Review on Energy Storage Systems in Microgrids

Georgious, Ramy; Elrazky, Rovan Refaat; Garcia, J.; Daoud, Ahmed A.

doi:10.3390/electronics10172134

Cited by 59 publications

(29 citation statements)

References 111 publications

(224 reference statements)

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“…Cost references for gearboxes, power converters and electrical generators are obtained from [13]. Additional inputs for cost, efficiency, energy density, power density and other qualitative attributes of storage technologies have been obtained from [14,15,16,17,18].…”

Section: Smoothened Electrical Power Drivetrain Component Sizes Costs...mentioning

confidence: 99%

Techno-economic analysis of power smoothing solutions for pumping airborne wind energy systems

Joshi

Terzi

Kruijf³

et al. 2022

J. Phys.: Conf. Ser.

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In pumping airborne wind energy (AWE) systems, the kite is operated in repetitive crosswind patterns, pulling the tether from a winch that drives a generator on the ground. During the reel-out phase of its operation, it produces power, whereas, during the reel-in phase, it consumes a small fraction of the produced power. This leads to an oscillating power profile that requires smoothing before it can be supplied to the electricity grid. This paper proposes three drivetrain concepts as a solution to this power smoothing challenge. The three concepts are based on three different types of storage technologies: electrical, hydraulic and mechanical. Techno–economic models of the drivetrains were developed and a case-study on sizing and costing of the three drivetrain concepts for a MW–scale AWE system was performed. Conclusions were drawn that provide guidance to AWE developers for choosing a suitable drivetrain concept for their systems.

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Section: Smoothened Electrical Power Drivetrain Component Sizes Costs...mentioning

confidence: 99%

Techno-economic analysis of power smoothing solutions for pumping airborne wind energy systems

Joshi

Terzi

Kruijf³

et al. 2022

J. Phys.: Conf. Ser.

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“…In recent years, worldwide scholars have completed numerous research on ESSs configuration method (Sarvesh and Geena, 2017;Xing et al, 2017;Zhang et al, 2020;Georgious et al, 2021;Li et al, 2022c). In reference (Cui et al, 2017), aiming at minimizing the sum of voltage over-limit of each node, genetic algorithm was used to optimize the capacity of battery energy storage systems (BESSs).…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of hybrid electric-hydrogen energy storage systems via multi-objective particle swarm optimization

Xuan

Chen²,

Jin³

et al. 2023

Front. Energy Res.

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In recent years, hydrogen is rapidly developing because it is environmentally friendly and sustainable. In this case, hydrogen energy storage systems (HESSs) can be widely used in the distribution network. The application of hybrid electric-hydrogen energy storage systems can solve the adverse effects caused by renewable energy access to the distribution network. In order to ensure the rationality and effectiveness of energy storage systems (ESSs) configuration, economic indicators of battery energy storage systems (BESSs) and hydrogen energy storage systems, power loss, and voltage fluctuation are chosen as the fitness function in this paper. Meanwhile, multi-objective particle swarm optimization (MOPSO) is used to solve Pareto non-dominated set of energy storage systems’ optimal configuration scheme, in which the technique for order preference by similarity to ideal solution (TOPSIS) based on information entropy weight (IEW) is used select the optimal solution in Pareto non-dominated solution set. Based on the extended IEEE-33 system and IEEE-69 system, the rationality of energy storage systems configuration scheme under 20% and 35% renewable energy penetration rate is analyzed. The simulation results show that the power loss can be reduced by 7.9%–22.8% and the voltage fluctuation can be reduced by 40.0%–71% when the renewable energy penetration rate is 20% and 35% respectively in IEEE-33 and 69 nodes systems. Therefore, it can be concluded that the locations and capacities of energy storage systems obtained by multi-objective particle swarm optimization can improve the distribution network stability and economy after accessing renewable generation.

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“…Microgrids (MGs) are self-sufficient intelligent grids that can accommodate a plethora of Distributed Energy Resources (DER), such as Renewable Energy Sources (RES) [ 1 , 2 , 3 , 4 ], and also integrate storage units [ 5 , 6 , 7 , 8 ]. Their self-sufficiency stems from the ability to operate either in association with a power grid (grid-following mode) or independently (islanded mode) [ 9 , 10 ]. In addition, they contain intelligent capabilities such as Decision Support Systems (DSS) that can operate independently or guide the system’s operator [ 11 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Decision Support System for Emergencies in Microgrids

Fotopoulou

Rakopoulos

Petridis

2022

Sensors

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The usual operation of a microgrid (MG) may often be challenged by emergencies related to extreme weather conditions and technical issues. As a result, the operator often needs to adapt the MG’s management by either: (i) excluding disconnected components, (ii) switching to islanded mode or (iii) performing a black start, which is required in case of a blackout, followed by either direct reconnection to the main grid or islanded operation. The purpose of this paper is to present an optimal Decision Support System (DSS) that assists the MG’s operator in all the main possible sorts of emergencies, thus providing an inclusive solution. The objective of the optimizer, developed in Pyomo, is to maximize the autonomy of the MG, prioritizing its renewable production. Therefore, the DSS is in line with the purpose of the ongoing energy transition. Furthermore, it is capable of taking into account multiple sorts of Distributed Energy Resources (DER), including Renewable Energy Sources (RES), Battery Energy Storage Systems (BESS)—which can only be charged with renewable energy—and local, fuel-based generators. The proposed DSS is applied in a number of emergencies considering grid-forming and grid-following mode, in order to highlight its effectiveness and is verified with the use of PowerFactory, DIgSILENT.

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Review on Energy Storage Systems in Microgrids

Cited by 59 publications

References 111 publications

Techno-economic analysis of power smoothing solutions for pumping airborne wind energy systems

Techno-economic analysis of power smoothing solutions for pumping airborne wind energy systems

Optimal planning of hybrid electric-hydrogen energy storage systems via multi-objective particle swarm optimization

Decision Support System for Emergencies in Microgrids

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