Quantum Particle Swarm Optimization Applied to Distinct Remuneration Approaches in Demand Response Programs

The development of demand response programs has been allowing to improve power system performance in several ways, both in terms of the management of electricity markets, as well as regarding benefits in its operation. In order to model the remuneration for the participation of consumers in the scheduling of resources, this paper proposes a methodology based on the use of four incentive-based tariffs for the remuneration of demand response participation. It considers steps, quadratic, constant and linear remuneration. The optimization model enables Virtual Power Players to minimize operation costs, considering different critical situations of management and operation. The optimization problem has been solved by Quantum Particle Swarm Optimization. The case study concerns 168 consumers, classified into 5 consumer types, 118 distributed generation resources and 4 external suppliers.