Optimal Control of Residents’ Controllable Load Resources Considering Different Demands of Users

doi:10.12096/j.2096-4528.pgt.23110

Abstract

Abstract:

Optimizing the controllable load resources of residents on the demand side is a major aspect of virtual power plant (VPP). It is an important means to improve the utilization rate of new energy such as wind power and photovoltaic power, to achieve carbon neutrality. The key to control controllable load is to meet the basic power demand of users, and different users have different power demand. Therefore, an optimal control method of residents’ controllable load resources considering user demand was proposed. Considering the different demand of different users, as an intermediate link between the power grid and the load group, VPP manages the controllable load. Firstly, an improved K-means clustering algorithm for optimal k selection was proposed for clustering. Then, according to the demand difference function to quantify the deviation between the power consumption and the original demand of each group after load regulation, a priority division rule based on the demand difference was proposed. Finally, in the regulation, VPP compensated different costs for different priority groups to achieve the regulation goal of maximizing VPP revenue and minimizing changes in user electricity consumption behavior. The simulation results show that the proposed optimal control strategy can effectively cut the peak and fill the valley of the load curve, and ensure the VPP revenue with less impact on the power demand of users.

Key words: virtual power plant (VPP), controllable load, load clustering, user demand, peak shaving and valley filling

CLC Number:

TK 01

He HUANG, Yan WANG, Nian JIANG, Qiang WU, Yajing ZHANG, Xiuyuan YANG. Optimal Control of Residents’ Controllable Load Resources Considering Different Demands of Users[J]. Power Generation Technology, 2023, 44(6): 896-908.

Figures/Tables 14

Fig. 1 Classification of residential load

Fig. 2 Load cluster control considering user demand

Fig. 3 Flexible load clustering process based on improved selection of optimal k

Fig. 4 Original electricity consumption curve of residents in a residential area

Fig. 5 Clustering results of electric vehicles when k=13

Tab. 1 Demand difference of each load group under different k of electric vehicles

类别	不同k值下的诉求差量
类别	k=5	k=13	k=20
1	0.070 8	0.186 2	0.056 0
2	0.120 4	0.066 6	0.089 8
3	0.131 5	0.069 4	0.104 0
4	0.082 7	0.083 7	0.328 5
5	0.198 0	0.075 5	0.035 9
6	—	0.068 1	0.050 4
7	—	0.095 3	0.066 2
8	—	0.085 9	0.063 9
9	—	0.153 1	0.166 1
10	—	0.059 7	0.050 3
11	—	0.054 1	0.054 2
12	—	0.049 9	0.038 9
13	—	0.229 7	0.046 7
14	—	—	0.047 8
15	—	—	0.050 3
16	—	—	0.123 6
17	—	—	0.060 2
18	—	—	0.088 7
19	—	—	0.042 1
20	—	—	0.273 6

Fig. 6 Clustering results of washing machines when k=10

Tab. 2 Clustering results of air conditioning

类别	温度下限 $T a c, m i n$ /℃	温度上限 $T a c, m a x$ /℃
1	23	26
2	23	27
3	23	28
4	24	26
5	24	27
6	24	28
7	25	26
8	25	27
9	25	28

Tab. 2 Clustering results of air conditioning

类别	温度下限 $T a c, m i n$ /℃	温度上限 $T a c, m a x$ /℃
1	23	26
2	23	27
3	23	28
4	24	26
5	24	27
6	24	28
7	25	26
8	25	27
9	25	28

Tab. 3 Load demand difference of each group for three typical flexible loads

类别	诉求差量
类别	空调(k=9)	电动汽车(k=13)	洗衣机(k=10)
1	0	0.186 2	0.099 3
2	0	0.066 6	0.105 6
3	0	0.069 4	0.087 3
4	0	0.083 7	0.077 8
5	0	0.075 5	0.055 1
6	0	0.068 1	0.057 3
7	0	0.095 3	0.060 5
8	0	0.085 9	0.073 2
9	0	0.153 1	0.127 1
10	—	0.059 7	0.052 1
11	—	0.054 1	—
12	—	0.049 9	—
13	—	0.229 7	—

Tab. 4 Regulation priority and demand difference range of various loads

优先级	诉求差量范围
优先级	空调(k=9)	电动汽车(k=13)	洗衣机(k=10)
1	0	0~0.06	0~0.06
2	—	0.06~0.08	0.06~0.08
3	—	0.08~0.10	0.08~0.10
4	—	0.10~0.23	0.10~0.13

Tab. 5 Load compensation price under different regulation priorities

优先级	负荷补偿价格/[元/(MW⋅h)]
优先级	空调	电动汽车	洗衣机
1	80	6.5	12
2	—	7.3	18
3	—	8.0	24
4	—	8.7	32

Fig. 7 Load curves before and after regulation

Fig. 8 Reduction and transfer of various loads

Tab. 6 Comparison of different control strategies

控制策略	VPP收益/元	负荷削减量/(MW⋅h)	诉求差量
随机分组控制	402	7.83	0.480
聚类控制	415	8.14	0.104
本文控制	409	8.11	0.033

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