MULTI-OBJECTIVE RISK CONSTRAINED SELF-SCHEDULING OF THE GENCOS FOR THE CORRELATED DEREGULATED MARKETS
industrial electrical power systems
This paper addresses a Multi-Objective Particle Swarm Optimization-Dynamic Crowding Distance (MOPSO-DCD) based algorithm to solve the Multi-Objective Risk Constrained Self-Scheduling (MORCSS) problem of the Generator companies (Gencos), in the correlated energy and spinning reserve markets. The proposed MOPSO-DCD method is demonstrated on the single generator system and the standard IEEE 30-bus system and its corresponding results are analyzed. The effectiveness of the proposed approach is demonstrated by comparing the reference Pareto front, generated by using multiple runs of the Cauchy Mutated Mimetic Particle Swarm Optimization (CMMPSO) method. Minimum spacing and the diversity of the Pareto front are taken into account for the performance assessment process
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