Scholarlink

<p>This paper proposes a Hybrid Stackelberg-Markov framework for adaptive load scheduling and dynamic pricing in smart grids. The framework integrates a Stackelberg game to model the interaction between the utility and consumers with a Markov process that captures consumer behavioral dynamics. By combining economic incentives with behavioral adaptation, the model achieves a balance between reducing the peak-to-average ratio (PAR), lowering consumer costs, and increasing utility profit. Simulation results demonstrate that the proposed approach reduces PAR by 43% compared with the baseline, decreases average consumer costs by 28%, and improves utility profit by 10%. The behavioral state analysis further shows that most consumers transition into the <i>Content</i> state, indicating long-term acceptance of dynamic pricing strategies. Moreover, the computational analysis confirms faster convergence and reduced run time compared with conventional demand response schemes. These results establish the proposed framework as a scalable and practical demand response solution for modern smart grids.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A hybrid Stackelberg–Markov framework for adaptive load scheduling and dynamic pricing in smart grids

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月7日

查看全文
  • Syed Ashraf Ali,
  • Sohail Imran Saeed,
  • Jehanzeb Khan,
  • Shujaat Ali,
  • Dilawar Shah,
  • Muhammad Tahir

摘要

This paper proposes a Hybrid Stackelberg-Markov framework for adaptive load scheduling and dynamic pricing in smart grids. The framework integrates a Stackelberg game to model the interaction between the utility and consumers with a Markov process that captures consumer behavioral dynamics. By combining economic incentives with behavioral adaptation, the model achieves a balance between reducing the peak-to-average ratio (PAR), lowering consumer costs, and increasing utility profit. Simulation results demonstrate that the proposed approach reduces PAR by 43% compared with the baseline, decreases average consumer costs by 28%, and improves utility profit by 10%. The behavioral state analysis further shows that most consumers transition into the Content state, indicating long-term acceptance of dynamic pricing strategies. Moreover, the computational analysis confirms faster convergence and reduced run time compared with conventional demand response schemes. These results establish the proposed framework as a scalable and practical demand response solution for modern smart grids.