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Fair electricity transfer price and unit capacity selection for microgrids


  • Zhang, Di
  • Samsatli, Nouri J.
  • Hawkes, Adam D.
  • Brett, Dan J.L.
  • Shah, Nilay
  • Papageorgiou, Lazaros G.


Microgrids are defined as an area of electricity distribution network that can operate autonomously from the rest of the network. In order to achieve the best economic outcomes, the participants in a microgrid can benefit from cooperation in microgrid design and operation. In this paper, a mathematical programming formulation is presented for fair, optimised cost distribution amongst participants in a general microgrid. The proposed formulation is based on the Game-theory Nash bargaining solution approach for finding optimal multi-partner cost levels subject to given upper bounds on the equivalent annual costs. The microgrid planning problem concerning the fair electricity transfer price and unit capacity selection is first formulated as a mixed integer non-linear programming model. Then, a separable programming approach is applied to reform the resulting mixed integer non-linear programming model to a mixed integer linear programming form. The model is applied to a case study with a microgrid involving five participants.

Suggested Citation

  • Zhang, Di & Samsatli, Nouri J. & Hawkes, Adam D. & Brett, Dan J.L. & Shah, Nilay & Papageorgiou, Lazaros G., 2013. "Fair electricity transfer price and unit capacity selection for microgrids," Energy Economics, Elsevier, vol. 36(C), pages 581-593.
  • Handle: RePEc:eee:eneeco:v:36:y:2013:i:c:p:581-593
    DOI: 10.1016/j.eneco.2012.11.005

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    References listed on IDEAS

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    Cited by:

    1. Gamarra, Carlos & Guerrero, Josep M. & Montero, Eduardo, 2016. "A knowledge discovery in databases approach for industrial microgrid planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 615-630.
    2. Menon, Ramanunni P. & Paolone, Mario & Maréchal, François, 2013. "Study of optimal design of polygeneration systems in optimal control strategies," Energy, Elsevier, vol. 55(C), pages 134-141.
    3. Zhang, Di & Evangelisti, Sara & Lettieri, Paola & Papageorgiou, Lazaros G., 2015. "Optimal design of CHP-based microgrids: Multiobjective optimisation and life cycle assessment," Energy, Elsevier, vol. 85(C), pages 181-193.
    4. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing & Lao, Changshi, 2017. "Profit allocation analysis among the distributed energy network participants based on Game-theory," Energy, Elsevier, vol. 118(C), pages 783-794.
    5. repec:eee:appene:v:210:y:2018:i:c:p:748-763 is not listed on IDEAS
    6. repec:eee:appene:v:203:y:2017:i:c:p:972-981 is not listed on IDEAS
    7. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Ren, Jianxing, 2017. "Benefit allocation for distributed energy network participants applying game theory based solutions," Energy, Elsevier, vol. 119(C), pages 384-391.

    More about this item


    Microgrid; Electricity transfer pricing; Game theory; Mixed integer optimisation;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C71 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Cooperative Games
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling


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