Scheduling Method for Virtual Power Plants Based on Analysis and Forecasting of Heterogeneous Load Characteristics

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

Abstract

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

CLC Number:

TP391.9

Zhang Runzhao, Chen Yanbo, Huang Tao, Tian Haoxin, Qiang Tuben, Zhang Zhi. 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.

Figures/Tables 15

Fig. 1

Fig. 2

Table 1

Table 2

Fig. 3

Table 3

Model parameter input

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

Table 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 4

Fig. 10

Table 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