Abstract: In order to mitigate wind and photovoltaic power generation uncertainty on stable operation of virtual power plant, a multi- objective stochastic scheduling optimization model with consideration of uncertainty and demand response is proposed with robust stochastic optimization theory. Firstly, wind power, photovoltaic power generation, gas turbine(GT) power generation, energy storage systems (ESS) and demand response are integrated into a virtual power plant. Secondly, maximize operational benefits of virtual power plant and minimize system operating cost and abandoned energy costs are selected as objective functions. Then by application of robust stochastic optimization theory, a virtual power plant (VPP) scheduling optimization model is established. The proposed method is applied to distributed power demonstration project in Yunnan, China as an example. The results show that the proposed model can reduce system power shortage penalty cost. The introduction of dual Robust coefficients can provide flexible VPP scheduling decision tools for different risk attitudes of decision makers and respond to wind power and photovoltaic power generation stochastic characteristics effectively. ESS can replace GT unit to provide backup services for wind power and photovoltaic power generation because of its charge and discharge characteristics. It can also smooth VPP output power curve and promote grid connection between wind power and photovoltaic power generation. Demand response is incorporate into VPP to realize power generation side and power side linkage optimization, smooth electric load curve and improve overall operational effectiveness.

Key words: robust stochastic optimization theory, VPP, stochastic scheduling optimization model, wind power, photovoltaic power