Current Projects

Optimal Demand Response Strategies Using Biogas from Wastewater Treatment

National Science Foundation (NSF), Award Number 2501727

This project aims to develop new algorithms and solution approaches to optimize demand response strategies of wastewater treatment plant operators under uncertainty, focusing on the benefits and barriers of using on-site biogas generators in these programs. Wastewater treatment is an energy-intensive process, and biogas produced through anaerobic digestion is often flared but can be used to provide demand response by reducing or shifting the energy consumption of wastewater treatment plants. This project focuses on the key challenges of managing uncertainties within and the variability across treatment plants and demand response programs. The project will bring transformative change by providing wastewater treatment operators with optimal demand response strategies. Increasing demand flexibility will improve power grid reliability, particularly during periods of network stress. This will be achieved by developing an approach for characterizing the uncertainty in wastewater treatment and introducing novel optimization techniques to manage these uncertainties within a demand response scheduling optimization framework. Collaboration with utilities and water quality researchers throughout the project will support the adoption of these strategies by improving stakeholder understanding of demand response program requirements, financial incentives, and operational impacts.

Stochastic Temperature-Dependent Models for Evaluating Flexible Load Dispatch

Power Systems Engineering Research Center (PSERC), Award Number S-113
Co-PI, with PI Constance Crozier (Georgia Tech) and Co-PI Dan Molzahn (Georgia Tech)

The coordination of demand-responsive, flexible loads can offer extremely low-cost load balancing to offset the variability of renewable energy. However, the potential of flexible loads to provide these services is inherently uncertain and temperature dependent. This project develops stochastic multi-period models for aggregated flexible loads, yielding a load forecast with confidence intervals. To do this, we leverage physics-based models with data-driven parameter estimation for electric vehicles, thermostatically controlled loads, and water supply systems.

Development and Validation of Single-Axis Solar Tracking Systems for Agrivoltaics

Research Excellence Fund - Research Seed Grant, Michigan Technological University
PI, with co-PI Ana Dyreson (Michigan Tech) and co-PI Chelsea Schelly (Michigan Tech)

The growing demands for both energy generation and food production intensify land competition, making agrivoltaics—integrating solar photovoltaic (PV) panels with agricultural land—a promising solution. However, existing agrivoltaic systems often rely on fixed or pre-scheduled PV tracking, which does not consider the trade-offs between power generation and crop growth. This project will establish a research team at Michigan Tech to advance agrivoltaic system design and operation, with a focus on the development and experimental validation of real-time control algorithms for single-axis solar tracking systems. Specifically, the project will assess the accuracy and performance of control strategies that balance solar energy production with crop light requirements. The research will be conducted at the DOE-designated Regional Test Center for Emerging Solar Technologies at Michigan Tech, using a single-axis PV system, cameras, and meteorological sensors. Additional irradiance sensors will be deployed to monitor ground shading and evaluate the impact of monofacial versus bifacial PV panels on power production and control design. The findings from this work will refine agrivoltaic control strategies, ensuring dynamic solar tracking based on real-time environmental conditions. Beyond validation, the outcomes of this project will inform optimization frameworks for agrivoltaic system design and operation.

Completed Projects

Flexible Operation of Drinking Water Pumps Using Learning-Aided Optimization

Rapid Seedling Award, GLRC-ICC Joint Institute, Michigan Technological University

Drinking water distribution networks can be operated as flexible loads within the electric power grid due to their substantial pumping demands and water storage capabilities. Optimizing the flexible operation of a water distribution network poses significant challenges due to the complex physical laws within the network, where the hydraulic head difference equations for pipes and pumps are nonconvex. Standard nonconvex optimization solvers often fail to provide globally optimal solutions, and the time required for computation can be prohibitively large. To resolve these issues, we present an optimization approach that accurately approximates the nonconvex constraints using input convex neural networks (ICNNs). This method converts the mixed-integer nonconvex optimization problem into a mixed-integer linear program, improving computational efficiency and scalability while maintaining the optimization problem's intuitive structure. In a case study, we compare the ICNN-aided approach with the original nonconvex problem and found that the ICNN-aided approach outperforms the nonconvex solver in terms of computational time and optimality.

Output

• Publication: A. N. Sakib and A. Stuhlmacher, "Leveraging Drinking Water Pumps as Flexible Loads Using Input Convex Neural Networks", In: Proceedings of the IEEE Power and Energy Society General Meeting (PES GM), Austin, Texas, July 2025.
• Poster: A. N. Sakib and A. Stuhlmacher, "Flexible Operation of Drinking Water Pumps using Input Convex Neural Network Approximations", Institute of Computing and Cybersystem's Computing Showcase. Houghton, MI, October 2024.
  2nd place within Graduate Student Category
• Talks:
  • Texas A&M, "Coordination of the Water and Power Sectors to Provide Grid Flexibility", March 28th, 2025.
  • IEEE Northeastern Wisconsin Section, "Coordination of the Water Supply System and the Power Grid to Support System Performance", February 6th, 2025.