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Results for “Model Predictive Control”

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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Discrete-Time Average Model Based Predictive Control for Quasi-Z-Source Inverter

A discrete-time average model-based predictive control (DTA-MPC) is proposed for a quasi-Z-source inverter (qZSI). As a single-stage inverter topology, the qZSI regulates the dc-link voltage and the ac output voltage through the shoot-through (ST) duty cycle and the modulation index. Several feedback strategies have been dedicated to produce these two control variables, among which the most popular are the proportional–integral (PI)-based control and the conventional model-predictive control (MPC). However, in the former, there are tradeoffs between fast response and stability; the latter is robust, but at the cost of high calculation burden and variable switching frequency. Moreover, they require an elaborated design or fine tuning of controller parameters. The proposed DTA-MPC predicts future behaviors of the ST duty cycle and modulation signals, based on the established discrete-time average model of the quasi-Z-source (qZS) inductor current, the qZS capacitor voltage, and load currents. The prediction actions are applied to the qZSI modulator in the next sampling instant, without the need of other controller parameters’ design. A constant switching frequency and significantly reduced computations are achieved with high performance. Transient responses and steady-state accuracy of the qZSI system under the proposed DTA-MPC are investigated and compared with the PI-based control and the conventional MPC. Simulation and experimental results verify the effectiveness of the proposed approach for the qZSI.

42 ENGINEERING↗
DOE OSTI2022

Deploying a Model Predictive Traffic Signal Control Algorithm - A Field Deployment Experiment Case Study

This paper presents a field deployment experiment of a real-time traffic signal control algorithm. We implemented the model predictive control (MPC) algorithm based on the virtual phase-link (VPL) model. We selected the deployment locations and times based on an energy saving potential concept. We developed a set of experiment systems, which included sensing, processing, and actuating components, to enable field deployment. We tested the systems rigorously before the experiment days. We reported the key procedures on the experiment days, including the steps taken, the real-time control procedure, and the monitoring of the experiment. We evaluated the impact of the deployment by looking at the changes in delay and energy consumption.

deployment↗
DOE OSTI2016

Centipod WEC, Advanced Controls, WaveDyn Models

Archive containing the WaveDyn models used for analysis of the Centipod WEC with and without Advanced Controls, i.e. the WaveDyn files contain a baseline model which can be run alone, and a model predictive control (MPC) model which must be run when connected to the MPC controller. The controller dynamic link library (DLL) is also contained within this archive. The code archive for the MPC controller is linked in this submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Development of Predictive Models for Advanced Reactor Autonomous Control

Advanced reactor designs including microreactors and small modular reactors will contribute to the clean production of cheap energy, and autonomous control for advanced reactors is an appealing option for reducing cost. However, there is a lack of industry experience applying autonomous control for advanced nuclear reactors. To accelerate the development and industry acceptance of autonomous control software for nuclear reactors, we aim to demonstrate autonomous control of the Purdue University research reactor (PUR-1) using INL-developed model predictive control (MPC) methods. To prepare for this demonstration, data-driven predictive models based on process data collected from PUR-1 have been developed and integrated with MPC and used to control a physics-based model of PUR-1. A data-driven dynamics model and a gated recurrent unit (GRU) network were both trained on process data from PUR-1. The dynamics model was shown to effectively control the reactor model with MPC when provided reactivity as a control variable but failed to control the model through the control rod positions. The GRU network produced more accurate predictions than the dynamics model when evaluated on operational data, and future work will include the evaluation of the GRU network in the controller.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Nowcasting, predictive control, and feedback control for temperature regulation in a novel hybrid solar-electric reactor for continuous solar-thermal chemical processing

A novel 15 kW reactor for the hybridization of concentrated solar and conventional electric heat was fabricated for renewable and continuous chemical processing at temperatures up to 1700 °C. Solar-electric controllers based on feedback or predictive linear models were used to regulate the device at 925 °C for the production of syngas via the gasification of carbon. The system was challenged with cloud transients programmed on a 45 kW high-flux solar simulator that approximated weather observed in the San Luis Valley (Colorado, USA). In experiment it was found that model predictive control with a 1 min ahead nowcast of incipient clouding best regulated solar-electric reactor temperatures, potentially averting thermal fatigue. Upon clouding, model predictive control with a nowcast yielded temperature disturbances of ±10 °C, whereas feedback control alone featured ±25 °C excursions. Overall, the performance of model predictive control with a nowcast was 75% better than feedback as indicated by the integral squared error of temperature excursion residuals. The importance of nowcast accuracy was explored, and although the model-based controller was robust to forecast amplitude inaccuracy, temporal forecast inaccuracy wholly negated the benefits of predictive control.

14 SOLAR ENERGY↗
DOE OSTI2025

Extended State Observer-Based Robust Model Predictive Velocity Control for Permanent Magnet Synchronous Motor

This article proposes an extended state observer based robust model predictive velocity control to decrease system prediction error under parameter uncertainties for permanent magnet synchronous motor (PMSM). We develop a new PMSM model that consists of velocity and acceleration to lump the system information and an external disturbance into a disturbance. The extended state observer (ESO) is designed to estimate the velocity, acceleration, and disturbance. By estimating the state variables and disturbance using the ESO, the model predictive control (MPC) finds the optimal control input by predicting future system behavior. Additionally, the direct current controller is designed so that the direct current converges to zero. Because the proposed method is not designed based on the cascade structure from the viewpoint of velocity control, the optimization control for the velocity and currents can be defined. Thus, the proposed method is robust against external disturbances and parameter uncertainties owing to feedback linearization, state feedback, and ESO-based MPC using the acceleration PMSM model. The proposed control algorithm was experimentally verified and it showed improved velocity tracking performance compared with ESO-based MPC using the conventional PMSM model.

42 ENGINEERING↗
DOE OSTI2022

Model-based predictive control of multi-zone commercial building with a lumped building modelling approach

Here this study investigates the applicability of a lumped building modeling approach to model-based predictive control (MPC) to alleviate the complex modeling process of the grey-box multi-zone building model. Based on experimental data, two building models were estimated in this study. The detailed model as a reference case and a lumped model were estimated with decentralized and conventional approaches, respectively. Then, simulations were performed with two boundary conditions, including the comfort bound and electricity cost structure. The performances of the MPC with the detailed and lumped models were analyzed compared to the feedback control. More savings was achieved with a larger comfort bound and more aggressive electricity cost structure. The savings potential of the proposed lumped model approach was not as high as that of the detailed model. However, the proposed method yields good control performance, whose savings was approximately 8.6% over that of feedback control. These results suggest that the proposed method can be used to facilitate MPC implementation in multi-zone building applications.

42 ENGINEERING↗
DOE OSTI2025

Optimization-Based Dynamic Voltage Support of Microgrids Using Energy Storage Systems

A microgrid network is characterized by a high R/X ratio, making the voltage more sensitive to active power changes compared to bulk power systems, where the voltage is regulated primarily by reactive power. Due to its sensitivity, voltage control approaches for microgrids should also consider the active power input coupling, making it very different from conventional power systems. Additionally, as the energy costs associated with active and reactive powers are different and the operational conditions of microgrids connected to active distribution systems vary over time, the ideal controller to provide voltage support must be flexible enough to handle these technical and operational constraints. This paper proposes a model predictive control approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system to solve the model predictive control problem. By proper selection of model predictive control weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in MATLAB/Simulink validates the proposed approach for the Cordova, Alaska microgrid. Results show that the performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control

This paper evaluates the virtual storage capability of a residential air-conditioning (AC) system by utilizing the building mass as a thermal storage to enable sustainable cities through model-based predictive control (MPC). The control-oriented building model was developed with a grey-box model structure based on the experimental data to predict the indoor air temperature. Based on the model, the operation cost-saving potential of the MPC was investigated in a single house with different comfort levels, electricity prices, and prediction horizons. The cost-saving of the MPC was approximately 10.6 % compared to the conventional control. The MPC was expanded to a residential building cluster considering the peak demand charge. In an hourly shedding and load-up scenario, the performances of the MPC and the heuristic prior-based control (PBC) are promising. When shedding was enforced all day, the peak demand saving of the MPC was 36~38 %, whereas that of the PBC was 25.7 % compared to the baseline. In addition, the monthly electricity bill, including the operation and peak demand cost, was investigated with different demand charge rates and weather conditions. The total saving decreased with a low demand charge rate and hot conditions, and the operation cost saving was compromised with more aggressive demand shedding.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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 OSTI2020

DECENTRALIZED APPROACH TO MULTI-ZONE GREY-BOX MODELING FOR MODEL-BASED PREDICTIVE CONTROL

This study aims to improve the easiness of utilizing the grey-box model (i.e., Resistance-Capacitance circuit) for Model-based Predictive Control (MPC). The primary barrier of implementing the MPC is estimating the control-oriented building model that needs to be computationally inexpensive and quick but reasonably precise in predicting building load and indoor conditions. The estimating of the model parameters becomes more complicated when the building scale is larger; e.g., multi-zone building. In this study, a decentralized approach is introduced; each zone is split and individually estimated with measured boundary temperature from adjacent zones integrated into one single system model. The proposed decentralized method is demonstrated with experimental data from a full-scale multi-zone test cell compared with the centralized reference case.

Joe, Jaewan↗
DOE OSTI2023

Control of Behind-the-Meter Resources for Enhancing the Resilience of Residential Buildings

There is increased concern about the impacts of frequent power outages, caused primarily by extreme weather conditions. With increasing behind-the-meter resources such as solar photovoltaics (PV), battery energy storage, and controllable loads, these resources - if properly coordinated - can meet critical loads even during an outage. Resilience building controls can coordinate and operate these resources to enhance the resiliency of buildings supporting critical loads for longer duration. In this paper, we present two resilience building controls: rule-based control and model predictive control (MPC). We simulated various scenarios considering different locations, seasons, outage types, and times of outages to evaluate the performance of resilience controls. The results show that MPC-based control can enhance resilience up to 65% compared to rule-based control. Similarly, PV self-consumption and occupant thermal comfort both increase during outages with MPC-based control.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2018

A simplified regression building thermal modelling method for detached two-floor house in U.S.

The residential building sector accounts for approximately 37% of total U.S. electricity consumption. Within the residential building sector, heating and cooling is the main target for peak load shifting/reduction since it is the largest contributor to peak demand. In fact, the flexibility of residential HVAC loads can provide continuous variation of demand to provide grid services by varying their demand over a baseline. The performance of HVAC load control to provide grid services relies heavily on the accuracy of indoor air temperature or cooling/heating demand predictions and therefore the quality of building model. Besides forward models, popular building models are data-driven models which can be broken down into two categories: gray-box model, e.g. Resistance-Capacitance (RC) model and black-box model. RC model, also called lumped capacitance or network model, which is constituted with electrical analogue pattern with resistance (R) and capacitance (C). In general, RC models require considerable computation burden and long periods of data to train limited number of model coefficients. Black-box models have gained increasing interest due to their capability in analyzing large-scale data and flexibility in practical applications. But, the data-mining based (machine learning algorithms/techniques based) models tend to have invisible model structures which poses a problem when trying to use the model for optimal control or model predictive control of the HVAC system. Hence, there is a continuing need for efficient online system identification techniques, which can provide explicit parameters for the model. Traditional regression models fit well for this specific purpose. This paper presents an innovative way to predict average indoor temperature in separate floors of typical detached residential house. A rolling horizon linear regression model, which includes online adaptive correction component, is proposed to predict the temperature difference between downstairs and upstairs. A RC model is used to predict the overall mean indoor air temperature. Since the adaptive algorithm needs to be implemented online, a less computation-demanding polynomial fitting algorithm is adopted. This kind of fitting problem can be cast as linear regression problem with multiple variables, parameters of which can be efficiently obtained by well-known gradient descent method.The validation is conducted by comparing the predicted results with the results from data-mining based models as well as measured data from a real typical detached two-floor house. The results show that the developed method has satisfactory performance in predicting the building indoor temperature in 1st and 2nd floors.

Cui, Borui↗
DOE OSTI2017

Identification Approach to Alleviate Effects of Unmeasured Heat Gains for MIMO Building Thermal Systems

It is important to have practical methods for constructing a good mathematical model for a building's thermal system for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. Identification approaches based on semi-physical model structures are popular in building science for those purposes. However conventional gray box identification approaches applied to thermal networks would fail when significant unmeasured heat gains present in estimation data. Although this situation is very common and practical, there has been little research to tackle this issue in building science. This paper presents an overall identification approach to alleviate influences of unmeasured disturbances, and hence to obtain improved gray-box building models. The approach was applied to an existing open space building and the performance is demonstrated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Advances in supervisory control strategies for a heat pump centric HVAC system − a comprehensive review on applications

There is an increase in research investigating the development and deployment of supervisory controllers that enable high efficiency electrically driven vapor compression heat pumps operating within grid interactive efficient buildings to provide demand side management. This paper reviews over sixty relevant case studies within this domain that focus on commercial off-the-shelf heat pumps whose primary task is providing space conditioning. The concept of a heat pump-centric heating, ventilation, and air-conditioning system is introduced, accompanied by a detailed overview of various kinds of electric heat pumps and building-level thermal energy storage configurations. Additionally, the different types of supervisory controller designs, including rule-based control and model predictive control, are discussed, along with the various methods for communication between a supervisory controller and a downstream heat pump’s local controller. A comparative analysis is conducted in order to categorize the reviewed case studies based on their system design, supervisory control algorithm, and validation methodology. This detailed analysis allows the review to establish current research trends, identify potential gaps, and suggest future directions for the development of this technology. Overall, the authors recommend that more future research be devoted to low-cost practical retrofits that allow for easy integration of active thermal energy storage within heat pump-centric heating, ventilation, and air-conditioning system systems that utilize direct expansion heat pumps. We also suggest more research into the development and deployment of supervisory controllers that can properly communicate with commercial off-the-shelf heat pump local controller available control inputs, e.g., zone temperature setpoint. Lastly, more rigorous experimental demonstrations of advanced supervisory control within real and or closed-loop, transient/ quasi-steady state environments are necessary to reduce industry wide skepticism of this technology.

Demand side management↗
DOE OSTI2023

Model Predictive Voltage Control of Large-Scale PV or Hybrid PV-BESS Plants

Increased penetration level of inverter-based resources (IBRs) and renewable energy in the power grid has called for more requirements from the control of IBRs. In this paper, the voltage-reactive power control of a photovoltaic (PV) power plant or hybrid PV-battery energy storage system (BESS) plant connected to a bulk power grid, whilst meeting the grid requirements, is studied. In this paper, a continuous-set model predictive control (MPC) formulation is proposed for voltage-reactive power control. For the same, an aggregated dynamic PV plant model is developed based on the recursive system equivalencing method. The formulation is then implemented in PSCAD simulation on a 125 MW PV plant or hybrid PV-BESS plant. The MPC implementation achieves 10.13% voltage improvement based on PSCAD simulation results in a balanced fault case study. Simulation results further demonstrate that MPC provides improved voltage support over the conventional proportional-integral (PI) controller post-fault occurrence.

Abu rub, Omar↗
DOE OSTI2022

Impact of model predictive control-enabled home energy management on large-scale distribution systems with photovoltaics

Residential customers use more than one-quarter of the electricity in the world. Optimally managing home energy consumption is an effective way of easing the operational challenges facing the electric grid with increasing solar photovoltaics (PV). This paper studies the impact of the future proliferation of home energy management systems (HEMS) in the presence of PV on large-scale distribution systems. First, we present a stochastic HEMS model that minimizes residential customers' thermal discomfort and energy costs under uncertainty. The HEMS model schedules the optimal operations of residential appliances in the presence of PV within a mixed-integer linear programming-based model predictive control framework that links the proposed HEMS to a quasi-steady-state time-series simulation tool. Additionally, extensive simulations are conducted for a stand-alone residential home using two tariff structures and for 1977 homes on an 8,500-node distribution feeder. Simulation results quantify the impact of the future proliferation of HEMS on the large-scale distribution system with PV.

14 SOLAR ENERGY↗