Management of an Electrical Storage System for Joint Energy Arbitrage and Improvement of Voltage Profile

2020 Workshop on Communication Networks and Power Systems (WCNPS) Pub Date : 2020-11-12 DOI:10.1109/WCNPS50723.2020.9263720

Jonas V. de Souza, Felipe M. dos S. Monteiro, R. B. Otto, Mauricio Biczkowski, E. Asada

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Abstract

Due to the increasing inclusion of renewable energy sources in the Distribution System (DS), the interest in Energy Storage Systems (ESSs) connected to the network and how to justify the investment has grown. By its attractive features such as fast response and decreasing price, the ESS can be used in various scenarios in the electrical system. Among them, we highlight the profit from the purchase and sale of electricity and the improvement of the voltage profile. The objective of this work is to evaluate the operation of the storage system by using the Multi-objective Evolutionary Particle Swarm Optimization (MEPSO) to perform energy arbitrage and jointly improve the voltage profile of the network. The MEPSO is used to find a set of operational decisions to buy or store energy using the prices of the Day-Ahead Market (DAM) and, within these decisions, to operate the ESS during the Real-Time Market (RTM) hours. The results are promising and evidence that, through the proposed methodology, it is possible to perform energy arbitrage with the improvement of the voltage profile and a low number of charging/discharging cycles.

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联合能源套利与电压分布改善的蓄电系统管理

由于可再生能源越来越多地纳入配电系统(DS)，对与电网连接的储能系统(ess)的兴趣以及如何证明投资的合理性已经增长。由于其响应速度快、价格低廉等特点，可应用于电力系统的各种场合。其中，我们强调了购电和售电的利润和电压剖面的改善。本研究的目的是利用多目标进化粒子群优化(MEPSO)进行能量套利，共同改善电网的电压分布，从而评估储能系统的运行情况。MEPSO用于找到一组使用日前市场(DAM)价格购买或存储能源的操作决策，并在这些决策的范围内，在实时市场(RTM)时间内运行ESS。结果是有希望的，并且证据表明，通过提出的方法，可以通过改善电压分布和减少充电/放电循环次数来进行能源套利。

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2020 Workshop on Communication Networks and Power Systems (WCNPS)

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