基于异构负荷特征解析预测的虚拟电厂调度方法

doi:10.16182/j.issn1004731x.joss.25-0602

摘要/Abstract

摘要：

为合理通过需求响应资源改善电力供需形势，提出了一种基于异构负荷特征解析预测的虚拟电厂双层优化调度模型。考虑多类型负荷的响应特征差异，通过使用动态场景生成和K-means++聚类的用户基线负荷曲线预测方法，构建了多类型负荷的需求响应模型；建立考虑负荷聚合商和需求响应的虚拟电厂双层优化调度模型，上层以虚拟电厂运营净收益最大化为目标进行优化调度，下层以负荷聚合商净利润最大化为目标进行市场出清模拟。仿真实验结果表明：充分发挥可调节资源的灵活性，虚拟电厂经济效益提升4.58%，验证了所提优化模型的有效性。

关键词: 虚拟电厂, 多元负荷聚合商, 用户基线负荷预测, 需求响应, 优化调度

Abstract:

To improve the electricity supply-demand situation by rationally utilizing demand response resources, a two-layer optimal scheduling model for virtual power plants (VPPs) based on the analysis and forecasting of heterogeneous load characteristics was proposed. With the differences in response characteristics of multi-type loads considered, a demand response model for multi-type loads was constructed by using a customer baseline load (CBL) curve forecasting method that integrated dynamic scenario generation and K-means++ clustering. A two-layer optimal scheduling model for VPPs that incorporated load aggregators and demand response was established. In this model, the upper layer conducted optimal scheduling targeting maximizing the net operating profit of VPPs, while the lower layer simulated market clearing aimed at maximizing the net profit of load aggregators. The simulation experimental results indicate an improvement of 4.58% in the economic benefit of VPPs through the full utilization of the flexibility of adjustable resources, which validates the effectiveness of the proposed optimal model.

Key words: virtual power plant (VPP), multi-type load aggregator, customer baseline load (CBL) forecasting, demand response, optimal scheduling

中图分类号:

TP391.9

张润昭,陈艳波,黄涛等 . 基于异构负荷特征解析预测的虚拟电厂调度方法[J]. 系统仿真学报, 2025, 37(12): 2994-3006.

Zhang Runzhao,Chen Yanbo,Huang Tao,et al . Scheduling Method for Virtual Power Plants Based on Analysis and Forecasting of Heterogeneous Load Characteristics[J]. Journal of System Simulation, 2025, 37(12): 2994-3006.

图/表 15

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图2

表1

表2

图3

表3

模型参数输入

参数	参数取值	参数	参数取值
$k w t / (元 / (k W · h))$	0.03	$ε$	0.85
$k p v / (元 / (k W · h))$	0.02	$χ e s, m i n / (M W · h)$	2
$k e s / (元 / (k W · h))$	0.03	$χ e s, m a x / (M W · h)$	500
$k f / (元 / (k W · h))$	0.26	$η$	0.90
$δ d$	0.9	$α$	0.55
$η s i, 0$	0.41	$γ$	0.04

表3

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图5

图6

图7

图8

图9

表4

图10

表5

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类型	安全保障负荷占比	经营性负荷占比	非经营性负荷占比	综合可调负荷占比
大型商业	1 ~ 5	60 ~ 85	5 ~ 10	36
宾馆	5 ~ 10	70 ~ 90	1 ~ 5	23
商场	10 ~ 15	60 ~ 80	1 ~ 5	30
办公室	1 ~ 5	70 ~ 90	5 ~ 10	32

行业	安全保障负荷占比	主要生产负荷占比	辅助生产负荷占比	非生产性负荷占比	综合可调负荷占比
非金属矿物制品业	8 ~15	55 ~ 60	15 ~ 20	2 ~ 5	24
黑色金属冶炼和压延加工业	10 ~15	55 ~ 60	5 ~ 10	2 ~ 5	20
有色金属冶炼和压延加工业	3 ~10	75 ~ 85	5 ~ 10	1 ~ 5	22
化学原料和化学制品制造业	3 ~10	75 ~ 85	5 ~ 10	1 ~ 5	20
通用及专用设备制造业	5 ~10	60 ~ 70	5 ~ 10	5 ~ 10	20
纺织业	5 ~10	60 ~ 70	5 ~ 10	5 ~ 10	35

场景	DR主体	不考虑的对象
Ⅰ	工商业可调负荷、EV	储能设备
Ⅱ	工商业可调负荷、储能设备	EV
Ⅲ	EV、储能设备	工商业可调负荷

场景	VPP	LA
考虑所有设备负荷	310 998.25	980.20
不考虑储能	290 733.50	980.20
不考虑EV	303 530.90	554.88
不考虑工商业负荷	289 333.40	723.17