Stackelberg equilibrium-based energy management strategy for regional integrated electricity–hydrogen market

Wang, Qinghan; Wang, Yanbo; Chen, Zhe

doi:10.3389/fenrg.2023.1169089

Cited by 2 publications

(1 citation statement)

References 37 publications

(38 reference statements)

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“…Li et al (2022) proposed a hierarchical, partitioned approach to optimize multi-energy supply and demand in integrated energy systems in the framework of a master-slave game. Wang et al (2023) develops an optimal energy bidding mechanism for the regional integrated electricity-hydrogen system considering complex electricity-hydrogen energy flow and further presents an electricity-hydrogen optimization management strategy based on Stackelberg game. Although the above studies have considered the game-based interactions among the community energy operator, the aggregator of residential customers, the energy storage operator, and other participants within the CIES, they have mostly focused on the collaborative optimization of such sources of energy such as electricity, heat, and cooling, and have not adequately addressed the joint optimization of coupling devices for heterogeneous sources of energy, such as electric heating and electric cooling on the side of the customer in the residential community.…”

Section: Introductionmentioning

confidence: 99%

Master–slave game-based optimal scheduling of community-integrated energy system by considering incentives for peak-shaving and ladder-type carbon trading

Dai,

Jiang,

Chen

et al. 2023

Front. Energy Res.

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To alleviate the challenges posed by high energy consumption, significant carbon emissions, and conflicting interests among multiple parties in a community-level microgrid, the authors of this study propose a master–slave game-based optimal scheduling strategy for a community-integrated energy system (CIES). First, we analyze the decision variables and revenue-related objectives of each stakeholder in the CIES, and use the results to construct a framework of implementation. Second, we develop a model to incentivize peak regulation and a ladder-type carbon trading model that consider the correlation between the load owing to residential consumers, the load on the regional grid, and the sources of carbon emissions. Third, we propose a master–slave game-based mechanism of interaction and a decision-making model for each party to the game, and show that it has a Stackelberg equilibrium solution by combining genetic algorithms and quadratic programming. The results of evaluations showed that compared with an optimization strategy that considers only the master–slave game, the proposed strategy increased the consumption surplus of the user aggregator by 13.65%, the revenue of the community energy operator by 7.95%, increased the revenue of the energy storage operator, reduced CO2 emissions by 6.10%, and adequately responded to peak-cutting and valley-filling by the power grid company.

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Section: Introductionmentioning

confidence: 99%