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At least 73 records · Page 4

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DOE OSTI2020

Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration

Model predictive control (MPC) has been proven in simulations and pilot case studies to be a superior control strategy for large buildings. MPC can utilize the weather and occupancy schedule forecasts, together with the system model, to predict the future thermal behavior of the building and minimize the overall energy use and maximize thermal comfort. However, these advantages come with the cost of increased modeling effort, computational demands, communication infrastructure, and commissioning efforts. Thus a typical approach is to, often rapidly, simplify the building modeling and MPC optimization problem while paying a price of not reaching the full performance potential. It has been shown that by employing accurate physics-based models, MPC performance can be notably increased closer to its theoretical performance bound. However, implementation of such high-fidelity MPC in real buildings remains a challenge, resulting in a lack of successful field test studies. This work presents the methodology and field test demonstration of a computationally efficient implementation of the white-box MPC in an office building in Belgium. The detailed model of the building is based on first-principle physical equations. The deployment and supervision of MPC operation in a practical setting are supported by an automated cloud-based communication infrastructure. The motivating factor behind the cloud-based architecture is its compatibility with a commercially appealing control as a service concept. The building is equipped with a ground source heat pump (GSHP) and thermally activated building structures (TABS), where the combination of both is also known as GEOTABS. From a control perspective, GEOTABS buildings are particularly challenging systems due to large scale, complex heating, ventilation and air conditioning (HVAC) system, and slow dynamics with time delays. On the other hand, there is an increased potential for energy savings due to the high thermal mass, which acts as thermal storage. The MPC operation is demonstrated during the challenging transient seasons (switching between heating and cooling), and its performance is compared to a traditional rule-based controller (RBC). We provide a proof of concept of real MPC operation for the most difficult seasons with notable GSHP energy use savings equal to 53.5% and thermal comfort improvement by 36.9%. Other MPC applications found in the literature describe tests for only cooling or only heating, and up to now only for a black-box or a grey-box approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Model predictive control for active insulation in building envelopes

Active insulation systems (AISs) refer to building envelopes with insulation materials that can change their thermal conductivity and are coupled with thermal mass to reduce building energy consumption and peak power. In this research, a novel optimal control approach is proposed to evaluate the maximum theoretical energy and cost-saving potential of AISs. A time-varying model predictive control (TV-MPC) controller was used to optimally select the AIS mode and simultaneously determine the operation of the heating, ventilation and air conditioning (HVAC) system so that the maximum saving potential of the entire system can be realized. To comprehensively evaluate the power shifting flexibility of AISs, two optimization objectives—minimizing weekly electric energy consumption and minimizing weekly electricity cost—were considered. The summer season simulation results show that under the first objective, more than 50% electric and thermal energy was saved when the upper boundary of the indoor air temperature was set to 25 °C. Furthermore under the second optimization objective, 38% of the cost was saved. It can be expected that the developed approach can be easily applied to multiple types of AISs with different mechanisms.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Comparative Analysis of Model Predictive Control and MPC-Informed Rule-Based Control for Thermal Storage Operation in Ultra-Low Temperature 4th Generation District Heating Networks

The integration of thermal storage and heat pumps in district heating networks (DHNs) can significantly enhance operational flexibility and energy efficiency; however, the practical deployment of advanced control strategies is often hindered by forecasting requirements and computational complexity. This study presents a comparative analysis of thermal storage control strategies in an ultra-low-temperature fourth-generation DHN, focusing on the development of a simplified rule-based control (RBC) explicitly informed by Model Predictive Control (MPC) behavior. The proposed methodology systematically analyzes the charging and discharging decisions of an MPC-controlled system under ideal forecasting conditions and extracts recurrent control patterns as a function of key system variables, including outdoor temperature, thermal demand, and electricity price. These patterns are translated into a set of structured time- and condition-based rules, resulting in an MPC-informed RBC that embeds predictive insights while preserving implementation simplicity and operational transparency. The approach is validated on a realistic mixed-use urban district in Denver, Colorado, USA, equipped with a centralized air-source heat pump, distributed water-to-water heat pumps, and a central thermal storage unit. Results show that the tuned RBC attains approximately 96% of ideal MPC economic performance (-27% of costs), preserves values of technical and environmental indicators (reduction only of 2-3%), and substantially reduces complexity. Sensitivity analyses further demonstrate the robustness of the RBC under varying operational conditions (i.e., ambient temperature, electricity price). Overall, the study demonstrates that MPC-informed rule-based control represents an effective trade-off between control performance and real-world applicability, enabling the integration of additional system components while maintaining simplicity, robustness, and ease of implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Data-Driven Model Predictive Control for Temperature Management of Heat Pipe Microreactor

A data-driven model predictive control (MPC) was developed to enable the self-regulating capability of heat pipe (HP) nuclear microreactors. The MPC can proactively respond to potential disturbances of HP microreactors using three approaches for system identifications: linear state-space model, feedforward neural network, and recurrent neural networks with long short-term memory units. We present numerical results of data-driven MPCs to control the temperatures of selected HPs in a 37-HP test article. Our results show qualitatively that all data-driven MPCs produced similar control actions, while quantitatively, with artificial neural nets (especially feedforward neural nets), MPC can better follow drastic changes in setpoints with small errors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

A Generic Two-Vector Model Predictive Control for Hybrid Multilevel Converters

In this article, a generic two-vector model predictive control (TV-MPC) strategy is proposed for the hybrid multilevel converters (HMCs). The proposed method selects two optimal voltage vectors among all the vector candidates using a geometric positioning approach to reduce the computational burden, which is a common issue in the existing MPC methods for HMC. Then duty cycles of the two selected vectors are optimized to minimize the current tracking error, such that the current tracking performance can be enhanced compared with the conventional MPC. In addition, the voltages of the floating dc capacitors can be balanced by evaluating all switching sequences that belong to the optimal voltage vectors with optimal duty cycles. Here, the concept of the proposed TV-MPC is generic and applicable for any HMCs. A typical HMC based on active neutral-point-clamped topology is adopted as a case study in this work. Comprehensive simulation and experimental studies are performed on an all silicon-carbide HMC prototype to validate the effectiveness of the proposed control scheme.

42 ENGINEERING↗

DOE OSTI2023

Experimental validation of model predictive control for solid oxide fuel cells

Here, this paper presents implementation of a model predictive controller (MPC) for an experimental solid oxide fuel cell (SOFC) system. The MPC controller is based on a gain-scheduled predictor with block-oriented structure that can capture important non-linear effects while still keeping the computational complexity low enough to meet real time control requirements. Experimental results show the MPC is able to regulate the SOFC cathode outlet temperature in the face of startup transients and input perturbations.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy *

Abstract Time-dependent SOLPS-ITER simulations have been used to identify reduced models with the sparse identification of nonlinear dynamics (SINDy) method and develop model-predictive control of the boundary plasma state using main ion gas puff actuation. A series of gas actuation sequences are input into SOLPS-ITER to produce a dynamic response in upstream and divertor plasma quantities. The SINDy method is applied to identify reduced linear and nonlinear models for the electron density at the outboard midplane n e , s e p O M P and the electron temperature at the outer divertor T e , s e p d i v . Note that T e , s e p d i v is not necessarily the peak value of T e along the divertor. The identified reduced models are interpretable by construction (i.e. not black box), and have the form of coupled ordinary differential equations. Despite significant noise in T e , s e p d i v , the reduced models can be used to predict the response over a range of actuation levels to a maximum deviation of 0.5% in n e , s e p O M P and 5%–10% in T e , s e p d i v for the cases considered. Model retraining using time history data triggered by a preset error threshold is also demonstrated. A model predictive control strategy for nonlinear models is developed and used to perform feedback control of a SOLPS-ITER simulation to produce a setpoint trajectory in n e , s e p O M P using the integrated plasma simulator framework. The developed techniques are general and can be applied to time-dependent data from other boundary simulations or experimental data. Ongoing work is extending the approach to model identification and control for divertor detachment, which will present transient nonlinear behavior from impurity seeding, including realistic latency and synthetic diagnostic signals derived from the full SOLPS-ITER output.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Limits on the Range and Rate of Change in Power Take-Off Load in Ocean Wave Energy Conversion: A Study Using Model Predictive Control

Previous work comparing power take-off (PTO) architectures for ocean wave-powered reverse osmosis suggests that variable displacement in the wave energy converter (WEC)-driven pump does not offer a significant performance advantage. A limitation of that study is that the WEC was subject to a constant load within a given sea state (“Coulomb damping”) and did not account for controlled, moment-to-moment variation of the PTO load enabled by a variable displacement pump. This study explores the potential performance advantage of a variable PTO load over Coulomb damping. Model predictive control is used to provide optimal load control with constraints on the PTO load. The constraints include minimum and maximum loads and a limit on the rate of load adjustment. Parameter studies on these constraints enable conclusions about PTO design requirements in addition to providing an estimated performance advantage over Coulomb damping. Numerical simulation of the Oyster 1 WEC is carried out with performance weighted by historical sea state data from Humboldt Bay, CA. The results show a performance advantage of up to 20% higher yearly-average power absorption over Coulomb damping. Additionally, the parameter studies suggest that the PTO load should be adjustable down to at least 25% of the maximum load and should be adjustable between the minimum and maximum loads within a few seconds.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Feed-Forward Compensation for Model Predictive Control in Tri-port Current-Source Medium-Voltage String Inverters for PV-Plus-Storage Farms

The novel tri-port current-source medium-voltage string inverter (TCS-MVSI) is a promising candidate for large-scale PV-plus-storage (PVS) farms owing to its galvanic isolation, easy storage integration, soft-switching capability across entire load range, controlled low dv/dt and EMI, benign fault tolerance, etc. Due to its low inertia feature, traditional PI-based control cannot manage large transients effectively. Instead, a model-based predictive control (MPC) is proposed to achieve robust and stable operation. As is well known, the control performance of MPC is compromised by the sampling and computational delay during implementation significantly, if not well addressed. This paper analyzed and quantified these delays and then proposes feed-forward compensation (FFC) for the MPC method to compensate the delays and the large parameter variations due to the low-inertia nature. In addition, this method also compensates for the high dc-link ripple within each switching cycle, a unique issue for low-inertia converters. Here, the proposed method requires low computational effort, allowing it to be extended for multiple ports. The effectiveness of the proposed method has been validated in experiments. In 10 kW test, the proposed method decreases the average dc-link current by 17%, leading to ~20% conduction losses and ~0.5% increase in converter efficiency. In addition, the peak dc-link current also decreases by 15%, resulting in reduced transformer size. As a result, an increased power density can be achieved with the proposed method. Similar improvements have been observed across the power range from 2 kW to 10 kW.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

A comparison of efficiency-aware model-predictive control approaches for wave energy devices

In this study, we compare four different formulations of model predictive control that attempt to maximise electrical power generated by a wave energy converter (WEC). Control laws include (1) pure maximisation of mechanical power, (2) maximisation of mechanical power with a control penalty factor, (3) maximisation of electrical power using power conversion efficiency, and (4) maximisation of electrical power using the full electro-mechanical model of a system. For this study, a wave-to-wire model is developed for a floating spherical buoy connected to a permanent magnet synchronous generator. The performance of the controllers, including the mechanical and electrical power outputs, is compared in irregular wave conditions for the unconstrained and force-constrained scenarios. The results demonstrate that the controller designed to maximise mechanical power is not suitable for practical applications and may lead to negative electrical power output due to the non-ideal power take-off efficiency. Moreover, the replacement of the power take-off dynamics by the efficiency coefficient does not guarantee the maximum electrical power production.

16 TIDAL AND WAVE POWER↗

DOE OSTI2022

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Zhou, Yujing↗

DOE OSTI2020

A Generic Multivector Model Predictive Control With Symmetric Pulse Pattern for Hybrid Multilevel Converters

This article presents a generic multivector model predictive control (MV-MPC) method that has the potential to be applied to all hybrid multilevel converters (HMCs). It first locates the reference voltage vector in the 120° oblique coordinate to select the three adjacent voltage vectors to be applied over one control cycle. Then, the current tracking is guaranteed through duty cycle optimization and dc capacitor voltages are balanced by evaluating possible switching sequences that belong to the voltage vectors with optimal duty cycles. At last, the optimal switching sequence with a symmetric five- or seven-segment pulse pattern is generated using an external modulator. Here, the proposed generic MV-MPC can significantly improve the quality of the output current, while achieving a constant equivalent switching frequency at the same time. Experimental studies on an all silicon carbide HMC prototype, i.e., an active neutral point clamped converter with cascaded H-bridge, are presented to validate the effectiveness of the proposed MV-MPC strategy.

42 ENGINEERING↗

DOE OSTI2023

Adaptive Data-Driven Model Predictive Control for Heat Pipe Microreactors

To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Adaptable Data Driven Model Predictive Control for Heat Pipe Microreactors

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Stochastic Model Predictive Control With Gaussian Wind Direction Preview for Wake Steering

This article addresses the problem of wake steering control for wind farms that explicitly consider the tradeoff between farm-level power generation and yaw duty cycle under variable and uncertain wind conditions. A novel stochastic model predictive control (MPC) algorithm is presented, which utilizes a stochastic model of the freestream wind field components in a receding horizon framework to compute optimal yaw set points that maximize the expected value of the farm power while constraining the yaw actuation. Different configurations of the algorithm are evaluated using a steady-state wind farm simulator. The proposed stochastic MPC algorithm can plan control actions over a future prediction horizon based on probabilistic estimates of the incoming wind magnitude and direction.

17 WIND ENERGY↗

DOE OSTI2020

Multiscale model predictive control of battery systems for frequency regulation markets using physics-based models

In this work, we propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals (compared to MPC formulations that use low-fidelity models). Our results also provide evidence that sophisticated battery models can be embedded within closed-loop MPC simulations by using modern nonlinear programming solvers (we provide an efficient and easy-to-use implementation in Julia). We use insights obtained with our simulations to design a low-complexity MPC formulation that matches the behavior obtained with high-fidelity models. This is done by designing a suitable terminal penalty term that implicitly captures long-term degradation. The results suggest that complex degradation behavior can be accounted for in low-complexity MPC formulations by properly designing the cost function. We believe that our proof-of-concept results can be of industrial relevance, as battery vendors are seeking to participate in fast-changing electricity markets while maintaining asset integrity.

42 ENGINEERING↗

DOE OSTI2024

Adaptive Online Model Update Algorithm for Predictive Control in Networked Systems

In this article, we introduce an adaptive on-line model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case.

data-driven model predictive control↗

DOE OSTI2020

A Virtual Space Vector Model Predictive Control for a Seven-Level Hybrid Multilevel Converter

This paper proposes a virtual space vector (VSV) model predictive control (MPC) for a three-phase seven-level (7L) hybrid multilevel converter (HMC), where each phase consists of an active-neutral-point-clamped converter with a floating H-bridge. To achieve the best current tracking, which is the primary goal of the proposed algorithm, a novel geometrical positioning approach is proposed to select the optimal voltage vector among all the realspace vectors and VSVs. Then, all the possible switching sequences that belong to the optimal voltage vector are evaluated to realize the dc capacitor voltage balancing and common-mode voltage reduction. Through an external modulator, the optimal voltage vector can be synthesized by using either one-, three-, or seven-segment switching sequence. Compared with the conventional MPC, the proposed VSV-MPC can reduce not only the computational burden but also the current THD. Both simulation and experimental results obtained on silicon carbide based 7L-HMC prototype are presented to validate the feasibility and effectiveness of the proposed VSVMPC strategy.

42 ENGINEERING↗