Real-Time Implementation of Multiagent-Based Game Theory Reverse Auction Model for Microgrid Market Operation

Cintuglu, Mehmet H.; Martin, Harold L.; Mohammed, Osama A.

doi:10.1109/tsg.2014.2387215

Cited by 157 publications

(66 citation statements)

References 15 publications

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“…Likewise, the form of control to be adopted to route information among the actors, whether distributed or central control, needs to be harmonized. Finally, the operational mechanism and operation optimization (for instance, game theory method [33]) must be defined.…”

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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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confidence: 99%

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

et al. 2017

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“…There are mainly two concepts that can be applied to form a multiagent platform. Some multiagent systems in microgrids are based on the market concept to maximize the benefits of generation units by analyzing the effects and evaluation of different auction algorithms [16][17][18][19]. This is also consistent with the market scenarios in a large power system.…”

Section: Introductionmentioning

confidence: 71%

“…One is based on centralized control [9][10][11][12][13], while the other is based on distributed control [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. In order to dispatch the power optimally, in [9][10][11][12], the authors use a centralized controller to make the decisions for generating the optimal power generation commands based on the generation cost.…”

Section: Introductionmentioning

confidence: 99%

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Operation Cost Minimization of Droop-Controlled AC Microgrids Using Multiagent-Based Distributed Control

Savaghebi

Guerrero

et al. 2016

Energies

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Abstract:Recently, microgrids are attracting increasing research interest as promising technologies to integrate renewable energy resources into the distribution system. Although many works have been done on droop control applied to microgrids, they mainly focus on achieving proportional power sharing based on the power rating of the power converters. With various primary source for the distributed generator (DG), factors that are closely related to the operation cost, such as fuel cost of the generators and losses should be taken into account in order to improve the efficiency of the whole system. In this paper, a multiagent-based distributed method is proposed to minimize the operation cost in AC microgrids. In the microgrid, each DG is acting as an agent which regulates the power individually using a novel power regulation method based on frequency scheduling. An optimal power command is obtained through carefully designed consensus algorithm by using sparse communication links only among neighbouring agents. Experimental results for different cases verified that the proposed control strategy can effectively reduce the operation cost.

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“…In [16], a hybrid stochastic/robust optimization model is proposed to render a bidding strategy for a microgrid (MG) to participate in the dayahead market. At the real-time stage, a multiagent-based gametheoretic reverse auction model is presented in [17] for MG market operations. Considering the uncertainties of market prices, the loss payment is minimized in [18] by scheduling DR and energy storage system (ESS).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Trading Strategies of Proactive DISCO with Heterogeneous DG Owners

Zhang¹,

Wang

et al. 2016

IEEE Trans. Smart Grid

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Abstract-This paper presents a methodology to obtain the optimal trading strategies between the proactive distribution company (PDISCO), heterogeneous distributed generation owners (DGOs) and wholesale market in a real-time trading framework. In this framework, the PDISCO's decisions cover the power procurements from DGOs and the transactions within the real-time market. A one-leader multi-follower-type bilevel model is proposed to embody the PDISCO-DGO gaming structure. The upper-level (UL) problem is to maximize the PDISCO's profit, while the lower-level (LL) problem indicates the profit maximization per DGO. Since the UL problem is non-linear and non-convex and the LL problems are linear and convex, we reformulate the proposed model to a solvable mathematical program with equilibrium constraints (MPEC) by an equivalent primal-dual approach. The numerical results of the case studies show the effectiveness and scalability of the proposed model. Mapping of the set of DGs and SDs onto the set of DGOs. NOMENCLATURE Sets and IndicesBidding price and generation quantity of respective Type1,2,3 DGOs at time t .Active and reactive power generated by respective Type1,2,3 DGOs at time t for scenario ω. P Reactive power supplied by the shunt compensator at bus i at time t for scenario ω. P f t,ij,ω , Q f t,ij,ω Active and reactive power flows through feeder ij at time t for scenario ω. e tiω , δ tiω Voltage magnitude and phase angle at bus i at time t for scenario ω. Profit guarantee factor of the DGO of Type1,2,3 respectively at time t.

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Real-Time Implementation of Multiagent-Based Game Theory Reverse Auction Model for Microgrid Market Operation

Cited by 157 publications

References 15 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

Operation Cost Minimization of Droop-Controlled AC Microgrids Using Multiagent-Based Distributed Control

Real-Time Trading Strategies of Proactive DISCO with Heterogeneous DG Owners

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