On Hierarchical Power Scheduling for the Macrogrid and Cooperative Microgrids

Wang, Yu; Mao, Shiwen; Nelms, R.M.

doi:10.1109/tii.2015.2417496

Cited by 134 publications

(77 citation statements)

References 27 publications

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“…This will further reduce the need for large energy storage systems and provide cost savings for the prosumers participating in the cooperation. In that regard, energy trading through cooperation among microgrids [24,[90][91][92][93][94][95] can be motivated through Nash bargaining theory [27]. Furthermore, Ref.…”

Section: Classification Based On Cooperative Game Theorymentioning

confidence: 99%

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

et al. 2017

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Abstract:Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation.

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Section: Classification Based On Cooperative Game Theorymentioning

confidence: 99%

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

et al. 2017

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“…Content may change prior to final publication. Citation information: DOI 10.1109/TSG.2017.2784902, IEEE Transactions on Smart Grid 7 A coalitional game in characteristic form [21] is uniquely defined by the pair (M, υ). M represents the set of players participating in the game, |M| = M , while υ : 2 M → R is the characteristic function of the game.…”

Section: The Coalitional Game Model For Householdsmentioning

confidence: 99%

“…Different problems involving energy trading and cooperation among microgrids equipped with RESs and ESSs have been studied in [1]- [7]. Methods based on Nash bargaining theory to incentivize the collaboration between microgrids are proposed in [1] and [2].…”

Section: Introductionmentioning

confidence: 99%

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

Chis

Koivunen

2019

IEEE Trans. Smart Grid

120

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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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“…In this way, a micogrid uses a point of common coupling to connect itself to the larger macrogrid, regarded as the distributed infrastructure interconnecting the microgrids [7]. Plus, microgrids are deemed as limited in power and size when compared to macrogrids, due to the fact that the latter typically mount distributed energy resources (DERs) as small photovoltaic panels (1-10 kW) and microturbines (25-100 kW) [8]. However, a microgrid is still larger than a nanogrid, which can be described as "a single domain for voltage, reliability, and administration.…”

Section: Generic Challenges Smart Grid Challenges Devices Registrationmentioning

confidence: 99%

Development of Middleware Applied to Microgrids by Means of an Open Source Enterprise Service Bus

et al. 2017

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Abstract:The success of the smart grid relies heavily on the integration of Distributed Energy Resources (DERs) and interoperability among the hardware elements that are present as part of either the smart grid itself or in a smaller size deployment, such as a microgrid. Therefore, establishing an accurate design for software architectures that guarantee interoperability and are able to abstract hardware heterogeneity in this application domain, along with a clearly defined procedure on how to implement and test a solution like this, becomes a desirable objective. This paper describes the requirements needed to design a secure, decentralized and semantic middleware architecture for microgrids and the procedures used to develop it, so that the mandatory software components that have to be encased by the solution, as well as the steps that should be followed to make it happen, become clear for any designer, software architect or programmer that has to tackle similar challenges. In order to demonstrate the usability of the ideas put forward here, two successful pilots where middleware solutions were created according to these principles have been described.

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On Hierarchical Power Scheduling for the Macrogrid and Cooperative Microgrids

Cited by 134 publications

References 27 publications

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

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

Development of Middleware Applied to Microgrids by Means of an Open Source Enterprise Service Bus

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