This paper presents a comprehensive approach to improve the daily performance of an active distribution network (ADN), which includes renewable resources and responsive load (RL), using distributed network reconfiguration (DNR). Optimization objectives in this work can be described as (i) reducing active losses, (ii) improving the voltage profile, (iii) improving the network reliability, and (iv) minimizing distribution network operation costs. The suggested approach takes into account the probability of renewable resource failure, given the information collected from their initial state at the beginning of every day, in solving the optimization problem. Furthermore, solar radiation variations are estimated based on past historical data and the impact of the performance of renewable sources such as photovoltaic (PV) is determined hourly based on the Markov model. Since the number of reconfiguration scenarios is very high, stochastic DNR (SDNR) based on the probability distance method is employed to shrink the scenarios set. At the final stage, an improved crow search algorithm (ICSA) is introduced to find the optimal scenario. The effectiveness of the suggested method is verified for the IEEE 33-bus radial distribution system as a case study.

中文翻译：

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通过每日重新配置对配电网络进行多目标优化

本文提出了一种使用分布式网络重新配置 (DNR) 提高主动配电网络 (ADN) 日常性能的综合方法，其中包括可再生资源和响应负载 (RL)。这项工作中的优化目标可以描述为 (i) 减少有源损耗，(i​​i) 改善电压分布，(iii) 提高网络可靠性，以及 (iv) 最小化配电网络运营成本。在解决优化问题时，建议的方法考虑了可再生资源故障的可能性，考虑到从每天开始时从其初始状态收集的信息。此外，太阳辐射变化是根据过去的历史数据估算的，光伏 (PV) 等可再生能源的性能影响是根据马尔可夫模型每小时确定的。由于重构场景的数量非常多，因此采用基于概率距离方法的随机DNR（SDNR）来缩小场景集。在最后阶段，引入了改进的乌鸦搜索算法（ICSA）来寻找最佳场景。作为案例研究，对 IEEE 33 总线径向配电系统验证了所建议方法的有效性。引入了改进的乌鸦搜索算法（ICSA）来寻找最佳场景。作为案例研究，对 IEEE 33 总线径向配电系统验证了所建议方法的有效性。引入了改进的乌鸦搜索算法（ICSA）来寻找最佳场景。作为案例研究，对 IEEE 33 总线径向配电系统验证了所建议方法的有效性。