Resilience-oriented schedule of microgrids with hybrid energy storage system using model predictive control

Tobajas, Javier; García-Torres, Félix; Roncero‐Sánchez, Pedro; Vázquez, Javier; Bellatreche, Ladjel; Nieto, Emilio

doi:10.1016/j.apenergy.2021.118092

Cited by 61 publications

(25 citation statements)

References 31 publications

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“…It provides high-efficiency energy conversion without harmful emissions. However, the requirement for larger land spaces limits its scope in modern microgrids [63], [64].…”

Section: Microgrids Secondary Control: Mpc-based Power Sharingmentioning

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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“…It provides high-efficiency energy conversion without harmful emissions. However, the requirement for larger land spaces limits its scope in modern microgrids [63], [64].…”

Section: Microgrids Secondary Control: Mpc-based Power Sharingmentioning

confidence: 99%

Model Predictive Control Strategies in Microgrids: A Concise Revisit

et al. 2022

View full text Add to dashboard Cite

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“…R&D projects with higher levels of risk are supposed to be supported with the following mechanisms: economic subvention, technology-specific public procurement, and nonreimbursable financing for joint ventures. While R&D investment is a rather short-term measure, long-run development of renewable energy sector requires a wider range of instruments and a deeper interconnection between linked areas of energy industry, such as renewable energy distribution, storage, and end-use (Li et al, 2020;Guneser et al, 2022;Sendstad et al, 2022;Tobajas et al, 2022;Umar et al, 2022).…”

Section: Renewable Energy Incentivesmentioning

confidence: 99%

Renewable energy incentives on the road to sustainable development during climate change: A review

Tryndina¹,

An²,

Varyash³

et al. 2022

Front. Environ. Sci.

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This review is dedicated to the analytical literature concerning the dynamics of different approaches to renewable energy promotion. Examples of major types of green energy incentives, including carbon tax, Feed-in Tariffs, and investments in research and development are covered in the paper, as well as the barriers and limitations to such practices and the contradictions existing in the field of renewable energy. The dynamics of the means of green energy promotion over this period have been addressed. This review analyzes energy considerations and the importance of raising public awareness on the issue. The evidence collected through the literature analysis, has proven that despite of a significant amount of work done in the field in the transition to the renewable energy sources, including the implementation of various incentives, controversial aspects remain that demand attention both from economists and policymakers. Modern best practices in the field of green energy incentives can be based on government initiatives or stem from the private sector. The most widely used policies for renewable energy promotion discussed in the reviewed studies are tax incentives; however, at the same time, numerous countries are providing fossil fuels subsidies to minimize the level of inequality. Finally, the outlook of different practices concerning financing of the transition from traditional energy sources to renewable ones is presented.

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“…A common approach is to ensure the power delivery to loads by storing an additional fixed amount of energy in the storage systems (typically 20-30% of the capacity) to be used in the case of utility grid fault without taking into account the future load demand [28], [29]. In [30], the amount of backup energy is adapted to the minimum future power requirement of the loads, and in [31], it is computed by solving an optimization problem. The drawback of such methods is that they require to share with the centralized controller all the future loads' demands.…”

Section: Introductionmentioning

confidence: 99%

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

Casagrande

Prodan

Spurgeon

et al. 2023

IEEE Systems Journal

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This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles).

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Resilience-oriented schedule of microgrids with hybrid energy storage system using model predictive control

Cited by 61 publications

References 31 publications

Model Predictive Control Strategies in Microgrids: A Concise Revisit

Model Predictive Control Strategies in Microgrids: A Concise Revisit

Renewable energy incentives on the road to sustainable development during climate change: A review

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

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