在具有可再生生成的大量份额的网格中，由于负载和发电的波动性增加，运营商将需要其他工具来评估运营风险。正向不确定性传播问题的计算要求必须解决众多安全受限的经济调度（SCED）优化，是这种实时风险评估的主要障碍。本文提出了一个即时风险评估学习框架（Jitralf）作为替代方案。 Jitralf训练风险代理，每天每小时一个，使用机器学习（ML）来预测估计风险所需的数量，而无需明确解决SCED问题。这大大减轻了正向不确定性传播的计算负担，并允许快速，实时的风险估计。本文还提出了一种新颖的，不对称的损失函数，并表明使用不对称损失训练的模型的性能优于使用对称损耗函数的模型。在法国传输系统上评估了Jitralf，以评估运营储量不足的风险，减轻负载的风险和预期的运营成本。

安全约束的经济调度（SCED）是传输系统运营商（TSO）的基本优化模型，以清除实时能源市场，同时确保电网的可靠操作。在不断增长的运营不确定性的背景下，由于可再生发电机和分布式能源资源的渗透率增加，运营商必须实时监控风险，即，他们必须在负载和可再生生产的各种变化下快速评估系统的行为。遗憾的是，鉴于实时操作的严格约束，系统地解决了每个这样的场景的优化问题。为了克服这种限制，本文提出了学习SCED，即机器学习（ML）模型的优化代理，其可以预测用于以毫秒为单位的最佳解决方案。本文提出了对MISO市场清算优化优化的原则性分析，提出了一种新颖的ML管道，解决了学习SCES解决方案的主要挑战，即负载，可再生产量和生产成本以及组合结构的变化，以及组合结构承诺决定。还提出了一种新的分类 - 然后回归架构，以进一步捕获SCED解决方案的行为。在法国传输系统上报告了数值实验，并展示了该方法在与实时操作兼容的时间范围内生产的能力，精确的优化代理产生相对误差低于0.6 \％$。

Reliability Assessment Commitment (RAC) Optimization is increasingly important in grid operations due to larger shares of renewable generations in the generation mix and increased prediction errors. Independent System Operators (ISOs) also aim at using finer time granularities, longer time horizons, and possibly stochastic formulations for additional economic and reliability benefits. The goal of this paper is to address the computational challenges arising in extending the scope of RAC formulations. It presents RACLEARN that (1) uses Graph Neural Networks (GNN) to predict generator commitments and active line constraints, (2) associates a confidence value to each commitment prediction, (3) selects a subset of the high-confidence predictions, which are (4) repaired for feasibility, and (5) seeds a state-of-the-art optimization algorithm with the feasible predictions and the active constraints. Experimental results on exact RAC formulations used by the Midcontinent Independent System Operator (MISO) and an actual transmission network (8965 transmission lines, 6708 buses, 1890 generators, and 6262 load units) show that the RACLEARN framework can speed up RAC optimization by factors ranging from 2 to 4 with negligible loss in solution quality.

电价是影响所有市场参与者决策的关键因素。准确的电价预测非常重要，并且由于各种因素，电价高度挥发性，电价也非常具有挑战性。本文提出了一项综合的长期经常性卷积网络（ILRCN）模型，以预测考虑到市场价格的大多数贡献属性的电力价格。所提出的ILRCN模型将卷积神经网络和长短期记忆（LSTM）算法的功能与所提出的新颖的条件纠错项相结合。组合的ILRCN模型可以识别输入数据内的线性和非线性行为。我们使用鄂尔顿批发市场价格数据以及负载型材，温度和其他因素来说明所提出的模型。使用平均绝对误差和准确性等性能/评估度量来验证所提出的ILRCN电价预测模型的性能。案例研究表明，与支持向量机（SVM）模型，完全连接的神经网络模型，LSTM模型和LRCN模型，所提出的ILRCN模型在电价预测中是准确和有效的电力价格预测。

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling," held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.

低压网络中分布式能源的渗透不断增加，这将最终用户从消费者转变为生产者。但是，由于零售和网络服务提供的监管分离，智能电表数据的不完整智能电表的推出和缺乏智能电表数据，这使主动分配网络管理变得困难。此外，分销网络运营商通常无法访问实时智能电表数据，这会带来额外的挑战。由于缺乏更好的解决方案，他们使用毯子屋顶太阳能出口限制，从而导致次优结果。为了解决这个问题，我们设计了一个有条件的生成对抗网络（CGAN）的模型来预测家庭太阳能产生和电力需求，这是用于在不确定性下用于计算公平操作信封的机会约束最佳功率流的输入。

The energy sector is facing rapid changes in the transition towards clean renewable sources. However, the growing share of volatile, fluctuating renewable generation such as wind or solar energy has already led to an increase in power grid congestion and network security concerns. Grid operators mitigate these by modifying either generation or demand (redispatching, curtailment, flexible loads). Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector. In this paper, we propose an AlphaZero-based grid topology optimization agent as a non-costly, carbon-free congestion management alternative. Our experimental evaluation confirms the potential of topology optimization for power grid operation, achieves a reduction of the average amount of required redispatching by 60%, and shows the interoperability with traditional congestion management methods. Our approach also ranked 1st in the WCCI 2022 Learning to Run a Power Network (L2RPN) competition. Based on our findings, we identify and discuss open research problems as well as technical challenges for a productive system on a real power grid.

评估能源转型和能源市场自由化对资源充足性的影响是一种越来越重要和苛刻的任务。能量系统的上升复杂性需要足够的能量系统建模方法，从而提高计算要求。此外，随着复杂性，同样调用概率评估和场景分析同样增加不确定性。为了充分和高效地解决这些各种要求，需要来自数据科学领域的新方法来加速当前方法。通过我们的系统文献综述，我们希望缩小三个学科之间的差距（1）电力供应安全性评估，（2）人工智能和（3）实验设计。为此，我们对所选应用领域进行大规模的定量审查，并制作彼此不同学科的合成。在其他发现之外，我们使用基于AI的方法和应用程序的AI方法和应用来确定电力供应模型的复杂安全性的元素，并作为未充分涵盖的应用领域的储存调度和（非）可用性。我们结束了推出了一种新的方法管道，以便在评估电力供应安全评估时充分有效地解决当前和即将到来的挑战。

A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.

本文在线学习和优化框架内提出并开发了一种用于电力市场中风能交易的新算法。特别是，我们将梯度下降算法的组成部分自适应变体与功能驱动的新闻册模型的最新进展相结合。这导致了一种在线产品的方法，能够利用数据丰富的环境，同时适应能源发电和发电市场的非平稳特征，并且具有最小的计算负担。根据几个数值实验，对我们的方法的性能进行了分析，既显示了对非平稳性不确定参数的更好适应性和显着的经济增长。

PV power forecasting models are predominantly based on machine learning algorithms which do not provide any insight into or explanation about their predictions (black boxes). Therefore, their direct implementation in environments where transparency is required, and the trust associated with their predictions may be questioned. To this end, we propose a two stage probabilistic forecasting framework able to generate highly accurate, reliable, and sharp forecasts yet offering full transparency on both the point forecasts and the prediction intervals (PIs). In the first stage, we exploit natural gradient boosting (NGBoost) for yielding probabilistic forecasts, while in the second stage, we calculate the Shapley additive explanation (SHAP) values in order to fully comprehend why a prediction was made. To highlight the performance and the applicability of the proposed framework, real data from two PV parks located in Southern Germany are employed. Comparative results with two state-of-the-art algorithms, namely Gaussian process and lower upper bound estimation, manifest a significant increase in the point forecast accuracy and in the overall probabilistic performance. Most importantly, a detailed analysis of the model's complex nonlinear relationships and interaction effects between the various features is presented. This allows interpreting the model, identifying some learned physical properties, explaining individual predictions, reducing the computational requirements for the training without jeopardizing the model accuracy, detecting possible bugs, and gaining trust in the model. Finally, we conclude that the model was able to develop complex nonlinear relationships which follow known physical properties as well as human logic and intuition.

预先完成的操作涉及一个复杂且计算密集的优化过程，以确定发电机的承诺时间表和调度。优化过程是一个混合企业线性程序（MILP），也称为安全受限的单位承诺（SCUC）。独立的系统操作员（ISO）每天运行SCUC，并需要最先进的算法来加快流程。可以利用历史信息中的现有模式来减少SCUC模型，这可以节省大量时间。在本文中，研究了基于机器学习（ML）的分类方法，即逻辑回归，神经网络，随机森林和K-Nearest邻居，以减少SCUC模型。然后，使用可行性层（FL）和后处理技术来帮助ML，以确保高质量的解决方案。提出的方法在多个测试系统上进行了验证，即IEEE 24总线系统，IEEE-73总线系统，IEEE 118总线系统，500个总线系统和波兰2383-BUS系统。此外，使用可再生生成的改良IEEE 24总线系统，证明了随机SCUC（SSCUC）的模型降低。仿真结果证明了高训练的准确性，以确定承诺时间表，而FL和后处理确保ML预测不会导致溶液质量损失最小的可行解决方案。

作为行业4.0时代的一项新兴技术，数字双胞胎因其承诺进一步优化流程设计，质量控制，健康监测，决策和政策制定等，通过全面对物理世界进行建模，以进一步优化流程设计，质量控制，健康监测，决策和政策，因此获得了前所未有的关注。互连的数字模型。在一系列两部分的论文中，我们研究了不同建模技术，孪生启用技术以及数字双胞胎常用的不确定性量化和优化方法的基本作用。第二篇论文介绍了数字双胞胎的关键启示技术的文献综述，重点是不确定性量化，优化方法，开源数据集和工具，主要发现，挑战和未来方向。讨论的重点是当前的不确定性量化和优化方法，以及如何在数字双胞胎的不同维度中应用它们。此外，本文介绍了一个案例研究，其中构建和测试了电池数字双胞胎，以说明在这两部分评论中回顾的一些建模和孪生方法。 GITHUB上可以找到用于生成案例研究中所有结果和数字的代码和预处理数据。

Machine learning (ML) algorithms are remarkably good at approximating complex non-linear relationships. Most ML training processes, however, are designed to deliver ML tools with good average performance, but do not offer any guarantees about their worst-case estimation error. For safety-critical systems such as power systems, this places a major barrier for their adoption. So far, approaches could determine the worst-case violations of only trained ML algorithms. To the best of our knowledge, this is the first paper to introduce a neural network training procedure designed to achieve both a good average performance and minimum worst-case violations. Using the Optimal Power Flow (OPF) problem as a guiding application, our approach (i) introduces a framework that reduces the worst-case generation constraint violations during training, incorporating them as a differentiable optimization layer; and (ii) presents a neural network sequential learning architecture to significantly accelerate it. We demonstrate the proposed architecture on four different test systems ranging from 39 buses to 162 buses, for both AC-OPF and DC-OPF applications.

Energy consumption in buildings, both residential and commercial, accounts for approximately 40% of all energy usage in the U.S., and similar numbers are being reported from countries around the world. This significant amount of energy is used to maintain a comfortable, secure, and productive environment for the occupants. So, it is crucial that the energy consumption in buildings must be optimized, all the while maintaining satisfactory levels of occupant comfort, health, and safety. Recently, Machine Learning has been proven to be an invaluable tool in deriving important insights from data and optimizing various systems. In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient. For the convenience of readers, we provide a brief introduction of several machine learning paradigms and the components and functioning of each smart building system we cover. Finally, we discuss challenges faced while implementing machine learning algorithms in smart buildings and provide future avenues for research at the intersection of smart buildings and machine learning.

Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced Plug and Play control strategies have been recently developed with such a decentralized framework in mind, more easily allowing for the interconnection of nomadic communities, both to each other and to the main grid. In light of the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) is implemented for the design and planning problem tackled. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access.

本文考虑了最佳功率流（OPF）的优化代理，即近似于OPF的输入/输出关系的机器学习模型。最近的工作重点是表明此类代理可能具有高忠诚。但是，他们的培训需要大量数据，每个实例都需要（离线）解决输入分布样本的OPF。为了满足市场清除应用程序的要求，本文提出了积极的桶装采样（ABS），这是一个新型的活跃学习框架，旨在培训在一个时间限制内培训最佳OPF代理。ABS将输入分布分配到存储桶中，并使用采集函数来确定接下来的何处。它依靠自适应学习率，随着时间的推移会增加和降低。实验结果证明了ABS的好处。

能源部门的深度脱碳将需要大量的随机可再生能源渗透和大量的网格资产协调。对于面对这种变化而负责维持电网稳定性和安全性的电力系统运营商来说，这是一个具有挑战性的范式。凭借从复杂数据集中学习并提供有关快速时间尺度的预测解决方案的能力，机器学习（ML）得到了很好的选择，可以帮助克服这些挑战，因为在未来几十年中，电力系统转变。在这项工作中，我们概述了与构建可信赖的ML模型相关的五个关键挑战（数据集生成，数据预处理，模型培训，模型评估和模型嵌入），这些模型从基于物理的仿真数据中学习。然后，我们演示如何将单个模块连接在一起，每个模块都克服了各自的挑战，在机器学习管道中的顺序阶段，如何有助于提高训练过程的整体性能。特别是，我们实施了通过反馈连接学习管道的不同元素的方法，从而在模型培训，绩效评估和重新训练之间“关闭循环”。我们通过学习与拟议的北海风能中心系统的详细模型相关的N-1小信号稳定性边缘来证明该框架，其组成模块的有效性及其反馈连接。

本文介绍了电力系统运营商的域知识如何集成到强化学习（RL）框架中，以有效学习控制电网拓扑以防止热级联的代理。由于大搜索/优化空间，典型的基于RL的拓扑控制器无法表现良好。在这里，我们提出了一个基于演员 - 评论家的代理，以解决问题的组合性质，并使用由RTE，法国TSO开发的RL环境训练代理。为了解决大型优化空间的挑战，通过使用网络物理修改环境以增强代理学习来纳入训练过程中的基于奖励调整的基于课程的方法。此外，采用多种方案的并行训练方法来避免将代理偏置到几种情况，并使其稳健地对网格操作中的自然变异性。如果没有对培训过程进行这些修改，则RL代理失败了大多数测试场景，说明了正确整合物理系统的域知识以获得真实世界的RL学习的重要性。该代理通过RTE测试2019年学习，以运行电力网络挑战，并以精确度和第1位的速度授予第2位。开发的代码是公共使用开放的。

多阶段随机线性问题（MSLP）的解决方案代表了许多应用程序的挑战。长期水热调度计划（LHDP）在影响全球电力市场，经济和自然资源的现实世界中实现了这一挑战。没有用于MSLP的封闭式解决方案，并且具有高质量的非预期策略的定义是至关重要的。线性决策规则（LDR）提供了一个有趣的基于模拟的框架，可通过两阶段随机模型为MSLP找到高质量的策略。但是，在实际应用中，使用LDR时要估计的参数数量可能接近或高于样本平均近似问题的场景数量，从而在样本外产生样本外的过度效果和差的表现不佳模拟。在本文中，我们提出了一个新型的正则LDR来基于Adalasso（自适应最少的绝对收缩和选择算子）求解MSLP。目的是使用高维线性回归模型中所研究的简约原理，以获得应用于MSLP的LDR的更好的样本外部性能。计算实验表明，使用经典的非规范LDR来求解LHDP时，过度合适的威胁是不可忽略的，这是研究最多的MSLP之一，其中具有相关应用在行业中。我们的分析强调了拟议框架与非规范化基准相比的以下好处：1）非零系数的数量显着减少（模型简约），2）2）大幅度降低样本外评估的成本降低， 3）改善了现货价格概况。