Optimization of Feed-in Tariff mechanism for residential and industrial photovoltaic adoption in Hong Kong

Zhang, Ruixiaoxiao; Lee, Minhyun

doi:10.1016/j.jclepro.2023.137043

Cited by 3 publications

(1 citation statement)

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“…In the pricing methods for energy sharing among prosumers, the feed‐in tariff encouraged the widespread integration of renewable energy sources by offering financial incentives to prosumers 30 . However, prosumers could not flexibly determine their electricity prices under the feed‐in tariff, which causes their benefits not to be maximized.…”

Section: Introductionmentioning

confidence: 99%

Game‐based distributed energy‐sharing model for prosumers in microgrids considering carbon emissions and its fast equilibrium‐finding method

Zheng,

Li,

Yuan

2024

Energy Science & Engineering

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In recent years, energy sharing has attracted a lot of attention. However, the intermediate platforms in centralized energy‐sharing methods cause the rapid growth of communication complexity and the risk of privacy leakage. Unlike complex energy‐sharing market mechanisms, in this paper, a simple and efficient distributed energy sharing for microgrids (MGs) is proposed, where game theory is employed to form flexible prices for prosumers and only local information is used to improve the privacy protection of prosumers. First, prosumers located in different areas are characterized more precisely and a two‐tier carbon emission cost model is built. Next, a game‐theory‐based distributed energy‐sharing model is proposed, where a flexible pricing mechanism is developed to enable prosumers to independently reach price agreements and achieve supply–demand balance within MGs. In the process, optimization models for obtaining equilibrium are formed and only local information is needed. However, solving these optimization models is generally time‐consuming. So, a distributed optimization method based on weighted subgradients is proposed to accelerate the equilibrium‐finding process. Finally, four cases are designed, and simulation results demonstrate that the prosumers' costs of our method are reduced by 7.5%–22.5% compared to the costs obtained by feed‐in tariff. Moreover, in the case of solving the distributed trading model for an MG at a 24‐h time scale, the iteration numbers of our method are only 38.9% and 49.3% of the two traditional solving methods.

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Section: Introductionmentioning

confidence: 99%