阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优...阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优化(proximal policy optimization,PPO)算法求解CIES低碳优化调度问题。该方法基于低碳优化调度模型搭建强化学习交互环境,利用设备状态参数及运行参数定义智能体的状态、动作空间及奖励函数,再通过离线训练获取可生成最优策略的智能体。算例分析结果表明,采用PPO算法得到的CIES低碳优化调度方法能够充分发挥阶梯式碳交易机制减少碳排放量和提高能源利用率方面的优势。展开更多
随着电力系统的数字化和智能化发展,配变重过载预测成为了实现智能状态检修的关键技术之一。配变过载时空因子在现实场景中通常呈偏置分布。其中,部分高风险罕见(high risk and rare,HRR)因子一旦出现,将对变压器造成无法逆转的伤害。为...随着电力系统的数字化和智能化发展,配变重过载预测成为了实现智能状态检修的关键技术之一。配变过载时空因子在现实场景中通常呈偏置分布。其中,部分高风险罕见(high risk and rare,HRR)因子一旦出现,将对变压器造成无法逆转的伤害。为此,该文提出一种基于提高关联规则关键重要性(improved association rules‐criticality importance,IAR‐CI)模型的配变过载预测方法。首先,考虑内部与外部因素,收集多个数据源并建立配变运行状态数据库,且通过ICA识别与配变重过载强关联的罕见高危时段与HRR;其次,基于关键性重要度(criticality importance,CI)度量计算,设计一种因子权重计算方法,准确衡量因子的风险权重;最后,应用TBFP‐Growth算法,增强模型的运行效率。采用中国南方某地区电网数据进行算例仿真。研究表明,该方法能够提升配变重过载的预测性能,有助于后续巡检、检测策略的合理统筹和科学规划,可在降低电力设备运维检修成本的同时提高供电的可靠性。展开更多
GitHub Actions, a popular CI/CD platform, introduces significant security challenges due to its integration with GitHub’s open ecosystem and its use of flexible workflow configurations. This paper presents Sher, a Py...GitHub Actions, a popular CI/CD platform, introduces significant security challenges due to its integration with GitHub’s open ecosystem and its use of flexible workflow configurations. This paper presents Sher, a Python-based tool that enhances the security of GitHub Actions by automating the detection and remediation of security issues in workflows. Self-Hosted Ephemeral Runner, or Sher, acts as a broker between GitHub’s APIs and a customizable, isolated environment, analyzing workflows through a static rules engine and automatically fixing identified issues. By providing a secure, ephemeral runner environment and a dynamic analysis tool, Sher addresses common misconfigurations and vulnerabilities, contributing to the resilience and integrity of DevSecOps practices within software development pipelines.展开更多
文摘随着电力系统的数字化和智能化发展,配变重过载预测成为了实现智能状态检修的关键技术之一。配变过载时空因子在现实场景中通常呈偏置分布。其中,部分高风险罕见(high risk and rare,HRR)因子一旦出现,将对变压器造成无法逆转的伤害。为此,该文提出一种基于提高关联规则关键重要性(improved association rules‐criticality importance,IAR‐CI)模型的配变过载预测方法。首先,考虑内部与外部因素,收集多个数据源并建立配变运行状态数据库,且通过ICA识别与配变重过载强关联的罕见高危时段与HRR;其次,基于关键性重要度(criticality importance,CI)度量计算,设计一种因子权重计算方法,准确衡量因子的风险权重;最后,应用TBFP‐Growth算法,增强模型的运行效率。采用中国南方某地区电网数据进行算例仿真。研究表明,该方法能够提升配变重过载的预测性能,有助于后续巡检、检测策略的合理统筹和科学规划,可在降低电力设备运维检修成本的同时提高供电的可靠性。
文摘GitHub Actions, a popular CI/CD platform, introduces significant security challenges due to its integration with GitHub’s open ecosystem and its use of flexible workflow configurations. This paper presents Sher, a Python-based tool that enhances the security of GitHub Actions by automating the detection and remediation of security issues in workflows. Self-Hosted Ephemeral Runner, or Sher, acts as a broker between GitHub’s APIs and a customizable, isolated environment, analyzing workflows through a static rules engine and automatically fixing identified issues. By providing a secure, ephemeral runner environment and a dynamic analysis tool, Sher addresses common misconfigurations and vulnerabilities, contributing to the resilience and integrity of DevSecOps practices within software development pipelines.
