Weighted sum approach using Parallel Particle Swarm Optimization to Solve Multi-objective Energy Scheduling

This paper presents a Particle Swarm Optimization (PSO) methodology to solve the problem of energy resource management with high penetration of Distributed Generation (DG) and Electric Vehicles (EVs), based in multi-objective optimization. The high penetration of unpredictable DG, results in the increase of the operation cost, due to the additional constraints on the system, and has a direct influence on the reducing of carbon dioxide (CO2) emissions. The proposed methodology consists in a multi-objective function, in which is intended to maximize the profit, corresponding to the difference between the income and operating costs, and minimize CO2 emissions. In this case study it was considered a real Spanish electric network, from the city of Zaragoza, applied to the productions and consumption values expected in 2030. This network is constituted by 1300 EVs and 70% DG penetration of its total installed capacity.