Design Methodology of a DC Nanogrid incorporating the V2G Technology

Skouros, Ioannis; Karlis, Athanasios

doi:10.1109/iecon.2019.8926776

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Similarly, an EV battery has been utilised as a residential storage solution (i.e., a controllable load or a controllable source) for improving the self-consumption of the nanogrid by implementing the Vehicle-to Home technology [42]. On the other hand, the Vehicle-to-Grid technology has also been successfully implemented, by using the EV battery as a grid-connected storage device during the hours that it is connected to the grid [43]. Finally, an off-grid nanogrid has been implemented for the charging of EVs.…”

Section: Adoption Of Nanogrids and Their Impactmentioning

confidence: 99%

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

et al. 2021

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Distributed generation (DG) systems are growing in number, diversifying in driving technologies and providing substantial energy quantities in covering the energy needs of the interconnected system in an optimal way. This evolution of technologies is a response to the needs of the energy transition to a low carbon economy. A nanogrid is dependent on local resources through appropriate DG, confined within the boundaries of an energy domain not exceeding 100 kW of power. It can be a single building that is equipped with a local electricity generation to fulfil the building’s load consumption requirements, it is electrically interconnected with the external power system and it can optionally be equipped with a storage system. It is, however, mandatory that a nanogrid is equipped with a controller for optimisation of the production/consumption curves. This study presents design consideretions for nanogrids and the design of a nanogrid system consisting of a 40 kWp photovoltaic (PV) system and a 50 kWh battery energy storage system (BESS) managed via a central converter able to perform demand-side management (DSM). The implementation of the nanogrid aims at reducing the CO2 footprint of the confined domain and increase its self-sufficiency.

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Section: Adoption Of Nanogrids and Their Impactmentioning

confidence: 99%

Design of a Smart Nanogrid for Increasing Energy Efficiency of Buildings

et al. 2021

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“…The design of centralized control hybrid nano-grid for the small communities has been proposed in [39], to supply the power to AC and DC load through inverter or tie converter having centralized control through main controller. In [40], a PV based nanogrid having V2G technology has been used in which PID controller has been used to control the power and provide the power to vehicles (load). Similarly, an approach of optimal power control was designed in [41] to minimize operational cost for the management of power flow in PV to the load.…”

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confidence: 99%

“…Even though there are many algorithms like DE [52], binary GA and real coded GA [50], CSO-PSO [53], dynamic programming algorithm [41], etc., which have been used in the literature to optimize the performance of the proposed controllers. Various controllers are also used to control the power parameters in a nano-grid ( PI controller [45], [48], [49], [51], [52], PID controller [40], [50], DPC and SMC [54], fuzzy-logic controller [42], smart load controller [38], keating controller [33] ). There are still limitations that can be improved further.…”

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A Comparative Analysis of Various Controller Techniques for Optimal Control of Smart Nano-Grid Using GA and PSO Algorithms

et al. 2020

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A nano-grid is an independent hybrid sustainable framework that utilizes non-renewable and renewable power resources for supplying continuous electrical energy to the load. Considering this scenario, in this research work, photovoltaic (PV) array, wind turbine, and fuel cell are taken as the three generation resources that have been used in the nano-grid. The active and reactive power of the all three generation resources is controlled using various controllers, i.e. integral, proportional-integral, proportional derivative, proportional integral derivative, fractional-order proportional-integral, fractional order proportional integral derivative (FOPID) and sliding mode controller (SMC). An advanced optimization technique based on a genetic algorithm (GA) and particle swarm optimization (PSO) algorithm has been utilized to optimize all of these controllers. The integral square error is taken as the objective function for both optimization algorithms. Finally, a graphical and tabular comparative analysis of all optimized controllers along with their control parameters and performance indexes is evaluated to find the best optimal solution. The performance of SMC has surpassed the performance of all other optimized controllers for power stability. In less than 0.267 seconds, the actual power yielded by using SMC is within 1% of the desired power. PSO algorithm has performed better than GA algorithm with all controllers. The worst performance is by FOPID controller with a steady state error of 6071.3W using GA algorithm and have a high magnitude of overshoot and undershoot. Moreover, a smart switching algorithm has been introduced for switching between the generation resources in accordance with the load demand and cost of the system in order to operate the nano-grid more economically. Finally, a case study has been performed in which the smart switching algorithm is utilized to switch to the best available generation resource in case of any fault at the generation side to provide uninterrupted power to the attached loads.

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