The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increasingly complex and costly. In our prior work, we have introduced a congestion management approach consisting of a redispatching optimizer combined with a machine learning-based topology optimization agent. Compared to a typical redispatching-only agent, it was able to keep a simulated grid in operation longer while at the same time reducing operational cost. Our approach also ranked 1st in the L2RPN 2022 competition initiated by RTE, Europe's largest grid operator. The aim of this paper is to bring this promising technology closer to the real world of power grid operation. We deploy RL-based agents in two settings resembling established workflows, AI-assisted day-ahead planning and realtime control, in an attempt to show the benefits and caveats of this new technology. We then analyse congestion, redispatching and switching profiles, and elementary sensitivity analysis providing a glimpse of operation robustness. While there is still a long way to a real control room, we believe that this paper and the associated prototypes help to narrow the gap and pave the way for a safe deployment of RL agents in tomorrow's power grids.
翻译:目前向可再生能源的过渡正在增加风能和太阳能等变化不定的电源的份额,提高电网的波动性,使电网运行变得日益复杂和昂贵。在先前的工作中,我们采用了一种拥堵管理办法,包括再喷射优化器,加上机械学习的地形优化剂。与典型的再喷射单一剂相比,它能够保持模拟电网的运行时间更长,同时降低运营成本。我们的方法在欧洲最大的电网运营商RTE发起的L2RPN 2022竞争中排名第1位。本文的目的是让这一有希望的技术更接近真正的电网运营世界。我们把基于RL的代理物放在两个类似既定工作流程、AI辅助日头规划和实时控制的环境里。我们试图展示这种新技术的好处和洞穴。我们然后分析拥塞、再喷射和转换剖面图谱,以及基本灵敏度分析,为业务的稳健性提供了一瞥。虽然距离仍然很远,但我们认为,在真正的控制室里仍有很长的路要走,但我们相信这张纸和相关的电网原型号有助于未来安全地铺隔。