Suchergebnisse

Four-wire low-voltage microgrids supply one-phase consumers with electricity, responding to a continuously changing demand. For addressing climate change concerns, national governments have implemented incentive schemes for residential consumers, encouraging the installation of home PV panels for covering self-consumption needs. In the absence of adequate storage capacities, the surplus is sold back by these entities, called prosumers, to the grid operator or, in local markets, to other consumers. While these initiatives encourage the proliferation of green energy resources, and ample research is dedicated to local market designs for prosumer–consumer trading, the main concern of distribution network operators is the influence of power flows generated by prosumers' surplus injection on the operating states of microgrids. The change in power flow amount and direction can greatly influence the economic and technical operating conditions of radial grids. This paper proposes a metaheuristic algorithm for prosumer surplus management that optimizes the power surplus injections using the automated control of three-phase inverters, with the aim of reducing the active power losses over a typical day of operation. A case study was performed on two real distribution networks with distinct layouts and load profiles, and the algorithm resulted efficient in both scenarios. By optimally distributing the prosumer generation surplus on the three phases of the network, significant loss reductions were obtained, with the best results when the generated power was injected in an unbalanced, three-phase flow.

The global climate change mitigation efforts have increased the efforts of national governments to incentivize local households in adopting PV panels for local electricity generation. Since PV generation is available during the daytime, at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of local generation to a later time. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation conditions in the network. This paper proposes a metaheuristic approach based on a genetic algorithm that considers three different scenarios of using energy storage for reducing the energy losses in the network. Two cases considers the battery placement and operation under the direct control of the network operator, with single and multiple bus and phase placement locations. Here, the aim was to maximize the benefit for the whole network. The third case considers selfish prosumer battery management, where the storage owner uses the batteries only for their own benefit. The optimal design of the genetic algorithm and of the solution encoding allows for a comparative study of the results, highlighting the important strengths and weaknesses of each scenario. A case study is performed in a real distribution system.