This work presents a strategy, from the perspective of the distribution system operator (DSO), that aims at simultaneously estimating the maximum penetration levels of renewable-based distributed generation (DG) and electric vehicles (EVs) that can be accommodated into an electric distribution system (EDS). To estimate such capacity, operational resources such as generation curtailment and controllable features of EVs can be managed to ensure the safe operation of the EDS and avoid infeasible operational conditions. Through a multi-period representation, the proposed strategy models the variability in demand consumption and DG power production. In addition, driving patterns of EV owners and energy requirements of EVs, obtained through probability density functions, are incorporated in this representation. Inherently, the problem is represented as an optimization model, and to determine its solution, an algorithm based on the metaheuristics greedy randomized adaptive search and tabu search (GRASP-TS) is developed. The applicability of the planning strategy is assessed on a 33-bus EDS under different test conditions and the numerical results show that higher penetrations of EVs and renewable-based DG can be accommodated without impacting the safe operation of the EDS. The results also demonstrate that by controlling the power draw by EV aggregators, an increase of 9% can be obtained in the DG installed capacity compared to the case of uncontrolled charging of EVs. In addition, the scalability of the proposed approach is studied using two distribution systems, the 83-bus system and the 135-bus system, where the results show that the convergence of the algorithm is achieved in a few iterations.INDEX TERMS Electric vehicle aggregator, GRASP-TS, hosting capacity, distributed generation.
The increasing generation and flexible demand at a local level enable the development of a local market structure, in which its participants could trade with each other and/or with other market structures. Therefore, demands such as electric vehicle charging stations could take advantage of the flexibility in charging electric vehicles to participate in the market. In this sense, this paper intends to explore the optimal charging of electric vehicles to enable the participation of charging stations in the local energy market, considering that energy transactions in the market are constrained by the physical limits of the network. A mathematical model is proposed to represent the operation of prosumers and charging stations, as well as their commercial interaction in a local market structure. Charging stations' interests in the market are represented by an aggregator. The objective of the proposed model is to minimize energy purchase costs from the aggregator, with the coordination of charging as the control variable. The impact of this control was analyzed considering using the IEEE 33-node distribution system, and the results obtained show that this coordination enabled a better participation of the charging stations in the local market.Resumo: O aumento de geração e de demanda flexível no sistema de distribuição de energia eléctrica possibilita o desenvolvimento de estruturas de mercado locais, nas quais seus participantes poderiam negociar entre si e/ou com outras estruturas de mercado. Portanto, demandas como estações de carregamento de veículos elétricos poderiam aproveitar a flexibilidade associada ao carregamento dos veículos elétricos, participando ativamente nos esquemas de mercados elétricos locais. Nesse sentido, este artigo explora carregamento ótimo de veículos elétricos para viabilizar a participação de estações de carregamento no mercado local de energia, considerando que as transações de energia no mercado devem respeitar os limites físicos da rede. Um modelo matemático é proposto para representar a operação de prosumers e de estações de carregamento, e a interação comercial entre eles em uma estrutura de mercado local. Os interesses das estações de carregamento no mercado são representados por um agregador. O objetivo do modelo proposto consiste em minimizar os custos com compra de energia do agregador, sendo a coordenação do carregamento a variável de controle. O impacto deste controle foi analisado considerando o sistema de distribuição IEEE de 33 barras, e os resultados obtidos mostraram que esta coordenação possibilitou uma melhor participação das estações de carregamento no mercado local.
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