Harmonic Distortion Minimization in Power Grids with Wind and Electric Vehicles

Misra, Ritam; Paudyal, Sumit; Ceylan, Oğuzhan; Mandal, Paras

doi:10.3390/en10070932

Cited by 8 publications

(4 citation statements)

References 23 publications

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“…The transformer purchase price is 166.1 $/kVA [17]. The harmonic current content of PEV chargers is tabulated in Table Ⅵ [38]. The simulations are conducted for a typical spring weekday in MATLAB R2019a and the interiorpoint method is employed to solve the optimization problems.…”

Section: Simulation Resultsmentioning

confidence: 99%

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Madahi

Nafisi

Abyaneh

et al. 2021

IEEE Trans. Transp. Electrific.

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The global transport sector has a significant share of greenhouse gas emissions. Thus, plug-in electric vehicles (PEVs) can play a vital role in the reduction of pollution. However, high penetration of PEVs can pose severe challenges to power systems, such as an increase in energy losses and a decrease in the transformers expected life. In this paper, a new day-ahead cooptimization algorithm is proposed to reduce the unwanted effects of PEVs on the power system. The aim of the proposed algorithm is minimizing the cost of energy losses as well as transformer operating cost by the management of active and reactive powers simultaneously. Moreover, the effect of harmonics, which are produced by the charger of PEVs, are considered in the proposed algorithm. Also, the transformer operating cost is obtained from a method that contains the purchase price, loading, and losses cost of the transformer. Another advantage of the proposed algorithm is that it can improve power quality parameters, e.g., voltage and power factor of the distribution network by managing the reactive power. Afterward, the proposed algorithm is applied to a real distribution network. The results show that the proposed algorithm optimizes the daily operating cost of the distribution network efficiently. Finally, the robustness of the proposed algorithm to the number and distribution of PEVs is verified by simulation results. Index Terms-plug-in electric vehicle (PEV), transformer aging, energy losses, daily operating cost reduction. NOMENCLATURE A. Indices and Sets ℎ Harmonic order ℎ Maximum harmonic order Set of power system nodes Time slot B. Plug-in Electric Vehicle and Parking Lot Parameters PEV battery capacity [kWh] Capacity of the n-th parking lot

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Section: Simulation Resultsmentioning

confidence: 99%

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Madahi

Nafisi

Abyaneh

et al. 2021

IEEE Trans. Transp. Electrific.

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show abstract

“…The OpenDSS (Version 8.6.5.2, Electric Power Research Institute, Inc., Palo Alto, CA, USA) [22] is an electric power distribution system simulator, which is designed to support DERs, including the photovoltaic, wind power, electrical energy storage, grid integration, and smart grid applications. It is a powerful and reliable tool for solving the power flow, harmonic, and so on, in unbalanced distribution systems and is capable of co-operating with an objective oriented optimal program designed by other programming languages [22][23][24][25][26]. Figure 3 shows the structure diagram of the optimal phase arrangement module, which was divided into two parts: a data processing program and a PSO algorithm model.…”

Section: The Solution Procedures Of the Proposed Approachmentioning

confidence: 99%

Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data

Lee

Jiang

et al. 2020

Energies

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An increase in the neutral current results in a malfunction of the low energy over current (LCO) protective relay and raises the neutral-to-ground voltage in three-phase, four-wire radial distribution feeders. Thus, the key point for mitigating its effect is to keep the current under a specific level. The most common approach for reducing the neutral current caused by the inherent imbalance of distribution feeders is to rearrange the phase connection between the distribution transformers and the load tapped-off points by using the metaheuristics algorithms. However, the primary task is to obtain the effective load data for phase rearrangement; otherwise, the outcomes would not be worthy of practical application. In this paper, the effective load data can be received from the feeder terminal unit (FTU) installed along the feeder of Taipower. The net load data consisting of customers’ power consumption and the power generation of distributed energy resources (DERs) were measured and transmitted to the feeder dispatch control center (FDCC). This paper proposes a method of establishing the equivalent full-scale net load model based on FTU data format, and the long short-term memory (LSTM) was adopted for monthly load forecasting. Furthermore, the full-scale net load model was built by the monthly per hour load data. Next, the particle swarm optimization (PSO) algorithm was applied to rearrange the phase connection of the distribution transformers with the aim of minimizing the neutral current. The outcomes of this paper are helpful for the optimal setting of the limit current of the LCO relay and to avoid its malfunction. Furthermore, the proposed method can also improve the three-phase imbalance of distribution feeders, thus reducing extra power loss and increasing the operating efficiency of three-phase induction motors.

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“…Two new feeders can accommodate more number of EVs. The work by Misra et al addressed issues of harmonic distortion in power networks due to the integration of EVs. Introduction of wind generators into the feeder network is recommended to lessen harmonics produced by EVs in power grids.…”

Section: Introductionmentioning

confidence: 99%

“…The study concluded that peak load factor is substantially increased by applying level 2 power charging. Unbalancing issues resulted from the massive single‐phase charging of EVs are not considered for assessment of realistic EV charging model in the aforementioned studies . Moreover, problems associated with added EV charging demand with location are not analyzed in terms of customers with voltage issues.…”

Section: Introductionmentioning

confidence: 99%

Risk Evaluation of Distribution Networks Considering Residential Load Forecasting with Stochastic Modeling of Electric Vehicles

et al. 2019

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Large‐scale integration of electric vehicles (EVs) into residential distribution networks (RDNs) is an evolving issue of paramount significance for utility operators. Similarly, electric load forecasting is an operational process permitting the utilities to manage demand issues for optimal energy utilization. Unbalanced voltages prevent the effective and reliable operation of RDNs. This study implements a novel framework to examine risks associated with RDNs by applying a residential forecasting model with a stochastic model of EVs charging pattern. Diversified EV loads require a stochastic approach to predict EVs charging demand; consequently, a probabilistic model is developed to account for several realistic aspects comprising charging time, battery capacity, driving mileage, state‐of‐charge, travelling frequency, charging power, and time‐of‐use mechanism under peak and off‐peak charging strategies. Peak‐day forecast of various households is obtained in summer and winter by implementing an optimum nonlinear auto‐regressive neural‐network (NN) with time‐varying external input vectors (NARX). Outputs of the EV stochastic model and residential forecasting model obtained from Monte‐Carlo simulations and the NARX‐NN model, respectively, are utilized to evaluate power quality parameters of RDNs. Performance specifications of RDNs including voltage unbalance factor (VUF) and voltage behavior are assessed in context to EV charging scenarios with various charging power levels under different penetration levels.

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Harmonic Distortion Minimization in Power Grids with Wind and Electric Vehicles

Cited by 8 publications

References 23 publications

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Co-Optimization of Energy Losses and Transformer Operating Costs Based on Smart Charging Algorithm for Plug-In Electric Vehicle Parking Lots

Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data

Risk Evaluation of Distribution Networks Considering Residential Load Forecasting with Stochastic Modeling of Electric Vehicles

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