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Two-stage coordinated optimal dispatching model and benefit allocation strategy for rural new energy microgrid

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Listed:
  • Yang, Shenbo
  • Fang, Jiangpeng
  • Zhang, Zheyu
  • Lv, ShuoShuo
  • Lin, Hongyu
  • Ju, Liwei

Abstract

To aggregate rural biomass energy, distributed power supply, flexibility load, and other resources, a novel structure of the rural Biomass-derived Fuel -based new energy microgrid (BDF-NEM) is proposed. It includes the Biomass Waste Conversion System (BWS), Distributed Renewable Energy (DRE), and Flexible Load Cluster (FLC). The two-stage scheduling optimization framework is designed for the BDF-NEM, minimizing operating costs in the Day-ahead phase and system deviation costs in the Intra-day phase. Uncertainty analysis of wind power plant (WPP) and photovoltaic generators (PV) is conducted using Latin hypercube sampling and the Kantorovich distance approach. To guarantee the equitable allocation of benefits, a benefit allocation mechanism grounded in the enhanced Sharp value approach takes into account operational risks. The analysis of a rural area in northern China demonstrates: (1) After BDF - NEM joined the tiered carbon trading mechanism, although it increased carbon trading costs by 31.27 %, wind power and photovoltaic output increased by 36.08 %, improving new energy consumption; (2) The two-stage scheduling optimization model with daily plan and daily correction can effectively alleviate the deviation caused by wind and solar uncertainty. Although the adjustment cost is 3966.41 yuan, it can avoid the penalty of load loss; (3) The improved benefit allocation based on the Shapley value method reflects the marginal contribution of each system module at the risk level, and when the system modules cooperate with each other, the return is 18.17 % higher than when operating alone, effectively stimulating the enthusiasm to participate in transactions. In summary, the BDF NEM proposed in this article can improve the utilization rate of wind and solar energy, promote the low-carbon development of rural power plants, and achieve local resource allocation in rural areas.

Suggested Citation

  • Yang, Shenbo & Fang, Jiangpeng & Zhang, Zheyu & Lv, ShuoShuo & Lin, Hongyu & Ju, Liwei, 2024. "Two-stage coordinated optimal dispatching model and benefit allocation strategy for rural new energy microgrid," Energy, Elsevier, vol. 292(C).
  • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224000458
    DOI: 10.1016/j.energy.2024.130274
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    1. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    2. Fang, Fang & Yu, Songyuan & Liu, Mingxi, 2020. "An improved Shapley value-based profit allocation method for CHP-VPP," Energy, Elsevier, vol. 213(C).
    3. Zhang, Lihui & Li, Songrui & Nie, Qingyun & Hu, Yitang, 2022. "A two-stage benefit optimization and multi-participant benefit-sharing strategy for hybrid renewable energy systems in rural areas under carbon trading," Renewable Energy, Elsevier, vol. 189(C), pages 744-761.
    4. Tajeddini, Mohammad Amin & Rahimi-Kian, Ashkan & Soroudi, Alireza, 2014. "Risk averse optimal operation of a virtual power plant using two stage stochastic programming," Energy, Elsevier, vol. 73(C), pages 958-967.
    5. Zahid Ullah & Arshad & Hany Hassanin & James Cugley & Mohammed Al Alawi, 2022. "Planning, Operation, and Design of Market-Based Virtual Power Plant Considering Uncertainty," Energies, MDPI, vol. 15(19), pages 1-16, October.
    6. Kong, Xiangyu & Xiao, Jie & Liu, Dehong & Wu, Jianzhong & Wang, Chengshan & Shen, Yu, 2020. "Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties," Applied Energy, Elsevier, vol. 279(C).
    7. Chi, Yuan-ying & Zhao, Hao & Hu, Yu & Yuan, Yong-ke & Pang, Yue-xia, 2022. "The impact of allocation methods on carbon emission trading under electricity marketization reform in China: A system dynamics analysis," Energy, Elsevier, vol. 259(C).
    8. Ali Ahmadian & Kumaraswamy Ponnambalam & Ali Almansoori & Ali Elkamel, 2023. "Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning," Energies, MDPI, vol. 16(2), pages 1-17, January.
    9. Zhang, Tianhan & Qiu, Weiqiang & Zhang, Zhi & Lin, Zhenzhi & Ding, Yi & Wang, Yiting & Wang, Lianfang & Yang, Li, 2023. "Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets," Applied Energy, Elsevier, vol. 329(C).
    10. Yang, Hanyu & Dou, Xun & Pan, Feng & Wu, Qiuwei & Li, Canbing & Zhou, Bin & Hao, Lili, 2022. "Optimal planning of local biomass-based integrated energy system considering anaerobic co-digestion," Applied Energy, Elsevier, vol. 316(C).
    11. Sun, Qie & Fu, Yu & Lin, Haiyang & Wennersten, Ronald, 2022. "A novel integrated stochastic programming-information gap decision theory (IGDT) approach for optimization of integrated energy systems (IESs) with multiple uncertainties," Applied Energy, Elsevier, vol. 314(C).
    12. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    13. Fusco, Andrea & Gioffrè, Domenico & Francesco Castelli, Alessandro & Bovo, Cristian & Martelli, Emanuele, 2023. "A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets," Applied Energy, Elsevier, vol. 336(C).
    14. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
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