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.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE Manchester PowerTech
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)978-1-5090-4237-1
Publication statusPublished - Jun 2017
Event12th IEEE PES PowerTech Manchester 2017 Conference: Towards and Beyond Sustainable Energy Systems - Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017
Conference number: 12


Conference12th IEEE PES PowerTech Manchester 2017 Conference
Abbreviated titleIEEE PowerTech Manchester 2017
CountryUnited Kingdom

    Research areas

  • optimal reactive power management, mean-variance mapping optimization, on-load tap changer

ID: 34760721