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计及需求响应不确定性的综合能源系统协同优化配置
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1.新能源电力系统国家重点实验室(华北电力大学),北京市 102206;2.国网山西省电力公司经济技术研究院,山西省 太原市 030000

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Collaborative Optimal Configuration for Integrated Energy System Considering Uncertainties of Demand Response
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Affiliation:

1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University), Beijing 102206, China;2.Economic and Technical Research Institute of State Grid Shanxi Electric Power Company, Taiyuan 030000, China

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    摘要:

    提出了一种计及综合需求响应(IDR)不确定性的综合能源系统(IES)设备优化配置方法。首先,以提高能源利用效率为目标,基于冷/热/电联供系统构建了计及IDR的IES基本结构。然后,采用证据理论对IDR中的随机和认知不确定性进行分析,并利用可信水平约束,优化一定电价方案下的负荷曲线。在此基础上,建立兼顾设备优化配置及运行策略的双层协同规划模型,上层以规划总成本最低为目标进行设备选型和容量配置,下层以运行成本最低为目标优化设备出力。通过比较所有电价方案下的总成本,获得最优电价与设备配置方案。最后,通过算例验证了计及IDR不确定性的优化配置结果对风险的抵御能力更强,且采用证据理论能够实现概率理论和区间理论的相统一。

    Abstract:

    This paper proposes a method of equipment optimal configuration for an integrated energy system (IES) considering the uncertainties of integrated demand response (IDR). Firstly, in order to improve efficiency of energy use, the basic structure of IES considering IDR is constructed based on a combined cooling, heating and power system. Then, the aleatory and epistemic uncertainty of IDR are analyzed by the evidence theory, and the load curve under a certain price scheme is optimized by using a credible level constraint. On this basis, a bi-level collaborative planning model considering the optimal configuration and operation strategy of the equipment in IES is established. In the upper level, the equipment selection and capacity allocation are conducted with the goal of minimizing the total planning cost. And in the lower level, the equipment output is optimized with the lowest operation cost as the objective. By comparing the total costs of all tariff schemes, the optimal tariff and equipment allocation scheme can be obtained. Finally, the proposed approach is illustrated on an example and the results demonstrate that the optimal allocation results considering the uncertainties of IDR are more resistant to risks. Meanwhile, evidence theory can be used to achieve the unification of probability theory and interval theory.

    表 3 Table 3
    表 7 Table 7
    表 1 各场景优化配置结果及成本对比Table 1 Optimized configuration results and cost comparison for each case
    表 6 Table 6
    表 8 Table 8
    表 2 Table 2
    表 4 Table 4
    图1 IES能量流图Fig.1 Energy flow diagram of IES
    图2 优化负荷计算的流程图Fig.2 Flow chart for calculating optimized load
    图3 夏季14:00冷负荷的累积概率分布Fig.3 Cumulative probability distribution of cold load at 14:00 in summer
    图4 不同情况下的累积概率分布Fig.4 Cumulative probability distribution with different conditions
    图 IES协同规划框图Fig. Collaborative planning block diagram of IES
    图 计及IDR不确定性的IES优化配置模型求解流程Fig. Solution flow of IES optimal configuration model with IDR uncertainty
    图 春秋季典型日负荷曲线Fig. Load curve for typical spring and autumn days
    图 夏季典型日负荷曲线Fig. Load curve for a typical day in summer
    图 冬季典型日负荷曲线Fig. Load curve for a typical day in winter
    表 5 Table 5
    图 计及IDR前后负荷曲线对比Fig. Comparison of load curves before and after considering IDR
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引用本文

刘文霞,李征洲,杨粤,等.计及需求响应不确定性的综合能源系统协同优化配置[J/OL].电力系统自动化,http://doi.org/10.7500/AEPS20190731013.
LIU Wenxia,LI Zhengzhou,YANG Yue,et al.Collaborative Optimal Configuration for Integrated Energy System Considering Uncertainties of Demand Response[J/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190731013.

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  • 收稿日期:2019-07-31
  • 最后修改日期:2020-03-07
  • 录用日期:2019-11-18
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