Decentralized energy management system of distributed energy resources as virtual power plant: Economic risk analysis via downside risk constraints technique

Abstract

A virtual power plant (VPP), as a decentralized energy management system (EMS), can be a handy application to aggregate and schedule distributed energy resources (DER). The DERs can be dispatchable and non-dispatchable energy resources wishing to participate in the day-ahead (DA) electricity market. Therefore, they can provide the grid with higher reliability and safety margins. However, there are uncertainties in the market that VPP should manage and the associated risks to obtain robust scheduling in such a probabilistic environment. In this paper, risk-oriented stochastic optimization is proposed to investigate the market's uncertainties and obtain the maximum profit while ensuring the safety margins of the network. Downside risk constraints (DRC) are integrated into the model to cover the risks. So, the risk-based scheduling of VPP is obtained. The proposed risk-based framework is formulated as the mixed-integer linear programming (MILP) model, solved by the CPLEX solver of GAMS software. The proposed model is applied to an 18-bus test system to investigate the proposed risk-oriented stochastic optimization of VPP. Finally, the costs, revenues, and total profit are illustrated, compared, and analyzed in the model's risk-neutral (RN) and risk-averse (RA) states so that an appropriate decision can be made. Moving toward the RA strategy lowers the amount of generated power of the DG and the purchased power from the upstream grid, consequently leading to a lower amount of sold power by the VPP within the studied power grid. The analysis shows that the RA strategy decides with low risk by decreasing the cost and revenue of the VPP simultaneously. In this regard, the results indicate that the average profit of the VPP decreased by 8.62%, from $15407 to $14079 when decision-maker tends to take risk-averse decisions with zero risk.