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Multi-objective battery sizing optimisation for renewable energy communities with distribution-level constraints: A prosumer-driven perspective

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  • Secchi, Mattia
  • Barchi, Grazia
  • Macii, David
  • Moser, David
  • Petri, Dario

Abstract

A Renewable Energy Community (REC) is a legal entity aggregating different users sharing their own resources to reduce both electricity bills and CO2 emissions. This paper presents and analyses the impact of a bi-objective strategy to optimise the capacity of the Battery Energy Storage Systems (BESSs) of REC prosumers equipped with photovoltaic (PV) generators. The optimisation problem is solved through a custom implementation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and has two contrasting objectives: maximising the self-sufficiency of the REC from the main grid, while minimising the BESS capacity of all REC members. A key novelty of this study is the prosumer-driven perspective, which allows to exclude the REC members who do not want to install a BESS through a linear optimisation constraint. Moreover, the proposed approach ensures that probabilities of over- or under-voltages are compliant with the limits specified by Distribution System Operators (DSOs). Such probabilities, as well as the line and BESS losses, are estimated within the optimisation loop through grid-level simulations performed in OpenDSS. Both a standard peer-to-grid (P2G) and a more REC-oriented peer-to-peer (P2P) energy sharing policy are analysed and their performance is assessed in different seasons and considering both the current energy demand and a possible future scenario, in which electrical heat pumps are widely used. The results of a case study based on a modified version of the IEEE 906-bus European Low Voltage distribution grid show that a if the total BESS capacity assigned to all REC prosumers exceeds a given threshold value, the benefits for the REC become minor. Assuming to choose the optimal BESS capacity solutions corresponding to the threshold value in the summer season (i.e., when PV and BESSs are most exploited), the overall energy losses are reduced roughly by 20%–40% for both P2G and P2P battery controls. The CO2 emissions instead, are reduced by 10% to 50% with the P2P policy having a slight edge over the P2G one. The P2P energy sharing policy spreads the economic benefits of energy savings more evenly among REC members, and the return on investment is generally higher if the electricity demand increases.

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  • Secchi, Mattia & Barchi, Grazia & Macii, David & Moser, David & Petri, Dario, 2021. "Multi-objective battery sizing optimisation for renewable energy communities with distribution-level constraints: A prosumer-driven perspective," Applied Energy, Elsevier, vol. 297(C).
  • Handle: RePEc:eee:appene:v:297:y:2021:i:c:s0306261921006024
    DOI: 10.1016/j.apenergy.2021.117171
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    1. Rodrigues, Daniel L. & Ye, Xianming & Xia, Xiaohua & Zhu, Bing, 2020. "Battery energy storage sizing optimisation for different ownership structures in a peer-to-peer energy sharing community," Applied Energy, Elsevier, vol. 262(C).
    2. Prina, Matteo Giacomo & Lionetti, Matteo & Manzolini, Giampaolo & Sparber, Wolfram & Moser, David, 2019. "Transition pathways optimization methodology through EnergyPLAN software for long-term energy planning," Applied Energy, Elsevier, vol. 235(C), pages 356-368.
    3. Andrea Mazza & Hamidreza Mirtaheri & Gianfranco Chicco & Angela Russo & Maurizio Fantino, 2019. "Location and Sizing of Battery Energy Storage Units in Low Voltage Distribution Networks," Energies, MDPI, vol. 13(1), pages 1-20, December.
    4. Wang, Jianxiao & Zhong, Haiwang & Wu, Chenye & Du, Ershun & Xia, Qing & Kang, Chongqing, 2019. "Incentivizing distributed energy resource aggregation in energy and capacity markets: An energy sharing scheme and mechanism design," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    5. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
    6. Hector Beltran & Pablo Ayuso & Emilio Pérez, 2020. "Lifetime Expectancy of Li-Ion Batteries used for Residential Solar Storage," Energies, MDPI, vol. 13(3), pages 1-18, January.
    7. Daghi, Majid & Sedghi, Mahdi & Ahmadian, Ali & Aliakbar-Golkar, Masoud, 2016. "Factor analysis based optimal storage planning in active distribution network considering different battery technologies," Applied Energy, Elsevier, vol. 183(C), pages 456-469.
    8. Saboori, Hedayat & Hemmati, Reza & Jirdehi, Mehdi Ahmadi, 2015. "Reliability improvement in radial electrical distribution network by optimal planning of energy storage systems," Energy, Elsevier, vol. 93(P2), pages 2299-2312.
    9. Sardi, Junainah & Mithulananthan, N. & Hung, Duong Quoc, 2017. "Strategic allocation of community energy storage in a residential system with rooftop PV units," Applied Energy, Elsevier, vol. 206(C), pages 159-171.
    10. Panos, Evangelos & Densing, Martin, 2019. "The future developments of the electricity prices in view of the implementation of the Paris Agreements: Will the current trends prevail, or a reversal is ahead?," Energy Economics, Elsevier, vol. 84(C).
    11. Long, Chao & Wu, Jianzhong & Zhou, Yue & Jenkins, Nick, 2018. "Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid," Applied Energy, Elsevier, vol. 226(C), pages 261-276.
    12. Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
    13. Müller, Simon C. & Welpe, Isabell M., 2018. "Sharing electricity storage at the community level: An empirical analysis of potential business models and barriers," Energy Policy, Elsevier, vol. 118(C), pages 492-503.
    14. Alessandro Pitì & Giacomo Verticale & Cristina Rottondi & Antonio Capone & Luca Lo Schiavo, 2017. "The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case," Energies, MDPI, vol. 10(2), pages 1-25, February.
    15. Quoilin, Sylvain & Kavvadias, Konstantinos & Mercier, Arnaud & Pappone, Irene & Zucker, Andreas, 2016. "Quantifying self-consumption linked to solar home battery systems: Statistical analysis and economic assessment," Applied Energy, Elsevier, vol. 182(C), pages 58-67.
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    2. Berg, Kjersti & Rana, Rubi & Farahmand, Hossein, 2023. "Quantifying the benefits of shared battery in a DSO-energy community cooperation," Applied Energy, Elsevier, vol. 343(C).
    3. Mattia Pasqui & Lorenzo Becchi & Marco Bindi & Matteo Intravaia & Francesco Grasso & Gianluigi Fioriti & Carlo Carcasci, 2024. "Community Battery for Collective Self-Consumption and Energy Arbitrage: Independence Growth vs. Investment Cost-Effectiveness," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    4. Pu, Yuchen & Li, Qi & Zou, Xueli & Li, Ruirui & Li, Luoyi & Chen, Weirong & Liu, Hong, 2021. "Optimal sizing for an integrated energy system considering degradation and seasonal hydrogen storage," Applied Energy, Elsevier, vol. 302(C).
    5. 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).
    6. Song, Hui & Gu, Mingchen & Liu, Chen & Amani, Ali Moradi & Jalili, Mahdi & Meegahapola, Lasantha & Yu, Xinghuo & Dickeson, George, 2023. "Multi-objective battery energy storage optimization for virtual power plant applications," Applied Energy, Elsevier, vol. 352(C).
    7. Felice, Alex & Rakocevic, Lucija & Peeters, Leen & Messagie, Maarten & Coosemans, Thierry & Ramirez Camargo, Luis, 2022. "Renewable energy communities: Do they have a business case in Flanders?," Applied Energy, Elsevier, vol. 322(C).
    8. Maria Alessandra Ancona & Francesco Baldi & Lisa Branchini & Andrea De Pascale & Federico Gianaroli & Francesco Melino & Mattia Ricci, 2022. "Comparative Analysis of Renewable Energy Community Designs for District Heating Networks: Case Study of Corticella (Italy)," Energies, MDPI, vol. 15(14), pages 1-18, July.
    9. Elisa Veronese & Luca Lauton & Grazia Barchi & Alessandro Prada & Vincenzo Trovato, 2024. "Impact of Non-Residential Users on the Energy Performance of Renewable Energy Communities Considering Clusterization of Consumptions," Energies, MDPI, vol. 17(16), pages 1-18, August.
    10. Smolenski, Robert & Szczesniak, Pawel & Drozdz, Wojciech & Kasperski, Lukasz, 2022. "Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).

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