Variable Weighted Multi-Objective Multi-Dimensional Genetic Algorithm for Demand Response Scheduling in a Smart Grid

This research presents the optimized scheduling of demand response loads of a residential community of 30 houses using a multi-objective multi-dimensional genetic algorithm (MOMD-GA) with a variable weighted objective function. Incorporating day ahead hourly real time pricing (RTP), the MOMD-GA attempts to present possible optimized dispatch patterns with their associated penalties and constraints (environmental, consumers and suppliers) thus providing system operators (SOs) and distribution network operators (DNOs) sufficient data for real time decision making. The variable weights for each considered component of the cost function is chosen to force the MOMD-GA towards exploring optimum solutions with lower environmental cost. Further shown are the trade-offs in selecting particular dispatch bias (consumer, supplier, environmental and optimized) and the impact of the various dispatch scenarios on the cost of overall electricity bill of the community.