Cooperative Optimization of Networked Microgrids for Supporting Grid Flexibility Services Using Model Predictive Control

García-Torres, Félix; Báez-González, Pablo; Tobajas, Javier; Vázquez, Francisco; Nieto, Emilio

doi:10.1109/tsg.2020.3043821

Cited by 17 publications

(14 citation statements)

References 19 publications

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“…The complex mathematical optimization problem has been converted into mixed integer linear programming using the strong duality theorem. A model predictive control technique has been adopted in [11] for flexibility provision from grid side using cooperative optimization framework. In [12] a clustering approach for utilizing the flexibility from demand-side has been investigated.…”

Section: Ummentioning

confidence: 99%

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Exploitation of Microgrid Flexibility in Distribution System Hosting Prosumers

MansourLakouraj

Sanjari

Javadi

et al. 2021

IEEE Trans. on Ind. Applicat.

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Increasing the penetration of renewables in prosumers' side brings about operational challenges in the distribution grid due to their variable and uncertain behavior. In fact, these resources have increased the distribution grid net load fluctuation during recent years. In this paper, the flexibilityoriented stochastic scheduling of a microgrid is suggested to capture the net load variability at the distribution grid level. In this scheduling, the flexibility limits are set to manage the net load fluctuation at a desirable level for the main grid operator. The uncertainties of load and renewables are considered, and their uncertainties are under control by the risk-averse strategy. Moreover, multi-period islanding constraints are added to the problem, preparing the microgrid for a resilient response to disturbances. The model is examined on a typical distribution feeder consisting of prosumers and a microgrid. The numerical results are compared for both flexibility-oriented and traditional scheduling of a microgrid in the distribution level. The proposed model reduces the net load ramping of the distribution grid using an efficient dispatch of resources in the microgrid. A sensitivity analysis is also carried out to show the effectiveness of the model.

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Section: Ummentioning

confidence: 99%

“…Constraint (11) guarantees that the unintentional load shedding should be less than the total load in MG.…”

Section: V V Tmentioning

confidence: 99%

Exploitation of Microgrid Flexibility in Distribution System Hosting Prosumers

MansourLakouraj

Sanjari

Javadi

et al. 2021

IEEE Trans. on Ind. Applicat.

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“…T HE increase in the integration of renewable-based generation also increases the flexibility needs of system operators [1]. To avoid the operation of expensive and fossil-fuel based generators for the sole purpose of containing infrequent high-peak periods, new flexibility sources are urgently needed.…”

Section: Introductionmentioning

confidence: 99%

Scenario-Based Stochastic Optimization for Energy and Flexibility Dispatch of a Microgrid

Antoniadou-Plytaria

Steen

Tuấn

et al. 2022

IEEE Trans. Smart Grid

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“…Historically, researchers and companies tried to design the best modelling and control techniques for these large grids and one of the most applied methods was Model Predictive Controls (MPC) [5], [6], [7]. This kind of controller tries to reach an optimal solution considering the current state of the system and the prediction of its near future behaviour [8].…”

Section: Introductionmentioning

confidence: 99%

Generic Framework for the Optimal Implementation of Flexibility Mechanisms in Large-Scale Smart Grids

Real¹,

Pastor²,

Durán³

2021

Preprint

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This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (Model Predictive Control) framework explicitly including a generic flexibility mechanism which is easy to particularise to specific strategies such as Demand Response, Flexible Production and Energy Efficiency Services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case which aims to further clarify the use of the framework and thus, to ease its adoption by other researchers in their specific flexibility mechanisms applications.

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Cooperative Optimization of Networked Microgrids for Supporting Grid Flexibility Services Using Model Predictive Control

Cited by 17 publications

References 19 publications

Exploitation of Microgrid Flexibility in Distribution System Hosting Prosumers

Exploitation of Microgrid Flexibility in Distribution System Hosting Prosumers

Scenario-Based Stochastic Optimization for Energy and Flexibility Dispatch of a Microgrid

Generic Framework for the Optimal Implementation of Flexibility Mechanisms in Large-Scale Smart Grids

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