Solving day-ahead scheduling problem with multi-objective energy optimization for demand side management in smart grid

Abstract

Demand side management (DSM) strategy implementation plays a vital role in energy management of smart grid (SG) by involving distributed energy resources (DERs) to reduce operational cost, pollution emission and provide end users satisfaction. In this study, day ahead scheduling problem in SG is adopted by using DSM strategy in SG considering different types of consumers to reduce operational cost and pollution emission, load curtailment cost by considering curtailable loads (CLs), and coordination between shiftable loads (SLs) and wind turbines (WTs) output power. The consumers participating in the DSM strategy are responsive consumers, which can shift and curtail loads, and non-responsive consumers who cannot shift or curtail loads. The DERs used in the proposed day-ahead scheduling problem consists of wind energy source (WES), energy storage systems (ESSs), and diesel generators (DGs). Before integrating wind energy sources with SG, its forecasting is necessary; thus, the probability distribution function (PDF) is used to forecast wind speed. The day-ahead scheduling problem with tri objective function is solved using the multi-objective wind driven optimization (MOWDO) technique using the decision-making mechanism (DMM) to obtain the best solution in search space. Simulation results show that the day ahead scheduling multi-objective problem is solved using MOWDO algorithm. To check the effectiveness of the proposed model, it is applied to SG considering different constraints to receive balance power at the user end.

Keywords

• Wind power
• Consumer price
• Multi-objective optimization
• Demand-side management
• Smart grid
• Multi-objective wind driven