Elsevier

Applied Energy

Volume 175, 1 August 2016, Pages 368-379
Applied Energy

The value of energy storage in decarbonizing the electricity sector

https://doi.org/10.1016/j.apenergy.2016.05.014Get rights and content

Highlights

  • Energy storage value increases with tighter carbon dioxide (CO2) emissions limits.

  • The marginal value of storage declines as storage penetration increases.

  • Large-scale deployment of available battery technologies requires cost reductions.

  • Energy storage increases utilization of the cheapest low-CO2 resources.

  • Longer-duration storage increases the share of wind more than solar photovoltaics.

Abstract

Electrical energy storage could play an important role in decarbonizing the electricity sector by offering a new, carbon-free source of operational flexibility, improving the utilization of generation assets, and facilitating the integration of variable renewable energy sources. Yet, the future cost of energy storage technologies is uncertain, and the value that they can bring to the system depends on multiple factors. Moreover, the marginal value of storage diminishes as more energy storage capacity is deployed. To explore the potential value of energy storage in deep decarbonization of the electricity sector, we assess the impact of increasing levels of energy storage capacity on both power system operations and investments in generation capacity using a generation capacity expansion model with detailed unit commitment constraints. In a case study of a system with load and renewable resource characteristics from the U.S. state of Texas, we find that energy storage delivers value by increasing the cost-effective penetration of renewable energy, reducing total investments in nuclear power and gas-fired peaking units, and improving the utilization of all installed capacity. However, we find that the value delivered by energy storage with a 2-hour storage capacity only exceeds current technology costs under strict emissions limits, implying that substantial cost reductions in battery storage are needed to justify large-scale deployment. In contrast, storage resources with a 10-hour storage capacity deliver value consistent with the current cost of pumped hydroelectric storage. In general, while energy storage appears essential to enable decarbonization strategies dependent on very high shares of wind and solar energy, storage is not a requisite if a diverse mix of flexible, low-carbon power sources is employed, including flexible nuclear power.

Introduction

The electric power sector must play a central role in any effort to mitigate the worst impacts of climate change. Most climate stabilization scenarios envision the global power sector emitting very low or zero carbon dioxide (CO2) by 2050 while also expanding to electrify and decarbonize portions of the industry and transportation sectors [1], [2]. Electrical energy storage could play an important role in the deep decarbonization of the power sector by offering a new, carbon-free source of operational flexibility in the power system, improving the utilization of generation assets, and facilitating the integration of variable renewable energy sources (i.e., wind and solar power) [3], [4]. Most of the value of energy storage is accrued from its ability to arbitrage wholesale prices during peak and non-peak hours, thereby leveling out the system load [5], [6], [7], [8], but also from providing a carbon-free source of operating reserves and flexibility [9], [10], [11], [12] that might potentially defer investments in other more expensive generation assets [13], [14].

To date, many studies have examined the short-run impact of energy storage on electric power system operations and economics [5], [6], [7], [8], [9], [14], [15], [16], [17], [18]. Some of these studies have focused on the role of energy storage for integrating large amounts of variable renewable energy generation in power system operations [9], [15], [16], and others have assessed the impact of storage operation on carbon emissions in conventional power systems [17], [18]. Studies assessing the short-run value of energy storage in different electricity markets typically employ price-taker arbitrage models (i.e., models that maximize the profits of the storage unit assuming that storage does not impact electricity prices) [5], [6], [7], [8], [14], while others calculate the short-run price equilibrium minimizing the system operating costs but ignoring long-run capacity expansion decisions [11], [12].

The long-run impact of energy storage on renewable energy utilization is explored in [19]. However, this study does not account for economic considerations and maximizes a multi-objective function composed of renewable penetration minus storage and backup requirements, instead of using the standard criterion of maximizing social welfare—or, equivalently, minimizing total generation costs. Conversely, the long-run economic impact of storage is analyzed in [13], [20] based on cost minimization, but these studies do not include binding CO2 emissions limits for the electricity sector. Other studies that consider the long-run market dynamics under stringent CO2 emissions limits [21], [22] do not consider detailed unit-commitment constraints in the operation of the plants, underestimating the flexibility value energy storage technologies bring to power systems.

In contrast to the existing literature discussed above, this paper focuses explicitly on the total generation-system value of energy storage.1 We explore in detail the impact of energy storage on short-run power systems operations—accounting for detailed unit-commitment decisions, the contribution of storage to system flexibility and operating reserves, and the resulting influence on wholesale electricity prices. We also consider the impact of energy storage on long-run power plant investment decisions, in the context of stringent CO2 emissions reduction goals. This work therefore adds to the existing literature by providing a more complete assessment of the economic value of energy storage through jointly capturing both the short- and long-run interaction between storage, renewable energy, and other zero-carbon electricity sources and their relative contributions to meet demands for energy and operating reserves along with emissions reduction objectives. The novel analytical framework used in this work can be applied to more accurately value energy storage in indicative planning [23] for future low-carbon power systems, where the CO2 emissions and flexibility attributes of the different generation technologies play a critical role in determining the minimum cost generation fleet that is operationally feasible and complies with a given carbon emissions limit.

In our analysis we made extensions to the Investment Model for Renewable Electricity Systems (IMRES) [24], an advanced generation capacity expansion model that considers unit commitment constraints for individual power plants, system-wide reliability requirements, and individual power plant investment decisions. The model selects the cost-minimizing set of investments in electricity generation capacity to reliably meet the electricity demand in a future year, subject to a CO2 emissions limit.2 We model a power system with electricity demand and wind and solar resource data from the Electricity Reliability Council of Texas (ERCOT) grid. To explore the impacts of storage on the long-run portfolio of power generation capacity, we increase demand consistent with 2035 projections in Texas and employ the model in a “greenfield” configuration—i.e., selecting the entire generation mix from scratch. Eligible technologies include pulverized coal, combined cycle gas turbines (CCGTs), open cycle gas turbines (OCGTs), wind turbines, solar photovoltaics (PV), and nuclear power. The nuclear power plants are modeled as capable of flexible operation consistent with reactors in France, Germany and other locations [25], [26], [27] as well as modern reactor capabilities [28], [29]. We model this experimental power system assuming no transmission network constraints and imposing both increasing levels of energy storage capacity and increasingly stringent limits on the average CO2 emissions rate of the electricity system. Specifically, we model 0–30 gigawatts (GW) of energy storage, representing approximately 0–30% of the system’s peak demand, and emissions limits of 200–50 metric tons of CO2 per gigawatt-hour (tCO2/GWh), approximately 60–90% below prevailing 2013 emissions rates in the United States (489 tCO2/GWh) [30] or the European Union EU28 (337 tCO2/GWh) [30].

The contributions of this paper can be summarized as follows: (1) We present a comprehensive analytical framework for assessment of the full generation-system value of energy storage technologies in long-run economic equilibrium, accounting for detailed, short-term operational constraints as well as CO2 emissions goals. The novel analytical framework allows for a more accurate assessment of energy storage benefits compared to what is found in the existing literature. (2) We conduct a detailed case study of the role of energy storage in a future power system based on ERCOT data and with increasingly stringent CO2 emissions targets. We find that the value of energy storage increases with tighter emissions targets. At the same time, the marginal value of storage declines significantly as storage capacity increases and substantial cost reductions are likely needed to economically justify large-scale deployment of most storage technologies.

The paper is organized as follows: Section 2 introduces the methodological approach and the experimental design used in the analysis. Section 3 presents the economic and technical results under three different hypothetical conditions, each of which is exposed to increasingly stringent emissions limits: a power system without energy storage and a diverse range of generation resources, a power system with energy storage and the same generation resources, and a power system with storage that relies exclusively on renewable energy technologies to reduce carbon emissions. Sections 4 Discussion, 5 Conclusion present respectively the discussion and the conclusions derived from the analysis.

Section snippets

Demand, renewables and generation technology cost data sets

This study models an experimental electricity system with electricity demand and wind and solar resource data from the Electricity Reliability Council of Texas (ERCOT) grid. The selection of a ‘Texas-like’ test system was motivated by the relative lack of hydroelectric resources in Texas and weak interconnection with other neighboring power systems of the ERCOT interconnection, which allows a clear interpretation of the results. To project electricity demand in ERCOT in 2035, we increased

Reducing the carbon footprint of electricity without energy storage

Fig. 3 reports the optimal portfolio of electricity generation under the increasingly stringent emissions limits as well as the average generation cost in the absence of energy storage. Average generation cost (AGC) is defined as the quotient between the total annual generation costs (TGC) and the total annual load:AGC=TGCΘ·h=1HDh[USD/MWh],where h is the index for the hours in the four weeks selected; H is the total number of hours considered in the simulation (H = 672 with a four-week

Discussion

Energy storage has been presented in many studies as a necessary element to significantly reduce the carbon footprint of the electricity sector. Indeed, our results indicate that meeting strict emissions reduction targets with variable renewable energy sources alone may be impossible without scalable energy storage or another zero-carbon source of operating flexibility. If flexible nuclear is precluded from our set of eligible technologies, our model cannot produce a feasible electricity

Conclusion

The results presented in this work help inform the current debate about the value and role of energy storage in decarbonizing electricity systems. Using a capacity expansion model with detailed unit commitment constraints we quantify the value of different capacity levels of 2-h and 10-h energy storage under stringent carbon emissions limits.

We first show that there is no silver bullet to decarbonize the electricity sector: the least-cost generation mix includes a diverse mix of resources and

Acknowledgements

The authors would like to thank J.I. Pérez-Arriaga and two anonymous reviewers for helpful comments and review. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE AC02-06CH11357. J.D.J. also gratefully acknowledges support from the U.S. National Science Foundation Graduate Research Fellowship program.

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