摘要
由于可再生能源出力的间歇性、负荷用电量和市场电价的不确定性这两方面因素,使得微电网内的竞价策略存在较大的风险和和较高的计算难度。为应对这些问题,设计采用三阶段混合随机/区间优化(Hybrid Stochastic/Interval Optimization,HSIO)模型来构建微电网内部的竞价问题,通过微电网内潜在灵活资源和实时市场支撑这两种方式来处理上述波动。其中,采用经济有效的随机规划来实现日前市场决策中的微电网利益最大化,从而解决日前市场价格的不确定性。此外,设计了一种基于快速鲁棒区间优化的灵活方法来实现实时阶段微电网的平衡成本最小化,从而应对可再生能源能出力的间歇性和实时市场电价的不确定性。算例综合比较分析了所提出方法的有效性、鲁棒性和计算复杂度。结果表明HSIO模型能够同时兼具随机规划模型的成本效益性和区间优化模型计算简单和鲁棒性的特点。
Volatile impact of intermittent renewable energy sources(RESs)output on the one hand and the uncertainties of load consumptions and market electricity prices,on the other hand,make the bidding strategy of micro-grids(MGs)too risky and high-computational problem.To cope with these challenges,the bidding problem of MGs based on a three-stage hybrid stochastic/interval optimization(HSIO)is devised in this study,which provides a trade-off between covering the volatilities by means of the MG potential flexibilities resources or by means of the energy provision from the real-time market(RTM).To tackle the uncertainties of the day-ahead market prices,the cost-effective stochastic programming(SP)is applied to maximize the profit of MG in the day-ahead stage of decision-making.Moreover,in order to handle the volatilities of RESs production and uncertainties of RTM prices,a flexibility scheme based on the robust and low-computational interval optimization(IO)approach is designed to minimize the balancing cost of MG in the real-time stages.Comprehensive numerical results are provided to compare the effectiveness,robustness,and computational complexity of the proposed method.Results show that the HSIO model takes advantage of the cost-effective solution from the SP model,and the robust solution with computational simplicity from the IO model.
作者
罗晓东
孙晋凯
郭晓霞
王晖南
Luo Xiaodong;Sun Jinkai;Guo Xiaoxia;Wang Huinan(Metrology Center of State Grid Shanxi Electric Power Company,Shuozhou 030012,Shanxi,China)
出处
《电测与仪表》
北大核心
2021年第6期130-139,共10页
Electrical Measurement & Instrumentation
基金
国网公司总部科技项目(520531180003)。
关键词
竞价策略
混合随机/区间优化
需求响应
储能灵活性
日前市场
bidding strategy
hybrid stochastic/interval optimization
demand response
energy storage flexibility
day-ahead market
