基于国家电网ESG平台的人工智能在清洁能源调度优化中的应用
Tianyi Zhu1, Xin Guan2, Chuan Chen3
1Business School, Suzhou University of Science and Technology, Suzhou, 215009, China.
Scientific reports
|December 29, 2024
概括
本研究介绍了一种人工智能驱动的方法,它结合了粒子优化 (PSO) 和深度Q网络 (DQN) 以实现更清洁的能源电网调度. 这种方法显著提高了清洁能源的使用,并通过大数据洞察优化调度来降低成本.
科学领域:
- * 动力系统工程 * 动力系统工程
- * 人工智能 * 人工智能
- * 数据科学数据科学
背景情况:
- * 清洁能源的固有随机性和波动性使电网调度变得复杂.
- * 传统的调度方法在多目标优化和实时适应方面扎.
- *现有系统在有效整合和利用各种清洁能源投入方面面临挑战.
研究的目的:
- * 开发一种人工智能驱动的方法来优化清洁能源电网调度.
- *通过先进的算法来提高清洁能源的利用率和降低调度成本.
- * 在可再生能源整合的背景下,解决电网调度的复杂性.
主要方法:
- * 整合一个环境,社会和治理 (ESG) 大数据平台,用于全面的数据分析.
- * 应用粒子集群优化 (PSO) 进行初始调度优化.
- * 实现双层架构与深度Q网络 (DQN) 实现基于动态电网条件的实时调度调整.
主要成果:
- *与传统方法相比,清洁能源利用率从62.4%增加到87.7%.
- *通过优化资源配置,调度成本降低了22%.
- * 该方法在各种负载需求和清洁能源供应场景中表现出适应性和稳定性.
结论:
- * 拟议的PSO-DQN方法为优化清洁能源调度提供了一种新且有效的解决方案.
- *与ESG大数据平台的整合提高了决策准确性和效率.
- * 这项研究为改善电网清洁能源整合提供了坚实的基础.
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