Informed mutation of wind farm layouts to maximise energy harvest

Abstract

Correct placement of turbines in a wind farm is a critical issue in wind farm design optimisation. While traditional "trial and error"-based approaches suffice for small layouts, automated approaches are required for larger wind farms with turbines numbering in the hundreds. In this paper we propose an evolutionary strategy with a novel mutation operator for identifying wind farm layouts that minimise expected velocity deficit due to wake effects. The mutation operator is based on constructing a predictive model of velocity deficits across a layout so that mutations are inherently biased towards better layouts. This makes the operator informed rather than randomised. We perform a comprehensive evaluation of our approach on five challenging simulated scenarios using a simulation approach acceptable to industry [1]. We then compare our algorithm against two baseline approaches including the Turbine Displacement Algorithm [2]. Our results indicate that our informed mutation approach works effectively, with our approach identifying layouts with the lowest aggregate velocity deficits on all five test scenarios.

Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

“…A heuristic is a method of searching for an optimal solution based on probabilistic theory, and mathematical programming is a method of formulating and optimizing the variables and boundary conditions of a problem. Algorithms that use heuristic methods for the WFLO problem include the GA [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], evolutionary strategy [20][21][22], particle swarm optimization [23][24][25], and greedy heuristic [26,27]. Moreover, other works on the development of various algorithms have been conducted [28][29][30][31][32][33].…”

Realistic Optimization of Parallelogram-Shaped Offshore Wind Farms Considering Continuously Distributed Wind Resources

“…A hybrid method based on a genetic algorithm (GA) combined with local search was proposed by Mittal et al [11]. A newly reported mutation operator was developed by Mayo with the objective of maximizing energy yield [12]. In a similar way, but referring to large wave farm consisting of fully submerged wave energy converters, Neshat et al [13] use different approaches to evolutionary algorithms to maximize the harnessed power.…”

Aesthetic Local Search of Wind Farm Layouts

“…One approach is to simply calculate the total expected power generated by a wind farm, which must be maximised (e.g., [23]). Another approach is to calculate the expected cost of energy: take the total expected power; convert it into units of currency that would be obtained if the power were sold at market; and divide that revenue by the cost of building and maintaining the wind farm (e.g., [13]). This is an objective that must be minimised.…”

“…Sometimes, other objectives may also be considered (e.g., construction costs), but the commonality amongst many papers in the literature is a focus on wake effect minimisation. Examples include the seminal work in the field by Mosetti et al [8], as well more recent works, such as that by Wagner et al [9], Rodrigues et al [10], Guirguis et al [11] and Mayo et al [12,13].…”