Multi-Objective Optimization for Demand Response Management

Demand Response (DR) plays a vital in maintaining the energy balance between supply and demand, in today's open electricity market. Instead of adjusting the generation levels every time, it introduces a flexibility in the Power system which allows the system operator to adjust the loads at the demand-side itself at different time-windows of operation. The recent implementation of newer smart grid technologies in the system has added communication network to the existing grid which paves the way for DR. There are many objectives that can be optimized through Demand Response Management (DRM) such as cost reduction for consumers who have participated in DR, reduction in carbon emissions, etc. For an optimization problem which is multi-objective, it is difficult to get the exact optimized solutions in response to which all the objective functions will be optimized. Thus, it leads to conflicting or sometimes ambiguous case study, giving rise to a set of feasible solutions. This paper uses the SPEA II i.e., Strength Pareto Evolutionary Algorithm II to obtain different Pareto-optimal fronts for different hours of the day. The objectives are to meet the peak load demands, and to decrease the expenditure to the consumers as well as the inconvenience faced by them.