Wind Farm Layout Optimization: Project Economics and Risk. Afanasyeva, Svetlana (2023-09-01) Väitöskirja.
Tiivistelmä
Wind energy is an integral resource for the transition of the energy sector to cleaner electricity production. While the advantages, such as its fuel- and water-free production, are evident, at present the economic viability compared with traditional fossil energy sources is uncertain. As each project is site-specific through its unique topography and wind map, wind farms require a custom design.
This doctoral dissertation focuses on optimizing wind farm design and provides guidance for solving this task. The aim of the study is to investigate the extent to which the layout affects the profitability of a wind farm project and, more specifically, to identify the factors that most influence the optimal solution. A methodology using techno-economic performance metric is developed. The rationale for the selection of this metric is also explained.
The wind farm layout problem can be formulated as a mixed-integer nonlinear programming problem with nonlinear constraints, in which the location of each turbine is considered continuous, and their type and total number are considered discrete. A relatively recent metaheuristic nature-inspired algorithm is used for the optimization routine. Furthermore, for wind farm infrastructure design, the dissertation presents a novel combined road and electrical cable layout search approach based on a least cost pathfinding algorithm. This approach considers the dependence of the cable laying costs on the road network solution. For each layout produced by the main algorithm, the infrastructure design is found.
Another focus of the study is to determine whether uncertainty in input parameters affects the design of a wind farm. Using a global variance-based sensitivity analysis technique, the work takes into account the uncertainties of the input parameters and analyzes their impact on the financial viability. A project risk assessment method is presented, which can be incorporated into the optimization framework. The analysis reveals that for a given number of turbines the risk of the project cannot be mitigated through the locations of the turbines.
The proposed optimization methodology performs reliably in solving the wind farm optimization problem. It was found that the number and location of turbines are driven by the existing infrastructure, the wind direction and the fixed part of the initial investments. Yet, the viability of the project as a whole is determined by the average wind speed, the price of electricity, and the discount rate.