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

智能能源选择异构偏好的结构统计学习以及协调电动汽车充电的案例研究

• 批准号: 2342215
• 负责人: Ricardo Daziano
• 金额: $ 39.04万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2342215&HistoricalAwards=false
• 关键词: Structural; statistical; learning; heterogeneous; preferences

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.

该项目正在开发用户友好的工具，以指导有关清洁能源的最佳选择，尤其是关于电动汽车（EV）的最佳选择。努力专注于创建能够从现实世界数据中学习的简单有效的计算机算法，以提出理想的电动汽车充电时间和条件。意义在于用户适应日益复杂的能源环境，采用更多可持续的选择和行为，并获得了对能源使用的更多控制。该项目以电动汽车为中心是减少运输中碳排放不可分割的组成部分。尽管电池和充电技术取得了重大进步，但规划电动汽车的充电方式仍存在许多挑战，并且这种情况被毒死了，担心电气化运输的预期渗透率会增加。目的是确保所有电动汽车用户都可以访问这些工具，不仅重点是提出促进可再生能源的建议，而且还要考虑考虑整个能源系统的个人偏好和潜在的好处。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。采用高级统计技术，利用高效的计算机算法，以确保速度，灵活性，可解释性和现实世界中的政策相关性。拟议的系统适应并学习实时的充电偏好，在协调的EV充电的背景下提供能源管理建议，同时考虑电动需求，价格和电网稳定性等因素。 The methodical modular Markov chain fast and scalable sampler of the proposed incentive-compatible recommendation 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),贝叶斯优化用于调整超参数并与系统等效算法集成，以及在战略剂之间和内部的偏好和动机中的异质性的灵活的半参数表示。研究团队以更简单的方式实现了该项目，为驾驶电动汽车的个人创建了有用的指南，建议根据其偏好和驾驶方式提出理想的充电时间和需求。智能技术用于收集信息，为用户提供个性化建议。该项目的影响超出了个人帮助，为能源系统的整体可持续性做出了贡献，并为能源相关的组织，公用事业和决策者提供了有价值的工具。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估而被认为是珍贵的支持。