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.
The paper presents a Mining Data peak load estimation of household consum uses an unsupervised learning method (clu the polynomial regression. With K-means cl the consumption categories of household determined. The input data for the consume consumption categories (monthly energy cons loads) are obtained from processing the ty provided by Smart Meters. For each consu polynomial regression model is built for peak household consumers equipped with cla obtained results demonstrate that the metho with the success in peak load estimation customers from distribution systems when i poor (based on the data provided by classic m
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