With economic development, the complexity of infrastructure has increased
drastically. Similarly, with the shift from fossil fuels to renewable sources
of energy, there is a dire need for such systems that not only predict and
forecast with accuracy but also help in understanding the process of
predictions. Artificial intelligence and machine learning techniques have
helped in finding out wellperforming solutions to different problems in the
energy sector. However, the usage of state-of-the-art techniques like
reinforcement learning is not surprisingly convincing. This paper discusses the
application of reinforcement techniques in energy systems and how explanations
of these models can be helpful

经济发展导致基础设施复杂度的急剧增加，同时，从化石燃料转向可再生能源，迫切需要能够准确预测和预测的系统，并且帮助理解预测过程。人工智能和机器学习技术已经在能源部门找到了良好的解决方案，然而，令人信服的最先进技术如强化学习的使用并不令人惊讶。本文讨论了强化学习技术在能源系统中的应用以及这些模型解释如何有帮助。

能源系统中为何需要强化学习解释

Why Reinforcement Learning in Energy Systems Needs Explanations

Long-term planning of a robust power system requires the understanding of
changing demand patterns. Electricity demand is highly weather sensitive. Thus,
the supply side variation from introducing intermittent renewable sources,
juxtaposed with variable demand, will introduce additional challenges in the
grid planning process. By understanding the spatial and temporal variability of
temperature over the US, the response of demand to natural variability and
climate change-related effects on temperature can be separated, especially
because the effects due to the former factor are not known. Through this
project, we aim to better support the technology & policy development process
for power systems by developing machine and deep learning 'back-forecasting'
models to reconstruct multidecadal demand records and study the natural
variability of temperature and its influence on demand.

通过开发机器和深度学习的 “后向预测” 模型，重建多十年需求记录，研究温度的自然变化以及其对需求的影响，以更好地支持电力系统技术和政策的发展过程。

长期历史需求数据的重建

Reconstruction of Long-Term Historical Demand Data

The AC Optimal Power Flow (AC-OPF) is a key building block in many power
system applications. It determines generator setpoints at minimal cost that
meet the power demands while satisfying the underlying physical and operational
constraints. It is non-convex and NP-hard, and computationally challenging for
large-scale power systems. Motivated by the increased stochasticity in
generation schedules and increasing penetration of renewable sources, this
paper explores a deep learning approach to deliver highly efficient and
accurate approximations to the AC-OPF. In particular, the paper proposes an
integration of deep neural networks and Lagrangian duality to capture the
physical and operational constraints. The resulting model, called OPF-DNN, is
evaluated on real case studies from the French transmission system, with up to
3,400 buses and 4,500 lines. Computational results show that OPF-DNN produces
highly accurate AC-OPF approximations whose costs are within 0.01% of
optimality. OPF-DNN generates, in milliseconds, solutions that capture the
problem constraints with high fidelity.

本文介绍了一种名为 OPF-DNN 的模型，它将深度神经网络和 Lagrangian 对偶相结合，以在满足物理和运行约束条件的同时确保发电机设定点的最小成本，从而在大型电力系统中提供高效准确的交流最优功率流（AC-OPF）近似解决方案。