An Efficient Improved Constrained Greedy Optimization Algorithm for Phase Load Balancing in Low-Voltage Distribution Networks

With regard to low-voltage (LV) distribution networks, the quality of distributed electricity can be compromised by the level of phase load imbalance. Consequently, numerous phase load balancing (PLB) algorithms have been proposed in the specialized literature. However, those models have been focused on the quality of the solution obtained rather than performance, which leads to reduced practical applicability for the distribution network (reduced scalability, slow convergence, and a higher computational cost). Furthermore, certain constraints regarding the electrical network and the switching operations of consumers must be integrated into the mathematical model. In this context, the proposed PLB algorithm represents an improved constrained greedy optimization (ICGO), capable of achieving fast convergence even on large datasets, with a lower computational cost. Three scenarios (30, 250, and 500 consumers), each with 20 distinct initial non-symmetries, were simulated. The results support the practical effectiveness and scalability of the ICGO: an absolute value of the neutral current below 0.63 A (99.53% relatively reduction), a current unbalance index below 0.1%, a small number of iterations (between 4 and 11 iterations), and an execution time between 0.00051 and 0.01149 s). Therefore, this research proposes an efficient PLB algorithm, with the possibility for its improvement in future work.