Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach

Shuai, Xuanyue; Wang, Xiuli; Xiong, Wei; Wang, Yifei; Song, Zhenzi; Wang, Bangyan; Ma, Zhicheng

doi:10.1016/j.ijepes.2021.107892

Cited by 31 publications

(9 citation statements)

References 24 publications

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“…It was emphasized in [59] that more research into models that control the coordinated operation of many microgrids is necessary in order to provide a reference framework for such a cooperative. The goal of this research was to act as a reference framework; thus, the authors established an operational architecture of the system, which allowed for the development of a model for the cooperative operation of several microgrids.…”

Section: Related Work On Money Flow In Istmgsmentioning

confidence: 99%

Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches

Machele,

Onumanyi,

Abu-Mahfouz

et al. 2024

JSAN

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The deployment of isolated microgrids has witnessed exponential growth globally, especially in the light of prevailing challenges faced by many larger power grids. However, these isolated microgrids remain separate entities, thus limiting their potential to significantly impact and improve the stability, efficiency, and reliability of the broader electrical power system. Thus, to address this gap, the concept of interconnected smart transactive microgrids (ISTMGs) has arisen, facilitating the interconnection of these isolated microgrids, each with its unique attributes aimed at enhancing the performance of the broader power grid system. Furthermore, ISTMGs are expected to create more robust and resilient energy networks that enable innovative and efficient mechanisms for energy trading and sharing between individual microgrids and the centralized power grid. This paradigm shift has sparked a surge in research aimed at developing effective ISTMG networks and mechanisms. Thus, in this paper, we present a review of the current state-of-the-art in ISTMGs with a focus on energy trading, energy management systems (EMS), and optimization techniques for effective energy management in ISTMGs. We discuss various types of trading, architectures, platforms, and stakeholders involved in ISTMGs. We proceed to elucidate the suitable applications of EMS within such ISTMG frameworks, emphasizing its utility in various domains. This includes an examination of optimization tools and methodologies for deploying EMS in ISTMGs. Subsequently, we conduct an analysis of current techniques and their constraints, and delineate prospects for future research to advance the establishment and utilization of ISTMGs.

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Section: Related Work On Money Flow In Istmgsmentioning

confidence: 99%

Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches

Machele,

Onumanyi,

Abu-Mahfouz

et al. 2024

JSAN

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“…Gas boilers (GB) consume natural gas, liquefied gas, and others as the fuel to produce heat and are currently the most common heating equipment. Referring to research (Xuanyue et al, 2022), the thermal power output of a gas boiler can be modeled as in ( 6) with natural gas being the fuel:…”

Section: Gas Boilermentioning

confidence: 99%

“…Xuan et al (Xuan et al, 2021) measured the potential risk loss using CVaR in the operation scenarios of the multi-energy microgrid. In terms of operating strategies, an operation coordination model of the multiple multi-energy microgrids is proposed by Xuanyue et al (Xuanyue et al, 2022), which took active and passive demand responses and CVaR into consideration. Based on CVaR, Li et al (Li et al, 2023) explored energy trading methods of grid-tied multi-energy microgrids participating in the market, which provides a novel view in dealing with uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

Yang²,

Liang³

et al. 2023

Front. Energy Res.

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The uncertainty caused by the growing use of renewable energy sources, such as wind and solar energy, makes it difficult to forecast the operation costs of micro-energy systems, particularly those in remote rural areas. Motivated by this point, this paper analyzes the possible operational risks and then introduces Condition Value at Risk (CVaR) to quantify the cost of the operational risk. On this basis, stochastic programming based on a multi-energy microgrid planning model that minimizes the investment cost, the operating cost, and the cost of operational risk, while considering the physical limitations of the multi-energy microgrid, is presented. Especially, scenarios of wind and solar energy output are generated using the Latin hypercube sampling method and reduced using the crowding measure-based scenario reduction method. After piecewise linearization and second-order cone relaxation, the model proposed in this paper is processed as a mixed integer linear model and solved by CPLEX. According to the achieved typical scenarios processed by the reduction method, the simulation shows that the presented configuration model can balance the investment cost and the cost of the operational risk, which effectively enhances the system’s ability to cope with uncertainties and fluctuations. Moreover, by adjusting the risk preference coefficient, the conservativeness of the planning scheme can be correspondingly adjusted.

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“…Given the current objectives of achieving carbon peaking and carbon neutrality, environmental concerns compel the energy management department to continuously elevate carbon emission standards for power systems. Hence, the obtained optimization results in [12][13][14][15][16][17] may exacerbate environmental degradation and violate the carbon emission policies set by the local government.…”

Section: Introductionmentioning

confidence: 99%

“…A tri‐layer non‐cooperative energy trading model among multiple grid‐tied MEMGs in the restructured integrated energy market was developed to simulate market competition behavior [15]. Furthermore, the advanced generalized Nash bargaining [16] and asymmetric Nash bargaining models [17] were investigated and applied to assign benefits impartially, ensuring the willingness of individual MEMGs to participate in the alliance. However, the issues addressed in the aforementioned studies were discussed solely from an economic perspective.…”

Section: Introductionmentioning

confidence: 99%

Distributed optimization for joint peer‐to‐peer electricity and carbon trading among multi‐energy microgrids considering renewable generation uncertainty

Hou,

Wang,

Zhao

et al. 2024

Energy Conversion and Econom

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The increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low‐carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer‐to‐peer (P2P) electricity and carbon trading in multi‐energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value‐at‐risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision‐making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.

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Peer-to-peer multi-energy distributed trading for interconnected microgrids: A general Nash bargaining approach

Cited by 31 publications

References 24 publications

Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches

Interconnected Smart Transactive Microgrids—A Survey on Trading, Energy Management Systems, and Optimisation Approaches

A multi-energy microgrid configuration method in remote rural areas considering the condition value at risk

Distributed optimization for joint peer‐to‐peer electricity and carbon trading among multi‐energy microgrids considering renewable generation uncertainty

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