A practical probabilistic approach for load balancing in data-scarce LV distribution systems using discrete PSO and 2 m + 1 PEM

Unbalanced distribution of single-phase customers among the three phases is an important factor in causing unbalance in low-voltage (LV) distribution systems. This unbalance phenomenon has some important consequences as higher losses in the distribution system, increasing voltage unbalance, and higher neutral wire current. The key challenge is that these networks only have the monthly reading of customers’ meters which is called data-scarce distribution systems due to the lack of enough information. In this paper, a method is proposed in order to have a balanced distribution of single-phase customers between the phases based on particle swarm optimization (PSO) algorithm to reduce the average-load losses (which is calculated losses in the average load of all of the customers and is an attainable data in real-world data-scarce distribution systems). First, a comparative study is performed between two load balancing methods. In the first method, the transformer neutral wire current is reduced. This method is used generally in electricity distribution companies. In the second method, the balancing is performed in order to reduce the losses in the average-load. Then, a probabilistic approach is proposed in order to manage the uncertainty of single-phase customer's consumption, which is an important issue in low-voltage distribution systems balancing. Numerical studies are performed on a practical system and the results show that the proposed method has higher performance comparing to the first method used by distribution companies. Also, it is observed that by rephasing of some single-phase customers, losses are reduced significantly.