Virtual Power Plant Profit Maximization in Day Ahead Market using Different Evolutionary Optimization Techniques

Virtual Power Plant (VPP) is a cloud-based software-controlled distributed power plant that aggregates heterogeneous distributed generation units into a single operating profile to participate in the energy trading with the wholesale energy market. The concept of VPP is mainly employed to deal with the uncertain nature of RESs. This paper discourses an electricity trading scheme involving VPP, consisting of a photo-voltaic (PV), wind turbine, and a micro-turbine (MT) unit in addition to load. The VPP participates in the Day-Ahead Market (DAM) with an objective of profit maximization. The generation scheduling is performed using different evolutionary optimization techniques to maximize the profit of VPP and its participants. Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Manta Ray Foraging optimizer (MRFO) and RUNge Kutta Optimizer (RUN) are the four algorithms being considered and compared in this study. The results show a comparative study in terms of maximum profit of VPP and execution time of optimization techniques. The optimal result is obtained consistently by MRFO.