Structural statistical learning of heterogeneous preferences for smart energy choices with a case study on coordinated electric vehicle charging

该项目正在开发用户友好的工具，以指导清洁能源的最佳选择，特别是在电动汽车充电方面。努力的重点是创建简单有效的计算机算法，能够从现实世界的数据中学习，为电动汽车提出理想的充电时间和条件。其意义在于用户适应日益复杂的能源环境，采用更可持续的选择和行为，并获得对能源使用的更多控制。该项目以电动汽车为中心，将其作为减少交通运输碳排放的组成部分。尽管电池和充电技术取得了重大进步，但在规划电动汽车充电方式方面仍存在许多挑战，随着电动交通的普及率预计将不断提高，情况将进一步恶化。其目的是确保所有电动汽车用户都能使用这些工具，不仅要提出促进可再生能源使用的充电建议，还要考虑个人偏好和对整个能源系统的潜在好处。最终目标是简化决策，使用户更容易以最佳方式管理能源消耗，而不会感到不知所措。采用先进的统计技术，利用高效的计算机算法，将确保速度，灵活性，可解释性和现实世界的政策相关性。该系统实时适应和学习充电偏好，在协调电动汽车充电的背景下提供能源管理建议，同时考虑电力需求，定价和电网稳定性等因素。所提出的激励兼容推荐系统的有条理的模块化马尔可夫链快速和可扩展的采样器通过集成来设计：用于多指标选择模型的终极Pólya-Gamma数据增强的扩展，以在不存在共轭的情况下创建共轭，用于效率的摊销和非因子化变分推理，用于跨服务功能的复杂非参数权衡的Choquet聚合（放松线性补偿行为），贝叶斯内隐控制，贝叶斯优化调整超参数和系统均衡算法的整合，以及灵活的半参数表示的异质性偏好和动机之间和内部的战略代理。该研究团队通过实际的住宅项目来实施该项目，以协调电动汽车充电的调度，例如通过离散目标捆绑的自我选择价格歧视来避免有效的价格内隐和约束。简而言之，为驾驶电动汽车的个人创建了一个有用的指南，根据他们的偏好和驾驶模式建议理想的充电时间和需求。采用智能技术收集信息，为用户提供个性化建议。该项目的影响超出了个人援助，有助于能源系统的整体可持续性，并为能源相关组织，公用事业和政策制定者提供宝贵的工具。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project is developing user-friendly tools to guide optimal choices about clean energy, especially regarding charging electric vehicles (EVs). Efforts focus on creating simple and efficient computer algorithms capable of learning from real-world data to suggest ideal charging times and conditions for EVs. The significance lies in users adapting to increasingly complex energy environments, adopting more sustainable options and behaviors, and gaining more control over energy use. The project centers on EVs as integral to reducing carbon emissions from transportation. Despite significant advancements in battery and charging technologies, there are many challenges in planning how EVs are charged, and the situation is poised to worsen with the expected increasing penetration of electrified transportation. The aim is to ensure these tools will be accessible to all EV users, focusing not only on making charging recommendations that promote the use of renewable energy but also on considering individual preferences and potential benefits to the entire energy system. The ultimate objective is to simplify decision-making, making it easier for users to manage energy consumption in optimal ways without feeling overwhelmed. Advanced statistical techniques are employed, utilizing highly efficient computer algorithms that will ensure speed, flexibility, interpretability, and real-world policy relevance. The proposed system adapts and learns charging preferences in real-time, providing energy management recommendations in the context of coordinated EV charging while considering factors such as electricity demand, pricing, and grid stability. The methodical modular Markov chain fast and scalable sampler of the proposed incentive-compatible recommender system is designed by integrating: an expansion of Ultimate Pólya-Gamma data augmentation for multi-index choice models to create conjugacy where it does not exist, amortized and non-factorized variational inference for efficiency, Choquet aggregation for complex nonparametric tradeoffs across service features (relaxing linear compensatory behavior), Bayes endogeneity controls, Bayesian optimization for tuning hyperparameters and for integration with a system equilibrium algorithm, and flexible semiparametric representation of heterogeneity in preferences and motives between and within strategic agents. The research team implements the project through an actual residential program for coordinated scheduling of electric-vehicle charging, such as avoiding in-force price endogeneity and constraints via self-selection price discrimination through discrete targeted bundles. In simpler terms, a helpful guide is created for individuals driving electric vehicles, suggesting ideal charging times and needs based on their preferences and driving patterns. Smart technology is employed to gather information, enabling the provision of personalized suggestions to users. The impact of the project extends beyond individual assistance, contributing to the overall sustainability of energy systems and providing valuable tools for energy-related organizations, utilities, and policymakers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.