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。

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.

Decision-making problems are commonly formulated as optimization problems, which are then solved to make optimal decisions. In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model. This statistical learning problem is referred to as data-driven inverse optimization. We focus on problems where the underlying decision-making process is modeled as a convex optimization problem whose parameters are unknown. We formulate the inverse optimization problem as a bilevel program and propose an efficient block coordinate descent-based algorithm to solve large problem instances. Numerical experiments on synthetic datasets demonstrate the computational advantage of our method compared to standard commercial solvers. Moreover, the real-world utility of the proposed approach is highlighted through two realistic case studies in which we consider estimating risk preferences and learning local constraint parameters of agents in a multiplayer Nash bargaining game.

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。

由于数据量增加，金融业的快速变化已经彻底改变了数据处理和数据分析的技术，并带来了新的理论和计算挑战。与古典随机控制理论和解决财务决策问题的其他分析方法相比，解决模型假设的财务决策问题，强化学习（RL）的新发展能够充分利用具有更少模型假设的大量财务数据并改善复杂的金融环境中的决策。该调查纸目的旨在审查最近的资金途径的发展和使用RL方法。我们介绍了马尔可夫决策过程，这是许多常用的RL方法的设置。然后引入各种算法，重点介绍不需要任何模型假设的基于价值和基于策略的方法。连接是用神经网络进行的，以扩展框架以包含深的RL算法。我们的调查通过讨论了这些RL算法在金融中各种决策问题中的应用，包括最佳执行，投资组合优化，期权定价和对冲，市场制作，智能订单路由和Robo-Awaring。

我们考虑一个一般的在线随机优化问题，在有限时间段的视野中具有多个预算限制。在每个时间段内，都会揭示奖励功能和多个成本功能，并且决策者需要从凸面和紧凑型措施中指定行动，以收集奖励并消耗预算。每个成本函数对应于一个预算的消费。在每个时期，奖励和成本函数都是从未知分布中得出的，该分布在整个时间内都是非平稳的。决策者的目的是最大化受预算限制的累积奖励。该配方捕获了广泛的应用程序，包括在线线性编程和网络收入管理等。在本文中，我们考虑了两个设置：（i）一个数据驱动的设置，其中真实分布未知，但可以提供先前的估计（可能不准确）； （ii）一个不信息的环境，其中真实分布是完全未知的。我们提出了一项基于统一的浪费距离措施，以量化设置（i）中先验估计值的不准确性和设置（ii）中系统的非平稳性。我们表明，拟议的措施导致在两种情况下都能获得统一后悔的必要条件。对于设置（i），我们提出了一种新的算法，该算法采用了原始的偶视角，并将基础分布的先前信息集成到双重空间中的在线梯度下降过程。该算法也自然扩展到非信息设置（II）。在这两种设置下，我们显示相应的算法实现了最佳秩序的遗憾。在数值实验中，我们演示了如何将所提出的算法与重新溶解技术自然整合，以进一步提高经验性能。

Uncertainty is prevalent in engineering design, statistical learning, and decision making broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to address uncertainty by formulating and solving conservative optimization models expressed using measure of risk and related concepts. We survey the rapid development of risk measures over the last quarter century. From its beginning in financial engineering, we recount their spread to nearly all areas of engineering and applied mathematics. Solidly rooted in convex analysis, risk measures furnish a general framework for handling uncertainty with significant computational and theoretical advantages. We describe the key facts, list several concrete algorithms, and provide an extensive list of references for further reading. The survey recalls connections with utility theory and distributionally robust optimization, points to emerging applications areas such as fair machine learning, and defines measures of reliability.

我们提出了一种专门的方案生成方法，该方法利用预测信息来生成用于日期调度问题的方案。特别是，我们使用归一化的流量来通过从有条件的分布进行采样，该分布使用风速预测来定制方案到特定的一天。我们将生成的方案应用于风能生产者的随机日期招标问题中，并分析该方案是否产生有利可图的决策。与高斯Copulas和Wasserstein基因的对抗网络相比，正常化的流程成功地缩小了每日趋势周围的各种场景范围，同时保持了各种可能的实现。在随机日间招标问题中，与历史场景的无条件选择相比，所有方法的条件情况都会导致更稳定的盈利结果。归一化流量始终获得最高利润，即使对于小型场景。

随机双动态编程（SDDP）是一种用于解决多级随机优化的最新方法，广泛用于建模现实世界流程优化任务。不幸的是，SDDP具有最糟糕的复杂性，其在决策变量的数量中呈指数级级别，这严重限制了仅限于低维度问题的适用性。为了克服这一限制，我们通过引入培训神经模型来扩展SDDP，该培训神经模型将在内部低维空间内将问题实例映射到内在的低维空间内的分型线性值函数，该尺寸低维空间是专门用于与基础SDDP求解器进行交互的架构成型，因此可以在新实例上加速优化性能。通过解决连续问题，提出的神经随机双动态编程（$ \ nu $ -sddp）不断自我提高。实证调查表明，$ \ nu $ -sddp可以显着降低解决问题的问题，而不会在一系列合成和实际过程优化问题上牺牲竞争对手的解决方案质量。

我们通过反馈信息研究了离线和在线上下文优化的问题，而不是观察损失，我们会在事后观察到最佳的动作，而是对目标功能充分了解的甲骨文。我们的目标是最大程度地减少遗憾，这被定义为我们的损失与全知的甲骨所产生的损失之间的区别。在离线设置中，决策者可以从过去段中获得信息，并且需要做出一个决策，而在在线环境中，决策者在每个时期内都会动态地基于一组新的可行动作和上下文功能，以动态进行决策。 。对于离线设置，我们表征了最佳的最小策略，确定可以实现的性能，这是数据引起的信息的基础几何形状的函数。在在线环境中，我们利用这种几何表征来优化累积遗憾。我们开发了一种算法，该算法在时间范围内产生了对数的第一个遗憾。

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.

预测组合在预测社区中蓬勃发展，近年来，已经成为预测研究和活动主流的一部分。现在，由单个（目标）系列产生的多个预测组合通过整合来自不同来源收集的信息，从而提高准确性，从而减轻了识别单个“最佳”预测的风险。组合方案已从没有估计的简单组合方法演变为涉及时间变化的权重，非线性组合，组件之间的相关性和交叉学习的复杂方法。它们包括结合点预测和结合概率预测。本文提供了有关预测组合的广泛文献的最新评论，并参考可用的开源软件实施。我们讨论了各种方法的潜在和局限性，并突出了这些思想如何随着时间的推移而发展。还调查了有关预测组合实用性的一些重要问题。最后，我们以当前的研究差距和未来研究的潜在见解得出结论。

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.

本研究提出了两个新的动态分配算法，将难民和寻求庇护者与东道国内的地理区域相匹配。目前在瑞士的多年来飞行员中实施的第一个，旨在通过最小不和谐的在线分配算法来最大限度地提高难民的平均预期就业水平（或利息的任何衡量结果）。尽管与后视最佳解决方案相比，所提出的算法达到了近乎最佳的预期就业，但它可能会随着时间的推移而导致定期不平衡的分配。这导致了移民资源和代理商的不良工作量低效，他们无法在地方之间移动。为了解决这个问题，第二种算法平衡了改善难民结果的目标，随着时间的推移甚至对每个地方的甚至分配。拟议方法的性能是使用来自美国最大的移民安置机构之一的真正难民移民安置数据进行说明。在此数据集上，我们发现分配平衡算法可以随着时间的推移实现接近完美的平衡，而与纯就业最大化算法相比，预期就业几乎没有损失。此外，分配平衡算法提供了许多辅助益处，包括对未知到达流量的鲁棒性，并通过更大的探索增加弹性。

Global power systems are increasingly reliant on wind energy as a mitigation strategy for climate change. However, the variability of wind energy causes system reliability to erode, resulting in the wind being curtailed and, ultimately, leading to substantial economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) that serve as onsite backup sources. Yet, this auxiliary role may significantly hamper the BESS's capacity to generate revenues from the electricity market, particularly in conducting energy arbitrage in the Spot market and providing frequency control ancillary services (FCAS) in the FCAS markets. Ideal BESS scheduling should effectively balance the BESS's role in absorbing onsite wind curtailment and trading in the electricity market, but it is difficult in practice because of the underlying coordination complexity and the stochastic nature of energy prices and wind generation. In this study, we investigate the bidding strategy of a wind-battery system co-located and participating simultaneously in both the Spot and Regulation FCAS markets. We propose a deep reinforcement learning (DRL)-based approach that decouples the market participation of the wind-battery system into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while simultaneously bidding in the wholesale Spot and FCAS markets to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy for the wind-battery system and find that our strategy generates significantly higher revenue and responds better to wind curtailment compared to an optimization-based benchmark. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to individual market participation.

这项工作解决了逆线优化，其中目标是推断线性程序的未知成本向量。具体地，我们考虑数据驱动的设置，其中可用数据是对应于线性程序的不同实例的最佳解决方案的嘈杂的观察。我们介绍了一个问题的新配方，与其他现有方法相比，允许恢复较少的限制性和一般更适当的可允许成本估算。可以表明，该逆优化问题产生有限数量的解决方案，并且我们开发了一个精确的两相算法来确定所有此类解决方案。此外，我们提出了一种有效的分解算法来解决问题的大实例。该算法自然地扩展到在线学习环境，可以用于提供成本估计的快速更新，因为新数据随着时间的推移可用。对于在线设置，我们进一步开发了一种有效的自适应采样策略，指导下一个样本的选择。所提出的方法的功效在涉及两种应用，客户偏好学习和生产计划的成本估算的计算实验中进行了证明。结果表明计算和采样努力的显着减少。

许多实际优化问题涉及不确定的参数，这些参数具有概率分布，可以使用上下文特征信息来估算。与首先估计不确定参数的分布然后基于估计优化目标的标准方法相反，我们提出了一个\ textIt {集成条件估计 - 优化}（ICEO）框架，该框架估计了随机参数的潜在条件分布同时考虑优化问题的结构。我们将随机参数的条件分布与上下文特征之间的关系直接建模，然后以与下游优化问题对齐的目标估算概率模型。我们表明，我们的ICEO方法在适度的规律性条件下渐近一致，并以概括范围的形式提供有限的性能保证。在计算上，使用ICEO方法执行估计是一种非凸面且通常是非差异的优化问题。我们提出了一种通用方法，用于近似从估计的条件分布到通过可区分函数的最佳决策的潜在非差异映射，这极大地改善了应用于非凸问题的基于梯度的算法的性能。我们还提供了半代理案例中的多项式优化解决方案方法。还进行了数值实验，以显示我们在不同情况下的方法的经验成功，包括数据样本和模型不匹配。

Virtually all machine learning tasks are characterized using some form of loss function, and "good performance" is typically stated in terms of a sufficiently small average loss, taken over the random draw of test data. While optimizing for performance on average is intuitive, convenient to analyze in theory, and easy to implement in practice, such a choice brings about trade-offs. In this work, we survey and introduce a wide variety of non-traditional criteria used to design and evaluate machine learning algorithms, place the classical paradigm within the proper historical context, and propose a view of learning problems which emphasizes the question of "what makes for a desirable loss distribution?" in place of tacit use of the expected loss.

资源限制的在线分配问题是收入管理和在线广告中的核心问题。在这些问题中，请求在有限的地平线期间顺序到达，对于每个请求，决策者需要选择消耗一定数量资源并生成奖励的动作。目标是最大限度地提高累计奖励，这是对资源总消费的限制。在本文中，我们考虑一种数据驱动的设置，其中使用决策者未知的输入模型生成每个请求的奖励和资源消耗。我们设计了一般的算法算法，可以在各种输入模型中实现良好的性能，而不知道它们面临的类型类型。特别是，我们的算法在独立和相同的分布式输入以及各种非静止随机输入模型下是渐近的最佳选择，并且当输入是对抗性时，它们达到渐近最佳的固定竞争比率。我们的算法在Lagrangian双色空间中运行：它们为使用在线镜像血管更新的每个资源维护双倍乘数。通过相应地选择参考功能，我们恢复双梯度下降和双乘法权重更新算法。与现有的在线分配问题的现有方法相比，所产生的算法简单，快速，不需要在收入函数，消费函数和动作空间中凸起。我们将应用程序讨论到网络收入管理，在线竞标，重复拍卖，预算限制，与高熵的在线比例匹配，以及具有有限库存的个性化分类优化。

This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.