Congestion Management in Distribution Network: An Exploration of Distributed Generation and Demand Response Programs by Using the MOPSO Algorithm

The distribution network is a critical component of the electrical power infrastructure that provides electricity to consumers. Ensuring the reliability of the distribution network is vital for safe and steady electricity delivery without interruptions or power outages. Line congestion, which occurs when electricity demand exceeds the capacity of the distribution network lines, can harm the reliability of the network by leading to voltage drops, increased losses a higher chance of system failures and power outages. Increasing adoption of Distributed Generation (DG) can both alleviate and exacerbate line congestion, depending on proper planning and generation capabilities. Therefore, effective management of line congestion is crucial to guarantee the reliability of the distribution network, reduce costs and enhance overall efficiency. In this article, congestion management of distribution network lines is investigated. For this purpose, demand response programs are utilized to modify or reduce customers' electricity consumption during peak periods. Additionally, management of DG units production under uncertainty is approached in a manner that has a beneficial impact on the network. Three objectives of reducing losses, voltage deviation and network costs are considered as goals of the problem. Given the significance of distributed generation's impact on the network, the production of these units is regarded as an uncertainty. To obtain the optimal solution, Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is employed. Finally, the proposed method is applied to a 33-bus system with the aim of estimating network requirements. The superiority of the MOPSO algorithm is discussed and compared with Particle Swarm Optimization (PSO) and Genetic Algorithms (GA).