(690a) Remuneration of Energy Storage in Electricity Markets Using Virtual Links

The US power grid is undergoing significant changes, largely driven by increasing adoption of renewable energy. Multiple states have set ambitious renewable portfolio standards for the near future; for instance, California, Colorado, and Virginia, aim to achieve 100% renewable power by 2050 or earlier [1]. Due to the high level of generation uncertainty and volatility, large-scale incorporation of renewable energy requires much more flexibility in the grid. For power systems, flexibility means the ability to adjust electricity production or consumption to respond to variability of all kinds [2]. It has been demonstrated that various energy storage resources (e.g. battery, pumped hydro) have significant potentials of temporal flexibility provision [3]; however, current electricity markets (ISOs) are not well designed to incorporate ESRs, especially for MW-scale storage ESRs. To address this issue, the Federal Energy Regulatory Commission (FERC) released Order 841 in 2018 to remove barriers to wholesale electricity market participation of ESR systems [4].

Recent work has attempted to design new energy market clearing procedures to incorporate ESR participants. This is important, especially for large-scale ESR systems in order to ensure operational feasibility and to capture potential effect of ESR systems on market prices. Coordinated (centralized) market clearing frameworks have been proposed, where operational constraints of ESR systems are explicitly captured and ESR systems act as price-takers [5], [6]. Complementarity constraints are introduced to prevent simultaneous charge and discharging, meaning that the dual prices cannot be obtained directly from the dual solution of the centralized market clearing problem. In addition, much research work on market design for ESR explores the potential price-making behavior of ESRs using bi-level programming approaches [7], [8], [9]. The bi-level programming models include prices as decision variables, thus solving the issue above, but are challenging to scale. Regardless, the models mentioned above include charge/discharge power as dispatch decisions for ESRs. While this is a straightforward way to capture ESRs, it is not obvious how ESRs are remunerated in these markets. Also, most of them assume ESRs benefit from charging (as a load) and lose from discharging (as a generator).

Here, we propose a new market clearing design that properly incentivizes flexibility from ESR systems. Our new market design decomposes the charging and discharging operations of ESR systems into energy transfer and net-charging/discharging. Energy transfer is captured based on the concept of virtual links [10]. This reveals how the new market framework incentivizes flexible operations of ESRs via temporal price differences (time volatility). Also, our new market design treats both discharging and charging as a service that ESRs provide. In addition, the new market design applies a robust bound on ESR operation trajectory to remove complementarity constraints at the expense of reducing the feasible operations of ESRs. This allows the market framework to compute prices directly (from the dual solution). Via numerical experiments, we also explore the optimal investment and operational strategy of ESRs under our proposed market design, as well as the desired ESR distribution from ISO's perspective.

References:

[1] State renewable portfolio standards and goals. https://www.ncsl.org/research/energy/renewable-portfolio-standards.aspx. Accessed: 2022-01-19.

[2] International Energy Agency. Harnessing Variable Renewables: A Guide to the Balancing Challenge. OECD, 2011. DOI.org (Crossref), https://doi.org/10.1787/9789264111394-en.

[3] Ramteen Sioshansi, Paul Denholm, Thomas Jenkin, and Jurgen Weiss. Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects. Energy economics, 31(2):269--277, 2009.

[4] Federal Energy Regulatory Commission. Electric storage participation in markets operated by regional transmission organizations and independent system operators. https://www.ferc.gov/media/order-no-841, 2018. Accessed: 2021-06-11.

[5] Shahin Parvar, Hamidreza Nazaripouya, and Ailin Asadinejad. Analysis and modeling of electricity market for energy storage systems. In 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pages 1--5. IEEE, 2019.

[6] Dapeng Chen and Zhaoxia Jing. An improved market mechanism for energy storage based on flexible state of energy. CSEE Journal of Power and Energy Systems, 2020. doi: 10.17775/CSEEJPES.2020.02980.

[7] Hrvoje Pandvzi'c and Igor Kuzle. Energy storage operation in the day-ahead electricity market. In 2015 12th International Conference on the European Energy Market (EEM), pages 1--6. IEEE, 2015.

[8] Karl Hartwig and Ivana Kockar. Impact of strategic behavior and ownership of energy storage on provision of flexibility. IEEE Transactions on Sustainable Energy, 7(2):744--754, 2015.

[9] Hamed Mohsenian-Rad. Coordinated price-maker operation of large energy storage units in nodal energy markets. IEEE Transactions on Power Systems, 31(1):786--797, 2015.

[10] Weiqi Zhang, Line A Roald, Andrew A Chien, John R Birge, and Victor M Zavala. Flexibility from networks of data centers: A market clearing formulation with virtual links. Electric Power Systems Research, 189:106723, 2020.