Abstract
With the increased distributed generation (DG) and the combination of residential, commercial, and industrial loads connected to the distribution networks, it is more difficult to ensure the safe and economic operation of the distribution networks because of the great volatility and randomness of DG and complex loads. In this paper, with the aim of minimizing network loss, load balance, and maximum voltage deviation, a multiobjective reconfiguration model of the distribution network is established under the condition of satisfying network constraints. Moreover, a new social beetle swarm optimization algorithm (SBSO) considering two social behaviors is adopted to solve the complex problem according to the characteristics of the distribution network reconfiguration (DNRC). Based on the SBSO algorithm, grey target decision-making (GTDM) strategy is used to choose the best beetle in the process of solving the multiobjective problem. Additionally, The grey relation projection (GRP) method is used to divide the time period of DNRC according to the change of DG and loads in a day, in order to reduce the number of switching operations. Finally, the effectiveness of the proposed multiobjective model and algorithm are verified on the standard IEEE-33 system and IEEE-69 system.
Highlights
distribution network reconfiguration (DNRC) is used to change the topological structure of the distribution network by changing the state of switches under the condition of satisfying network constraints, thereby enhancing the efficiency and stability of the distribution network [1], so the DNRC has been considered a powerful tool in distribution system planning and operation
distributed generation (DG) into distribution networks is implemented for reducing network losses, balancing demand overloads, improving the node voltage level, and absorbing renewable energy [5], which further increases the complexity of the distribution network
social beetle swarm optimization algorithm (SBSO) based on a grey target decision-making (GTDM) strategy was designed to solve the multiobjective optimization problem
Summary
DNRC is used to change the topological structure of the distribution network by changing the state of switches under the condition of satisfying network constraints, thereby enhancing the efficiency and stability of the distribution network [1], so the DNRC has been considered a powerful tool in distribution system planning and operation. [31] integrated decision-making by combining the fuzzy c-means algorithm (FCM) with GRP aims to extract the best compromise solutions which reflect the preferences of decisionmakers from the POSs. A GTDM theory based on the entropy weight method is proposed in [30] to identify the best trade off scheduling scheme among all the solutions. To improve the efficiency of optimization, the search speed of the global optimal solution is accelerated by changing its operator, increasing social learning behavior, and sorting after each iteration On this basis, this paper uses SBSO algorithm combined GTDM to solve the multiobjective model to reduce the calculation time through the individual advantage of the beetle and this method is effective to avoid volatility and subjectivity. According to the above analysis, the net load on each node is obtained
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