Scholarlink

<p>Seaport Virtual Power Plants (SVPP) face challenges, such as low economic efficiency and limited renewable energy utilization in the process of energy trading and integration. To address these issues, this study proposes an energy-sharing trading model for SVPPs that accounts for multiple uncertainties, including fluctuations in energy demand and renewable energy supply. A multi-agent collaborative optimization mechanism is established through a Stackelberg game to coordinate energy trading strategies among key port entities, including ships, cranes, and battery swapping stations for electric heavy-duty trucks. Monte Carlo simulation is used to model the stochastic energy demand of ships and swapping stations, capturing uncertainties in energy consumption and supply that are often overlooked in traditional models. A bi-level optimization framework is then developed: the upper-level problem focuses on maximizing the economic benefit of the SVPP, while the lower level aims to optimize the electricity utility for users, considering their comfort, consumption stability, and sensitivity to price fluctuations. By integrating game-theoretic optimization with uncertainty modeling, the proposed method enables a joint improvement in both objectives. Case studies demonstrate that the economic performance of the SVPP and the electricity utility of users is significantly improved, with ship users’ power purchases increasing by 42.2% and electricity sales rising by 34.9%, indicating the applicability and effectiveness of the model in complex port environments.</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 bi-level Stackelberg game-based energy sharing trading method for port virtual power plants considering multiple uncertainties

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月7日

查看全文
  • Jie PAN,
  • Wang JIANG,
  • Hui ZHU,
  • Shuiming JIANG,
  • Feng CHEN,
  • Xuefei WANG,
  • Haitao HUANG

摘要

Seaport Virtual Power Plants (SVPP) face challenges, such as low economic efficiency and limited renewable energy utilization in the process of energy trading and integration. To address these issues, this study proposes an energy-sharing trading model for SVPPs that accounts for multiple uncertainties, including fluctuations in energy demand and renewable energy supply. A multi-agent collaborative optimization mechanism is established through a Stackelberg game to coordinate energy trading strategies among key port entities, including ships, cranes, and battery swapping stations for electric heavy-duty trucks. Monte Carlo simulation is used to model the stochastic energy demand of ships and swapping stations, capturing uncertainties in energy consumption and supply that are often overlooked in traditional models. A bi-level optimization framework is then developed: the upper-level problem focuses on maximizing the economic benefit of the SVPP, while the lower level aims to optimize the electricity utility for users, considering their comfort, consumption stability, and sensitivity to price fluctuations. By integrating game-theoretic optimization with uncertainty modeling, the proposed method enables a joint improvement in both objectives. Case studies demonstrate that the economic performance of the SVPP and the electricity utility of users is significantly improved, with ship users’ power purchases increasing by 42.2% and electricity sales rising by 34.9%, indicating the applicability and effectiveness of the model in complex port environments.