A Multiobjective Evolutionary Algorithm based on Decomposition for Unit Commitment Problem with Significant Wind Penetration

In this paper, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) problem in presence of significant wind penetration as a multi-objective optimization problem considering cost, emission, and reliability as the multiple objectives. The uncertainties occurring due to thermal generator outage, load forecast error, and wind forecast error are incorporated using expected energy not served (EENS) reliability index and EENS cost is used to reflect the reliability objective. Since, UC is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables while differential evolution (DE) evolves the continuous variables. The performance of the proposed algorithm is investigated on a 20 unit test system. To improve the performance of the algorithm in terms of distribution of solutions obtained, an external archive strategy based on is an element of-dominance principle is implemented. The simulation results demonstrate that the proposed algorithm can efficiently obtain a well-distributed set of trade-off solutions on the multiobjective wind-thermal UC problem.