Multi-Objective Optimization of Distribution Networks via Daily Reconfiguration

TL;DR

This paper develops a stochastic multi-objective optimization approach for daily distribution network reconfiguration, incorporating renewable resource variability, to enhance efficiency, reliability, and cost-effectiveness in active distribution networks.

Contribution

It introduces a stochastic distributed network reconfiguration method using scenario reduction and an improved crow search algorithm for optimal daily operation.

Findings

Effective reduction in active losses and operation costs.

Improved voltage profile and network reliability.

Validated approach on IEEE 33-bus system.

Abstract

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.