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The benefits of sharing in off-grid microgrids: A case study in the Philippines

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  • Prevedello, Giulio
  • Werth, Annette

Abstract

Microgrids are a key technology in granting universal access to affordable energy. To compare the efficacy of these systems, a quantitative approach is required to evaluate implemented solutions and energy sharing benefits from interconnected units. In this paper, real data from an off-grid microgrid in the Philippines were analyzed and used for simulating different sharing scenarios. The microgrid consisted of 62 solar home system units arranged in 10 clusters, each unit paying a daily fee for a given allowance of energy usage. An agent-based model was built from the parameters and topology of this microgrid, closely approximating recorded energy flows and enabling the simulation of different sharing schemes. Key performance indicators were then introduced to evaluate the actual sharing performances, against scenarios with no interconnections (standalone) and with all units connected (ideal). As the microgrid panels and batteries were over-dimensioned to sustain streaks of bad weather days, sharing seemed to provide moderate benefits. A further analysis on the unit types showed that peer-to-peer exchange promoted economic development, since productive units, which consumed more energy, could exploit energy excesses that would otherwise be curtailed. To improve microgrid performance, two solutions were proposed: the diversification of energy sources; the addition of flexible load. The latter was validated by simulations, showing that the setup analyzed could sustain a supplemental flexible load of about 250 W, that could power communal appliances or agro-processing machines, increasing the grid utility by 10%. These results confirmed that additional grid heterogeneity enhances the benefits of energy sharing.

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  • Prevedello, Giulio & Werth, Annette, 2021. "The benefits of sharing in off-grid microgrids: A case study in the Philippines," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921009764
    DOI: 10.1016/j.apenergy.2021.117605
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    1. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    2. Syed Rizvi & Ahmed Abu-Siada, 2023. "A Review on Active-Power-Sharing Techniques for Microgrids," Energies, MDPI, vol. 16(13), pages 1-17, July.
    3. Sheridan, Steve & Sunderland, Keith & Courtney, Jane, 2023. "Swarm electrification: A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).

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