Risk-based Optimal Bidding and Operational Scheduling of a Virtual Power Plant Considering Battery Degradation Cost and Emission

Abstract

A virtual power plant (VPP) is a system combining various types of distributed energy resources (DERs) to provide a reliable power system operation. It provides the advantage of making changes in generation according to variety, price, and demand conditions with bringing renewable energy sources (RES) in a single portfolio and using their flexibility. In this study, it is tried to find optimal bidding and operational scheduling of a VPP containing Wind Power Plant (WPP), Photovoltaic Power Plant (PVPP), Heat-Only Unit (HOU), Battery Energy Storage System (BESS), Combined Heat and Power Plant (CHPP), and electrical/thermal demands and participating in the day-ahead electricity market in 24-h time interval. It is aimed to maximize profit and minimize emissions with considering the battery cost. A stochastic model is formed by considering the uncertainty arising from RES. In addition, CVaR (Conditional Value at Risk) as a risk measurement technique is applied against the risk arising from low profit scenarios. The proposed optimization problem formulated as a stochastic Mixed Integer Nonlinear Programming (MINLP) model and is solved by solver LINDO in GAMS (General Algebraic Modelling System). The case studies are implemented to show the applicability and effectiveness of the presented model.