Demand-side Fuel-cells and Controllable Loads to Reduce Operational Costs of Micro-grid through Optimal Unit Commitment

H. Howlader, A. Saber, T. Senjyu
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引用次数: 3

Abstract

Nowadays, the price of photovoltaic (PV) has been reducing drastically, that has been increasing the installation of PVs in both power supply-side and demand-side. Definitely, it is a positive development, but huge penetration of PVs in the daytime changes the load demand of thermal generators (TGs) and increases the peak and off-peak gap. This change may create duck curve. Duck curve increases the TG’s fuel cost and start-up cost(SUC) because of frequently start-up and shutdown, and ramping up and down of generators reduce the efficiency. Therefore, load leveling is essential, but it is a challenging issue. This research proposes a smart micro-grid operation and management system for a large island. The micro-grid considers PVs, TGs in supply-side and demand-side considers smart houses which include rooftop gird connected PVs, fuel-cells(FCs), controllable loads and other loads. Real-time pricing (RTP) based demand response (DR) has been introduced for shifting the usages of controllable loads and FCs for leveling the loads. Neural Networks predicts the PVs output power and MATLAB® INTLINPROG optimization toolbox determines simulation results.
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需求侧燃料电池和可控负荷通过优化机组承诺降低微电网运行成本
目前,光伏发电价格大幅下降,这使得电力供给侧和需求侧的光伏安装量都有所增加。当然,这是一个积极的发展,但pv在白天的大量渗透改变了火力发电机组(tg)的负载需求,并增加了峰值和非峰间隙。这种变化可能会产生鸭曲线。由于频繁的启停,鸭子曲线增加了TG的燃料成本和启动成本(SUC),而发电机的上下起伏降低了效率。因此,负载均衡是必要的,但它是一个具有挑战性的问题。本研究提出一种面向大岛的智能微电网运行管理系统。微电网考虑了光伏,供应侧的tg,需求侧考虑了智能住宅,包括屋顶并网的光伏,燃料电池(fc),可控负载和其他负载。引入基于实时定价(RTP)的需求响应(DR)来转移可控负荷的使用,引入基于fc的负荷均衡。神经网络预测pv输出功率,MATLAB®INTLINPROG优化工具箱确定仿真结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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