申建建,陈光泽,魏巍,程春田,苗树敏,王亮.耦合联动分析理论的多调峰指标水电短期调度模型[J].水利学报,2021,52(8):936-947 |
耦合联动分析理论的多调峰指标水电短期调度模型 |
Short-term optimal scheduling model with multiple peak shaving indexes based on linkage analysis |
投稿时间:2020-12-28 |
DOI:10.13243/j.cnki.slxb.20201079 |
中文关键词: 调峰 指标 联动分析 发电调度 |
英文关键词: peak shaving indicators linkage analysis generation scheduling |
基金项目:国家自然科学基金项目(52079014);大连市青年科技之星项目(2019RQ004) |
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中文摘要: |
构建适应性调峰模型是发挥水电优质调峰作用,动态适应不同电网负荷特点的关键问题之一。本文综合考虑4种调峰压力指标,引入经济学联动分析理论对指标数据序列进行平稳性和因果关系检验,明了指标间的联动变化规律,采用回归分析量化相互影响关系,以根据电网负荷需求自适应确定多调峰指标耦合建模方式,减小以往主观选取调峰指标和权重系数可能产生的不利调峰结果影响。以某水电站参与省级电网调峰为例,给出了不同典型日负荷、不同单一调峰指标模型的对比分析案例,结果表明通过多个调峰指标的联动分析构建发电优化调度模型能够更客观地描述电网的综合调峰要求,与多个常规单一调峰指标优化模型相比,调峰结果得到显著改善。 |
英文摘要: |
Building an adaptive peak-shaving model is one of the key issues to play the role of hydropower peak-shaving and dynamically adapt to different grid load characteristics. This article considers four peak-shaving pressure indicators, and introduces a linkage analysis theory in economics in order to clarify the related change law between multiple indicators by conducting the stationary and causality tests of the indicator data series. The regression analysis method is utilized to quantify the relationship between any two indicators. Based on this, a multi-index coupling modeling method for peak-shaving is self-adaptive determined by grid load to reduce the negative influence of subjective selection of peak-shaving indicators and weighting coefficients on the results. Taking a hydropower plant participating in peak operations of a provincial power grid as an example,several comparative analysis cases with different typical daily loads and different single peak-shaving indicator models are presented. The results show that it is more objective to describe the peak-shaving requirements of the power grid by using the multi-indicator coupling optimization model developed by linkage analysis theory. And the results of peak-shaving operation have been significantly improved compared with multiple single peak-shaving indicator optimization models. |
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