考虑短期调度效益的风电系统月机组承诺

Zhang Na, Ji Xing, Liu Xinglong, Song Zhuoran, Panxiao, D. Xiaoyu, Liu Gang, Meng Xiangyu
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引用次数: 0

摘要

近年来,风力发电在许多国家的渗透率一直在迅速增长。在系统调度调度中采用更合理的方式考虑风电,可以获得额外的节能效果。将风电输出纳入中长期规划与短期调度相协调,可以通过优化资源配置进一步降低燃料消耗。根据现有技术,风速和风电功率无法提前一周以上预测,这意味着考虑风电出力的中长期机组承诺与传统的中长期机组承诺和考虑风电的短期机组承诺存在很大差异。提出了一种动态聚类思想,在中长期规划中确定部分常规机组的投入计划,在短期规划中确定其余机组的投入计划。正是基于两个调度周期的优势,中长期规划更有利于优化资源配置和降低总启动成本,而短期规划由于风电输出的不确定性显著降低,有利于降低负负荷率和备用需求。为使风电集成系统的月总成本最小化,建立了考虑短期调度的两阶段月机组承诺模型。有效决策变量包括各常规机组所属的组和月度优化组所属机组的承诺变量。在改进型IEEE-118总线系统上的仿真结果验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monthly Unit Commitment for Systems with Wind Power Considering Short-term Scheduling Benefits
Wind power penetration in many countries has been increasing rapidly in recent years. Additional energy saving can be obtained by considering wind power with more proper approach in system scheduling and dispatching. Considering wind power output in mid-long term planning coordinated with short-term scheduling can further reduce the fuel consumption by optimizing resource distribution. Wind speed and wind power cannot be predicted more than one week earlier according to the present technology, which means the mid-long term unit commitment considering wind power output much differs from the traditional one and the short-term unit commitment considering wind power. A dynamic clustering idea is presented which determines the commitment plan of some part of conventional units in the mid-long term planning and determines that of the rest units in the short-term planning. It is based on the advantages of the two scheduling periods that mid-long term planning is more conducive to optimize resource distribution and reduce the total start-up costs, whereas short term planning contribute to reduce the load rate and reserve requirement due to the significant reduction in the uncertainty of wind power output. A two-stage monthly unit commitment model considering short term scheduling for systems with wind power integration is developed to minimize the expected monthly total costs. The effective decision variables contain the group of each conventional unit belongs to and the commitment variables of the units belonging to the monthly optimizing group. Simulation results on the revised IEEE-118 bus system verified the proposed method.
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