考虑随机性及光热电站参与的多源发电系统两阶段随机优化调度

针对多种新能源发电的联合优化调度问题,以风力发电、光伏发电、光热发电、火力发电构成多源发电系统(Muti-Source Power System, MSPS),提出了一种基于鲁棒随机优化理论的MSPS两阶段随机优化调度方法。首先,介绍了MSPS基本结构,并建立了MSPS出力模型。然后,提出了基于区间法及概率距离的场景生成及削减框架以处理风光出力的随机性,在此基础上建立了MSPS两阶段优化调度模型。同时,采用鲁棒随机优化理论将含有随机变量的约束条件转化为可以反映系统管理者承担系统风险态度的约束条件,建立了含双鲁棒系数的MSPS随机优化调度模型。通过仿真验证该模型可以降低系统的缺电风险,并能够为风险态度不同的系统管理者提供优化调度决策依据。

A two-stage stochastic scheduling optimization for multi-source power system considering randomness andconcentrating solar power plant participation

In allusion to the problem of integrated scheduling optimization in a variety of new energy power generation, the paper integrates the wind power, photovoltaic power, concentrating solar power and thermal power generation as the Multi-Source Power System(MSPS), and a two-stage stochastic scheduling optimization method based on robust stochastic optimization theory is proposed. First, this paper introduces the basic structure of MSPS and establishes the output power models of MSPS. Then, the scenario generation and reduction frame based on the interval method and the probability distance is proposed for dealing with the randomness of new energy output power. On this basis, a two-stage stochastic scheduling optimization model for MSPS is established. Simultaneously, this paper transforms the constraints containing random variables into the constraints reflecting the decision maker’s attitude of bearing systematic risks based on robust stochastic optimization theory, and then establishes MSPS stochastic scheduling optimization model including two robust coefficients. The simulation proves that the model can reduce the risk of power shortage and provide scheduling optimization decision-making foundation for different-risk-attitude system manager.