Probabilistic Modeling and Stochastic Optimization for Effective Demand Response Decision Management under Uncertainties in Emerging Smart Energy Markets

随着可再生能源资源和住宅智能家庭网络的快速扩张，未来的电力分销商必须应对一个极其动态和复杂的供需平衡问题，这将导致一个截然不同的能源市场。为了解决这种市场转变，需求响应（DR）的概念被提出，通过受动态市场电价调节的负荷和发电量的客户侧响应来实现动态供需平衡。尽管在灾难恢复研究方面取得了许多令人鼓舞的进展，但目前的数据分析和优化模型在能力和可扩展性方面仍然有限，无法在各种系统和市场不确定性下实现有效的灾难恢复决策。该项目的主要目标是弥合这一知识差距，并提供新的预测建模和优化方法，使负载服务实体（lse）能够有效地进行灾难恢复决策管理，从而在能源市场中部署实用且可持续的灾难恢复计划。该项目在方法上的突破对于促进未来几年可再生能源和住宅智能家居网络大规模整合的智能电网转型具有广泛的社会影响。该项目将组织外展和教育活动，以扩大不同学生的参与，特别是代表性不足的少数民族，在整合数据科学，统计学，机器学习和大数据时代复杂系统决策优化的高度有影响力的研究领域。这项研究的结果将通过一系列关于决策分析、概率机器学习和智能能源技术的研讨会和研讨会进行传播。为了解决在随机智能电网市场中部署实用和可持续的需求响应（DR）计划的挑战，本研究将通过三个综合研究任务建立新的数学建模和随机优化方法：1)开发一种新的概率深度学习方法来研究复杂的时空变量相互作用并进行不确定性量化的顺序预测；并针对电价、负荷和可再生能源发电等关键市场变量开发新的预测模型；2)开发了一种多智能体自适应动态规划（ADP）方法，用于全面的容灾规划和运行优化框架，以实现实时最优的单日容灾操作；3)建立两阶段优化框架，对日前能源市场的能源交易和DR决策进行优化。概率深度学习方法将贝叶斯非参数方法与深度学习结构相结合，解决复杂的多元序列到序列预测问题。准确量化预测误差对于降低不确定市场中的决策风险至关重要。多智能体ADP和两阶段优化问题都将使用计算机实验方法的设计和分析，这将为LSE与动态客户代理和随机智能能源市场互动提供有效的实时和提前决策过程。本研究的结果将为新兴智能能源市场开发有效和可持续的DR管理方案奠定坚实的方法论基础。该项目将推进概率深度学习和随机优化研究的前沿知识，以解决高度随机环境中许多具有挑战性的现实世界决策问题。该奖项反映了美国国家科学基金会的法定使命，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。

With rapid expansion of renewable energy resources and residential smart home networks, power distributors in the future must respond to an extremely dynamic and complex supply-demand balancing problem, which will lead to a dramatically different energy market. To address such market transformation, the concept of demand response (DR) has been proposed to achieve dynamic supply-demand balance through customer side response of load and generation regulated by dynamic market electricity prices. Although there have been many encouraging advances in DR research, the current data analytics and optimization models are still limited in capability and scalability to achieve effective DR decision-making under various system and market uncertainties. The primary target of this project is to bridge this knowledge gap and provide new predictive modeling and optimization methods enabling effective DR decision management for load service entities (LSEs) to deploy practical and sustainable DR programs in the energy markets. The methodological breakthroughs in this project have broad societal impact on facilitating smart grid transformation in the upcoming years for large-scale integration of renewable energy resources and residential smart home networks. This project will organize outreach and educational activities to broaden a diverse student participation, particularly underrepresented minorities, in highly impactful research areas that integrate data science, statistics, machine learning, and optimization for complex system decision-making in the big data era. Results of this research will be disseminated through a series of workshops and seminars on decision analytics, probabilistic machine learning, and smart energy technologies. To address the challenges of deploying practical and sustainable demand response (DR) programs in a stochastic smart grid market, this research will establish new mathematical modeling and stochastic optimization methods via three integrated research tasks: 1) develop a new probabilistic deep learning method to investigate complex spatial-temporal variable interactions and make sequential forecasts with uncertainty quantification; and accordingly develop new forecasting models for key market variables, such as electricity price, load and renewable generation, etc.; 2) develop a multi-agent adaptive dynamic programming (ADP) approach for a comprehensive DR planning and operational optimization framework to achieve real-time optimal intra-day DR operations; 3) develop a two-stage optimization framework to optimize energy transactions and DR decisions in the day-ahead energy markets. The probabilistic deep learning method combines Bayesian nonparametric methods with deep learning structures to solve complex multivariate sequence-to-sequence forecasting problems. It is critical to quantify forecasting errors precisely to mitigate risks of decision making in uncertain markets. Both the multi-agent ADP and two-stage optimization problems will use a design and analysis of computer experiments approach that will enable effective real-time and day-ahead decision-making processes for an LSE to interact with dynamic customer agents and stochastic smart energy markets. The outcomes of this research will construct a solid methodological foundation to develop effective and sustainable DR management programs for the emerging smart energy markets. This project will advance the frontiers of knowledge in probabilistic deep learning and stochastic optimization research to solve many challenging real-world decision-making problems in highly stochastic environments.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.