Fuzzy logic based frequency control by V2G aggregators

Datta, Manoj

doi:10.1109/pedg.2014.6878702

Cited by 8 publications

(3 citation statements)

References 30 publications

(31 reference statements)

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“…These rules are achieved logic and analysis. Fuzzy rule i in this controller is expressed as follows [19]: Rule i: IF f ∆ is L x and SOC is M y , THEN P I is Z l x= 1,2, … , 7, y=1,2, …,5 , l=1,2,3,…,9…”

Section: Design Of Controllermentioning

confidence: 99%

Smart Deregulated Grid Frequency Control in Presence of Renewable Energy Resources by EVs Charging Control

Aliabadi

Taher

Shahidehpour

2018

IEEE Trans. Smart Grid

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Nowadays, due to the increasing price of fossil fuels and its decreasing resources on the one hand and environmental pollutions on the other hand, use of electric vehicles (EVs) has been increased. Charging EVs has imposed new loads on power systems. These new and major loads along with the deregulation of power systems, which introduces new uncertainties to grid, have caused new challenges for the frequency control and stability of power systems. Use of EVs as moving batteries is one of the ways for dealing with this problem. In this method, EV charging is controlled and, when necessary, EV battery is discharged in grid. This concept is so-called vehicle to grid (V2G). V2G concept is employed in this study for the control of a smart deregulated grid frequency. For this purpose, an optimized fuzzy controller is used to control EVs. Using the proposed method, charging or discharging batteries is carried out with respect to grid frequency and battery state of charge (SOC). To investigate the proposed approach, a modified IEEE 39-bus system in the presence of renewable energy resources is assumed. Then, this system is converted into a three area system in order for the frequency analysis. Investigating the performance of the proposed method for the charging of EVs is done in another part of paper. Simulations are carried out in MATLAB/SIMULINK environment and their results illustrate good performance of the proposed method in the frequency control of deregulated system and EV charging.

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Section: Design Of Controllermentioning

confidence: 99%

Smart Deregulated Grid Frequency Control in Presence of Renewable Energy Resources by EVs Charging Control

Aliabadi

Taher

Shahidehpour

2018

IEEE Trans. Smart Grid

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“…In addition to the more traditional approaches, the various proposed methods have utilized soft computing solutions. A fuzzy logic based frequency control method was developed by Datta (Datta, 2014), where electric vehicle charging was controlled to provide frequency regulation. Similar fuzzy methods were also utilized for photovoltaics inverter control (Datta and Senjyu, 2013).…”

Section: Controlling (Via) Frequencymentioning

confidence: 99%

On Demand Response Modeling and Optimization of Power in a Smart Grid

Kilkki¹,

Zenger²

2018

Linköping Electronic Conference Proceedings

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The electrical grid is under reform; the increasing volatile renewable energy production and distributed local generation compel the development of a future where the consumption of electricity can also participate in maintaining the production-consumption balance of the grid. There is a vast amount of recent research activity related to exploiting residential and industrial consumption elasticity. This paper presents a selected overview of various facets of modeling, optimizing and simulating the demand response potential and effects, especially with emergent soft computing methods. In addition, some illustrating examples are presented, where various relevant approaches from recent state-of-the-art research, including soft computing methods, are reviewed.

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“…Furthermore, the work done in [20] proposed a method for primary frequency control (PFC) using a simple constant droop characteristic, implemented in an isolated network model with a conventional generator and inertial emulation. Some other studies [21,22] suggested a better approach for frequency control, using fuzzy logic. The fuzzy controller receives information from PEV batteries and the grid, such as SOC and frequency deviation, controlling the output power in a more efficient and effective fashion.…”

Section: Introductionmentioning

confidence: 99%

Efficient Model for Accurate Assessment of Frequency Support by Large Populations of Plug-in Electric Vehicles

Dakanalis

Kanellos

2021

Inventions

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In recent years, plug-in electric vehicles (PEVs) have gained immense popularity and are on a trajectory of constant growth. As a result, power systems are confronted with new issues and challenges, threatening their safety and reliability. PEVs are currently treated as simple loads due to their low penetration. However, as their numbers are growing, PEVs could potentially be exploited as distributed energy storage devices providing ancillary services to the network. Batteries used in PEVs are developed to deliver instantaneously active power, making them an excellent solution for system frequency support. This paper proposes a detailed dynamic model that is able to simulate frequency support capability from a large number of PEVs, using an innovative aggregate battery model that takes into account the most significant constraints at PEV and aggregate battery levels. The cost optimization algorithm, which is the most time-consuming process of the problem, is executed only at the aggregate battery level, thereby reducing the computational requirements of the model without compromising the obtained accuracy. The proposed method is applied to the power system of Crete exploiting detailed statistical data of EV mobility. It is proven that PEVs can effectively support power system frequency fluctuations without any significant deviation from their optimal operation.

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Fuzzy logic based frequency control by V2G aggregators

Cited by 8 publications

References 30 publications

Smart Deregulated Grid Frequency Control in Presence of Renewable Energy Resources by EVs Charging Control

Smart Deregulated Grid Frequency Control in Presence of Renewable Energy Resources by EVs Charging Control

On Demand Response Modeling and Optimization of Power in a Smart Grid

Efficient Model for Accurate Assessment of Frequency Support by Large Populations of Plug-in Electric Vehicles

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