Energy loss minimization using PHEVs as distributed active and reactive power resources: a convex quadratic local optimal solution

Nafisi, Hamed; Abyaneh, Hossein Askarian; Abedi, Mehrdad

doi:10.1002/etep.2134

Cited by 23 publications

(22 citation statements)

References 33 publications

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“…Several targets such as power losses minimization, 26 load variance minimization, 10 and peak load minimization 27 are proposed to maintain the grid reliability. Several targets such as power losses minimization, 26 load variance minimization, 10 and peak load minimization 27 are proposed to maintain the grid reliability.…”

Section: Minimizing Load Variancementioning

confidence: 99%

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

Shang

Zheng

Shao

et al. 2019

Int Trans Electr Energ Syst

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With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods.

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Section: Minimizing Load Variancementioning

confidence: 99%

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

Shang

Zheng

Shao

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Numerous researchers have assessed the technical impacts of electric vehicles (EVs) on radial distribution systems (RDSs) . In a parallel way, other researchers have investigated the technical impacts of PV systems on RDSs .…”

Section: Introductionmentioning

confidence: 99%

Voltage behaviour in radial distribution systems under the uncertainties of photovoltaic systems and electric vehicle charging loads

Ruíz-Rodríguez

Hernández

Jurado

2017

Int Trans Electr Energ Syst

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Summary This paper presents a probabilistic method that accurately verifies the fulfilment of voltage constraints in radial distribution systems (RDSs) with photovoltaic (PV) systems and electric vehicle (EV) charging loads. This problem has not been comprehensively discussed. The proposed probabilistic method (PPM) involves the calculation of input variable cumulants, the linearization of load‐flow equations, and the application of the cumulant method and the Cornish‐Fisher expansion. Obviously, it is first necessary to model the PV systems and EV charging loads as random variables. In addition, the correlation of the input variables is suitably handled. As part of this research, a Monte Carlo simulation was performed, which confirmed the accuracy of the results obtained with the PPM. As an added value, when compared to the Monte Carlo simulation, there was a significant reduction in computational cost. The results obtained by the PPM demonstrate that the connection of PV systems in the IEEE 33‐node RDS with EVs clearly contributes to keeping voltages within regulatory limits, once the distributions of the RDS output variables are inspected. Nonetheless, the greater dispersion in the distributions in combined PV and EV scenarios means that voltage constraint fulfilment cannot be absolutely guaranteed. This probabilistic approach provides a more accurate assessment than one based on a simple deterministic vision. Thus, the PPM is extremely useful because it provides a better understanding of the combined technical impact under different correlated and uncorrelated scenarios.

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“…Meanwhile, the minimization of power system losses is also a popular topic in the V2G application. In [42], EVs were used as reactive power resources to reduce the power grid energy losses. Furthermore, V2G technology was also utilized to maximize the renewable energy generation by solving the renewable energy intermittency issue [43,44].…”

Section: Introductionmentioning

confidence: 99%

Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

et al. 2017

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Abstract:The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles' state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data.

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Energy loss minimization using PHEVs as distributed active and reactive power resources: a convex quadratic local optimal solution

Cited by 23 publications

References 33 publications

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

Voltage behaviour in radial distribution systems under the uncertainties of photovoltaic systems and electric vehicle charging loads

Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

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