Abstract
In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) based is proposed to solve the unit commitment (UC) problem in uncertain environment as a multi-objective optimization problem considering cost, emission, and reliability as the multiple objectives. The uncertainties occurring due to thermal generator outage and load 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 decomposition-based MOEA such that genetic algorithm (GA) evolves the binary variables while differential evolution (DE) evolves the continuous variables. To enhance the performance of the presented algorithm, novel non-uniform weight vector distribution strategies are proposed. The effectiveness of the non-uniform weight vector distribution strategy is verified through stringent simulated results on different test systems.
This work was supported by the Ministry of Education under Grant R-263-000-B49-112 and Energy Market Authority of Singapore under Grant R-263-000-B14-279.
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Trivedi, A., Srinivasan, D., Pal, K., Reindl, T. (2017). A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment. In: Wagner, M., Li, X., Hendtlass, T. (eds) Artificial Life and Computational Intelligence. ACALCI 2017. Lecture Notes in Computer Science(), vol 10142. Springer, Cham. https://doi.org/10.1007/978-3-319-51691-2_32
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