A Hybrid Heuristic Algorithm for Energy Management in Electricity Market with Demand Response and Distributed Generators

Optimal energy management trends are indispensable in improving the power grid's reliability. However, power usage scheduling for energy management (EM) poses several challenges on a practical and technical level. This paper develops an energy consumption scheduler (ECS) to solve the power usage scheduling problem for optimal EM and overcome the major challenge in demand response (DR) implementation. This work aims to solve the power usage scheduling problem for EM to optimize utility bill, peak energy demand, and pollution emission while considering the varying pricing signal, distributed generators (DGs), household load, energy storage batteries, users, and EUC constraints. The ECS is based on a stochastic algorithm (genetic wind-driven optimization (GWDO) algorithm) because generation, DGs, demand, and energy price are stochastic and uncertain. The ECS based on the GWDO algorithm determines the optimal operation schedule of household appliances and batteries charge/discharge for a day time horizon. The developed model is analyzed by conducting simulations for two cases: home is not equipped with DGs, and home is equipped DGs in terms of utility bill, peak energy demand, and pollution emission. The simulation results validated the proposed model's applicability to EM problems.