Distributed Energy Trading: The Multiple-Microgrid Case

Gregoratti, David; Matamoros, Javier

doi:10.1109/tie.2014.2352592

Cited by 293 publications

(182 citation statements)

References 29 publications

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“…First, we have incorporated the CHP and DR into the model, which makes the optimal scheduling model more realistic with respect to the practical applications. Second, the proposed algorithm has shown a better convergence performance as compared with the algorithm proposed in [9]. Finally, the optimal operation cost obtained by Algorithm 1 is almost equal to the centralized method, which is shown in Table 7.…”

Section: Comparison With the Related Workmentioning

confidence: 84%

“…Besides, sharing information of load Fig. 9 Iterative process comparison of price in MG1 between this study and that in [9] and sources can lead to serious privacy and business information leakage, since MGs may belong to different business owners. For [7], all the expected exchange power of MGs should be shared with each other in the IMS.…”

Section: Comparison With the Related Workmentioning

confidence: 90%

“…For simplicity, we assume that the cost in all connection topologies of IMS is the same. According to [9], the cost of network tariff can be modeled as a cubic polynomial.…”

Section: Network Costmentioning

confidence: 99%

“…Only the expected exchanging power information needs to be shared among all the MGs during the iterative process to minimize the total operation cost. Similarily, a distributed convex optimization framework is developed for energy trading among islanded MGs in [9] and [10] with the objective of minimizing the total operation cost.…”

Section: Introductionmentioning

confidence: 99%

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Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Liu

Jie

Wang

2017

J. Mod. Power Syst. Clean Energy

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From the perspective of transactive energy, the energy trading among interconnected microgrids (MGs) is promising to improve the economy and reliability of system operations. In this paper, a distributed energy management method for interconnected operations of combined heat and power (CHP)-based MGs with demand response (DR) is proposed. First, the system model of operational cost including CHP, DR, renewable distributed sources, and diesel generation is introduced, where the DR is modeled as a virtual generation unit. Second, the optimal scheduling model is decentralized as several distributed scheduling models in accordance with the number of associated MGs. Moreover, a distributed iterative algorithm based on subgradient with dynamic search direction is proposed. During the iterative process, the information exchange between neighboring MGs is limited to Lagrange multipliers and expected purchasing energy. Finally, numerical results are given for an interconnected MGs system consisting of three MGs, and the effectiveness of the proposed method is verified.

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Section: Comparison With the Related Workmentioning

confidence: 84%

Section: Comparison With the Related Workmentioning

confidence: 90%

“…For simplicity, we assume that the cost in all connection topologies of IMS is the same. According to [9], the cost of network tariff can be modeled as a cubic polynomial.…”

Section: Network Costmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Liu

Jie

Wang

2017

J. Mod. Power Syst. Clean Energy

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“…An approach for cooperative power control in a network of interconnected microgrids is proposed in [5]. A problem for energy trading among islanded microgrids is formulated in [6]. A problem for power management in a network composed of a macrogrid and cooperative minigrids is investigated in [7].…”

Section: Introductionmentioning

confidence: 99%

Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities

Chis

Koivunen

2019

IEEE Trans. Smart Grid

112

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Abstract-In this paper we propose two novel coalitional game theory based optimization methods for minimizing the cost of electricity consumed by households from a smart community. Some households in the community may own renewable energy sources (RESs) conjoined with energy storage systems (ESSs). Some other residences own ESSs only, while the remaining households are simple energy consumers. We first propose a coalitional cost optimization method in which RESs and ESSs owners exchange energy and share their renewable energy and storage spaces. We show that by participating in the proposed game these households may considerably reduce their costs in comparison to performing individual cost optimization. We further propose another coalitional optimization model in which RESs and ESSs owning households not only share their resources, but also sell energy to simple energy consuming households. We show that through this energy trade the RESs and ESSs owners can further reduce their costs, while the simple energy consumers also gain cost savings. The cost savings obtained by the coalition are distributed among its members according to the Shapley value. Simulation examples show that the proposed coalitional optimization methods may reduce the electricity costs for the RESs and ESSs owning households by 18%, while the sole energy consumers may reduce their costs by 3%.Index Terms-Coalitional game, demand side management, smart households, renewable energy, energy storage, cost reduction. NOMENCLATURE Abbreviations DSM Demand side management ESS Energy storage system RES Renewable energy sourceSets, cardinalities and indicesOptimization period, number of time-slots, and their index N , N, n Households in the community, their number, and index M, M, m Households owning RESs and/or ESSs (grand coalition), their number, and index P, P, p Sole energy consuming households, their number, and index G, G, gHouseholds forming a coalition, their number, and indexWe acknowledge the financial support received from KAUTE Foundation for the research reported in this article.The Cost incurred by household m, g in periodPayoff assigned to a player m by Shapley value This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TSG.2017.2784902, IEEE Transactions on Smart Grid 2 a n,m,p (t) Amount of energy exchanged by household n, m, p with other households in time-slot t b n,m,p (t) Amount of energy bought by household n, m, p from utility company in time-slot t

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A distribution‐market based game‐theoretical model for the coordinated operation of multiple microgrids in active distribution networks

Zhao

Ban

et al. 2019

Int Trans Electr Energ Syst

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With an increasing number of microgrids being integrated into active distribution networks (ADN), it becomes a challenge how to efficiently coordinate the operations of multiple microgrids (MGs) which are individual entities seeking for their self‐interests. In this paper, a bi‐level noncooperative game‐theoretical model is proposed for MGs operation in ADN based on the distribution system operator (DSO) market and distribution locational marginal prices (DLMPs). The upper level is modeled as a game among MGs who develop their optimal strategies to bid into the DSO market aiming at maximizing their own payoffs. In the lower‐level, the DSO of the ADN implements the market‐clearing and calculates the DLMPs. An iterative solving algorithm is proposed to find the Nash equilibrium of the game model. The case studies show that through the proposed model, the coordinated operation of MGs is achieved while satisfying the security constraints of the ADN.

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Distributed Energy Trading: The Multiple-Microgrid Case

Cited by 293 publications

References 29 publications

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Distributed energy management for interconnected operation of combined heat and power-based microgrids with demand response

Coalitional Game-Based Cost Optimization of Energy Portfolio in Smart Grid Communities

A distribution‐market based game‐theoretical model for the coordinated operation of multiple microgrids in active distribution networks

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