| 引用本文: | 颜 伟,李 翔,梁文举,赵 霞,余 娟,戚 飞,李一铭.基于负荷分段技术的多目标月度发电计划及其遗传算法[J].电力自动化设备,2013,33(10): |
| YAN Wei,LI Xiang,LIANG Wenju,ZHAO Xia,YU Juan,QI Fei,LI Yiming.Multi-objective monthly generation scheduling based on load partition technology and its genetic algorithm[J].Electric Power Automation Equipment,2013,33(10): |
|
| 摘要: |
| 兼顾节能减排与经济调度的要求，建立了月度发电计划的多目标优化模型。模型中除考虑基于直流潮流方程的电网安全、机组动态调节特性、月度合同电量等约束条件外，重点考虑了能耗、排污量以及机组的启停费用目标。针对小时级发电计划的模型规模问题，提出了基于融合思想的月负荷曲线分段处理方法，大幅减小了模型规模。提出了基于目标相对占优策略的多目标改进遗传算法求解所提的月度发电计划多目标优化问题。IEEE 57节点系统的仿真分析验证了所提模型和算法的有效性。 |
| 关键词: 负荷分段 月度发电计划 多目标优化 目标相对占优 遗传算法 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金资助项目（51177178）；输配电装备及系统安全与新技术国家重点实验室自主研究课题（2007DA10512712203） |
|
| Multi-objective monthly generation scheduling based on load partition technology and its genetic algorithm |
|
YAN Wei1, LI Xiang1, LIANG Wenju2, ZHAO Xia1, YU Juan1, QI Fei3, LI Yiming1
|
|
1.State Key Laboratory of Power Transmission Equipment & System Security and New Technology，Chongqing University，Chongqing 400030，China;2.Chongqing Electric Power Research Institute，Chongqing Electric Power Corporation，Chongqing 401123，China;3.Xuzhou Power Supply Company，Jiangsu Electric Power Company，Xuzhou 221005，China
|
| Abstract: |
| With the consideration of both ESER （Energy Saving and Emission Reduction） and ED（Economic Dispatch），a multi-objective optimization model is established for monthly generation scheduling，which mainly focuses on the objectives of energy consumption，emission amount and unit startup／shutdown cost，as well as the constraints of DCPF-based grid safety，unit dynamic regulation performance and contracted monthly generation. A method is presented to avoid the model scale problem existing in hourly generation scheduling，which partitions the monthly load curve based on fusion concept to sharply decrease the model scale. An improved multi-objective genetic algorithm based on the strategy of relatively dominant objective is presented to solve the multi-objective optimization model of monthly generation scheduling. Simulation for IEEE 57-bus system demonstrates the effectiveness of the proposed model and algorithm. |
| Key words: load partition monthly generation scheduling multi-objective optimization relatively dominant objective genetic algorithms |
