Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D

Wake effect and wind uncertainty are the key factors resulting in low efficiency in wind energy extraction. Classic micro-siting approaches focus on reducing the wake effect to determine the best number and positions of the turbines. However, very little literature has addressed the issue of risk due to wind uncertainty which causes the expected production to be distantly deviated from what is actually produced. Multi-objective modeling is of particular interest due to its potential of managing risk. This paper proposes several multi-objective risk management (MORM) models which set the foundation on Monte Carlo simulation to conduct cost, benefit, and risk analyses. We develop an enhanced multi-objective evolutionary algorithm with decomposition (MOEA/D) algorithm by taking advantages of wind farm structure. The experiment result with real wind farm data shows the application differences in gauging the risks with various MORM models. The enhanced MOEA/D is compared with two state-of-the-art algorithms and the former produces the best frontier in the objective space in most of the simulations with mean absolute percentage improvement (API) of 46%. We demonstrate what-if analysis with various risk scenarios to assist the decision maker to realize his/her risk tolerance and to reach quality tradeoff decisions.