Distributed Cooperation Optimization of Multi-Microgrids Under Grid Tariff Uncertainty: A Nash Bargaining Game Approach with Cheating Behaviors

Du, Jianan; Han, Xiaoqing; Wang, Jinning

doi:10.2139/ssrn.4415335

Cited by 1 publication

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“…C CEES,om represents the daily operational and maintenance expenses for the cloud storage system; C CEES represents the daily leasing costs for the CES system. The same principles apply to the cloud thermal storage system and will not be elaborated on here (Du et al, 2022). The carbon trading costs are described in Section 3.1.1.…”

Section: The Objective Function For the Upper-level Decision-makermentioning

confidence: 99%

Master–slave game operation scheduling strategy of an integrated energy system considering the uncertainty of wind and solar output

Zhang,

Shen

2024

Front. Energy Res.

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Introduction: With the development of the energy market and the gradual rise of emerging market players, the linkage of interests between energy sources and loads in the Integrated Energy System (IES) has become increasingly complex. Additionally, the reliability of the system has been impacted by the growing proportion of renewable energy output.Methods: To address the challenges posed by the above issues. This paper first proposes an operational strategy for an integrated energy system that incorporates the uncertainty of wind and solar output using a master-slave game approach. To enhance system robustness and cost-effectiveness, the paper introduces the information gap decision theory (IGDT). Second, building on this foundation, the system operator is considered as the leader, adding a tiered carbon trading mechanism and cloud energy storage system, and building a system revenue maximization model. Then, the user is regarded as the follower, and an optimization model is developed based on integrated demand response (IDR). Finally, the two-layer model is converted into a mixed-integer linear programming problem (MILP) to be solved by the Karush-Kuhn-Tucker conditions (KKT) combined with the big M method.Results: The analysis of the example shows that according to the difference of the decision maker’s attitude towards risk, different scheduling schemes can be obtained through the two perspectives of risk-seeking and risk-avoiding, which can provide guidance for the dynamic operation of the system, and at the same time, the users can be guided by the energy differentials to reasonably use the energy under this strategy.Discussion: Therefore, the proposed strategy in this paper can balance the economy and robustness of the system.

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Section: The Objective Function For the Upper-level Decision-makermentioning

confidence: 99%

Master–slave game operation scheduling strategy of an integrated energy system considering the uncertainty of wind and solar output

Zhang,

Shen

2024

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

Distributed Cooperation Optimization of Multi-Microgrids Under Grid Tariff Uncertainty: A Nash Bargaining Game Approach with Cheating Behaviors

Cited by 1 publication

References 0 publications

Master–slave game operation scheduling strategy of an integrated energy system considering the uncertainty of wind and solar output

Master–slave game operation scheduling strategy of an integrated energy system considering the uncertainty of wind and solar output

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