Optimal Control of Power Flows and Energy Local Storages in a Network of Microgrids Modeled as a System of Systems

Ouammi, Ahmed; Dagdougui, Hanane; Sacile, Roberto

doi:10.1109/tcst.2014.2314474

Cited by 91 publications

(40 citation statements)

References 37 publications

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“…It also represents the most frequent starting point of stochastic analysis, simulation, and forecasting of wind speed. Its general formulation is represented as follows [15]:…”

Section: B Wind Power Generationmentioning

confidence: 99%

“…The predicted powers sold and purchased to/from the DNO are constrained by upper and lower bounds u g,s,min < u g,s (i, t + k) < u g,s,max (15) u g,p,min < u g,p (i, t + k) < u g,p,max .…”

Section: ) State Equations and Constraintsmentioning

confidence: 99%

“…Ouammi et al [15] studied the optimal energy management in a network of MGs based on the use of the original linear quadratic Gaussian problem. The proposed problem is a formulation that may be taken into account as a preliminary attempt to model and control the exchange of information and power in a network of MGs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

160

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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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“…It also represents the most frequent starting point of stochastic analysis, simulation, and forecasting of wind speed. Its general formulation is represented as follows [15]:…”

Section: B Wind Power Generationmentioning

confidence: 99%

“…The predicted powers sold and purchased to/from the DNO are constrained by upper and lower bounds u g,s,min < u g,s (i, t + k) < u g,s,max (15) u g,p,min < u g,p (i, t + k) < u g,p,max .…”

Section: ) State Equations and Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

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

160

View full text Add to dashboard Cite

show abstract

“…where ↵ x , ↵ and ↵ µ are positive scalars controlling the amount of change in the gradient direction and Proj µ (·) is a projection operator that ensures non-negativity of µ j , using bounds µ, µ, and a boundary-layer thickness ✏ (see (24) for details). The application of this method to the optimization problem corresponding to (16) and (17) forms the DMM.…”

Section: Market Negotiations Of the Dmmmentioning

confidence: 99%

“…Our framework also presupposes that suitable aggregation of various types of demands is feasible (see for example, [19]- [24]), which are for the most part noncooperative [16]. This paper has been organized as follows: In Section II we present a brief introduction to electricity markets.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Market Mechanism for the Integration of Renewables and Demand Response

Knudsen

Hansen

Annaswamy

2016

IEEE Trans. Contr. Syst. Technol.

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Abstract-The most formidable challenge in assembling a Smart Grid is the integration of a high penetration of renewables. Demand Response, a largely promising concept, is increasingly discussed as a means to cope with the intermittent and uncertain renewables. In this paper, we propose a dynamic market mechanism that reaches the market equilibrium through continuous negotiations between key market players. In addition to incorporating renewables, this market mechanism also incorporates a quantitative taxonomy of demand response devices, based on the inherent magnitude, run-time, and integral constraints of demands. The dynamic market mechanism is evaluated on an IEEE 118 Bus system, a high fidelity simulation model of the Midwestern United States power grid. The results show how the proposed mechanism can be utilized to determine combinations of demand response devices in the presence of intermittent and uncertain renewables with varying levels of penetration so as to result in a desired level of Social Welfare.

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Optimal Planning and Performance Assessment of Multi‐Microgrid Systems in Future Smart Cities

Wang

Liu

et al. 2018

Transportation and Power Grid in Smart Cities

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Optimal Control of Power Flows and Energy Local Storages in a Network of Microgrids Modeled as a System of Systems

Cited by 91 publications

References 37 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

A Dynamic Market Mechanism for the Integration of Renewables and Demand Response

Optimal Planning and Performance Assessment of Multi‐Microgrid Systems in Future Smart Cities

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