基于 TCSC 的广泛的 ADN 在山区,考虑到水电-光伏-ESS 的互补性
Yao Guo1, Shaorong Wang1, Dezhi Chen1
1School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究介绍了用于山区的活跃分销网络 (ADN),该网络使用广泛的电阻控制系列补偿 (TCSC) 和深度强化学习 (DRL). 该战略通过优化能源存储系统 (ESS) 来提高可再生能源的消耗,并减少线路损耗.
科学领域:
- 电气工程 电气工程
- 电力系统工程 电力系统工程
- 整合可再生能源的整合
背景情况:
- 山区现有的交流电配电网络面临着整合分布式发电 (DG) 和消耗可再生能源的挑战,原因是辐射结构,小线路截面和轻载荷.
- 主动配电网络 (ADN) 提供了一个潜在的解决方案,但需要先进的运营策略来有效利用可再生能源.
研究的目的:
- 为山区提供一个基于TCSC的广域电阻控制系列补偿 (ADN) 定制的DNA.
- 开发基于深度强化学习 (DRL) 的最佳运营策略,以提高可再生能源消耗和减少线路损失.
主要方法:
- 一个新的ADN架构,结合广泛的TCSC连接子网络与水电和光伏 (PV) 系统.
- 储能系统 (ESS) 对每个可再生能源的配置.
- 将最佳操作作为具有连续行动空间的马尔科夫决策过程 (MDP) 的表述.
- 应用双延迟深定论政策梯度 (TD3) 算法来解决MDP.
主要成果:
- 拟议的宽范围TCSC可促进功率逆转并提高功率传输效率.
- 基于DRL的战略有效地协调ESS充/放电和TCSC运行.
- 实现了可再生能源 (RES) 的最大消耗和线路损耗最小化.
结论:
- 开发的基于TCSC的ADN和DRL最佳运行策略有效地改善了在具有挑战性的地形中改进可再生能源的整合.
- 这种方法提高了消耗可再生能源的能力,同时减少了电力线路的损失.
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