Evolutionary Approach for Optimal Bidding Strategies in Electricity Markets

Since smart grids have become available, utility companies now offer demand response programs to help residents manage the gap between supply and demand. The first contribution in the paper presents an optimization-based energy management (EM) method that controls power consumption with real-time price signals from the grid and photovoltaic (PV) batteries in order to limit energy costs, decrease carbon dioxide emissions, and decrease peak energy consumption while relieving rebound power generation. Second, a Grey Wolf Optimization (GWO) algorithm has been employed for solving the complete planning problem for 3 case studies: one sans PV batteries, one with PV systems, and one with PV batteries. Simulations have been carried out to compare the suitability of this suggested approach to existing approaches. According to the outcomes, this suggested GWO algorithm helped reduce energy costs, carbon dioxide emissions, and peak loads across a range of case studies.