Reactive Power Compensation Optimization Planning for Distribution Networks Using Tabu Search Algorithm

Reactive power compensation optimization planning in distribution networks serves as a critical method for enhancing energy efficiency and stability in power system operations. Traditional compensation approaches often rely on empirical formulas or local search techniques, struggling to address complex and dynamic grid environments. The tabu search algorithm presented here offers an intelligent global optimization solution for this challenge.

The core concept of this algorithm employs a memory mechanism to avoid revisiting invalid solutions. In distribution network scenarios, the algorithm records historical operations (such as capacitor switching locations and capacity selections) in a tabu list, temporarily prohibiting these operations from being repeated to escape local optima traps. This strategy proves particularly effective for handling nonlinear reactive power compensation problems, effectively preventing premature convergence commonly encountered in conventional optimization methods. Code implementation typically involves maintaining a circular buffer for tabu list management with configurable tenure parameters.

Targeting the primary objective of network loss reduction, the algorithm first establishes a mathematical model incorporating line parameters, load distribution, and voltage constraints. During the search process, it dynamically evaluates power loss indicators for candidate solutions while considering constraints like voltage deviations. By setting appropriate tabu tenure and aspiration criteria, the algorithm achieves balance between exploring new solutions and retaining high-quality ones. The evaluation function can be implemented using power flow calculations with penalty terms for constraint violations.

Compared to traditional methods, this intelligent optimization algorithm significantly improves the economic efficiency of compensation device configuration. Practical applications allow flexible adjustment of search parameters according to grid scale, finding optimal balance between computational efficiency and optimization results. Another advantage lies in its adaptability to various compensation equipment types, enabling unified optimization frameworks for both switched capacitor banks and continuously adjustable static var compensators. The algorithm's neighborhood generation function can be designed to handle discrete and continuous variables simultaneously.

Future development directions may include hybrid approaches combining tabu search with other intelligent algorithms, or integrating real-time data for dynamic compensation strategies, further enhancing the intelligence and economic performance of distribution network operations. Potential code extensions could incorporate machine learning modules for adaptive parameter tuning.