Implication of Market Impact on Co-optimization Scheduling Policy for Electricity Merchants with Energy Storage and Wind Farms

Abstract

Wind power generation has increased with the participation of renewable energy power generation in power grid transactions. However, because wind power generation is easily affected by factors such as geography and climate, its high level of uncertainty and intermittent issues have attracted widespread attention. To satisfy the requirements of the power market to maintain a balance between power supply and demand, this study introduces energy storage, a useful tool for mitigating power supply fluctuations through the charging and discharging of energy storage devices to satisfy electricity market demand. By acquiring power to store when prices are low and sell to the market when prices are high, electricity merchants can also use energy storage to engage in energy arbitrage and maximize profit. Co-optimization of renewable power plants with energy storage by storing electricity when the demand for power is lower than the supply of renewable energy can potentially provide future value by mitigating the intermittent nature of renewable energy generation. Most models used in current studies assume that the energy storage capacity is sufficiently small with respect to the wholesale power market and that charging and discharging decisions do not affect the price of electricity. Consequently, a merchant (also known as a price-taker merchant) sells or purchases a certain amount of power at a price that is unrelated to his or her own trading actions. Price arbitrage, in which the merchant′s operational decisions affect electricity prices in the market (i. e., a pricemaker merchant), in turn influences the merchant' s actions, thus illustrating the value of large energy storage, such as pumped storage hydropower. We investigate the best multi-period scheduling decision made by electricity merchants by considering market impact, unreliable wind energy, and energy storage limitations. To facilitate merchant decision-making in considering the impact of the market, we approximate the price of electricity by a linear function of the amount of power that the merchant transacted in the reward function. This research overcomes the challenges in obtaining analytical conclusions when incorporating market impact, because nonlinear reward functions are used instead of the conventional linear reward and value functions. The energy storage capacity, pumping/charging and generating/discharging capacity, and facility pumping/charging and generating/discharging efficiency must all be considered in modeling. To maximize profit, we construct this problem as a Markov decision process and investigate the electricity merchant′s optimal co-optimization operational trading decisions. To address this optimization problem, we first divide the original optimization problem into three sub-optimization problems corresponding to three possible activities of the electricity merchant and conditional value functions in each period. On the basis of the Bellman equation, the best solution for each sub-optimization problem is discussed. Our closed-form analytical results may support merchants' multi-period decision-making for energy storage scheduling. We finally combine and compare the best sub-optimization situation to draw overall conclusions regarding the original problem and determine the best decision rules across the entire optimization horizon. We analytically show that three state of charge (SOC) reference points depend on the energy that is currently available, the predicted price, the market price impact, and the energy of wind generation that is currently available. The storage SOC is divided into four subregions by using three SOC reference points relating to four distinct activities. By simply comparing the current storage SOC and the SOC reference points in the following period, the merchant can obtain the corresponding optimal decisions, which are discharging stored power and selling all wind generated power to the market; selling all wind generated power and storage remaining idle; storing and selling a portion of wind generated power; and storing all wind generated power and purchasing power from the market. The pricing differential between peak and off-peak periods shrinks when the market impact is taken into account, thus raising the cost of acquiring power from the electricity market and lowering the revenue from selling power to the electricity market. We analytically demonstrate that when both the price-maker and the price-taker merchants offer the same generating/discharging and pumping/charging limits to the independent system operator, the market impact may decrease electricity merchants′ expected profit and substantially alter the optimal storage policy structure. An electricity merchant who merely has storage is a special case different from that of a merchant who also has a wind farm. The decision-making approach is also supported by a case study of synthesized data and a study of a midcontinent independent system operator′s real data. The relationship between market impact strength and anticipated maximum profit is investigated. With an increase in the strength of the operator′s trading decisions on the market, the operator′s income declines. Therefore, throughout the optimization horizon, merchants should decide to limit the power transaction quantity to lessen the adverse market impact on trade. Our findings demonstrate that, by affecting the values of reference points, market impact substantially alters the best storage scheduling strategy. Furthermore, compared with the use of separate schedules for energy storage and wind farms, the co-optimization scheduling strategy benefits merchants with energy storage and wind power plants. This research may provide guidance and practical application value for merchants participating in wholesale power market scheduling who have energy storage and renewable power sources.

Department(s)

Electrical and Computer Engineering

Comments

National Natural Science Foundation of China, Grant 71671092

Keywords and Phrases

价格制定者; 市场影响; 最优调度决策; 电力存储; 风力发电

International Standard Serial Number (ISSN)

1004-6062

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 Zhejiang University, All rights reserved.

Publication Date

01 Jan 2024

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