Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant

Abstract

Abstract. Optimizing turbine layout is a challenging problem that has been extensively researched in the literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant in a land-constrained site. We optimized several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit compared to the wind power plant optimized for maximum profit, which corresponded to a loss of about USD 2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

Exploring the Impact of Near-Term Innovations on the Technical Potential of Land-Based Wind Energy

“…Thus, for our typical comparative cases where the Innovations turbines have a significantly higher tip height than the Baseline case, the capacity estimated by cell and nationally is likely significantly lower than is empirically likely as tip heights and tip height differences between turbines increase (Stanley et al 2021). We acknowledge this discrepancy; however, we expect that other factors such as the price of power, installed costs, and site optimization of turbine placement may be larger factors in the overall error in cost potential.…”

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

“…It can be seen that 21 of the initial 38 optional lines are optimized which can meet the demands of power load, security, and reliability. We selected the MOLS [20] and MOGA [21] algorithms respectively to verify the optimization performance of the algorithms. Table 3 shows the three algorithms with 100 iterations, and Fig.…”

Multi-Timescale Wind-Based Hybrid Energy Systems

“…To perform the discrete optimization of this hybrid plant, we used a simple in-house genetic algorithm as was used in [14]. For the plants that we optimized, we performed a uniform crossover with a rate of 0.1, with a mutation rate of 0.02.…”