New hardware and control technologies will be essential to improving the power system and making possible any of a variety of future grid architectures. Most of the technologies that will be implemented in the next several decades are generally known today. As researchers look further into the future, some of the technologies that will enable future changes may only be a gleam in the eyes of today’s engineers and entrepreneurs. Some emerging technologies could fit comfortably into the current grid architecture, and some could be used to implement significantly different architectures in the future. This chapter discusses several clusters of technologies—for power generation, storage, transmission, and power electronics—that together comprise the elements of “no-regret” investments for the future grid.
Make it-move it-use it has been the governing method to get electricity to the consumers. This paradigm may undergo a drastic change with progress in all three power systems-centric technologies discussed in the first half of this chapter. The second half discusses concomitant information-centric technologies, and the advances they are experiencing. Three specific technologies are delineated related to communication, advanced grid management, and automation. All three involve the ways in which data about the grid are collected through sensors, data are processed from various locations at various time scales ensuring security and privacy, and decisions are made for grid-wise planning and operations at multiple time scales ensuring reliability and resilience, using coordinated, and where possible automated, actions based on control principles and market mechanisms.
When integrated, these pivotal technologies will enable new functionalities that may change the very relationship between society and the way energy is used (as discussed in Chapters 1 and 2), enable more rapid technological innovation, and, in some cases, call into question the adequacy of regulatory models that were designed for an era when there was a single direction of electricity flow, from centralized generation to customers. As elaborated in Chapter 6, some of these new technologies and capabilities also raise new questions and concerns about cybersecurity in a rapidly digitizing world in which millions of intelligent devices capable of real-time control are deployed at the grid-edge (Chapter 6). It will take well-trained people to make all this happen, and to design, manage, and operate the power system of the future. These human resource needs are discussed in the final portion of this chapter.
Today’s generation fleet releases less conventional air pollution and carbon dioxide to the atmosphere than in the past. This trend will likely continue in order to address concerns about public health, the drive to achieve deep
decarbonization, and increased electrification of transportation and process industries. Accordingly, the United States will need more sources of abundant clean energy that do not release greenhouse gases to the atmosphere together with a variety of strategies that capture carbon dioxide and prevent its release to the atmosphere. Clean generation technologies include advanced photovoltaics (PV), carbon capture and sequestration and/or large-scale use of CO2, terrestrial, shallow, and deep-water offshore wind, hybrid generation plants, geothermal energy, and the production, storage, distribution, and use of cost-effective carbon-free gaseous and liquid fuels, such as H2 and NH3, highlights of which are included below.
Utility-Scale Wind and Solar
Utility-scale wind has matured over the past decade, with turbines rated at up to 5 MW onshore and up to 12 MW offshore (Siemens Gamesa 2020; GE Renewable Energy, n.d.). Wind capacity has been growing rapidly, doubling roughly every 6 years. EIA (2020) reports that wind farms in the United States generated 300 billion kWh in 2019, a total of about 7.3 percent of total U.S. utility-scale electricity generation. If regulatory and public acceptance issues can be overcome, offshore wind farms in shallow and deep waters offer an even greater potential for generation and have capacity factors as high as 65 percent, compared with approximately 35 percent for land-based wind (Klippenstein, 2018). The International Energy Agency (2019) estimates the global technical potential for shallow offshore wind generation to be 36,000 TWh per year, and IEA’s estimate is 77,000 TWh/year for deep-water offshore wind. For comparison, total global electricity production was 26,000 TWh in 2017.
Many challenges remain in developing offshore wind, including streamlining international and national regulations; the cost to build, anchor, stabilize, and maintain large offshore turbines and ancillary gear; ensuring survivability under extreme weather conditions; collecting and transporting the energy to the shore; and impacts on navigation and marine and bird life. As installed capacity becomes very large, there are possible local and regional climatic impacts of extracting large amounts of energy from the boundary layer (Adams et al., 2016; Butterfield et al., 2005; DOE-EERE, 2018a; Hu et al., 2016; Keith et al., 2004; Miller and Keith, 2018; Miller and Cox, 2014; Myhr et al., 2014; Shafiullah et al., 2013).
Utility-scale PV solar has also shown rapid growth and continuing price declines globally, with individual PV farms now exceeding 500 MW (Stoker, 2020). Already, price declines have changed the economics of PV plants, enabling siting in areas with more modest solar resources (NREL, 2019) and allowing overbuild of PV panels to increase energy extraction (Perez et al., 2019). The large plant rating has also resulted in the use of higher DC collection voltages in the farm, and the replacement of 1 MW centralized inverters with many 60–100 kW string inverters so as to simplify installation and maintenance (Zipp, 2016). PV continues to decline in price, even as efficiency increases and price decreases are expanding the market.
The recent trajectories of power purchase agreement prices for wind and solar are shown in Figure 5.1. While there are certainly limits to how much further costs can drop, grid-price parity has already been reached for many locations, and these trends will continue to be important in reshaping the grid. At the same time, there needs to be a continued focus on decreasing Balance of System (BoS) costs for PV and wind plants, as those costs sometimes exceed the cost of the generation resource itself (DOE-EERE, 2019).
As penetration of PV and wind resources has increased, the ability to maintain a grid that is stable and can ride through faults has become increasingly challenging and needs to be addressed. As the size of the PV farms has increased, they have also been required to play a bigger role in supporting the bulk grid: providing volt-amp reactive, dynamic frequency regulation, and fault ride through characteristics (Liu et al., 2014; Bravo et al., 2014; Mirhosseini et al., 2015), which now allows voltage support. The residential inverters output voltage was 230 volts line-to-ground, and the commercial was 400 volts line-to-line, reflecting the typical German residential and commercial voltage configurations, respectively. The inverters frequency was adjusted to 60 Hz in order to reflect American standard practices. The testing was performed in Southern California Edison’s (SCE as electric systems continue to deploy much more of these resources. One recent development has been proposals to use inverters to provide voltage support, especially at night (Varma et al., 2015, 2019). Another important emerging need is for PV and wind resources to operate to offset the impact of loss of rotational inertia on the system as generators are taken
out of service, and to be able to improve system resiliency by providing black-start and grid-forming capability when needed—functionality that has not yet been demonstrated in realistic deployments at scale.
In addition to challenges associated with grid integration with high levels of penetration of wind and solar, another key challenge for the next decade is developing higher efficiencies. Larger offshore wind turbines in deep water need higher efficiencies and capacity factors for an efficient transfer of energy to the grid. In addition to DC, low-frequency AC (facilitated by power electronics) holds promise. For deep-water wind that is very far from shore, neither AC nor DC transmission may be practical, and an alternative approach using dynamic positioning and making hydrogen from seawater that can then be compressed and taken to shore via tanker may become an option. Even more hypothetical may be airborne wind turbines that tap into the jet stream to provide a wind resource with even higher capacity factors (ARPA-E, 2016a). Bifacial silicon solar cells have increased efficiency to 22 percent (Rodriguez et al., 2018, with a promise of greater than 45 percent efficiency using quantum dots (Irving, 2020). Similarly, solar cells sprayed onto paper or plastic promise even lower cost, while the use of thermal photovoltaics that convert infrared photons to electricity can further augment system efficiency.
While both wind and solar are widely viewed as environmentally benign, they consume a great deal of space (relative to an equivalent fossil fuel plant, not including land and subsurface areas for mining or extraction) and can have a significant impact on land use and habitat disruption (Ausubel, 2007; NAS-NAE-NRC, 2010), and it is possible that available supply of developable land could be a limiting factor. There is a need for improved life cycle analysis of wind and solar systems, along with a focus on end-of-life for these technologies. Last, as noted above, if wind farms extract large enough amounts of energy from the boundary layer (or the jet stream), that could also give rise to local and regional climatic impacts that need to be better understood.
Managing High Ramp Rate and Dispatchability Issues
In a system with high penetration of intermittent renewable resources, there is a need for ensuring energy availability (i.e., guaranteeing that power will be available when it is needed) through dispatchability, and for managing high ramp rates (e.g., 13 GW over a few hours in California) (Ela et al., 2012). Without extensive storage, flexible demand, and larger capacity interregional transmission, and/or new dispatchable generation with net-zero CO2 emissions, these problems cannot be easily solved using traditional tools such as forecasting, Locational Margin Pricing (LMP)-based market forces, Automatic Generation Control (AGC), and fast-responding ancillary units. Two approaches are possible in the short run and need to be further explored. The repurposing of existing plants, such as the addition of pumped hydro capability to existing hydro plants (Penn et al., 2018), may be a cost-effective approach. Another approach is the integration of short-duration energy storage with peaker gas plants: allowing for instant start, fast dynamic response, and dispatchability. This also reduces the need for spinning reserve—that is, the need to keep a generation plant operating, even without load, to address unexpected contingencies, and can significantly reduce system emissions. The need for 24/7 power from renewable sources is also being addressed using technologies like solar power towers that convert solar energy to high-temperature heat, using molten salt for energy storage and for driving a turbine. Last, while it is presently expensive, dispatchable generation that uses carbon-neutral fuel or captures any CO2 emissions and power to gas (using electricity to produce a gaseous energy carrier) may offer a strategy for longer-term energy storage and transfer (Bailera et al., 2017; Walker et al., 2016).
Similarly, if PV and wind plants could be made more dispatchable, that would greatly increase their value to the system. An increasing number of PV and wind farms are being deployed with approximately 4 hours of energy storage at very competitive bid prices. For example, Xcel Energy has received a median bid price of $21/MWh for wind-plus-storage, and a median bid of $36/MW for solar with storage (Walton, 2018). The configuration with storage improves dispatchability of the plant, allowing it to participate in the market to get more value, and can also help to mitigate resource variability and ramp-rate issues. Such hybrid plants allow “value-stacking” because of distinct capabilities of the two technologies that are coupled, and can address some of the new challenges that the future grid will pose.
Last, the integration of flexible loads that can respond to real-time pricing signals can provide a virtual generation resource, and needs extensive investigation (CAISO-First Solar Demo 2017; Correa-Posada et al., 2017; Dobber et al., 2005; Feng, 2013; Hirth and Ziegenhagen, 2015; Pierpont et al., 2017; Pillai et al., 2011). Such virtual resources include commercial buildings, utility-scale PV and wind farms, and coordinated charging for fleets of electric vehicles (Correa-Posada et al., 2017; Dobber et al., 2005; Feng, 2013; Hirth and Ziegenhagen, 2015; Kreikebaum, 2012; Pierpont et al., 2017; Pillai et al., 2011). This is covered in more detail later in the chapter.
Carbon Capture with Sequestration or Use
Carbon capture and sequestration (CCS) refers to a family of technologies that can reduce the net carbon dioxide (CO2) emissions from different carbon-based fossil fuels (coal, natural gas, and biomass) when those fuels are used to generate electricity. CCS includes technologies to capture the CO2 and transport it to a permanent underground storage site where is it deposited and not emitted to the atmosphere.
Once captured, carbon dioxide must either be sequestered by injecting it into appropriate geological formations or used to create products including as mineral carbonates (used to make concrete and cement), fuels (e.g., combined with green hydrogen), polymers, and chemicals (NASEM, 2019). Assessments of the volume of available geological disposal sites suggest that the United States probably has decades of capacity to store decades of CO2 otherwise emitted into the atmosphere. Whether induced seismicity, other geophysical issues, or problems with public acceptance will allow the use of all that capacity is less clear. To date, no practical strategies have been developed to use more than a tiny fraction of the enormous volumes of carbon dioxide that would be produced by a large-scale capture program (NASEM, 2019).
Fossil fuel power plant types that could benefit from CCS include pulverized coal, fluidized-bed combustion, oxycombustion, integrated gasification combined cycle, natural gas combined cycle, and reciprocating internal combustion engines. The extent to which CCS and other low- to zero-emitting electricity generation technologies are deployed will depend on the relative performance and costs of technologies combined with the influence of different policy and economic factors.
There are six or so major carbon capture technologies currently being developed by DOE and other global research organizations. Several technologies for carbon capture, including “end of pipe” capture at conventional combustion plants, oxyfuel systems, and coal gasification systems, all exist at various stages of development and commercialization (Bui et al., 2018; NASEM, 2018). Because costs are high, until there are significant constraints or cost penalties on emitting carbon dioxide to the atmosphere or high-value uses are realized for the extracted carbon, such systems are unlikely to see significant deployment beyond the level of demonstrations.1 Currently, absorption or carbon scrubbing with amines is the dominant capture technology and is the only carbon capture technology that has been used industrially at limited scale. Several technical challenges exist, including significant cost reductions and decrease in the parasitic load on the fossil plant from the carbon capture technology.
While the concentration of carbon dioxide in the atmosphere is much lower than that in combustion exhaust streams, several projects around the world are exploring direct air capture (DAC) (NASEM, 2018). Pilot systems have been built that capture carbon dioxide from the air and combine it with hydrogen to make synthetic fuel (Leahy, 2018). As noted in Chapter 2, DAC plants require a substantial amount of electricity and could present a meaningful energy burden if deployed at scale. While DAC will likely remain expensive, there may be some applications, such as making long-haul air transport carbon neutral, for which it could end up being the lowest cost option.
1 For a map of existing plants, see https://sequestration.mit.edu/tools/projects/ccs_map.html.
Production of carbon-neutral gaseous and liquid fuels, not from agriculture stock but from clean energy sources such as renewables (or nuclear), can provide energy needed for applications such as ground transportation (especially heavy freight), aviation, power generation, and industrial processes that are now fueled by fossil fuel. It could also be used for electricity generation, especially for peaker plants and plants with high ramp rates. Examples include conversion of water to hydrogen using electrolysis, or the production of hydrocarbons from sunlight and carbon dioxide. Once releasing CO2 to the atmosphere is no longer free, the ability to produce such fuels at energy breakeven and cost parity with equivalent fossil fuels could transform this sector, and would allow utilization of many of the infrastructure elements that exist (with some adaptation), as well as newer technologies such as fuel cells with combined heat and power (CHP). Hydrogen production and storage at scale already exists, and the Los Angeles Department of Water and Power has announced a plan to replace 1,900 MW of coal generation with hydrogen (Malik, 2020). However, the time frame for commercial and technical viability for carbon-neutral fuel systems is unclear. Specific chemical energy carriers that have the potential for deployment in a carbon-neutral fashion are described in Box 5.1.
Micro-Reactors, Small Modular Reactors, and Nuclear Fusion
Many existing nuclear plants continue to operate, often with life extension, supported by state programs designed to keep them operating as well as policies to compensate plants for their zero-carbon generation. It appears unlikely that the United States will build any additional large conventional light water reactors after the completion of Vogtle Units 3 and 4 in Georgia (Morgan et al., 2018).
At the same time, significant research continues in nuclear energy. While larger traditional nuclear plants are falling out of favor owing to cost overruns and delays, small modular reactors (SMRs) and micro-reactors may redefine the field. Various companies are commercializing SMRs rated around 300 MW, which can be assembled in the factory and transported to the final location—cutting assembly time, cost, and risk. Micro-reactors are even smaller, rated at 0.2–10 MW, and can be transported as fully operational units by semi-tractor trailer to provide power at the grid edge, in remote locations not easily served by the grid, or in emergency situations. Regulatory approvals are in progress for both SMRs and micro-reactors. Both will likely be available commercially within the next few decades. A key concern continues to be life cycle cost, including the issues around long-term fuel management and safety/security of installations and stored spent fuel.
Nuclear fusion, including a number of innovative designs developed by private companies, also continues to attract research funding. While progress is being made, to date no system has yet demonstrated net positive energy production. If and when that occurs, even if that is accomplished at some point, commercialization will take time, so deployment at significant scale appears highly unlikely before mid-century, at the earliest (Horvath and Rachlew, 2016; Lopes Cardozo et al., 2016).
Central-station geothermal presents the opportunity to harness thermal energy for electricity. While there are geothermal facilities at a few places like the Geysers in California, and across Iceland, where thermal sources are close to the surface, the high cost of drilling technology has prevented exploitation of the abundant energy that is available deep in the earth as a result of the thermal flux from the core. Wendell Duffield and John Sass of USGS note that “even if only 1 percent of the thermal energy contained within the uppermost 10 kilometers of our planet could be tapped, this amount would be 500 times that contained in all oil and gas resources of the world” (Duffield and Sass, 2003). However, until the cost of drilling is reduced dramatically, it is unlikely that much of this resource will be realized.
The electricity system needs to dynamically balance generation and load in real-time. This balancing has traditionally been accomplished using fast-responding generation capacity, or energy storage resources such as hydropower and pumped hydro to balance must-run generation resources such as nuclear or large coal plants. Consistent and sustained declines in prices of highly variable and nondispatchable sources of energy from wind and solar have resulted in rapid growth and high penetration levels of these resources in some parts of the grid. The increasing share of generation coming from intermittent renewable sources motivates increased consideration of energy storage technologies to help balance the system.
Operational reliability of the electric system requires a continuous need to balance load and generation over periods ranging from milliseconds to seasons. This is true both at the bulk level (traditionally, the area of focus for utilities), and for more local applications in distribution systems, microgrids, and on customer’s premises. There are two basic methods for achieving the needed local- and system-level balancing. Today, the most cost-effective approach is usually to balance the system using fast-ramping generation sources and flexible loads through direct control or price signals (Sun et al., 2016). While this is an important component of balancing and is discussed in the Advanced Grid Management Systems section of this chapter, the complexity and impact on customers limits the extent to which this can be effectively used today. A second approach for balancing is energy storage—to store any excess energy in energy storage devices and convert it back to the grid when it is needed. Energy storage devices range from electrochemical batteries, flow batteries, thermal storage, clean liquid fuels, compressed air energy storage, and gravity-based systems including pumped hydro. Many of these technologies have been a focus for DOE and ARPA-E research initiatives, some of which are described in in Box 5.2. In addition to some of the traditional uses shown in Figure 5.2, energy storage can also address several key requirements on the evolving future grid, including fast responding generation; ramp rate control; spinning reserve (e.g., synthetic momentum) to address contingencies; maximizing energy recovery from and improving dispatchability of variable resources; improving grid resiliency; and addressing seasonal mismatch between renewable generation and loads (Barton and Infield, 2004). Energy storage is seeing high growth across the globe and provides a unique opportunity for the United States to gain a strong position as a developer and supplier of energy storage solutions.
Rapid development and price declines in EV batteries have allowed such batteries to also be used for grid-scale storage, with deployed plants in the 100 MW+ range. In addition, as the numbers of EVs grow, the charging patterns of large fleets can be controlled to help balance the grid. If adverse impacts on battery cycle life can be reduced, technologies such as vehicle to grid (V2G) could also allow energy stored in batteries to help support the grid. Current improvements include rapid price declines in EV batteries (averaging $156/kWh in 2019 [Bloomberg NEF, 2020]) and improvement in life, with current batteries modeled to last between 5.2 years in Florida and 13.3 years in Alaska under typical driving conditions and a 30 percent battery degradation limit (Yang et al., 2018). The falling costs of EV batteries are displayed in Figure 5.2. In addition to technological advances, forward-leaning policies have helped make grid-scale energy storage based on electrochemical batteries an area of strong growth. These systems can provide a few minutes to many hours of energy storage. Other technologies such as thermal energy storage can provide a few days to a week of stored energy, filling another gap in terms of need. The potential role for grid management through electric vehicle coordination is discussed further in this chapter’s Advanced Grid Management System section.
Perhaps the most demanding requirement in a future with high penetration of nondispatchable and fuel-limited resources is the availability of long-duration energy storage. The ability to store low-cost energy when it is available and to return it to the grid at scale, many hours, days, or months after the energy was stored will be needed, with the blended cost of energy over the year being competitive with what is available today. Some of the key technologies that are likely to have an impact on grid-level storage requirements are shown below. (See Box 5.2.)
Finding 5.1: Energy storage, for periods ranging from seconds to months, will play an increasingly important role in most future grid realizations. Short-term storage will enable dynamic grid balancing and stabilization; mid-term storage will allow optimization of grid operations and dispatch of renewable resources for lowest cost; and long-term storage will enable cost-effective low-carbon generation even when PV and wind are not available for extended periods of time.
The use of low-cost high-performance power electronics holds the potential to dramatically transform many aspects of the power system: from high-voltage DC at one end of the spectrum, to small consumer devices on the other (Gueguen, 2015; Kassakian and Jahns, 2013). This technology can add fast, efficient, and accurate active control mechanisms to what has traditionally been a passive grid. It can make much greater use of HVDC feasible and provide the interface between the grid and new forms of renewable energy and energy storage. It can also support the development of new devices such as compact high-frequency power transformers. In these and other ways, power electronics is a key enabler of a flexible, affordable, secure, reliable, and resilient grid.
Power semiconductor devices based on silicon (Si), and more recently wide band gap devices using silicon carbide (SiC) and gallium nitride (GaN) that can switch at high frequency (1 kHz to 100 kHz) and can respond in microseconds, are at the heart of this capability (Bindra, 2015; Millán et al., 2014). Continually declining prices and improved performance of power semiconductors and other elements used in power converters—for example, microprocessors and sensors—have enabled explosive growth in PV, wind, energy storage, microgrids, EVs, and data centers. This technology currently enables the miniscule charger plug for the cell phone, the electric vehicle, and the microgrid, and is the force behind renewable wind, PV energy, and battery energy storage, and helps transport gigawatts of power thousands of miles using HVDC links (Huang, 2017; Baliga, 2018). DOE, EPRI, ARPA-E, and DoD have long recognized the importance of power electronics and have maintained research initiatives in the area (ARPA-E, 2016b; DOE-EERE, n.d.; DOE-OE, 2020; EPRI, 2011; ONR, n.d.).
Finding 5.2: Advances in power semiconductor devices, as well as integration of electrical, thermal, and mechanical integrated structures for compact high-power converters, are critical core technology capabilities that are enablers of many future grid architectures. These fast-moving technology areas will need sustained research investment, as well as the development of effective translation mechanisms to reach commercially relevant scale.
Power electronics enable new functionalities that can be foundational for a variety of possible future grid architectures. At high power levels on the bulk power system, HVDC converters allow conversion of AC power to DC to enable transfer over longer distances and reconversion to the AC grid (Hingorani et al., 1996; Lesnicar and Marquardt, 2003; Van Hertem and Ghandhari, 2010). Newer voltage source converter (VSC) technology can support multiterminal operation that may facilitate regulatory approval of long lines crossing several states (Flourentzou et al., 2009). It can facilitate longer underground links and can be used to move power from offshore wind resources (Breseti et al., 2007). Flexible AC Transmission Systems (FACTS) provide controllability on the AC system itself (Padivar, 2007), allowing increases in system capacity and utilization, as well as the integration of larger levels of solar and wind energy (Burman et al., 2011; Steimer, 2010).
The large centralized systems that have been developed over the past 50 years are beginning to be challenged by more distributed systems, such as controllable hybrid transformers and distributed series impedances, offering lower cost, higher reliability, and faster deployment (Kreikebaum et al., 2010; DOE-OE, n.d.). These technologies could create a more flexible and resilient bulk power system, enabling new paradigms such as the operation of the national grid as a set of independent electrical AC islands interconnected by strong DC links (Mureddu et al., 2016); the building of a DC network with high controllability to augment the existing AC grid; or a “bottom-up” grid that provides reliability and resiliency from distributed energy and primarily uses the bulk-power system for accessing low-cost energy whenever it is available (Kristov, 2019; Retiere et al., 2017).
Another key use of power electronics is to connect a wide variety of distributed energy resources to the grid—including PV solar, wind, energy storage, fuel cells and microgrids, as well as electrical loads such as electric vehicles, motor drives, data centers, and all electronics loads. EPRI has estimated that by 2030, more than 80 percent of substations would have Intelligent Electronic Devices (EPRI, 2011). A key technology in these devices is the DC/AC inverter that can bidirectionally transfer power between a DC source/load and the grid. Inverters today span a wide range, from 200 W microinverters for rooftop solar, to highly modular, rapidly deployable inverter systems rated at 100 MW and higher that are used with large-scale PV, wind, and storage. With high penetration of PV solar and wind energy, the characteristics of the AC grid are increasingly being governed by inverters, which intrinsically behave very differently from the existing large generators that use rotating machines. Widespread use of the term “smart inverter” may suggest that all issues have been resolved, but significant gaps remain in our understanding of how to model, simulate and actually control systems with millions of intelligent fast-responding inverters. These inverters are already being deployed in a wide range of applications: from stiff AC grids dominated by generators, to resilient microgrids, where the inverters essentially form the grid. The impact of this unpredictable and poorly controlled interaction is becoming very visible, resulting in degraded grid stability and controllability, and is perhaps one of the most important research issues that needs to be addressed (Bayo, 2018; Bayo et al., 2016; Shu et al., 2018). Further, in the face of fast 2- to 3-year technology cycles in power electronics, standards governing inverters significantly lag behind the state of the art, often by 6 to 8 years, and can inhibit innovation if enforced blindly. New mechanisms are required to ensure compatibility between inverters and the grid across vendors and through technology obsolescence cycles, and to ensure that innovation continues to be enabled.
Finding 5.3: As the number of AC/DC inverters used to connect generation, storage, and loads to the grid increases, new techniques will be needed to ensure coordination between these inverters, and to minimize interactions between them as well as with other grid components, over time periods ranging from milliseconds
to minutes. These inverters should manage issues related to communications, cybersecurity, protection, and resiliency.
Power electronics for grid control is a critical technology area (Box 5.3), and much of this technology was initially developed in the United States, but has since been commercially produced by other countries in Europe and Asia. The United States currently has little manufacturing and design involvement for HVDC, VSC, or FACTS devices. The era of large, centralized grid control systems may be increasingly challenged by highly modular systems that do not require the high level of customization and can be rapidly deployed. U.S. companies are well positioned in this fast-growing new segment, and the United States has the potential to be a leader in these newer areas, including energy storage systems, and if legal and regulatory barriers to wide adoption can be overcome, modular high-power microgrids. If the future grid moves in the direction of greater decentralization, with greater use of HVDC, VSC, and related technologies, power electronics will be an important enabler.
Technological advances in power electronics are key to enabling many future grid architectures—for integration of utility-scale renewables at high power levels, moving power over long distances or to shore from offshore wind, networking clusters of AC islands with DC links, connecting a wide variety of DER and clusters of microgrids. DC transmission is a critical technology for moving power over long distances, and is a key component for integrating large portfolios of PV and wind generation, but represents an area where the United States has little technical and manufacturing capability. Hallmarks are low cost, fast switching, and high efficiency. Significant progress needs to occur in developing methods for reliable autonomous operation, addressing issues such as transient stability with increasing loss of rotating inertia, lack of universal design rules that prevent different types of inverters and generators from working together, and huge uncertainties in available DER and network topologies.
Power electronics is a critical enabling technology that will essentially be used to manage electricity flows between virtually every source and every load in a future grid, including extensions of centralized systems with HVDC links from distant PV and wind farms, to highly distributed and decentralized systems with microgrids, EVs, and rooftop solar. This is a fast-growth technology where the United States should have high technical and manufacturing capability—but does not.
Recommendation 5.1: To meet the challenge of dramatically lowering U.S. CO2 emissions, the Department of Energy (DOE), Electric Power Research Institute (EPRI), universities, and industry should focus on developing generation technologies with zero direct CO2 emissions; low-carbon technologies with high dispatchability and fast ramping capabilities; storage systems for multihour, multiday, and seasonal time-shifting; and power electronics to enable real-time control of the grid.
Recommendation 5.2: The United States has lost ground in the manufacturing of conventional grid-scale power control technologies (e.g., high-voltage direct current [HVDC] and flexible alternating current transmission system [FACTS]) and is deploying very little of these advanced solutions. Developments in rapidly growing technologies, such as photovoltaics (PVs), wind, electric vehicles (EVs), and energy storage, suggest that a new paradigm may be rapidly emerging that is more modular, distributed, and edge-intelligent, and that may be able to compete with and outperform the existing grid paradigm in terms of sustainability, reliability, resilience, and affordability. A rapidly changing paradigm for electrical power and the grid offers a unique opportunity for U.S. research and manufacturing to reclaim their global lead in this critical area. The Department of Energy (DOE), Electric Power Research Institute (EPRI), other domestic and international research organizations, universities, and worldwide industry should identify such “breakaway” threads early, and work with industry, investors, and regulators to understand the potential roadmap and impact. Then, DOE, EPRI, and industry should collaborate to develop and fund a research agenda that creates fast-moving programs that help to de-risk such solutions from technology, market, and regulatory perspectives.
In order to make all critical decisions, today’s grid requires a strong communications backbone and Supervisory Control and Data Acquisition (SCADA) system. These decisions enable coordination and communication between thousands of critical assets in generating plants and substations with the control room. These assets may grow in number, in complexity, and in type, as evidenced by the three aforementioned technologies. The situation is further complicated by new applications such as advanced metering infrastructures (AMI) that connect to millions of smart meters and can generate terabytes of data. The advances in generation, storage, and power electronics discussed thus far in this section, as well as the explosive growth in AMI and grid-edge sensing and control, necessitate large advances in the underlying communications technologies, as well as data management at a scale that utilities are not accustomed to.
Communications technologies evolve at a more rapid pace than the electricity grid, and therefore the capabilities and vulnerabilities as new generations of communication networks are introduced will have to be continually addressed (Ma et al., 2013). These represent additional challenges to several others such as interoperability across utilities and regions, critical effects of latencies on outages and failures, and issues of scale and technology migration. While some of these challenges can be addressed through hardware technologies, others require software solutions that require changes in the network protocols, all of which are discussed below.
Wireless technology comprises an array of different types of systems, with the cellular/mobile network being the prevalent technology today. Cellular communications technology has evolved dramatically over the past four decades; it has gone through four distinct generations and the fifth is currently being rolled out. Each generation has brought in new capabilities that other technologies are built on, with the smartphone being a notable by-product of the third generation. Key aspects of the emerging fifth generation are lower latencies, a high degree of densification, lower cost per bit, and greater energy efficiency—all enablers of Internet of Things (IoT) capability that can be built on top of 5G networks. The future electricity grid, being a primary application domain for IoT technology (Bedi et al., 2018), can benefit greatly from these advances, better enabling functionalities such as power balance using inverter coordination, provided cost-effective integration with grid operating systems can be achieved.
At this pace of development, which is not likely to subside, the cellular communication networks will go through three more generations during the time frame that this study addresses. Like previous generations, the new ones will respond to emerging applications. Thus, predicting the exact form and capabilities of future networks is quite difficult. Certainly, predicting 5G capabilities from the vantage point of 1990, when 3G was being introduced, would have been very difficult. However, it is almost certain that successive generations will bring greater capacity, lower costs, greater energy efficiency, and more ubiquitous coverage, driven (like previous generations) by major advances in microelectronics and radio technologies.
An issue with terrestrial cellular technologies is that they are largely deployed to provide capacity where population is concentrated. This means that there can be coverage gaps, particularly in rural and remote areas where power systems may still need to operate. Therefore, other communications technologies may also be critical to the increased cyber-sophistication of the electricity grid. An example is satellite-based cellular systems, in which the role of terrestrial base stations is assumed by satellites in low earth orbit. Such systems have existed for decades (Iridium, Globalstar), but a new generation of these technologies currently under development will provide greater capabilities than these earlier systems, thereby providing more ubiquitous coverage than terrestrial systems (Qu et al., 2017). Issues with satellite systems in supporting IoT includes greater latencies owing to round-trip propagation times, and the additional vulnerabilities associated with having part of the infrastructure deployed in space. Another challenge associated with satellite-based communications for grid sensors also concerns the cost, particularly when one considers its use for distribution asset monitoring, where ultra-low cost is likely to be required for millions
of points. Another challenge is the cost and complexity of migrating from multiple communications systems that many utilities often use today to a new communications backbone that is likely to change again in a few years.
Dedicated networks of microwave links or other radio frequency (RF) technologies, fiber optic cable, or power line communications are all used to support grid communications, although these will not be as able to take advantage of the economies and other advantages of dual-use that cellular networks offer.
The growth in the number of IoT as well as IoT-enabled devices has provided a new platform for grid operators to facilitate controlling loads in the power grid. The most noteworthy demonstration of load control using IoT devices came during the total solar eclipse on August 21, 2017, when Nest could leverage the popularity of its thermostats to decrease the demand in the path of the eclipse by 700 MW by synchronously reducing air conditioning power usage to help avoid power outages owing to the loss in solar energy generation (S. Tsao, 2017).
Besides being controllable remotely, IoT devices can provide valuable data about power usage patterns in different households without a need for extra investment by the regional grid operators to install smart meters at all the houses. This information can be used to more accurately predict the power demand in the system, and to perform more efficient and personalized load control without causing any inconvenience for the consumers. Moreover, these devices can constantly be pinged to detect power outages in the power distribution networks without requiring the consumers to report their outages (currently the only way for the utilities to detect such outages) in areas where smart meters are not deployed.
In order to fully benefit from controllable loads as ancillary services (e.g., frequency control), the grid operator should be able to change the loads in milliseconds (Sauer and Pai, 2018). However, as the number of IoT devices in households increases—there are already more connected things than people (Tung, 2017)—reaching this level of latency with current network bandwidths seems very difficult. Hence, the success of using the IoT platform for grid operation relies on the successful development and implementation of the emerging generation of wireless technology (i.e., 5G) (Schulz et al., 2017) and the subsequent generations. Current narrowband Long-Term Evolution (LTE) technologies such as enhanced Machine-Type Communication (eMTC) and Narrowband-Internet of Things (NB-IoT) have already provided some solutions to the “massive IoT” problem that form a foundation for the deployment of 5G technologies. An important point to consider is network overload or downtime, which occurs frequently, and can make signal reception unreliable or with unpredictable latencies. It is important that any grid control paradigm using IoT devices be able to operate even when connection with the IoT sensors is compromised.
Another challenge in making load control via IoT a reality is the need for processing huge amounts of data in short periods of time (from milliseconds for control to hours for planning) by the grid operators. This requires grid operators to invest in huge computational power infrastructure and also to attract new workforces (e.g., data scientists) that traditionally are not part of the grid operation. One way to make this challenge less demanding is to develop distributed control algorithms and rely on edge computing to perform some of the computation at IoT devices (i.e., at the “edge”) (Chiang and Zhang, 2016). The edge computing approach helps not only to reduce the computational load from the servers on the cloud but also to lower the network bandwidth requirements by reducing the amount of data that needs to be transferred over the network. Moreover, edge computing, if designed carefully, can address the privacy concerns around transferring consumers’ personal data to a central server—for example, power consumption patterns can reveal the degree of religious practices (Perera, 2015).
Communications, IoT, and cloud computing technologies are widely used today to support a wide variety of grid operations, ranging from metering, work-force management, protection coordination, PMU measurements, and SCADA for monitoring and control. Most of these systems are designed to operate with substantial latencies, and to have human operators fill in gaps in knowledge as needed. In a future grid with a high penetration of inverters acting in microseconds, loss of communications or latencies could cause system malfunction or collapse, unless the system is designed to operate under such conditions. Last, increased numbers of IoT elements on the grid motivate considerations pertaining to cybersecurity and the ability of the electric power system to operate while subject to cyber intrusion. A detailed discussion of these implications is presented in Chapter 6.
Grid assets such as DER are expected to grow rapidly in number, in complexity, and in type. In order to enable time-critical decisions in a power grid and grid-edge devices, and ensure grid reliability and resilience, advances in communications technologies will likely be needed to guarantee system performance, while limiting new vulnerabilities and operating in the face of technology migration and obsolescence. R&D methods are needed to develop resilience to cyberattacks, through a combination of analysis, isolation, and reconfiguration.
ADVANCED GRID MANAGEMENT SYSTEMS
As discussed earlier, for many decades the bulk power system has been considered to be the foundation for the grid, maintaining reliability while coordinating generation resources to supply demand to ultimate customers at the lowest cost. The distribution system has traditionally been a passive network with unidirectional power flows, poor visibility, and almost no real-time control. Most of the investments on the distribution system have been to improve operations and processes for the grid operators. distribution management systems (DMS), a collection of applications traditionally designed to monitor and control the entire distribution network efficiently and reliably, are being increasingly deployed to provide more decision support to assist utility planning, workflows, and operation with monitoring of the electric distribution system, alerts to minimize outage times, and support for managing feeder voltage profiles (Cassel, 1993). These are however slow processes with little by way of real-time visibility or control capability, and are challenged to meet the requirements of the future distribution grid.
As the distribution system evolves to include a higher percentage of distributed energy resources including PV solar, energy storage, distributed generation, and flexible loads, these existing DMS are being challenged (Agalgaonkar et al., 2016). The complexity and costs associated with coordinating a very large number of non-utility-owned or controlled generation assets, particularly under current operating paradigms, give pause for concern. A lack of local real-time visibility, and the ability for utilities to analyze and process this data if it were available, makes it difficult to compute or securely communicate guidance to tens of thousands of specific inverters for functions such as local reactive power control for voltage support, or augmenting system inertia following large grid transients with high rate of change of frequency values (Rezkalla et al., 2016. The majority of distributed generation resources, whether PV solar, battery storage, small generators, fuel cells, or other technology, connect to the grid using inverters that can respond much faster than the utility can exert control on them. Even if such centralized control was feasible, there is no standard low-cost low-latency cyber-secure communication means that is accepted by industry and is deployed in the millions of inverters that are already in the field, or are about to be installed over the next few years. Grid coordination and stable operation of a large number of inverters on distribution systems, especially at high penetration levels, is a significant issue as well (Demirok et al., 2011; Johal and Divan, 2007).
All of the above challenges necessitate significant advances in the overall integration of DER. The associated pivotal technologies span across both distribution and transmission systems, and form the focus of this section.
Finding 5.4: Even though the reliability of the U.S. bulk power system is very high, the reliability and resiliency at the grid-edge for real customers is not as high, especially following high-impact low-frequency events, such as wildfires and hurricanes. The ability to add control into the power grid can enable operational paradigms where reliability and resiliency are achieved at the distribution system level, while the bulk power system delivers low-cost energy when it is available—thus reducing cost while improving reliability and resiliency at the grid edge. The technical, economic, and regulatory issues with technologies that can enable such a paradigm are not fully understood or proven.
Distributed Energy Resources
The main component ubiquitous in an Advanced Grid Management System is a DER, which encompasses distributed generation, storage devices, and controllable loads. Earlier sections in this chapter addressed the first two aspects of DER. This section discusses controllable loads and their role as a DER. Sources of controllable
loads include residential, commercial, industrial, and transportation assets. As discussed in Chapter 2, thermal loads from HVAC devices in commercial and residential buildings represent cost-effective assets for fast power balancing. In transportation, EVs with suitable coordination of their charging schedules are assets that can be leveraged (Papadaskalopoulos et al., 2013). These, together with other resources such as headroom in solar plants, flexible thermostatically controlled loads, and increased electrification of industrial processes—offer a cost-effective “zero investment” approach for grid dynamic balancing (DOE-EERE, 2019; Freire et al., 2010; Hao et al., 2018; Salmani et al., 2010).
Large clustered loads such as buildings and facilities may now participate in demand-response programs and are beginning to use thermal storage to manage and shape electricity consumption (Tindemans et al., 2015). Increased use of local generation and energy storage can allow these facilities to become active players in grid support. With respect to flexible loads, commercial facilities have the potential of achieving 15 to 25 percent load response without any major change in operations or comfort (Paulson Institute, 2014; Samad et al., 2016). A commercial building including charging for a number of plug-in EVs can provide charging while also coordinating grid support and services (Qi et al., 2016). Large customers playing in the “demand response” market can turn off flexible loads to help in slow system-level balancing of generation and demand. Some utilities have agreements with customers allowing the tripping of hot water heaters and air conditioners under high system load conditions.
Buildings represent a significant load with temporal flexibility, allowing for demand response and load shifting. Designing flexibility into building heating and cooling allows these systems to dial back when demands are high on the overall system. Appliance loads, such as water heaters, are also possible to shift to more advantageous times. Lighting (particularly in infrequently used areas) is also an area where larger buildings can reduce demand in response to system signals. For residential buildings in some regions of the country and eventually over time in most regions, space and water heating can be instrumented and coordinated that may reduce customers’ lifetime costs while providing demand flexibility (Billimoria et al., 2018).
While the above illustrates the potential for controllable loads to provide grid support, controllable loads also introduce challenges. New load patterns, especially when present in large volume, need a tight coordination. Data centers are a specific example of load growth, in contrast to other traditional electric loads that have been flat. Increased electrification of established industry segments is another. Data centers can consume as much as 100 MW of electrical power, and have been forecast to grow to 8 percent of projected global energy demand (Jones, 2018). Such large loads can present a challenge for grid integration, unless there is close coordination between the utility and the plant owners. These large loads, including industrial plants and large commercial facilities, often have integrated back-up power generation. This generation is highly modularized, rapidly deployed, and can often provide energy at a price point where arbitrage is feasible—allowing the use of this generation resource to reduce energy costs and demand charges and to provide frequency support to the grid (Kelly et al., 2016; Yu et al., 2016). This local generation, especially when tightly coordinated, represents a vast untapped DER that can provide power balance in real time and resiliency for the nation’s electricity supply. Such new services are technically and commercially feasible and could serve as part of the critical infrastructure to provide electricity services for nearby communities when the grid is down for extended periods (NASEM, 2017).
Rapid growth in customer-owned BtM DER pose similar issues of coordination. Given their small size and large numbers, it is challenging for utilities to coordinate and control them, especially when they are geographically dispersed. Their total impact on the distribution feeder can be significant with issues such as voltage volatility and reverse power flows (Horowitz et al., 2019; Federal Energy Regulatory Commission, 2017). Connectivity and coordination issues become worse with customer ownership of the resource and diversity of equipment manufacturers (NERC, 2017a; NREL and EPRI, 2019). Without real-time information from these DER, it is difficult for utilities to calculate the system state, and without dispatch of distributed generation, utilities do not have the levers to affect control (Johnson, 2018). One approach for addressing ownership boundaries and diverse stakeholders is through transactive energy. By enabling transactive energy on the distribution grid similar to what is available in the wholesale markets (Hardin and Kaufman, 2017), where customers can respond to value signals based on demand, price, time of day, or other considerations, appropriate coordination and control may be realizable.
The past few years have also seen an increased frequency of large outages of long duration precipitated by hurricanes, floods, and wildfires that impacted the availability of the bulk power system for extended periods of time (Epstein et al., 2019; Georgia Power, 2018; Mazzei et al., 2020). Many customers who had invested in PV panels and EVs still sat in the dark, or had to get back-up generators for emergency power (Tita and Carlton, 2020). Behind-the-meter DER have the potential to provide cost-effective back-up power for their own owners, and for their communities, but neither the equipment nor the homes have been designed for that functionality. Similarly, the ability to use the energy stored in the EV battery as an integral part of energy management at the home level (vehicle to home, or V2H) or at the grid level (vehicle to grid) can optimize the use of assets that are owned by the customer (Lazzeroni et al., 2019).
Finding 5.5: There is likely to be significant growth in DG at the grid edge to meet emerging needs for critical loads, for high-value loads, for fast evolving large new loads, and for resilience. If that DG cannot be dispatched, there will also be a growing need for storage. If the growth of clean dispatchable DG is limited, there will likely be a growing need to increase the capacity of existing transmission corridors and/or build new transmission.
Finding 5.6: As the distribution system evolves to include a higher percentage of distributed energy resources including PV, energy storage, distributed generation, and flexible loads, existing DMS are being challenged. The complexity and costs associated with coordinating a very large number of generation assets and flexible loads that are not owned or controlled by the local utility, is a source of concern, particularly under current operating paradigms. A lack of local real-time visibility, and the ability of utilities to analyze and process these data if they were available, makes it difficult to compute or securely communicate guidance to tens of thousands of specific edge-devices (e.g., inverters) for functions such as local reactive power control for voltage support, or augmenting system inertia following large grid transients. This is an almost impossibly complex problem, motivating the development of other decentralized solutions.
As DER proliferate throughout the grid, the current approach for decision making in a power grid requires significant customization at each site. In addition, if these DER are geographically dispersed, they may be difficult to operate especially under resiliency conditions when the communications, sensing, and actuation backbone may be compromised (Habib et al., 2018). In this context, microgrids could provide an excellent avenue for grid resilience. Microgrids are typically configured and operated as mini-utilities, using centralized control based on system state estimation and dispatch of loads to ensure cost optimization and dynamic balancing, and have been implemented at army bases, hospitals, data centers, industrial plants, commercial facilities, and small communities (Con Edison, 2018; DOE-EERE, 2018b; Galvin Center for Electricity Innovation, n.d.).
Microgrids have the potential for increasing system resiliency at the edge of the grid (Navigant, 2019). What may prove to be attractive, from a reliability and a resilience perspective, is to design a microgrid that is itself flexible and resilient, and in addition can be configured in an ad hoc manner using standard modules that follow specific rules, without requiring detailed real-time information on connected devices, can work in grid-connected and grid-islanded modes over a wide and varying range of energy surplus/scarcity, can prioritize loads, is cyber-secure, and continues to operate even as physical and communication layers are compromised (Cook et al., 2018). A smart interface device to connect microgrids to the grid is needed to simplify the interconnection approval process while ensuring that grid rules are enforced, removing a major impediment to the deployment of DER and microgrids (Bilakanti et al., 2019). Proceeding in the same vein, what may be even more desirable is microgrids and clusters of microgrids that perform autonomously and automatically allow both normal and abnormal operation and meet the requisite specifications, even as they break up into fragments or coalesce into a whole as needed
(Vorobev et al., 2019; Xue et al., 2018; Graham et al., 2017; Fox-Penner et al., 2018; Choi et al., 2013; PJM, 2017). An illustration of a smart grid composed of integrated microgrids is illustrated in Figure 5.3.
A distribution system that can operate as a cluster of such flexible microgrids could have a transformative impact on the reliability, resiliency, equity, sustainability and affordability of electrical power right down to the grid edge (Li et al., 2017; Liu et al., 2017). While microgrids hold great promise, powering them solely with wind and solar presents serious challenges, and as noted in Chapter 3, the pace at which some types of microgrids are being deployed and used is being severely constrained by law and regulations limiting which entities can build and operate such systems.
Finding 5.7: System reliability and resiliency at the grid edge could be dramatically improved by the operation of clusters of microgrids that function autonomously and automatically in grid-connected or grid-independent mode under both normal and abnormal operating conditions, while meeting requisite specifications and grid interoperability requirements, and ensuring that critical loads and critical customers are supported even as the grid itself is compromised.
Electric Vehicle Coordination
As discussed in Chapter 2, electrification of many existing industry segments seems likely and can pose challenges for the grid. Electrification of transportation can pose a significant challenge for grid integration, both now and as a transition occurs to the future grid (Graham et al., 2017). Estimates for EV penetration in the United States range from 3 million to 40 million by 2030 (U.S. DRIVE, 2019). Estimates for energy consumed for EV charging range from 74–190 TWHr by 2030, increasing to 290–590 TWHr by 2040—representing 1.8 percent to 15 percent of the 4,000 TWHr of total U.S. electrical energy consumption, representing a worst-case growth rate of <0.75 percent/year (Fox-Penner et al., 2018). The coordinated charging and discharging of millions of vehicles could, under existing business models and regulations, offer a virtual storage resource for balancing the grid (Choi et al., 2013; PJM, 2017). Aggregation of EV fleet charging and time-of-use pricing are two strategies that utilities can use to help manage the impact of the EV load on the grid (Black et al., 2019).
An even bigger concern for grid integration is posed by fast-charging, which is needed both for electric automobiles and heavier vehicles. Direct current fast charging (DCFC) would enable EVs to charge quite rapidly (e.g., in 10 minutes or less). However, DCFC stations require high-capacity grid access. These stations operate at 480 V or higher and require approximately 50–100 kW or more for each charger, leading to large power demands on electric feeders where installed. For example, if a bank of 10 Tesla Superchargers (each drawing up to 150 kW) were used simultaneously, it could create 1.5 MW of incremental demand.
Even assuming that 20 million EVs (low estimate for 2030) might be charging simultaneously at 50 kW (low end for fast charging) represents a peak load of 1,000 GW, auto manufacturers are rolling out electric vehicles that can fast-charge at 250 kW, increasing to up to 3 MW for large tractors for semi-trailers (Field, 2019; Lambert, 2019). An EV charging station handling 10 vehicles at a time represents up to 2.5 MW of peak load, often with very poor average loading. Clearly, depending on the character of incentives for customers to charge at various times of day, this will create a challenge for utility infrastructure. For a service center simultaneously handling 50 electric trucks, the charging rate could reach up to 20 MW (Earl et al., 2018), which, if supplied by the grid, would almost certainly need a new transmission line and substation. At the same time, with few EVs on the road today, the economic viability of a business model built around EV fast-charging stations has been challenging (Eckhouse et al., 2019).
New business models are needed to support the rollout of EV charging infrastructure. To avoid introducing costly impacts on the system to satisfy EV charging requirements, it is critical that the operation and pricing of charging stations reflect system costs and that they be closely integrated with grid operations and needs. There is an opportunity for utilities or private companies to strengthen the grid infrastructure, including the deployment
of storage and microgrids, to address local reliability and resiliency needs, while using the same infrastructure to deliver DC as a service for electric vehicle fast charging (Ashique et al., 2017).
Finding 5.8: Electrification of transportation simultaneously represents a challenge and an opportunity. Coordinated charging and discharging of millions of vehicles may offer the opportunity of a virtual storage resource. On the other hand, the peak load represented by large quantities of EVs with fast charging could represent a very large fraction of peak U.S. generating capacity. This, in turn, motivates investments in research directions of DER-aggregation, load management, and viable retail and wholesale market mechanisms.
Sensing, Monitoring, and Protection
Grid operations require visibility to operating conditions, and the ability to respond, either locally or through central coordination. The process of measurement is accomplished using sensors, typically measuring parameters such as voltage, current, frequency, and phase angle. This data is either fed to a local actuator, such as a breaker for fast protection, or is conveyed to the utility control center using a SCADA system for slower but coordinated actions.
The traditional bulk power system has three operating rhythms. For protection, devices need to sense abnormal operating conditions locally, and act autonomously and fast to keep the system safe. The second is to monitor the health of key assets for diagnostics and prognostics, which is through SCADA. A third is to help manage and optimize grid operations and is based on state estimation, power flow calculations and scheduled economic dispatch. In all three contexts, key data from the grid needs to be collected. Voltage and current sensors at substations couple the data back to control centers through the SCADA system, providing data for state estimation and control. For stability considerations, phasor measurement units (PMUs) that measure precise phase angle differences between two remote points on the bulk power system are used, but are susceptible to channel losses and latencies. As the grid edge becomes more advanced, technological advances are needed to address these inadequacies and ensure better data collection.
On the distribution side, utilities (Saleh et al., 2019) have been deploying AMI using “smart meters” to automate the billing systems, and to gain visibility to the grid edge (Wang et al., 2019). Fault location isolation and service restoration (FLISR), as well as smart switches based on Load Tap Changers, Capacitor Banks, and Voltage Regulators are used for volt-VAR control (VVC) (Sun et al., 2019). Smart reclosers are also being increasingly used to allow rapid interruption of a fault, followed by automatic feeder reconfiguration to maintain service (S&C Electric Company, 2019) and improve resilience. However, digitalization of the distribution feeders, with millions of assets and points to be monitored/controlled, is hard to be accommodated through AMI alone because of the cost and complexity of managing the communications, computing, and data management for millions of meters (Padullaparti et al., 2019). The conventional paradigm of utilities collecting and analyzing data from all assets, determining an appropriate response, communicating it to the edge asset, and then ensuring that the action has been carried out, is very difficult to scale to millions of devices (Lo and Ansari, 2013). While significant efforts are being made through a variety of ADMS programs by utilities and vendors across the nation, a new paradigm is needed to manage the emerging millions of intelligent and dynamically controllable grid-edge assets on the distribution system (Kulkarni et al., 2019).
Examples of advances needed for distribution systems include greater use of fast-acting intelligent reclosers that can perform the FLISR function and microgrid-to-grid connect/disconnect autonomously; distributed VVC that can autonomously manage voltage profile of a distribution feeder even in the presence of high DER levels (Sun et al., 2019); low-cost AMI that does not require a communications backbone or tremendous levels of customization and data management; asset monitoring across the transmission and distribution system using remote monitoring and UAVs (Shakhatreh et al., 2019); and low-cost sensor networks that provide visibility and advanced analytics across the distribution network. Last, there is need for cost-effective advanced protection, both for DC and AC systems, including solid-state breakers and breakers that can dynamically modify their protection regimes based
on operating conditions, and potentially fault current limiters that can manage fault currents at distinct points on the AC and DC systems.
Enabled by the advent of ubiquitous and precise time synchronization, system operators have been deploying synchrophasor measurements to enhance system reliability for several years (Martin et al., 1998). DOE, working closely with other stakeholders from industry and academia, has been nurturing the deployment and utilization of this technology (Dagle, 2008). Funded in part by the American Recovery and Reinvestment Act of 2009, DOE provided substantial investment to promote the deployment of this advanced technology (DOE, 2009). Moving forward, there is significant opportunity to leverage this technology to support Wide Area Monitoring, Protection and Control (WAMPAC), which is the topic of an emerging body of research; one representative example is given in Johnson et al. (2011). These WAMPAC research initiatives seek to expand upon a class of controls referred to as system integrity protection schemes (IEEE, 2019). Additionally, research into continuous point-on-wave measurement systems is looking beyond synchrophasors (Silverstein and Follum, 2020).
Sensing and Controlling High-Voltage Components
High-voltage high-power handling components, including large power transformers, cables and conductors, surge arrestors, breakers, insulators, reactors and capacitors will continue to be at the heart of the transmission and distribution network. Tens of thousands of large power transformers rated at above 50 MW, and an estimated million utility-owned transformers rated at >10 MVA, are the central nodes of the transmission and sub-transmission system. The loss of a key transformer or substation can result in extended blackouts over large geographical regions impacting millions of customers (NASEM, 2017; Pesin, 2019; Wu et al., 2017). While the United States has made progress in recent years in developing stockpile of replacement transformers (Smith, 2016), manufacturing capacity in North America and Europe is very limited.
While much of this represents mature technology, integration of sensing and autonomous control into these devices can further enhance system reliability and resiliency. Advanced surge suppressors that can protect semiconductors from lightning surges and system faults will accelerate deployment of grid-connected power electronics at the medium voltage level (Khan Khadem et al., 2010). Power electronics holds the potential to enhance the capabilities of large power transformers to realize transformers that are more flexible and that can be more readily “tuned” to match the electrical properties needed in various applications (TRAC program DOE). Power electronics can similarly be used to implement FACTS technology to change the electrical characteristics and capacity of transmission systems. Improved dielectrics and magnetics, especially to handle high-frequencies at high-voltage are needed.
While key assets such as transmission substations and large transformers are monitored for performance and potential degradation, there are still thousands of assets on most utility’s bulk power systems, including towers, surge arrestors, capacitors, and splices; the loss of any one of which could have a huge impact on system reliability and availability. Chapter 6 presents a discussion of the cybersecurity considerations pertaining to having thousands of these assets on the bulk power system, as well as the potential ability for the grid to restart after a critical outage. The advent of low-cost sensing, communications, and analytics together with the possibility of distributed decision making has created an opportunity to extend continuous monitoring of the transmission and distribution power system, providing visibility and actionable information to system operators and helping to improve system reliability and availability.
Standards and Regulations for Pivotal Technologies
As elaborated in Chapter 3, the utility market is highly regulated. It has operated for about 100 years as an industry that moved slowly and conservatively to fulfill its public mission—focusing on safety, reliability, and cost. The industry often resisted making changes until technology, safety, and economic risks were eliminated. Owing to a noncompetitive environment, they collaborated with each other, setting up steep hurdles before technology
adoption could occur. Technical standards were developed by industry-wide bodies through a deliberative process that could take years. As the industry was mature and moved slowly, this was considered to be adequate. As has been discussed in Chapter 4, this also led to the development of large vendors who developed the needed technologies and equipment and recouped their investments over decades. This process has also made it very challenging for new companies, VCs and investors to promote new technologies, especially those outside the current operating paradigm, for use in the utility sector.
The new pivotal technologies have challenged these processes and paradigms. For instance, as DER deployment has exploded, IEEE 1547, the standard that applies to grid-connected inverters, continues to lag significantly. Microgrids that have to operate in grid-connected or grid-independent modes, to power geographically dispersed communities for resiliency in the face of fires and storms, can now be built, but do not have a standard that applies. Similarly, even as EV fast charging is rapidly increasing, multiple formats driven by EV manufacturers continue to be deployed. Many of these fast-moving technologies are evolving outside the sphere of direct control of the utility industry but enormously impact their operations. One concern is that if utilities do not evolve at a compatible pace, the grid-edge will build (out of economic necessity) so they can operate without the utility.
There are some additional challenges with standards processes today. As standards are often written by groups that have strong representation from the incumbents (industry and utility), they can often inhibit the rate of innovation and change. As many of the newest features and control principles are proprietary, there is hesitation to discuss these with standards-committees or with utility customers (e.g., VSC inverter controls). Further, with all these fast-moving new technologies, it is often very difficult for utilities to assess the potential impact on their grid, both in terms of controllability and safety. This can dramatically slow down the interconnection permitting process, and still does not ensure that there will be no problems.
Perhaps a new approach is needed for when and how new technologies are considered safe to connect to the grid. What is critical is that devices that connect to the grid (sources or loads) not impact grid operations in a negative manner, and are autonomously disconnected if they do so. This was not possible in the past and had to be ensured through a painstaking study of all modes of interaction between the grid and the connected devices. Today, such grid impact monitoring can be integrated at the point of common coupling (PCC) in a utility-owned device (e.g., smart meter), which can then act to protect grid integrity and safety while ensuring interoperability. Such an approach can ensure utilities of safety and controllability, while unleashing innovation downstream of the PCC, and allowing microgrids to achieve their potential of supporting the grid and providing resiliency at the grid edge.
Electricity Markets to Include DER
As noted in Chapter 3, as the adoption of DER begin to increase, distribution systems will need to contend with significant changes in power flows. These issues arise not only in traditional utility settings where the local utility must dispatch its resources to meet changing load conditions, but also in so-called organized wholesale markets.
Wholesale markets that govern centralized dispatch and operations—termed day ahead (DA) and real-time (RT) markets—are designed to ensure that the instantaneous supply and demand for electric power are balanced in a least-cost manner. The more recent market structures such as the Western Energy Imbalance Markets (Bonneville Power Administration, 2019; CAISO, 2020) improve the integration of clean energy. The above and all market structures may need to be revisited, revised, or overhauled depending on the extent of grid transformation, and changes may need to occur not just at the wholesale/bulk power level, but within distribution systems as well. A potential large penetration of DER accompanied by a mix of scales brings in a correspondingly large variability and uncertainty in generation assets. Market structures will have to accommodate the associated costs of increased volatility and risks, as they can lead to high congestion costs and significant reliability challenges (Kyritsis et al., 2017). As the total system inertia and contingency reserve capacity decrease, as nondispatchable renewables displace conventional generation, the critical operating decisions in terms of market clearing times may need to be faster and flexible. The following are some of the emerging directions that attempt to addresses these challenges.
Dispatchable renewables: Much of current practice is to treat renewables as negative load for planning and operational purposes. As the penetration of renewable power increases up from single digits to 50 percent and higher, RTOs with larger fractions of wind integration, such as MISO and CAISO, have run into issues with such current practice, and have started to dispatch and, when there is too much supply, curtail renewables. Curtailment of renewables has been growing, with CAISO’s wind and solar curtailments displayed in Figure 5.4. The challenges here are to address the design of penalties and curtailments when energy commitments are not met. Secondary market designs that address partnerships with other infrastructures such as natural gas and EV-networks are beginning to emerge (Fitzgerald et al., 2016; Karas, 2018). Another approach to addressing this issue is to integrate the renewable resource with 2 to 4 hours of energy storage, which provides dispatchability, as well as additional benefits (Stenclik et al., 2017).
Direct load control: The coordination and control of electric loads to provide various grid services were discussed in the previous sections in this chapter. Direct load control (DLC) has been used for many years by utilities across the country for peak shaving and load-shifting, through thermostatically controlled loads, plug-in electric vehicles, and data-center servers. These have also been used for frequency regulation and load following. Recent efforts in this direction pertain to the design of financial contracts between an aggregator and individual loads. While DLC has the advantages of achieving reliable and accurate aggregated load response, several challenges related to privacy and security concerns have to be addressed (Haring et al., 2016). How DLC can accommodate the finer grain load variations in space and time introduced by the changing landscape of the distribution grid, and in particular its advantages over the use of rate structures based on price-responsive control (PRC) such as time-of-use (TOU), critical peak pricing (CPP), and real-time-pricing (RTP), needs to be investigated.
Transactive control: A specific example of PRC that has been tested on a large scale is transactive control, which has the potential to efficiently integrate DER and DR in distribution grids. Rather than use the wholesale price, an internal price is designed to accommodate specific, local and control objectives. This allows an accommodation of some of the advantages of DLC and leads to a better-aggregated load response. It also accommodates a few microeconomics-based principles, which motivates self-interested consumers to participate in the program. Three large demonstrations have been reported in the literature, the Olympic Peninsula Demonstrations (2006–2007) (Hammerstrom et al., 2007), AEP gridSMART demonstration (2010–2014) (Widergren et al., 2014), and Pacific Northwest Smart Grid Demonstration (2010–2015) (Battelle Memorial Institute, 2015), which show that this approach has a significant potential in accommodating renewable penetration, offers flexibility to consumers, and improves system stability and reliability. Significant challenges remain in extending this concept to a larger scale across the country.
New market structures: As DER proliferate and continue to dot the landscape of distribution grids, the need for a distribution market, which is currently absent, becomes stronger. Several retail demand response programs have been proposed of late to compensate for reductions in electrical demand through reservation payments. But they either over- or undercompensate customers owing to several latencies, variabilities, and uncertainties (Borenstein, 2014; Wolak, 2006; Wolfram, 2017). Dynamic rate structures outlined under PRC above attempt to provide better pricing signals to customers, so as to alter their consumption behavior and accurately recover the associated costs. However, it remains to be investigated as to when these rate structures are adequate and if new market structures with underlying pricing signals are dynamic and also different from the wholesale pricing.
In Haider et al. (2020), a distributed LMP-like price is proposed based on a distributed optimization algorithm and is shown to ensure voltage control. In Jinsiwale and Divan (2020), an AGC-like market structure is proposed by utilizing a price-frequency curve for different DER to participate. The development of a streamlined retail market is nontrivial and needs to address significant challenges in scalability, reliability, security, and privacy. While many of the market structures assume communications, central computation, and compliance from actors, it is also important to consider resiliency and continued real-time system operations when this backbone is compromised or is not working.
Finding 5.9: The increasing penetration of intermittent renewables, storage devices, and flexible loads is introducing operational challenges in distribution grids. The proper coordination and scheduling of these resources using a distributed approach might be achieved through local retail markets employing transactive energy schemes, although (as noted in Chapter 2) this can raise issues of equity. Fine-grain variations of generation, flexible demand, and storage in space and time would need to be suitably incentivized, which may be beyond the reach of wholesale markets, and may need to be operated by a Distribution System Operator (DSO). Related ICT needs would need to be in place to ensure secure, accurate, fast, and resilient communication in such markets.
Recommendation 5.3: The Department of Energy (DOE), Electric Power Research Institute (EPRI), other domestic and international research organizations, universities, and worldwide industry should develop relevant supporting information and communications technology (ICT) to permit (1) secure, reliable, private, and fast communication; and (2) security, safety, accuracy, privacy, and speed in computation, so as to incentivize various asset owners to participate in a retail market structure that allows distributed energy resources (DER) to participate and be compensated for distributed generation, grid support services, and/or flexible load consumption.
COORDINATION, ULTRA-AUTOMATION, AND CONTROL
The discussions in the preceding sections point to the proliferation of three highly distributed assets: small-scale generation, storage, and flexible consumption. These are facilitated by continued growth in dispatchable large-scale wind and PV farms with energy storage, continued growth in utility-scale solar and wind, grid-scale batteries with large discharge cycles becoming available at low cost, and an acceleration of customer-side demand management with greater functionality and lower cost (Pierluigi, 2014). Under these circumstances, managing and coordinating thousands of grid-edge assets under the direct supervision of a centralized grid controller/operator becomes challenging (von Meier, 2011). Performing distributed actions at various points in the distribution network based on central dispatch requires system knowledge, communications, coordination, and data management at an unprecedented scale, and will likely be very expensive. The idea that we will know our neighbors requires a level of topology detail and control coordination that may be impractical. Further, without a high level of automation, it will require trained personnel that simply may not be available. Introducing elements of distributed control, coordination, and automation also presents increased cybersecurity risk, discussed in greater detail in Chapter 6. Last, while the technology to monitor all assets at the grid edge is available, the cost associated with implementation has been so high that it has been challenging to build a business case.
The possibility that a future grid may be composed of hundreds of millions of generators, control devices, and loads—all of which must be automatically coordinated at all times and at all locations while ensuring a range of grid services including reliability and affordability during normal operation, and resilience and fast recovery under stressed conditions—presents a formidable challenge for reliable grid operations (Favre-Perrod et al., 2019). Unlike many other systems that consist of very large numbers of devices (e.g., cell phones, PCs, and IoT devices), the grid is characterized by the physics of the system including high voltage and power levels, as well as interaction between connected devices over a vast time scale that ranges from microseconds to years. Latencies in communication, which may be inconvenient in many applications, can cause dangerous conditions and can result in system failure and outages with catastrophic consequences. Therefore, an array of systems, technologies, and capabilities has to be addressed to realize this grand vision of a future grid (Annaswamy et al., 2013; Stoustrup et al., 2018). Three important technologies are enumerated below.
The first step toward ultra-automation with millions of assets is their coordination. Coordination of spatially distributed assets with disparate capacities and time scales using today’s centralized approach is extremely challenging, if not impossible. This is because such coordination becomes highly expensive to manage and operate, as well as more fragile and vulnerable. A strategy separating the slow logistical and management processes from operational real-time control of millions of distributed assets is needed for the future distribution system to achieve its desired performance goals. That is, a systems-wide manual that prescribes guidelines for who needs to talk to whom, when, and for what (i.e., a less-centralized grid architecture) may be needed. In this case, an extensive application of controls (Molzahn et al., 2017)—concepts that outline the tenets of decentralized, distributed, hierarchical systems—would need to be carried out. Uncertainties and intermittencies across the three different assets of generation, storage, and flexible consumption necessitate the development of new tools and methodologies in control systems. Integration of fast-acting autonomous devices, such as controllable transformers and power flow control devices would also need to be carried out (Kandula et al., 2018). Most importantly, coordinating the growing and transforming distribution system and its intelligent edge with the transmission system that is responsible for the bulk generation, serving as the backbone of the current grid, is a challenge that needs to be overcome.
Utilities continue to be challenged by the cost and complexity of implementations of ADMS and AMI, and with deriving the full value potential for these technologies. Here too, coordination through a distributed architecture and leveraging of the presence of DER is highly warranted. Low-cost sensors (e.g., IoT), edge-computing, local rule-based actuation (not dependent on detailed knowledge of topology), and a simplified “universal” communication to “cloud” (such as a pub-sub model) of summary data and actionable information can reduce data intensity and system complexity, allowing scaling to millions of nodes. Further, edge intel-
ligence and actuation, flexible low-cost global communications, cloud computing, advanced data analytics, artificial intelligence (AI) and machine learning (ML) can together provide the foundation for an intelligent and autonomous grid. The increased proliferation of DER also introduces new challenges for protection, owing to bidirectional flow (Jahangiri and Aliprantis, 2013). This, together with the fact that the reliability levels in the current distribution systems are lower, introduces yet another challenge when it comes to designing reliable distributed control architectures. How a distributed and coordinated architecture can be leveraged to ensure resilience under high-impact, low-frequency events (e.g., identify and operate electrical islands in isolation from the rest of the grid) is another important problem to be addressed.
The coordination of thousands (or millions) of inverters in real-time (microseconds to hours) requires control principles that are still evolving. Issues of cost and latency (or loss) of communications, cybersecurity, imperfect system knowledge and real-time interactions need to be managed—ensuring continued system operation under these constraints.
The ultimate extension of the distributed architecture is one that consists of billions of real-time active endpoints with control. This scenario would require the deployment of sensors, actuators, and communication devices to be employed at a large scale at generators, substations, renewable energy sites, in power-electronic devices, in storage devices and in electric vehicles, and in buildings, microgrids, and homes. This conglomerate entity would need to evolve in accordance to a tightly coordinated blueprint of closed-loop control that must be implemented in a distributed manner. Every one of these nodes would have to act intelligently, independently, and autonomously, once again based on local information and set rules, to ensure power balance, and regulate key variables such as voltage and frequency, even under widely changing ratios of generation supply to demand (Kroposki et al., 2017). The control loops have to function in real time, will have to employ various degrees of feedback, be scalable, and address resilience and security issues. The time scales involved have to span a wide range, with slower ones corresponding to optimization and reliability, and faster ones that correspond to real-time control, as well as high-stress events and disturbance mitigation. Associated technologies of sensing, computation, communication, and actuation have to be commensurate with these temporal specifications.
An idealized instantiation of ultra-automation is the realization of the entire grid as a cluster of flexible microgrids (Heidel and Miller, 2018). A system that can operate as a cluster of such flexible microgrids could have a transformative impact on the reliability, resiliency, equity, sustainability, and affordability of electrical power right down to the grid edge (Li et al., 2017). It is likely that if the grid were being built today from the ground up, it would be built from the bottom up as a cluster of microgrids, which can operate connected to or independent of, with automatic fail-over from, the bulk power system. In such a system, reliability and resiliency at the grid edge would be baked into the distribution system itself, while bulk power is transacted whenever it is available at lower cost. This would flip the current grid paradigm on its head, enabling the use of highly variable generation with widely varying costs and matching those resources with flexible loads in real time. Given that many microgrids and DER are being installed, interconnecting them to achieve the desired properties for the power system requires some technical innovation, and the removal of some regulatory barriers (Hanna et al., 2017). Developing technological and manufacturing leadership in such next-generation grid architecture can position U.S. companies to build the next-generation infrastructure in the United States and for emerging economies. Both physical and economic architectures have to be jointly designed as to ensure affordability, equitability, and sustainability simultaneously with reliability, security, and resiliency.
A future ultra-distributed scenario would require the deployment of sensors, actuators, and communication devices to be employed en masse, at generators, at substations, in renewable energy sites, in power-electronic devices, in storage devices and electric vehicles, and in microgrids, buildings, and homes. While a power grid with such an ultra-automation may be a highly idealized concept, a concerted effort in developing technologies in this direction can lead to the necessary building blocks for the future “grid as an ecosystem,” where millions of
individual players, aggregators, and utilities work together to achieve real-time balance and operation, and realize a clean, safe, affordable, reliable, and resilient grid.
Finding 5.10: An ultimate extension of distributed architecture is one that consists of billions of real-time active endpoints with control. This becomes especially important when accompanied by fast obsolescence in communications, sensing, cyber, and cloud technologies—as it becomes increasingly challenging to roll out intelligence to millions of devices at the grid edge, and to integrate these devices into grid operations and control systems. Autonomous operation may be inevitable for such complex systems, and will require regulators to stay apprised of new ICT developments. The challenge is to introduce such advanced autonomous ICT technologies even while ensuring cybersecurity at the component and system levels.
Machine Learning and Artificial Intelligence
The explosion of sensing, actuation, and communication technologies implies that a large amount of data that are relevant to real-time grid performance begin to become available (Ramchurn et al., 2012; Simmhan et al., 2013). The question then is if the current huge advances in AI and ML-based methods can be brought to bear on this data and lead to important grid services. Machine learning allows for the use of self-improving algorithms, and AI could perform operations typically done by a human. These methods can be employed at the grid edge in edge devices, or in the cloud (Liu et al., 2019). A particular problem that would be apt for the deployment of ML and AI is power-system security and resiliency, as it involves monitoring, extracting, and synthesizing relevant information and real-time decision making. Problems with either unstructured information, where the underlying physics has significant uncertainty, or is too complex to render the problem intractable, are all candidates for the deployment of ML and AI. The application of ML and AI to the grid will likely require advances in these techniques outside of the electricity sector first, leading the way to adoption for the grid. The challenges are to ensure that the solutions lead to grid services with desirable properties such as reliability, safety, and affordability.
Recommendation 5.4: The Department of Energy (DOE), Electric Power Research Institute (EPRI), other domestic and international research organizations, universities, and worldwide industry should support the development of new suites of technologies that can enable the high levels of automation needed in a future grid. These include (1) ultra-automation strategies to allow millions of edge-intelligent devices (e.g., fast responding inverters) that coordinate in real time in a secure manner; (2) technologies grounded in power-electronics, communications, computing, and control to ensure a cyber-enabled, distributed energy resources (DER)-rich distribution grid that ensures resiliency and reliability, with the bulk-power system transporting low-cost energy as needed; and (3) technologies that enable a resilient cluster of electrical islands interconnected through direct current (DC) or controllable alternating current (AC) links.
MODELING AND SIMULATION OF NEW TECHNOLOGIES AND EMERGING ARCHITECTURES
In the preceding sections of this chapter, a wide variety of new and emerging technologies are presented that are changing the nature of the grid. Together with the changing policies for decarbonization and higher resiliency, the architecture of the grid is evolving into new configurations. These trends in the architecture are discussed in Chapter 1, and some possible new architectures are presented in Chapter 2. The only way to study the performance of the present and evolving grid has been the analytical tools that can model and simulate the grid. These software tools have also evolved over the past six decades but will now have to develop much more rapidly to keep up with the qualitative and quantitative changes in the grid. This section provides some findings and recommendations
about these analytical tools needed to ensure that the evolving architectures meet the sustainability, reliability, economic, and security standards.
Understanding the performance of the grid requires ongoing analysis and attention by electrical engineers in operational settings, commercial enterprises, and academic institutions. Simulating all aspects of a large grid from end-to-end across all time scales presents difficult challenges and may not even be feasible. The simulation tools available today can only simulate limited portions of the grid for a particular behavior at a given time. Many different simulation tools are commercially available, and engineers must choose the appropriate tool to study one particular behavior for one portion of the power system, such as the electromechanical behavior after a short circuit on the generation-transmission portion of a grid, which can be studied using a transient stability program. The study of the voltage control of a distribution system, however, requires a completely different set of software applications. Moreover, these different analytical tools are not built on any standard databases or platforms; instead they use proprietary databases and programs, thus making it difficult if not impossible to connect these tools together to give a comprehensive view of the whole grid. Thus, preparing data for analysis and interpreting results from each simulation tool requires significant engineering time before decisions can be made, making it very inefficient in these times when both technology and policy are changing at a rapid pace (NASEM, 2020).
Decision making with respect to performance of the grid involves three overlapping time frames:
- Planning decisions: What components are needed in upcoming years to ensure that demand in a region can be supplied with the needed electrical energy in an economic, reliable, and sustainable manner? The time horizon for such analysis depends on the type of equipment being considered because some (e.g., transmission lines) can require many years to put in place whereas others (e.g., transformers) can be installed in a few months.
- Operational decisions: How to operate the existing grid for the next few minutes (e.g., disconnecting a transmission line) to many months in the future (e.g., scheduling water flow through hydro generators). This is also known as operations planning.
- Real-time decisions: What actions must be taken in real time as system conditions change unexpectedly (e.g., a tripping of a generator or a short circuit)? Some decisions are automated, while others are activated by the human operator.
Another simulation tool is that used for training grid operators. These simulators have to simulate in real time the behavior of the grid including all the ICT components so that the operator trainee can perform all the operator actions in simulated operational scenarios.
The simulation methodologies that are appropriate for these different time frames are obviously quite different, but all are beneficial for understanding the future development of grid’s architecture throughout its layers. Similarly, the simulations needed for the different layers of the architecture and the simulation methods for the different phenomena—like fast electromagnetic phenomena (micro-secs) to hydro-generation scheduling (months)—are all very different. In all these cases, the physics are well understood, and models are available for all except the very new technologies, so these balkanized set of simulation tools can be easily upgraded for the new technologies or policies.
In his classic book, Science of the Artificial, Herbert Simon (1981) defines a complex system as:
One made up of a large number of parts that interact in a non-simple way. In such systems the whole is more than the sum of the parts, not in an ultimate, metaphysical sense but in the important pragmatic sense that, given the properties of the parts and the laws of interaction, it is not a trivial matter to infer the properties of the whole.
He goes on to argue that:
How complex or simple the structure is depends critically upon the way in which we describe it. Most of the complex structures found in the world are enormously redundant, and we can use this redundancy to simplify their description.
But to use it, to achieve this simplification, we must find the right representation.
Simon argues that most such systems are hierarchical and thus can be decomposed into subsystems in which many of interactions among the different parts are negligible. Of course, interactions between some of the parts are more consequential, and in studying such systems, it is those interactions on which one should focus.
Building on Simon’s insight, Morgan (2017) has argued that “electric power systems provide a good example of a system often analyzed through decomposition.” For example, in terms of different time scales, he writes:
Electric fault processes can involve time scales of fractions of seconds; voltage and frequency transients can involve periods of seconds to minutes; questions of economic dispatch and load management typically involve time scales of minutes to hours; and expansion planning involves time scales of years to decades. Rather than constructing a single model that spans the entire problem space from milliseconds to decades, different models are used to study each of the different issues.
Other interactions can be similarly decomposed and separately modeled.
The problem, however, is that today there are a plethora of models, many of which are seriously incompatible. If there is to be a means for modeling potential architectural developments for the grid, this intercompatability must be overcome. Most members of the committee believe that setting out to build a single large integrated simulation model that encompasses all aspects of the power system is not likely to be a productive strategy. However, all members of the committee agree that the analysis community should push hard to make existing and future models substantially more compatible—using inputs and providing outputs in forms that allow far more effective interaction. Outside the United States, some success in intercompatibility has been achieved by using the international common data standard known as the Common Information Model (CIM). In China, all grid control centers are now designed on the CIM, while the European Union has recently also adopted this standard.
Finding 5.11: Because they use different strategies, data, and formats, many of the current generation of models used to assess and plan the power system are incompatible and do not adequately work together. The development, maintenance, and continuous improvements of simulation software will continue to depend on private vendors, but there is a need for standards, frameworks, and platforms such that all new analytical tools are developed to be interoperable with each other.
Recommendation 5.5: The Department of Energy (DOE) should support a sustained collaboration of national laboratories, academia, utilities, and vendors to develop a family of intercompatible simulation tools that have common standard interfaces to work together to assess the performance of the present grids and better anticipate the implications of the various ways the grid architectures may evolve in the future. Because having a single, large, integrated model of very large, complex grids is impractical, the development and standardization of common interfaces between simulation tools will enable the studies of evolving architectures of generation, transmission, distribution, and information and communications technology (ICT).
While different regions may have different architectures, there is typically only one architecture in any single region, which may be as large as some or all of a continent. The United States and Canada are tightly interconnected and are planned and operated according to the same NERC standards. There are four separate synchronously connected areas—the Eastern, Western, Texas, and Quebec interconnections—which are then further electrically connected by HVDC connections. To study the implications of different possible evolving architectures, one need not always simulate the whole North American grid, as many engineering questions can be answered by studying a portion of the grid for particular behaviors under certain scenarios. That said, it is very important to be able to move more easily from studying one scenario to another, or to quickly connect the scenarios when they impact each other. For example, if wind generation is curtailed unexpectedly in the Midwest, how might manufacturing
loads in the Southeast be affected? If the rooftop solar panels in the distribution system in the Southwest start ramping down near sunset, what distant generation will the transmission system need to bring in to the Southeast?
The characteristics of the nation’s power grid have a variety of serious national security implications. Hence, much of the detailed data on the national grid is of a sensitive nature and is kept out of the public domain. This means that many detailed studies cannot be conducted in the public domain. However, R&D is more effective if it can be done in the public domain, and in anticipation, ARPA-E has already started developing synthetic grid data that can mimic certain characteristics of the real grid. The idea is to conduct R&D and develop improved and more compatible simulation tools by using these sets of synthetic data as test systems. Sensitive data from the real grid can be kept proprietary and only used by the utility industry and their contractors for the actual planning and engineering studies.
As part of their reliability responsibilities, FERC and NERC have had to depend on studies done by regional entities, as FERC and NERC have not had direct access to the underlying data to run the simulation studies themselves. As the regional entities often only run studies for their region and not the whole interconnection, increasingly FERC and NERC will need studies that encompasses the whole grid as the reliability of the grid depends on the holistic behavior.
Finding 5.12: FERC and NERC rely on standards to oversee planning of the grid to ensure adequate levels of reliability. In the future, they will need to ensure that the models and intercompatible analytic tools that are used by planners provide comparable insights about future scenarios of the interconnected grid. Also, the many interconnected power companies in the same grid need to use common processes, like comparable simulation tools, to determine reliability metrics.
Finding 5.13: The operation and control of the interconnected grid is coordinated by large numbers of control centers, protective devices, and controllers, and FERC and NERC again regulate the reliability and market standards that must be followed. Online analytical tools need to be tested on large-scale simulations of the grid, and such online analytical tools and the process of their testing need to be standardized so that, to the extent possible, all power companies and their control centers operate by the same standards.
Recommendation 5.6: As new technologies that impact the architecture of the grid are deployed in the grid, the North American Electric Reliability Corporation (NERC) should develop and the Federal Energy Regulatory Commission (FERC) should approve standards that more specifically address new technologies and ensure that information is available to enable the development of improved modeling and simulation tools.
Recommendation 5.7: As more capable and intercompatible simulation tools become available, system planners and operators should use the results and insights that are gained to develop better grid architectures, plans, and operational procedures; they should also inform regulators and policy makers, such as the Federal Energy Regulatory Commission (FERC) and the North American Electric Reliability Corporation (NERC), about potential issues and opportunities for improving grid operations and planning, so that this information can be used to update the regulations and standards.
A primary motivation for developing improved simulation capabilities is the limit on what can be done experimentally in the grid while it is operating. Testing and evaluating different architectures is important for ensuring operation which satisfies key attributes for the grid as it evolves. Clearly keeping the power on for customers must be a very high priority. That said, there are sometimes limits to just how much can be learned through simulation, or how much confidence one can place in the results. If a promising architectural evolution shows excellent results in simulation, some large-scale field experiments to verify the simulation results could provide the confidence needed to commit to large-scale investments. Any large-scale grid experiment will require the active participation of key stakeholders like the generation, transmission, and distribution owners as well as reliability coordinators, customers, and regulators.
Finding 5.14: When they can be conducted in a manner that does not create a high risk of service disruption, experimental studies conducted in the operating grid can provide insights that are difficult, and sometimes impossible, to develop solely through the use of simulation.
Recommendation 5.8: Because there will always be limits to what can be learned through simulation, the Department of Energy (DOE) should choose the most promising new architectures indicated by large-scale simulation studies in order to identify and plan a number of large-scale field experiments that could verify the advantages of such grid architectures under actual operations. Such field experiments of grid architecture would be qualitatively and quantitatively much larger in scope than the usual prototyping of a component such as a storage device, and should be reserved for when adequate resources and opportunities are available.
CREATING THE WORKFORCE TO DESIGN, MANAGE, AND OPERATE THE FUTURE POWER SYSTEM
These transformative technological changes to the grid, in conjunction with an existing aging workforce and the need to ensure the industry has access to a workforce that can ensure safe and efficient operation of the electric system, pose challenges in terms of workforce education, training and development. Workforce needs include educating a new generation of managers, designers, and operators, including people with training on both OT and IT elements of the grid (including cybersecurity); expanding the pool of skilled craft workers; providing job placement and retraining for displaced workers; and adopting and implementing federal policy to help meet these needs.
A few decades ago, the electricity sector found it difficult to recruit top students from business and engineering schools because the industry was seen as dull and unexciting. That impression is now changing, with many students realizing that the fast pace of change in the sector offers promising career opportunities. Although many junior colleges, 4-year colleges, and universities had dramatically reduced their relevant programs, they are now rebuilding. This needs to continue to ensure that the industry will have the benefit of diverse, creative and well-educated leadership in the decades ahead.
Employment policy in the United States over the past several decades has often emphasized fast job placement rather than vocational training. A recent meta-analysis of labor market policies has found that job search assistance programs have positive effects on fast reemployment, whereas subsidized retraining programs are not always effective (Card, 2010).2 However, both displaced workers and policy makers tend to underestimate the level of training required to recoup earning losses (Jacobson et al., 2011). Most of these reports, however, are not electricity-sector specific; a more rigorous account of electricity-specific workforce retraining is necessary.
Workforce retraining becomes especially important given the emergence of new technologies and tools outlined in this report. Chapters 1 and 2 describe the many ways in which the electricity system is expected to change—notably across all three physical, ICT, and organizational layers.
2 Note that as a means of mitigating unemployment from decreases in coal sector jobs, for example, the Department of Labor recently created geographically targeted grants for workforce retraining programs (DOL, 2019).
The emerging cyber physical system, for example, has several new pathways for monitoring and decision making that are essential for power system operations. This in turn underscores the need for workforce education, training and retraining to ensure that the nation has the human resources to work in these emergent pathways. NSF initiatives such as “Training-based Workforce Development for Advanced Cyberinfrastructure (CyberTraining)” need to be reinforced with similar research endeavors by other organizations such as DOE to address such retraining. It should be noted that, as with all employment transitions, there are positives and negatives distinct to different employment outcomes. We discuss below both near-term and long-term considerations of this retraining.
In the near term, changes in the workforce needs across the electricity industry will tend to track changes in the power production portfolio.
Renewables are expected to be a key sector for job growth, in manufacturing, construction/installation, and operations and maintenance. However, the number of full-time job equivalents and skillset requirements for operating solar or wind plants will be very different than what is needed for thermal power plants and individuals previously (or currently) employed at thermal facilities may not find seamless transitions to the renewables industry. While one study that assessed the potential transition of coal workers to the solar PV industry estimated that a relatively minor investment in worker retraining could allow the majority of workers to find employment in the PV industry (Louie and Pearce, 2016), there remains significant skepticism regarding the effectiveness of worker retraining programs. This skepticism is because workers want to be confident that retraining will result in jobs that pay well and are unlikely to be outsourced or automated (Schimmel, 2019). The potential for jobs to be relocated is also an impediment.
Small modular reactor (SMR) manufacturer NuScale has suggested that retired coal plant infrastructure could be repurposed to house SMRs, and that most plant operation positions are directly transferable from coal to nuclear with no wage reduction (NuScale Power, 2017). In that context, Utah Associated Municipal Power Systems has planned two coal plant closures and anticipates SMRs will play a major role in replacing retired coal generation. Should that happen, it clearly has implications for future workforce opportunities, but if it does come to pass, the affected communities are unlikely to see those jobs for many years.
The 2020 U.S. Energy and Employment Report points to employment-related difficulties across all the energy-related employment groups. In electricity, it indicates that
- 90 percent of construction employers in power generation reported that it was somewhat difficult or very difficult to hire new employees, with 29 percent reporting it to be very difficult.
- 84 percent of manufacturing employers reported that it was somewhat difficult or very difficult to hire new employees.
- 93 percent of utility employers in electric power generation reported that it was either somewhat difficult or very difficult to hire new employees. However, only 7 percent of those reported that it was very difficult.
Finding 5.15: Studies on the employment impacts of major changes in the energy systems tend to offer highly varying estimates. The lack of firmer evidence has made it difficult to understand and debate important employment-related aspects of energy transitions and energy policy, and has also impeded a firm understanding of whether active training and support will be needed for communities and workers on the losing side of these transitions.
Recommendation 5.9: Congress should provide funding for the Department of Labor, Department of Education, and Department of Energy (DOE) to build on previous experiences in funding workforce training programs (e.g., 2009 Recovery Act Workforce Development, Grid Engineering for Accelerated Renewable Energy Deployment [GEARED], etc.) and provide funding to support vocational, profes
sional, and academic programs to train, retrain, and educate the current and future workforce in the electric utility sector and electrical manufacturing industries. These programs should be implemented in a way that allows for state-of-the-art quantitative analysis of program effectiveness and learning for future policy development.
Recommendation 5.10: The Bureau of Labor Statistics (BLS), in conjunction with the Department of Energy (DOE), should invest in accurately estimating job numbers in more granular categories of work in industries that are part of the electricity system supply chain of the future. An analysis of the overlap of electricity with other employment sectors is necessary to understand the economic significance of electricity-specific employment changes. Furthermore, an analysis of wage impacts following displacement and/or retraining owing to transition-related job loss is necessary to better address equitable worker transitions. This includes those employed in commissioning and installation as well as end-use manufacturers, with a focus on moving from fossil-focused to renewables and electrification-focused employment.
As the grid becomes more complex and more heavily dependent on ICT, more workers who have an understanding of ICT among OT staff will be needed, particularly for utilities that will not be able to support individual staff positions for each grid security skill. This need is particularly acute and compelling in light of the legacy in which OT systems were initially designed with an assumption that they would be deployed in an environment of implicit trust; in that context, cybersecurity controls have been less extensively integrated into the devices, equipment, and network architecture, and OT security in the industry. Compared to IT security, relevant OT skills in the electric industry are less prevalent and less mature. Educational programs for grid engineers and operators have only recently begun to incorporate cybersecurity training into curricula, and many of today’s engineers and operators do not have expertise in OT cybersecurity. Additionally, within the vendor community there is often an organizational separation between the individuals who design and build the substation and operational systems architecture, and those who design the ICT architecture and implement the communications systems that will operate the equipment.
With the sharp increase in the use of power electronics all across the power system, there is also a critical need for workers who are trained in power electronics systems as well as in generation, distribution, microgrids, storage, and/or EVs. This skillset is different from traditional IT and cybersecurity skillsets, as well as from those who install PV and wind systems or are traditional power systems. There is shortage of people trained in these areas and the skills from these fields do not directly crossover.
As of 2019, the International Information System Security Certification Consortium ((ISC)2), a nonprofit membership association of certified cybersecurity professionals, estimated that the U.S. cybersecurity workforce needed to grow by 62 percent to meet demand. The cybersecurity workforce shortage in utilities could be even more extreme considering only a small portion of the existing workforce specializes in ICS cybersecurity. Of respondents from North America, 11 percent were in an operational technology security team and none were employed at organizations with less than 250 employees ((ISC)2, 2019b). Cybersecurity positions that remain vacant for too long expose organizations to unnecessary risk (Olyaei et al., 2018). Of 3,237 respondents from across the United States, 51 percent of the cybersecurity professionals said their organization was at moderate or extreme risk owing to a cybersecurity staff shortage ((ISC)2, 2019b).
As elaborated at greater length in Chapter 6, there are also workforce needs in cybersecurity. Curricula in cybersecurity need to be added or upgraded into the core education and training for future electrical and other engineers, operators and computer scientists. There is, of course, a tension in that adding to core educational requirements will force different content out of the curriculum or potentially add more time to a degree program. Given the trend to reduce specific degree requirements, embedding cybersecurity training into multiple classes
across these academic disciplines could be an effective and efficient strategy to enhance cybersecurity training. A more broadly shared knowledge could help mitigate the language and cultural divides between the engineers and operators, on the one hand, and IT/OT staff on the other Developing model job descriptions that include the minimum set of cybersecurity skills required for future positions in these fields can help students understand the market value of cybersecurity curricula, and can help utilities evaluate their current and future job descriptions. Again, the importance of these issues is explored in greater depth in Chapter 6.
An important related concept also gaining prominence is that of Cyber Physical Human Systems (Annaswamy and Yildiz, 2020; Baillieul and Gelenbe, 2012), where the human, the physical system, and enabling cyber technologies are interconnected through complex interactions to accomplish a certain goal. This approach sharply contrasts with a conventional perspective, where the human is treated as an isolated element who operates or uses the system. This concept is highly appropriate and applicable in the current context, as the control operator needs to leverage the emerging technologies that provide visibility and enable fast and accurate decision making appropriately. These interactions between the operator and the overall cyber-enabled grid become especially important in the context of emergencies where security and resilience become crucial. Notions of cognitive resilience, capacity for maneuver, graceful degradation have to be explored systematically to the functioning, education, and training of control room operators (Hollnagel et al., 2006).
The committee’s recommendations for cybersecurity and training are continued in Chapter 6.
While by no means exhaustive, this chapter has covered technologies whose development and application hold the potential to substantially influence the future grid. These first include increasing amounts of generation from clean sources and the implementation of energy storage (especially long-duration storage and storage to support intermittent generation sources). Accompanying clean generation and storage are advanced power electronics and communications technologies facilitating advanced grid management systems and ultra-coordination, automation, and control.
A compelling trend across both the physical layer and the ICT layer of the grid (see Figure 1.7 in Chapter 1) is the rapid and sustained trend in declining costs across the board. In the physical layer, the price of renewable resources, including utility scale wind and PV solar plants, has been declining over several decades. In addition, grid-scale batteries with large discharge cycles are becoming available at low cost, and there is an acceleration of customer-side demand management with greater functionality and lower cost. In the ICT layer, a large-scale implementation of AMI has facilitated digitalization. Wireless technology has progressed dramatically over the past four decades with increasing performance and decreasing cost. Implementation of IoT networks is becoming increasingly possible with low complexity and low cost, thereby continuously lowering barriers for implementation. These concomitant cost and complexity reductions, in both the power- and information-layers, are paving the way for distinctly different viable alternatives for the evolution of America’s electricity grid. These trends result in more distributed ICT, more non-utility ICT and increased points of connectivity with utility ICT assets. As a result there is an increased cybersecurity vulnerability landscape. Key elements for ensuring security and cyber resiliency for a modernized electric grid are detailed in the following chapter.
Making all of this happen will require well-educated and trained people capable of designing, managing, and operating the power system of the future, whatever specific form it may take.
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