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A novel pseudo-spectral method is proposed to tackle the non-linear control problem of a wave energy converter (WEC) integrated with an all-electrical power-take-off (PTO). The WEC hydrodynamic model is constructed using linear wave theory together with a non-linear viscous damping term. The PTO model encompasses a permanent magnet synchronous generator (PMSG) whose local field weakening mechanism imposes non-linear operational constraints on the WEC's motion and PTO torque. The energy-maximizing control problem of this wave-to-wire model is non-linear and has not been investigated before. In comparison to pseudo-spectral methods previously studied, the proposed pseudo-spectral method is exempt from the assumption of ideal incoming wave prediction. This is made possible by the introduction of a novel mapping function to convert the finite control horizon onto the Gaussian quadrature interval. This mapping function increases more slowly with time at the beginning of the control horizon where wave prediction attains greater precision. As a result, the collocation points are utilized to the advantage of the better approximation of the objective function, thus better overall control performance. • A novel model predictive control (MPC) for wave energy converters (WEC) is proposed. • The case study involves a WEC integrated with an electric power take-off model. • Non-linear dynamics and constraints can be handled explicitly and optimally. • The novel MPC controller is robust against. wave prediction errors. [ABSTRACT FROM AUTHOR]

To improve the wave energy capture performance of a raft-type wave energy converter, a novel hybrid control strategy consisting of a passive control strategy and an active control strategy is proposed. In the passive control strategy, a nonlinear stiffness mechanism is introduced to generate a nonlinear passive control force. The passive control strategy has the advantage of broadening the capture width, whereas the active control strategy, which uses nonlinear model predictive control, is mainly utilized to improve the capture efficiency. Considering the high dimensions of the dynamic model, a dimension reduction strategy is given. Moreover, a mechanism for selecting the initial value is proposed to avoid the local optimum. Then, a dimension reduction nonlinear model predictive control controller is proposed to obtain the optimal active control force. Finally, the wave energy capture performance is evaluated for both regular and irregular waves. Numerical simulation results show that the wave energy capture performance can be improved using the novel hybrid control strategy. • A novel hybrid control strategy consisting of a passive control strategy and an active control strategy is investigated. • A nonlinear stiffness mechanism plays the role of phase control to achieve the inverse phase. • An initial value selecting mechanism is proposed to avoid the local optimum. • A dimension reduction strategy is proposed to enhance real-time performance. [ABSTRACT FROM AUTHOR]

This research aims to improve the performance and economics of fuel cell hybrid electric vehicles (FCHEVs), validated and established by introducing an innovative energy management strategy (EMS) based on a speed-predictive fusion model. Firstly, a mixed prediction model was built based on BiLSTM, TCN, and Self-attention (SA) mechanism to accurately search, capture and fuse multi-granularity features in time series. Then, Harris-Hawk Optimization (HHO) was used to optimize the dropout rate and model learning rate of the combined BiLSTM-TCN-SA time series model to improve the prediction accuracy and generalization ability of the model. Finally, stochastic model predictive control was combined with BiLSTM-TCN-SA to form SMPC-NSGA III algorithm, which was used for multi-objective optimization of fuel economy, fuel cell durability and battery durability. In this study, the effectiveness of the proposed strategy was verified under the condition of CLTC-P driving cycle. The experimental results showed that RMSE and R2 of HHO-BiLSTM-TCN-SA velocity prediction model are 1.169 and 0.998, respectively. In addition, the output of the model is within the confidence interval of 97.5 % of the real speed, and there is no significant difference, which is statistically significant. Under the SMPC-NSGA III strategy, the average efficiency of the fuel cell was increased by 12 % and 1 % respectively. • A new energy management strategy was created to improve fuel cell durability. • HHO-based optimisation of BiLSTM-TCN-Self-Attention hyperparameter fusion model improves speed prediction performance. • NSGA-III multi-objective optimization algorithm was combined with SMPC for collaborative optimization results. • The average efficiency of the fuel cell in the SMPC strategy is higher than the DP and FLC. [ABSTRACT FROM AUTHOR]

This paper presents a heuristic model predictive control (MPC) methodology to activate energy flexibility in fully electric school buildings in cold climates to reduce electricity demand during peak demand periods of the electric grid. To streamline the implementation of MPC, the proposed approach employs grey-box archetypes, a clustering of weather conditions to identify typical scenarios and a limited number of possible setpoint profiles. A data-driven grey-box approach is used to create archetype models for different thermal zones in a typical school building; this approach enables rapid development and requires much less calibration data than black-box models. A third-order resistance-capacitance thermal network for zones with convective heating and a fourth-order model for zones with radiant floor heating are developed and calibrated using measured data from an all-electric school building in Québec, Canada. The weather data are clustered into several categories, representing different weather conditions (6 clusters representing two ambient temperature ranges and three solar radiation ranges). The heuristic MPC strategy uses predefined optimal setpoint profiles for each cluster and weather prediction one day ahead to shift the building load from on-peak to off-peak hours. For each heuristic MPC scenario, the model runs a simulation using forecast weather data to quantify and activate energy flexibility in response to grid requirements. The developed MPC framework was implemented in the school used as the case study. Ten classrooms are investigated, with six using the MPC and four as a reference case with the reactive control system and default zone setpoint profiles. Results indicate that the school building can provide between 47% and 95% energy flexibility (load shifting relative to reference) during on-peak hours and up to 44% electricity cost reduction while satisfying acceptable temperature constraints. By implementing the proposed MPC, energy flexibility of 32 W/m2 of floor area for the zones with a convective heating system and 65 W/m2 of floor area for the zones with radiant heating can be achieved during a demand response event. The proposed strategy can be generalized and replicated in other school buildings. • A model predictive control methodology for school buildings was presented. • The methodology was applied to a fully electric school building near Montréal, Canada. • Data-driven grey-box models were developed and calibrated using measured data. • The model predictive control provided between 47% and 95% energy flexibility during on-peak hours. • The model predictive control reduced the cost of electricity by 44%. [ABSTRACT FROM AUTHOR]

To meet the active regeneration requirements of the diesel particulate filter (DPF), the outlet temperature of the diesel oxidation catalyst (DOC) must be maintained within the range of 585 °C–615 °C. Firstly , this paper explores the DOC system using a set of partial differential equations that describe the coupled flow and heat transfer of both the gas phase and solid phase carriers. Subsequently, the DOC model is significantly simplified, and the equations are solved based on their characteristics. Secondly , a Luenberger observer is designed, and the appropriate range of values for the observer gain is calculated. By minimizing the cost function and adhering to constraints within a finite prediction horizon, the optimal sequence for injecting hydrocarbon(HC) is determined. Thirdly , in this study, a Worldwide Harmonized Transient Cycle test is conducted on the engine, following the China VI emission standard. The computational results demonstrate that the mean absolute errors of the DOC outlet temperatures obtained from the model prediction and the observer are 0.535 °C and 1.994 °C, respectively. Finally , a nonlinear model predictive control is implemented to ensure that the DOC outlet temperature can be maintained between 585 °C and 615 °C. [Display omitted] • The DOC gas phase and solid phase temperature differential equations were simplified. • The Backward Differentiation Formulas method was used to solve the DOC model and the parameters were identified by the Levenberg-Marquardt algorithm. • The Luenberger observer was designed and the Lyapunov function was constructed to prove its stability. • The DOC model was combined with the minimised cost function and NMPC was implemented to optimise the HC injection strategy. [ABSTRACT FROM AUTHOR]

This article offers a cohesive design optimization and control framework of a large-scale grid-connected battery and battery-less hybrid solar/wind system. Primarily, a techno-enviro-socio-economic design optimization and feasibility analysis were performed for eight distinct energy alternatives. Secondly, a finite-set model predictive current control (FS-MPCC) was proposed to improve both stability and dynamic behavior of the optimal energy alternative under various climatic circumstances. Moreover, finite-set model predictive control is furtherly employed to exploit the maximum permissible power from both solar and wind energies. The proposed framework is verified over a new case-study in Sokhna Industrial Zone in Suez, Egypt. The studied load demand combines both industrial and residential load sectors with a daily consumption of 40,500 kWh and of 4-MW peak. The design optimization results revealed that the grid-connected photovoltaic/wind turbines/converter system has superior sustainable and economic performances with the least net present cost ($17,371,980) and energy cost (0.0782 $/kWh). These values were less than those of the base-case system (grid-only) by 17 % and 22 %, respectively. Moreover, the reliability analysis found a tiny and tolerable daily capacity shortage of 5835-kWh despite the five annual grid outages. Besides, around 52 % of the total energy production was acquired from both solar and wind generation systems, which interpreted in a maximum saving in carbon dioxide emissions by 45.8 % compared to the base-case system and offered substantial social benefits in terms of creating nearly 10 jobs. The FS-MPCC results proved a reinforced dynamic behavior for the maximum power tracking profiles of solar and wind systems as well as in the DC-bus voltage profile regarding the over- and undershoot and the steady-state response. Compared to PI controller, the proposed FS-MPCC creates dq-axis voltages and currents with less disturbances and ripples which inject current/voltage to the grid with fewer harmonics and thus increases the generator's lifetime. To sum up, the proposed method features an integrative and compelling case to be used as an efficient tool to implement large-scale renewable energy projects. It could act as a blueprint for energy developers, researchers, investors, and authorities to achieve affordable and sustainable energy access. • On-grid solar/wind energy system is optimized to meet industrial and residential loads. • PI. FS-MPCC technique is developed and compared with PI for optimal system control. • Optimal design has the least net present and energy costs and maximum carbon savings. • FS-MPCC prove a strengthened dynamic performance under different climatic conditions. [ABSTRACT FROM AUTHOR]

District energy systems through fourth and fifth generations have shown great promises to help integrate renewable energy sources at large scale and to decarbonize the built environment. However, older generations using boilers represent a large proportion of systems currently in operation, and significantly contribute to greenhouse gas (GHG) emissions. Moreover, operational data has become increasingly available for these older generation systems and represents a suitable opportunity for Artificial Intelligence (AI)-based modelling and advanced controls. In this context, model predictive control (MPC) has appeared as a powerful control solution; however, field implementation remains relatively scarce. This paper aims to develop an AI-based MPC strategy for multi-boiler district heating systems. It relies on heating load forecasting machine learning models and data-driven boiler performance curves to optimize boiler thermal outputs. This strategy was implemented in two Canadian demonstration sites and showed GHG emissions reductions of 1.3 % and 2.8 %. Although relatively modest, statistical analysis confirmed the realization of these savings. In absolute terms, the strategy helped avoid 45 t and 77 t CO2eq emissions and save $10,268 and $19,975 CAD during the testing period of 2–3 months. The model and strategy developed in this work could be easily scalable to similar district heating systems. • AI predictive control was developed for multi-boiler district heating systems. • The strategy relies on data-driven load forecasting and boiler performance curves. • The strategy was implemented in two Canadian demonstration sites. • Natural gas boiler consumption was reduced by 1.3 and 2.8 %. • GHG emissions, energy costs were reduced by 45–77 t CO2 eq and $ 10,268–19,975 CAD. [ABSTRACT FROM AUTHOR]

Ocean waves hold a promising potential as a sustainable source of clean energy. However, in order to make wave energy converters financially viable, particularly in the case of the oscillating water column (OWC), it is crucial to optimize the power take-off system to maximize performance. Most control strategies focus solely on turbine performance, often at the expense of overlooking the overall wave-to-wire system performance. Therefore, a model predictive control (MPC) strategy using short-term air pressure prediction was proposed to enhance electrical energy delivered to the grid. The Mutriku wave power plant (Spain) was case-studied for evaluating the control approaches, which focused on maximizing average power (turbine or generator) from an OWC equipped with the innovative biradial turbine. In comparison to the reference baseline strategy, the implementation of MPC (with a 16-second forecast) resulted in a minor 2%-increase in the average turbine power. However, when it comes to the generator power accumulated over a year, the MPC strategy produced an improvement of more than 8%. Since generator power is the ultimate product, this improvement is significant. Nonetheless, the MPC strategy exhibited a non-smooth control response compared to other strategies. Improving control action quality should be a primary focus of future research. • A model predictive control was proposed for an oscillating-water-column power plant. • Short-term air pressure forecasting was performed using an autoregressive model. • The proposed algorithm was applied to the Mutriku wave power plant. • Improvements in the annual average generator power output of over 8% were observed. [ABSTRACT FROM AUTHOR]

The effective utilization of ultra-capacitor (UC) in the energy allocation process is crucial for improving the efficiency of the energy management strategy (EMS) for hybrid energy storage system (HESS). In this paper, we present an adaptive energy management strategy framework based on a model predictive control (MPC) with real-time tuning weight to optimize UC utilization and extend battery lifetime. Firstly, the adaptive autoregressive integrated moving average model (AARIMA) is developed to accurately predict vehicle velocity and road gradient for online power demand computation, based on local historical information. The differencing order and lags of the model in AARIMA are updated using the augmented Dickey-Fuller (ADF) test and Bayesian Information Criterion (BIC). Secondly, the MPC is developed to optimize power allocation using quadratic programming. Finally, the weight in the cost function is real-time tuned via the fuzzy logic rule according to the predicted power. Furthermore, the proposed strategy is validated using a scale-down hardware-in-the-loop verification platform. Simulation results demonstrate that the UC utilization ratio has a 9.91% improvement, and the battery lifetime has a prolonged by 24.38% across two tested actual and two typical driving cycles. Consequently, the proposed EMS achieves optimization of both energy management efficiency and battery lifetime. • An adaptive energy management strategy based on a model predictive control with real-time tuning weight strategy is proposed to optimize UC utilization and extend battery lifetime for hybrid energy storage system. • The AARIMA with variable differencing order and lags of the model is proposed to predict the velocity and gradient. • The fuzzy logic is developed for realizing reliable real-time tuning weight, and active adjustment of power allocation. • A scale-down hard-in-loop verification platform is established to verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

The heat dissipation performance of batteries is crucial for electric vehicles, and unreasonable thermal management strategies may lead to reduced battery efficiency and safety issues. Therefore, this paper proposed an optimization strategy for battery thermal management systems (BTMS) based on linear time-varying model predictive control (LTMPC). To begin, based on the results of three-dimensional thermal flow analysis, a reduced order substitution model for the battery cooling system was established. Next, to address the issue of high computational complexity caused by the nonlinear model in the controller, a linearization method based on staged control was designed. Furthermore, the control parameters were optimized using the particle swarm optimization algorithm. Finally, a BTMS physical model based on SIMSCAPE was established for performance simulation. The performance of the logic threshold controller (LTC), linear model predictive controller (LMPC), and nonlinear model predictive controller (NMPC) was compared and analyzed. LTMPC was found to reduce the average temperature of the battery by up to 2.33 °C and 1.90 °C, and the cumulative temperature difference is reduced by 13.9% and 16.6% compared to LTC and NMPC, respectively. Compared to LMPC, LTMPC reduces thermal management energy consumption by 21.2%. • A complete mathematical model of the liquid cooling cycle circuit was derived through the order reduction method. • Reasonably incorporate future information into the controller to improve robustness of system. • Adopting a staged control strategy to segment the model, establishing a linear time-varying control model, greatly improving computational efficiency. • The upper controller uses the heat exchange between the coolant and the air conditioning system as the control variable, improving control efficiency. • The combination of particle swarm optimization algorithm and pareto curve fully exploits the optimization potential of parameters. [ABSTRACT FROM AUTHOR]

This work is concerned with modelling, Model Predictive Control (MPC) and on-line energy optimisation of a residential building in which an underfloor heating system based on electric heating foils is used. At first, the first-principle model is developed to determine air temperature inside the building. Low-order linear models for MPC are found. Tuning of MPC is discussed, both set-point change and disturbance compensation cases are discussed. Next, two control system structures are considered: MPC with a constant temperature set-point and MPC cooperating with a set-point optimiser which repeatedly calculates on-line the set-point in order to minimise the amount of energy used. Effectiveness of these structures is validated in simulations using a real scenario of outdoor temperature changes. In the first structure a natural method to reduce energy used is to slightly reduce the temperature set-point, e.g. reduction of the set-point from 21 ∘ C to 18.5 ∘ C leads to reducing the energy usage by approx. 1430 KWh during the heating season. Introduction of the set-point optimiser makes it possible to further reduce energy used by approx. 300 KWh annually. The presented solution is computationally efficient since in MPC and set-point optimisation the classical quadratic optimisation method is used on-line. • A residential building with underfloor electric heating foils is considered. • First-principle model of the process is thoroughly described. • Development and tuning of MPC algorithm for temperature control is detailed. • Development of on-line set-point optimisation algorithm is described. • The set-point optimiser reduces energy usage up to approx. 300 KWh annually. [ABSTRACT FROM AUTHOR]

Real processes with heat exchange have usually complex behaviour and are energy intensive. In practical applications, the process variables are always bounded, and it is suitable to include these boundaries into the controller design. The soft-constrained robust model predictive controller has been designed to improve the control performance and energy consumption in comparison with the robust model predictive control with only hard constraints. Experimental application of soft-constrained robust model predictive control (SCR MPC) for a laboratory plate heat exchanger is presented in this paper. The plate heat exchanger is a non-linear process with asymmetric dynamics and is modelled as a system with parametric uncertainties. The controlled variable is the temperature of the heated fluid at the outlet of the heat exchanger and the manipulated variable is the volumetric flow rate of the heating fluid. The actuator is a peristaltic pump and the influence of the linear and non-linear actuator characteristics on the control performance is also investigated. The set-point tracking using SCR MPC is studied for the laboratory plate heat exchanger in an extensive case study. The experimental results confirmed the improvement of the control responses and reduction of energy consumption by introducing the soft constraints into MPC design. • The complex behaviour of the heat exchanger was modelled by parametric uncertainty. • The linear and non-linear characteristic of actuator were considered. • Soft-constrained robust MPC with integral action was investigated. • Experimental results of the laboratory case study were analysed. • Designed robust MPC improved control performance and energy savings. [ABSTRACT FROM AUTHOR]

Energy saving potential of radiant floor cooling combined with underfloor ventilation (RFCUV) system has been restricted by its control problems. Existing conventional control methods of radiant cooling system have several disadvantages, such as control lag, poor control performance, and low economy, etc. The objectives of this study were to: (i) build a dynamic simplified model and validate its precision experimentally; (ii) implement advanced model predictive control (MPC) on RFCUV system; and (iii) demonstrate MPC control performance by comparing with existing conventional proportional-integral-derivative (PID) experimentally. Experimental results indicated that under experimental step setpoint variations, the adjusting time of indoor air temperature or operative temperature was only 12 min with MPC controller, and was 30 min with PID controller; it took only 1 min to reach recommended thermal comfort range with MPC controller, and 17 min with PID controller. During 9:00 to 17:00 in typical design day of Nanjing city, compared with PID controller, MPC controller yielded 17.5% energy saving when maintaining equal or better indoor comfort. Thus, compared with PID, MPC demonstrated the advantages of rapid responses, good stability and excellent energy saving effect in RFCUV system. • A dynamic simplified RC-network model of RFCUV system was created and validated. • Constrained multi-objective MPC controller of RFCUV system was developed. • MPC performance of RFCUV system was demonstrated by comparing with PID experimentally. [ABSTRACT FROM AUTHOR]

Abstract When developing an energy management strategy (EMS) including a battery aging model for plug-in hybrid electric vehicles, the trade-off between the energy consumption cost (ECC) and the equivalent battery life loss cost (EBLLC) should be considered to minimize the total cost of both and improve the life cycle value. Unlike EMSs with a lower State of Charge (SOC) boundary value given in advance, this paper proposes a model predictive control of EMS based on an optimal battery depth of discharge (DOD) for a minimum sum of ECC and EBLLC. First, the optimal DOD is identified using Pontryagin's Minimum Principle and shooting method. Then a reference SOC is constructed with the optimal DOD, and a model predictive controller (MPC) in which the conflict between the ECC and EBLC is optimized in a moving horizon is implemented. The proposed EMS is examined by real-world driving cycles under different preview horizons, and the results indicate that MPCs with a battery aging model lower the total cost by 1.65%, 1.29% and 1.38%, respectively, for three preview horizons (5, 10 and 15 s) under a city bus route of about 70 km, compared to those unaware of battery aging. Meanwhile, global optimization algorithms like the dynamic programming and Pontryagin's Minimum Principle, as well as a rule-based method, are compared with the predictive controller, in terms of computational expense and accuracy. Highlights • A model predictive energy management strategy is proposed for PHEVs. • The energy management strategy considers the battery aging. • Optimal battery depth of discharging is identified. • Real-world driving cycles are used to examine the controller. • Typical methods are compared to the proposed method. [ABSTRACT FROM AUTHOR]

Abstract With the increase of wind power penetration in generation profile, the contribution of wind power to load frequency control (LFC) has become more significant. To improve the frequency characteristics of a wind-power-contained power system, this paper establishes an analytical linearized model for the frequency response characteristics of wind turbine generator (WTG) during LFC which is helpful to the design of frequency controller. Furthermore, the gap metric measure to the above models under different wind speed ranges is calculated. Under the per unit system, it can be extended as an equivalent model for the whole wind farm. Combining the derived wind farm model and a known thermal power model, an integrated model of hybrid power system can be built. To optimize the frequency-response performance to the power system, an improved LFC method based on model predictive control (MPC) is presented and applied to a multi-areas hybrid system. Then, the wind farms and thermal power plants in the same area can be controlled simultaneously and obtain their reference orders from the predictive controller. The simulation results show that the proposed method can effectively raise the frequency response level of the both power supplies and then improve the frequency performance of power system. Highlights • Frequency response characteristics of wind turbine is studied. • Wind farm equivalent model is established by gap metric calculation. • Hybrid power system is modeled, containing multiple forms of energy. • Coordinated model predictive control is used to improve frequency response. [ABSTRACT FROM AUTHOR]

The consistency of lithium-ion battery packs is extremely important to prolong battery life, maximize battery capacity and ensure safety operation in electric vehicles. In this paper, a model predictive control (MPC) method with a fast-balancing strategy is proposed to address the inconsistency issue of individual cell in lithium-ion battery packs. Firstly, an optimal energy transfer direction is investigated to improve equalization efficiency and reduce energy loss. Then, a MPC-based equalization algorithm is developed to obtain the optimal constant equalization current by directly minimizing equalization time of battery's SOC. Moreover, a fast-solving strategy for MPC is designed to reduce the computational burden of cells' equalization. Finally, the performance of proposed MPC algorithm has been compared with other MPC-based equalization methods in three different equalization topologies (cell-to-cell, cell-to-pack and module-based equalization topology), the results indicate that the proposed algorithm achieves faster equalization speed and less energy loss in three equalization topologies. Importantly, the proposed algorithm avoids the repeated charging and discharging of intermediate batteries effectively, and ensures the single-point convergence of cells' SOC. Furthermore, the effectiveness and accuracy of proposed fast-solving strategy for MPC algorithm is verified by comparison with common solving strategies, the results show the proposed method takes less computational time to obtain the accurate optimal balancing current, indicating that the proposed fast-solving strategy can improve computation speed and reduce computational burden. • A MPC-based algorithm is developed for three different equalization topologies. • Optimal energy transfer direction is investigated to improve equalization efficiency. • Optimal constant current is obtained by minimizing equalization time of cells' SOC. • Fast-solving strategy is designed to reduce computation cost of cells' equalization. [ABSTRACT FROM AUTHOR]

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime. • Health-aware MPC energy management strategy is proposed for fuel cell electric vehicle. • Fuel cell internal parameters improper change is considered in the proposed strategy. • The fuel cell health state model is established based on the hybrid modeling method. • Comparison between optimization strategies exhibit the effectiveness of the proposed strategy. • Results verify the advantages of the proposed strategy under congested urban conditions. [ABSTRACT FROM AUTHOR]

The access to electricity is increasing worldwide with a considerable growing rate per year. Unfortunately, traditional large fossil power technologies based on coal and oil lead to a major concern in environmental pollution, so the distributed power generation utilizing natural gas engine is taken into consideration. In this paper, a hybrid model predictive control (HMPC) strategy is proposed for natural gas engines in distributed power generation. The original fourth-order double-input double-output (DIDO) nonlinear engine model is transformed into two simpler models, one is a first-order model for air–fuel ratio (AFR) control, and a linear model predictive control (LMPC) strategy is designed. The other is a second-order nonlinear model for speed control, and the AFR is used as one of its two inputs to design nonlinear model predictive control (NMPC) strategy. The advantage of this is that each model is simpler than the original fourth-order model, and prediction horizon can be lengthened to achieve better control performance. For the optimization problem of NMPC, a switching mechanism strategy is proposed. It can effectively reduce the number of SQP iterations in both transient and steady-state conditions. Both simulations and experiments verify the feasibility of the HMPC and the effectiveness of the switching mechanism. • The original model is splited into two simpler models, and two MPC controllers are designed. • The switching mechanism is proposed to reduce iterations of optimization problem solution. • Based on above methods, longer prediction horizon achieves better control performance. [ABSTRACT FROM AUTHOR]

This paper establishes an adaptive correction predictive energy management strategy (EMS) to obtain optimal power distribution in the case of inaccurate prediction with excellent computational efficiency for parallel hybrid electric vehicle (HEV). Firstly, deep neural network (DNN) models are trained to forecast future speed sequence with different prediction horizons to prepare for online model predictive control (MPC) implementation. According to the degree of forecasting errors, a novel tolerant sequential correction algorithm as the solver of MPC strategy is selected in tuning mechanism instead of traditional dynamic programming (DP) to cope with the rough accumulated prediction. Besides, to compromise between fuel optimization and lower computation burden, statistical analysis of battery SOC variation distribution is gained from historical driving cycles' calculation through offline DP. Then, nearest neighbor interpolation model is fitted to generate optimal ranges of state variable in each step, which adaptive mesh discretization method is intelligently reducing state and control calculation grid's number. Numerical simulations demonstrate that the proposed tolerant sequential correction algorithm applied in MPC scheme with adaptive mesh discretization have yielded the favorable capability of the fuel economy in the consideration of inaccurately velocity prediction and excellent computational efficiency, which exhibits the practical adaptability in real driving routes. • A novel tolerant sequential correction method is to cope with the mechanism of rough prediction. • Adaptive mesh discretization is generated by statistical analysis of battery SOC variation. • The proposed strategy is achieved online better optimization with more less computational time. [ABSTRACT FROM AUTHOR]

Organic Rankine Cycle (ORC) stands out in low-grade waste heat recovery (WHR) technology for its significant performance. Considering the system's coupling dynamics, model predictive control (MPC) has adopted its wide application in realizing reasonable adjustment of the ORC based WHR system and has been proved to be effective. MPC is usually applied under the premise of having established a relatively accurate model, thus achieving a satisfactory control performance. However, the popular first principles model of the ORC system turns out to be high-dimensional and will result in computationally costly during typical nonlinear MPC adoption. While model linearization around operating points would enable the employment of linear MPC and reduce online calculation amount to some extent, the obtained local model loses global validity, leading to possible unstable control performance. To address these problems, a practical input–output data-driven quasi-linear parameter varying (QLPV) model is constructed for the ORC based WHR system by introducing the Koopman operator to ensure the global control effect. The corresponding MPC algorithm is thus presented via QLPV representation which solves the constructed constrained optimization problem iteratively in the form of a series of quadratic programming (QP) problems at each time step. Moreover, considering the possible lack of adequate training data covering the important dynamics of the ORC based WHR systems in practical application, an online updating mechanism that involves recursive equations is proposed to realize prediction accuracy improvement. Simulations on prediction, setpoint tracking and disturbance rejection are performed to verify the established model accuracy and the control effectiveness of the proposed strategy. • The practical data-driven QLPV model and MPC algorithm is proposed for ORC system. • QLPV model outdoes those models built up through local linearization. • QLPV model update mechanism is presented considering lack of training data. • QLPV-IMPC strategy has shown its advantages compared with NMPC algorithm. [ABSTRACT FROM AUTHOR]

—This paper proposes a real-time energy management strategy (EMS) for hybrid electric vehicles by incorporating reinforcement learning (RL) in a model predictive control (MPC) framework, which avoids the inherent drawbacks of RL—the excessive learning time and lack of adaptability—and remarkably enhances the real-time performance of MPC. First, the MPC framework for the energy management problem is formulated. In that, a novel long short-term memory (LSTM) neural network is utilized to construct the velocity predictor for a more accurate prediction, and its prediction capability is verified by a comparative analysis. Then, the HEV prediction model and the velocity predictor are regarded as the RL model with which the RL agent can interact. On this basis, the optimal control sequence in the prediction horizon can be learned through model-based RL, but only the first element is actually executed, and the RL process begins anew after the prediction horizon moves forward. In the simulation, the algorithm's convergence is analyzed and the influence of the prediction horizon length is evaluated. Then, the proposed EMS is compared with DP, conventional MPC, and RL method, the results of which demonstrate its performance and adaptability. As last, a hardware-in-the-loop test validates its actual applicability. • An energy management strategy is developed by incorporating RL in MPC framework. • The strategy avoids RL's inherent drawbacks and enhances MPC's real-time ability. • A long short-term memory neural network is utilized to predict the velocity. • Simulation and HIL test verify its fuel economy and real-time ability. [ABSTRACT FROM AUTHOR]

The large ownership of direct-expansion (DX) air-conditioning (A/C) systems in small and medium-sized buildings brings with it the need to reduce their energy consumption without damaging the thermal comfort of the occupants. Model predictive control (MPC) is an effective method to optimally control the operation of air-conditioners. However, most existing MPC methods require the investment of additional equipment and labor-intensive work, which greatly increases the cost of MPC and hinders its practical application. To solve the problem, this paper presents an economical and practical MPC algorithm for DX A/C systems, capable of achieving a balance between thermal comfort and energy saving. The proposed algorithm was experimentally validated on both an experimental DX A/C system and a market available split-type air-conditioner. Experimental results on the experimental DX A/C system show that temperature and humidity set-points selected at α = 1 saved 23.3% of energy consumption compared to those selected at α = 0, while keeping indoor thermal comfort within acceptable range. And results on the split-type air-conditioner demonstrate energy savings of up to more than 32% compared to the baseline and proved that the algorithm can be practically applied on market available D/X air-conditioners. • A practical application-oriented model predictive control algorithm is developed for DX A/C systems. • A cooling capacity prediction ANN model of the DX A/C system is used to predict the cooling load. • An ANN model for predicting the operational status of the DX A/C system is developed without increasing the training data. • A cost function is used to balance indoor thermal comfort levels and energy consumption. • The algorithm has been validated in both an experimental DX A/C system and a market available split-type air-conditioner. [ABSTRACT FROM AUTHOR]

The major concern of modern power plants is changing from tracking control to environmental and economic issues. The economic model predictive control (EMPC) scheme, which incorporates the boiler-turbine unit's dynamic tracking and economic optimization into one online framework, can well enhance the dynamic economic performance. Considering the strong nonlinearity that existed in the boiler-turbine system, this paper presents an advanced EMPC scheme based on the input/output feedback linearization (IOFL) approach. The boiler-turbine dynamics are converted via the IOFL method to a linear form, which can be readily used to constitute the standard EMPC scheme. A dual-mode method is adopted in this paper to guarantee the stability of the IOFL EMPC strategy for the boiler-turbine system, in which the first-mode optimizes the economic objective function while preserving the states of the system within a feasible region, and the second-mode moves the system state to the optimum operating point using an auxiliary controller. The simulations under the MATLAB environment demonstrate that the application of the IOFL-based EMPC scheme enhances the economic and dynamic output performance under load demand changes in comparison with fuzzy hierarchical MPC and fuzzy economic MPC schemes. • A stable economic model predictive control is proposed for boiler-turbine unit. • The nonlinear model is transferred to a new linear form via feedback linearization. • Linear matrix inequalities are constructed to guarantee the system stability. • The proposed scheme enhances dynamic economic performance for boiler-turbine unit. [ABSTRACT FROM AUTHOR]

The energy management problem of hybrid unmanned aerial vehicles (UAVs) is studied in this paper, and an energy management strategy based on hierarchical model predictive control (HMPC) is proposed. The structure of HMPC is divided into the trajectory optimization layer and the control layer. The trajectory optimization layer primarily considers the factors like economic costs, including hydrogen consumption, equipment purchase, use costs, and equipment lifetime. To determine the optimal trajectory of the battery state of charge, the trajectory optimization layer is optimized and solved. The control layer is model predictive control, and its key function is to follow the reference trajectory to obtain the optimal fuel cell output power. A grey Markov prediction model is proposed and used to predict the future power demand of UAVs. The superiority of the prediction model is demonstrated by comparing it with the typical prediction methods. Based on the simulation and experimental comparison, it can be concluded that the effect of the HMPC is satisfactory and has a positive impact on the endurance of the UAV. • A hierarchical model predictive control-based energy management strategy is proposed. • A grey Markov prediction model is proposed for forecasting the power demand. • The proposed strategy outperforms the traditional strategies for endurance time. [ABSTRACT FROM AUTHOR]

Thermal management is becoming a major challenge for electronics, and a better temperature control algorithm that could maximize the system performance will play a greater role in fully utilizing the existing cooling capacity. Unfortunately, the simplest look-up table method is still widely used as the temperature control algorithm in current portable electronic devices, especially laptops, resulting in a significant performance loss of devices. In this paper, a general temperature control framework for a commercial laptop that considers the skin temperature constraints is proposed based on the model predictive control algorithm. In specific, a high-accuracy compact thermal model is first generated through the model order reduction method and validated by abundant experimental data. Then the proposed MPC is numerically evaluated in three test scenarios, covering different workloads and performance indexes. The results show that the proposed MPC outperforms the baseline look-up table method by achieving about 10–20% higher performance index in different test scenarios. The open-loop optimal control method is also considered to estimate the optimality of the proposed MPC. Moreover, a parametric study is conducted to analyze the influence of different control parameters, indicating broad prospects for the future application of the proposed MPC algorithm. • A general temperature control framework for commercial laptops is proposed via MPC algorithm. • The constraints of skin temperature are considered. • A compact thermal model of the commercial laptop is generated and validated by abundant experimental data. • The proposed MPC could obtain about 10–20% higher performance index compared to the baseline method of the look-up table. • The proposed MPC could achieve more than 90% of the optimal value calculated from the open-loop optimal control algorithm. [ABSTRACT FROM AUTHOR]

The need to increase renewable energy sources deployment and to reduce consumption of fossil fuels has led to the diffusion of small-scale DG (Distributed Generation) systems, which may be effectively integrated in micro-grids. The role of control logic in defining microgrid performances and reliability is predominant and can be improved by using advanced control logics such as the ones based on MPC (Model Predictive Control). In a previous paper, a MPC logic, based on the use of weather forecasts to improve the performances, has been applied to the analysis of power management in a domestic micro-grid system composed by: PV (Photovoltaic panels), FC (Fuel Cells) and a battery pack; in that case, the system was not affected by real uncertainties. In this paper the same system has been considered for domestic microgrid applications. The system control logic has been implemented by assuming real weather forecast as input data. DMPC and SMPC (Deterministic and Stochastic Model Predictive Control) concepts have been applied to the system and results have been compared to both MPC and to a standard RBC (Rule Based Control) logic. The impact of forecast uncertainties has been evaluated showing the advantages of a stochastic approach. In that case, the SMPC showed encouraging performances compared to standard control logics, primarily in terms of primary energy savings and downsizing potential of the power-delivering sub-systems using programmable energy sources. [ABSTRACT FROM AUTHOR]

This paper is framed within the context of intra-day optimal control of polygeneration systems and storage connected to microgrids. In particular, the paper proposes an optimal control strategy that accounts for both electrical and thermal processes taking place in multi-building energy networks. To this end, the proposed optimal control strategy, based on the use of a Model Predictive Control, has been developed with the aim of providing optimal resource set-points for the coming 24 h. The proposed approach takes into account: day-ahead and spot prices of electricity, resource prices in terms of fuel cost and shut-down, start-up costs as well as the presence of electrical and thermal storage. The ability of this thermo-electric optimal control strategy is then demonstrated through simulations considering three case studies, namely: (a)fixed electricity price, (b)electricity spot-price and (c)different seasonal cases. The paper also studies the shaving of peaks through the use of optimally sized units in a system with the proposed method. [ABSTRACT FROM AUTHOR]

In order to solve the problems of power distribution taking a long time and large fluctuation of propeller speed in the process of COGAG propulsion system switching operating patterns, model predictive control (MPC) is introduced to control power distribution and a SDNN-MPC method is proposed. Discrete incremental LPV model is established as the prediction model and MPC for power distribution process is designed. In order to make MPC suitable for COGAG, which is a nonlinear time-varying system with short sampling period, a simplified dual neural network (SDNN) with simplified constraints is used to solve the quadratic programming in MPC. A large number of simulation results show that the established LPV model has high prediction accuracy and SDNN is helpful to shorten the execution time of MPC. The softening factor and weight factors in MPC can affect the performance of power distribution control. Two principles are summarized to choose weight factors and the recommended range of the softening factor is 0.026–0.034. Compared with the PI controller, based on the SDNN-MPC the time of power distribution process is shortened and the fluctuation of propeller speed is reduced. The maneuverability and stability of the vessel are improved. • A SDNN-MPC algorithm based on LPV model is proposed for the power distribution process of COGAG propulsion system. • The designed control method can significantly improve the maneuverability and stability of COGAG propulsion system. • The robustness and good computing efficiency of the SDNN-MPC are verified. [ABSTRACT FROM AUTHOR]

To improve the energy economy and speed tracking qualities of an unmanned electric vehicle (EV) having a dual-motor powertrain, this paper proposes a model predictive control (MPC) based longitudinal control strategy considering energy consumption. Firstly, an enhanced vehicle longitudinal dynamic model considering powertrain response performance is built as predictive model to guarantee the high precision and robustness of speed prediction. Secondly, pedal command is solved by an online activity set method aiming at minimizing speed tracking errors to realize fast and reliable real-time solving. Finally, an efficient energy management strategy (EMS) is developed to optimize the demand torque distribution and gear shifting. Acquiring these two quantities with an offline global optimization method, the strategy addresses frequent gear shifting problems by online adjusting gear shifting lines. The real-time performance of the proposed strategy is validated in a HIL test. Results show that the proposed MPC-based strategy improves the speed tracking accuracy by 58.93% and expands the high efficiency range of powertrain by 40.93%. The equivalent electric consumption of the EV is reduced by 9.29%. This study provides a foundation for the practical application of longitudinal control algorithms on EVs in the future. • An enhanced predictive model for MPC-based longitudinal control strategy is built. • An energy management strategy for a new dual-motor drive system is proposed. • An offline global optimization method and an online adjustment rule are combined for EMS. • Using MPC-based control strategy, the energy consumption of EV is lowered by 9.29%. • Joint HIL tests verify the effectiveness and real-time performance of the strategy. [ABSTRACT FROM AUTHOR]

Energy management of Parallel Hybrid Electric Vehicles (PHEVs) involves computation of energy efficient torque-speed set-points for the engine and the motor. Application of Nonlinear Model Predictive Control (NMPC) for this problem has been reported in the recent literature. In this work, a fast iterative implementation of the NMPC, known as Model Predictive Static Programming (MPSP) is deployed for the first time for energy management of PHEVs, with an appropriate multi-objective cost function and the applicable equality and inequality constraints. Additionally, a systematic approach based on the Theory of Dominance is used to tune parameters of the controller efficiently. Performance is evaluated with a validated PHEV model on the ADVISOR simulation environment, in terms of various performance metrics on standard drive cycles. The results show that the performance of the MPSP-based technique is close to the ideal performance achieved with Dynamic Programming (DP) and improved over a standard implementation using the Linear Time-Varying MPC (LTV-MPC) and Sequential Quadratic Programming MPC (SQP-MPC) algorithm. Moreover, its execution time on an industry standard micro-controller board is seen to be significantly less than that of LTV-MPC and SQP-MPC. This algorithm therefore appears to be one of the best candidates for on-board vehicle implementation of an optimal energy management strategy for a PHEV. • A new improved energy management strategy for a Parallel HEV based on the MPSP framework. • Numerical implementation showing improved performance over a contemporary MPC algorithm. • Low execution time and feasibility of an on-board implementation. • A new effective approach for tuning the algorithm. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality and meet the increased charging demand of electric vehicles, microgrids incorporating renewable energy and charging stations are considered one of the potential solutions. However, the inevitable uncertainties of renewable energy and load demand become a challenge for the online charging dispatch of the microgrid. Considering these uncertainties, this paper proposes a two-stage optimal framework for the online dispatch of a grid-connected DC microgrid. The first stage presents a power coordination model to obtain the schedule plans of the main grid, the energy storage unit and the charging station, where the combined robust and stochastic model predictive control approach with different granular models is developed to solve this problem and effectively deal with these uncertainties. In the second stage, the charging station allocation model is designed to determine the charging power for every EV, which takes into account the max-min fairness of the charging power. Numerical cases in the presence of uncertainties are studied to evaluate the proposed dispatch framework and the solving approaches. The simulation results show its superiority in both computational efficiency and operating cost. • A novel two-stage optimal framework for a microgrid including charging station is proposed. • The proposed considers uncertainties introduced by EV, PV, WT, and load. • RS-MPC with different granular models is adopted to deal with uncertainties. • Power allocation model of EV charging station considers the max-min fairness of charging power. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) method shows superiority in enhancing system performance. But its actual performance in operation is hampered by forecast uncertainties. Different methods have been proposed to mitigate the impacts of these uncertainties, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC). However, the year-round performance of these methods has rarely been investigated, and little is known about their relative performance, particularly in utilization of building flexibility-resources. In this study, the year-round performance of conventional MPC (CMPC), SHMPC and SMPC strategies when used to optimize utilization of flexibility-resources in buildings with PV power generation and battery storage systems are compared using real-time optimal control (RTC) as the benchmark, and their enhancement is ultimately proposed. Forecast uncertainties are quantified based on real meteorological data. Different optimization horizon start times and shrinking intervals are tested. Results show that there is no one control strategy which is superior to the other strategies under all operating conditions. The SMPC strategy has a higher probability of achieving daily cost saving than CMPC and SHMPC strategies, but still has higher daily costs than the RTC strategy in the winter. Hybrid control strategies with proper control schemes implemented under different operating conditions are therefore recommended. • Year-round performance of alternative MPC strategies is investigated and compared. • Their enhancement for optimal use of building flexibility-resources is proposed. • Forecast uncertainties are quantified based on real meteorological data. • No one MPC strategy is superior to other MPC strategies under all conditions. • Hybrid control method is recommended for optimal building flexibility-resource use. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization strategy based on redundant control variables under the zone control framework and can improve the economic optimization effect. First, we briefly reviewed the general concepts of redundant control and zone control. Then we analyzed the limitations of this optimization strategy, the reason is the impact of the change of redundant control variables on the system state variables. This will cause the switching of the controller on the boundary of the zone control target, which will affect the economic benefits of the system. Based on the above problems, we have designed two different optimization strategies to avoid this switching action. The first method is to temporarily disable the switching conditions during the process transition to avoid triggering the switching action. The second method is to dynamically change the rate of change of redundant control variables so that the system can not trigger the switching conditions. Finally, we verified the feasibility of the two improved optimization strategies we proposed through numerical simulation, and achieved greater economic benefits compared with previous studies. • The factors were analyzed which affect energy consumption in the transition process. • A smooth transition strategy was designed in the zone control framework. • The transition process was used to abstract as a generally overlooked stage in optimization. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC), as an important part of the process of realizing self driving, is very consistent with the current concept of vehicle production vigorously advocating safety, energy saving and environmental protection. However, how to integrate energy optimization into the design of the controller and realize the trade-off between the tracking performance and economy for the host vehicle is a very important problem. Therefore, this paper proposes a strategy for energy-optimal adaptive cruise control (EACC) for pure electric vehicles based on model predictive control (MPC) algorithm. By the way to introduce motor energy consumption as the economic evaluation index, the optimized torque can make the host vehicle track the preceding vehicle more economically with the constraints related to safety, comfort and tracking satisfied. At the same time, based on the nonlinear autoregressive with external input (NARX), the acceleration prediction of the preceding vehicle is selected as the disturbance for MPC, which can effectively improve the speed tracking performance for the host vehicle in the situation of driving at low speed and braking at high speed. In addition, the variable weight strategy is used to balance tracking performance and economy, which improves the adaptability of EACC vehicles under different working conditions like high speed and low speed. The results under the cycle condition of NEDC and WVUCITY show that the proposed EACC based on MPC+ (introducing leading vehicle speed prediction and motor energy consumption) shows a good energy saving compared with the other strategies based on PID, LQR and MPC. Compared with the classic Eco-ACC strategy based on dynamic programming, the proposed MPC + control strategy has the advantage of realizing more gentle torque output, thus the peak of the battery discharge current is relatively less than that of the controller based on DP, which is beneficial for prolong the battery life. The hardware-in-loop test evaluates the response characteristics of the controller and validates the effectiveness of the proposed MPC + strategy under real-time conditions. [Display omitted] • With the motor energy consumption as the economic index to reduce energy consumption. • With the predicted leading vehicle speed obtained by NARX as the disturbance of MPC. • MPC with variable weight can effectively balance tracking performance and economy. • EACC vehicles' adaptability of different working conditions is improved. • Multi-objective of safety, comfort, economy and tracking performance is balanced. [ABSTRACT FROM AUTHOR]

The present study analyses the performance of a heating system controlled by a model predictive control strategy, where the impact of different combinations of thermal energy storage tank volumes and installed PV power capacities are analysed. The novelty of the paper lies in studying both economic and energy impacts of each equipment combination in different locations, buildings, and indoor occupancy schedules. The payback period, the reduction of the electricity grid consumption, and the behaviour of the coefficient of performance of the heat pump are studied in detail for all cases. Results point out that from an economic point of view, to invest in a thermal energy storage tank provides shorter payback periods in comparison to scenarios with PV panels, due to the high price of the solar elements. However, the energy performance analysis highlighted that the use of PV panels contributes to achieve up to 34%, 54%, and 90% of reduction of the electricity grid consumption in Helsinki, Strasbourg, and Athens, respectively. Finally, it is worth noting that the increase of the thermal energy storage volume improves the coefficient of performance of the heat pump. [Display omitted] • An analysis of the MPC behaviour at the energetic and economic level is presented. • The MPC strategy is implemented in different occupancy schedules and locations. • The aim of the MPC strategy is to reduce the final energy cost in all scenarios. • Different combinations of PV panels and TES tanks were assessed. • Combining TES, PV, and MPC contributed to enhancing the heat pump performance. [ABSTRACT FROM AUTHOR]

District heating systems may support an increased penetration of stochastic renewable energy technologies and a reduction in centralized combined heat and power plants to reduce carbon dioxide emissions. Ultra low temperature district heating minimizes transport heat losses while enabling the utilization of low-grade surplus heat. Local heat booster substations can heat water to useable temperatures using a heat pump and a hot water tank for storage and flexible operation. This paper proposes a hybrid model predictive control strategy in which an existing heat booster substation is modelled and its charging schedule optimized in real-time over a 24-h forecasted prediction horizon. This enables load shifting whereby scheduling of the heat pump minimizes operation costs. The realisation of energy flexibility can support greater utilization of renewable energy sources and surplus heat in energy supply systems to reduce primary energy consumption. The linear hybrid model predictive controller was successfully implemented in a real 22-flat multifamily building in Copenhagen to verify the control strategy. A comparison of the proposed model predictive control scheduling to the standard rule-based control showed average savings of 23 % on the electricity costs. • Modelling of a heat booster substation (HBS) for a cost-effective optimal operation. • Field test of a hybrid Economic MPC (EMPC) in the HBS with real-time feed-back. • Average saving of 23 % by comparing the proposed hybrid EMPC to a rule-based control. • Load shifting of EMPC for HBS enabling renewables integration in ULTDH. [ABSTRACT FROM AUTHOR]

Abstract: Considerable amounts of energy are consumed in supermarket refrigeration systems worldwide. Due to the thermal capacity of refrigerated goods and the rather simplistic control most commonly applied, there is a potential for distributing the system load over time in a more cost-optimal way. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial for the system itself, while crucial services can be delivered to a future flexible and intelligent power grid (Smart Grid). Furthermore, we discuss a novel incorporation of probabilistic constraints and Second Order Cone Programming (SOCP) with economic MPC. A Finite Impulse Response (FIR) formulation of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller. [Copyright &y& Elsevier]

Abstract: There are various governmental policies aimed at reducing the dependence on fossil fuels for space heating and the reduction in its associated emission of greenhouse gases. DHNs (District heating networks) could provide an efficient method for house and space heating by utilizing residual industrial waste heat. In such systems, heat is produced and/or thermally upgraded in a central plant and then distributed to the end users through a pipeline network. The control strategies of these networks are rather difficult thanks to the non-linearity of the system and the strong interconnection between the controlled variables. That is why a NMPC (non-linear model predictive controller) could be applied to be able to fulfill the heat demand of the consumers. The main objective of this paper is to propose a tuning method for the applied NMPC to fulfill the control goal as soon as possible. The performance of the controller is characterized by an economic cost function based on pre-defined operation ranges. A methodology from the field of experiment design is applied to tune the model predictive controller to reach the best performance. The efficiency of the proposed methodology is proven throughout a case study of a simulated NMPC controlled DHN. [Copyright &y& Elsevier]

The day-ahead operational schedule of the integrated electricity and heat system may be suboptimal due to prediction errors of renewables and loads. This paper proposes a real-time optimal operation scheme for the integrated electricity and heat system considering reserve provision from large scale heat pumps, which utilizes model predictive control and different operating reserves to gradually balance forecast errors of renewables and loads in the co-optimization of reserve deployment and heat regulation. The real-time operation is divided into two stages including real-time pre-scheduling and real-time balancing. A two-stage model predictive control approach is proposed to deploy following reserve and regulating reserve for real-time pre-scheduling and real-time balancing, respectively. The following reserve in the real-time pre-scheduling is used to deal with the day-ahead forecast errors, while the regulating reserve in the real-time balancing is to handle real-time forecast errors. In addition, a detailed reserve provision model of large-scale heat pumps is built. The case studies are conducted on a 6-bus integrated electricity and heat system. The simulation results show that the proposed two-stage approach uses following and regulating reserves from large-scale heat pumps to further reduce operational cost, wind power curtailment, and load shedding. The MPC approach can obtain a feasible solution closer to the ideal solution. • Real-time optimal operation of IEHS with real-time prescheduling and balancing. • Large-scale heat pumps providing following and regulating reserves. • Reserve deployment and heat regulation are co-optimized. [ABSTRACT FROM AUTHOR]

Scheduling strategies are crucial for combined cooling, heating, and power-containing renewable energy source (RES-CCHP) systems to realize cost-effective operation. However, the prediction error generated by renewable energy sources (RES) and load leads to mismatch between the scheduling plan and actual demand. To address this problem, an interval scheduling method is proposed for the RES-CCHP system based on the model predictive control method. A genetic algorithm to optimize a back propagation (GA-BP) neural network combined with historical and weather data is used to predict the multistep RES and load to improve prediction accuracy. An interval optimization model (cost optimization) is then constructed based on RES and load prediction data to achieve an output consistent with the actual situation. Finally, feedback correction is used to compensate the RES and load prediction data online. The prediction error and interval are reduced, thereby improving the performance of the interval optimization method in achieving an optimal solution. This research is applicable to living spaces such as residential areas, schools, and hospitals. In this study, a hospital was considered as a case study to verify the effectiveness of the proposed method. A comparison of the results of the proposed method with those obtained in the following electrical load and following thermal load modes in a typical summer day revealed a cost reduction of 16.21% and 16.92%, respectively. The proposed method can improve the prediction accuracy of the RES and load of the RES-CCHP system and help decision makers to develop reasonable operation schemes and maximize profits. • Interval scheduling method for RES combined cooling, heating, and power systems based on model predictive control. • The weather data and genetic algorithm–BP neural network method accurately predict multistep source and load. • Interval optimization used upon considering source and load error of prediction caused by randomness. • Feedback correction to compensate prediction results and reduce optimization interval. [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]

Thermal energy storage (TES) is a key technology for enabling increased utilization of industrial waste heat in district heating. The ability of TES to equalize offsets in demand and supply depends strongly on the sizing, control and integration in a heating plant. We consider the problem of sizing TES in heating plants utilizing a varying waste-heat source. To this end, we propose a combined dynamic simulation and model predictive control approach that accounts for the dynamics and optimal control of the heating plant with TES. A case study has been carried out on a district-heating plant located in Norway, with 90% of its annual heat production being heat recovered from the off-gas from a ferrosilicon plant. We evaluate the effective peak-heating reduction with different TES sizes and the energy-to-heat-flow-ratio for the TES discharging periods as performance metrics. For the case study, our results suggest that a modest TES tank volume of 1500 m3 is sufficient to achieve a half-year peak-heating reduction of 12% and comparable performance with larger volumes. The proposed methodology constitutes a numerically tractable means of incorporating the impact of model predictive control on the sizing of TES for heating plants with time-varying waste-heat supply and demand. • Combined dynamic simulations and model predictive control for TES sizing. • The methodology handles varying waste-heat availability and demand. • Model predictive control enables assessing maximum achievable performance with TES. • Energy-to-heat-flow ratio is introduced as performance metric of TES. • A real case study on a Norwegian district-heating plant recovering fluctuating waste heat. [ABSTRACT FROM AUTHOR]

District heating networks transport thermal energy from one or more sources to a plurality of consumers. Lowering the operating temperatures of district heating networks is a key research topic to reduce energy losses and unlock the potential of low-temperature heat sources, such as waste heat. With an increasing share of uncontrolled heat sources in district heating networks, control strategies to coordinate energy supply and network operation become more important. This paper focuses on the modeling, control, and optimization of a low-temperature district heating network, presenting a case study with a high share of waste heat from high-performance computers. The network consists of heat pumps with temperature-dependent characteristics. In this paper, quadratic correlations are used to model temperature characteristics. Thus, a mixed-integer quadratically-constrained program is presented that optimizes the operation of heat pumps in combination with thermal energy storages and the operating temperatures of a pipe network. The network operation is optimized for three sample days. The presented optimization model uses the flexibility of the thermal energy storages and thermal inertia of the network by controlling its flow and return temperatures. The results show savings of electrical energy consumption of 1.55%–5.49%, depending on heat and cool demand. • MIQCP optimization model of low-temperature district heating is presented. • MIQCP formulation allows to regard temperature characteristics. • Good fit between simulation and optimization. • MPC with simulation and optimization shows improved operation. [ABSTRACT FROM AUTHOR]

Intelligent control schemes are essential for the implementation of smart energy systems, where district heating and electric networks are tightly interconnected. The increasing complexity of such networked infrastructure has resulted in the need to test and assess control algorithms before field deployment. This work presents a method to assess advanced control schemes for thermal-electric appliances with explicit consideration of coupled heat and power networks. It is based on closed loop simulation of high-fidelity physical system models, using dynamic thermal-hydraulic district heating and electric distribution network models, and low-fidelity time-discrete advanced control models. Co-simulation is used to perform coupled simulations of the different involved domains and tools. A test case is presented where a model predictive control scheme for grid friendly operation of domestic hot water electric booster heaters is implemented in a low-temperature district heating and low-voltage electric distribution network. Test case results show that the control is able to reduce peaks in district heating and electric networks compared to a simple reference controller. A comparison between using perfect and naive forecasts shows that control performance highly depends on the availability of accurate predictions. The results underline the versatility of the method to assess different control schemes in integrated networks. • Closed-loop simulation of dynamic heat and power network models with advanced control. • Introduction of grid-friendly model predictive control for electric booster heaters. • Example heat and power networks with multiple distributed electric booster heaters. • Performance comparison of perfect and naive forecasting. • Results show reduced peaks in heat and power network and high self-consumption of PV. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid construction vehicles are attractive for future fuel cell applications. Model predictive control as an energy management strategy has been applied to various hybrid electric vehicles. In this paper, a hierarchical model predictive control-based energy management strategy for fuel cell hybrid construction vehicles is explored. The hierarchical model predictive control framework consists of economic and control levels. The economic level considers economic costs, including of hydrogen consumption and components use cost. Real-time optimization is implemented at the economic level to identify the optimal reference trajectory. The control layer is a model predictive control that controls the system to follow the reference trajectory. A prediction methodology-based on wavelet transformation and Levenberg-Marquardt optimised neural network is proposed for the complex load prediction of fuel cell hybrid construction vehicle. The simulations performed with cyclic loading show that the accuracy of the proposed prediction methodology is satisfactory, and demonstrate the superiority of the proposed energy management strategy. The proposed hierarchical model predictive control-based energy management strategy considering economy and controllability is important for commercial application in hybrid electric vehicles. • A hierarchical predictive control based energy management strategy is presented. • Wavelet transformation and neural network-based prediction method are presented. • Economic cost optimization is considered in the hierarchical predictive control. • A system-level model of fuel cell hybrid construction vehicle is established. [ABSTRACT FROM AUTHOR]

Two thirds of the total buildings final energy are used for heating purposes, specifically during the peak period. There is a mismatch between the power generation from renewable energy resources and demand. Thermal energy storage systems have been used not only to fill the gap between supply and demand, but also to take advantage of the time-of-use tariff structures. Nowadays, the application of smart controls to regulate heating systems is growing in popularity. Within this context, a model predictive control strategy to improve the operation of a space-heating system coupled with renewable resources is proposed. This model uses a dynamic approach based on forecasting all energy inputs into the system over a given period of time in advance, before taking any operational decisions. The model predictive control strategy was applied to minimize annual energy costs of the heating system of a detached house located in Puigverd de Lleida (Spain) and, based on a heat pump coupled to a thermal energy storage unit and photovoltaic panels. The results show the potential of the model predictive control with a 24-h horizon. In that case, energy cost savings of 58% can be achieved, compared to the same heating system without smart control. • MPC is applied in a heating system with TES, PV panels, and electricity grid supply. • The impact of different MPC settings in the heating system was studied. • An inner control algorithm was created to obtain more accurate results. • The MPC performance was assessed against the weather variability. • Heating energy cost savings were obtained by using MPC. [ABSTRACT FROM AUTHOR]

Renewable energy based multi-energy supply microgrids not only can cover different types of demands (such as, electricity/heat/gas), but also can interconnect with different utility grid networks (electricity/heat/gas). When there are large numbers of grid-connected microgrids, how to operate these multiple microgrids in real-time is a problem. In this paper, day-ahead stochastic optimization scheduling and real-time sliding window model predictive control are used to control the operation of microgrids. In order to consider the influence of future prediction on the current optimal decision results, different prediction methods are adopted to predict the load demands and renewable energy output. For example, online learning Markov chain prediction, and support vector machine are used to predict the future values. As for comparison, robust prediction and bilevel optimization are adopted to describe the future prediction uncertainty. The real-time operation of microgrids aims to follow the day-ahead exchanged energy with utility grids, which can minimize the impact of the microgrid on the utility grids. The supply network is an IEEE30 + gas20+heat14 network. The results show that: 1) when the sliding window number is smaller, the total operation cost is larger, but the calculation time is smaller, the trade-off between sliding window numbers and calculation time should be considered; 2) the accuracy of the prediction impacts the 2-norm error of the operation cost, when we decrease by "1" unit of 2-norm prediction error of the whole system, the 2-norm operation cost will decrease by "0.15" unit; 3) from the view of the post-event analysis (total operation cost), for the Markov chain prediction method, the relative error is about 0.32%, is better than the support vector machine method; 4) in the robust cases, the larger the conservative value, the higher the stored hydrogen energy. At last, the results of real-time sliding window model predictive control problem are influenced by the future prediction methods and the window numbers. • Two-stage coordinated scheduling method to control grid-connected microgrids. • Considering the associated power flow in electric/heat/gas utility grid networks. • k-means is used to reduce the scenario numbers in day-ahead prediction. • Different prediction methods are compared in real-time model predictive control. • Robust method and bilevel optimization to tackle future uncertainty. [ABSTRACT FROM AUTHOR]

This paper develops a model predictive multi-objective control framework for HEVs in car-following scenarios to investigate the interplay between fuel economy, vehicle exhaust emissions, and inter-vehicle safety. Specifically, an MPC-based controller is developed to optimize the vehicle speed and engine torque for better fuel economy and fewer exhaust emissions while ensuring inter-vehicle safety. The engine-out emission model and its impact on energy management are considered in the optimization. The proposed controller is evaluated at different driving conditions, such as urban driving and highway driving. The proposed controller is compared with conventional controllers used in ADVISOR. The comparison results demonstrate that the proposed controller can reduce fuel consumption by 10.49%, CO by 48.02%, HC by 55.38%, and NO x by 22.79% in the UDDS driving cycle. • An online multi-objective optimization framework for HEV in car-following scenarios. • The interplay between fuel economy and vehicle exhaust emissions is investigated. • The designed controller is evaluated at different traffic conditions. • Comparisons with conventional control methods are made. • Improved performance of fuel economy and emission reduction is achieved. [ABSTRACT FROM AUTHOR]

Biomass gasification is an environment-friendly energy conversion process that utilizes bio-waste materials to produce combustible gases. In recent literature, machine learning-based techniques are used to model biomass gasification process. Even though these methods are reported for being viable, developed models' time-independent structure fundamentally limited their prediction capabilities. Furthermore, control of biomass gasification is not studied in the literature despite its importance for industrial applications. We conducted this study in two parts. Firstly, we developed a time-dependent identification model to describe and predict outcomes of biomass gasification using non-linear autoregressive with exogenous neural networks (NARXNN) and experimentally collected data set. The developed model showed exceptional success by reaching R 2 > 0.98 for all output variables. Secondly, we designed a model predictive controller (MPC) in order to control a certain output variable at the desired state. For this purpose, we created polynomial regression models and online optimization routines. Moreover, the designed controller is challenged in practical scenarios such as maximum hydrogen production to test its usability in practical applications. MPC showed satisfactory performance for all scenarios and also showed high compliance with the experimental data which further strengthened its practical usability potential. • First study focuses on the control of biomass gasification process. • Identification is performed by NARXNN with an experimentally collected data set. • NARXNN model predicted all output variables with R 2 > 0.98. • MPC is used to control outputs of the biomass gasification. • MPC applications showed great compliance with the experimental data. [ABSTRACT FROM AUTHOR]

The rise of the intelligent transportation system (ITS) brings golden opportunity to accelerate the development of environment-friendly smart mobility eco-system. The intelligent control of connected autonomous vehicles (CAV) platoon with V2V communication as the core technology exhibits superior energy-saving potential. However, there still exist plentiful technologies of the emerging vehicle platoon need to be improved. Hence, this paper describes a predictive optimization strategy as ecological cooperative adaptive cruise control (eCACC) based on nonlinear model predictive control (NMPC) to minimize the energy consumption of an electrified CAV platoon considering V2V topological communication structure of leader predecessor follower. The cost function for NMPC includes the following velocity, range deviation and energy consumption. Through the simulation analysis under various drive cycles, the advantage of the proposed scheme emerges that the platoon consisted of three vehicles possesses the nice string stability, excellent following performance and significant energy-saving potential at same time. Moreover, the acceleration of the following vehicles is in a small range, improving the drive comfort. By the comparison with the existed Eco ACC controller, the simulation results demonstrate the proposed controller owns better following performance and energy-saving behavior of 16.1%, 6.2% and 11.7% under full UDDS, HWFET and NEDC drive cycle, respectively. • The eCACC improves car-following and energy performance with string stability. • NMPC is utilized to optimize In-Wheel-Motor of the vehicles in platoon. • The strategy is verified both in the simulation level and real-time implementation. • The strategy exhibits better comprehensive performance than traditional ACC. [ABSTRACT FROM AUTHOR]