A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem

作者：Yang, Zhile; Li, Kang^*; Niu, Qun; Xue, Yusheng

来源：Knowledge-Based Systems, 2017, 134: 13-30.

DOI：10.1016/j.knosys.2017.07.013

• Unit commitment
• Hybrid meta-heuristic optimisation
• Binary particle swarm optimisation
• Differential evolution
• Renewable generation
• Plug-in electric vehicles

Unit commitment is a traditional mixed-integer non-convex problem and remains a key optimisation task in power system scheduling. The high penetration of intermittent renewable generations such as wind and solar as well as mass roll-out of plug-in electric vehicles (PEVs) impose significant challenges to the traditional unit commitment problem, not only by significantly increasing the complexity of the problem in terms of the dimension and constraints, but also dramatically change the problem formulation. In this paper, a new hybrid unit commitment problem considering renewable generation scenarios and charging and discharging management of plug-in electric vehicles is first formulated. To effectively solve the problem, a novel parallel-series hybrid meta-heuristic optimisation method is then proposed, which combines a hybrid topology binary particle swarm optimisation, the self-adaptive differential evolution algorithm and a lambda iteration method, to simultaneously and intelligently determine the binary on/off status of each thermal unit, the generation power of online units, as well as the demand side management of plug-in electric vehicles. The proposed parallel-series hybrid method is first assessed on a 10-unit benchmark, and then on a case where renewable generation and smart PEV management are integrated. Numerical results confirm the superiority of the proposed new algorithm in comparison with some popular meta heuristic approaches.

• 出版日期2017-10-15
• 单位国网电力科学研究院; 上海大学