This paper introduces a new approach for the optimal management of reactive power, with emphasis on offshore wind power plants. The approach follows a predictive optimization scheme (i.e. day-ahead, intraday application).
Predictive optimization is based on the principle of minimizing the real power losses, as well the number of On-load Tap Changer (OLTC) operations for daily time horizon (discretized in 24 hours). The mixed-integer nature of the problem and the restricted computing budget is tackled by using an emerging
metaheuristic algorithm called Mean-Variance Mapping Optimization (MVMO). The evolutionary mechanism of MVMO is enhanced by introducing a new mapping function, which improves its global search capability. The effectiveness of MVMO to find solutions that ensure minimum losses, minimum impact on OLTC lifetime, and well as optimal grid code compliance is demonstrated by investigating the case of a real world far-offshore wind power plant with HVDC connection.

Mean-variance mapping optimization (MVMO) is an emerging metaheuristic optimization algorithm, whose evolutionary mechanism performs within a normalized search space. The most remarkable aspect of this mechanism resides in the application of a special mapping function to generate new values of the optimization variables based on their statistical significance throughout the search process. This paper concerns with the feasibility of the MVMO to tackle the problem of online optimal reactive power management in near-shore wind power plants. The main challenges reside in the restricted computing budget and mix-integer nature of the problem. To this aim, MVMO is configured to evolve a single solution throughout the search process, and a new mapping function is proposed to improve the global search capability. Numerical tests on a benchmark system proposed by the IEEE Working Group on Modern Heuristic Optimization as well as a real world wind power plant demonstrate the effectiveness of MVMO.