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Techno-economic performance analysis of synergistic energy sharing strategies for grid-connected prosumers with distributed battery storages

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  • Zheng, Siqian
  • Huang, Gongsheng
  • Lai, Alvin CK.

Abstract

Renewable energy planning, electrochemical storages, and prosumer-based energy management and sharing in smart grids are critical elements for the transition towards carbon neutrality, whereas the synergistic effects between distributed renewables and storages have not been sufficiently investigated regarding spatiotemporal compensation between energy supply and demand. Moreover, the economic benefits of individual prosumers need to be quantified to motivate their participation in the sharing scheme. In this study, a peer-to-peer (P2P) energy sharing paradigm was formulated, consisting of hybrid solar-wind renewable systems, battery storages, grid-connected commercial prosumers (a high-rise office building and a high-rise hotel), and flexible energy management. Different sharing scenarios with/without storage sharing were studied for performance improvements. Case studies show that, compared to the traditional peer-to-grid operation, the proposed P2P sharing operation with storage sharing can promote the self-consumption from 0.591 to 0.795 and reduce the net cost from 80.4 to 52.7 HKD/m2·a by 34.5%. The formulated energy sharing strategies can also contribute to energy bill savings for each prosumer when considering the profit from transactive energy and grid feed-in tariff, whereas the storage sharing may shorten the battery service life. The research can provide synergistic sharing strategies, energy planning, and economic policy guidelines to achieve carbon-neutral communities.

Suggested Citation

  • Zheng, Siqian & Huang, Gongsheng & Lai, Alvin CK., 2021. "Techno-economic performance analysis of synergistic energy sharing strategies for grid-connected prosumers with distributed battery storages," Renewable Energy, Elsevier, vol. 178(C), pages 1261-1278.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1261-1278
    DOI: 10.1016/j.renene.2021.06.100
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    3. Zhou, Yuekuan, 2023. "A dynamic self-learning grid-responsive strategy for battery sharing economy—multi-objective optimisation and posteriori multi-criteria decision making," Energy, Elsevier, vol. 266(C).
    4. Herenčić, Lin & Kirac, Mislav & Keko, Hrvoje & Kuzle, Igor & Rajšl, Ivan, 2022. "Automated energy sharing in MV and LV distribution grids within an energy community: A case for Croatian city of Križevci with a hybrid renewable system," Renewable Energy, Elsevier, vol. 191(C), pages 176-194.
    5. Zhou, Yuekuan, 2022. "A regression learner-based approach for battery cycling ageing prediction―advances in energy management strategy and techno-economic analysis," Energy, Elsevier, vol. 256(C).
    6. Chen, Xi & Liu, Zhongbing & Wang, Pengcheng & Li, Benjia & Liu, Ruimiao & Zhang, Ling & Zhao, Chengliang & Luo, Songqin, 2023. "Multi-objective optimization of battery capacity of grid-connected PV-BESS system in hybrid building energy sharing community considering time-of-use tariff," Applied Energy, Elsevier, vol. 350(C).
    7. Zhou, Yuekuan, 2022. "Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    8. Sima, Catalina Alexandra & Popescu, Claudia Laurenta & Popescu, Mihai Octavian & Roscia, Mariacristina & Seritan, George & Panait, Cornel, 2022. "Techno-economic assessment of university energy communities with on/off microgrid," Renewable Energy, Elsevier, vol. 193(C), pages 538-553.
    9. Heilmann, Jakob & Wensaas, Marthe & Crespo del Granado, Pedro & Hashemipour, Naser, 2022. "Trading algorithms to represent the wholesale market of energy communities in Norway and England," Renewable Energy, Elsevier, vol. 200(C), pages 1426-1437.
    10. Han, Ouzhu & Ding, Tao & Zhang, Xiaosheng & Mu, Chenggang & He, Xinran & Zhang, Hongji & Jia, Wenhao & Ma, Zhoujun, 2023. "A shared energy storage business model for data center clusters considering renewable energy uncertainties," Renewable Energy, Elsevier, vol. 202(C), pages 1273-1290.
    11. Zhou, Yuekuan, 2022. "Energy sharing and trading on a novel spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area," Applied Energy, Elsevier, vol. 318(C).
    12. Zheng, Siqian & Jin, Xin & Huang, Gongsheng & Lai, Alvin CK., 2022. "Coordination of commercial prosumers with distributed demand-side flexibility in energy sharing and management system," Energy, Elsevier, vol. 248(C).

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