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In the current studies on energy management strategy (EMS) for vehicle-following scenarios, the accuracy of vehicle state predictions based on mechanistic models is influenced by the time-varying conditions, affecting the optimization control performance. To address this issue, a data-driven Koopman model predictive control for hybrid energy storage system (HESS) of electric vehicles (EVs) in vehicle-following scenarios is proposed, combining the safety speed planning and energy management strategy. Firstly, a data-driven Koopman vehicle state prediction model is constructed in the upper layer for estimating parameters, such as road surface smoothness and slope. This model is then integrated into Model Predictive Control (MPC) to optimize the speed of the following vehicle. Subsequently, in the lower layer, utilizing the output from the upper layer and predicting load power, the load power is further allocated within the HESS. Simulation results demonstrate that in scenarios considering factors like slope, the hierarchical MPC with the Koopman model reduces energy consumption by 5.55% compared to the hierarchical MPC with mechanistic model. • Data-driven Koopman MPC for EVs under vehicle-following scenarios is proposed. • A Koopman prediction model is developed considering parameters such as slope. • The proposed method reduces energy consumption by 5.55%. [ABSTRACT FROM AUTHOR]

Recently, battery fast charging strategies have gained increasing interest as range anxiety and long charging time have been the main obstacles to the wider application of electric vehicles. While simply increasing the current can reduce charging time, it might also tend to accelerate the irreversible capacity degradation and power fade. To solve the dilemma between charging speed and battery lifetime, in this work, we proposed a life-extending optimal charging method that considers the charging time and the aging-related effects within the battery. A multi-physics battery model coupled with thermal and electrochemical degradation dynamics is developed and integrated into a model predictive control framework to manipulate the optimal charging current considering the constraints of safe requirements and the rate of internal aging side reactions. The design method was extensively validated by in-situ and ex-situ experiments. Results show that by reducing the rates of side reactions and minimizing detrimental morphological changes in the anode material, the proposed charging method can prolong the battery lifetime by at least 48.6%, compared with the commonly used constant current and constant voltage charging method without obviously sacrificing charging speed. [Display omitted] • An electrochemical-thermal-aging battery model is developed and widely validated. • Battery degradation mechanisms are analyzed at various operating conditions. • Optimal charging profile is designed by considering multiple physical constraints. • The impact of algorithm hyper-parameters is discussed. • Capacity loss is reduced by 48.6% without sacrificing the charging speed. [ABSTRACT FROM AUTHOR]

Dual fluidized bed (DFB) gasification is a promising method for producing valuable gaseous energy carriers from biogenic feedstocks as a substitute for fossil fuels. State-of-the-art DFB gasification plants mainly rely on manual operation or single-input single-output control loops, and scientific contributions only exist for controlling individual process variables. This leaves a research gap in terms of comprehensive control strategies for DFB gasification. To address this gap, we propose a multivariate control strategy that focuses on crucial process variables, such as product gas quantity, gasification temperature, and bed material circulation rate. Our approach utilizes model predictive control (MPC), which enables effective process control while explicitly considering process constraints. A simulation study is given demonstrating how different MPC parametrizations influence the behavior of the closed-loop system. Experimental results from a 100 kW pilot plant at TU Wien demonstrate the successful control achieved by the proposed control algorithm. [Display omitted] • Model predictive control for product gas quantity and gasification temperature. • Consideration of process constraints such as remaining oxygen in the flue gas. • Control structure composed of a high-level MPC and a bed material circulation MPC. • A simulation study compares and evaluates different controller parametrization. • Experimental results are shown for a 100 kW pilot plant at TU Wien. [ABSTRACT FROM AUTHOR]

The use of model predictive control (MPC) to optimally control district heating (DH) networks can support the transition to a carbon-neutral heating sector. DH systems are inherently subject to non-linear physics and integer controls, which results in a mixed-integer non-linear program (MINLP). In this work, an MINLP-based MPC strategy is developed for the optimal control of a DH network, building upon an existing decomposition approach (combinatorial integral approximation). The main novelty of this work is the application of an integrated MINLP-based MPC to a DH network and its comparison to a non-linear program (NLP)-based MPC. To successfully develop this MINLP-based approach, the pycombina tool is efficiently integrated in the existing NLP-based MPC toolchain (TACO) and the concept of an augmented time horizon is introduced to manage dwell time constraints. The MINLP-based MPC is applied to two use cases: a relatively simple nine-zone terraced house and a more complex fourth generation DH network. The simulation study shows that the MINLP-based MPC yields a comparable control performance to that of a previously developed NLP-based MPC, but the CPU time is approximately eight times higher. However, the absence of a post-processing step (which requires ample engineering practice and time) and the improved match between the MPC's controller model and the actual system show promise for the MINLP-based MPC to control complex DH networks. • An MINLP-based MPC methodology for district heating systems is developed. • The MINLP-based MPC methodology can cope with dwell time constraints. • NLP- and MINLP-based MPC reach similar performance for the envisaged use cases. • NLP-based MPC has a computational advantage. • MINLP-based MPC avoids parameter tuning and better matches the actual system. [ABSTRACT FROM AUTHOR]

Building photovoltaic (PV) power generation is intermittent, volatile, random, and uncontrollable; thus, the use of solar grid-connected power generation can lead to a series of problems, such as grid voltage fluctuations and power imbalance. As a typical flexible load, the scheduling and regulation of the heating, ventilation, and air conditioning (HVAC) system load can help a grid-connected solar grid achieve balanced and flexible operation. This study proposes a PV capacity dynamic tracking model predictive control strategy for air-conditioning systems with flexible loads. This strategy aims to effectively address the issue of unstable grid-connected output of solar PV systems. In particular, a solar radiation prediction model based on a long short-term memory neural network optimized by the grey wolf optimization algorithm was established, which effectively improved the model prediction accuracy. The cost function of HVAC flexible load control considers the law of solar PV output and the human thermal comfort model. A genetic algorithm (GA) is used to obtain the optimal control parameters for dynamic regulation. The GA was employed to efficiently and effectively optimize the control of flexible HVAC loads, considering the variability of the solar PV output and ensuring human thermal comfort. A dynamic regulation model was developed for a typical office building with a grid-connected solar PV power generation system. TRNSYS was utilized for verification based on the actual measured data and relevant meteorological parameters of the case building. A new evaluation metric, volatility, was proposed to evaluate net load fluctuation. The simulation platform based on the physical building yield results indicated a strong resemblance between the energy consumption curve under the predicted control conditions using the flexible model and the PV generation curve. Additionally, the net load volatility was measured to be 2.63. Compared with the predicted control condition without the flexible model, the net load volatility of the grid was reduced by 47.08%, and the energy-saving rate reached 10.89% in the summer. [Display omitted] • Tracking MPC strategy is proposed for fine-tuning control of HVAC system. • The law of photovoltaic output is considered in the proposed strategy. • Grey Wolf Optimization algorithm is used to tune LSTM neural network. • Control strategies comparisons exhibit the effectiveness of the proposed strategy. • A new metric-"volatility" is proposed to evaluate load fluctuations. [ABSTRACT FROM AUTHOR]

Grid-interactive communities are an emerging solution for optimal energy dispatching, improving grid stability, and enhancing the usage of on-site renewable energy by leveraging community flexibilities. This work introduces a novel Physically Consistent Deep Learning (PCDL)-based optimization framework for day-ahead energy dispatching and thermal comfort control within grid-interactive communities. Specifically, the PCDL model is developed to capture the thermal dynamics within the community while ensuring strict adherence to physics consistency. This consistency is defined with the aim of generating physically viable solutions in response to modified system inputs. Subsequently, the PCDL model is utilized for load estimation and indoor climate prediction within a proposed scheduling and control strategy: (1) an optimal energy dispatching plan is calculated based on the day-ahead grid market; (2) a real-time model predictive control (MPC) method is applied to minimize the utilization error and indoor comfort constraint violations caused by following the scheduled energy dispatching plan. A simulation case of a residential hall community at Cornell University with on-site renewable energy resources is implemented to demonstrate the effectiveness of the proposed approach. Comparative simulations, which include the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Gaussian Process (GP) regression models, confirm the performance advantage of capturing community thermal dynamic and control-oriented generalization ability when using the PCDL model. These results leads to notable improvements in indoor thermal comfort control, ranging between 65.4 and 68.7%, 63.6–79.3%, and 60.4–62.2% for each comparative model, respectively. Moreover, by harnessing community demand flexibility, we achieve energy cost savings between 29.5 and 39.7% compared to the baseline controller. • A physically consistent deep learning (PCDL) model for thermal dynamic prediction. • A PCDL-based community energy dispatching and indoor comfort control framework. • Novel case study results compared to alternative data-driven models. [ABSTRACT FROM AUTHOR]

This article presents the results of a 182 day implementation of a model predictive controller (MPC) in a test house in Ottawa, Canada. The MPC controls the timing of hydronic floor heating and cooling within the highly glazed house (13.5% south-facing window-to-floor ratio). The objectives of the MPC are to optimally control the on/off times of active heating and cooling to minimize energy use while also minimizing air temperature violations outside of a prescribed range. It was found that 71% of the days in the experimental period saw successful operation of the MPC towards these goals. Days that experienced overheating were found to be concentrated in January–February and days that experienced overcooling were found to be concentrated in March–May. Problematic decision making by the MPC was found to be rooted in 4 main issues: (1) global horizontal irradiance (GHI) is a bad indicator of passive solar on its own; (2) weather forecasting (specifically GHI forecasting) is critical to MPC success in highly glazed buildings; (3) the GHI history regressor used in the prediction model regularly influenced predictions illogically; and (4) the MPC lacked decision-making logic on the timing of hydronic floor cooling. • A model predictive controller was implemented in a highly glazed, full-scale, test house. • Performance of the model predictive controller was evaluated over 7 months. • The design of the MPC is simple and practical for implementation in a real building. • A recommendation is made on what a model predictive controller could look like for a highly glazed house. [ABSTRACT FROM AUTHOR]

• Study of microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV system. • Development of energy management system based on model predictive control (MPC-EMS). • Results compared with proportional sharing algorithm based on SOC level (SOC-EMS). • Best results obtained from MPC-EMS. This paper presents a new energy management system (EMS) based on model predictive control (MPC) for a microgrid with solar photovoltaic (PV) power plants and a quasi-Z-source cascaded H-bridge multilevel inverter that integrates an energy storage system (ES-qZS-CHBMLI). The system comprises three modules, each with a PV power plant, quasi-impedance network, battery energy storage system (BESS), and voltage source inverter (VSI). Traditional EMS methods focus on distributing the power among the BESSs to balance their state of charge (SOC), operating in charging or discharging mode. The proposed MPC-EMS carries out a multi-objective control for an ES-qZS-CHBMLI topology, which allows an optimized BESS power distribution while meeting the system operator requirements. It prioritizes the charge of the BESS with the lowest SOC and the discharge of the BESS with the highest SOC. Thus, both modes can coexist simultaneously, while ensuring decoupled power control. The MPC-EMS proposed herein is compared with a proportional sharing algorithm based on SOC (SOC-EMS) that pursues the same objectives. The simulation results show an improvement in the control of the power delivered to the grid. The Integral Time Absolute Error, ITAE, achieved with the MPC-EMS for the active and reactive power is 20 % and 4 %, respectively, lower than that obtained with the SOC-EMS. A 1,3 % higher charge for the BESS with the lowest SOC is also registered. Furthermore, an experimental setup based on an OPAL RT-4510 unit and a dSPACE MicroLabBox prototyping unit is implemented to validate the simulation results. [ABSTRACT FROM AUTHOR]

The integration of new elements and technologies to the power grid, driven by the expansion of electric microgrids, brings various challenges requiring a suitable control architecture for systems to operate ensuring economic and reliable access to electricity. This paper presents the development of a time-varying constraint real-time model predictive secondary voltage control (MPVC) strategy for microgrids built up in a multi-class Python environment with InfluxDB and SQLExpress databases storing the controller and microgrid data, and its experimental implementation with Modbus communication to physical devices at the Universidad Pontificia Bolivariana (UPB) campus microgrid. The microgrid characteristics are described and the experimental setup is presented. The mathematical model of the resource used as case study is obtained through experimental identification, and it is integrated to the MPVC scheme. Polytopic invariant sets are used as terminal sets in the MPVC scheme to ensure stability of the solution, and time-varying constraints of reactive power availability are considered. Validation studies of the proposed control system in computational simulation are presented prior to real application in the UPB campus microgrid. Controller performance results are described for three different implementation scenarios of low, medium and high variability of external disturbances, presenting adequate voltage regulation around the reference value and considerable dependence on the meteorological forecast quality. The model predictive voltage controller is finally benchmarked against a commercial microgrid controller to evaluate its performance, standing out as a more comprehensive and tailor-made solution. • Five campus microgrids across Colombia with multilevel hierarchical EMS are presented. • A secondary voltage control architecture is proposed and tested in UPB microgrid.. • Implementation scenarios of low, medium and high irradiance variability are tested. • Controller performance is compared to the regulation functions of a commercial device. • The voltage controller architecture depends on the accuracy of disturbance forecast. [ABSTRACT FROM AUTHOR]

Highlights • Presented a model-predictive control strategy for radiant floor systems. • Developed data-driven building models with solid prediction accuracy. • Deployed a robust optimization formulation that achieves good solution quality. • Implemented the developed smart operation strategy in an actual building. • Presented a realistic evaluation and demonstrated significant energy savings. Abstract This paper introduces a smart operation strategy based on model predictive control (MPC) to optimize the performance of hydronic radiant floor systems in office buildings and presents results from its implementation in an actual building. Our MPC approach uses dynamic estimates and predictions of zone loads and temperatures, outdoor weather conditions, and HVAC system models to minimize energy consumption and cost while meeting equipment and thermal comfort constraints. It includes data-driven building models estimated and validated using data from an actual building, and deploys an optimizer based on constraint linear/quadratic programming with hard comfort bounds that yields a global minimum with predicted exogenous disturbances. The MPC results show 34% cost savings compared to baseline feedback control during the cooling season and 16% energy use reduction during the heating season. Also, the radiant floor system with the predictive controller shows 29–50% energy savings when compared with a baseline air delivery system serving two identical thermal zones located in the same building. [ABSTRACT FROM AUTHOR]

• A hierarchical model predictive controller is developed. • Little to no modification is required on the architecture of the existing system. • A stable power exchange between the renewable system and the grid is achieved. • Fast variations are completely removed from the battery power. • Increased utilization of intermittent renewable energy is achieved. This paper presents a two-layer control strategy designed for easy integration of supercapacitors in a grid-integrated solar photovoltaic-battery hybrid renewable system, initially controlled by a typical model predictive control method. To operate the upgraded energy system, either without or with little modifications of the pre-existing architecture, an additional control layer is applied at the bottom of the original control system. Considering the complementary characteristics of batteries and supercapacitors, the design of the new model predictive control layer and its coordination with the original one help to deliver a stable power flow between the hybrid renewable system and the utility grid, and remove fast variations from the battery power. Actual measurements of solar radiation in South Africa are used to test the effectiveness of the proposed strategy. Simulations carried out on a 1-MW photovoltaic plant confirm the benefits in terms of adherence to power quality regulations, improved conditioning of the power generated by the intermittent renewable sources, and lifetime extension of the battery. [ABSTRACT FROM AUTHOR]

• A test-bench for PV-CHP hybrid system has been build. • Input profiles (PV and load) are emulated with accuracies between 0.1% and 14%. • The CHP and gas burner should have modulated power supply for higher efficiency. • Hardware and metering generates additional deviations from forecast schedules. • A battery needs a faster control added to the MPC for higher accuracy. In order to reduce the global warming to less than two degrees, a large increase in renewable energy resources like photovoltaic systems is necessary. A business case based on feed-in tariffs does not exist in every countries and self-consumption is limited due to the unsteady nature of solar radiation on earth. A combination with other systems such as batteries, heat-pumps or even combined heat and power plants can enhance the use of generated power by photovoltaic systems, particularly in private households and small businesses. Rule-based controllers and optimization algorithms (model predictive control) can both realise the efficient operation of a photovoltaic system in combination with storage systems and a combined heat and power plant. However, different controllers and energy management systems have hitherto only been compared theoretically. A comparison of such controllers in a real, controllable hardware environment has not yet been carried out. In this study, a test-bench is introduced to test different control algorithms for photovoltaic systems in combination with storage systems and a micro- combined heat and power plant. The operation has been tested for a one day period. Key performance parameters have been derived and compared for a rule-based control, an optimized control and simulation results including a forecast. The results show that the operational costs can be reduced by 7.3% for the chosen test-period using the optimized algorithm in the laboratory compared to the same system with a rule-based control. The results also indicate that even under perfect forecast conditions the hardware, metering and energy management cause latencies and inaccuracies leading to deviations, which are not accounted for in simulations. Hence, the accuracy of the forecast methods need not be higher than the deviations introduced by the hardware. These deviations often lead to unwanted charging and discharging events of the battery. A faster way of processing data and a second order or low level control is needed for short term reaction and higher efficiency. [ABSTRACT FROM AUTHOR]

• An MPC approach is devised for efficient management of building HVAC systems. • Electrical and thermal storage, PV generation and demand-response are considered. • A low-complexity two-stage optimization approach is introduced. • The proposed method is suitable for application to large-scale buildings. • The approach is robust against inaccurate forecasts of exogenous inputs. This paper deals with the problem of cost-optimal operation of smart buildings that integrate a centralized HVAC system, photovoltaic generation and both thermal and electrical storage devices. Building participation in a Demand-Response program is also considered. The proposed solution is based on a specialized Model Predictive Control strategy to optimally manage the HVAC system and the storage devices under thermal comfort and technological constraints. The related optimization problems turn out to be computationally appealing, even for large-scale problem instances. Performance evaluation, also in the presence of uncertainties and disturbances, is carried out using a realistic simulation framework. [ABSTRACT FROM AUTHOR]

• A novel physically consistent deep learning framework for building thermal modeling. • A PCDL-based MPC for optimizing energy efficiency and indoor thermal comfort. • A simulation case study of an office building at Cornell University. Being a primary contributor to global energy consumption and energy-related carbon emissions, the building and building construction sectors are a crucial player in the decarbonization and energy efficiency efforts. This article proposes a novel physically consistent deep learning (PCDL) approach for building thermal modeling and assesses its potential for optimizing building energy efficiency and indoor thermal comfort through model predictive control (MPC). The PCDL model considers physical relationships between system inputs and outputs and is applied to predict the indoor thermal climate. With the strict guarantee of satisfying the laws of physics, the proposed PCDL model has better generalization ability than other machine learning approaches. Subsequently, the PCDL model is integrated into an MPC controller to optimize building energy consumption and indoor thermal comfort. The proposed approach is tested on Carpenter Hall, a multi-zone office building located in Cornell University Campus, through simulations. Based on the simulation results, the PCDL model demonstrate a much better generalization ability for yielding physically-feasible predictions compared to the long short-term memory (LSTM) model. Compared to an On/Off controller, a state space model-based MPC controller, and an LSTM-based MPC controller, the proposed PCDL-based MPC reduces by 5.8%, 4.5%, and 8.9% energy consumption and improves by 55%, 59%, and 64% indoor thermal comfort, respectively, and therefore enhances the building decarbonization progress. The results emphasize the importance of considering physics information in data-driven models and highlight the advantages of the proposed PCDL-based MPC controller. [ABSTRACT FROM AUTHOR]

Actively controlling a building's heating, ventilation, and air conditioning (HVAC) system can reduce costs and improve indoor comfort. Model predictive control (MPC) is an effective control algorithm that can facilitate the active control of complex systems such as the HVAC system. However, the uncertainty of the prediction model engenders many challenges in practical application. To address these issues, we propose a tube-based MPC strategy. First, a reduced-order thermal capacitance and thermal resistance model is established for the target system. Subsequently, a tube-based MPC scheme is designed to effectively handle uncertainties in real systems. The prediction uncertainty space is re-assumed in the tube, combined with the actual prediction error, to more closely correspond to the actual situation. The proposed model is tested and validated using the BOPTEST open-source testing framework. The results show that the proposed tube-based MPC can reduce the operating cost by at least 24%, compared with the traditional open-loop and closed-loop MPC, and can better control the indoor temperature when considering multiple uncertain predictions. • The operation control of the HVAC system is carried out by tube-based MPC. • Taking into account the indoor temperature and economic factors. • All algorithms are verified on the BOPTEST open source framework. • Tube-based MPC can obtain robust results in the presence of prediction uncertainty. [ABSTRACT FROM AUTHOR]

Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimization-based control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería , Spain. • Decentralized control of large-scale parabolic-trough solar collector fields. • Reducing the computational burden of model predictive controllers for solar plants. • Splitting large-scale solar plants by clustering model predictive controllers. • Improving the scalability of model predictive control for large-scale solar plants. • Evolutionary game theoretical assistance for model predictive control in solar plants. [ABSTRACT FROM AUTHOR]

• An analysis model for dynamic available energy of UPS in IDC is proposed. • Integrated dispatching strategy of the UPS and conventional units. • A reallocation method based on dispatching error and SOC of BESS improve the optimization of SOC and the revenue of the IDC increased by about ¥5950. • Proposed strategy increases UPS utilization by about 85% and reduces the MAE and RMSE of the SOC fluctuation to 12%-19%. The internet data center (IDC) can improve the stability of power system and increase the utilization of uninterruptible power supply (UPS) with battery energy storage system (BESS) and hydrogen fuel cell (HFC) by participating in dispatch operations. This paper proposes a hierarchical dispatch strategy assisted by model predictive control (MPC) for UPS in IDC including available energy analysis, the upper-level power system dispatch strategy and the lower-level IDC dispatch strategy. The data security can be ensured through available energy analysis based on load forecasting information of IDC. Then, the upper-level dispatch model aims at increasing the utilization of UPS and the revenue of IDC via minimizing the total generation cost. Meanwhile, the lower-level dispatch method with MPC reallocates the upper-level dispatch instructions between BESS and HFC by balancing power tracking error and the state of charge of BESS. Finally, the effectiveness of the strategy is verified by several experiments in MATLAB platform. Numerical results show that the proposed strategy increases the utilization rate of the UPS by about 85% and the revenue by about ¥5950, and the SOC fluctuation is reduced to 12%-19%. [ABSTRACT FROM AUTHOR]

Floating offshore wind turbines (FOWTs) present a cost-competitive advantage over their fixed-bottom counterparts, but also have technical challenges of achieving the desired stability and power reliability of the wind turbine. It is believed that co-locating of wave energy converters (WECs) and a FOWT can be the solution to these challenges. However, as the power generation of WECs is strongly associated with their hydrodynamic response, their addition tends to have a detrimental effect on the FOWT's performance. To address this challenge, this paper proposes a framework for combining a FOWT with a small wave array that will make it possible to simultaneously achieve a reliable overall power production, and minimise the motion of a floating platform. It is done by properly controlling the hydrodynamic coupling between FOWT and WEC via a model predictive control approach. The results demonstrate that this novel approach manages to achieve platform stability and power reliability simultaneously, although it might require to collaborate with an aerodynamic control at high wind speeds. This work can be used as a guidance for operation of co-located wind–wave power systems. • A novel control framework for co-located wind–wave power system is proposed. • The platform and power reliability of floating offshore wind turbines are achieved. • Up to 46% peak reduction in nacelle velocity and acceleration can be achieved. • The power shortage of the FOWT at low wind speeds can be fully compensated. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive power management strategy for a novel anti-idling system, regenerative auxiliary power system (RAPS), designed for service vehicles. RAPS is able to utilize recovered braking energy for electrified auxiliary systems; this feature distinguishes it from its counterparts - auxiliary power unit (APU) and auxiliary battery powered unit (ABP). To efficiently operate the RAPS, a power management strategy is required to coordinate power flow between different energy sources. Thus, a model predictive controller (MPC) is developed to improve the overall efficiency of the RAPS. As an optimization-based approach, the MPC-based power management strategy usually requires the drive cycle or the drivers’ command to be known a priori. However, in this study, an average concept based MPC is developed without such knowledge. MPC parameters are tuned over an urban drive cycle; whereas, the robustness of this MPC is tested under different drive cycles (e.g. highway and combined). Analysis shows that, the presented MPC has a comparable performance as the prescient MPC regarding fuel consumption, which assumes knows the drive cycle beforehand. Meanwhile, with the help of the proposed MPC and RAPS, the service vehicle saves up to 9% of the total fuel consumption. The proposed MPC is independent of powertrain topology such that it can be directly extended to other types of hybrid electric vehicles (HEVs), and it provides a way to apply the MPC even though future driving information is unavailable. [ABSTRACT FROM AUTHOR]

• An MPC framework for space heating system with PCM-based active latent heat storage. • A linear, high-fidelity active latent heat storage model for fast building control. • Highly dynamic electricity prices considered for demand-responsive building control. • The MPC approach reduces electricity costs for space heating by 53.2% to 122.7% • A good synergy between the MPC approach and active latent heat storage is observed. Active latent heat storage (ALHS) involving phase-change materials constitutes a promising energy-efficient solution for building energy management (BEM) by reshaping building energy demands without occupant comfort degradation. Current BEM systems based on conventional reactive control lack the level of control delicacy required to exploit the full potential of ALHS for BEM under certain conditions, such as highly dynamic electricity prices. This study proposes a smart model predictive control (MPC) approach for BEM to minimize the energy cost while maintaining the indoor climate by fully applying ALHS. More specifically, a reduced-order, high-fidelity state-space model (SSM) of ALHS is proposed for fast building control. An MPC framework considering highly dynamic electricity prices and ALHS dynamics is developed based on the proposed ALHS SSM integrated with a building SSM. A case study entailing a set of simulations is designed based on a single-family house with a space heating system, including an ALHS, ground source heat pump, and radiator. The proposed MPC approach, compared to conventional reactive control, enables substantial reductions in the electricity cost (ranging from 53.2% to 122.7% depending on the MPC settings and ALHS capacity), even financial gains under certain scenarios. Further analysis reveals that coupling ALHS with MPC is critical to ensure that ALHS adoption is economically convincing: while conventional reactive control of an ALHS-equipped building increases the electricity cost, an MPC-enabled building could reduce the electricity cost by 45.1% due to ALHS adoption. The proposed MPC approach also exhibits promising feasibility for real-world BEM applications. [ABSTRACT FROM AUTHOR]

• Battery SOC and SOP are co-estimated by combining the fractional-order calculus and the model predictive control theory. • A fractional-order modified moving horizon estimation algorithm is proposed for online SOC estimation. • A fractional-order model predictive control algorithm is devised to optimize current sequences for online SOP estimation. • Different battery current–voltage behaviors in the prediction horizon are researched over a battery operating range. Accurate battery modelling is the cornerstone to state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries in electric vehicles. Due to strong battery nonlinearity over a broad frequency range, traditional integer-order models are incapable of capturing complex battery dynamics for SOC and SOP co-estimation. This paper proposes a fractional-order modified moving horizon estimation (FO-mMHE) algorithm and a fractional-order model predictive control (FO-MPC) algorithm. Firstly, a second-order FOM is constructed by performing a series of hybrid pulse tests at different SOC regions, and its model parameters are identified through a particle swarm optimization-genetic algorithm method. Secondly, online SOC estimation is converted into a constrained optimization problem in a past moving horizon and then solved by the FO-mMHE algorithm, which enables fast convergence speed and proactive smoothing of estimation outcomes. Thirdly, the FO-MPC algorithm is devised to manipulate the current sequence in a prediction horizon for maximizing discharge/charge power accumulation and determining battery SOP in real time. Moreover, different battery current–voltage behaviors are comprehensively researched in the prediction horizon over a whole battery operating range. The proposed co-estimation method is validated under different dynamic load profiles. The experimental results demonstrate a SOC estimation error reduction of up to 1.2 % compared with the commonly used fractional-order extended Kalman filter while the SOP estimation error could be limited below 0.35 W. [ABSTRACT FROM AUTHOR]

• Stochastic co-optimization of kinematic states and EMS for CAEVs is developed. • Dynamic probabilistic constraints ensuring driving safety are formulated. • A deep learning model predicting the PDF of preceding vehicle speed is constructed. • An efficient hierarchical solver for the co-optimization problem is designed. Ameliorating energy efficiency and enhancing driving safety are both extremely concerning issues for connected and automated electric vehicles (CAEVs) driving in a random traffic environment. To enhance driving safety and fully coordinate the potential conflict between driving safety and energy efficiency, an adaptive co-optimization method of speed planning and energy management strategy (EMS) with dynamic probabilistic constraints is proposed under the framework of stochastic model predictive control. The dynamic probabilistic constraints are enabled by the proposed composite sequence generation model, which predicts the future speed distribution of the preceding vehicle according to the probability relationship among future speed sequence, historical speed sequence, and macroscopic traffic state of downstream road segments, effectively modeling the macro and micro disturbance from random traffic environment and improving the prediction accuracy by about 10% (along with an over 57% decrease in distribution divergence) compared with pure sequence generation model. Comparison with existing co-optimization methods under the same car-following tasks validates the promising performance of the proposed adaptive co-optimization method, which produces dynamic feasible regions for kinematic states according to downstream traffic state and the driving state of the preceding vehicle, raising the driving safety by 14.81% and retaining the relatively high energy efficiency. [ABSTRACT FROM AUTHOR]

Home energy management systems (HEMS) have been shown to reduce energy bills and to provide grid services including peak demand reduction and demand flexibility. However, uncertainty in residential energy systems is a significant issue and can reduce the benefits of a HEMS to the homeowner or grid operator. Sources of uncertainty include weather forecasts, predictions of energy-related occupant activities (e.g., hot water draws), and parameter estimation for the building envelope and energy-consuming equipment. This paper tackles the problem of uncertainty by developing a framework that simulates HEMS in uncertain conditions and evaluates the performance of multiple control strategies. A linear, reduced-order residential building model for model predictive control applications is derived and compared to a full-order model. Stochastic model predictive control is shown to perform better than deterministic and heuristic methods when considering realistic forecasts with uncertainty. The framework can evaluate the performance of HEMS in real-world applications, which can help de-risk HEMS deployment. • Uncertainty from weather and occupant forecasts impact home energy management. • A stochastic control framework is developed for a home energy management system. • A reduced-order linear house model is developed for model predictive control. • Incorporating uncertain forecasts reduces control performance in HEMS. • Stochastic MPC outperforms common deterministic and heuristic control methods. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the performance of Model Predictive Control (MPC) for a typical commercial building heating, ventilation and air conditioning (HVAC) system across a wide range of climate and weather conditions. The motivation of the study comes from the fact that although there is a large body of work on MPC for HVAC systems, there is a lack of studies that examine the range of possible performance of MPC, in terms of both energy savings and maintaining indoor climate (temperature and humidity) as a function of outdoor weather. A challenge in conducting such a study is developing an MPC controller that can be used in a wide range of weather. The root cause of this challenge is the need for a tractable cooling and dehumidification coil model that can be used by the MPC controller, since the coil may operate in quite distinct modes depending on weather. We present such an MPC controller, and then leverage it to conduct an extensive simulation campaign for fourteen climate zones in the United States and four weather conditions (winter, spring, summer, and fall) in each climate zone. The performance of the proposed controller is compared with not only a rule-based baseline controller but also with a simpler MPC controller that ignores humidity and latent heat considerations. There are several results the arise from this comparative study. One such result is that energy savings from MPC over baseline can vary dramatically based on climate and season. Another is that the effect of ignoring humidity in the MPC formulation can lead to poor indoor humidity control more in milder weather rather than in hot weather. The results from this study can help practitioners and researchers assess costs and benefits of proposed MPC formulations for HVAC control. • Performance of two MPC controllers for HVAC systems is studied. • The study spans a wide range of climate and weather conditions, a first of its kind. • MPC controller can perform poorly unless latent heat is considered in design. • Poor performance occurs typically in moist and marine climate zones. [ABSTRACT FROM AUTHOR]

• A bottom-up intra-hour proactive scheduling for household peak avoiding. • Physic-based modeling and simulation of thermal appliances. • MPC controller formulation with soft constraints and flat tariffs. • Minimization of high-power appliances' concurrent operations considering user comfort. • Key factors and computational complexity analyzed. Residential-level peak shaving is beneficial for the supply–demand balance of buildings with a limited power capacity of renewable energy sources like building-integrated photovoltaics. Thermostatically controlled loads (TCLs) are recognized as flexible resources for peak shaving. In practice, most thermal appliances are controlled by local on–off controllers without any communication and coordination. Multiple appliances may operate simultaneously and produce load peaks. Existing studies assume the existence of an electricity market and time-varying tariffs, which are not always available. This paper developed a model predictive control (MPC) based bottom-up proactive method to leverage the thermal inertia of residential thermal appliances for avoiding household peak load without relying on time-varying tariffs. First, we used a physics-based modeling approach to model the thermal dynamics of several residential thermal devices. Then, we designed a centralized MPC controller to minimize the appliance concurrent operations considering occupant comfort zones and appliance rate powers. Consequently, the house peak load is reduced by minimizing concurrent operations. Simulations over homogenous and heterogenous appliance set validated that the MPC-based proactive scheduling method can effectively reduce household peak load without compromising user comfort too much. Finally, we discussed the impact of control interval, appliance rated powers, and penalty factors on peak avoiding and the computational feasibility of the MPC controller. We concluded that the MPC-based proactive scheduling is feasible and effective for a limited number of thermal appliances with short horizons (i.e., six-step). Replacing a mixed-integer nonlinear program solver with a mixed-integer quadratic program solver could significantly reduce the computational burden. However, the application of the MPC controller to multiple households at the system level is still impractical. Further research efforts are emphasized, like mining and forecasting occupant demand and appliance usage patterns in households and designing scalable control strategies for system-level peak shaving. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) has shown potential in improving building performance but is bottlenecked by the difficulty in constructing control-oriented models. The challenge lies in evaluating the sufficiency of the model and the data usage beforehand. This paper bridges the knowledge gaps in the interactions between data requirements, model quality, and control performance by integrating real-world measurements and simulation-based experiments. The data usage related to occupancy and Internal heat gain (IHG) was studied considering its importance and the absence of consensus in the literature. Varying occupant-related data sources were tested as RC model inputs, including none, schedule, electricity consumption, CO 2 ppm, and ideal measurement. Combinations of model inputs and complexities were examined for prediction and control in an office, a classroom, and multi-zone offices on one floor. The results indicated that the usefulness of data is jointly affected by three factors: measurement suitability, model complexity, and modeling purpose. Given the adequate model structure, satisfying prediction and control performance was achieved in offices with no detailed measurement. Meanwhile, electricity and CO 2 were needed together to capture the IHG influence and realize the good performance for classrooms. The experiments also uncovered the heterogeneous requirements on models from traditional prediction tests and the control tasks. Lower prediction error did not always mean better control. More importantly, we provided the first quantitative demonstration of the complementary relationship between model adequacy and data informativeness with respect to different purposes. This study advocates the pioneering idea of sparse data usage and parsimonious modeling, which promotes the actual application of MPC in buildings by guiding control-oriented model development. [Display omitted] • Over 10% energy saving and acceptable PMV achieved without occupant-related data. • Occupant-related data is needed only if the internal heat gain has high variability. • Adequate model with sufficient data yields physically representative identification. • Better physical representation could lead to larger RMSE but more precise control. • Sparse data and parsimonious modeling should be pursued in real-world implementation. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to efficiently integrate long prediction horizons subject to uncertainty into a stochastic model predictive control (MPC) framework for the energy management of hybrid electric vehicles. By exploiting Pontryagin's minimum principle, we show that the energy supply required to obtain a certain change in the state of charge (SOC) of the battery can be approximated using a quadratic equation. The parameters of these mappings depend on the power request imposed by the driving mission and thus allow to compress the time-resolved power profile into only three scalar variables. Having a driving mission divided into several segments of arbitrary length, the corresponding sequence of quadratic approximations allows to reformulate the original energy management problem as a quadratic program, which offers an efficient way to include a large number of future scenarios. The resulting scenario-based stochastic MPC approach prevents SOC boundary violations with a certain probability, which can be controlled by the number of scenarios considered. To validate the quadratic approximation, we study two numerical examples using two different vehicles, a series hybrid electric passenger car and a battery-assisted trolley bus. Finally, a case study based on the operation of the latter is provided, which demonstrates the expected behavior and the real-time capability of the stochastic MPC approach. While the SOC is maintained in predefined boundaries with high probability, the required energy supply is only increased by 1.41% compared to the non-causal optimum. • New approach for long-horizon stochastic MPC for the energy management of HEVs. • Using Pontryagin's minimum principle and the scenario approach. • Numerical validations based on a series HEV passenger car and a hybrid trolley bus. • Case study demonstrating the performance and the realtime capability of the SMPC. • Only 1.4% more energy demand than optimum with low SOC violation probability. [Display omitted] [ABSTRACT FROM AUTHOR]

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. • MPC saved energy compared to existing control in a real office building HVAC system. • Open-source software was used for the MPC implementation. • Effort for data collection and MPC deployment was comparable to model generation. • Workflows that streamline implementation processes would help reduce effort. [ABSTRACT FROM AUTHOR]

Sports facilities are considered complex buildings due to their high energy demand and occupancy profiles. Therefore, their management and optimization are crucial for reducing their energy consumption and carbon footprint while maintaining an appropriate indoor environmental quality. This work is part of the SportE3.Q project, which aims to manage and optimize the operation of sports facilities. A neural network (NN)-based model predictive control (MPC) management and optimization system is proposed for the heating, ventilation, and air conditioning (HVAC) system of a sports hall in the sports and events complex of Qatar University (QU). The proposed approach provides an integrated dynamic optimization method that accounts for future system behavior in the decision-making process, consisting of a prediction element and an optimizer. A NN is used to implement the dynamic prediction element of the MPC system and is compared with other machine learning (ML)-based models, which are support vector regression (SVR), k -nearest neighbor (k NN), and decision trees (DT). The NN-based model outperforms the other ML models with an average root mean squared error (RMSE) of around 0.06 between the actual and the predicted values, and an average R of 0.99 as NNs are popular for their high accuracy and reliability. Two schemes of the proposed NN-based MPC system are investigated for managing and optimizing the operation of the hall's HVAC system for enhanced energy use and indoor environment quality, as well as for providing occupancy profile recommendations to aid the facilities' managers in handling their operation. In alignment with the objective of the SportE3.Q project, up to 46% energy reduction was achieved while jointly optimizing the thermal comfort and indoor air quality. In addition, Scheme 2 of the proposed system provided productive occupancy recommendations for a healthier indoor environment. [Display omitted] • High energy use and occupancy profiles are main characteristics of sports facilities. • Developing a neural network (NN)-based model predictive control (MPC) system. • Used to optimize energy use and indoor environment quality in sports facilities. • Provides dynamic optimization accounting for current and future behavior. • Reduces energy by up to 46% without altering indoor thermal comfort and air quality. [ABSTRACT FROM AUTHOR]

• Enhanced battery power constraint compliance is realized for an electric vehicle by integrating SoP prediction and driving torque regulation together. • A tailored adaptive model reduction technique is proposed to ensure real-time model predictive control of the integrated SoP prediction and driving torque regulation. • Guaranteed sufficient accuracy of the model reduction is mathematically justified. • Forward dynamic programming is employed to solve the control optimization problem. To comply with battery power constraint in the operation of electric vehicles, traditional methods usually estimate battery's power capability first and then regulate vehicle's driving torque based on the estimation. These methods have fundamental drawbacks due to their separated nature. A better way is to integrate the estimation and regulation together, which increases the model complexity of the control problem though. To tackle this issue, a reduced-order model predictive control (MPC)-based approach is proposed in this paper, where the dimension of the control model is reduced from two to one by exploiting a quasi-linear relationship between the two state variables. Rigorous mathematical justification proves that sufficient accuracy is retained as long as MPC's prediction horizon is determined according to the initial states and battery's polarization dynamics. The superiority of the proposed method is validated by model-in-the-loop tests, which demonstrate that the proposed method reduces the possibility of over-discharge and can make a flexible trade-off between power constraint handling and target vehicle speed tracking. [ABSTRACT FROM AUTHOR]

Continued focus on reducing carbon emissions and improving energy efficiency requires buildings to become grid-interactive and not just behave as static consumers. A distributed model predictive control (DMPC) algorithm known as Limited-Communication (LC) DMPC is modified to enable grid-interactive buildings. A grid-aggregator subsystem is added that allows for a bulk grid power reference signal to be followed while the individual building subsystems also achieve their local comfort objectives. The LC-DMPC algorithm is applied for the first time to systems with multiple buildings. Adequate power tracking is shown for different simulation scenarios involving heterogeneous buildings, and next steps are discussed. • Grid-aggregator system for distributed, coordinated, grid-interactive building control. • Limited-Communication Distributed MPC applied to multiple, heterogeneous buildings. • Achieves higher-level grid requests and local building comfort priorities [ABSTRACT FROM AUTHOR]

• A novel MPC-based optimal dispatch and control strategy is proposed for CIES. • Rolling forecast and feedback correction are developed to handle the uncertainties. • A closed-loop dynamic performance optimization control model is presented. • The approach manages to optimize the system dispatch and dynamic response performance. Integrated energy systems have recently attracted interest for the purpose of energy development and utilization owing to the increase in environmental pollution and the shortage of fossil energy. However, the integration of a high proportion of renewable energies and controllable loads in the system increases the uncertainty of system operation significantly. In order to rapidly track the system fluctuations and accurately control the operating equipment, a bi-level and multi-timescale dispatch and control strategy based on model predictive control is proposed for a community integrated energy system (CIES) considering dynamic response performance. The upper-level optimizer completes the rolling forecast of renewable outputs and loads over a long-time horizon, and builds an economic rolling optimal scheduling model with consideration of feedback correction. The lower-level controller establishes a closed-loop dynamic performance optimization control model over a short time scale by real-time controlling the system's upstream energy equipment. The simulation results on a modified CIES located in Beijing, China demonstrate that the proposed bi-level strategy increases the prediction accuracy while ensuring the economic efficiency of the system operation, and improves the dynamic tracking ability of the equipment and the overall energy supply dynamic response rate of the system, which provides a fundamental way for efficient application of CIES. [ABSTRACT FROM AUTHOR]

Buildings need advanced control for the efficient and climate-neutral use of their energy systems. Model predictive control (MPC) and reinforcement learning (RL) arise as two powerful control techniques that have been extensively investigated in the literature for their application to building energy management. These methods show complementary qualities in terms of constraint satisfaction, computational demand, adaptability, and intelligibility, but usually a choice is made between both approaches. This paper compares both control approaches and proposes a novel algorithm called reinforced predictive control (RL-MPC) that merges their relative merits. First, the complementarity between RL and MPC is emphasized on a conceptual level by commenting on the main aspects of each method. Second, the RL-MPC algorithm is described that effectively combines features from each approach, namely state estimation, dynamic optimization, and learning. Finally, MPC, RL, and RL-MPC are implemented and evaluated in BOPTEST, a standardized simulation framework for the assessment of advanced control algorithms in buildings. The results indicate that pure RL cannot provide constraint satisfaction when using a control formulation equivalent to MPC and the same controller model for learning. The new RL-MPC algorithm can meet constraints and provide similar performance to MPC while enabling continuous learning and the possibility to deal with uncertain environments. • MPC and RL are compared and merged for the same building optimal control problem. • RL-MPC is an algorithm that combines methods from machine learning and control theory. • MPC, RL, and RL-MPC are evaluated and benchmarked in the BOPTEST simulation framework. • MPC effectively uses the controller model while pure RL violates the constraints. • RL-MPC enables learning and meets the constraints with similar performance to MPC. [ABSTRACT FROM AUTHOR]

• A machine learning-based model predictive control with instantaneous linearization. • The instantaneous linearization linearizes the machine learning models recurrently. • The proposed control is implemented in an office for air-conditioning control. • The proposed control achieves 26% energy savings with better thermal comfort. • The proposed control is 70 times faster than nonlinear model predictive control. Machine-learning (ML) –based building models have been gaining popularity in constructing model predictive control (MPC) for building energy management applications. However, ML-based building models are usually nonlinear so to capture the building dynamics, leading to high computation load for MPC, prohibiting its application for real-time building control. This study proposes a ML-based MPC with an instantaneous linearization (IL) scheme, which employs real-time building operation data to linearize the nonlinear ML-based building model for constructing a linear MPC at each control interval. The proposed ML-based MPC with IL system is implemented to control an air conditioning system in an office of a general hospital building located in Singapore for experimental evaluation of its control performance. The ML-based MPC with IL is compared to a ML-based MPC that directly uses a nonlinear ML-based building model and the original reactive-control-based thermostat of the office. Results show that the ML-based MPC with IL significantly reduced the computation time (by more than 70 times) as compared to the ML-based MPC while retained most of the advantages of the ML-based MPC. The ML-based MPC with IL and the ML-based MPC achieved 31.6% and 26.0% reductions, respectively, in cooling energy consumption as compared to the original thermostat. Meanwhile, both the MPC systems significantly improved indoor thermal comfort for the office as compared to the original thermostat. The study demonstrated that using IL for ML-based MPC could substantially improve computation efficiency with no obvious performance degradation in terms of thermal comfort and energy saving. [ABSTRACT FROM AUTHOR]

• A novel tri-stage online dispatch framework for a microgrid is proposed. • The proposed framework coordinates the EV charging behavior at different timescales. • Multiple uncertainties introduced by EV, PV, and load are taken into account. • The real-world data of eight EVs in Beijing is collected to model EV charging behavior. This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced. [ABSTRACT FROM AUTHOR]

• Speed prediction MPC is proposed for fuel cell electric vehicle energy management. • Fuel cell performance degradation cost is considered in the proposed MPC strategy. • Speed prediction influences on the energy management strategies are discussed. • Optimization strategies comparisons exhibit the effectiveness of the proposed MPC. • HIL test results verify the real-time performance of the proposed strategies. Due to the poor dynamic response ability of the fuel cell, the battery is normally applied to integrate with fuel cell to configure the hybrid power system in electric vehicles. In this paper, a vehicle speed prediction model predictive control (SP-MPC) energy management strategy is developed for the hybrid power system in fuel cell electric vehicles. The main principle of the proposed SP-MPC is that the future vehicle total power demand is forecasted via the Markov speed predictor and imported into the energy management system response prediction model to improve the control performance by more accurate disturbance description. The objective function is set for equivalent hydrogen consumption minimization and fuel cell degradation inhibition. As a contrast, the normal MPC strategy, the speed prediction dynamic programming (SP-DP) strategy and the DP offline strategy are formulated. Comparing with the normal MPC strategy, the SP-MPC strategy has a 3.74% reduction in the total operation cost under MANHATTAN condition. The SP-MPC strategy also has a 1.39% reduction in the total operation cost than the SP-DP strategy. Moreover, two scenarios are introduced with different disturbance prediction accuracy to verify the influences of the prediction inaccuracy on the SP-MPC and SP-DP results. For SP-DP strategy, the total operation cost under actual forecast scenario has increased by 5.03% compared with the perfect forecast scenario. The similar result can be seen in the SP-MPC, but the increase between perfect and actual forecast scenario is only 1.02%, which indicates a better robustness to the disturbance prediction inaccuracy compared with the SP-DP strategy. A DSP hardware in loop (HIL) test is conducted for real-time performance verification of the proposed SP-MPC. [ABSTRACT FROM AUTHOR]

• An optimization operation of a CO 2 air source heat pump water heater is presented. • The model predictive control strategy is proposed to optimize system performance. • A high-fidelity physical model is built in Dymola to provide the data source. • A data-driven control-oriented model is developed via dynamical system identification. • The simulation results prove the effectiveness of the proposed strategy. This paper presents a model predictive control (MPC) approach to optimize the operation of a transcritical CO 2 air source heat pump (ASHP) water heater. Past findings have revealed that the heat rejection pressure is the key factor affecting the system performance of the ASHP. However, the currently used PI controls based on correlations and other control strategies reported in the literature have some limitations. In this paper, we applied the MPC strategy in an ASHP water heater to optimize its operational performance in real-time. Different from the current control strategy aiming to search for the optimal heat rejection pressure, MPC predicts the future operation condition and calculates the optimal inputs based on the control-oriented model and objective function. A high-fidelity physical model is first built in Dymola to produce the operational data, then a data-driven control-oriented model for MPC is derived from the data source via a dynamical system identification. MPC is designed to maximize the coefficient of performance of the ASHP water heater under the state-space equations and constraints. The evaluation is carried out under several cases including fixed ambient temperature, realistic ambient temperature, and variable water outlet temperature. The simulation results prove the control effectiveness of MPC. [ABSTRACT FROM AUTHOR]

• Four optimization strategies for the operation of greenhouse systems are studied. • The proposed strategy improves water use efficiency and reduces water demand. • A sensitivity analysis of prices and constraints is conducted. • A model predictive controller is designed to address system disturbances. Greenhouse systems consume lots of energy, water and carbon dioxide ( CO 2 ) to provide a suitable growth environment for crops. Due to the problems of operation mode, some greenhouse systems are inefficient and need to be optimized. In this paper, four optimization strategies for improving the operation efficiency of greenhouse systems are studied. Strategy 1 minimizes the energy consumed for greenhouse heating, cooling, ventilation and irrigation. Strategy 2 minimizes the water consumed for irrigation. Strategy 3 minimizes the CO 2 consumed for greenhouse CO 2 enrichment. Strategy 4 minimizes the total cost of energy, water and CO 2 consumed. These optimization strategies are based on a multi-input multi-output (MIMO) climate model and a modified evapotranspiration model. Moreover, a sensitivity analysis is conducted to study the influence of electricity price, water price, CO 2 price and the range of system constraints on the optimization results. Finally, a model predictive controller (MPC) is designed to reject system disturbances and address model plant mismatch. The MPC controller is compared with a commonly used open loop controller. A performance index relative average deviation (RAD) is introduced to evaluate the tracking performance of the proposed MPC and the compared open loop control. Simulation results show that Strategy 4 reduce the total cost by 66.60 %, 92.68 % and 68.83% compared with Strategy 1, Strategy 2 and Strategy 3 respectively. Changes in electricity price have a greater impact on optimization results than changes in water price and CO 2 price. Both temperature constraints and relative humidity constraints have a great influence on the optimization results. The controller designed is verified to be effective. [ABSTRACT FROM AUTHOR]

We describe a method for embedding advanced weather disturbance models in model predictive control (MPC) of energy consumption and climate management in buildings. The performance of certainty-equivalent controllers such as conventional MPC for smart energy systems depends critically on accurate disturbance forecasts. Commonly, meteorological forecasts are used to supply weather predictions. However, these are generally not well suited for short-term forecasts. We show that an advanced physical and statistical description of the disturbances can provide useful short-term disturbance forecasts. We investigate the case of controlling the indoor air temperature of a simulated building using stochastic differential equations (SDEs) and certainty-equivalent MPC using the novel short-term forecasting method. A Lamperti transformation of the data and the models is an important contribution in making this SDE-based approach work. Simulation-based studies suggest that significant improvements are available for the performance of certainty-equivalent MPC based on short-term forecasts generated by the advanced disturbance model: Electricity savings of 5%–10% while at the same time improving the indoor climate by reducing comfort violations by up to over 90%. [Display omitted] • An advanced short-term weather forecasting model for buildings based on stochastic differential equations. • The formulated models are based on grey-box principles which enable usage of combined information from physics and data. • Results suggest improved performance for model predictive control when using the advanced forecasts. • The smart building controller using advanced forecasts perform almost as good as perfect forecasts. [ABSTRACT FROM AUTHOR]

We present the design, implementation, and experimental validation of a supervisory model predictive control approach for the simultaneous optimization of thermal and electrical building energy supplies. The system comprises a heat pump, a photovoltaic system, an electric battery, and a thermal storage tank. The system uses a phase change slurry as both the heat storage and heat transfer medium, which, beyond an increased storage capacity, results in an improved performance of the heat pump and an increased load-shifting potential. In contrast to approaches that use volumetric flow rates as inputs, the supervisory model predictive control used here determines optimal energy flows. As a result, the online optimization problems do not require impractical computational power in spite of the system complexity. We show the control can be implemented on low-cost embedded hardware and validate it with an experimental demonstrator comprising an installation of the complete supply system, including all hydraulic and all electrical components. Experimental results demonstrate the feasibility of both, the heat pump based heating system with a phase change slurry, and the optimal control approach. The main control objectives, i.e., thermal comfort and maximum self-consumption of solar energy, can be met. In addition, the system and its controller provide a load shifting potential. • Operation of a low-temperature heating system with a phase change slurry. • Design of supervisory model predictive control. • Simultaneous optimization and storage of power and heat. • Experimental demonstration of innovative heat transfer fluid and control technology. [ABSTRACT FROM AUTHOR]

Hydrogen-based hybrid storage system has a high energy density, which can operate as the long-term storage system, and play an important role in future smart cities. In the hydrogen storage system, fuel cell, hydrogen tanks, and electrolyzer are often combined together and operating with complex electrochemical reactions. How to efficiently operate the hydrogen storage system and considering the convoluted electrochemical reactions is a problem. In addition, multiple hydrogen storage systems are often grouped together to supply the demands. Thus, cooperating the dispatching of these storage systems is another complicated problem. In this paper, we first present a two-dimension model considering temperature influences for hydrogen-based microgrid, where a regression method is adopted. Moreover, a combined allocating-and-dispatching methodology involving two layers is proposed to cooperate the multiple storage systems. Specifically, both TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and fuzzy logic are adopted as the first-layer allocating algorithm. Then, the model predictive control (MPC) is utilized as the second-layer dispatching algorithm. Based on the combined method, power is firstly allocated to hybrid storage system considering each hybrid storage system health conditions, and secondly scheduled to battery storage and hydrogen storage based on MPC method. The simulation results showed that with the combined Dematel-TOPSIS and MPC algorithm, the degradation index and operation cost were the smallest among three algorithms, and can further extend the lifetime of hybrid hydrogen storage systems in microgrids. • Temperature has a positive influence on the operation of the hybrid storage system. • A combined TOPSIS and model predictive control methodology. • Two-layer operation algorithm can healthily dispatch power to storage system. • The performance is improved based on the multi-criteria decision. • Dematel-TOPSIS algorithm can extend the lifetime of storage systems. [ABSTRACT FROM AUTHOR]

The performance of model predictive control can be significantly affected by different choices of controller parameters such as the time intervals for model discretization and control sampling. Due to the lack of a systematic understanding on how these parameters affect the control performance, they are usually selected arbitrarily in practice. In this paper, the combined impacts of selected time intervals for model discretization and control sampling on the performance of model predictive control are comprehensively investigated for the first time through detailed simulations. The simulation results reveal that the time interval for model discretization has a much greater influence on the performance of model predictive control than that for control sampling by affecting the prediction performance, cost saving, and computation time simultaneously. In particular, there are three findings on the time interval for model discretization that are specific to the case studies considered herein. First, it affects the performance of multi-period prediction in a non-linear manner so that it is not always better to have a smaller time interval for model discretization. Second, it has four times greater influence on the resulting cost saving from model predictive control than the time interval for control sampling. Last, it affects the computation time significantly. The increase of the time interval for model discretization from 1 min to 5 min can reduce the computation time by roughly 96%. The proposed simulation-based performance evaluation sheds light on the importance of selecting appropriate time intervals for practical applications of model predictive control to building energy management. • Evaluated performance of model predictive control for building energy management. • Investigated combined impacts of model and control intervals on control performance. • Provided useful insights on selection of model and control intervals in practice. [ABSTRACT FROM AUTHOR]

Even though energy-efficient climate control of commercial buildings using model predictive control (MPC) has been widely investigated, most MPC formulations ignore humidity and latent heat. The inclusion of moisture makes the problem considerably more challenging, primarily since a cooling and dehumidifying coil model which accounts for both sensible and latent heat transfers is needed. In this work, we propose an MPC controller in which humidity and latent heat are incorporated in a principled manner. We construct low order data-driven models of a cooling and dehumidifying coil that can be used in the MPC formulation. The resulting controller's performance is tested in simulation using a plant that differs significantly from the model used by the optimizer. Additionally, the performance of the proposed controller is compared with that of a naive MPC controller which does not explicitly consider humidity, and also to that of a conventional rule-based controller. Simulations show that the proposed MPC controller outperforms the other two in terms of energy use and thermal comfort. It is also observed that the naive MPC formulation which does not consider humidity leads to poor humidity control under certain conditions. Such violations in humidity can adversely affect occupant comfort and health. • Proposes an energy-efficient climate controller for HVAC systems in buildings. • The controller is based on MPC and incorporates humidity in a principled manner. • Performance comparison is made with a naive MPC which ignores humidity. • Proposed controller outperforms the naive MPC and a rule-based controller. • MPC ignoring humidity causes poor humidity control that can affect occupant health. [ABSTRACT FROM AUTHOR]

• Shown that thermostat controls for multiple RTUs inefficiently operate economizers. • MPC that optimally operates economizers for open space buildings is proposed. • A CFD-coupled model was developed/validated for the control evaluation. • Significant energy savings up to 35% was achieved by the proposed MPC. "Free cooling" with cool outside air can meet or reduce a building's cooling loads in an energy efficient manner when conditions are favorable. For rooftop air conditioning units (RTUs), this is achieved with air-side economizers that are integrated with the packaged RTU. However, current RTU economizers are independently controlled at the unit level and may not fully leverage the free resource for economic benefit especially when multiple units serve a common zone of a building, such as in retail stores. That is, the independent control of multiple RTUs could lead to some units operating with mechanical cooling even when the outdoor air temperature or enthalpy is low enough to meet the load with economizer cooling. This paper presents a model-predictive control (MPC) approach to optimally supervise multiple RTUs in order to fully utilize the free cooling resource. The algorithm is applied to the evaluation of the energy savings potential for a case study building served by four identical RTUs under various climate zones over a cooling season. [ABSTRACT FROM AUTHOR]

• A new comprehensive control method based on Model Predictive Control is proposed. • A new dynamic equation of Continuously Variable Transmission is proposed. • Take engine torque and clamping force as control objectives. • The method can improve transmission efficiency by 8.92% and fuel economy by 4.9%. This paper proposes an optimal comprehensive control framework to maximize the powertrain transmission efficiency and fuel economy meeting vehicle owners' travel needs. The proposed control framework consists of vehicle layers and verification. In the vehicle layer, creatively, each set of automotive Continuously Variable Transmission (CVT) supply equipment is equipped with original comprehensive controller that estimates engine fuel consumption and CVT efficiency based on a newly dynamic equation of CVT, engine torque, clamping force, and upcoming trip information. The controller obtains the optimal input of engine torque and clamping force by repeatedly solving the optimization problem of each sampling point in real-time. In the verification layer, with the powertrain system model received from the comprehensive controller, the robustness was verified by bench and hardware-in-the-loop simulation platform. The proposed comprehensive optimization approach can enhance accuracy and solve the multi-objective control problems compared with traditional methods. It is also scalable to the expanding CVT fleet and robust to uncertainties future system condition and in further to meet the vehicle owner's travel comfort and economy. The proposed method is studied using a prototypical driving platform developed at State Key Laboratory of Automotive Safety and Energy of Tsinghua University and detailed trip information extracted from 2019 BEIJING Transport Annual Report Survey. The main contribution of this study is to explore a novel way to optimize the comprehensive transmission efficiency of engine and CVT, compared with the Proportional Integral Derivative (PID) control, the comprehensive controller can improve the transmission efficiency by 8.92%, and reduce the fuel consumption by 4.9%, it also can save about 90 million liters of fuel and 70 million US dollar for CVT vehicles in Beijing. [ABSTRACT FROM AUTHOR]

This paper presents a hierarchical two-layer home energy management system to reduce daily household energy costs and maximize photovoltaic self-consumption. The upper layer comprises a model predictive controller which optimizes household energy usage using a mixed-integer linear programming optimization; the lower layer comprises a rule-based real-time controller, to determine the optimal power settings of the home battery storage system. The optimization process also includes load shifting and battery degradation costs. The upper layer determines the operating schedule for shiftable domestic appliances and the profile for energy storage for the next 24 h. This profile is then passed to the lower energy management layer, which compensates for the effects of forecast uncertainties and sample time resolution. The effectiveness of the proposed home energy management system is demonstrated by comparing its performance with a single-layer management system. For the same battery size, using the hierarchical two-layer home energy management system can achieve annual household energy payment reduction of 27.8% and photovoltaic self-consumption of 91.1% compared to using a single layer home energy management system. The results show the capability of the hierarchical home energy management system to reduce household utility bills and maximize photovoltaic self-consumption. Experimental studies on a laboratory-based house emulation rig demonstrate the feasibility of the proposed home energy management system. [ABSTRACT FROM AUTHOR]

• Developed microgrid control algorithm that includes energy market participation. • Model predictive control showed up to 13.73% improvement over logic-based control. • Shorter time steps for control actions and longer prediction horizons reduce costs. • Participation in ancillary service market reduces operating expenses up to 23.47%. • Battery maintained 38.78% greater SOC to bid into ancillary service markets. This study develops two model predictive control approaches to optimize microgrid dispatch, one with participation in real-time ancillary service markets and the other without participation. Results are compared to a baseline logic-based control with case study data taken from a grid-tied 326 kW solar photovoltaic, 634 kW/634 kWh battery, and 350 kW diesel generator microgrid portfolio designed to serve an office building. Annual performance evaluations show that model predictive control algorithms can reduce operating expenses by up to 13.73% when compared to logic-based controls, and through participation in ancillary service markets, model predictive control can reduce net operating expenses by up to 23.47%. Revenue from ancillary service equated to 12.03% of operating costs, with approximately two-thirds of revenue from spinning reserve and one-third from non-spinning reserve. Model predictive control with ancillary services maintained battery state of charge an average of 38.78% higher than batteries dispatched by model predictive control without market participation. This reduced battery cycling losses, minimized battery operation and maintenance expenses, and improved battery lifetime. Sensitivity analyses indicate that model predictive control with more granular time steps and longer prediction horizons changes the dispatch schedule to further reduce operating costs. Intraday simulations indicate that both model predictive control algorithms can adapt to differences in environmental conditions and pricing signals to minimize operational costs. This generalizable finding suggests the inherent modularity, scalability, and robustness of the proposed algorithms can benefit a variety of microgrid configurations and use cases. [ABSTRACT FROM AUTHOR]

• A dynamic J-type battery thermal control system is established. • The mode switching control succeeds to maintain the battery thermal performance. • The energy efficiency is improved by 15.8% by employing model predictive control. Battery thermal control plays an indispensable role in terms of the safety and performance for electric vehicles. For air-based cooling technologies, one of the most pressing challenges is to balance the temperature uniformity and constrain the maximum temperature simultaneously under varying driving conditions. This paper proposes a self-adaptive intelligent neural network-based model predictive control strategy for a J -type air-based battery thermal management system. The J -type structure is first optimized through surrogate-based optimization to improve the temperature uniformity before control. Based on the optimized J -type configuration, an operation mode switching module is developed to mitigate the temperature unbalance. The thermal control approach is tested using an integrated driving cycle, and its evaluations are threefold: (i) the neural network-based control without mode switching fails to meet the thermal requirements; (ii) the control with mode switching succeeds in constraining the maximum temperature and maintaining the temperature uniformity within 1.33 K; (iii) the added model predictive control approach slightly enhances the thermal performance but improves the energy efficiency significantly by 15.8%. The results show that the J -type structure with its appropriate control strategy is a promising solution for light-duty electric vehicles using an air-cooling technology. [ABSTRACT FROM AUTHOR]