Stochastic Risk-Constrained Scheduling of Renewable-Powered Autonomous Microgrids with Demand Response Actions: Reliability and Economic Implications

Abstract

The unpredictable and volatile nature of renewable energy sources (RESs) will increase the burden of a system operator for maintaining the system reliability in different conditions. In this paper, a stochastic risk-constrained framework is proposed for short-term optimal scheduling of autonomous microgrids to evaluate the influence of demand response (DR) programs on reliability and economic issues, simultaneously. The objective is to maximize the expected profit of the microgrid operator through optimal scheduling of resources in a more reliable manner considering both supply and demand side uncertainties. In the proposed approach, the microgrid operator’s risk aversion is modeled by using the conditional value-at-risk (CVaR) method to control and avoid non-desirable profit distributions due to various system uncertainties. Moreover, AC optimal power flow (AC-OPF) technique is employed to calculate the amount of energy and reserve of dispatchable distributed generation (DG) units and responsive loads for the operational hour of the next day. Eventually, the applicability of the proposed method is studied on different test systems and impacts of various parameters such as level of DR participants and values of lost load (VOLL) as well as risk aversion parameter on the system’s economy and reliability indices are investigated deeply.