With the growth of Renewable Energy (RE) generation, the operation of power
grids has become increasingly complex. One solution is automated grid
operation, where Deep Reinforcement Learning (DRL) has repeatedly shown
significant potential in Learning to Run a Power Network (L2RPN) challenges.
However, only individual actions at the substation level have been subjected to
topology optimization by most existing DRL algorithms. In contrast, we propose
a more holistic approach in this paper by proposing specific Target Topologies
(TTs) as actions. These topologies are selected based on their robustness. As
part of this paper, we present a search algorithm to find the TTs and upgrade
our previously developed DRL agent CurriculumAgent (CAgent) to a novel topology
agent. We compare the upgrade to the previous CAgent agent and can increase
their scores significantly by 10%. Further, we achieve a 25% better median
survival with our TTs included. Later analysis shows that almost all TTs are
close to the base topology, explaining their robustness.

通过使用特定的目标拓扑结构（TTs），以其鲁棒性为基础，本研究提出了一种更全面的自动化电网运行解决方案，采用深度强化学习算法对电网进行拓扑优化，与之前的算法相比，结果表明得分提高了 10％，并且加入 TTs 后，存活率提高了 25％。

HUGO: 结合深度强化学习与启发式目标拓扑方法的突出显示未见格点选项

HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement  Learning with a Heuristic Target Topology Approach

Achieving net zero carbon emissions by 2050 requires the integration of
increasing amounts of wind power into power grids. This energy source poses a
challenge to system operators due to its variability and uncertainty.
Therefore, accurate forecasting of wind power is critical for grid operation
and system balancing. This paper presents an innovative approach to short-term
(1 to 6 hour horizon) windpower forecasting at a national level. The method
leverages Automated Deep Learning combined with Numerical Weather Predictions
wind speed maps to accurately forecast wind power.

本文介绍了一种创新的方法，利用自动深度学习结合数值天气预报风速地图，准确预测全国范围内的短期（1 到 6 小时）风力发电。

风龙：自动深度学习增强风力发电预测

WindDragon: Enhancing wind power forecasting with Automated Deep  Learning

Flexible district heating grids form an important part of future, low-carbon
energy systems. We examine probabilistic state estimation in such grids, i.e.,
we aim to estimate the posterior probability distribution over all grid state
variables such as pressures, temperatures, and mass flows conditional on
measurements of a subset of these states. Since the posterior state
distribution does not belong to a standard class of probability distributions,
we use Markov Chain Monte Carlo (MCMC) sampling in the space of network heat
exchanges and evaluate the samples in the grid state space to estimate the
posterior. Converting the heat exchange samples into grid states by solving the
non-linear grid equations makes this approach computationally burdensome.
However, we propose to speed it up by employing a deep neural network that is
trained to approximate the solution of the exact but slow non-linear solver.
This novel approach is shown to deliver highly accurate posterior distributions
both for classic tree-shaped as well as meshed heating grids, at significantly
reduced computational costs that are acceptable for online control. Our state
estimation approach thus enables tightening the safety margins for temperature
and pressure control and thereby a more efficient grid operation.

这篇论文提出一种使用马尔可夫蒙特卡罗采样和深度神经网络来估计柔性区域能源网格的概率状态分布的方法，从而实现更高效的对温度和压力的控制和更加安全的网格运营。