Frequency control of a microgrid including renewable resources with energy management of electric vehicles

Kermani, Homa Rashidizadeh; Dahraie, Mostafa Vahedipour; Najafi, Hamidreza

doi:10.1109/icredg.2016.7875905

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…With the help of this signal, EVs are made to charge and discharge. When the reserve power in MG is high, EVs are charged, and when the power reserve is low, EVs are discharged (Kermani et al, 2016). As more DGUs and RESs are present in MG, controlling of each source is important.…”

Section: Other Control Methodsmentioning

confidence: 99%

Control strategies for frequency regulation in microgrids: A review

Verónica

Kumar

2019

Wind Engineering

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The electric power generation over the past decade has moved from conventional fossil fuel-fired thermal power plants to tiny-scale system generating power through distributed generation units. A group of such distributed generation units and loads are termed as microgrids. Microgrids can be located near the load centers to supply the load without any loss of power. Frequency regulation in a microgrid operating in autonomous mode is critical because of the intermittent nature of the renewable sources employed. To maintain the frequency regulation within a tolerance limit in a microgrid, proper control schemes have to be adopted in order to increase or decrease the real power generation. Hence, this article explores and presents a critical review of different types of control strategies employed for frequency regulation in microgrids.

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Section: Other Control Methodsmentioning

confidence: 99%

Control strategies for frequency regulation in microgrids: A review

Verónica

Kumar

2019

Wind Engineering

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“…The results confirm that the proposed system can successfully enhance grid security, reduce operational costs and emissions, and manage DG resources with higher degrees of freedom. An innovative strategy for charging and discharging EVs in the presence of renewable resources has been endorsed [22]. A cost signal is obtained based on the day-ahead load and the generation of renewables to manage the EVs' charging and discharging processes.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust PID Control of EV Aggregator for C-FCAS Using Sequential Multi-Objective Optimization

Pradana,

Haque,

Krismanto

et al. 2024

IEEE Access

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Integrating Electric Vehicles (EVs) charging into power grids through EV aggregator, offers promising benefits to society for reliable, sustainable, and cost-effective energy solutions. A key aspect of this integration is the possibility of provision of Contingency-Frequency Control Ancillary Services (C-FCAS). While EVs primarily serve owners' transportation needs, they must return to their idle positions with a satisfactory State of Charge (SoC) after providing frequency support following a contingency. However, real-world implementations of grid-connected EV aggregator control for C-FCAS are complex and challenging because several factors, including uncertainties, perturbation magnitude, renewable energy penetration, and the SoC of EVs need to be considered. To implement the C-FCAS considering these factors, this paper presents a robust PID control approach for an EV aggregator. The objective is to effectively manage EV units willing to participate, ensuring frequency regulation support, system stability, and smooth return to their idle position. The strategy exploits a sequential multi-objective optimization approach with two main objectives: mitigating frequency perturbations caused by contingencies and restoring EV units power discharge to zero after a critical time period. Sequential Multi-objective Optimization (SMO) algorithms optimize the control parameters under diverse scenarios and uncertainties. The simulation results validate the effectiveness of the proposed strategy. The control strategy by an EV aggregator successfully handled different market conditions and uncertainties, ensuring reliable frequency regulation. This service opportunity will enhance income of EV owners and their willingness to participate while helping to keep the integrity of system operation.

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“…Higher bandwidth limits, for example, is a feature that answers to new requirements coming from the spread of Distributed Generation (DG) along the power network. Due to DG presence, voltages and currents may contain harmonics up to 100 kHz, hence the measurement equipment must also behave correctly in that range of frequencies [4,5].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of a Test Bed for Calibrating Voltage Transformers Vs. Temperature

Mingotti

Peretto

Tinarelli

et al. 2019

Sensors

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The paper addresses the evaluation of the uncertainty sources of a test bed system for calibrating voltage transformers vs. temperature. In particular, the Monte Carlo method has been applied in order to evaluate the effects of the uncertainty sources in two different conditions: by using the nominal accuracy specifications of the elements which compose the setup, or by exploiting the results of their metrological characterization. In addition, the influence of random effects on the system accuracy has been quantified and evaluated. From the results, it emerges that the choice of the uncertainty evaluation method affects the overall study. As a matter of fact, the use of a metrological characterization or of accuracy specifications provided by the manufacturers provides respectively an accuracy of 0.1 and 0.5 for the overall measurement setup.

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Frequency control of a microgrid including renewable resources with energy management of electric vehicles

Cited by 5 publications

References 9 publications

Control strategies for frequency regulation in microgrids: A review

Control strategies for frequency regulation in microgrids: A review

Robust PID Control of EV Aggregator for C-FCAS Using Sequential Multi-Objective Optimization

Uncertainty Analysis of a Test Bed for Calibrating Voltage Transformers Vs. Temperature

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