引用本文: | 贺建波,胡志坚,刘宇凯.大规模多目标水-火-风协调优化调度模型的建立及求解[J].电力系统保护与控制,2015,43(6):1-7.[点击复制] |
HE Jianbo,HU Zhijian,LIU Yukai.Establishment and solution of the large-scale multi-objective hydro-thermal-wind power coordination optimization dispatching model[J].Power System Protection and Control,2015,43(6):1-7[点击复制] |
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摘要: |
考虑水、火、风能源各自的自然特性和某省级电网工程实际要求,在风电全额上网的前提下,以上网电价费用最小和煤耗量排放最小为目标,同时考虑火电、水电机组出力平滑性约束和各水电厂调度日总出力定值等约束,建立动态多目标风-火-水协调优化调度模型。由于所建立的模型是一个大规模、多维数,多时段、多约束非连续问题,直接求解较困难。为此,在求解阶段,结合分层求解思想,通过应用提出的改进多目标“教”与“学”优化算法(Modified Multi-Objective Teaching-Learning Algorithm,MMTLA)进行求解。以某省级电网某一典型调度日实际数据进行仿真验证,结果表明,所建立的模型的有效性以及所提出算法的优越性。 |
关键词: 上网电价 水-火-风协调调度 多目标调度 反向学习 MMTLA |
DOI:10.7667/j.issn.1674-3415.2015.06.001 |
投稿时间:2014-06-13修订日期:2014-07-31 |
基金项目:高等学校博士学科点专项科研基金项目(20110141110032);西安交通大学电力设备电气绝缘国家重点实验室资助(EIPE13205) |
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Establishment and solution of the large-scale multi-objective hydro-thermal-wind power coordination optimization dispatching model |
HE Jianbo,HU Zhijian,LIU Yukai |
(School of Electrical Engineering, Wuhan University, Wuhan 430072, China) |
Abstract: |
Considering practical requirements of a provincial power grid project and natural characteristics of hydro power, fire power and wind power, in the premise of full amount of wind power integrated into the grid, with minimum the power purchase cost and emissions as the objective, taking into account the thermal and hydropower units’ output smoothness constraints and each day hydropower scheduling fixed constraints, a dynamic multi-objective water-fire-wind coordination optimal scheduling model is built. In order to simplify the solution of the built model, this paper proposes a new constraint processing methods. Through the introduction of quasi-opposition-based learning and self-learning mutation, an improved multi-objective "teaching" and "learning" optimization algorithm (MMTLA) is got and applied to solve the model. Taking a provincial power grid as an example, the simulation results show the validity of the built model and the superiority of the proposed algorithm. |
Key words: power purchase cost water-fire-wind coordination scheduling multi-objective dispatch quasi-opposition- based learning MMTL |