Optimal stochastic scheduling of electrical and thermal renewable and non-renewable resources in virtual power plant

Abstract

For coordinated operation of distributed energy resources, virtual power plants are introduced as a solution to maximize the net profit of all participants considering the uncertainties. In this study, a stochastic scheduling problem for a virtual power plant is modeled to meet the thermal and electrical load considering the network security constraints and uncertainties of electrical and thermal loads, wind speed, solar radiation, and market price. The virtual power plant consists of conventional generators, photovoltaic panels, wind turbines, photovoltaic-thermal panels, combined heat and power, energy storage systems, and boilers. To model all uncertain parameters, a scenario reduction approach is used to decrease the number of possible scenarios. Also, to increase the accuracy of the uncertainty modeling, a new approach for modeling the wind speed uncertainty is proposed. By utilizing a proper linear model for the conventional generators, the scheduling problem is formulated as a mixed integer linear programming. Two cases are introduced to study the effects of including photovoltaic-thermal panels in the scheduling problem using the IEEE 33-bus distribution test system. The problem is modeled in GAMS environment and is solved using CLPEX solver. The results show that the proposed model for virtual power plants scheduling for the next 24 h increases the expected net profit. Simulation results show that considering photovoltaic-thermal panels increases the expected net profit. Also, addition of photovoltaic-thermal panels has decreased the dependency of the virtual power plant on the boiler and combined heat and power for meeting the thermal load. A sensitivity analysis is performed to investigate the effects of electricity price on virtual power plant profit.