Electric Power ›› 2024, Vol. 57 ›› Issue (3): 34-42.DOI: 10.11930/j.issn.1004-9649.202312036
• New Type Distribution Network Driven by Digital Technology • Previous Articles Next Articles
Heng LIANG1(), Geng HUANG2, Bin HOU2, Xi YANG3, Xiaohu LUO3(
), Da ZHANG1(
)
Received:
2023-12-11
Accepted:
2024-03-10
Online:
2024-03-23
Published:
2024-03-28
Supported by:
Heng LIANG, Geng HUANG, Bin HOU, Xi YANG, Xiaohu LUO, Da ZHANG. Accurate Estimation Method of Customer Baseline Load for Continuous Participation of Industrial Users in Demand Response[J]. Electric Power, 2024, 57(3): 34-42.
企业 | S | 企业 | S | |||
A | 1.000 | D | 0.799 | |||
B | 0.849 | E | 0.795 | |||
C | 0.810 | F | 0.772 |
Table 1 Similarity of load curves within the same group in enterprise A
企业 | S | 企业 | S | |||
A | 1.000 | D | 0.799 | |||
B | 0.849 | E | 0.795 | |||
C | 0.810 | F | 0.772 |
组内企业 | MAE/kW | 组内企业 | MAE/kW | |||
A | 2 285.5 | ABCD | 1 540.8 | |||
AB | 1 620.7 | ABCDE | 1 402.6 | |||
ABC | 1 561.2 | ABCDEF | 1 470.3 |
Table 2 Optimization effect of CBL estimation for enterprise A by transfer learning
组内企业 | MAE/kW | 组内企业 | MAE/kW | |||
A | 2 285.5 | ABCD | 1 540.8 | |||
AB | 1 620.7 | ABCDE | 1 402.6 | |||
ABC | 1 561.2 | ABCDEF | 1 470.3 |
算例 | 响应日期 | 响应时段 | 单次响应容量/% | |||
1 | 11月14日 | 12:00—20:00 | 10~30 | |||
2 | 11月14—18日 | 12:00—20:00 | 10~30 |
Table 3 Case design
算例 | 响应日期 | 响应时段 | 单次响应容量/% | |||
1 | 11月14日 | 12:00—20:00 | 10~30 | |||
2 | 11月14—18日 | 12:00—20:00 | 10~30 |
方案 | MAE/kW | RMSE/kW | MAPE/% | |||
1 | 4 405.76 | 4 745.28 | 15.59 | |||
2 | 4 921.30 | 5 106.05 | 17.36 | |||
3 | 4 353.98 | 4 556.30 | 15.36 | |||
4 | 2 320.26 | 2 950.66 | 8.03 | |||
5 | 3 608.09 | 3 727.07 | 12.78 | |||
6 | 1 269.38 | 1 455.03 | 4.50 | |||
7 | 708.75 | 753.31 | 2.49 |
Table 4 Deviation of CBL from the true value based on 7 methods in case 1
方案 | MAE/kW | RMSE/kW | MAPE/% | |||
1 | 4 405.76 | 4 745.28 | 15.59 | |||
2 | 4 921.30 | 5 106.05 | 17.36 | |||
3 | 4 353.98 | 4 556.30 | 15.36 | |||
4 | 2 320.26 | 2 950.66 | 8.03 | |||
5 | 3 608.09 | 3 727.07 | 12.78 | |||
6 | 1 269.38 | 1 455.03 | 4.50 | |||
7 | 708.75 | 753.31 | 2.49 |
方案 | MAE/kW | RMSE/kW | MAPE/% | |||
1 | 6670.74 | 6934.52 | 28.32 | |||
2 | 7164.99 | 7328.42 | 30.07 | |||
3 | 3696.04 | 3999.12 | 15.74 | |||
4 | 4195.92 | 4541.79 | 16.86 | |||
5 | 6224.07 | 6400.32 | 27.28 | |||
6 | 1380.38 | 1747.09 | 5.63 | |||
7 | 616.92 | 746.38 | 2.34 |
Table 5 Deviation of CBL from the true value based on 7 methods in case 2
方案 | MAE/kW | RMSE/kW | MAPE/% | |||
1 | 6670.74 | 6934.52 | 28.32 | |||
2 | 7164.99 | 7328.42 | 30.07 | |||
3 | 3696.04 | 3999.12 | 15.74 | |||
4 | 4195.92 | 4541.79 | 16.86 | |||
5 | 6224.07 | 6400.32 | 27.28 | |||
6 | 1380.38 | 1747.09 | 5.63 | |||
7 | 616.92 | 746.38 | 2.34 |
算例 | 方案 | 平均负荷响应率/% | 平均激励金额/ (元·(kW·次)–1) | |||
1 | 参考值 | 100 | 12.00~12.00 | |||
1 | 204.40~134.80 | 14.40~14.40 | ||||
2 | 216.60~138.90 | 14.40~14.40 | ||||
3 | 203.10~134.40 | 14.40~14.40 | ||||
4 | 48.40~82.80 | 0.00~9.94 | ||||
5 | 185.50~128.50 | 14.40~14.40 | ||||
6 | 107.00~102.30 | 12.84~12.28 | ||||
7 | 88.70~96.20 | 10.65~11.55 | ||||
2 | 参考值 | 100 | 12.00~12.00 | |||
1 | 257.20~152.40 | 14.40~14.17 | ||||
2 | 269.50~156.50 | 14.40~14.27 | ||||
3 | 184.40~128.10 | 14.21~13.88 | ||||
4 | 149.40~116.50 | 8.64~10.63 | ||||
5 | 247.40~149.10 | 14.40~14.21 | ||||
6 | 122.90~107.60 | 14.04~12.92 | ||||
7 | 90.00~96.70 | 10.80~11.60 |
Table 6 DR contribution and incentive of the case enterprise
算例 | 方案 | 平均负荷响应率/% | 平均激励金额/ (元·(kW·次)–1) | |||
1 | 参考值 | 100 | 12.00~12.00 | |||
1 | 204.40~134.80 | 14.40~14.40 | ||||
2 | 216.60~138.90 | 14.40~14.40 | ||||
3 | 203.10~134.40 | 14.40~14.40 | ||||
4 | 48.40~82.80 | 0.00~9.94 | ||||
5 | 185.50~128.50 | 14.40~14.40 | ||||
6 | 107.00~102.30 | 12.84~12.28 | ||||
7 | 88.70~96.20 | 10.65~11.55 | ||||
2 | 参考值 | 100 | 12.00~12.00 | |||
1 | 257.20~152.40 | 14.40~14.17 | ||||
2 | 269.50~156.50 | 14.40~14.27 | ||||
3 | 184.40~128.10 | 14.21~13.88 | ||||
4 | 149.40~116.50 | 8.64~10.63 | ||||
5 | 247.40~149.10 | 14.40~14.21 | ||||
6 | 122.90~107.60 | 14.04~12.92 | ||||
7 | 90.00~96.70 | 10.80~11.60 |
1 | 蒋棹骏, 向月, 谈竹奎, 等. 计及需求响应的高比例清洁能源园区储能容量优化配置[J]. 中国电力, 2023, 56 (12): 147- 155, 163. |
JIANG Zhaojun, XIANG Yue, TAN Zhukui, et al. Optimal allocation of energy storage capacity in high proportion clean energy parks considering demand response[J]. Electric Power, 2023, 56 (12): 147- 155, 163. | |
2 | 黄蔚亮, 苏志鹏, 梁欣怡, 等. 考虑可调市场和外部需求响应的虚拟电厂优化运行策略[J]. 中国电力, 2023, 56 (12): 156- 163. |
HUANG Weiliang, SU Zhipeng, LIANG Xinyi, et al. Optimal operation strategy for virtual power plant considering regulation market and external demand response[J]. Electric Power, 2023, 56 (12): 156- 163. | |
3 | 毕锐, 王孝淦, 袁华凯, 等. 考虑供需双侧响应和碳交易的氢能综合能源系统鲁棒调度[J]. 电力系统保护与控制, 2023, 51 (12): 122- 132. |
BI Rui, WANG Xiaogan, YUAN Huakai, et al. Robust dispatch of a hydrogen integrated energy system considering double side response and carbon trading mechanism[J]. Power System Protection and Control, 2023, 51 (12): 122- 132. | |
4 | 赵梓潼, 顾兵. 需求响应下基于电动汽车负荷聚合商的充放电电价与时段研究[J]. 东北电力大学学报, 2023, 43 (6): 79- 86. |
ZHAO Zitong, GU Bing. Research on charging and discharging price and time period based on electric vehicle load aggregator under demand response[J]. Journal of Northeast Electric Power University, 2023, 43 (6): 79- 86. | |
5 | 申洪涛, 刘宝铭, 任鹏, 等. 基于辨识解耦的含高渗透分布式光伏用户集群基线负荷估计方法[J]. 电力系统保护与控制, 2022, 50 (3): 164- 173. |
SHEN Hongtao, LIU Baoming, REN Peng, et al. Aggregated baseline load estimation method under high distributed photovoltaic penetration based on identification and decoupling[J]. Power System Protection and Control, 2022, 50 (3): 164- 173. | |
6 | 刘金朋, 刘胡诗涵, 张雨菲, 等. 考虑居民用户可调节潜力的负荷聚合商日前投标决策优化模型研究[J]. 智慧电力, 2024, (2): 71- 78, 122. |
LIU Jinpeng, LIU Hushihan, ZHANG Yufei, et al. Day-ahead bidding decision optimization model of load aggregators considering adjustable potential of residential users[J]. Smart Power, 2024, (2): 71- 78, 122. | |
7 | 陈海鹏, 周越豪, 赵畅, 等. 考虑高载能负荷参与的多时间尺度风电消纳调度[J]. 东北电力大学学报, 2022, 42 (6): 39- 51. |
CHEN Haipeng, ZHOU Yuehao, ZHAO Chang, et al. Multi-time scale wind power consumption scheduling with high energy load participation is considered[J]. Journal of Northeast Electric Power University, 2022, 42 (6): 39- 51. | |
8 | 李东东, 张凯, 姚寅, 等. 基于信息间隙决策理论的电动汽车聚合商日前需求响应调度策略[J]. 电力系统保护与控制, 2022, 50 (24): 101- 111. |
LI Dongdong, ZHANG Kai, YAO Yin, et al. Day-ahead demand response scheduling strategy of an electric vehicle aggregator based on information gap decision theory[J]. Power System Protection and Control, 2022, 50 (24): 101- 111. | |
9 | 丁琦欣, 覃洪培, 万灿, 等. 基于机会约束规划的配电网分布式光伏承载能力评估[J]. 东北电力大学学报, 2022, 42 (6): 28- 38. |
DING Qixin, QIN Hongpei, WAN Can, et al. Chance-constrained optimization-based distributed photovoltaic hosting capacity assessment of distribution networks[J]. Journal of Northeast Electric Power University, 2022, 42 (6): 28- 38. | |
10 | 刘雪飞, 刘洋, 马国真, 等. 考虑负荷差异化需求响应的配电网多目标扩展规划[J]. 电力系统保护与控制, 2022, 50 (22): 131- 141. |
LIU Xuefei, LIU Yang, MA Guozhen, et al. Multi-objective extended planning for a distribution network considering demarcation of demand response schemes[J]. Power System Protection and Control, 2022, 50 (22): 131- 141. | |
11 | 何胜, 徐玉婷, 陈宋宋, 等. 我国电力需求响应发展成效及“十四五” 工作展望[J]. 电力需求侧管理, 2021, 23 (6): 1- 6. |
HE Sheng, XU Yuting, CHEN Songsong, et al. Prospect and the 14th Five-Year Plan of power demand response development effect in China[J]. Power Demand Side Management, 2021, 23 (6): 1- 6. | |
12 | 贾巍, 黄裕春. 基于小样本数据差分扩容的微电网负荷预测方法[J]. 中国电力, 2023, 56 (8): 151- 156, 165. |
JIA Wei, HUANG Yuchun. Method of load forecasting in microgrid based on differential expansion of small sample data[J]. Electric Power, 2023, 56 (8): 151- 156, 165. | |
13 | 欧阳福莲, 王俊, 周杭霞. 基于改进迁移学习和多尺度CNN-BiLSTM-Attention的短期电力负荷预测方法[J]. 电力系统保护与控制, 2023, 51 (2): 132- 140. |
OUYANG Fulian, WANG Jun, ZHOU Hangxia. Short-term power load forecasting method based on improved hierarchical transfer learning and multi-scale CNN-BiLSTM-Attention[J]. Power System Protection and Control, 2023, 51 (2): 132- 140. | |
14 | 李文武, 石强, 李丹, 等. 基于VMD和PSO-SVR的短期电力负荷多阶段优化预测[J]. 中国电力, 2022, 55 (8): 171- 177. |
LI Wenwu, SHI Qiang, LI Dan, et al. Multi-stage optimization forecast of short-term power load based on VMD and PSO-SVR[J]. Electric Power, 2022, 55 (8): 171- 177. | |
15 |
LEE E, LEE K, LEE H, et al. Defining virtual control group to improve customer baseline load calculation of residential demand response[J]. Applied Energy, 2019, 250, 946- 958.
DOI |
16 | 杨彪, 颜伟, 莫静山. 考虑源荷功率随机性和相关性的主导节点选择与无功分区方法[J]. 电力系统自动化, 2021, 45 (11): 61- 67. |
YANG Biao, YAN Wei, MO Jingshan. Pilot-bus selection and network partitioning method considering randomness and correlation of source-load power[J]. Automation of Electric Power Systems, 2021, 45 (11): 61- 67. | |
17 | 高欣, 纪维佳, 赵兵, 等. 不平衡数据集下基于CVAE-CNN模型的智能电表故障多分类方法[J]. 电网技术, 2021, 45 (8): 3052- 3060. |
GAO Xin, JI Weijia, ZHAO Bing, et al. Multi-classification method of smart meter fault types based on CVAE-CNN model under imbalanced dataset[J]. Power System Technology, 2021, 45 (8): 3052- 3060. | |
18 | 黄南天, 郭玉, 赵暄远. 计及辐照区间划分的含光伏电源配电网源-荷联合场景生成[J]. 东北电力大学学报, 2023, 43 (5): 78- 84. |
HUANG Nantian, GUO Yu, ZHAO Xuanyuan. Combined source-load scenario generation for PV-containing distribution networks with calculation and irradiation interval classification[J]. Journal of Northeast Electric Power University, 2023, 43 (5): 78- 84. | |
19 | 肖白, 李梦雪, 尉博旭. 基于AP聚类-跳转持续MC的风电出力时间序列模拟生成方法研究[J]. 东北电力大学学报, 2023, 43 (1): 35- 44. |
XIAO Bai, LI Mengxue, YU Boxu. Research on simulation method to generate wind power output time series based on AP clustering-transition and persistence Markov chain[J]. Journal of Northeast Electric Power University, 2023, 43 (1): 35- 44. | |
20 | 张露, 颜宏文, 马瑞. 基于改进DBSCAN-RNN的电力负荷建模及可调特征提取[J]. 智慧电力, 2023, 51 (3): 39- 45. |
ZHANG Lu, YAN Hongwen, MA Rui. Power load modeling and adjustable feature extraction based on improved DBSCAN-RNN[J]. Smart Power, 2023, 51 (3): 39- 45. | |
21 | RENCHER A C, CHRISTENSEN W F. Classification analysis: allocation of observations to groups[J]. Methods of Multivariate Analysis, 2003: 299–321. |
22 | 邝梓佳, 邱桂华, 汤志锐. 基于迁移学习的OMS配网瞬态稳定性控制方法[J]. 信息技术, 2024, 48 (1): 182- 188. |
KUANG Zijia, QIU Guihua, TANG Zhirui. Transient stability control method of OMS distribution network based on Transfer Learning[J]. Information Technology, 2024, 48 (1): 182- 188. | |
23 | ZHU Z D, LIN K X, JAIN A K, et al. Transfer learning in deep reinforcement learning: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45 (11): 13344- 13362. |
24 | 赵源上, 林伟芳. 基于皮尔逊相关系数融合密度峰值和熵权法典型场景研究[J]. 中国电力, 2023, 56 (5): 193- 202. |
ZHAO Yuanshang, LIN Weifang. Research on typical scenarios based on fusion density peak value and entropy weight method of pearson's correlation coefficient[J]. Electric Power, 2023, 56 (5): 193- 202. | |
25 | 安军, 周永超, 周毅博, 等. 考虑风荷不确定性的电源无功电压调差系数整定方法研究[J]. 东北电力大学学报, 2023, 43 (4): 30- 38. |
AN Jun, ZHOU Yongchao, ZHOU Yibo, et al. Optimization method for var-voltage adjustment CoefficientConsidering wind power and load uncertainty[J]. Journal of Northeast Electric Power University, 2023, 43 (4): 30- 38. | |
26 | 刘文丽, 张涛, 杨晓雷, 等. 计及负荷随机性含风电电力系统TCSC多目标优化配置[J]. 电力系统保护与控制, 2023, 51 (5): 58- 69. |
LIU Wenli, ZHANG Tao, YANG Xiaolei, et al. Multi-objective optimal allocation of TCSC for a power system for wind power and load randomness[J]. Power System Protection and Control, 2023, 51 (5): 58- 69. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||