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<p>The full-scale integration of Electric Vehicles (EVs) into residential power networks presents new challenges for maintaining grid stability, particularly due to the stochastic and time-varying nature of EV charging demand. This study introduces a comprehensive Demand Side Management (DSM) framework tailored for a smart grid where all the consumers are considered to have an EV. A novel emission estimation approach is proposed, incorporating the nonlinear relationship between generation cost and pollutant emissions to more accurately evaluate environmental impact. To address the competing objectives of minimizing peak load, operational cost, and total emissions, a multi-objective optimization problem is formulated and solved using a newly developed hybrid Butterfly Green Anaconda Optimization (BFGAO) algorithm. The algorithm also determines the optimal sizing of Battery Energy Storage Systems (BESS) while coordinating EV charging and discharging under a real-time pricing scheme. The arrival and departure behaviors of EVs are modelled using Gamma and Weibull distributions to reflect realistic user behavior. The proposed approach achieves a peak load reduction of 73.68%, a total operational cost reduction of 23.67%, and a total emission reduction of 72.54%, demonstrating its effectiveness in improving grid performance and environmental sustainability. The robustness of the algorithm is validated through benchmark functions, sensitivity analysis, and ANOVA testing. The results confirm the superiority of the proposed method in optimizing DSM strategies for a smart grid environment.</p>

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Multi-Objective Emission-Conscious Demand-Side Management in Electric Vehicle–Integrated Smart Grids Using a Hybrid Butterfly–Green Anaconda Optimization Algorithm

  • 期刊论文

  • 发表日期：

    2025年10月18日

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  • Sampatirao Nanibabu,
  • Shakila Baskaran,
  • Prakash Marimuthu

摘要

The full-scale integration of Electric Vehicles (EVs) into residential power networks presents new challenges for maintaining grid stability, particularly due to the stochastic and time-varying nature of EV charging demand. This study introduces a comprehensive Demand Side Management (DSM) framework tailored for a smart grid where all the consumers are considered to have an EV. A novel emission estimation approach is proposed, incorporating the nonlinear relationship between generation cost and pollutant emissions to more accurately evaluate environmental impact. To address the competing objectives of minimizing peak load, operational cost, and total emissions, a multi-objective optimization problem is formulated and solved using a newly developed hybrid Butterfly Green Anaconda Optimization (BFGAO) algorithm. The algorithm also determines the optimal sizing of Battery Energy Storage Systems (BESS) while coordinating EV charging and discharging under a real-time pricing scheme. The arrival and departure behaviors of EVs are modelled using Gamma and Weibull distributions to reflect realistic user behavior. The proposed approach achieves a peak load reduction of 73.68%, a total operational cost reduction of 23.67%, and a total emission reduction of 72.54%, demonstrating its effectiveness in improving grid performance and environmental sustainability. The robustness of the algorithm is validated through benchmark functions, sensitivity analysis, and ANOVA testing. The results confirm the superiority of the proposed method in optimizing DSM strategies for a smart grid environment.