Application of Renewable Energy in Charging Station for Electric Vehicles: A Comprehensive Review

Prasad, Bugatha Ram Vara; Sai, Brundavanam Sesha; Vijaychandra, Joddumahanthi; Babu, Rohit

doi:10.1007/978-981-19-4975-3_18

Cited by 2 publications

(1 citation statement)

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“…In [43], the modeling and genetic algorithm optimization of an electric car charge in the presence of RENs were described. In [44], the finest energy sources for EVs charging stations are solar and wind power. For addressing the stated UCP, a meta-heuristic solution tool is presented in [3], where real power system data are used, a thorough investigation into four distinct examples of unit commitment challenges with varying seasonal and weather scenarios is carried out and addressed; intelligent EVs charging and discharging management is included into the problem.…”

Section: Introductionmentioning

confidence: 99%

Modified Arithmetic Algorithms for Optimal Power System Operation Considering Uncertainties From Intermittent Renewable Energy and Electric Vehicles

Abdelhakeem,

Hasaneen,

Helmy

et al. 2024

IEEE Access

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The integration of Renewable Energy Resources (RENs) and the increasing usage of Electric Light Duty Vehicles (LDVs) are considered decisive measures for meeting international commitments towards reducing greenhouse gas (GHG) emissions in pursuit of sustainability. However, the complexity that arises from the inherent instability and uncertainty associated with these measures affects power grids and significantly complicates the Unit Commitment Problem (UCP). This research paper introduces two unique optimization techniques which are the Main Arithmetic Approach (AOT) and the Modified Arithmetic Approach (IAOT) to address the optimal decisions for the UCP considering intermittent RENs and LDVs. The AOT exploits the statistical characteristics inherent in basic mathematical operators. Additionally, AOT leverages the probability distributions associated with fundamental mathematical operators to optimize its operations. Conversely, the proposed IAOT boosts effectiveness by harnessing the benefits of natural logarithms and exponential operators with high-density values for operator selection to produce a more flexible search process. Moreover, the IAOT takes advantage of the unique properties of natural logarithms and exponential operators with high-density values to achieve improved performance. Real power system data is utilized to conduct a comprehensive analysis involving four distinct UCP case studies with twelve relevant scenarios ranging from SA1 to SF2 showing the low and prominent levels of penetration of LDVs in different seasonal conditions that incorporate uncertainties of RENs. The effectiveness of the elected approaches is examined through key performance indicators including the overall operating costs per day (OCD) and the costeffectiveness ratio (CER), considering distinct LDVs profiles and levels of RENs penetration. The study shows significant insights into the scenarios analyzed. Scenario SF1 -IAOT is the most cost-effective alternative, with OCD of $528,430, closely followed by SB1 -IAOT, which costs $529,550. Conversely, Scenario SD2 -AOT has the greatest OCD of $559,667. Furthermore, the CER and OCD offer useful insights. SB1 -IAOT has the lowest OCD (20.69), whereas SD2 -AOT has the greatest OCD (21.92). In terms of CER, Scenario SE1 -AOT has the greatest value of 0.91, whereas SB1 -IAOT, SA1 -AOT, SC1 -IAOT, and SF2 -IAOT have the lowest CER of 0.86., highlighting the effectiveness of incorporating RENs and LDVs in achieving favorable financial implications for grid operators and decision-makers. The comprehensive analysis results demonstrate the elected techniques' applicability and superiority in finding the best optimal solutions to the UCP incorporating different RENs and LDVs scenarios.

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Section: Introductionmentioning

confidence: 99%