Robust Data Predictive Control Framework for Smart Multi-Microgrid Energy Dispatch Considering Electricity Market Uncertainty

Brahmia, Ibrahim; Wang, Jingcheng; Xu, Haotian; Wang, Hongyuan; Turci, Luca de Oliveira

doi:10.1109/access.2021.3060315

Cited by 22 publications

(13 citation statements)

References 46 publications

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“…Meanwhile, for the stable and economic operation of MMG, a multi-microgrid energy management system is required to manage and coordinate dispatchable distributed generators, controllable loads, and energy trading among networked MGs. The operation objectives of MMG can be summarized as follows: 1) to minimize the overall operation cost of MMG in the grid-connected mode [17] and islanded mode [18]; 2) to maximize the distributed renewable energy penetration with the influence of uncertainties from various DREs, loads and electricity price [19][20][21]; 3) to minimize energy transmission loss from energy exchanges with the main-grid and among networked MGs [22]; 4) to maximize the economic goals of different subjects by designing multimicrogrids cluster architecture and its energy transaction mechanism [23]; 5) to minimize the communication delay between MMGs and improve the security and reliability of communication system [24].…”

Section: Figure1 the Global Microgrid Market Size (2021-2027)mentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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Microgrid (MG) is one of the most effective solution to integrate distributed renewable energy into power system. However, modern MG has several technical challenges such as migration to multi vector energy system, increasing renewable energy penetration, and the uncertainties that arise from the large-scale incorporation of renewable energy. Recent development of multi-microgrid (MMG) could potentially mitigate these challenges. MMG has obvious advantages in improving the system stability, reliability, economy, and energy efficiency of power grid operation through autonomous management and coordinated control among networked MGs. In the meantime, uncertainties from many elements and operators in the MMG system also pose huge challenges in modeling. This paper will present and review typical architecture of MMG, including physical layer, information layer and application layer. Moreover, this paper will critically review and analyze challenges in uncertainty modelling and solution in MMGs. Finally, the paper will also discuss future research areas and development trends of MMG.

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Section: Figure1 the Global Microgrid Market Size (2021-2027)mentioning

confidence: 99%

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Zhang

2022

IEEE Access

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“…The coupling of microgrids may be fully connected, which has a complex infrastructure and is limited to a small number of systems, or partially associated, which is more practical with many subsystems. The former provides complete information sharing with neighbors, whereas the latter is limited to a subset of others [24], [25]. Communication among subsystems may be serial or parallel.…”

Section: A Distributed Mpc Approaches In Microgrid Clustermentioning

confidence: 99%

Model Predictive Control Strategies in Microgrids: A Concise Revisit

et al. 2022

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The world is rapidly integrating renewable energy resources into the existing grid systems. However, the unpredictable nature of renewables and uncertain load profiles cause issues such as poor power quality, lower system reliability, complex power management, battery degradation, high operating costs, and lower efficiency. Microgrids can help smart grid technology overcome several problems associated with renewable energy integration. Distant locations can obtain electricity without building extensive transmission infrastructure, cutting development costs, or transmission losses. The intermittent nature of renewable energy sources contributes to microgrid problems such as poor power quality, decreased reliability, and high operating costs. Model predictive control (MPC) is an effective method to address challenging industrial and scientific issues. Advancements in MPC that accept different system constraints have solved multiple concerns in uncertain microgrid systems. MPC applied to three hierarchal control layers in a microgrid resolves the problems of power quality, power sharing, energy management, and economic optimization. This study demonstrates that MPC microgrid control is suitable for low-cost operation, improved management, and reliable control. The shortcomings of recent model predictive control techniques for microgrids are reviewed, and future research directions for MPC microgrids are identified.INDEX TERMS Microgrids, renewable energy resources, model predictive control, power quality enhancement, energy management system, hybrid energy storage system, demand side management, demand response, distributed systems

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“…Modelling the relationship between independent and dependent data (regression) is well practised in fields including (but not limited to) medicine [52][53][54], cyber security [55], finance [56], scheduling [57,58], vehicle routing [59], data classification [60] and multiobjective optimisation [61,62]. The task of regression-the prediction of this relationshipcan be performed via any one of a variety of methods, including heuristics [55,59], natureinspired algorithms [52,56,57,60-62] and non-linear statistical models [53,54,58].…”

Section: Advanced Regression Models and Artificial Intelligencementioning

confidence: 99%

Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks

Dixon

Koukoura

Brand

et al. 2021

Future Transportation

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Predicting car ownership patterns at high spatial resolution is key to understanding pathways for decarbonisation—via electrification and demand reduction—of the private vehicle fleet. As the factors widely understood to influence car ownership are highly interdependent, linearised regression models, which dominate previous work on spatially explicit car ownership modelling in the UK, have shortcomings in accurately predicting the relationship. This paper presents predictions of spatially disaggregated car ownership—and change in car ownership over time—in Great Britain (GB) using deep neural networks (NNs) with hyperparameter tuning. The inputs to the models are demographic, socio-economic and geographic datasets compiled at the level of Census Lower Super Output Areas (LSOAs)—areas covering between 300 and 600 households. It was found that when optimal hyperparameters are selected, these neural networks can predict car ownership with a mean absolute error of up to 29% lower than when formulating the same problem as a linear regression; the results from NN regression are also shown to outperform three other artificial intelligence (AI)-based methods: random forest, stochastic gradient descent and support vector regression. The methods presented in this paper could enhance the capability of transport/energy modelling frameworks in predicting the spatial distribution of vehicle fleets, particularly as demographics, socio-economics and the built environment—such as public transport availability and the provision of local amenities—evolve over time. A particularly relevant contribution of this method is that by coupling it with a technology dissipation model, it could be used to explore the possible effects of changing policy, behaviour and socio-economics on uptake pathways for electric vehicles —cited as a vital technology for meeting Net Zero greenhouse gas emissions by 2050.

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Robust Data Predictive Control Framework for Smart Multi-Microgrid Energy Dispatch Considering Electricity Market Uncertainty

Cited by 22 publications

References 46 publications

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

A Review on Optimal Energy Management of Multimicrogrid System Considering Uncertainties

Model Predictive Control Strategies in Microgrids: A Concise Revisit

Spatially Disaggregated Car Ownership Prediction Using Deep Neural Networks

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