Optimal operation of energy-intensive load considering electricity carbon market
Bowen Zhou,
Jianing Li,
Qihuitianbo Liu,
Guangdi Li,
Peng Gu,
Liaoyi Ning,
Zhenyu Wang
Affiliations
Bowen Zhou
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang, 110819, China; Corresponding author. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
Jianing Li
State Grid Changchun Electric Power Supply Company, Changchun, 130012, China
Qihuitianbo Liu
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang, 110819, China
Guangdi Li
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang, 110819, China
Peng Gu
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; Key Laboratory of Integrated Energy Optimization and Secure Operation of Liaoning Province, Northeastern University, Shenyang, 110819, China
Liaoyi Ning
State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang, 110006, China
Zhenyu Wang
State Grid Electric Power Research Institute, Wuhan Efficiency Evaluation Company Limited, Wuhan, 430072, China
Energy-intensive load benefits from low electricity tariff and carbon emission, since they occupy certain amounts in the total cost of the product. This paper considers energy-intensive load participation in the electricity as well as carbon trading to reduce the cost. Firstly, an electricity-carbon model is established based on the correlation value method to calculate the carbon emissions of energy-intensive load based on their electricity consumption to realize the carbon amount. Afterwards, the baseline method is used to allocate free carbon emission quotas to energy-intensive load and a reward-penalty carbon trading price mechanism considering offset is proposed. Next, the objective function to achieve maximum benefits, and to reduce output fluctuation, and to improve new energy accommodation is proposed. The case studies show that, by comparing multi-objective function optimization, the optimization target proposed in this paper can effectively reduce wind power output fluctuations and improve wind power accommodation. Through the total participation in carbon trading and electricity market income, multi-objective optimization can increase the system income while ensuring that energy-intensive load meets production requirements under the premise of reducing carbon emissions, verifying the effectiveness of the low-carbon optimal operation model proposed in this paper.
