论文标题
深度多代理的强化学习，用于具有成本效益的分布载荷频率控制
Deep Multi-Agent Reinforcement Learning for Cost Efficient Distributed Load Frequency Control
论文作者
论文摘要
基于微电网的体系结构的兴起正在大力修改分配系统中的能量控制格局，从而使分布式控制机制确保可靠的电源系统操作所需的分布式控制机制。在本文中，我们建议使用加固学习技术来实施负载频率控制，而无需中央权威。为此，我们使用多代理深层确定性策略梯度（MADDPG）算法近似于主要，次级和第三级控制的最佳解决方案。发电单元的特征是通过行动和与环境互动以以具有成本效益的方式来平衡生成和负载来学习如何最大程度地提高其长期性能。网络效应还在我们的框架中建模，以恢复频率为标称值。我们通过数值结果来验证加强学习方法，并表明它可以用分布式和具有成本效益的方式来实现负载频率控制。
The rise of microgrid-based architectures is heavily modifying the energy control landscape in distribution systems making distributed control mechanisms necessary to ensure reliable power system operations. In this paper, we propose the use of Reinforcement Learning techniques to implement load frequency control without requiring a central authority. To this end, we approximate the optimal solution of the primary, secondary, and tertiary control with the use of the Multi- Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Generation units are characterised as agents that learn how to maximise their long-term performance by acting and interacting with the environment to balance generation and load in a cost efficient way. Network effects are also modelled in our framework for the restoration of frequency to the nominal value. We validate our Reinforcement Learning methodology through numerical results and show that it can be used to implement the load frequency control in a distributed and cost efficient way.
