Wind Farm electrical system design and optimization

◀ Back to projects overview P201101-005-ECN

Objectives

Although the exploitation of offshore wind energy is rapidly expanding, the energy price of this renewable energy resource is still too high to be market competitive. Hence, there has been a recent increase in the demand for optimization strategies to accelerate the development of offshore wind farms while reducing the Levelized Cost of Energy (LCoE). This project was started in 2011 with the aim of developing a framework to integrate, automate and optimize the design of offshore wind farms. Furthermore, introducing a state-of-
the-art multi-objective optimization algorithm, which is tailored to the design process of offshore wind farms, differentiating the approach from other existing algorithms. The second objective of this project was applying the optimization framework to optimize the wind farm electrical system introduced by the industrial partners of the project. The third objective was extension and validation of the existing steady state and dynamic models for
the design and analysis of electrical systems in offshore wind farms.

Approach and results

This project has made a significant contribution to reducing the cost of energy. Cost reductions resulted from a better design with lower investment costs, or better operation with lower power losses. In this regard, most of the project work was conducted based on RWE Innogy UK’s system design for Tromp-Binnen offshore wind farm, located about 90 km off the Dutch coast. For the defined systems, combined electrical, aerodynamic and
economic analysis of the systems has been done by ECN tools FarmFlow (aerodynamic analysis tool) and EeFarm-II (electrical systems analysis tool for wind farms), and the cost reduction scenarios were quantified for each system. In the case studies of this project different design options were evaluated and the most economic design options were indicated. Moreover, different operation methods for reactive power and voltage control were evaluated, and the selected designs were optimized for having minimum power losses. For this purpose, the steady state and dynamic models for the design and evaluation of electrical system of offshore wind farms were used. The models have been extended and verified by comparing to benchmark models, and also partially validated against the measurement data from Gwynt-y-Môr offshore wind farm.

Furthermore, in this project a multi-objective optimization framework was developed for offshore wind farm design. The developed optimization framework allows developers to separate the design of a project into independent optimization and decision phases. This framework provides optimized trade-offs between different project parameters, such as energy production, investment costs and operational expenditure, by independently and simultaneously optimizing these parameters using a multi-objective optimization algorithm. The developed optimization framework cuts down the cost of far-offshore wind energy, reduces the risk, and accelerates the preliminary design phase of offshore wind farm development. In other words, having a standard framework for automatic evaluation of multiple designs in the wind farm development stage, reduces the duration of preliminary phase (prior to financial close), which contributes to reducing Development Expenditures
(DevEx). Moreover, it reduces the risk of design and development of offshore wind farms, by considering a wide range of design options and possibilities. Thus, provides the possibility to investigate abundant of design options faster, and iterate over a large number of optimized trade-offs in an early design phase.

The optimization framework was applied to the Dutch Borssele offshore wind farm area. The results demonstrate the optimized trade-offs, giving wind farm developers a clear picture of attainable trade-offs between the chosen objectives such as Capital Expenditures (CAPEX) and Annual Energy Production (AEP). By applying different economic parameters, such as levelized production cost, and net present value, optimized designs were demonstrated. For example, the optimization framework was able to design an offshore wind farm layout with
7.1% lower LCoE than the layout designed with a standard sequential approach; also an optimized layout achieved 71% higher Net Present Value (NPV) than the standard layout.

Subsequently, the developed optimization framework has been implemented on RWE Innogy’s case study of Tromp-Binnen offshore wind farm to make the best trade-off between the contradictory design choices. The results of this study demonstrated that
designing the system using optimization framework reduces the total power losses in the system considerably, which leads to lower cost of energy.