| 引用本文: | 沈赋,杨光兵,王健,蔡子龙,陈雪鸥,曹旸,翟苏巍.计及电-气园区综合能源系统多重不确定性的变置信区间优化调度[J].电力自动化设备,2024,44(11):33-40. |
| SHEN Fu,YANG Guangbing,WANG Jian,CAI Zilong,CHEN Xueou,CAO Yang,ZHAI Suwei.Optimal scheduling with variable confidence interval considering multiple uncertainties of electricity-gas park integrated energy system[J].Electric Power Automation Equipment,2024,44(11):33-40. |
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| 摘要: |
| 电-气园区综合能源系统(EGPIES)受到天然气管道泄漏与风电出力波动的多重不确定性影响，导致系统供电可靠性受到严重威胁。计及天然气管道泄漏与风电出力不确定性，提出了一种EGPIES自适应优化调度方法。根据天然气泄漏量与储能对系统负荷损失的影响划分失荷程度，结合场景法与条件风险价值理论对风电不确定性进行量化，并通过隶属函数对自适应选择的多目标函数进行处理。利用遗传粒子群优化算法对处理后的多目标函数进行求解，得到不同泄漏程度下的机组调度结果与自适应改变的风电置信区间。通过算例验证所提模型能在具有良好经济性的同时，提高系统的供电可靠性。 |
| 关键词: 园区综合能源系统 多重不确定性 自适应优化调度 遗传粒子群优化算法 置信区间 |
| DOI：10.16081/j.epae.202405009 |
| 分类号: |
| 基金项目:国家自然科学基金资助项目(52107097)；云南省兴滇英才支持计划项目(KKRD202204021)；云南省应用基础研究计划项目(202101BE070001-061,202201AU070111)；昆明理工大学高层次人才平台建设项目(KKZ7202004004) |
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| Optimal scheduling with variable confidence interval considering multiple uncertainties of electricity-gas park integrated energy system |
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SHEN Fu1, YANG Guangbing1, WANG Jian1, CAI Zilong1, CHEN Xueou2, CAO Yang1, ZHAI Suwei3
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1.Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China;2.Training and Evaluation Center of Yunnan Power Grid Co.,Ltd.,Kunming 650106, China;3.Electric Power Research Institute of Yunnan Power Grid Co.,Ltd.,Kunming 650217, China
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| Abstract: |
| The electricity-gas park integrated energy system(EGPIES) is affected by the multiple uncertainties of natural gas pipeline leakage and wind power output fluctuation, which seriously threatens the power supply reliability of the system. Considering the leakage of natural gas pipeline and the uncertainty of wind power output, an adaptive optimization scheduling method for EGPIES is proposed. According to the influence of natural gas leakage and energy storage on system load loss, the load loss degree is divided. The uncertainty of wind power is quantified by combining the scenario method and conditional value at risk theory, and the adaptive multi-objective function is processed by membership function. The genetic algorithm particle swarm optimization algorithm is used to solve the modified multi-objective function to obtain the scheduling results of units and the self-adaptive variable wind power confidence intervals under different leakage degrees. An example is given to verify that the proposed model can improve the power supply reliability of the system while maintaining good economic performance. |
| Key words: park integrated energy system multiple uncertainties adaptive optimal scheduling genetic algorithm particle swarm optimization algorithm confidence interval |
