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Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks

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  • Janko, Samantha
  • Johnson, Nathan G.

Abstract

The integration of renewables and microgrids into the modern electric grid has forced financial, technical, and policy change. Control strategies that enable energy trading between microgrids provide more effective use of distributed energy resources. This study presents a decentralized, autonomous control approach to manage energy transactions between nodes of a grid-connected microgrid network. Agents in the network form relationships, with interactions between agents described by quantifying their reputation using historical knowledge of familiarity, acceptance, and value between nodes. Methods are demonstrated on a network of 9 nodes with varying levels of network connectivity for a simulated year. Results indicate that certain relationships between nodes allow some microgrids to achieve greater financial benefit than others through a reduction in operating cost. A baseline case with no trading is used to compare results, with nodes experiencing anywhere from 3% to 72% reduction in their annual cost of energy depending on network connectivity and configuration. Node pair connections with the most opportunities to trade had a significant effect on the amount of excess renewables successfully traded in the network, and network configurations containing those pairs also resulted in the lowest grid load factors. Enabling localized trading led to a decrease in utility revenue by 21–27%, which could be partly recouped by establishing wheeling charges or trading fees.

Suggested Citation

  • Janko, Samantha & Johnson, Nathan G., 2020. "Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks," Applied Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:appene:v:277:y:2020:i:c:s0306261920311077
    DOI: 10.1016/j.apenergy.2020.115598
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    References listed on IDEAS

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    6. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).
    7. Pascual, Julio & Arcos-Aviles, Diego & Ursúa, Alfredo & Sanchis, Pablo & Marroyo, Luis, 2021. "Energy management for an electro-thermal renewable–based residential microgrid with energy balance forecasting and demand side management," Applied Energy, Elsevier, vol. 295(C).
    8. Qiang Li & Feng Zhao & Li Zhuang & Qiulin Wang & Chenzhou Wu, 2023. "Steady-State Risk Prediction Analysis of Power System Based on Power Digital Twinning," Sustainability, MDPI, vol. 15(3), pages 1-20, January.

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