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A systematic approach for the joint dispatch of energy and reserve incorporating demand response

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  • Zhang, Menglin
  • Ai, Xiaomeng
  • Fang, Jiakun
  • Yao, Wei
  • Zuo, Wenping
  • Chen, Zhe
  • Wen, Jinyu

Abstract

The intermittent nature of wind power increases the need for flexibility of the power system. This paper proposes the systematic approach for the joint dispatch of energy and reserve incorporating demand response, including the formulation of the two-stage optimization, dynamic scenario generation, and inactive constraint identification. The incentive-based demand response model is adopted to improve flexibility by its cooperation with conventional units. The dynamic scenario generation method is developed to provide reasonable input for the two-stage optimization, considering the temporal correlations of the wind power. Three indicators are proposed to evaluate the quality of scenarios. To speed up the solution, the inactive constraint reduction has been applied to reduce the computational burden raised by the number of the scenarios and the system scale. Finally, the modified IEEE 118-bus test system with fifty incentive-based demand response aggregators is utilized to evaluate the effectiveness of the proposed method to improve operational economics and to promote wind power utilization. Simulation results show that 89.53% of the transmission line constraints can be removed, leading to a maximal reduction of 69.81% of the computational time. Compared to the conventional sampling method, the dynamic scenario set performs better in terms of three proposed indicators, and can reduce the total cost by 1.99%. Neglecting the constraint of response times, the economic efficiency would be overestimated by 0.98%.

Suggested Citation

  • Zhang, Menglin & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2018. "A systematic approach for the joint dispatch of energy and reserve incorporating demand response," Applied Energy, Elsevier, vol. 230(C), pages 1279-1291.
    • Handle: RePEc:eee:appene:v:230:y:2018:i:c:p:1279-1291
    DOI: 10.1016/j.apenergy.2018.09.044
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    2. Hlalele, Thabo G. & Naidoo, Raj M. & Bansal, Ramesh C. & Zhang, Jiangfeng, 2020. "Multi-objective stochastic economic dispatch with maximal renewable penetration under renewable obligation," Applied Energy, Elsevier, vol. 270(C).
    3. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Lin, Zhongwei & Fang, Fang & Chen, Qun, 2021. "Optimal operation of integrated electricity and heat system: A review of modeling and solution methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. Wu, Yunyun & Fang, Jiakun & Ai, Xiaomeng & Xue, Xizhen & Cui, Shichang & Chen, Xia & Wen, Jinyu, 2023. "Robust co-planning of AC/DC transmission network and energy storage considering uncertainty of renewable energy," Applied Energy, Elsevier, vol. 339(C).
    5. Xu, Fangyuan & Wu, Wanli & Zhao, Fei & Zhou, Ya & Wang, Yongjian & Wu, Runji & Zhang, Tao & Wen, Yongchen & Fan, Yiliang & Jiang, Shengli, 2019. "A micro-market module design for university demand-side management using self-crossover genetic algorithms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    6. Zhang, Menglin & Wu, Qiuwei & Wen, Jinyu & Zhou, Bo & Guan, Qinyue & Tan, Jin & Lin, Zhongwei & Fang, Fang, 2022. "Day-ahead stochastic scheduling of integrated electricity and heat system considering reserve provision by large-scale heat pumps," Applied Energy, Elsevier, vol. 307(C).
    7. Turk, Ana & Wu, Qiuwei & Zhang, Menglin & Østergaard, Jacob, 2020. "Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing," Energy, Elsevier, vol. 196(C).
    8. Wei Zhang & Ruoyao Liu & Xinyu Yang, 2019. "Study on Operating Strategy of Electric–Gas Combined System Considering the Improvement of Dispatchability," Energies, MDPI, vol. 12(23), pages 1-24, December.
    9. Tan, Jin & Wu, Qiuwei & Hu, Qinran & Wei, Wei & Liu, Feng, 2020. "Adaptive robust energy and reserve co-optimization of integrated electricity and heating system considering wind uncertainty," Applied Energy, Elsevier, vol. 260(C).
    10. Zhou, Bo & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2019. "Data-adaptive robust unit commitment in the hybrid AC/DC power system," Applied Energy, Elsevier, vol. 254(C).
    11. Hlalele, Thabo G. & Zhang, Jiangfeng & Naidoo, Raj M. & Bansal, Ramesh C., 2021. "Multi-objective economic dispatch with residential demand response programme under renewable obligation," Energy, Elsevier, vol. 218(C).
    12. Tan, Jin & Wu, Qiuwei & Wei, Wei & Liu, Feng & Li, Canbing & Zhou, Bin, 2020. "Decentralized robust energy and reserve Co-optimization for multiple integrated electricity and heating systems," Energy, Elsevier, vol. 205(C).

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