۱- Introduction

۲- Literature review

۳- Model

۴- Model analysis

۵- Operational policy analysis

۶- Model generalizations

۷- Conclusion

References

Abstract

Enabled by smart meters and Internet of Things (IoTs) technologies, we are now able to harness information systems and automatize the management of energy storages. Motivated by applications such as renewables integration and electrification of transportation, the paradigm shift towards smart-cities naturally inspires information systems design for energy storages. The goal of this paper is to understand the economic value of future market information to increase the efficiency of the energy market. From storages’ perspective, we investigate energy storages’ optimal decentralized buying and selling decisions under market uncertainty. Different potential policy interventions are discussed: (1) providing a publicly available market forecasting channel; (2) encouraging decentralized storages to share their private forecasts with each other; (3) releasing additional market information to a targeted subset of storages exclusively. Through these system level discussions, we evaluate different information management policies to coordinate storages’ actions and improve their profitability. The key findings of this work include (1) a storage’s payoff first increases then decreases in its private information precision. The over-precision in forecasts can lead to even lower payoffs; (2) communication among the storages could fail to achieve a coordinated effort to increase market efficiency; (3) it is optimal to release additional information to a subset of energy storages exclusively by targeted information release.

The economics of energy storages

Energy storages (ESs) are becoming increasingly common in the power system and are used in a host of services (Dunn et al., 2011; Pandziˇ c´ et al., 2015). In essence, these devices shift energy across time through charging and discharging operations. Energy storage will become a critical component in the transmission network because of their ability to mitigate the uncertainty and variability in renewable resources. Recent studies have concluded that California would require up to a total of 186 GWh/22 GW network energy storage by 2050 (Solomon et al., 2014) and Northwest Power Pool would require a total of 10 GWh/1 GW network energy storage by 2019 to balance 14.4 GW of installed wind capacity (Kintner-Meyer et al., 2010). However, both of these studies did not focus on the economics of storage. In particular, why storage owners would want to be in the system is not discussed in Solomon et al. (2014), and Kintner-Meyer et al. (2010) acknowledged that revenue for the owners may be thin. Both regulators and system operators (SOs) agree that since the deployment of storage can reduce the operating cost of the grid, forprofit owners of storage should able to connect sufficient revenue to justify their investment and keep them in the system. For instance, California Independent System Operator (CAISO) roadmap states that “realizing the full revenue opportunities consistent with the value ES can provide” is a priority of system design (CAISO, 2014). At the same time, Federal Energy Regulatory Commission (FERC) Order No. 792 specifically identifies storage as small generators and instructs that they should be able to earn their fair share of revenue (FERC, 2013).