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<p>This article reports on the development and evaluation of a proposed optimal network reconfiguration approach for total real power losses reduction in radial distribution systems. The proposed approach was developed in MATLAB and the IEEE 33-bus radial distribution system was used in evaluating its performance. Application of the developed approach on the IEEE 33-bus radial distribution system helped to reduce the total real power losses of the system by 76.31%, from 202.68 kW to 48.02 kW, whereas the best approaches reported in literature reduced the losses by 31.15%, from 202.68 kW to 139.55 kW. These results suggest that the approaches reported in literature fail to find the global optimum combination of tie and sectionalizing branches whose corresponding switches have to be closed and opened, respectively, in order to minimize the total real power losses in the system. Consequently, due to its significant reduction of the system's total real power losses, the developed approach proves to be a potentially reliable tool for power system operators to adopt and use when solving the radial tribution systems' optimal network reconfiguration problem for real power losses reduction.</p>

Kenya is experiencing a fast increase in grid-connected intermittent renewable energy sources (RESs) to meet its increased power demand, and at the same time be able to fulfill its Paris Agreement obligations of abating greenhouse gas emissions. For instance, Kenya has 102 MW of grid-tied solar power and 410 MW of grid-tied wind power. However, these sources are very intermittent with low predictability. Thus, after their installation and integration into the grid, they impose a new challenge for the secure, reliable, and economic operation of the system. To mitigate these and to ensure proper planning of the system operations, accurate and faster prediction of the generation output of the wind energy resources and optimal design and sizing of storage for the large-scale wind energy integration into the grid are of paramount importance. Artificial intelligence (AI) and metaheuristic techniques have proven to be efficient and robust in offering solutions to complex nonlinear prediction and optimization problems. Therefore, this study aims to utilize backpropagation neural network (BPNN) algorithm to conduct hourly prediction of the generation output of Lake Turkana Wind Power Plant (LTWPP), a 310 MW plant connected to the Kenyan power grid, and optimally size its battery energy storage system (BESS) using genetic algorithm (GA) to guarantee its dispatchability. The historical weather data, namely wind speed, ambient temperature, relative humidity, wind direction, and generation output from LTWPP, are employed in the training, testing, and validation of the neural network. LTWPP and BESS are modelled in MATLAB R2016a software. Thereafter, the developed BPNN and GA algorithms are applied to the modelled systems to predict the wind output and optimize the storage system, respectively. BESS optimization with neural prediction reduces the BESS capacity and investment costs by 59.82%, while the overall dispatchability of LTWPP is increased from 73.36% to 90.14%, hence enabling the farm to meet its allowable loss of power supply probability (LPSP) index of 0.1 while guaranteeing its dispatchability.

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