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.

Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Texas at Arlington
United States
Zip Code