Reactive power compensation with Capacitor Banks (CBs) is one of the most successful approaches used in distribution systems, mainly due to their versatility, long-term acceptance in the power industry, and reduced costs. Most allocation methods, however, lack specific strategies to handle the limited discrete nature of CBs sizes seeking to improve the overall optimization and computational performance. We present an algorithm for the Optimal Placement of Capacitor Banks (OPCB) in distribution systems by means of a hybrid Flower Pollination Algorithm (FPA)–Exhaustive Search (ES) approach. The pollination process itself determines the sets of buses for placement, while CBs sizes and the final fitness values of each pollen are selected after a full-search is conducted in the sizing space. As the sizing phase works on the limited search space of predetermined discrete bank values, the computational effort to find the optimum CB capacity is greatly reduced. Tests were performed on distribution systems of 10, 34, and 85 buses with respect to the objective function, final losses, and voltage profile. The algorithm offers an excellent compromise between solution quality and computational effort, when compared to similar approaches.
The ever-growing participation of Renewable Energy Sources (RES) in modern distribution networks is replacing an important portion of Conventional Generation (CG), which brings along new challenges in the planning and operation of distribution grids. As RES such as Photovoltaic Energy (PV) and Wind Power Generation (WPG) increase in distribution networks, studies regarding their integration and coordination become more important. In this context, the purpose of this paper is to propose a Multi-period Optimal Power Flow (MOPF) model for the optimal coordination of Battery Energy Storage Systems (BESSs) with PV, WPG, and CG in modern distribution networks. The model formulation was developed in A Modeling Language for Mathematical Programming (AMPL) and solved through the Knitro solver within a time horizon of 24 h. A distinctive feature and one of the main contributions of the proposed approach is the fact that BESSs can provide both active and reactive power. The proposed optimization model reduces power losses and improves voltage profiles. To show the applicability and effectiveness of the proposed model, several tests were carried out on the 33-bus distribution test system and a real distribution system of 141 buses located in the metropolitan area of Caracas. Power loss reductions of up to 58.4% and 77% for the test systems of 33 and 141 buses were obtained, respectively, when BESSs provided both active and reactive power. The results allow us to conclude that the proposed model for optimal coordination of BESSs with RES is suitable for real-life applications, resulting in important reductions of power losses and flattening of voltage profiles.
Este artigo apresenta uma metodologia para alocação de geração distribuída (GD) usando Otimização por Enxame de Partículas (PSO -Particle Swarm Optimization) e análise de sensibilidade (AS). O local para alocação das GD é determinado pelo PSO e a função é avaliada utilizando AS. A AS dispensa o uso do Fluxo de Carga (FC) para avaliar a rede, em cada proposta de alocação fornecida pelo PSO, tornando o algoritmo rápido. A AS empregada é de segunda ordem e apresenta ótima aproximação quando comparada com a solução do FC. Simulações computacionais foram realizadas em dois sistemas elétricos de distribuição com 34 e 70 barras, alocando dois geradores em cada sistema. Os resultados foram comparados com uma técnica de busca exaustiva (BE), atestando a exatidão das soluções, bem como a redução no tempo computacional. Foram obtidas reduções de até 83,0% nas perdas de potência ativa e consequente aumento no perfil de tensão do sistema elétrico, bem como diminuição de até 95,3% no tempo computacional, quando comparado com busca exaustiva.Palavras-Chave: Geração distribuída, Sistema elétrico de distribuição, Otimização por enxame de partículas, Análise de sensibilidade.
Este artigo apresenta uma eficiente metodologia híbrida composta pela metaheurística Particle Swarm Optimization (PSO) e uma Busca Exaustiva (BE) limitada, denominada PSO-BE. A metodologia foi aplicada ao problema de alocação e dimensionamento de banco de capacitores em redes de distribuição de energia, objetivando a minimização das perdas ativas considerando valores discretos de bancos de capacitores, sendo a alocação realizada pelo PSO, e o dimensionamento pela BE. A busca pela solução do problema de forma separada favorece a obtenção da solução ótima, o que foi comprovado em testes em sistemas de distribuição de 69 e 84 barras. A técnica proposta apresentou vantagens em relação a outras metodologias apresentadas na literatura especializada.
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