Tabu Search (TS) Algorithm for Reactive Power Optimization in Distribution Networks

This paper explores reactive power optimization planning in power system distribution networks. To ensure safe and economical operation of distribution networks, reactive power optimization serves as a vital measure for reducing network losses and improving voltage quality. However, optimal distribution system planning presents a highly complex nonlinear combinatorial optimization problem that remains inadequately solved. Therefore, we employ the Tabu Search algorithm to address this challenge - a highly efficient heuristic search technique that has gained widespread application in solving combinatorial optimization problems in recent years.

Our implementation uses fixed shunt capacitors as research objects for reactive power compensation, employing intelligent optimization methods to obtain optimal node solutions that maximize distribution network loss reduction. In the neighborhood search for compensation capacity, we implemented improvements by expanding the search range to achieve global optimization. The algorithm incorporates tabu lists and aspiration criteria to prevent cycling and explore new solution spaces effectively. We validated the proposed method's feasibility using two IEEE test cases, demonstrating the algorithm's convergence behavior and solution quality through comparative analysis.

In conclusion, this paper presents an effective Tabu Search algorithm for solving reactive power optimization problems in power system distribution networks. Our research not only holds theoretical significance but also offers substantial value for practical engineering applications, providing implementable code solutions for distribution system optimization.