Demand Response Capability Assessment of Regenerative Electric Heating Load under the Goal of “Double Carbon”

2021 ◽  
Author(s):  
Jie Ji ◽  
Mingliang Liang ◽  
Changzheng Gao ◽  
Fuqiang Li ◽  
Yuhan Liu ◽  
...  
Keyword(s):  
Demand Response ◽  
Electric Heating ◽  
Heating Load ◽  
Response Capability

scholarly journals Optimal scheduling of Park level integrated energy system considering electrothermal coupling

2021 ◽  
Vol 236 ◽  
pp. 02008
Author(s):  
LIU Dunnan ◽  
Gao Yuan ◽  
Wang Lingxiang ◽  
Liang Jiahao ◽  
Wang Zhenyu ◽  
...  
Keyword(s):  
Demand Response ◽  
Electric Heating ◽  
Energy System ◽  
Optimal Scheduling ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Total Energy Consumption ◽  
Integrated Energy System ◽  
Heating Load ◽  
Operation Cost

Considering the inherent characteristics of the park heating load, such as transmission delay, fuzzy heating comfort, etc., it can be used as a flexible load to participate in the optimal scheduling. Aiming at the minimum operation cost of the integrated energy system in the park, a collaborative optimal scheduling model of the park's integrated energy system with the participation of comprehensive demand response of electric heating load is constructed. The simulation results show that, compared with the optimization results of traditional power demand response, the application of integrated demand response of electric heating load improves the flexibility of production of cogeneration units in the park, reduces the total energy consumption cost of demand side users and the operation cost of the system on the premise of ensuring the balance of supply and demand of the system, improves the energy utilization efficiency, and realizes the environmental protection and economy of the system function.

Power grid two-level dispatch strategy with participation of electric heating load

2021 ◽  
Author(s):  
Zhang Jie ◽  
Chen Yongjie ◽  
Lu Zhenxi ◽  
Zhang Zhe
Keyword(s):  
Power Grid ◽  
Electric Heating ◽  
Heating Load

scholarly journals Joint planning of distribution network and substation considering the effect of electric heating load

2020 ◽  
Vol 446 ◽  
pp. 042018
Author(s):  
Zhi Zhang ◽  
Jingxiong Liu ◽  
Xi Duan ◽  
Yanhui Zhang ◽  
Haibo Zhao ◽  
...  
Keyword(s):  
Distribution Network ◽  
Electric Heating ◽  
Joint Planning ◽  
Heating Load

A framework for the assessment of electric heating load flexibility contribution to mitigate severe wind power ramp effects

2017 ◽  
Vol 142 ◽  
pp. 268-278 ◽  
Author(s):  
Antti Alahäivälä ◽  
Jussi Ekström ◽  
Juha Jokisalo ◽  
Matti Lehtonen
Keyword(s):  
Wind Power ◽  
Electric Heating ◽  
Heating Load

scholarly journals Regulation capability evaluation of individual electric heating load based on Radial basis Function neural network

2019 ◽  
Vol 23 (5 Part A) ◽  
pp. 2821-2829 ◽  
Author(s):  
Liwei Zhang ◽  
Xiaotian Liu ◽  
Jingbiao Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Evaluation Method ◽  
Back Propagation ◽  
Electric Heating ◽  
Northern Region ◽  
Thermal Parameters ◽  
Radial Basis ◽  
Heating Load

As a time-shifting load that is gradually popularized in the northern region, electric heating load has great adjustment potential. Because the electric heating operation characteristics are affected by many non-linear factors, the traditional equivalent thermal parameters model cannot accurately evaluate the regulation capability of individual electric heating load. Aiming at this problem, this paper proposes an evaluation method for the regulation capability of individual electric heating load based on radial basis function neural network. Firstly, electric heating load control experiments were carried out in a typical room of a residential quarter in winter and relevant experimental data were collected. Then, based on the operation data, the radial basis function neural network is used to evaluate the regulation capability of the individual electric heating load. Finally, the evaluation results based on radial basis function neural network are compared with those based on back propagation neural network and equivalent thermal parameters model. The results show that the proposed method has the least evaluation error and can more accurately evaluate the regulation capability of individual electric heating load.

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