参与气电市场的虚拟电厂内部优化随机模型

Internal Optimization Stochastic Model of Virtual Power Plant Participating in Gas and Electricity Market

虚拟电厂总体降低了不确定因素的影响,但若不考虑其内部的随机性,则会使虚拟电厂在参与电力、天然气市场过程中,因其调度方案的保守性,难以获得最高的经济效益。为了充分挖掘虚拟电厂的经济效益,提出了一种气电市场下计及电价和风光不确定性的电-热-气虚拟电厂随机优化调度模型。模型目标函数为虚拟电厂总效益,即售电售热售气收益与电转气成本、碳捕集成本、碳排放权成本和燃料成本之差。引入超分位数方法将含多随机变量的虚拟电厂总效益最优模型转化为其超分位数随机优化模型,为了便于计算进而处理为其离散化计算模型,并利用空间粒子群算法进行求解。算例分析表明:虚拟电厂通过优化售电和售气方案来获得最优效益,在参与气电市场过程中考虑多种随机变量,使得虚拟电厂规避风险后有更多的机会获得更高的经济效益。

The virtual power plant(VPP) generally reduces the impact of uncertainties, but due to the conservative nature of its scheduling schemes, it is difficult for a VPP to obtain the highest economic benefits without considering the internal randomness in the process of participating in the electricity and gas market. In order to fully exploit the economic benefits of VPPs, a stochastic optimization scheduling model is proposed for electric-thermal-gas VPPs with consideration of the uncertainty of electricity price and wind-photovoltaic under gas and electricity market. The objective function of the model is the total benefit of VPPs, which is the difference between the sales of electricity, heat and gas and the cost for electricity to gas conversion, carbon capture, carbon emission and fuel. The superquantile method is introduced to convert the total benefit optimal model of VPPs with multiple random variables into a super-quantile random optimization model. For the convenience of calculation, the model is further processed into a discretization calculation model, and is solved with the spatial particle swarm optimization algorithm. The simulation results show that the VPP obtains the optimal benefits through optimizing the sale schemes of electricity and gas, and with consideration of various random variables in the process of participating in gas and electricity market, the VPP has more opportunities to obtain higher economic benefits after avoiding risks.