| 引用本文: | 张靠社,冯培基,张 刚,等.考虑源荷不确定性的CCHP型微网多目标优化调度[J].电力系统保护与控制,2021,49(17):18-27.[点击复制] |
| ZHANG Kaoshe,FENG Peiji,ZHANG Gang,et al.Multi-objective optimization scheduling of CCHP-type microgrids considering source-load uncertainty[J].Power System Protection and Control,2021,49(17):18-27[点击复制] |
|
| 本文已被：浏览 4214次 下载 1618次 |
码上扫一扫！ |
| 考虑源荷不确定性的CCHP型微网多目标优化调度 |
| 张靠社,冯培基,张刚,侯金旺,解佗,李萌,何欣 |
|
|
| (1.西安理工大学电气工程学院, 陕西 西安 710048;2.西安理工大学西北旱区生态水利国家重点实验室, 陕西 西安
710048;3.陕西燃气集团有限公司, 陕西 西安 710016;4.国网甘肃省电力公司电力科学研究院, 甘肃 兰州 730050) |
|
| 摘要: |
| 为提高冷热电联供(Combined Cooling Heating and Power, CCHP)型微网的综合运行效益，建立了以运行费用最小和二氧化碳排放量最小为目标的优化模型。针对源荷的不确定性，提出了基于误差场景整体生成与缩减的典型场景获得方法，并引入伪F-统计(Pseudo F-statistics, PFS)指标用于确定最佳场景缩减数目。实例计算表明，与不考虑源荷不确定的确定性优化方法相比，所提方法在应对源荷的不确定性上具有较好效果，运行费用平均下降0.31%，二氧化碳排放量平均下降4.85%。此外，计算分析表明，应用PFS指标确定最佳聚类数目可以找到模型应对源荷不确定的能力与计算时间之间的平衡点，提高模型计算效率。 |
| 关键词: CCHP型微网 优化调度 场景生成与缩减 伪F-统计 源荷不确定性 |
| DOI：DOI: 10.19783/j.cnki.pspc.201360 |
| 投稿时间：2020-11-06修订日期：2021-03-06 |
| 基金项目:陕西省重点研发计划项目资助(2018ZDCXL-GY- 10-04)；西安理工大学西北旱区生态水利国家重点实验室自主研究课题项目资助(2019KJCXTD-10) |
|
| Multi-objective optimization scheduling of CCHP-type microgrids considering source-load uncertainty |
| ZHANG Kaoshe,FENG Peiji,ZHANG Gang,HOU Jinwang,XIE Tuo,LI Meng,HE Xin |
| (1. School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China;
2. State Key Laboratory of Eco-Hydraulics in Northwest Arid Region, Xi'an University of Technology,
Xi'an 710048, China; 3. Shaanxi Provincial Natural Gas Co., Ltd., Xi'an 710016, China;
4. Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730050, China) |
| Abstract: |
| In order to improve the comprehensive operating benefits of the CCHP-type microgrid, an optimization model with minimum operating cost and CO2 emissions is established. In order to deal with the uncertainty of source-load, a typical scene acquisition method based on the overall generation and reduction of error scenes is proposed. The Pseudo F-statistics (PFS) index is introduced to determine the optimal number of scene reductions. The example calculation shows that compared with the deterministic optimization method which does not consider the source-load uncertainty, the method proposed in this paper has a better effect in dealing with that uncertainty. The operating cost is reduced by 0.31%, and the CO2 emissions are reduced by 4.85%. In addition, the calculation example shows that applying the PFS index to determine the optimal number of clusters can find the balance between the model's ability to deal with uncertainty and the calculation time, and improve the calculation efficiency of the model.
This work is supported the Key Research and Development Plan of Shaanxi Province (No. 2018ZDCXL-GY-10-04) and the Research Fund of the State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology (No. 2019KJCXTD-10). |
| Key words: CCHP-type microgrid optimal scheduling scenario generation and reduction Pseudo F-statistics source-load uncertainty |
