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A Quasi-Feed-In-Tariff policy formulation in micro-grids: A bi-level multi-period approach

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  • Taha, Ahmad F.
  • Hachem, Nadim A.
  • Panchal, Jitesh H.

Abstract

A Quasi-Feed-In-Tariff (QFIT) policy formulation is presented for micro-grids that integrates renewable energy generation considering Policy Makers׳ and Generation Companies׳ (GENCOs) objectives assuming a bi-level multi-period formulation that integrates physical characteristics of the power-grid. The upper-level problem corresponds to the PM, whereas the lower-level decisions are made by GENCOs. We consider that some GENCOs are green energy producers, while others are black energy producers. Policy makers incentivize green energy producers to generate energy through the payment of optimal time-varying subsidy price. The policy maker׳s main objective is to maximize an overall social welfare that includes factors such as demand surplus, energy cost, renewable energy subsidy price, and environmental standards. The lower-level problem corresponding to the GENCOs is based on maximizing the players׳ profits. The proposed QFIT policy differs from the FIT policy in the sense that the subsidy price-based contracts offered to green energy producers dynamically change over time, depending on the physical properties of the grid, demand, and energy price fluctuations. The integrated problem solves for time-varying subsidy price and equilibrium energy quantities that optimize the system welfare under different grid and system conditions.

Suggested Citation

  • Taha, Ahmad F. & Hachem, Nadim A. & Panchal, Jitesh H., 2014. "A Quasi-Feed-In-Tariff policy formulation in micro-grids: A bi-level multi-period approach," Energy Policy, Elsevier, vol. 71(C), pages 63-75.
    • Handle: RePEc:eee:enepol:v:71:y:2014:i:c:p:63-75
    DOI: 10.1016/j.enpol.2014.04.014
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    References listed on IDEAS

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    2. Zhen, J.L. & Huang, G.H. & Li, W. & Liu, Z.P. & Wu, C.B., 2017. "An inexact optimization model for regional electric system steady operation management considering integrated renewable resources," Energy, Elsevier, vol. 135(C), pages 195-209.
    3. Chen, Yizhong & He, Li & Li, Jing & Cheng, Xi & Lu, Hongwei, 2016. "An inexact bi-level simulation–optimization model for conjunctive regional renewable energy planning and air pollution control for electric power generation systems," Applied Energy, Elsevier, vol. 183(C), pages 969-983.
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    7. Nouicer, Athir & Meeus, Leonardo & Delarue, Erik, 2023. "Demand-side flexibility in distribution grids: Voluntary versus mandatory contracting," Energy Policy, Elsevier, vol. 173(C).

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