Research on day-ahead transactions between multi-microgrid based on cooperative game model

Chen, Weidong; Wang, Junnan; Yu, Guanyi; Chen, Jiajia; Hu, Yumeng

doi:10.1016/j.apenergy.2022.119106

Cited by 54 publications

(16 citation statements)

References 40 publications

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“…• Each buyer with non-zero bid energy establishes its payoff preference list by calculating its expected payoff from trading with each seller with an ask price lower than its bid price using (24), sorts the expected payoffs in decreasing order. Compare the expected payoffs with the most preferred seller m * , i.e., the seller in its own first order, with the expected payoff from acquiring the same amount of energy, g acq n0 (t) = min(g bid n (t), g r,es nm * (t)) from the utility company via the DS, which is given by…”

Section: Pair Matching Algorithmmentioning

confidence: 99%

“…To incorporate the optimal energy management problem of MGs into the P2P energy trading framework design, hierarchical approaches have been proposed, where the local energy management problem in the lower level is integrated with the high-level P2P trading problem [20]- [24]. Shortterm optimization models are developed to determine dayahead optimal energy bidding and scheduling decisions in the lower level.…”

Section: Introductionmentioning

confidence: 99%

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Transmission Loss-Aware Peer-to-Peer Energy Trading in Networked Microgrids

2022

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Networked microgrids (MGs) have a great potential to improve the efficiency, reliability, resilience, security, and sustainability of power supply services. Peer-to-peer (P2P) energy trading built on a smart information system in networked MGs is an emerging economic approach to facilitate energy sharing among networked MGs to achieve mutual cost-effective operation and improve the reliability and stability of energy supply service. Such a distributed and competitive energy trading market urges the need for an efficient energy trading strategy that incentivizes the self-interested MGs with various energy production and consumption profiles to participate in energy trading. In this paper, we propose a distributed real-time P2P energy trading strategy that integrates energy trading into energy management and enables the MGs with renewable energy sources (RESs) and energy storage systems (ESSs) to manage their storage scheduling, energy supply, and energy trading in a dynamic manner, jointly considering the randomness of renewable energy generation and load demand, operational constraints of ESSs and transmission losses associated with energy exchange. The proposed energy control and bidding algorithm allows each MG to dynamically and independently determine its energy control actions and price-quantity bids/offers, while the proposed trading pair matching algorithm matches the MGs on a many-to-many basis with respect to their individual payoffs, which couple price-quantity bids/offers of the MGs with distance-dependent energy transmission losses associated with energy exchange. Numerical simulation results demonstrate that the proposed distributed energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while reducing energy transmission losses within the system.

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Section: Pair Matching Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transmission Loss-Aware Peer-to-Peer Energy Trading in Networked Microgrids

2022

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“…This method effectively addresses privacy issues and achieves a balance of interests for each MG subject by considering direct interaction between participants. The game theory mainly includes noncooperative game [18,19] and cooperative game [20][21][22][23][24][25][26][27][28]. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…Noncooperative games can maximise the interests of each agent, but it cannot guarantee the existence and uniqueness of Nash equilibrium solution, so it usually leads to non-social and non-Pareto optimal. Cooperative game is usually modelled as alliance game [20][21][22] or Nash bargaining [23][24][25][26][27][28]. In ref.…”

Section: Introductionmentioning

confidence: 99%

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Distributed robust operation strategy of multi‐microgrid based on peer‐to‐peer multi‐energy trading

Gao

Shao

Chen

et al. 2023

IET Energy Syst Integration

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In microgrid (MG) systems, traditional centralised energy trading models can lead to issues such as low energy efficiency due to unstable energy supply and lack of flexibility. Peer‐to‐peer (P2P) trading models have been widely used due to their advantages in promoting the sustainable development of renewable energy and reducing energy trading costs. However, P2P multi‐energy trading requires mutual agreements between two microgrids (MGs), and the uncertainties of renewable energy and load affects energy supply security. To address these issues, this article proposed a distributed robust operation strategy based on P2P multi‐energy trading for multi‐microgrid (MMG) systems. Firstly, a two‐stage robust optimisation (TRO) method was adopted to consider the uncertainties of P2P multi‐energy trading between MGs, which reduced the conservatism of robust optimisation (RO). Secondly, a TRO model for P2P multi‐energy trading among MGs was established based on the Nash bargaining theory, where each MG negotiates with others based on their energy contributions in the cooperation. Additionally, a distributed algorithm was used to protect the privacy of each MG. Finally, the simulation results based on three MGs showed that the proposed approach can achieve a fair distribution of cooperative interests and effectively promote cooperation among MGs.

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