SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This is a presentation of the paper "Multi-Model Predictive Control for Enhanced Load Following of a sCO2 Recompression Brayton Cycle" submitted to the International sCO2 Power Cycles Symposium.
Model predictive control (MPC) is a system or process control technique for making decisions under uncertainty via rolling look-ahead optimizations at each control step where only the current step decisions are applied, and the rest are discarded. In this work, we developed an MPC for a critical load restoration (CLR) in power distribution systems to recover system service (electricity delivery) following an extreme event-triggered substation outage. The method considers the problem of controlling distributed energy resources (DERs) of the distribution system with the objective of achieving maximum load pick up while satisfying distribution network flow and voltage constraints. A linearized optimal power flow (OPF) model is employed to represent the physics of the network. The problem formulation is augmented with a ramping (up) reserve product for the DERs to ensure improved and upward monotonic load restoration as time evolves. Simulation analysis and performance tests are performed using a modified IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage battery. The software is developed using various software packages in Julia and Python. The MPC model is implemented using the JuMP optimization language in Julia while the data analytics including renewable generation and load demand forecasts, running the MPC simulation and visualizations is performed in Python.
Every microgrid is unique in its diversity of assets, operation, and network structure. This uniqueness presents challenges for each microgrid implementation that need to be understood and addressed to prevent power quality concerns, stability issues, and blackouts. In this research work, we present one challenge faced during microgrid operation that could potentially cause a blackout in some microgrid configurations. Through the incorrect operation of the load tap changer controller during an islanding event for a microgrid with generation present on both primary and secondary busses, the primary voltage will increase or decrease beyond allowable bounds, causing a cascading failure. This work presents the issue first with simulation results to demonstrate the phenomenon and then presents results of load tap changer controller operation using a controller-hardware-in-the-loop experiment. Finally, details of the complete sequence of operation and the blackout scenarios are presented and summarized.
Gasoline compression ignition (GCI) is an emerging technology that can achieve high fuel efficiency and low criteria emissions in diesel engine platforms using gasoline and gasoline-like fuels. While low- and mid-range loads are enabled with low temperature combustion (LTC) approaches, high-loads are more efficient with high temperature combustion (HTC) requiring mixing-controlled combustion (MCC). Lower soot emissions are achieved with gasoline even in high-load MCC than with diesel, but detailed soot properties from gasoline and diesel have been rarely discussed. In order to better understand how high-load operations affect soot properties, in this study gasoline and diesel were tested in a six-cylinder heavy duty engine at 14 bar brake mean effective pressure (BMEP) at similar combustion phasing. Effects of pilot mass ratio and EGR on heat release rates, cylinder temperatures, ignition delay (ID) and combustion duration (CD) were examined, relating with emissions variations with applications of the two fuels. Soot mass and particle number emissions were slightly lower with gasoline than with diesel, resulting in a more favorable NOx-soot trade-off with gasoline like other reports. Contrary to other findings in the literature, gasoline and diesel soot samples presented similar particle distribution shapes, carbon crystalline structures and primary particle sizes, regardless of fuel type and EGR rate. Consequently, the current result proposes that soot particles have undergone similar soot formation and oxidation processes despite reactivity difference and oxygen availability under the examined high-load MCC with similar combustion phases.
To accomplish net-zero carbon in the built environment by 2050, we must equitably decarbonize commercial buildings, which includes reducing plug and process loads (PPLs). PPLs are plug-in or hardwired electric and gas loads that are not directly associated with major building end uses like lighting and heating, ventilating, and air conditioning. PPLs account for a growing portion of U.S. commercial building energy consumption. Although commercial building PPL strategies and technologies are available today, they have not been adopted at a level sufficient to achieve significant savings and load flexibility across the building stock. In our "Pathways to Plug and Process Load Efficiency and Control" study, we investigated why these technologies and strategies have not seen widespread adoption and identified five behavior and technology pathways to increase PPL reduction in commercial buildings. In this paper, we expand beyond identifying the pathways and discuss approaches for achieving them. We discuss the importance of collecting and sharing data and case studies on PPL energy consumption and savings from control technology implementation, including code-required measures, for increasing adoption. Centralizing case studies and data, engaging industry organizations, and promoting awareness of PPL efficiency benefits to relevant groups are also key approaches. Additionally, funding, incentives, and rebate programs play important roles in driving PPL efficiency and control adoption. Finally, we discuss integrating PPL efficiency into broader company goals, such as environmental, social and governance (ESG) strategies and green building certifications, to further drive adoption.
This paper presents a framework for modeling, scheduling, and controlling residential thermostatically controlled loads (TCLs) to provide multiple grid services, such as energy shifting, peak load reduction, and ancillary services. A modeling method is proposed to characterize the aggregate flexibility from heterogeneous TCLs using a battery-equivalent model. Based on the flexibility model, a multi-period optimal scheduling formulation is developed to best utilize the flexibility from building loads and maximize total benefits from stacked value streams. An algorithm is proposed to control individual TCLs to follow the desired power consumption in real-time. The proposed methods are illustrated and validated through simulations.
Hong, S.M.C., E. Hedrick, K. Hedrick, D. Beahr, D. Bhattacharyya, S.E. Zitney, and B.P. Omell, "Multiple And Nonlinear Model Predictive Control for Rapid Load Following Operation of Supercritical Pulverized Coal Power Plants," AIChE 2022 Annual Meeting, Phoenix, AZ, November 13-18 (2022).
Automation control is a key strategy to improve the economic competitiveness of nuclear power plants. Not only does it help reduce operational costs, but it also extends the value proposition of these plants to nontraditional markets, including unattended operations in remote villages and space. However, the dynamics of the operating environments of nuclear reactors are subject to changes over time, and there are no widely adopted methods to ensure that the automation strategy will remain effective over the extended durations required for these applications. Adaptive control is a discipline that offers the possibility to accommodate such changes online. However, it relies on mathematical assumptions that must be respected to ensure robustness and reliability. In this work, we derive an adaptive control formulation for linear systems in which all states are observable and apply it to an instance of load-follow operation for Boiling Water Reactors. We assumed uncertainty in two factors: the temperature coefficient and the control rod worth, both of which are affected over time by the evolution of the nuclear reactor core environment. With an arbitrary penalty factor of 5, we found that the mean absolute and integral time absolute errors can be reduced by more than 90%, underscoring the strength of adaptive control. To extend the application to more challenges, different uncertainties and load-follow trajectories, as well as new formulations that include non-linearity and partial observability, are currently being developed.
A study was conducted of an intermittent binary control strategy for trailing edge flaps and leading edge spoilers installed on wind turbine blades for the purpose of load alleviation. Cost estimation models were developed for the systems to predict overall impact on levelized cost of energy over the lifecycle of the turbine system. Aeroelastic simulations of turbines with the control strategy implemented showed improved levelized cost for some, but not all cases.
Virtual battery (VB) is an innovative method to model flexibility of building loads and effectively coordinate them with other resources at a system level. Unlike a real battery with a dedicated power conversion system for charging control, methods are required for operating building loads to deviate from the baseline to respond to grid signals. This paper presents a VB control for a commercial heating, ventilation, and air conditioning (HVAC) system to follow the desired power consumption in real-time by adjusting zonal airflow rates. The proposed method consists of two parts. At the system level, a mixed feedforward and feedback control is used to estimate the desired total airflow rate. At the zone level, two priority-based algorithms are then proposed to distribute the total airflow rate to individual zones. In particular, a zonal airflow limit estimation method is proposed using machine-learning techniques, in contrast to physics-based thermal models in existing studies, to more accurately capture zonal thermal dynamics and improve temperature control performance. An office building on the Pacific Northwest National Laboratory campus is implemented in EnergyPlus, and used to illustrate and validate the proposed control.
Smart outlets control the flow of power to devices plugged into them and measure their energy use. The energy use data can be accessed via an online dashboard or smartphone application, allowing the user to turn power to plug-in devices on or off based on a schedule established in the dashboard or application. This four-page resource is a fact sheet meant to educate commercial building owners on smart outlets: what they are, why we use them, and how to use them in a way that will achieve energy savings while maintaining occupant comfort. The resource also describe how to procure a smart outlet system and how to fully capture their benefits over time. This resource was developed to support the Better Buildings Alliance Plug and Process Loads Technology Research Team.
Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary services. This project aims to develop a robust, scalable hierarchical transactional control mechanism incorporating elements of model-free control (MFC) and game theory to harness buildings to provide ancillary services to the grid. This approach is being taken to address the challenges of incorporating traditional transactional control schemes into existing buildings. The challenges include small individual building sizes requiring aggregation of many buildings, unpredictable energy usage that makes model identification difficult, and satisfying the sensitive occupant comfort constraints. In the proposed approach, by separating the control mechanism into two layers above and below the load aggregator, MFC can be used below the aggregator to modulate flexible building loads in response to pricing signals with guaranteed performance. This allows the burden of identifying an accurate model of the system to be shifted to the above-aggregator layer, where fluctuations in individual building usage have less impact on predicted building system behavior. Game theory concepts can then be used to determine pricing curves and control signals among regional aggregators. Managing this control in a game-theoretic approach will allow us to build in financial incentives that increase customer engagement. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable on small, embedded devices. In a broader sense, developing a strategy capable of effectively incorporating residential and small commercial buildings will allow greater throughput of existing and emerging grid services in addition to future transactive energy grid management methods. Using MFC within a hierarchical control architecture will allow the shifting of existing forecasting challenges to an aggregate level, where dynamics are slower and more predictable. This will enable a smooth interface between the grid services requests of utilities and the reliable control required by participating buildings. MFC, which supports distributed control architecture, permits a scalable solution that can be deployed to neighborhood-size systems as well as individual buildings. This project focuses on three objectives: (1) developing the mathematical framework, algorithm toolkit, and software toolset of the two-layer transactive control testbed; (2) developing a scalable solution for application over many residential and small-size commercial buildings with sparse distributed communication; and (3) field testing and implementation on hardware of the control strategies developed in the previous two objectives. The research and development activities are focused and designed to be impactful within the relevant 2025 targets timeframe. An open-source control framework for exploiting variability and dispatchability of building loads will be delivered as the outcome of the project. This capability enables greater participation of loads in electricity markets and ancillary services that are both useful for the utility and financially beneficial for building owners.
This project developed the integration of a direct-contact heat exchanger (DCHX) based thermal energy storage (TES) system with a chiller–air handling unit (AHU) plant to evaluate its potential for reducing building energy costs. Detailed physical models of the TES unit, building envelope, and HVAC components were developed alongside simplified control-oriented models to support both high-fidelity simulation and real-time optimization. Two control strategies were implemented and compared: a rule-based control (RBC) aligned with utility time-of-use (ToU) rates, and a model predictive control (MPC) framework leveraging forecasts of building load, weather, and internal gains.Simulation results show that the RBC strategy reduced daily electricity costs by around 30% by shifting cooling production from on-peak to off-peak hours. In contrast, the MPC strategy achieved significantly greater performance, reducing daily operating costs by up to 44% and peak-hour costs by more than 60%. Both strategies maintained indoor thermal comfort within acceptable limits, with MPC further improving load distribution and reducing equipment cycling.The outcomes confirm that TES integration, particularly when coordinated with advanced predictive control, can provide substantial cost savings and on-peak demand reduction. . These findings directly support the U.S. Department of Energy’s goals for grid-interactive efficient buildings and demonstrate the potential of TES-enabled HVAC systems for scalable deployment across the commercial building.
This project developed the integration of a direct-contact heat exchanger (DCHX) based thermal energy storage (TES) system with a chiller–air handling unit (AHU) plant to evaluate its potential for reducing building energy costs. Detailed physical models of the TES unit, building envelope, and HVAC components were developed alongside simplified control-oriented models to support both high-fidelity simulation and real-time optimization. Two control strategies were implemented and compared: a rule-based control (RBC) aligned with utility time-of-use (ToU) rates, and a model predictive control (MPC) framework leveraging forecasts of building load, weather, and internal gains. Simulation results show that the RBC strategy reduced daily electricity costs by around 30% by shifting cooling production from on-peak to off-peak hours. In contrast, the MPC strategy achieved significantly greater performance, reducing daily operating costs by up to 44% and peak-hour costs by more than 60%. Both strategies maintained indoor thermal comfort within acceptable limits, with MPC further improving load distribution and reducing equipment cycling. The outcomes confirm that TES integration, particularly when coordinated with advanced predictive control, can provide substantial cost savings and on-peak demand reduction. . These findings directly support the U.S. Department of Energy’s goals for grid-interactive efficient buildings and demonstrate the potential of TES-enabled HVAC systems for scalable deployment across the commercial building.
Over the course of this project, we have successfully de-risked our distributed microgrid control architecture by tightly integrating its associated control algorithms into a unified software library, installing the software library on several industrial-grade target hardware platforms, and validating the performance of the resulting microgrid controller in a real-life microgrid. Upon completion of the project, we demonstrated that our distributed control architecture is resilient against (i) failures in control devices, (ii) unreliable communication links, (iii) delays in transmitted data, and (iv) imperfect knowledge of the number of (and state of) generation and load assets in the microgrid. In this final report, we present results from all the milestones that were accomplished over the course of this project.
Abstract To accomplish net-zero carbon emissions in the built environment by 2050, we must equitably decarbonize commercial buildings, including reducing plug and process loads (PPLs). PPLs are plug-in or hardwired electric and gas loads that are not associated with major building end uses like lighting and HVAC. Research shows PPL energy reduction strategies and control technologies have the potential to save energy. But even when implemented, these savings have rarely been achieved and there has not been widespread uptake in U.S. commercial buildings. We investigate why these technologies and strategies have not seen widespread adoption and identify behavior and technology pathways to increase PPL reduction in U.S. commercial buildings. We examined behaviors of commercial building stakeholders through 44 interviews and cross-referenced qualitative analysis findings with in-depth technical knowledge of existing PPL control technologies and reduction strategies. PPL control implementation must be paired with management strategies, such as occupant engagement and training, to achieve optimal savings, and best practices should be disseminated across the industry. We found that increasing access to cost and energy savings data will promote uptake of PPL control technologies and allow designers to better incorporate PPLs into building design. Improving access to funding for PPL energy efficiency projects and addressing the split-incentive problem will increase adoption of PPL efficiency and control. Code bodies should continue to include PPL monitoring and reduction measures in energy codes. Key building stakeholders, including cybersecurity and information technology teams, should be involved in PPL monitoring and reduction strategy processes for successful implementation.
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, photovoitaic 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.
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.