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Low Carbon Economic Dispatch of Integrated Energy System Considering Power Supply Reliability and Integrated Demand Response

Jian Dong, Haixin Wang, Junyou Yang*, Liu Gao, Kang Wang, Xiran Zhou

School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China

* Corresponding Author: Junyou Yang. Email: email

(This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)

Computer Modeling in Engineering & Sciences 2022, 132(1), 319-340. https://doi.org/10.32604/cmes.2022.020394

Abstract

Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy. This paper studies an electric-gas-heat integrated energy system, including the carbon capture system, energy coupling equipment, and renewable energy. An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost, carbon emission and enhance the power supply reliability. Firstly, the low-carbon mathematical model of combined thermal and power unit, carbon capture system and power to gas unit (CCP) is established. Subsequently, we establish a low carbon multi-objective optimization model considering system operation cost, carbon emissions cost, integrated demand response, wind and photovoltaic curtailment, and load shedding costs. Furthermore, considering the intermittency of wind power generation and the flexibility of load demand, the low carbon economic dispatch problem is modeled as a Markov decision process. The twin delayed deep deterministic policy gradient (TD3) algorithm is used to solve the complex scheduling problem. The effectiveness of the proposed method is verified in the simulation case studies. Compared with TD3, SAC, A3C, DDPG and DQN algorithms, the operating cost is reduced by 8.6%, 4.3%, 6.1% and 8.0%.

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Cite This Article

Dong, J., Wang, H., Yang, J., Gao, L., Wang, K. et al. (2022). Low Carbon Economic Dispatch of Integrated Energy System Considering Power Supply Reliability and Integrated Demand Response. CMES-Computer Modeling in Engineering & Sciences, 132(1), 319–340.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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