Risk management of wind farm micro-siting using an enhanced genetic algorithm with simulation optimization


Novel simulation optimization risk management (SORM) models for micro-siting are proposed.

SORM models embed Monte Carlo simulation to estimate uncertain wind energy production.

SORM models conduct what-if analysis trading profit, cost and risk.

Risk identification and risk control processes by using SORM models are illustrated.

An enhanced genetic algorithm (EGA) is developed to obtain effective SORM decisions.


Wind farm micro-siting is the decision problem for determining the optimal placement of wind turbines in consideration of the wake effect. Existing micro-siting models seek to minimize the cost of energy (COE). However, little literature addresses the production risk under wind uncertainty. To this end, we develop several versions of the simulation optimization based risk management (SORM) model which embeds the Monte Carlo simulation component for obtaining a large number of samples from the wind probability density function. Our SORM model is flexible and allowing the decision makers to conduct various forms of what-if analysis trading profit, cost and risk according to their business value. Then we propose an enhanced genetic algorithm (EGA) which is customized to the properties of wind farm dimensions. The experimental results show that the EGA can obtain the SORM decision both effectively and efficiently as compared to other metaheuristic approaches. We demonstrate how the risk under wind uncertainty can be effectively handled with our SORM models. The simulations with what-if analyses are conducted to disclose important characteristics of the risky micro-siting problem.


  • Enhanced genetic algorithm;
  • Micro-siting;
  • Risk management;
  • Simulation optimization;
  • What-if analysis;
  • Wind farm

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