Dynamic Economic Dispatch Problem Integrated With Demand Response (DEDDR) Considering Non-Linear Responsive Load Models

Abstract

Intelligent implementation of demand response programs (DRPs) not only decreases electricity price in electricity markets, but also improves network reliability. In this paper, the dynamic economic dispatch (DED) problem has been optimally integrated with the incentive-based DRPs. Moreover, mathematical load modeling can be so effective in the load curve estimation with the lowest error. So, economic models of the linear and non-linear responsive loads (power, exponential, and logarithmic) have been developed for time-based and incentive-based DRPs and integrated with DED. Also, a procedure to select the most conservative responsive load model for the load estimation has been presented too. Also, determining the optimal incentive in the incentive-based DRPs is one of the independent system operator’s challenges. In the proposed combined model, the fuel cost is minimized and the optimal incentive is determined simultaneously. Valve-point loading effect, prohibited operating zones, spinning reserve requirements, and the other non-linear practical constraints make the combined problem into a complicated, non-linear, non-smooth, and non-convex optimization problem, which has been solved with a population-based meta-heuristic algorithm namely random drift particle swarm optimization algorithm. The proposed combined model is applied on a ten units test system. Results indicate the practical benefits of the proposed model.