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A New Strategy for Economic Virtual Power Plant Utilization in Electricity Market Considering Energy Storage Effects and Ancillary Services
Journal of Electrical Engineering & Technology ( IF 1.6 ) Pub Date : 2021-06-07 , DOI: 10.1007/s42835-021-00811-8
Bo Li , Mohammad Ghiasi

A group of distributed generators (DGs) systems including wind, solar, diesel, energy storage (ES), etc., that are under a central management and control is often considered as virtual power plant (VPP) concept. One of the components of a VPP is ES, whose presence and participation in the electricity market can create business opportunities. In this paper, a new mathematical-based strategy for identifying different types of trading situations considering VPPs effects is proposed in the electricity market to obtain maximum benefit. Also VPP trading between energy and ancillary services is considered and analysed. The presented model considers all limitations of the VPP including network constrains and the structure of VPPs. The optimal management of distributed energy units determines the state of charge (SoC) or discharge of ES resources and the amount of intermittent load for the day ahead electricity market. By implementing the proposed model on the microgrid (MG), two different modes of trading for VPPs are examined and the changes of efficiency related to energy storages are analysed. In order to solve the issue of optimal operation strategy, an intelligent approach based on differential evolution (DE) algorithm is used. The obtained simulation results of both modes are compared with those VPP without energy storage. The results show notable profits in both modes.



中文翻译:

考虑储能效应和辅助服务的电力市场经济虚拟电厂利用新策略

一组分布式发电机(DG)系统,包括风能、太阳能、柴油、储能(ES)等,在中央管理和控制下,通常被认为是虚拟电厂(VPP)概念。VPP 的组成部分之一是 ES,它的存在和参与电力市场可以创造商机。在本文中,在电力市场中提出了一种新的基于数学的策略,用于识别考虑 VPP 影响的不同类型的交易情况,以获得最大的收益。还考虑和分析了能源和辅助服务之间的 VPP 交易。所提出的模型考虑了 VPP 的所有限制,包括网络约束和 VPP 的结构。分布式能源单元的优化管理决定了 ES 资源的充电状态 (SoC) 或放电以及日前电力市场的间歇负荷量。通过在微电网 (MG) 上实施所提出的模型,研究了 VPP 的两种不同交易模式,并分析了与储能相关的效率变化。为了解决最优运行策略问题,采用了基于差分进化(DE)算法的智能方法。将获得的两种模式的仿真结果与没有储能的 VPP 进行了比较。结果显示两种模式都有显着的利润。研究了 VPP 的两种不同交易模式,并分析了与储能相关的效率变化。为了解决最优运行策略问题,采用了基于差分进化(DE)算法的智能方法。将获得的两种模式的仿真结果与没有储能的 VPP 进行了比较。结果显示两种模式都有显着的利润。研究了 VPP 的两种不同交易模式,并分析了与储能相关的效率变化。为了解决最优运行策略问题,采用了基于差分进化(DE)算法的智能方法。将获得的两种模式的仿真结果与没有储能的 VPP 进行了比较。结果显示两种模式都有显着的利润。

更新日期:2021-06-07
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