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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 145 records · Page 8
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DOE OSTI2021

Enhancing Distribution Grid Resilience Through Model Predictive Controller Enabled Prioritized Load Restoration Strategy

Effective resilience improvement strategies enable the power grid to cope with disruptive extreme events. Most power grid outages are caused by disruptions in distribution grids. Motivated by the urgent need for power system resilience research, this paper proposes a priority-weighted optimal load restoration technique to enhance the resilience of distribution grids against extreme events. The proposed technique is based on smart distribution technology and framed as sequential multi-step decision process (MDP) and mixed integer linear program (MILP). It is formulated as optimal control problem with a model predictive control (MPC) approach. We applied the devised MILP-MPC-based load restoration technique to a simplified single-bus version of the IEEE 13-bus distribution system with integrated distributed energy resources (DERs) such as wind turbine, photovoltaic array, microturbine, and energy storage device. The technique executes a reducing and rolling horizon optimization in each control step in real-time using the forecasted information of the renewables, the fuel status of the microturbine and the state of charge of the energy storage device. We consider an extreme event which triggered outage of the upstream utility grid and caused islanded operation of the distribution grid. We demonstrated the effectiveness of the proposed MPC approach in restoring the distribution grid loads based on their priority during the main grid outage-caused islanded operation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

Experimental Study and Model Predictive Control of a Lean-Burn Gasoline Engine Coupled With a Passive Selective Catalytic Reduction System

Lean-burn gasoline engines have reflected 10–20% engine efficiency gain over stoichiometric engines and are widely considered as a promising technology for meeting the 54.5 miles-per-gallon (mpg) corporate average fuel economy standard by 2025. Nonetheless, nitrogen oxides (NO x ) emissions control for lean-burn gasoline for meeting the stringent Environmental Protection Agency tier 3 emission standards has been one of the main challenges toward the commercialization of highly efficient lean-burn gasoline engines in the United States. Passive selective catalytic reduction (SCR) systems, which consist of a three-way catalyst (TWC) and SCR, have demonstrated great potentials of effectively reducing NO x emissions for lean gasoline engines at low cost. Yet, passive SCR operation may cause significant fuel penalty since rich engine combustion is required for ammonia generation. The purpose of this study is to develop a model-predictive control (MPC) scheme for a lean-burn gasoline engine coupled with a passive SCR system to minimize the total equivalent fuel penalty associated with passive SCR operation while satisfying stringent NO x and ammonia (NH 3 ) emissions requirements. Simulation results demonstrate that the MPC approach can reduce the fuel penalty by 43.9% in a simulated US06 cycle and 28.0% in a simulated urban dynamometer driving schedule (UDDS) cycle, respectively, compared to the baseline control, while achieving over 97% DeNO x efficiency and less than 15 ppm tailpipe ammonia slip. The proposed MPC controller can potentially enable highly efficient lean-burn gasoline engines while meeting the stringent Environmental Protection Agency tier 3 emission standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Machine learning modeling and model predictive control of a closed-circuit reverse osmosis system

Closed-circuit reverse osmosis (CCRO) offers a flexible and energy-efficient alternative to conventional reverse osmosis by operating in a semi-batch mode that recycles brine, enabling higher recovery rates and reduced specific energy consumption (SEC). However, developing accurate, system-level dynamic models for CCRO remains challenging due to its nonlinear, multi-phase operation and sensitivity to variable feed water conditions. Traditional modeling approaches, such as NARMAX (nonlinear autoregressive moving average with exogenous inputs), often struggle to generalize across varying inlet feed concentrations, necessitating frequent parameter re-estimation and limiting their utility for real-time control applications. To address these limitations, we developed a long short-term memory (LSTM) neural network model trained on an extensive experimental data set from a CCRO pilot plant. The model accepts three inputs, feed flow rate, recirculation flow rate, and initial feed conductivity, and predicts three key outputs: reject conductivity, feed pump power draw, and recirculation pump power draw. We validated the LSTM model against experimental data, demonstrating its ability to distinguish between different feed conductivities and adapt to variable flow rates. Subsequently, we incorporated the LSTM model within a nonlinear model predictive control (MPC) scheme and conducted closed-loop simulations to optimize the integrated SEC (iSEC). In conclusion, the results project up to a 6% reduction in iSEC by using MPC to optimize performance over the entire experiment duration, without requiring any random excitation for data collection or parameter re-estimation.

Desalination↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
DOE OSTI2020

Optimization-Based Fast-Frequency Estimation and Control of Low-Inertia Microgrids

The lack of inertial response from non-synchronous, inverter-based generation in microgrids makes the power system vulnerable to a large rate of change of frequency (ROCOF) and frequency excursions. Energy storage systems (ESSs) can be utilized to provide fast-frequency support to prevent such large excursions in the system. However, fast-frequency support is a power-intensive application that has a significant impact on the ESS lifetime. In this paper, a framework that allows the ESS operator to provide fast-frequency support as a service is proposed. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. The framework utilizes moving horizon estimation (MHE) to estimate the frequency deviation and ROCOF from noisy phase-locked loop (PLL) measurements. These estimates are employed by a model predictive control (MPC) algorithm that computes control actions by solving a finite-horizon, online optimization problem. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low-pass filters as with traditional derivative-based (virtual inertia) controllers. MATLAB/Simulink simulations on a test system from Cordova, Alaska, show the effectiveness of the MHE-MPC approach to reduce frequency deviations and ROCOF of a low-inertia microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Field testing and validation of a low-cost MPC for demand flexibility for grid-interactive K-12 schools

K-12 school buildings account for the highest energy consumption within the public sector. Implementing advanced HVAC controls in grid-interactive K-12 schools could bring substantial economic advantages and grid flexibility. Our previous study demonstrated that a low-cost model predictive control (MPC) solution, which coordinates multiple packaged units, can enable demand flexibility without major hardware upgrades. However, a significant gap remains between academic pilots and market-ready scalable solutions. This paper extends the previous single-site pilot to a multi-site demonstration involving three school campuses (95 total units) through a commercial technology transfer process. Addressing the challenge of verifying performance with sparse field data, we present a new statistical approach using Bayesian methods to estimate the MPC’s effect on peak demand. Unlike traditional methods, this approach robustly quantifies uncertainty in non-normal, limited datasets. The results confirm the solution’s replicability, achieving a 21.6–38.9% reduction in HVAC peak demand (10.8–22.1% at the site-level) with > 98% probability across diverse locations. Finally, we document critical barriers to scaling software-as-a-service (SaaS) solutions–such as API instability and diverse legacy systems–and offer practical strategies to accelerate the commercial adoption of grid-interactive efficient buildings.

Ham, Sang Woo↗
DOE OSTI2019

Experimental Validation of Model Predictive Control Applied to the Azura Wave Energy Converter

Ocean wave energy is a promising area of renewable energy development. However, there are unique operational challenges, particularly with regards to modeling, estimation, and control. Model Predictive Control (MPC) is a widely studied control approach that has strong potential for successful application to ocean wave energy conversion. It combines a predictive element, which is necessary for optimal wave energy conversion, along with consideration of system limitations, which is extremely important when operating within the very large forces present in ocean hydrodynamics. This paper presents MPC formulation and experimental testing, applied to the 1/15th scale Azura wave energy converter developed and tested by Northwest Energy Innovations (NWEI). Here, the results demonstrate successful prototype testing, with MPC providing an average increase in power production of 36% over standard fixed damping for six cases of sea states.

16 TIDAL AND WAVE POWER↗
DOE OSTI2018

A Model Predictive Control Based Generator Start-Up Optimization Strategy for Restoration With Microgrids as Black-Start Resources

Microgrids (MGs) can operate in an islanded mode and serve as black-start resources for power system restoration (PSR). Here, a model predictive control (MPC) based generator start-up optimization strategy for PSR is proposed utilizing MGs as black-start resources. First, the generator start-up sequence (GSUS) optimization is formulated as a mixed integer linear programming. Then, the uncertainties of MG black-start resources (MBSRs) are modeled by discretizing the probability distribution of the forecast errors, and representative scenarios for MBSRs extracted by formulating the probability mass transportation problem. Third, the generator start-up optimization strategy considering MBSRs is proposed utilizing the MPC technique, in which the optimization objective is to maximize the energy capability of the power systems and minimize the load curtailment of the MGs in each looking-ahead interval. Simulations on the IEEE 118 bus system with MGs and Zhejiang provincial power system in China verify that the introduced strategy for PSR can successfully restore the power system and effectively determine the optimal GSUS.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy

Reinforcement learning (RL) was first demonstrated to be a feasible approach to controlling heating, ventilation, and air conditioning (HVAC) systems more than a decade ago. However, there has been limited progress towards a practical and scalable RL solution for HVAC control. While one can train an RL agent in simulation, it is not cost-effective to create a model for each thermal zone or building. Likewise, existing RL agents generally take a long time to learn and are opaque to expert interrogation, making them unattractive for real-world deployment. To tackle these challenges, we propose Gnu-RL: a novel approach that enables practical deployment of RL for HVAC control and re- quires no prior information other than historical data from exist- ing HVAC controllers. To achieve this, Gnu-RL adopts a recently- developed Differentiable Model Predictive Control (MPC) policy, which encodes domain knowledge on planning and system dynam- ics, making it both sample-efficient and interpretable. Prior to any interaction with the environment, a Gnu-RL agent is pre-trained on historical data using imitation learning, which enables it to match the behavior of the existing controller. Once it is put in charge of controlling the environment, the agent continues to improve its policy end-to-end, using a policy gradient algorithm. We evaluate Gnu-RL on both an EnergyPlus model and a real- world testbed. In both experiments, our agents were directly de- ployed in the environment after offline pre-training on expert demonstration. In the simulation experiment, our approach saved 6.6% energy compared to the best published RL result for the same environment, while maintaining a higher level of occupant comfort. Next, Gnu-RL was deployed to control the HVAC of a real-world conference room for a three-week period. Our results show that Gnu-RL saved 16.7% of cooling demand compared to the existing controller and tracked temperature set-point better.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Deploying advanced supervisory control strategies to small- and medium-sized commercial buildings: Case study and lessons learned

Deploying advanced supervisory control strategies (ASCSs) in small- and medium-sized commercial buildings (SMBs) is vital but faces two issues: (1) a lack of building control and communication infrastructure (BCCI) in SMBs and (2) the significant engineering efforts required to implement and configurate ASCSs. Despite hindering the large-scale adoption of ASCSs in SMBs, these issues have not been adequately explored in the literature, which tends to focus more on feasibility than scalability. This paper provides a comprehensive evaluation of these two issues through a case study of an occupied SMB in eastern Tennessee of the United States. Specifically, we design and implement a BCCI for the studied building with commercial off-the-shelf products to accommodate the needs for deploying ASCSs. Here, we then deploy two ASCSs—a rule-based setback control and a model predictive control (MPC)—with the BCCI and evaluate their performance throughout the summer of 2024. This study reveals that the main bottlenecks in establishing BCCIs for SMBs are the high initial cost (∼$56/m 2 ) and communication delays (up to 9 min). Additionally, the assessment of the two ASCSs indicates that the majority (∼75 %) of the engineering effort required for implementation and configuration is spent on model identification, debugging, and tuning.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Stochastic Optimal Scheduling of Residential Appliances with Renewable Energy Sources

This paper proposes a stochastic, multi-objective optimization model within a Model Predictive Control (MPC) framework, to determine the optimal operational schedules of residential appliances operating in the presence of renewable energy source (RES). The objective function minimizes the weighted sum of discomfort, energy cost, total and peak electricity consumption, and carbon footprint. A heuristic method is developed for combining different objective components. The proposed stochastic model utilizes Monte Carlo simulation (MCS) for representing uncertainties in electricity price, outdoor temperature, RES generation, water usage, and non-controllable loads. The proposed model is solved using a mixed integer linear programming (MILP) solver and numerical results show the validity of the model. Case studies show the benefit of using the proposed optimization model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

MPCPy: An Open-Source Software Platform for Model Predictive Control in Buildings

Within the last decade, needs for building control systems that reduce cost, energy, or peak demand, and that facilitate building-grid integration, district-energy system optimization, and occupant interaction, while maintaining thermal comfort and indoor air quality, have come about. Current PID and schedule-based control systems are not capable of fulfilling these needs, while Model Predictive Control (MPC) could. Despite the critical role MPC-enabled buildings can play in future energy infrastructures, widespread adoption of MPC within the building industry has yet to occur. To address barriers associated with system setup and configuration, this paper introduces an open-source software platform that emphasizes use of self-tuning adaptive models, usability by non-experts of MPC, and a flexible architecture that enables application across projects.

Blum, David↗
DOE OSTI2021

Parallelized POD-based suboptimal economic model predictive control of a state-constrained Boussinesq approximation

Motivated by an energy efficient building application, we want to optimize a quadratic cost functional subject to the Boussinesq approximation of the Navier-Stokes equations and to bilateral state and control constraints. Since the computation of such an optimal solution is numerically costly, we design an efficient strategy to compute a sub-optimal (but applicationally acceptable) solution with significantly reduced computational effort. We employ an economic Model Predictive Control (MPC) strategy to obtain a feedback control. The MPC sub-problems are based on a linear-quadratic optimal control problem subjected to mixed control and state constraints and a convection-diffusion equation, reduced with proper orthogonal decomposition. Finally, to solve each sub-problem, we apply a primal-dual active set strategy. The method can be fully parallelized, which enables the solution of large problems with real-world parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Physics-informed machine learning modeling for predictive control using noisy data

Due to the occurrence of over-fitting at the learning phase, the modeling of chemical processes via artificial neural networks (ANN) by using corrupted data (i.e., noisy data) is an ongoing challenge. Therefore, this work investigates the effect of both Gaussian and non-Gaussian noise on the performance of process-structure based recurrent neural networks (RNN) models, which take the form of partially-connected RNN models in this work, that are used to approximate a class of multi-input-multi-outputs nonlinear systems. Furthermore, two different techniques, specifically Monte Carlo dropout and co-teaching, are utilized in the development of partially-connected RNN models. Here, these two techniques are employed to reduce the over-fitting in ANNs when noisy data is used in the training process and, hence, to improve the open-loop accuracy as well as the closed-loop performance under a Lyapunov-based model predictive controller (MPC). Aspen Plus Dynamics, a well-known high-fidelity process simulator, is used to simulate a large-scale chemical process application in order to demonstrate the anticipated improvements in both open-loop approximation and closed-loop controller performance in the presence of Gaussian and non-Gaussian noise in the data set using physics-informed RNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Energy-Efficient Driving in Connected Corridors via Minimum Principle Control: Vehicle-in-the-Loop Experimental Verification in Mixed Fleets

Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin's Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Field demonstration and implementation analysis of model predictive control in an office HVAC system

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems

Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors’ impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor's influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20%25 in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

A Comparative Assessment of Economic Model Predictive Control Strategies for Fuel Economy Optimization of Heavy-Duty Trucks

This paper provides a comparative assessment of three control strategies that fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC) in an effort to minimize fuel consumption for a heavy-duty truck. The online MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. Three candidate economic MPC formulations are evaluated: a time-based formulation that directly penalizes predicted fuel consumption, a simplified time-based formulation that penalizes braking effort in place of fuel consumption, and a distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity based on DP. The performance of each approach is presented for three representative route profiles, using a medium-fidelity proprietary Volvo model of the heavy-duty truck’s longitudinal dynamics. Results demonstrate 4-7% fuel economy improvement across all three formulations, when compared to a baseline strategy. Furthermore, we present a detailed analysis of energy usage by “type” (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control approach.

33 ADVANCED PROPULSION SYSTEMS↗