Optimization techniques for grid-connected PV with retired EV batteries in centralized charging station with challenges and future possibilities: A review

Sharma, Pankaj; Naidu, Rani Chinnappa

doi:10.1016/j.asej.2022.101985

Cited by 34 publications

(11 citation statements)

References 85 publications

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“…A large portion of these works has been oriented towards developing mathematical models that describe all of the technical and operating constraints involved in the operation of electrical networks within an environment of distributed energy resources (DERs) [9,10]. These mathematical models formulate the objective functions or goals to be achieved through the optimal integration and operation of BS in GCNs, with two the most studied being the reduction of energy production costs and energy losses [11,12]. In [13,14], a literature review on the objective functions used in the optimal integration of BS in electrical systems was carried out, finding that a large number of works focus on economic and technical indicators.…”

Section: State Of the Artmentioning

confidence: 99%

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

2023

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This work addressed the problem regarding the optimal integration of battery systems (BS) in grid-connected networks (GCNs) with the purpose of reducing energy losses and CO2 emissions, for which it formulates a mathematical model that considers the constraints associated with the operation of GCNs in a distributed generation environment that includes BS and variable power generation related to photovoltaic (PV) distributed generation (DG) and demand. As solution strategies, three different master–slave methodologies are employed that are based on sequential programming methods, with the aim to avoid the implementation of commercial software. In the master stage, to solve the problem regarding the location and the type of batteries to be used, parallel-discrete versions of the Montecarlo method (PMC), a genetic algorithm (PDGA), and the search crow algorithm (PDSCA) are employed. In the slave stage, the particle swarm optimization algortihm (PSO) is employed to solve the problem pertaining to the operation of the batteries, using a matrix hourly power flow to assess the impact of each possible solution proposed by the master–slave methodologies on the objective functions and constraints. As a test scenario, a GCN based on the 33-bus test systems is used, which considers the generation, power demand, and CO2 emissions behavior of the city of Medellín (Colombia). Each algorithm is executed 1000 times, with the aim to evaluate the effectiveness of each solution in terms of its quality, standard deviation, and processing times. The simulation results obtained in this work demostrate that PMC/PSO is the master–slave methodology with the best performance in terms of solution quality, repeatability, and processing time.

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Section: State Of the Artmentioning

confidence: 99%

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

2023

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“…The routine of the EV charging systems is mostly affected by stability, grid interruption, blackout, and uncertainty problems [8][9]. Thus, to conquer the instabilities and uncertainties, more efficient controlling strategies and optimization approaches are used [10]. Many of the prevailing works focused on SPV-centric EV charging systems utilizing Artificial Intelligence (AI) over the past two decades.…”

Section: Introductionmentioning

confidence: 99%

Season Aware Solar Photovoltaic-Based Efficient EV Charging System Using A Novel JFL-BiLSTM And RF-SHO

Rajender,

Dubey,

Kumar

2024

Preprint

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The surge in Electric Vehicle (EV) adoption has intensified energy demand, prompting the integration of Solar Photovoltaic (S-PV) systems for optimization. To address the challenge of fluctuating load demand with seasonal changes, a novel approach is proposed. This system combines Laguerre Polynomial-based Ramp Rate Method (LP-RRM) to smooth DC power from S-PV, Generalized Space Vector Modulation-based Switching Regulators (GSVM-SR) to tackle grid interruptions, and Rosenbrock Function-based Sea-Horse Optimization (RF-SHO) for optimal Charging System (CS) selection. Pre-processing the dataset identifies seasons and extracts features, which are fed into Joint Fusion Layer – Bidirectional Long Short Term Memory (JFL-BiLSTM) for load demand forecasting. The chosen CS is determined from a constructed graph based on user requests. The proposed technique effectively mitigates blackout risks and facilitates efficient EV charging operations, yielding promising results according to simulation outcomes.

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“…Recently, environmental and economic factors have contributed to the popularity of electric vehicles (EVs) over traditional internal combustion vehicles. In particular, the battery electric vehicle (BEV) has become popular in this decade, which consists of an electric motor that receives power from the battery throughout the EV operation (Saravanakumar and Saravana kumar, 2023); (Sharma and Chinnappa Naidu, 2023). Charging methods in EVs draw power from sustainable energy sources which lead to a greener environment.…”

Section: Introductionmentioning

confidence: 99%

A novel digital control scheme for power management in a hybrid energy-source environment pertaining to electric vehicle applications

Mathesh

Saravanakumar²

2023

Front. Energy Res.

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In electric vehicle, the utilization of various input power sources demand an appropriate integration. This integration inevitably questions the effectiveness of a suitable power management system. The integration of sources causes complexity in power conversion and programming errors while switching between the sources and brings difficulties in real-time implementation. This study presents a novel digital control scheme specific to the integration of solar, battery, and fuel cell to solve these issues and also explains the power management system in different controllers for electric vehicles. The proposed scheme checks the reference currents based on the power generated by the different sources needed to meet the instantaneous demand from the permanent magnet synchronous machine, which is widely used in electric vehicles. The methodology constitutes maximum power point tracking in solar panels at every second of the day, retaining constant voltage in the DC bus and managing the power flow from sources to the permanent magnet synchronous machine based on the power availability from different sources. The power flow to a control module dedicated to permanent magnet synchronous machine control is considered. The major contribution of the paper is the design and development of a power management system for an electric vehicle with a novel digital control scheme. Power management is achieved with the aid of a digital controller using real-time solar data for a periodically fluctuating load demand in MATLAB/Simulink and also in an experimental investigation, the results of which are discussed.

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Optimization techniques for grid-connected PV with retired EV batteries in centralized charging station with challenges and future possibilities: A review

Cited by 34 publications

References 85 publications

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

Optimal Integration of Battery Systems in Grid-Connected Networks for Reducing Energy Losses and CO2 Emissions

Season Aware Solar Photovoltaic-Based Efficient EV Charging System Using A Novel JFL-BiLSTM And RF-SHO

A novel digital control scheme for power management in a hybrid energy-source environment pertaining to electric vehicle applications

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