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The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type and processes billets. The hardware innovation is related to the installation of an insulated tunnel at the end of the reheating furnace, in order to guarantee a higher heat retention of the billets before their path along the rolling mill stands. The software innovation refers to the design and the installation of an Advanced Process Control system which manipulates the gas flow rate and the stoichiometric ratio of the furnace zones in order to satisfy the control specifications on billets and furnace variables. The control system is based on Model Predictive Control strategy and on a virtual sensor which tracks and estimates the billet features inside/outside the furnace. The designed controller was commissioned on the real plant, providing significant performances in terms of service factor, process control, and energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators' manual conduction and/or previous level 2 systems control. [ABSTRACT FROM AUTHOR]

Building predictive control has proven to achieve energy savings and higher comfort levels than classical rule-based controllers. The choice of the model complexity needed to be used in model-based optimal control is not trivial, and a wide variety of model types is implemented in the scientific literature. This paper shares practical aspects of implementing different control-oriented models for model predictive control in a building. A real thermally activated test building is used to compare the white-, grey-, and black-box modeling paradigms in prediction and control performance. The experimental results obtained in our particular case reveal that there is not a significant correlation between prediction and control performance and highlight the importance of modeling the heat emission system based on physics. It is also observed that most of the complexity of the physics-based model arises from the building envelope while this part of the building is the most sensitive to weather forecast uncertainty. Dataset: https://doi.org/10.17632/xzdy23nzvj.1 [ABSTRACT FROM AUTHOR]

Modular building energy management strategy based on a three-level hierarchical model predictive control is proposed in the paper. Building zones, central medium conditioning and microgrid subsystems are controlled independently by individual linear and nonlinear model predictive controllers, and further integrated together as levels of hierarchical coordination control structure based on price-consumption information exchange. The three-level coordination provides a holistic energy management strategy and enables significant demand response ancillary services for buildings as prosumers, while retaining the independence of required expertise in very different building subsystems. The approach is applied for daily operation scheduling of a full-scale building consisting of 248 offices. Models of building subsystems are obtained by identification procedures on measurement data. Compared to rule-based control, detailed realistic simulations show that the overall building operation cost for typical days in summer is reduced by 9-12% for level-by-level energy-optimal and by 15-24% for price-optimal, coordinated operation. The application of predictive control in the proposed way also improves the indoor comfort substantially. [ABSTRACT FROM AUTHOR]

Pressure servo control plays a crucial role in the majority of industrial applications that use compressed air as their power source. How to reduce the energy consumption of the pneumatic system while ensuring the high accuracy of pressure control remains a key problem to be solved. This paper proposes a Gaussian Process (GP) 1 model predictive controller (MPC) applied to a pressure servo control system based on high-speed on-off valves. The smallest quantity of collected data is used to create the GP model. The entire model, which includes some predictive data, is produced once the generated model has been optimized. The GP model is combined with the MPC to perform comparative experiments using an industrial controller with a Field Programmable Gate Array (FPGA). The system responds quickly and with little tracking error in steady-state response studies as well as dynamic response experiments. The GP-MPC achieves the best outcomes in the dynamic comparison tests. The root mean square error (RMSE) is just 2.42 kPa, and the overshoot is less than 59.9% of the Proportional-Integral-Derivative (PID) controller and 68.5% of the sliding mode controller (SMC). Although the compressed air consumption of GP-MPC is basically the same as that of PID, it is significantly better than SMC, with a total saving of about 70.2%. All the experiments prove that the controller proposed in this paper can effectively improve the energy efficiency and tracking accuracy of the pressure servo control system. [Display omitted] • A Gaussian process model for the high-speed on-off valve is developed which is able to predict uncollected data. • A model predictive controller is established by combining the Gaussian process model with rolling optimization method. • The pressure tracking and energy-saving performance are intended to be improved by using the objective function. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a decision making and motion planning system for lane change maneuvers considering energy efficiency. A novel decision making algorithm is proposed to check the desirability of performing the lane change. The algorithm is based on a utility function that consists of different performance criteria, including energy consumption. The execution of the decided maneuver involves a lower-level motion planning and control system for the longitudinal and lateral directions. For the longitudinal direction, an energy-efficient Model Predictive Controller (MPC) is designed, which considers the safety boundaries as well as other constraints, such as comfort, traffic laws, and physical limitations of the system. For the lateral direction, the desired trajectory is planned based on a parameterized sigmoid function. The lateral tracking is then realized by a PID controller. Finally, to evaluate the performance of the designed algorithms, a fuel consumption map of an internal combustion engine (ICE) is approximated by a second-order multivariate polynomial. Simulation results demonstrate the capability of the proposed algorithm to safely perform the lane change maneuver in different scenarios and for two vehicle models, including a simplified vehicle dynamic model and a high-fidelity IPG CarMaker model. [Display omitted] • A new lane change decision making is proposed considering energy consumption. • An integrated energy-efficient planning and control framework is designed using MPC. • The framework considers safety, comfort, energy consumption, and actuator limitation. • The fuel map of an internal combustion engine is fitted to second-order polynomial. • The obtained polynomial is used in the optimization problem of energy-efficient MPC. • Simulation study for a simple mathematical model and a high-fidelity CarMaker model. [ABSTRACT FROM AUTHOR]

This paper addresses the design and implementation of a model predictive control framework for temperature control in buildings zones via direct control of their thermal energy inputs. Comfort-centric approach in ensured by selecting building thermal zones to be equal to the physical building rooms. The framework integrates different identification and estimation technologies, machine learning and model predictive control to assure systematic handling of non-modelled disturbances and offset-free control. It is envisioned as the lowest level in the hierarchical decomposition of building subsystems responsible for comfort and shaping the overall thermal energy consumption in building zones. The paper shows how it is deployed on a full scale occupied skyscraper building. To enable optimization of the whole building behaviour a special focus is put on developing the possibility for interaction and coordination with other building subsystems or energy distribution grids. This ensures the scalability of the approach, computational relaxation, technology independency, cost-effective implementation and enables upscaling towards the smart grid and smart city concepts where buildings play decisive roles. [Display omitted] • Direct control of thermal energy per zone. • Enabled interaction with other building subsystems. • Integral part for upscaling towards smart grid and smart city concepts. • Deployment and verification on a scale of the whole skyscraper building. • Modular service built on top of the existing building automation infrastructure. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements. [ABSTRACT FROM AUTHOR]

The deployment of model-predictive control (MPC) for a building's energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm. [ABSTRACT FROM AUTHOR]

In the heating, ventilation, and air-conditioning (HVAC) system of electric vehicles (EVs), an electric heater is often used for a reheating process that warms up the chilled evaporator outlet air for thermal comfort before it is supplied to the cabin. The usage of the electric heater can significantly reduce the driving range of an EV. Therefore, optimal control of the HVAC system with consideration of the reheating process becomes essential for an increased driving range. In addition, considering the primary role of cabin climate control, it is desirable to intelligently consider the passengers' thermal comfort in climate control design. In this paper, thermal comfort-conscious eco-climate control (TCC-ECC) based on model predictive control (MPC) is proposed to enhance the energy efficiency of the HVAC operation while ensuring passengers' thermal comfort. The MPC design uses a reduced-order HVAC system model based on an ideal vapor-compression cycle. For the integration of thermal comfort into the MPC, a new approach is proposed to obtain an approximate solution of a predictive mean vote (PMV)-based thermal comfort model, which aims to balance computational efficiency and prediction accuracy. With the proposed TCC-ECC, a parametric study is conducted to analyze the impact of weighting factors on energy consumption and thermal comfort under two different thermal load conditions. Then, the performance of the tuned TCC-ECC is evaluated in comparison with a rule-based (RB) controller and the baseline eco-climate control (ECC) without considering thermal comfort. In the performance evaluation, the proposed TCC-ECC demonstrates that with the inclusion of thermal comfort it performs better in terms of energy efficiency and thermal comfort than manually adjusting a target cabin temperature depending on the environmental thermal load. The energy consumption of the proposed TCC-ECC is 22.5% and 35.24% less than that of the RB controller at an ambient temperature of 24 °C and 35 °C, respectively, and 14.5% and 18.5% less than the baseline ECC at the same conditions, respectively. • Eco friendly climate control in an EV should consider both cooling and reheating. • Reheat process in an EV may consume significant energy. • Approximated solution for a PMV-based modified thermal comfort model. • Better energy efficiency by including thermal comfort in MPC-based climate control. [ABSTRACT FROM AUTHOR]

In seagoing ships in waves, the torque and thrust of the propeller may vary with the submergence depth of the propeller. A large ship motion can cause ventilation and loss of effective disc area, which degrade the ship's propulsion efficiency. Electric or electrified ships powered by electric motors can respond quickly to such load variations, unlike conventional ships powered by internal combustion engines. This paper proposes a model predictive anti-spin thruster control algorithm that can improve the propulsion efficiency of electric ships by controlling the rotational speed of the propeller with consideration of time-varying load conditions. The weight of the cost function for optimizing the propulsion efficiency is adjusted by using the propeller's submergence predicted by an autoregressive model. The feasibility of the proposed algorithm is shown through numerical simulations of ship motions and propeller depth variation in irregular waves. The performance of the proposed algorithm is validated and compared with that of shaft speed and anti-spin thruster controllers, and the results are discussed. [ABSTRACT FROM AUTHOR]

Building equipment accounts for almost 40% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants’ well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants’ comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants’ comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68% while providing a significant indoor thermal improvement. A conventional On–Off controller was used as a baseline reference to evaluate the system performance against the proposed approach.

The capability to dynamically plan, predict, and control indoor conditioning allows to adapt to individual preferences of inhabitants and enables demand side management. While former mainly improves thermal comfort of inhabitants so does latter unlock ecological and financial opportunities mostly for energy utilities. Commonly, dynamic indoor conditioning is based on piece-wise constant indoor temperature constraints. This paper's contribution is the presentation of additional constraints: in particular ones expressed relative to the nominal behavior of a hydronic heating system. This allows to simultaneously harness the relevant process variables in particular during the pre-loading and post-loading phases of a load reduction. The findings are based on data sets acquired on 10 inhabited, residential buildings in Stockholm over a whole year. One of the findings is that building models need to be adaptable if predictive control is applied in practice. This adaptability is assured by a novel concept, i.e. a so called model manager on which the control is relying for the selection of the most accurate model. Centralized optimal control of buildings connected to a district heating network is challenging in practice due to a high computational load. In order to reduce it, the herein presented method elaborates plans only every hour instead of at every control step for optimal control. Since these plans cannot be optimal due to the lack of regular update a hitherto unknown cascaded control logic has been developed that corrects planning errors and other disturbances. Capabilities are demonstrated and compared to conventional controllers in dynamic simulations of a multi-zoned building. The herein presented method is to our knowledge the first to provide all flexibility desired by energy utilities and inhabitants alike through harnessing the consequences of transitions. [ABSTRACT FROM AUTHOR]

Buildings are responsible for almost half of the world's energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves. [ABSTRACT FROM AUTHOR]

This work focuses on evaluating the potential of variable speed limits (VSLs) in a synthetic urban network to improve both environmental sustainability and traffic performance. The traffic system is modeled using the microscopic traffic simulator SUMO, and a physical fuel consumption and NOx emission model is used to assess the vehicles’ energy efficiency. Speed limits are controlled through a nonlinear model predictive control (NMPC) approach, in which the traffic evolution and fuel consumption are respectively predicted with a macroscopic traffic model, namely the cell transmission model (CTM), and a pre-calibrated artificial neural network (ANN). The results reveal that in transient phases between different levels of congestion, the proposed eco-VSL controller is faster to decongest the network, resulting in an improvement of the environmental sustainability and the traffic performance both in the controlled network, and at its boundary roads. [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]

Advanced optimization-based control, such as Model Predictive Control (MPC), has been shown to achieve increased energy savings and thermal comfort across different building types and climatic conditions. However, the success of such control algorithms is typically contingent on factors, such as sensor measurements used for the advanced control implementation. In this paper, we specifically investigate the role of occupancy sensors in improving the performance of MPC for a large office building situated in Chicago, IL weather conditions with VAV type HVAC system. Utilizing a detailed simulation-based study involving Occupancy-Based Model Predictive Control (OB-MPC), we infer that occupancy sensing could enable higher energy savings (∼ 5% on summer days) and improve thermal comfort relative to a baseline MPC that does not utilize occupancy information. Additionally, we investigate the impact of using occupancy presence sensors versus counting sensors, different levels of occupant density, and varying weather conditions on the control performance. Finally, we perform a systematic investigation of the impact of sensor non-idealities (specifically bias and latency errors) on the performance of OB-MPC algorithms. The study indicates that measurement bias could slightly degrade the realizable benefits of using occupant counting-based MPC (up to ∼ 1% reduction in energy savings and up to 2X increase in thermal discomfort compared to ideal sensing). However, measurement latency may not impact the control performance. [ABSTRACT FROM AUTHOR]

In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants' numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality. [ABSTRACT FROM AUTHOR]

The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type and processes billets. The hardware innovation is related to the installation of an insulated tunnel at the end of the reheating furnace, in order to guarantee a higher heat retention of the billets before their path along the rolling mill stands. The software innovation refers to the design and the installation of an Advanced Process Control system which manipulates the gas flow rate and the stoichiometric ratio of the furnace zones in order to satisfy the control specifications on billets and furnace variables. The control system is based on Model Predictive Control strategy and on a virtual sensor which tracks and estimates the billet features inside/outside the furnace. The designed controller was commissioned on the real plant, providing significant performances in terms of service factor, process control, and energy efficiency.

In this article, we investigate coordinated receding-horizon control of vehicle speed and transmission gearshift for energy-efficient operation of automated battery electric vehicles (BEVs). The introduction of multispeed transmissions in BEVs enables manipulation of electric motor operating point under given vehicle speed and acceleration command, thus creating the opportunity to further improve BEV energy efficiency. However, co-optimizing vehicle speed and transmission gearshift leads to a mixed-integer nonlinear programming (MINLP) problem, and it is well known that solving MINLP problems is computationally very challenging. To address this challenge, we propose a novel continuous relaxation technique that enables the computation of solutions to the speed and gearshift co-optimization problem using off-the-shelf nonlinear programming solvers. After analyzing theoretical properties of the proposed relaxation technique, we demonstrate its effectiveness through simulation-based case studies, where we show that co-optimizing vehicle speed and transmission gearshift can lead to considerably greater energy efficiency than optimizing them separately or sequentially and the proposed relaxation technique can reduce the computational cost of the co-optimization problem to a level that is comparable to the time budget available for onboard implementation. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi -level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation. • A real-time PCC system was developed to minimize the energy consumption of EVs. • Two scenarios of car-following and signal anticipation were evaluated. • An ANN model was developed to predict the energy economy performance of the EV. • The proposed PCC can realize 8.5%–15.6% of the energy saving in the urban areas. [ABSTRACT FROM AUTHOR]

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators’ manual conduction and/or previous level 2 systems control.

The energy efficiency of hydraulic cylinder drives can be increased by replacing the actuator with a multi-chamber cylinder, utilising multiple supply lines with unique pressures or a combination of the concepts. Previous studies have demonstrated significant energy savings using a cascaded control system, which requires velocity feedback to stabilise the system. To avoid the need of position or velocity sensors in harsh conditions of mobile machines, this article presents a throttling control scheme, which achieves good energy efficiency on multi-pressure systems without velocity feedback. A simulation study was performed to determine the efficiency of the system, robustness against load variations and the effect of valve response time on performance. [ABSTRACT FROM AUTHOR]

This paper intends to present a cooperative control framework for a connected cluster of microgrids with multi-smart greenhouses creating a smart local electric grid in the framework of smart grids. Each microgrid comprises renewable generators, pumps, advanced communication and metering infrastructure, water reservoir, energy storage device, and a set of greenhouses where each one includes heating, ventilation and air conditioning (HVAC), CO 2 injector, artificial lighting, sensors, local pump, and fans. The key objective is to formulate a coordinated optimization framework embedded in a model predictive control (MPC) scheme to optimally control the operation of the clustered microgrids and manage the power flows exchange ensuring a high quality of service. The microgrids are connected permitting the power exchanges to enhance the utilization of local renewable generations. Furthermore, the cluster is connected to the main grid through a power link permitting power exchange in excess/shortage case. The cooperation is achieved throughout a bidirectional communication infrastructure, where a centralized controller is responsible of managing the different control signals. A comprehensive scheduling optimization algorithm is developed and implemented to effectively control the clustered microgrids operation considering the operational constraints, where the purpose is to enhance energy efficiency, and manipulating effectively the microclimate variables defining the optimal environment for crops development in all greenhouses. An MPC-based energy management framework is implemented and applied to a case study to demonstrate its performance and effectiveness through extensive numerical simulations. • We present a control framework for a cluster of microgrids with multi-greenhouses. • We formulate a coordinated optimization framework embedded in a MPC scheme. • The purpose is to optimally control the operation of the clustered microgrids. • An MPC-based algorithm is developed to control the microclimate in each greenhouse. • The algorithm is applied to a case study to demonstrate its performance. [ABSTRACT FROM AUTHOR]

Building predictive control has proven to achieve energy savings and higher comfort levels than classical rule-based controllers. The choice of the model complexity needed to be used in model-based optimal control is not trivial, and a wide variety of model types is implemented in the scientific literature. This paper shares practical aspects of implementing different control-oriented models for model predictive control in a building. A real thermally activated test building is used to compare the white-, grey-, and black-box modeling paradigms in prediction and control performance. The experimental results obtained in our particular case reveal that there is not a significant correlation between prediction and control performance and highlight the importance of modeling the heat emission system based on physics. It is also observed that most of the complexity of the physics-based model arises from the building envelope while this part of the building is the most sensitive to weather forecast uncertainty.

Optimization of economic aspects of microgrid operation in both grid-connected and islanded mode leads to contradictive definitions of optimality for both modes. There is no general agreement on how to cope with this duality. To address this issue, as well as modern energy market requirements and a better renewable energy utilization necessity in the case of large facilities, a comprehensive control solution utilizing the appropriate model is needed. In response, the authors propose a hybrid microgrid model covering fundamental features and designed to work in conjunction with two switched receding horizon control laws. A relevant controller is chosen according to the current microgrid operation mode and its cost function tailored to specific demands of the islanded or grid-connected operation. Performed research led to a new switched hybrid model predictive control approach focused on microgrid economic optimization. This approach utilizes an appropriate hybrid microgrid model also contributed by the authors. The introduced solution turned out to be effective in overall energy cost reduction in the case of large commercial facilities, regardless of grid-connection and renewable generation scenarios. Furthermore, it also provides satisfactory renewable energy and storage capabilities utilization in changing grid connection conditions. [ABSTRACT FROM AUTHOR]