Battery energy storage systems management in a day-ahead market scenario with transactive energy and private aggregators

Héricles Eduardo Oliveira Farias, Camilo Alberto Sepulveda Rangel, L. Canha, Leonardo Weber Stringini, T. A. Silva Santana, Zeno Luiz Iensen Nadal
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Abstract

This paper presents a methodology for battery energy storage systems (BESS) management considering the concept of transactive energy. Transactive energy is defined as the economic and control technique used for energy management that allows the dynamic balance of supply and demand across the electrical system. In a transactive energy market, the consumer can produce energy and inject it into the grid, becoming a prosumer. Also, it is possible to have the presence of private aggregators. Aggregators have large energy production capacity and can negotiate this energy with the grid. The system is composed by two private aggregators, the consumers, and the distribution system operator (DSO). The aggregators are assigned to supply a specific number of consumers defined in the contractual demand with the DSO, and the DSO is responsible for serving the rest of the system. Both aggregators and the DSO have distributed energy resources (DERs), such as energy storage and/or photovoltaic generation. A neural network based on group method of data handling (GMDH) is used for forecasting the grid demand, energy prices and solar generation for the day-ahead operation. The BESS reserve for the day-ahead is optimized based on prediction model. The methodology is validated in a 33-bus distribution network simulated on software OpenDSS. The curve profiles are taken from real data of the Canadian distribution system.
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具有交易能源和私有聚合器的日前市场情景下的电池储能系统管理
本文提出了一种考虑交互能量概念的电池储能系统管理方法。交互能源被定义为用于能源管理的经济和控制技术,它允许整个电力系统的供需动态平衡。在一个互动性的能源市场中,消费者可以生产能源并将其注入电网,成为产消者。此外,也可能存在私有聚合器。聚合器有很大的能源生产能力,可以与电网协商这些能源。该系统由两个私有聚合器,消费者和分配系统运营商(DSO)组成。聚合器被分配给特定数量的消费者,这些消费者在与DSO的合同需求中定义,DSO负责为系统的其余部分提供服务。集热器和DSO都具有分布式能源(DERs),例如能源存储和/或光伏发电。采用基于分组数据处理方法(GMDH)的神经网络对日前运行的电网需求、能源价格和太阳能发电进行预测。基于预测模型对前日BESS储备进行优化。该方法在OpenDSS软件上的33总线配电网仿真中得到了验证。曲线轮廓取自加拿大配电系统的实际数据。
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