Decentralized Control of the Power Flows in a Network of Smart Microgrids Modeled as a Team of Cooperative Agents

Dagdougui, Hanane; Sacile, Roberto

doi:10.1109/tcst.2013.2261071

Cited by 90 publications

(61 citation statements)

References 23 publications

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“…The authors proposed a smart network of MGs with an optimal topology to enhance the RESs exploitation. In [7], the control approach aims at keeping the storage level in each MG around a reference value by exchanging power in a network of MGs. The proposed model is defined according to a linear quadratic formulation, which can allow an effective computation of the optimal solution, even for networks with a great number of MGs.…”

Section: Introductionmentioning

confidence: 99%

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

Ouammi¹,

Dagdougui

Dessaint

et al. 2015

IEEE Trans. Smart Grid

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163

107

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In this paper, a model predictive control (MPC) for the optimal power exchanges in a smart network of power microgrids (MGs) is presented. The main purpose is to present an innovative control strategy for a cluster of interconnected MGs to maximize the global benefits. A MPC-based algorithm is used to determine the scheduling of power exchanges among MGs, and the charge/discharge of each local storage system. The MPC algorithm requires information on power prices, power generation, and load forecasts. The MPC algorithm is tested through case studies with and without prediction errors on loads and renewable power production. The operation of single MGs is simulated to show the advantage of the proposed cooperative framework relative to the control of a single MG. The results demonstrate that the cooperation among MGs has significant advantages and benefits with respect to each single MG operation

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Section: Introductionmentioning

confidence: 99%

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

Ouammi¹,

Dagdougui

Dessaint

et al. 2015

IEEE Trans. Smart Grid

Self Cite

163

107

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show abstract

“…Proof of Lemma 1 Proof: We prove (18) in this section, while (19) can be proved in the same way. Rewrite the first update equation in (17) and sum up from t = 1 to T .…”

Section: Appendixmentioning

confidence: 97%

“…In [18], the authors present a decentralized control strategy modeling the MGs as a team of cooperative agents to minimize the costs of energy storage and the power exchanged among the MGs. The authors of [19] propose a game theoretic coalition to optimally reduce the total power losses in a MGs power system with power storage devices, and demonstrate the overhead of communications.…”

mentioning

confidence: 99%

On Hierarchical Power Scheduling for the Macrogrid and Cooperative Microgrids

Wang

Mao

Nelms

2015

IEEE Trans. Ind. Inf.

131

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Although considerable advances have been made in single Microgrid (MG) systems, the problem of cooperation among MGs and the Macrogrid has attracted considerable interest only recently. As in wireless communications systems, exploiting the temporal, spatial, and technological diversities in multiple cooperative MGs could bring about more efficient power generation and distribution. This paper investigates a hierarchical power scheduling approach to optimally manage power trading, storage and distribution in a smart power grid with a Macrogrid and cooperative MGs. We first formulate the problem as a convex optimization problem and then decompose it into a two-tier formulation: the first-tier problem jointly considers user utility, transmission cost, and grid load variance, while the secondtier problem minimizes the power generation and transmission cost and exploits distributed storage in the MGs. We develop an effective online algorithm to solve the first-tier problem and prove its asymptotic optimality, as well as a distributed optimal algorithm for solving the second-tier problem. The proposed algorithms are evaluated with trace-driven simulations and are shown to outperform several existing schemes with considerable gains.

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“…Multi-agent systems have been also used as optimization algorithm in Smart Grids grids [19]. Particularly in [20], smart microgrids have been modelled as a team of cooperative agents, where each microgrid can exchange power with other microgrids and the main grid. In [21], it is proposed the IEC/ISO 62264 standard for adapting the hierarchical control and energy storage in microgrids in different applications based on multiagent systems, such as market participation and control in virtual power plants.…”

Section: Introductionmentioning

confidence: 99%

A Robot-Soccer-Coordination Inspired Control Architecture Applied to Islanded Microgrids

Díaz

Guarnizo

Mellado

et al. 2017

IEEE Trans. Power Electron.

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Decentralized Control of the Power Flows in a Network of Smart Microgrids Modeled as a Team of Cooperative Agents

Cited by 90 publications

References 23 publications

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

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

On Hierarchical Power Scheduling for the Macrogrid and Cooperative Microgrids

A Robot-Soccer-Coordination Inspired Control Architecture Applied to Islanded Microgrids

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