Modified particle swarm optimization for day-ahead distributed energy resources scheduling including vehicle-to-grid

• Autor(a) principal: Soares, João André Pinto
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/10400.22/4408
• topic: Electric Vehicles; Electric Vehicles Demand Response; Optimization; Particle Swarm Optimization; Optimização; Gestão da Procura para Veículos Eléctricos; Veículos Eléctricos

This thesis proposes a modified Particle Swarm Optimization (PSO) approach for the day-ahead scheduling of Distributed Energy Resources (DER) in smart grids, considering Electric Vehicles (EVs) with gridable capability (vehicle-to-grid). The proposed methodology introduces several changes in traditional PSO meta-heuristic to solve effectively the scheduling problem of DER with EVs. This thesis proposes an intelligent mechanism for adjusting the velocity limits of the swarm to alleviate violations of problem constraints and to improve the quality of the solution, namely the value of the objective function. In addition, a hybridization of PSO method is used, which combines this meta-heuristic with an exact method, a full ac power flow in order to validate network constraints of the solutions explored by the swarm. This thesis proposes a trip reduce demand response program for EVs users. A datamining based methodology is used to support the network operator in the definition of this program and to estimate how much demand response is adequate for a certain operation condition. The case studies included in the thesis aim to demonstrate the effectiveness of the modified PSO approach to the problem of DER scheduling considering EVs. An application named EV Scenario Simulator (EVeSSi) has been developed. EVeSSi allows creating scenarios considering EVs in distribution networks. A case study comparison of the modified PSO with an accurate mixed integer non-linear programming is presented. Furthermore, it is also compared with other variants of PSO, and the traditional PSO. Addionatly, different methods of EV battery management, namely uncontrolled charging, smart charging and vehicle-to-grid, are compared. Finally, a test case is presented to illustrate the use of the proposed demand response program for EVs and the data-mining methodology applied to a large database of operation scenarios.