Utility-scale Lithium-Ion Storage Cost Projections for Use in Capacity Expansion Models

Transcription

1 Utility-scale Lithium-Ion Storage Cost Projections for Use in Capacity Expansion Models Wesley J. Cole,* Cara Marcy, Venkat K. Krishnan, Robert Margolis Strategic Energy Analysis Center National Renewable Energy Laboratory Golden, Colorado, USA *Corresponding author: Abstract This work presents U.S. utility-scale battery storage cost projections for use in capacity expansion models. We create battery cost projections based on a survey of literature cost projections of battery packs and balance of system costs, with a focus on lithium-ion batteries., mid, and high cost trajectories are created for the overnight capital costs and the operating and maintenance costs. We then demonstrate the impact of these cost projections in the Regional Energy Deployment System (ReEDS) capacity expansion model. We find that under reference scenario conditions, lower battery costs can lead to increased penetration of variable renewable energy, with solar photovoltaics (PV) seeing the largest increase. We also find that additional storage can reduce renewable energy curtailment, although that comes at the expense of additional storage losses. Keywords lithium-ion batteries; storage; capacity expansion; ReEDS E I. INTRODUCTION NERGY storage, over the years, has been long touted as one of the quintessential pieces to ensure economic and reliable means to modernize the grid, with utility-scale battery assessments going back nearly 3 years [1], [2]. Several studies have reported the benefits energy storage can bring to the grid in terms of load leveling, peak shaving, enhancing system reliability, and enabling higher integration of low carbon variable renewable resources (see [3] for a review of the literature, and see [4], [5] for additional discussion with respect to renewable energy and storage). In addition to the existing ~22 GW of pumped-hydro storage and 11 MW of compressed-air energy storage in the U.S., there have been several projects that have demonstrated short-term storage such as battery and flywheel integration into the grid for providing frequency regulation [6], [7]. Historically, the cost of battery storage has been one of the main prohibitive factors in its market uptake, and the recent decrease in their capital cost coupled with other market and system changes are spurring many battery storage projects [8] [1]. Following the FERC orders 755 and 784, several markets have devised strategies to monetize fast-responding storage performance and their ability to provide highly accurate regulation reserves in an effort to attract more storage ventures. While the above developments are at the utility-scale, the commercial-scale industries are seeing storage along with solar photovoltaics (PV) integration as a means to mitigate their peak demand charges [11], [12]. At the residential level, depending upon the net-metering and utility rate structures, customers installing rooftop solar PV are potentially looking to adopt battery storage to improve their reliability and reduce their energy bills [13], [14]. Finally, there have also been pilot projects and analyses that have demonstrated battery storage s value in enabling micro-grid systems and enhancing system resilience against catastrophic events [15], [16]. Despite all the aforementioned developments and challenges to deploy storage in current markets, storage capital costs have, in our view, remained the most significant factor in preventing this technology from experiencing broader adoption. However, declining costs and other market factors could to make that change. One industry projection call for a 26x increase in the size of the U.S. storage market by 22 compared to 214, with annual capacity additions reaching over 1.5 GW per year [17]. Therefore in this paper, we focus on developing future cost projections of battery energy storage. We then use those projections in a long-term electric system capacity expansion model to ascertain their impact on nationwide utility-scale battery storage penetration and the resulting impacts of that battery storage on the rest of the power system. The paper is organized as follows: Section II presents the battery storage cost projection methodology along with the resulting cost projections. Section III provides a short description of the long-term capacity expansion planning model, in which these battery cost projections are used. Section IV presents the numerical results from the scenario simulations and discusses the impact of cost assumptions on utility-scale battery deployment. Finally, Section V presents the conclusions and discusses future research directions. II. BATTERY COST PROJECTIONS The cost projections developed in this work are based on a review of battery pack and balance of system cost literature. We used the literature projections to develop low, mid, and high cost projections for utility-scale battery storage systems. Our goal is to generate battery systems costs through 25 that can be used in long-term capacity expansion models. All costs are presented in 214 USD, and the capital costs included here are intended to represent overnight capital costs. Because of the abundance of literature on lithium-ion batteries relative to other battery storage systems, and because the majority of recently completed battery systems have been lithium-ion systems, we /16/$ IEEE

2 focus on lithium-ion technologies [17]. This does not imply, however, that other battery technologies will not or cannot play a significant role in the U.S. power system. The bulk of the battery pack cost projections used here are from a review of vehicle battery pack costs by Nykvist and Nilsson [18]. In addition to the projections collected in [18], we also include projections from Bloomberg New Energy Finance, Goldman Sachs, Green Tech Media, Navigant, Rocky Mountain Institute, Energy Information Administration, and UBS. In total we use 25 battery pack cost projections in this work. For battery balance of systems (BOS) costs we rely on work by Ortiz and Manghani [19] because the literature on balance of systems cost is very limited. We also collected total system cost projections from the Energy Storage Association, Goldman Sachs, and Lazard [2]. Operations and maintenance (O&M) costs were taken from Zakeri and Syri [21] which reviews the life-cycle cost of various storage technologies. A. Battery Pack Cost Projections To create battery pack cost projections, we began by assigning the median of all 215 projections to be the starting 215 cost for the projections (see Fig. 1). For cost projections from , we used relative cost reductions were used according to Equation (1) and applied that cost reduction to the 215 starting point. From we used year-over-year cost reduction according to Equation (2) because of the relatively few literature projections that extend beyond 23 (see Fig. 1). CostReduct year = C pack,year / C pack,215 (1) CostReduct year = C pack,year / C pack,(year-1) (2) where CostReduct year is the cost reduction in a given year and C pack,year is the battery pack capital cost in a given year. Battery Pack Costs ($/kwh) Literature Projections Cost Projection Cost Projection Cost Projection Fig. 1., mid, and high battery pack capital cost trajectories compared to literature values. The high cost trajectory is created by assuming the 215 starting value decreases at the smallest rate of cost reduction in each year. The mid cost trajectory assumes the 215 starting value declines by the median cost reduction in each year, and the low cost trajectory assumes the 215 starting value declines by the large rate of cost reduction each year. The resulting battery pack cost projections are shown in Fig. 1. B. Balance of System Cost Projections The BOS cost trajectories could not be created in the same way as the battery pack costs because of lack of literature cost projections. The 215 starting cost for BOS capital costs is taken directly from [19]. The high BOS trajectory assumes there are no cost declines from 215 through 25. The mid cost trajectory assumes that the 22 value from [19] is reached in 24 and remains flat thereafter. The low cost trajectory assumes that the cost reduction from the mid cost trajectory applies to the minimum of the reported costs in [19]. The resulting BOS capital cost trajectories are shown in Fig. 2. Balance of System Capital Cost ($/kw) Fig. 2., mid, and high balance of system cost trajectories. C. Operations and Maintenance Cost Projections Operations and Maintenance costs are based on a literature review by Zakeri and Syri [21]. The 215 value is taken from their estimate of current O&M costs. The high case is assumed to have no cost reduction over time. The mid case assumes that the top of their lowest quartile of reported O&M costs is reached in 24. The low case assumes that the minimum of their reported O&M costs is reached in 24. Fixed and variable operations and maintenance cost projections are shown in Fig. 3 and Fig. 4. Battery System Fixed O&M ($/kw-yr) Fig. 3., mid, and high cost trajectories for fixed operations and maintenance (O&M) costs for the battery system.

3 Battery System Variable O&M ($/MWh) Fig. 4., mid, and high cost trajectories for variable operations and maintenance (O&M) costs for the battery system. D. Total System Cost Projections The total battery system capital costs (C tot ) are determined as sum of the battery pack capital costs (C pack ) and the balance of system capital costs (C BOS ): C tot = C pack hrs + C BOS (3) where hrs is the hours of storage. The resulting capital costs for an 8-hour battery system are shown in Fig. 5 (on a $/kw basis) and Fig. 6 (on a $/kwh basis). The high, mid, and low cost projections see a 34%, 57%, and 81% reduction, respectively, in capital costs from 215 to 25. Battery System Capital Cost ($/kw) Fig. 5. Battery system capital costs for an 8-hour battery on a $/kw basis for the low, mid, and high trajectories. Battery System Capital Cost ($/kwh) Fig. 6. Battery system capital costs for an 8-hour battery on a $/kwh basis for the low, mid, and high trajectories. III. IMPLEMENTATION IN A CAPACITY EXPANSION MODEL We implement the battery costs trajectories produced above in the Regional Energy Deployment System (ReEDS) capacity expansion model. ReEDS is a long-term electricity system optimization model that assesses the deployment and operation of the electricity sector (including both generation and transmission) in the contiguous United States from now through 25 [22], [23]. ReEDS captures renewable energy resource availability and costs through the use of 356 individual resource regions, and models system dispatch using 134 load balancing areas across the U.S. ReEDS is able to handle renewable energy integration issues such as variability in wind and solar output, transmission interconnection costs and constraints, and ancillary services requirements. The model version and assumptions in this work are the same as those in [24]. We model three scenarios in this work to assess the impact of these battery cost projections on long-term storage deployments. The three scenarios are: 1. Battery Cost scenario using the high battery system cost trajectory in Figs. 3-5, 2. Battery Cost scenario using the mid battery system cost trajectory in Figs. 3-5, and 3. Battery Cost scenario using the high battery system cost trajectory in Figs In each scenario we assume the only battery configuration available is a front-of-the-meter 8-hour lithium-ion battery storage configuration. We also assume that these battery systems have a round trip efficiency of 9% [25] and have a lifetime of 15 years, and we assume that the full purchased capacity is available for use. Pumped-hydro and compressed air energy storage are also included in all scenarios, and additional details related to their cost and performance assumptions are described in [23]. Behind-the-meter and customer-sited storage are not included in this work. Storage in ReEDS is able to provide several functions. It contributes to the planning reserve margin with a capacity value of.94 times the nameplate capacity (based on [26]). Storage also contributes to the operational reserve requirements modeled in ReEDS. Storage is able to provide energy arbitrage by charging when it is cost-effective to do so and then discharging during peak hours. Finally, storage is able to reduce renewable energy curtailments by charging during times when energy is curtailed according to (4): Curt n,t = existcurt n,t + newvre n,t,tech CurtMarg n,t,tech + NewMR n,t CurtMargMR n,t StorageIn n,t Effective n,t (4) where Curt n,t is the curtailment at each region n and each time period t, existcurt n,t is the curtailment from existing variable renewable energy generators, newvre n,t,tech is the amount of new variable renewable energy for each technology type tech, CurtMarg n,t,tech is the marginal curtailment rate for new generators, NewMR n,t, is the new must-run capacity, CurtMargMR n,t, is the marginal curtailment rate induced by new must-run generators, StorageIn n,t is the amount of energy going into storage, and Effective n,t is the effectiveness of

4 storage at reducing curtailment by charging. The purpose of the Effective n,t parameter is to help overcome the shortcomings of not doing sequential dispatch. For example, if there are three consecutive days of excess wind generation, storage may only be able to reduce curtailment on the first day and then may be unable to charge on the second and third days because it is already full. The Effective n,t parameter is a function of the ratio of renewable energy generation to load in each region, n, as shown in Fig. 7. That ratio can be greater than one if the region is an exporting region and has a relatively small load. ReEDS uses a system-wide, least-cost optimization algorithm to do the capacity expansion of a large-scale energy system (the contiguous United States) and, therefore, ReEDS does not evaluate the various economic value streams of storage. As such, ReEDS is unable to capture small market segments, niche opportunities, or short-time-scale storage contributions that are profitable and are likely to help the storage industry to expand, especially in the near-term. For example, ReEDS does not model sub-hourly load balancing phenomena and hence cannot value the fast responding storage ability to enhance system frequency response and get paid for its higher performance in regulation markets. Additionally, ReEDS does not include voltage considerations, and hence cannot ascertain the value of storage in providing voltage support. Given the lack in modeling all the economic value streams of storage systems in such a long-term planning model, the scenario assessments with respect to different storage costs represent only the opportunities for storage given the storage function mentioned above. Effective n,t Fig. 7. Total storage capacity over time across the three scenarios. battery costs leads to much higher levels of storage deployment. IV Ratio of Variable Renewable Energy to Load in a Region RESULTS LONG-TERM PLANNING SCENARIOS Figures 8 and 9 present cumulative generation capacity deployments in mid and low battery cost scenarios respectively (the high battery cost scenario is not shown because it is nearly identical to the mid battery cost scenario in Fig. 8). Under the mid battery cost scenario, the model projects the system storage capacity to increase from the current ~22 GW to about 32.5 GW in 25 (~2% of the total generation capacity). Under the low battery cost scenario, the total system storage capacity is projected to reach about 172 GW in 25 (~9.5% of the total generation capacity). Figures 8 and 9 demonstrate the correlation that exists between the storage deployments and variable renewable resource deployments. Capacity (GW) 2 18 Storage 16 Solar 14 Wind 12 Other RE 1 Gas-CT 8 Gas-CC 6 Oil-Gas-Steam Coal 4 Nuclear Fig. 8. Cumulative generation capacity in mid battery cost scenario. Capacity (GW) 2 18 Storage 16 Solar 14 Wind 12 Other RE 1 Gas-CT 8 Gas-CC 6 Oil-Gas-Steam Coal 4 Nuclear Fig. 9. Cumulative generation capacity in low battery cost scenario. Figure 1 isolates the battery capacity increase over time and presents it under different battery cost assumptions developed in Section II. In the Battery Cost scenario, though the storage capacity climbs from the current capacity of ~22 GW (existing pumped storage) to ~32.5 GW by 25, none of this storage capacity includes battery storage. The Battery Cost scenario does build 3 GW of battery storage by 25. It is not until we assume that battery costs follow the low cost trajectory that we see batteries begin to be deployed at significant levels. By 25, total battery storage capacity is projected to reach 148 GW (out of a total of 172 GW storage). Battery Capacity (GW) Battery Cost Battery Cost Battery Cost Fig. 1. Total battery capacity over time across the three scenarios. battery costs leads to much higher levels of battery deployment.

5 In the Battery Cost scenario, storage begins to grow rapidly in 228 (see Fig. 9 and Fig. 1). If one uses a natural gas combustion turbine as an indicator of capacity firming, then the capacity firming value of the battery is equal to the capital cost of a combustion turbine (which is about $8/kW in ReEDS). In 228, the 8-hour battery storage has a capital cost that is about 5% greater than a natural gas combustion turbine, indicating that the model is seeing substantial value in the battery storage beyond purely meeting capacity needs. One of the functions of additional storage is that it has the capability to reduce curtailments from variable renewable resources, which in turn increases the value of additional variable renewable resources. We observe that effect in Fig. 11 and Fig. 12. Fig. 11 shows the PV capacity in the three battery cost scenarios. The additional storage that is deployed leads to an increase in total PV capacity, primarily through reducing the solar generation curtailment. PV Capacity (GW) Fig. 11. Total PV capacity (utility-scale plus rooftop) over time across the three scenarios. er battery costs lead to more deployment of PV. Fig. 12 shows the total wind capacity across the scenarios. In the Battery Cost scenario where storage is costeffective enough in ReEDS to get built, wind capacity is increased relative to the other two scenarios. Wind is primarily benefited from the reduced curtailments offered by the battery storage. Wind Capacity (GW) Battery Cost Battery Cost Battery Cost Battery Cost Battery Cost Battery Cost Fig. 12. Total wind capacity over time across the three scenarios. er battery costs lead to slightly more wind deployment. The increases in PV and wind capacity in the Battery Cost scenario shown in Fig. 11 and Fig. 12 are largely offsetting natural gas generation (as seen from Fig. 9), though there is some small impact on coal generation. Hydropower and geothermal capacity is mostly unchanged due to the lowcost storage. Concentrating solar power (CSP) technologies, however, declines in capacity due to low-cost storage, which is not surprising given that CSP often includes energy storage (thermal energy storage). The impact of low-cost storage on renewable energy curtailment is demonstrated in Fig. 13. The Battery Cost scenarios has more PV and wind capacity (see Fig. 11 and Fig. 12), but far lower curtailments. The storage is predominantly charging during the daytime hours when solar PV curtailment is high and discharging during evening and nighttime hours, with the overall effect of lowering system-wide curtailments. Curtailed Energy (TWh) Battery Cost Battery Cost Battery Cost Variable Renewable Generation (TWh) Fig. 13. Curtailed energy versus the variable renewable energy in the three scenarios. er battery costs lead to lower levels of curtailed energy for the same amount of variable renewabel generation. The curtailments in Fig. 13 in the Battery Cost scenario is about 5 TWh lower than the Battery Cost scenario even though there is about 4 TWh more variable renewable energy in the Battery Cost scenario. However, there is additional lost energy because the storage does not have a 1% round trip efficiency. Storage losses between those same two scenarios increase by 3 TWh, which offsets some of the gain in recovering the curtailments. Additionally, the recovery of curtailments makes additional variable renewable energy investment more attractive, which in turn increases curtailments. However, the model still sees substantial benefit in shifting the energy and building additional wind and solar capacity. V. CONCLUSIONS In this work we have created low, mid, and high lithiumion battery cost trajectories from literature projections. We have demonstrated the impact of these battery cost trajectories in the ReEDS capacity expansion model. We found that the low battery cost trajectory leads to significant deployment of storage, with accompanying increases in renewable energy capacity and decreases in natural-gas-fired generation. Storage also has the potential to reduce variable renewable energy curtailments, though the reduction in curtailments comes with an increase in storage losses.

6 Future work includes improving the modeling framework to better capture the ability of storage to recover curtailed energy. Because capacity expansion models often have reduced temporal resolution, the storage potential often has to be represented in this reduced temporal framework. Methods that are able to translate hourly dispatch decisions into representative timeslices can improve the ability of long-term models to better quantify the value of storage. Because ReEDS solve sequentially, we plan to do this by using hourly load and renewable energy profiles between each solve to update variability parameters such as capacity value, curtailment, and the Effective n,t parameter discussed in this paper. Once the modeling framework is improved, we anticipate examining additional scenarios that allow for a more robust analysis of how storage might impact the power system. For example, we plan on running low-cost renewable energy scenarios and scenarios where we limit the construction of new transmission in order to better understand how storage interacts with a grid with very high level of renewable energy or in situations where additional transmission may be costprohibitive or otherwise infeasible. ACKNOWLEDGMENT We thank Paul Denholm and Aaron Bloom for helpful comments that helped us improve this work. This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-8GO2838 with the National Renewable Energy Laboratory. Funding provided by U.S. DOE Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Program. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. 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