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Robust energy systems scheduling considering uncertainties and demand side emission impacts

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  • Wang, Yunqi
  • Qiu, Jing
  • Tao, Yuechuan

Abstract

Distributed Companies (DISCOs) with distributed energy resources (DERs) are considered as one of the most flexible forms of power supply towards decarbonization society. However, to achieve this low-carbon goal, it would not be effective without appropriate incentives and the effective demand side involvement. Meanwhile, uncertainties in the system operation such as outputs of intermittent renewable energy, electric vehicles (EVs), and demand side behaviors introduce difficulty for scheduling, and further influence the system's carbon emission. This pa-per proposes a two-stage model to investigate the dynamic carbon impacts caused by the above uncertainties to system scheduling through active demand side from both the economic aspect and environmental aspect. The carbon impacts are reflected upon a defined integrated selling price of DISCO based on the result of carbon emission flow (CEF) and real-time carbon price from the carbon trading market. The uncertainties are modeled as individual probability distributions, and the electricity consumption is forecasted by the least absolute shrinkage and selection operator (LASSO)-quantile regression neural network (QRNN). To address the system uncertainties, a probability-weighted robust optimization (PWRO) approach is applied. Case studies are tested on a modified IEEE-33 bus system, the simulation result indicates that the proposed model with PWRO can effectively handle uncertainties, while a fairer bill plan for customers towards a low-carbon economy can be obtained.

Suggested Citation

  • Wang, Yunqi & Qiu, Jing & Tao, Yuechuan, 2022. "Robust energy systems scheduling considering uncertainties and demand side emission impacts," Energy, Elsevier, vol. 239(PD).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pd:s0360544221025652
    DOI: 10.1016/j.energy.2021.122317
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    Cited by:

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    2. Sun, Xiaocong & Bao, Minglei & Ding, Yi & Hui, Hengyu & Song, Yonghua & Zheng, Chenghang & Gao, Xiang, 2024. "Modeling and evaluation of probabilistic carbon emission flow for power systems considering load and renewable energy uncertainties," Energy, Elsevier, vol. 296(C).
    3. Ding, Bing & Li, Zening & Li, Zhengmao & Xue, Yixun & Chang, Xinyue & Su, Jia & Jin, Xiaolong & Sun, Hongbin, 2024. "A CCP-based distributed cooperative operation strategy for multi-agent energy systems integrated with wind, solar, and buildings," Applied Energy, Elsevier, vol. 365(C).
    4. Junpei Nan & Jieran Feng & Xu Deng & Chao Wang & Ke Sun & Hao Zhou, 2022. "Hierarchical Low-Carbon Economic Dispatch with Source-Load Bilateral Carbon-Trading Based on Aumann–Shapley Method," Energies, MDPI, vol. 15(15), pages 1-17, July.
    5. Meng, Weiqi & Song, Dongran & Huang, Liansheng & Chen, Xiaojiao & Yang, Jian & Dong, Mi & Talaat, M., 2024. "A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response," Energy, Elsevier, vol. 289(C).
    6. Rabea Jamil Mahfoud & Nizar Faisal Alkayem & Emmanuel Fernandez-Rodriguez & Yuan Zheng & Yonghui Sun & Shida Zhang & Yuquan Zhang, 2024. "Evolutionary Approach for DISCO Profit Maximization by Optimal Planning of Distributed Generators and Energy Storage Systems in Active Distribution Networks," Mathematics, MDPI, vol. 12(2), pages 1-33, January.

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