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<p>The world is continuously progressing towards an electrified future. Therefore, the importance of demand-side management (DSM) has also been evident in optimising energy demand while maintaining grid stability. This study introduces a holistic DSM framework designed to manage various load types, including the increasing demands of Electric Vehicle (EV) charging, which is highly dynamic. Utilising the Gaussian Mixture Model (GMM) for clustering, the study identifies distinct EV charging patterns across public, residential, and workplace locations. These EV charging profiles are integrated with conventional load data from commercial, industrial, and residential sectors to develop optimised load profiles. Advanced pricing schemes, such as Time-of-Use (TOU), Ultra Low Overnight (ULO), and Step-Pricing, are evaluated based on the effectiveness of peak load reduction and electricity cost savings. To reflect real-world operational scenarios, the developed optimisation models incorporate non-linear cost functions, multi-period constraints, and load flexibility ranges, enabling a more realistic representation of system dynamics and limitations. A sensitivity analysis was also performed, incrementing each load type by 10% one at a time to evaluate the robustness of the proposed framework. Results indicate substantial reductions in peak loads and energy costs: the ULO pricing model achieves a 13.09% peak load reduction and a 7.20% cost reduction, and the TOU model delivers an 11.32% peak load reduction and a 2.61% cost reduction. The Step-Pricing model yields a 3.81% peak load reduction and a 2.59% cost reduction. The sensitivity analysis further demonstrated that the ULO model remains the most effective under varying load increments, particularly in managing EV loads. Findings highlight the critical role of strategic pricing schemes and load flexibility management within DSM frameworks, demonstrating their effectiveness in managing EV charging demands while ensuring seamless integration into the broader energy infrastructure.</p>

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Strategic Pricing and Load Optimization for Effective Demand Side Management in Electric Vehicles Integrated Grid

  • 期刊论文

  • 发表日期：

    2025年8月21日

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  • Mayank Saklani,
  • Devender Kumar Saini

摘要

The world is continuously progressing towards an electrified future. Therefore, the importance of demand-side management (DSM) has also been evident in optimising energy demand while maintaining grid stability. This study introduces a holistic DSM framework designed to manage various load types, including the increasing demands of Electric Vehicle (EV) charging, which is highly dynamic. Utilising the Gaussian Mixture Model (GMM) for clustering, the study identifies distinct EV charging patterns across public, residential, and workplace locations. These EV charging profiles are integrated with conventional load data from commercial, industrial, and residential sectors to develop optimised load profiles. Advanced pricing schemes, such as Time-of-Use (TOU), Ultra Low Overnight (ULO), and Step-Pricing, are evaluated based on the effectiveness of peak load reduction and electricity cost savings. To reflect real-world operational scenarios, the developed optimisation models incorporate non-linear cost functions, multi-period constraints, and load flexibility ranges, enabling a more realistic representation of system dynamics and limitations. A sensitivity analysis was also performed, incrementing each load type by 10% one at a time to evaluate the robustness of the proposed framework. Results indicate substantial reductions in peak loads and energy costs: the ULO pricing model achieves a 13.09% peak load reduction and a 7.20% cost reduction, and the TOU model delivers an 11.32% peak load reduction and a 2.61% cost reduction. The Step-Pricing model yields a 3.81% peak load reduction and a 2.59% cost reduction. The sensitivity analysis further demonstrated that the ULO model remains the most effective under varying load increments, particularly in managing EV loads. Findings highlight the critical role of strategic pricing schemes and load flexibility management within DSM frameworks, demonstrating their effectiveness in managing EV charging demands while ensuring seamless integration into the broader energy infrastructure.