A multiobjective-based approach for demand-side management in smart distribution grids

Demand-side load management methods need to find a compromise between utility and consumer interests. However, oftentimes these entities have conflicting objectives, particularly in price-based demand response programs. This paper considers the demand-side management problem from the perspective of both the energy service provider and its consumers in two steps: a local and decentralized load schedule optimization, followed by a system-wide demand profile optimization. A multi-objective optimization model is formulated for the load scheduling problem to minimize consumer expenses and the cost associated with inconvenient load shifting. Then, the proposed approach to improve the aggregated demand profile by reducing the peak-to-average ratio is described. Ant colony optimization algorithms were implemented and compared with other heuristics. Numerical results based on simulated load profiles show that ant algorithms were able to improve the aggregated demand load profile.