Guo Yao, Wang Shaorong, Chen Dezhi
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2024 Sep 18;24(18):6028. doi: 10.3390/s24186028.
Due to the radial network structures, small cross-sectional lines, and light loads characteristic of existing AC distribution networks in mountainous areas, the development of active distribution networks (ADNs) in these regions has revealed significant issues with integrating distributed generation (DGs) and consuming renewable energy. Focusing on this issue, this paper proposes a wide-range thyristor-controlled series compensation (TCSC)-based ADN and presents a deep reinforcement learning (DRL)-based optimal operation strategy. This strategy takes into account the complementarity of hydropower, photovoltaic (PV) systems, and energy storage systems (ESSs) to enhance the capacity for consuming renewable energy. In the proposed ADN, a wide-range TCSC connects the sub-networks where PV and hydropower systems are located, with ESSs configured for each renewable energy generation. The designed wide-range TCSC allows for power reversal and improves power delivery efficiency, providing conditions for the optimization operation. The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. The simulation results demonstrate the effectiveness of the proposed method.
由于山区现有交流配电网具有辐射状网络结构、小截面线路和轻载等特点，这些地区有源配电网（ADN）的发展在分布式发电（DG）接入和可再生能源消纳方面暴露出重大问题。针对这一问题，本文提出了一种基于晶闸管控制串联补偿器（TCSC）的广域有源配电网，并提出了一种基于深度强化学习（DRL）的优化运行策略。该策略考虑了水电、光伏（PV）系统和储能系统（ESS）之间的互补性，以提高可再生能源的消纳能力。在所提出的有源配电网中，广域TCSC连接光伏和水电系统所在的子网，并为每种可再生能源发电配置储能系统。所设计的广域TCSC允许功率反转并提高输电效率，为优化运行提供条件。将优化运行问题表述为具有连续动作空间的马尔可夫决策过程（MDP），并使用双延迟深度确定性策略梯度（TD3）算法求解。优化目标是通过协调储能系统的充放电与TCSC的运行模式，最大化可再生能源（RES）的消纳并最小化线路损耗。仿真结果验证了所提方法的有效性。
