Coordinated Model Predictive-Based Power Flows Control in a Cooperative Network of Smart Microgrids

Ouammi, Ahmed; Dagdougui, Hanane; Dessaint, L.-A.; Sacile, Roberto

doi:10.1109/tsg.2015.2396294

Cited by 165 publications

(111 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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“…Alessandra Parisio et al [53,54] implemented a mixed integer linear programming (MILP), a model predictive control (MPC) approach of optimization in microgrid operation. MPC -MILP control economically optimizes the microgrid operation and computational burden by using commercial solvers without decomposition techniques.…”

Section: Optimization Techniques In Microgridmentioning

confidence: 99%

A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation

Praiselin¹,

Edward²

2018

IJISA

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Abstract-Due to the global demand for energy saving and reduction of greenhouse gas emissions, utilization of renewable energy sources have increased in electricity networks. The negative aspects of this technology are very complex and not well known which affect reliability and robustness of the grids. Microgrids based on renewable energy sources have gained significant popularity, due to the major benefits it has to offer for solving the increasing energy demand. Harmonic distortion in microgrids caused by the non-linear loads is an essential topic of study necessary for the better understanding of power quality impacts in microgrids. The various control techniques utilized to curtail the power quality impacts on micro grids are reviewed in this paper. Also, Optimization based control techniques utilized for power quality improvement in microgrids is discussed in this review.

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“…In [4], prospect theory has been proposed to formulate a static game between autonomous microgrids that are trading energy. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Chis

Koivunen

2019

IEEE Trans. Smart Grid

119

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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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Coordinated Model Predictive-Based Power Flows Control in a Cooperative Network of Smart Microgrids

Cited by 165 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

A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation

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

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