- Email: [email protected]
The value of demand response in Florida
The Electricity Journal 30 (2017) 57–64
Contents lists available at ScienceDirect
The Electricity Journal journal homepage: www.elsevier.com/locate/tej
Energy Policy Institute's Seventh Annual Energy Policy Research Conference
The value of demand response in Florida a,⁎
b
Brady Stoll , Elizabeth Buechler , Elaine Hale a b
T
a
National Renewable Energy Laboratory, United States Stanford University, United States
A R T I C L E I N F O
A B S T R A C T
Keywords: Demand response Power systems Solar photovoltaics Renewable integration
Many electrical loads may be operated flexibly to provide grid services, including peaking capacity, reserves, and load shifting. The authors model 14 demand end uses in Florida and analyze their operational impacts and overall value for a wide range of solar penetrations and grid flexibility options. They find demand response is able to reduce production costs, reduce the number of low-load hours for traditional generators, reduce starting of gas generators, and reduce curtailment.
1. Introduction Demand response (DR) is a broad descriptor for any electric utility or aggregator program that incentivizes or requires loads to reduce or otherwise modify their energy consumption in support of grid operations. These types of programs have been in use for many years in the United States in the industrial sector, where customers are contracted to reduce their demand during emergency periods. More recently, demand response programs have been used to shift residential and commercial loads away from peak periods. As renewable generation sources increase, the ability of demand response to help mitigate challenges with increased net load variability and uncertainty has become of higher interest to utilities due to its ability to provide flexibility to the electric grid. Additionally, rapidly developing communication and control technologies have made demand response more flexible and easier to implement. In this article we study the potential impact of demand response in Florida assuming both low and high levels of solar photovoltaic (PV) deployment. Florida currently has a high level of demand response and little PV generation, but the state is poised to see high growth in solar energy in coming years due to its high solar resource potential and the falling costs of solar installations (Gagnon et al., 2016; Lopez et al., 2012; NREL, 2016; Lazard, 2016). Challenges with the “duck curve” that are starting to be seen in California, including low net loads during the day and a steep ramp in net load during the evening, are likely to be seen in Florida as well in the 5- to 15-year timeframe (Hale et al., 2017). Demand response can help mitigate these issues by shifting loads from high net load periods to low net load periods, providing reserves to meet increased variability from solar, and by providing capacity for long-term system planning. This is in addition to the valuable capacity
⁎
Corresponding author at: 15013 Denver West Pkwy, MS RSF300, Golden, CO 80401. E-mail address: [email protected] (B. Stoll).
https://doi.org/10.1016/j.tej.2017.10.004
Available online 10 November 2017 1040-6190/ © 2017 Elsevier Inc. All rights reserved.
and peak load management services that demand response already provides (Lee et al., 2015). In this article we analyze the impact on grid operations of 14 different load types that could provide demand response in Florida. We include two demand response penetrations, one approximately equivalent to 2015 participation in demand response programs, and one assuming a high participation rate. We analyze the changing impacts of demand response as PV penetration increases from 5% to 45%, with a focus on the value of demand response to the system at higher penetrations in terms of system operation impacts. 2. Methods We analyzed the grid operations of the Florida Reliability Coordinating Council (FRCC) using the commercial software package PLEXOS (Energy Exemplar, 2014), and the FRCC model created for (Denholm et al., 2016). In this model, we include all operational constraints on generators and enforce line limits on all lines above 200 kV. It includes a connection to the only reliability region in the eastern interconnection that is electrically connected to FRCC, the SERC Reliability Corporation in Georgia, which is modeled as a single load and supply curve of generation. We include additional utility scale photovoltaic generators to enable penetrations up to 45%. For more information on the PLEXOS model, see (Hale et al., 2017; Denholm et al., 2016). We incorporate demand response resources for 14 different load end uses into the model of FRCC, dispersing the demand response into the two nodes with the highest load per region. We use hourly estimates of the total potential demand response resource from (Olsen et al., 2013), which uses several filters to calculate the fraction of a load that is
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B. Stoll et al.
Table 1 Demand response end-use constraints by sector, including grid services that can be provided. Sector
End-Use
Grid Services
Load Recovery Restrictions
Balancing Freq. (days)
Daily Duration Restriction (h)
Residential
Cooling Heating Water heating Cooling Heating Ventilation Lighting Wastewater pumping Water pumping Outdoor Lighting Datacenters Manufacturing Refrigerated warehouses Agricultural pumping
R, R, R, R, R, R, R, E
5 am–6 pm 3 am–7 pm – 5 am–6 pm 3 am–7 pm – – –
1 1 1 1 1 – – 1
1 1 – 2 2 – – 3
E
–
1
2
R, C
–
–
–
C, E C, E E
4 am–8 pm – –
1 1 1
4 – 4
C, E
–
7
8
Commercial
Municipal
Industrial
C, C, C, C, C, C C
E E E E E
Fig. 1. Scenario framework for the analysis. Each scenario consists of one PV level, one DR option and one flexibility option.
systems, any resource that can potentially shift generation from one time period to another in rhythm with the diurnal cycles of the sun and electricity demand becomes more valuable and can be used to perform this service on days of significant curtailment. Additionally, resources that can quickly increase generation or decrease loads during periods of rapidly changing net-load, such as during the afternoon when solar output decreases and total demand is increasing, can reduce stress on the grid and limit the use of high-cost generators during these times. These roles, and a description of how the presence of battery storage influences DR operations and vice versa, are covered in the second set of results.
controllable, sheddable, and acceptable to consumers. In this and our companion work we adjust these filters to better represent the current status of demand response (Low DR scenario) and a more realistic future scenario with high DR participation (High DR scenario) (Hale et al., 2017). These data provide an estimate of the total load a particular end use can change in response to a grid signal, including potential contributions to energy shifting and reserves provision. Each end use is subject to different constraints based on assumptions about how much that end use could reasonably be used. These constraints, listed in Table 1, are expanded from similar work done in (Hummon et al., 2013). These constraints consist of requirements on allowable time ranges for shifting energy, including when load shifting can be recovered based on building occupancy and comfort levels, restrictions on the amount of time a single load may be shifted, and a requirement that a demand response provider may not simultaneously provide reserves and energy shifting for more than its total capacity. For this work, we analyze a set of scenarios to study the impact of demand response in comparison with or in addition to other measures of flexibility. This includes battery capacities of 1 GW and 4 GW with 6 hours of energy storage, and a set of conditions, called the Flex System, to increase operational flexibility: reducing minimum generation levels of gas combined-cycle (CC) generators, enabling reserves sharing between regions, and allowing PV to provide reserves. The scenario framework can be seen in Fig. 1. Each scenario consists of a particular PV penetration, demand response option, and flexibility option.
3.1. Overall impacts of DR on FRCC 3.1.1. Production costs The total production cost of serving a region’s electricity demand consists of all direct costs incurred through generation, including the fuel costs, variable operation and maintenance costs (VO&M), and start and shutdown costs. This represents the direct costs of operating the electric system modeled. The total production cost of the combined FRCC-SERC system ranged from $11.6 million to $14.2 million for different PV penetrations before the addition of any flexibility options, as in Table 2. Increasing PV penetration reduces the production cost due to lower usage of fuel, reducing the overall fuel costs in the system. Demand response reduces the total production costs in all cases; however, the Low DR scenario does not significantly impact costs at low PV penetrations. Table 2 shows the total reduction in cost for a range of PV penetrations in the base flexibility scenario. The Low DR scenario reduces costs by 0.1% at 5% PV, and by 0.8% at 45% PV. The benefits of the Low DR scenario are more operational in nature, and will be discussed in subsequent sections. The High DR scenario has a higher impact, reducing costs by 0.5% at 5% PV, 1.0% at 25% PV and 2.2% at 45% PV. This is a much more significant impact on the overall costs, in addition to operational changes seen due to DR.
3. Results
Table 2 Production cost reduction from demand response in the baseline flexibility scenario.
The traditional role of demand response is to reduce load at peak times, or in times of system emergency, so as to improve system reliability and economic efficiency. The outcomes of the fulfillment of such roles include avoided new capacity builds, and reduced production costs. Production cost savings can accrue from reduced prices in peak times, reduced reserve costs (especially to cover contingencies), and reduced startups and shutdowns of other generators. These types of benefits are summarized both monetarily and from an operational point of view in the first part of this section. In emerging power systems that include more wind and solar, DR may assume additional roles. In particular for high penetration PV
Flexibility Scenario
DR Scenario
Total Production Cost (million $) 5% PV
58
Base
No DR
Base
Low DR High DR
15% PV
25% PV
35% PV
14,232 13,148 12,372 11,841 Reduction in Total Production Cost (million Base, no DR scenario 5% PV 15% PV 25% PV 35% PV 20.98 31.92 40.60 67.63 76.35 93.43 127.75 181.72
45% PV 11,607 $) from 45% PV 95.21 259.15
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Fig. 2. Contingency reserve provision by generator type for each PV penetration and flexibility scenario.
variation and uncertainty in net load. Many fewer types of demand response are able to provide regulation reserves because of this continuous signal-following requirement. In our modeling, demand response was able to provide nearly all required contingency reserves in all cases, even for the Low DR scenario, as in Fig. 2. Demand response predominantly displaced gas CC units from providing reserves, as well as a small amount of coal and gas CT units. Contingency reserves do not increase with rising PV penetration, so their quantity does not change with PV penetration. Regulation reserves increase in line with rising PV penetrations such that demand response in the High DR scenario can provide nearly all regulation reserves at 5% PV, but as the requirements rise it cannot maintain the same proportion of the acquired reserves, as in Fig. 3.
Generally, demand response has a greater impact on costs at higher PV penetrations due to its ability to reduce curtailment, enabling higher utilization of a zero-fuel-cost resource. At low PV penetrations, demand response is typically used to switch between coal and natural gas fuel types. 3.1.2. Reserves Many types of demand response are well suited for providing reserves, particularly contingency reserves. Contingency reserves are held to cover potential generator or transmission line outages, and are typically called infrequently. Regulation reserves are used to maintain system frequency at short time scales, and are called on throughout the day to change their output levels every 4–6 seconds to balance out
Fig. 3. Regulation reserve provision by generator type for each PV penetration and flexibility scenario.
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Fig. 4. Mean number of hours two generator types spent operating at their minimum stable levels for each PV penetration and several flexibility scenarios.
spent at minimum stable level, until 35% PV penetration, at which point it rises. A large part of the difference in this behavior between coal and gas CC units is that gas units have a lower startup cost, so are more likely to turn off during the daytime as solar penetrations increase (Denholm et al., 2016). DR is able to reduce the number of hours spent at the minimum stable level for CCs in all scenarios, with the High DR scenario having a higher impact than the 1 GW battery scenario. DR has a median reduction of 22 hours in the Low DR scenario and 50 hours in the High DR scenario, with a range of −1 to 123 hours and −4 to 250 hours, respectively. When other flexibility options are added DR has less of an impact than when DR is the only flexibility option. Increased flexible generation, including the large battery and the High DR scenario, are able to eliminate the increase in CC hours at minimum generation seen at high PV penetrations. Another important operational metric is the number of starts and shutdowns an average plant undergoes per year. This is another indicator of the cycling of these units, which can increase wear on the plants. Fig. 5 shows the number of starts in the No DR scenario for each generator type and flexibility scenario. In all scenarios, the number of starts increases for all generator types at higher PV penetrations, though coal starts remain relatively constant. Gas CC units see a high increase in the number of starts, rising from 50 starts/year at 5% PV penetration to 150 starts/year at 45% PV penetration. Gas CT plants see a similar three-fold rise in the number of starts in the base scenario. Demand response has a negligible impact on coal at low penetrations, and may increase the number of starts per year slightly at higher penetrations. The ability of DR to reduce the number of starts of gas and CC plants varies based on the other flexibility scenarios included, Fig. 6. DR is most effective at reducing starts of gas generators, which are typically used for their quick-start capabilities during evening ramping and high net-load periods. DR can reduce nearly half of the starts of these generators in the High DR scenario. The impact on CC generators varies by PV penetration; the impact of DR levels off or even decreases drastically at higher PV penetrations. The addition of flexibility options tends to increase the ability of DR to reduce starts in CC generators at high PV penetrations.
While it is fairly unrealistic that DR could provide all or nearly all reserves of either type since many balancing areas limit the amount of non-conventional capacity that can provide reserves, this does demonstrate the degree to which DR is capable of filling the reserves market. Other types of flexibility, such as batteries or allowing PV to provide reserves, also quickly fill the market. This is discussed further in Section 3.2.2. 3.1.3. Operational impacts on traditional generators An important aspect of demand response is how it impacts other generators and their operations. Traditional generators have a minimum stable level they must operate above; however, they operate much less efficiently at this level compared to full capacity. As solar penetrations increase, coal generators tend to reduce their output significantly during the day so they are available during the evening peakload hours (Denholm et al., 2016). However, this can be costly for generators in terms of efficiency losses and there are concerns about equipment wear. Demand response can help mitigate this issue by shifting energy to times of high solar output, allowing these generators to operate above their minimum stable level. Fig. 4 shows the number of hours coal and gas CC generators spend operating at their minimum stable level in the base and battery only scenarios, with and without DR. Coal generators spend an increasing number of hours at their minimum stable level as the PV penetration increases in all scenarios. The presence of batteries and demand response, individually or together, reduces the number of hours coal generators spend at their minimum levels. The Low DR scenario reduces the mean number of hours at minimum level by −11 to 112 hours per year and the High DR scenario reduces the mean number of hours by 51–214 hours per year. The median values across all scenarios are 37 and 102 hours of reduction, for the Low and High DR scenarios respectively. The High DR scenario and the 1 GW battery scenario reduce the number of hours at minimum stable level for coal generators by approximately the same amount. Gas CC generators show a more complicated pattern of time spent at their minimum stable levels as a function of rising PV penetration (Fig. 4). In the baseline scenario, a decreasing number of hours are
Fig. 5. Average number of times each generator type was started per year in the no-DR scenario, for each flexibility option and PV penetration. In general, higher PV penetrations lead to increased generator starts, whereas flexibility options reduce starts.
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Fig. 6. Reduction in the mean generator starts per year for each generator category by demand response, for all flexibility options.
most operational flexibility – manufacturing and wastewater pumping. We assumed that both manufacturing and pumping had enough excess capacity to increase their output during any hour of the day, making them valuable for curtailment reduction. On the other hand, many residential DR end uses have more limited flexibility during daytime hours due to occupancy and comfort restrictions, limiting their ability to impact curtailment. The capability to increase load grows at higher PV penetration for all DR types due to the higher number of hours during which curtailment occurs. As PV penetrations rise, curtailment tends to occur during more hours of the day, making it more likely that DR providers will be able to increase their load during those times.
3.2. Interactions between DR, PV, and batteries 3.2.1. Impact of DR on PV curtailment One way in which DR interacts with PV capacity is by reducing curtailment on high PV penetration systems. Curtailment decreases the value of PV and increases system costs, since the low-cost energy from PV must be replaced by higher-cost generation from other fuel sources. Curtailment increases significantly at higher penetrations (Fig. 7). Very little curtailment occurs before 20% PV penetration, but curtailment rises rapidly from 8% at 30% PV to 28% at 45% PV in the base scenario. Nearly all flexibility scenarios are able to reduce curtailment, with the Flex System scenario being the only one to increase curtailment at PV penetrations less than 30%. The large battery is most effective at reducing curtailment, reducing the overall curtailment by nearly half in the highest-penetration systems studied. Demand response is able to reduce curtailment in all scenarios. The High DR scenario is able to reduce curtailment by an amount approximately equivalent to a 1 GW battery, enabling up to 6 TWh of additional PV generation in the Base scenario at 45% PV. The different DR end-uses have varying potentials to reduce curtailment based on whether or not they have operational constraints that limit their ability to shift load to hours in which curtailment occurs. Fig. 8 shows the ability of each end use to reduce curtailment based on the total amount of load they increased during hours when curtailment occurred in the base scenario. The end uses with the highest ability to increase load, and thus impact curtailment, are those end uses with the
3.2.2. Reserves impacts Demand response can provide a large fraction of all required reserves, as discussed in Section 3.1.2. How this changes with rising PV penetration and the addition of other flexibility options (Fig. 9) provides interesting insights into the reserves market and the value of demand response. In the base scenario, contingency reserve provision is relatively constant or decreases at increasing PV due to higher use of these resources for energy shifting as curtailment increases. Regulation reserve provision increases steadily due to an increase in the reserve requirement as the PV penetration increases and excess availability to provide regulation reserves for many end uses in the High DR scenarios. The High DR scenario is able to provide more reserves, but a much smaller fraction of the DR resource is used to provide reserves than in Fig. 7. Total curtailed energy per year, TWh, for each flexibility option and penetration. Total curtailment increases sharply at rising PV penetrations.
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Fig. 8. Increased load by demand response end-uses during hours of curtailment in the no-DR scenario. This indicates the ability of DR end-use to reduce curtailment at each PV penetration. Some DR types have higher availability and flexibility during times of curtailment, making them more valuable for load shifting at higher PV penetrations.
Fig. 9. Total annual reserves provision by demand response for all flexibility scenarios.
when both are present. Fig. 10 shows the mean daily charge and discharge patterns of batteries (a) and demand response (b). The overall differences between battery and DR usage stem from the different constraints on how the technologies operate. Batteries have fewer restrictions on their usage compared to DR, which enables batteries to more fully shift load away from evening peak hours. We find, however, little to no interaction between battery and DR dispatch patterns at any PV penetration (Fig. 10). While the use patterns of both DR and batteries changes substantially at increasing PV penetrations, the addition of either DR or batteries to a system already containing capacity of the other technology does not substantially alter the charging or discharging patterns of either resource. Interestingly, we found that the presence of both resources increased the usage of both for generation. Fig. 11 shows the annual generation from batteries and DR, which tends to increase at higher PV penetration. Usage of batteries (Fig. 11a) increases as more demand response is included in the system. This also occurs for DR at low PV penetrations; however, at high PV penetrations the addition of batteries has no effect or a slightly negative impact (Fig. 11b). This increase with additional flexible resources is largely tied to the reserves market. When only batteries or only DR are included, much of the capacity ends up providing reserves. As discussed in Section 3.2.2, the addition of flexible resources eventually saturates the reserves market, leading to an increased use of both resources to provide energy shifting. The total provision of generation and reserves reduces for both resources when the other resource is included.
the Low DR scenario. The Low DR scenario itself can provide nearly all contingency reserves in the model, so there is little additional need for reserves provision in the High DR scenario. The presence of other flexibility options also reduces the amount of reserves provided by DR. The Flex scenario suite allows PV to provide reserves, which it does at higher PV penetrations in all scenarios including that flexibility option (Figs. 2–3). Batteries, which also have very low operational costs, compete with DR to provide low-cost reserves, and typically take away some market share from DR, reducing the overall amount of reserves DR provides at all penetrations. These factors indicate that market saturation of the reserves market is reached fairly quickly. The market itself is fairly shallow, with reserve prices ranging from 0 to 226 $/MWh in the 5% PV base scenario and 0 to 250 $/MWh in the 30% PV base scenario. The median reserve price is 41 $/MWh for both scenarios. As the number of resources that can cheaply provide reserves, including batteries and PV in addition to DR, increases, the reserves price will decrease, as will the value each can get from providing reserves. As renewable penetrations increase, reserve markets may change. For example, more jurisdictions may add reserve products similar to the flexible ramping product recently implemented in the California ISO1 (California I.S.O., 2015).
3.2.3. Impacts of batteries on DR generation Batteries and demand response provide very similar services: the ability to shift energy usage in time and provide reserves. Here we investigate the degree to which these resources compete with each other
4. Discussion 1
As of November 1, 2016, see https://www.caiso.com/informed/Pages/ StakeholderProcesses/FlexibleRampingProduct.aspx.
Demand response resources have the ability to shift generation 62
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Fig. 10. Battery (a) and Demand Response (b) operations on average for each hour of the day for 10%, 20% and 30% PV penetrations.
Fig. 11. Annual generation from batteries (a) and demand response (b). Total generation tends to increase with the addition of flexible resources, largely due to additional availability from providing fewer reserves.
low net load, particularly at higher PV penetrations, enables lower-cost resources to meet demand. This is also demonstrated by reducing number of times gas turbines are started, a decline of 20–29% in the Low DR scenario and 47–61% in the High DR scenario. Gas turbines are more expensive units that can rapidly start and ramp quickly, so are typically deployed during peak periods. Demand response reduces the reliance on these units by shifting small amounts of load for a few hours, which lessens the need for quick-start units and allows baseload
away from high-load periods, enabling more efficient use of resources and lowering the total cost of operating the electric grid. While the overall impact of demand response on total costs is small, typically less than a 1% cost reduction, the addition of this resource is able to improve other aspects of grid operations. Demand response was able to reduce the number of hours coal generators spent at their minimum stable level by 37 and 102 hours for the Low DR and High DR scenarios, respectively. The shifting of energy from peak load hours to hours of
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generators, such as coal units, to provide that demand. Another important potential role of DR is providing operating reserves. These resources are particularly well suited for providing contingency reserves; we found that in both the Low and High DR scenarios, nearly all contingency reserves can be provided by demand response. While this may be unrealistic due to requirements for firm capacity to provide some fraction of reserves, it demonstrates the capability of DR to provide this service, even given low penetrations of the resource. However, we found that the current reserves market is fairly shallow, and easily satisfied by a variety of low-cost resources such as batteries and PV. Demand response providers looking to reserves for a majority of their revenue should take this into consideration, particularly as batteries and other flexible resources decrease in cost and regulations change to allow variable generators to participate in reserves markets. We see a similar saturation of other values provided by DR as larger amounts of flexible generators are included in the model, though to a much smaller degree than the saturation of the reserves market. While the presence of energy storage capacity increases the amount of generation provided by DR, particularly at low penetrations, this increase is smaller than the reduction in reserves provision, leading to an overall lower use of the resource. The impacts of demand response on starting of gas and CC generators, as well as the number of hours at minimum stable level for coal units, are not greatly affected by increasing flexible generation, indicating that demand response can continue to provide operational value to the grid even as overall flexibility increases.
National Renewable Energy Laboratory, Golden, Colorado Technical Report NREL/ TP-6A20–65298, Jan. Lopez, A., Roberts, B., Heimiller, D., Blair, N., Porro, G., 2012. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. National Renewable Energy Laboratory, Golden, Colorado Technical Report NREL/TP-6A20–51946, Jul. NREL, 2016. Annual Technology Baseline. National Renewable Energy Laboratory (NREL), Golden, CO Sep. Lazard, 2016. Lazard’s Levelized Cost of Energy Analysis—Version 10.0. Dec. Hale, E.T., Stoll, B.L., Novacheck, J.E., 2017. Enabling More Solar in Florida: Potential Roles for Power System Flexibility. Appl. Energy [Forthcoming]. Lee, M.P., et al., 2015. Assessment of Demand Response & Advanced Metering. Federal Energy and Regulatory Commission (FERC), Washington, D.C Staff Report, Dec. Energy Exemplar, PLEXOS, 2014. https://energyexemplar.com/software/plexos-desktopedition/. Denholm, P., Novacheck, J., Jorgenson, J., O’Connell, M., 2016. Impact of Flexibility Options on Grid Economic Carrying Capacity of Solar and Wind: Three Case Studies. National Renewable Energy Laboratory, Golden, Colorado Technical Report NREL/ TP-6A20-66854, Dec. Olsen, D.J., et al., 2013. Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection. Lawrence Berkeley National Laboratory, Berkeley, CA Technical Report LBNL-6417E, Sep. Hummon, M., Palchak, D., Denholm, P., Jorgenson, J., 2013. Grid Integration of Aggregated Demand Response, Part 2: Modeling Demand Response in a Production Cost Model. NREL (National Renewable Energy Laboratory (NREL), Golden, CO Technical Report NREL/TP-6A20-58492, Dec. California I.S.O, 2015. Flexible Ramping Product, Revised Draft Final Proposal. December. Brady Stoll is an engineer in the Grid Systems Analysis Group at the National Renewable Energy Laboratory. Her research interests include the potential for demand response in the United States and abroad, in particular using production cost models to analyze the impacts of demand response and other flexible technologies on operations of the electric grid. She also is interested in capacity expansion modeling with the Resource Planning Model, a tool that analyzes future challenges for the electric grid including policy impacts and falling renewable energy costs. Dr. Stoll received her Ph.D. in Mechanical Engineering from the University of Texas at Austin. She also holds a master’s degree in Mechanical Engineering and a bachelor’s degree in Physics from University of Texas at Austin.
Acknowledgements The authors thank Josh Novacheck and Paul Denholm for providing the underlying model and providing their insight for analyzing flexibility in Florida. Thanks to Laurel Dunn (Lawrence Berkeley National Laboratory) for providing the demand response resource dataset used in this work, and to Galen Maclaurin for shepherding the dataset across cyberspace. The authors also thank Steve Capanna and Ookie Ma (U.S. Department of Energy) for supporting this work. This research was funded by the U.S. Department of Energy under contract number DEAC36-08GO28308. Any errors or omissions are the sole responsibility of the authors.
Elizabeth Buechler is a graduate student at Stanford University studying Mechanical Engineering. She studied demand response at the National Renewable Energy Laboratory during the summer of 2017 with a focus on the operational impacts and value of demand response end-uses. Ms. Buechler has also performed probabilistic, hygrothermal modeling of the indoor climates of residential buildings using Monte Carlo methods and the simulation engine EnergyPlus at Oak Ridge National Laboratory. She received her B.S. in Mechanical Engineering from Tufts University. Elaine Hale is a senior engineer in the Forecasting & Modeling Group at the National Renewable Energy Laboratory. She has over 15 years of experience in computational systems engineering, numerical optimization, and software development; including six years developing software for building energy modeling. Her current work focuses on modeling and analyzing power system flexibility options, including demand response, in production cost and capacity expansion models under high renewables futures. Dr. Hale received her Ph.D. and M.S. degrees in Chemical Engineering from the University of Texas at Austin. She also holds a bachelor’s degree in Chemical Engineering from the Georgia Institute of Technology.
References Gagnon, P., Margolis, R., Melius, J., Phillips, C., Elmore, R., 2016. Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment.
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Contents lists available at ScienceDirect
The Electricity Journal journal homepage: www.elsevier.com/locate/tej
Energy Policy Institute's Seventh Annual Energy Policy Research Conference
The value of demand response in Florida a,⁎
b
Brady Stoll , Elizabeth Buechler , Elaine Hale a b
T
a
National Renewable Energy Laboratory, United States Stanford University, United States
A R T I C L E I N F O
A B S T R A C T
Keywords: Demand response Power systems Solar photovoltaics Renewable integration
Many electrical loads may be operated flexibly to provide grid services, including peaking capacity, reserves, and load shifting. The authors model 14 demand end uses in Florida and analyze their operational impacts and overall value for a wide range of solar penetrations and grid flexibility options. They find demand response is able to reduce production costs, reduce the number of low-load hours for traditional generators, reduce starting of gas generators, and reduce curtailment.
1. Introduction Demand response (DR) is a broad descriptor for any electric utility or aggregator program that incentivizes or requires loads to reduce or otherwise modify their energy consumption in support of grid operations. These types of programs have been in use for many years in the United States in the industrial sector, where customers are contracted to reduce their demand during emergency periods. More recently, demand response programs have been used to shift residential and commercial loads away from peak periods. As renewable generation sources increase, the ability of demand response to help mitigate challenges with increased net load variability and uncertainty has become of higher interest to utilities due to its ability to provide flexibility to the electric grid. Additionally, rapidly developing communication and control technologies have made demand response more flexible and easier to implement. In this article we study the potential impact of demand response in Florida assuming both low and high levels of solar photovoltaic (PV) deployment. Florida currently has a high level of demand response and little PV generation, but the state is poised to see high growth in solar energy in coming years due to its high solar resource potential and the falling costs of solar installations (Gagnon et al., 2016; Lopez et al., 2012; NREL, 2016; Lazard, 2016). Challenges with the “duck curve” that are starting to be seen in California, including low net loads during the day and a steep ramp in net load during the evening, are likely to be seen in Florida as well in the 5- to 15-year timeframe (Hale et al., 2017). Demand response can help mitigate these issues by shifting loads from high net load periods to low net load periods, providing reserves to meet increased variability from solar, and by providing capacity for long-term system planning. This is in addition to the valuable capacity
⁎
Corresponding author at: 15013 Denver West Pkwy, MS RSF300, Golden, CO 80401. E-mail address: [email protected] (B. Stoll).
https://doi.org/10.1016/j.tej.2017.10.004
Available online 10 November 2017 1040-6190/ © 2017 Elsevier Inc. All rights reserved.
and peak load management services that demand response already provides (Lee et al., 2015). In this article we analyze the impact on grid operations of 14 different load types that could provide demand response in Florida. We include two demand response penetrations, one approximately equivalent to 2015 participation in demand response programs, and one assuming a high participation rate. We analyze the changing impacts of demand response as PV penetration increases from 5% to 45%, with a focus on the value of demand response to the system at higher penetrations in terms of system operation impacts. 2. Methods We analyzed the grid operations of the Florida Reliability Coordinating Council (FRCC) using the commercial software package PLEXOS (Energy Exemplar, 2014), and the FRCC model created for (Denholm et al., 2016). In this model, we include all operational constraints on generators and enforce line limits on all lines above 200 kV. It includes a connection to the only reliability region in the eastern interconnection that is electrically connected to FRCC, the SERC Reliability Corporation in Georgia, which is modeled as a single load and supply curve of generation. We include additional utility scale photovoltaic generators to enable penetrations up to 45%. For more information on the PLEXOS model, see (Hale et al., 2017; Denholm et al., 2016). We incorporate demand response resources for 14 different load end uses into the model of FRCC, dispersing the demand response into the two nodes with the highest load per region. We use hourly estimates of the total potential demand response resource from (Olsen et al., 2013), which uses several filters to calculate the fraction of a load that is
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Table 1 Demand response end-use constraints by sector, including grid services that can be provided. Sector
End-Use
Grid Services
Load Recovery Restrictions
Balancing Freq. (days)
Daily Duration Restriction (h)
Residential
Cooling Heating Water heating Cooling Heating Ventilation Lighting Wastewater pumping Water pumping Outdoor Lighting Datacenters Manufacturing Refrigerated warehouses Agricultural pumping
R, R, R, R, R, R, R, E
5 am–6 pm 3 am–7 pm – 5 am–6 pm 3 am–7 pm – – –
1 1 1 1 1 – – 1
1 1 – 2 2 – – 3
E
–
1
2
R, C
–
–
–
C, E C, E E
4 am–8 pm – –
1 1 1
4 – 4
C, E
–
7
8
Commercial
Municipal
Industrial
C, C, C, C, C, C C
E E E E E
Fig. 1. Scenario framework for the analysis. Each scenario consists of one PV level, one DR option and one flexibility option.
systems, any resource that can potentially shift generation from one time period to another in rhythm with the diurnal cycles of the sun and electricity demand becomes more valuable and can be used to perform this service on days of significant curtailment. Additionally, resources that can quickly increase generation or decrease loads during periods of rapidly changing net-load, such as during the afternoon when solar output decreases and total demand is increasing, can reduce stress on the grid and limit the use of high-cost generators during these times. These roles, and a description of how the presence of battery storage influences DR operations and vice versa, are covered in the second set of results.
controllable, sheddable, and acceptable to consumers. In this and our companion work we adjust these filters to better represent the current status of demand response (Low DR scenario) and a more realistic future scenario with high DR participation (High DR scenario) (Hale et al., 2017). These data provide an estimate of the total load a particular end use can change in response to a grid signal, including potential contributions to energy shifting and reserves provision. Each end use is subject to different constraints based on assumptions about how much that end use could reasonably be used. These constraints, listed in Table 1, are expanded from similar work done in (Hummon et al., 2013). These constraints consist of requirements on allowable time ranges for shifting energy, including when load shifting can be recovered based on building occupancy and comfort levels, restrictions on the amount of time a single load may be shifted, and a requirement that a demand response provider may not simultaneously provide reserves and energy shifting for more than its total capacity. For this work, we analyze a set of scenarios to study the impact of demand response in comparison with or in addition to other measures of flexibility. This includes battery capacities of 1 GW and 4 GW with 6 hours of energy storage, and a set of conditions, called the Flex System, to increase operational flexibility: reducing minimum generation levels of gas combined-cycle (CC) generators, enabling reserves sharing between regions, and allowing PV to provide reserves. The scenario framework can be seen in Fig. 1. Each scenario consists of a particular PV penetration, demand response option, and flexibility option.
3.1. Overall impacts of DR on FRCC 3.1.1. Production costs The total production cost of serving a region’s electricity demand consists of all direct costs incurred through generation, including the fuel costs, variable operation and maintenance costs (VO&M), and start and shutdown costs. This represents the direct costs of operating the electric system modeled. The total production cost of the combined FRCC-SERC system ranged from $11.6 million to $14.2 million for different PV penetrations before the addition of any flexibility options, as in Table 2. Increasing PV penetration reduces the production cost due to lower usage of fuel, reducing the overall fuel costs in the system. Demand response reduces the total production costs in all cases; however, the Low DR scenario does not significantly impact costs at low PV penetrations. Table 2 shows the total reduction in cost for a range of PV penetrations in the base flexibility scenario. The Low DR scenario reduces costs by 0.1% at 5% PV, and by 0.8% at 45% PV. The benefits of the Low DR scenario are more operational in nature, and will be discussed in subsequent sections. The High DR scenario has a higher impact, reducing costs by 0.5% at 5% PV, 1.0% at 25% PV and 2.2% at 45% PV. This is a much more significant impact on the overall costs, in addition to operational changes seen due to DR.
3. Results
Table 2 Production cost reduction from demand response in the baseline flexibility scenario.
The traditional role of demand response is to reduce load at peak times, or in times of system emergency, so as to improve system reliability and economic efficiency. The outcomes of the fulfillment of such roles include avoided new capacity builds, and reduced production costs. Production cost savings can accrue from reduced prices in peak times, reduced reserve costs (especially to cover contingencies), and reduced startups and shutdowns of other generators. These types of benefits are summarized both monetarily and from an operational point of view in the first part of this section. In emerging power systems that include more wind and solar, DR may assume additional roles. In particular for high penetration PV
Flexibility Scenario
DR Scenario
Total Production Cost (million $) 5% PV
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Base
No DR
Base
Low DR High DR
15% PV
25% PV
35% PV
14,232 13,148 12,372 11,841 Reduction in Total Production Cost (million Base, no DR scenario 5% PV 15% PV 25% PV 35% PV 20.98 31.92 40.60 67.63 76.35 93.43 127.75 181.72
45% PV 11,607 $) from 45% PV 95.21 259.15
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Fig. 2. Contingency reserve provision by generator type for each PV penetration and flexibility scenario.
variation and uncertainty in net load. Many fewer types of demand response are able to provide regulation reserves because of this continuous signal-following requirement. In our modeling, demand response was able to provide nearly all required contingency reserves in all cases, even for the Low DR scenario, as in Fig. 2. Demand response predominantly displaced gas CC units from providing reserves, as well as a small amount of coal and gas CT units. Contingency reserves do not increase with rising PV penetration, so their quantity does not change with PV penetration. Regulation reserves increase in line with rising PV penetrations such that demand response in the High DR scenario can provide nearly all regulation reserves at 5% PV, but as the requirements rise it cannot maintain the same proportion of the acquired reserves, as in Fig. 3.
Generally, demand response has a greater impact on costs at higher PV penetrations due to its ability to reduce curtailment, enabling higher utilization of a zero-fuel-cost resource. At low PV penetrations, demand response is typically used to switch between coal and natural gas fuel types. 3.1.2. Reserves Many types of demand response are well suited for providing reserves, particularly contingency reserves. Contingency reserves are held to cover potential generator or transmission line outages, and are typically called infrequently. Regulation reserves are used to maintain system frequency at short time scales, and are called on throughout the day to change their output levels every 4–6 seconds to balance out
Fig. 3. Regulation reserve provision by generator type for each PV penetration and flexibility scenario.
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Fig. 4. Mean number of hours two generator types spent operating at their minimum stable levels for each PV penetration and several flexibility scenarios.
spent at minimum stable level, until 35% PV penetration, at which point it rises. A large part of the difference in this behavior between coal and gas CC units is that gas units have a lower startup cost, so are more likely to turn off during the daytime as solar penetrations increase (Denholm et al., 2016). DR is able to reduce the number of hours spent at the minimum stable level for CCs in all scenarios, with the High DR scenario having a higher impact than the 1 GW battery scenario. DR has a median reduction of 22 hours in the Low DR scenario and 50 hours in the High DR scenario, with a range of −1 to 123 hours and −4 to 250 hours, respectively. When other flexibility options are added DR has less of an impact than when DR is the only flexibility option. Increased flexible generation, including the large battery and the High DR scenario, are able to eliminate the increase in CC hours at minimum generation seen at high PV penetrations. Another important operational metric is the number of starts and shutdowns an average plant undergoes per year. This is another indicator of the cycling of these units, which can increase wear on the plants. Fig. 5 shows the number of starts in the No DR scenario for each generator type and flexibility scenario. In all scenarios, the number of starts increases for all generator types at higher PV penetrations, though coal starts remain relatively constant. Gas CC units see a high increase in the number of starts, rising from 50 starts/year at 5% PV penetration to 150 starts/year at 45% PV penetration. Gas CT plants see a similar three-fold rise in the number of starts in the base scenario. Demand response has a negligible impact on coal at low penetrations, and may increase the number of starts per year slightly at higher penetrations. The ability of DR to reduce the number of starts of gas and CC plants varies based on the other flexibility scenarios included, Fig. 6. DR is most effective at reducing starts of gas generators, which are typically used for their quick-start capabilities during evening ramping and high net-load periods. DR can reduce nearly half of the starts of these generators in the High DR scenario. The impact on CC generators varies by PV penetration; the impact of DR levels off or even decreases drastically at higher PV penetrations. The addition of flexibility options tends to increase the ability of DR to reduce starts in CC generators at high PV penetrations.
While it is fairly unrealistic that DR could provide all or nearly all reserves of either type since many balancing areas limit the amount of non-conventional capacity that can provide reserves, this does demonstrate the degree to which DR is capable of filling the reserves market. Other types of flexibility, such as batteries or allowing PV to provide reserves, also quickly fill the market. This is discussed further in Section 3.2.2. 3.1.3. Operational impacts on traditional generators An important aspect of demand response is how it impacts other generators and their operations. Traditional generators have a minimum stable level they must operate above; however, they operate much less efficiently at this level compared to full capacity. As solar penetrations increase, coal generators tend to reduce their output significantly during the day so they are available during the evening peakload hours (Denholm et al., 2016). However, this can be costly for generators in terms of efficiency losses and there are concerns about equipment wear. Demand response can help mitigate this issue by shifting energy to times of high solar output, allowing these generators to operate above their minimum stable level. Fig. 4 shows the number of hours coal and gas CC generators spend operating at their minimum stable level in the base and battery only scenarios, with and without DR. Coal generators spend an increasing number of hours at their minimum stable level as the PV penetration increases in all scenarios. The presence of batteries and demand response, individually or together, reduces the number of hours coal generators spend at their minimum levels. The Low DR scenario reduces the mean number of hours at minimum level by −11 to 112 hours per year and the High DR scenario reduces the mean number of hours by 51–214 hours per year. The median values across all scenarios are 37 and 102 hours of reduction, for the Low and High DR scenarios respectively. The High DR scenario and the 1 GW battery scenario reduce the number of hours at minimum stable level for coal generators by approximately the same amount. Gas CC generators show a more complicated pattern of time spent at their minimum stable levels as a function of rising PV penetration (Fig. 4). In the baseline scenario, a decreasing number of hours are
Fig. 5. Average number of times each generator type was started per year in the no-DR scenario, for each flexibility option and PV penetration. In general, higher PV penetrations lead to increased generator starts, whereas flexibility options reduce starts.
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Fig. 6. Reduction in the mean generator starts per year for each generator category by demand response, for all flexibility options.
most operational flexibility – manufacturing and wastewater pumping. We assumed that both manufacturing and pumping had enough excess capacity to increase their output during any hour of the day, making them valuable for curtailment reduction. On the other hand, many residential DR end uses have more limited flexibility during daytime hours due to occupancy and comfort restrictions, limiting their ability to impact curtailment. The capability to increase load grows at higher PV penetration for all DR types due to the higher number of hours during which curtailment occurs. As PV penetrations rise, curtailment tends to occur during more hours of the day, making it more likely that DR providers will be able to increase their load during those times.
3.2. Interactions between DR, PV, and batteries 3.2.1. Impact of DR on PV curtailment One way in which DR interacts with PV capacity is by reducing curtailment on high PV penetration systems. Curtailment decreases the value of PV and increases system costs, since the low-cost energy from PV must be replaced by higher-cost generation from other fuel sources. Curtailment increases significantly at higher penetrations (Fig. 7). Very little curtailment occurs before 20% PV penetration, but curtailment rises rapidly from 8% at 30% PV to 28% at 45% PV in the base scenario. Nearly all flexibility scenarios are able to reduce curtailment, with the Flex System scenario being the only one to increase curtailment at PV penetrations less than 30%. The large battery is most effective at reducing curtailment, reducing the overall curtailment by nearly half in the highest-penetration systems studied. Demand response is able to reduce curtailment in all scenarios. The High DR scenario is able to reduce curtailment by an amount approximately equivalent to a 1 GW battery, enabling up to 6 TWh of additional PV generation in the Base scenario at 45% PV. The different DR end-uses have varying potentials to reduce curtailment based on whether or not they have operational constraints that limit their ability to shift load to hours in which curtailment occurs. Fig. 8 shows the ability of each end use to reduce curtailment based on the total amount of load they increased during hours when curtailment occurred in the base scenario. The end uses with the highest ability to increase load, and thus impact curtailment, are those end uses with the
3.2.2. Reserves impacts Demand response can provide a large fraction of all required reserves, as discussed in Section 3.1.2. How this changes with rising PV penetration and the addition of other flexibility options (Fig. 9) provides interesting insights into the reserves market and the value of demand response. In the base scenario, contingency reserve provision is relatively constant or decreases at increasing PV due to higher use of these resources for energy shifting as curtailment increases. Regulation reserve provision increases steadily due to an increase in the reserve requirement as the PV penetration increases and excess availability to provide regulation reserves for many end uses in the High DR scenarios. The High DR scenario is able to provide more reserves, but a much smaller fraction of the DR resource is used to provide reserves than in Fig. 7. Total curtailed energy per year, TWh, for each flexibility option and penetration. Total curtailment increases sharply at rising PV penetrations.
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Fig. 8. Increased load by demand response end-uses during hours of curtailment in the no-DR scenario. This indicates the ability of DR end-use to reduce curtailment at each PV penetration. Some DR types have higher availability and flexibility during times of curtailment, making them more valuable for load shifting at higher PV penetrations.
Fig. 9. Total annual reserves provision by demand response for all flexibility scenarios.
when both are present. Fig. 10 shows the mean daily charge and discharge patterns of batteries (a) and demand response (b). The overall differences between battery and DR usage stem from the different constraints on how the technologies operate. Batteries have fewer restrictions on their usage compared to DR, which enables batteries to more fully shift load away from evening peak hours. We find, however, little to no interaction between battery and DR dispatch patterns at any PV penetration (Fig. 10). While the use patterns of both DR and batteries changes substantially at increasing PV penetrations, the addition of either DR or batteries to a system already containing capacity of the other technology does not substantially alter the charging or discharging patterns of either resource. Interestingly, we found that the presence of both resources increased the usage of both for generation. Fig. 11 shows the annual generation from batteries and DR, which tends to increase at higher PV penetration. Usage of batteries (Fig. 11a) increases as more demand response is included in the system. This also occurs for DR at low PV penetrations; however, at high PV penetrations the addition of batteries has no effect or a slightly negative impact (Fig. 11b). This increase with additional flexible resources is largely tied to the reserves market. When only batteries or only DR are included, much of the capacity ends up providing reserves. As discussed in Section 3.2.2, the addition of flexible resources eventually saturates the reserves market, leading to an increased use of both resources to provide energy shifting. The total provision of generation and reserves reduces for both resources when the other resource is included.
the Low DR scenario. The Low DR scenario itself can provide nearly all contingency reserves in the model, so there is little additional need for reserves provision in the High DR scenario. The presence of other flexibility options also reduces the amount of reserves provided by DR. The Flex scenario suite allows PV to provide reserves, which it does at higher PV penetrations in all scenarios including that flexibility option (Figs. 2–3). Batteries, which also have very low operational costs, compete with DR to provide low-cost reserves, and typically take away some market share from DR, reducing the overall amount of reserves DR provides at all penetrations. These factors indicate that market saturation of the reserves market is reached fairly quickly. The market itself is fairly shallow, with reserve prices ranging from 0 to 226 $/MWh in the 5% PV base scenario and 0 to 250 $/MWh in the 30% PV base scenario. The median reserve price is 41 $/MWh for both scenarios. As the number of resources that can cheaply provide reserves, including batteries and PV in addition to DR, increases, the reserves price will decrease, as will the value each can get from providing reserves. As renewable penetrations increase, reserve markets may change. For example, more jurisdictions may add reserve products similar to the flexible ramping product recently implemented in the California ISO1 (California I.S.O., 2015).
3.2.3. Impacts of batteries on DR generation Batteries and demand response provide very similar services: the ability to shift energy usage in time and provide reserves. Here we investigate the degree to which these resources compete with each other
4. Discussion 1
As of November 1, 2016, see https://www.caiso.com/informed/Pages/ StakeholderProcesses/FlexibleRampingProduct.aspx.
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Fig. 10. Battery (a) and Demand Response (b) operations on average for each hour of the day for 10%, 20% and 30% PV penetrations.
Fig. 11. Annual generation from batteries (a) and demand response (b). Total generation tends to increase with the addition of flexible resources, largely due to additional availability from providing fewer reserves.
low net load, particularly at higher PV penetrations, enables lower-cost resources to meet demand. This is also demonstrated by reducing number of times gas turbines are started, a decline of 20–29% in the Low DR scenario and 47–61% in the High DR scenario. Gas turbines are more expensive units that can rapidly start and ramp quickly, so are typically deployed during peak periods. Demand response reduces the reliance on these units by shifting small amounts of load for a few hours, which lessens the need for quick-start units and allows baseload
away from high-load periods, enabling more efficient use of resources and lowering the total cost of operating the electric grid. While the overall impact of demand response on total costs is small, typically less than a 1% cost reduction, the addition of this resource is able to improve other aspects of grid operations. Demand response was able to reduce the number of hours coal generators spent at their minimum stable level by 37 and 102 hours for the Low DR and High DR scenarios, respectively. The shifting of energy from peak load hours to hours of
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generators, such as coal units, to provide that demand. Another important potential role of DR is providing operating reserves. These resources are particularly well suited for providing contingency reserves; we found that in both the Low and High DR scenarios, nearly all contingency reserves can be provided by demand response. While this may be unrealistic due to requirements for firm capacity to provide some fraction of reserves, it demonstrates the capability of DR to provide this service, even given low penetrations of the resource. However, we found that the current reserves market is fairly shallow, and easily satisfied by a variety of low-cost resources such as batteries and PV. Demand response providers looking to reserves for a majority of their revenue should take this into consideration, particularly as batteries and other flexible resources decrease in cost and regulations change to allow variable generators to participate in reserves markets. We see a similar saturation of other values provided by DR as larger amounts of flexible generators are included in the model, though to a much smaller degree than the saturation of the reserves market. While the presence of energy storage capacity increases the amount of generation provided by DR, particularly at low penetrations, this increase is smaller than the reduction in reserves provision, leading to an overall lower use of the resource. The impacts of demand response on starting of gas and CC generators, as well as the number of hours at minimum stable level for coal units, are not greatly affected by increasing flexible generation, indicating that demand response can continue to provide operational value to the grid even as overall flexibility increases.
National Renewable Energy Laboratory, Golden, Colorado Technical Report NREL/ TP-6A20–65298, Jan. Lopez, A., Roberts, B., Heimiller, D., Blair, N., Porro, G., 2012. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. National Renewable Energy Laboratory, Golden, Colorado Technical Report NREL/TP-6A20–51946, Jul. NREL, 2016. Annual Technology Baseline. National Renewable Energy Laboratory (NREL), Golden, CO Sep. Lazard, 2016. Lazard’s Levelized Cost of Energy Analysis—Version 10.0. Dec. Hale, E.T., Stoll, B.L., Novacheck, J.E., 2017. Enabling More Solar in Florida: Potential Roles for Power System Flexibility. Appl. Energy [Forthcoming]. Lee, M.P., et al., 2015. Assessment of Demand Response & Advanced Metering. Federal Energy and Regulatory Commission (FERC), Washington, D.C Staff Report, Dec. Energy Exemplar, PLEXOS, 2014. https://energyexemplar.com/software/plexos-desktopedition/. Denholm, P., Novacheck, J., Jorgenson, J., O’Connell, M., 2016. Impact of Flexibility Options on Grid Economic Carrying Capacity of Solar and Wind: Three Case Studies. National Renewable Energy Laboratory, Golden, Colorado Technical Report NREL/ TP-6A20-66854, Dec. Olsen, D.J., et al., 2013. Grid Integration of Aggregated Demand Response, Part 1: Load Availability Profiles and Constraints for the Western Interconnection. Lawrence Berkeley National Laboratory, Berkeley, CA Technical Report LBNL-6417E, Sep. Hummon, M., Palchak, D., Denholm, P., Jorgenson, J., 2013. Grid Integration of Aggregated Demand Response, Part 2: Modeling Demand Response in a Production Cost Model. NREL (National Renewable Energy Laboratory (NREL), Golden, CO Technical Report NREL/TP-6A20-58492, Dec. California I.S.O, 2015. Flexible Ramping Product, Revised Draft Final Proposal. December. Brady Stoll is an engineer in the Grid Systems Analysis Group at the National Renewable Energy Laboratory. Her research interests include the potential for demand response in the United States and abroad, in particular using production cost models to analyze the impacts of demand response and other flexible technologies on operations of the electric grid. She also is interested in capacity expansion modeling with the Resource Planning Model, a tool that analyzes future challenges for the electric grid including policy impacts and falling renewable energy costs. Dr. Stoll received her Ph.D. in Mechanical Engineering from the University of Texas at Austin. She also holds a master’s degree in Mechanical Engineering and a bachelor’s degree in Physics from University of Texas at Austin.
Acknowledgements The authors thank Josh Novacheck and Paul Denholm for providing the underlying model and providing their insight for analyzing flexibility in Florida. Thanks to Laurel Dunn (Lawrence Berkeley National Laboratory) for providing the demand response resource dataset used in this work, and to Galen Maclaurin for shepherding the dataset across cyberspace. The authors also thank Steve Capanna and Ookie Ma (U.S. Department of Energy) for supporting this work. This research was funded by the U.S. Department of Energy under contract number DEAC36-08GO28308. Any errors or omissions are the sole responsibility of the authors.
Elizabeth Buechler is a graduate student at Stanford University studying Mechanical Engineering. She studied demand response at the National Renewable Energy Laboratory during the summer of 2017 with a focus on the operational impacts and value of demand response end-uses. Ms. Buechler has also performed probabilistic, hygrothermal modeling of the indoor climates of residential buildings using Monte Carlo methods and the simulation engine EnergyPlus at Oak Ridge National Laboratory. She received her B.S. in Mechanical Engineering from Tufts University. Elaine Hale is a senior engineer in the Forecasting & Modeling Group at the National Renewable Energy Laboratory. She has over 15 years of experience in computational systems engineering, numerical optimization, and software development; including six years developing software for building energy modeling. Her current work focuses on modeling and analyzing power system flexibility options, including demand response, in production cost and capacity expansion models under high renewables futures. Dr. Hale received her Ph.D. and M.S. degrees in Chemical Engineering from the University of Texas at Austin. She also holds a bachelor’s degree in Chemical Engineering from the Georgia Institute of Technology.
References Gagnon, P., Margolis, R., Melius, J., Phillips, C., Elmore, R., 2016. Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment.
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