In this paper, a real-time capable reference governor superordinate model predictive controller (RG-MPC) is developed for fuel cell (FC) control suitable for automotive application. The RG-MPC provides reference trajectories for the subordinate proportional-integral (PI) controllers, which act directly on the FC system. Antiwindup and decoupling schemes, which are common problems in multivariable PI control, are unnecessary, given that the RG-MPC can inherently consider constraints and multivariable systems. The PI dynamics are incorporated into the prediction model used for control, ensuring the feasibility of the provided references for the PI controllers. The successive linearization technique is used in the RG-MPC to cope with the model’s nonlinear nature in real-time. The concept has been illustrated in a simulation scenario featuring efficient and safe power control of an FC stack in automotive application using real driving data obtained from an in-house-built FC vehicle. This w... [more]

Faced with the increasingly serious energy crisis and environmental pollution problems, traditional internal combustion engine vehicles are receiving more and more resistance, which has rapidly promoted the development of new energy electric vehicles. Permanent magnet synchronous motors are widely used in new energy electric vehicles and in other fields because of their simple structure, light weight, small size, and high power density. With the continuous advancement of production technology, the requirements of accuracy, rapidity, and stability in permanent magnet synchronous motor systems have gradually increased. Among many advanced control technologies, this paper proposes an optimized model predictive torque control strategy based on voltage vector expansion. This strategy involves the construction of a reference stator flux linkage vector based on the analytical relationship between electromagnetic torque, reference stator flux linkage amplitude, and rotor flux linkage and the t... [more]

A gas-lifted system in a mature oil well can experience casing-heading instability, which reduces the mean oil production and it is not healthy for the downstream equipment. This instability was removed by implementing a terminal equality-constrained nonlinear model predictive control (NMPC) having input targets with control zones in the system. The input-dependent stability behaviour of the gas-lifted system was visualised through the bifurcation diagram and the step responses of the linearised model at various operating points. The controller was then presented. Then, the close-loop feasibility, as well as the convergence, were discussed. The controller stabilised the undisturbed gas-lifted system, improving production by 5.63% compared to the open-loop operation when the system was in casing-heading instability. For the two input case, the steady-state production, aided by the high-input target, reached 12.25 kg/s, which was far more than 9.57 kg/s for the one input case. This contr... [more]

An optimal dispatching strategy for a multi-source complementary power generation system taking source−load uncertainty into account is proposed, in order to address the effects of large-scale intermittent renewable energy consumption and power load instability on power grid dispatching. The uncertainty problem is first converted into common situations for study, such as load power forecasting and solar and wind power. The backward scenario reduction and Latin hypercube sampling techniques are used to create these common situations. Based on this, a multi-timescale coordinated optimum scheduling control method for a multi-source complementary power generation system taking the demand response into account is presented, and the optimal operation of a wind−PV−thermal-pumped storage hybrid system is examined. The time-of-use power price optimizes the electrical load in the day-ahead pricing mode, and the two types of demand response loads are selected in the day-ahead scheduling. Second,... [more]

Weather conditions have significant impacts on the solar concentration processes of the heliostat fields in solar tower power plants. The cloud shadow movements may cause varying solar irradiance levels received by each heliostat. Hence, fixed aiming strategies may not be able to guarantee the solar concentrating performance. Dynamic aiming strategies are able to optimize the aiming strategy based on real-time shadowing conditions and short-term forecast, and, therefore, provide much more robust solar concentration performance compared to fixed strategies. In this work, a model predictive control approach for s heliostat field power regulatory aiming strategy was proposed to regulate the total concentrated solar flux on the central receiver. The model predictive control method obtains the aiming strategy, leveraging real-time and forecast shadowing conditions based on the solar concentration model of the heliostat field. The allowable flux density of the receiver and the aiming angle a... [more]

An electric multiple unit (EMU) high-speed train is the dynamic load that degrades the power quality in an electric railway system. Therefore, a power quality improvement system using an active power filter (APF) must be considered. Due to the oscillating load current in the dynamic load condition, a fast and accurate harmonic current-tracking performance is required. As such, this paper proposes the design of a model predictive control (MPC) since the minimization of cost function in the MPC process can suitably determine APF switching states. The design technique of MPC is based on the APF mathematical model. This controller was designed to compensate the time delay in the digital control. Moreover, the synchronous detection (SD) method applied the reference current calculations, as shown in this paper. To verify the proposed MPC, the overall control of APF was implemented on a eZdsp F28335 board by using the hardware-in-the-loop technique. The testing results indicated that the prop... [more]

The development of strategies for distribution network management is an essential element for increasing network performance and reducing the upgrade of physical assets. This paper analyzes a multi-timescale framework to control the voltage of distribution grids characterized by a high penetration of renewables. The multi-timescale solution is based on three levels that coordinate Distributed Generation (DG) and Energy Storage Systems (ESSs), but differs in terms of the timescales and objectives of the control levels. Realistic load and photovoltaic generation profiles were created for cloudy and clean sky conditions to evaluate the performance features of the multi-timescale framework. The proposed solution was also compared with different frameworks featuring two of the three levels, to highlight the contribution of the combination of the three levels in achieving the best performance.

This work introduces a new method for computing the angular position of the voltage of the grid—based on a finite set of angles—in the condition of failures in the distribution systems, as symmetrical and asymmetric voltage sags, unbalance, harmonic distortions, and frequency changes. This method is inspired in the model predictive control finite control set principles. In this way, the proposal employs the One-Cycle Fourier filter (OCF) to estimate the positive sequence of the voltage vector into the stationary αβ-frame. The positive sequence voltages extracted from this filter is then handled by an algorithm that is implemented by a finite position set (FPS) for estimating the phase angle. In this way, the minimized cost function chooses the optimal angular position while using the predicted behavior of the grid voltage vector elements in dq frame. The structure, called One-Cycle Fourier Finite position Set Phase Locked Loop (OCF-FS-PLL), here is a composition of the OCF and the FPS.... [more]

Wave energy is nowadays one of the most promising renewable energy sources; however, wave energy technology has not reached the fully-commercial stage, yet. One key aspect to achieve this goal is to identify an effective control strategy for each selected Wave Energy Converter (WEC), in order to extract the maximum energy from the waves, while respecting the physical constraints of the device. Model Predictive Control (MPC) can inherently satisfy these requirements. Generally, MPC is formulated as a quadratic programming problem with linear constraints (e.g., on position, speed and Power Take-Off (PTO) force). Since, in the most general case, this control technique requires bidirectional power flow between the PTO system and the grid, it has similar characteristics as reactive control. This means that, under some operating conditions, the energy losses may be equivalent, or even larger, than the energy yielded. As many WECs are designed to only allow unidirectional power flow, it is ne... [more]

This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of t... [more]

Inductive power transfer (IPT) systems have become more and more popular recently. To improve transient responses and load disturbance responses, this paper proposes a predictive controller design for a three-winding inductive power transfer (IPT) system. First, a three-winding IPT is presented. Next, a predictive controller is designed based on augmented variables and a performance index. Finally, a digital signal processor, TMS 320F2808, made by Texas Instrument, is used to execute the predictive control algorithms and to control the switching states of the power devices. An IPT system, with DC 220 V input, DC 130 V output, and a rated power of 2 kW, is implemented. A buck converter is used to provide an adjustable output voltage and output current to charge a battery set. Experimental results show that the proposed predictive controllers of the IPT system have better performance than proportional-integral (PI) controllers, including faster transient responses and better load disturb... [more]

Renewable generation and energy storage systems are technologies which evoke the future energy paradigm. While these technologies have reached their technological maturity, the way they are integrated and operated in the future smart grids still presents several challenges. Microgrids appear as a key technology to pave the path towards the integration and optimized operation in smart grids. However, the optimization of microgrids considered as a set of subsystems introduces a high degree of complexity in the associated control problem. Model Predictive Control (MPC) is a control methodology which has been satisfactorily applied to solve complex control problems in the industry and also currently it is widely researched and adopted in the research community. This paper reviews the application of MPC to microgrids from the point of view of their main functionalities, describing the design methodology and the main current advances. Finally, challenges and future perspectives of MPC and it... [more]

The integration of a variety of heterogeneous energy sources and different energy storage systems has led to complex infrastructures and made apparent the urgent need for efficient energy control and management. This work presents a non-linear model predictive controller (NMPC) that aims to coordinate the operation of interconnected multi-node microgrids with energy storage capabilities. This control strategy creates a superstructure of a smart-grid consisting of distributed interconnected microgrids, and has the ability to distribute energy among a pool of energy storage means in an optimal way, formulating a virtual central energy storage platform. The goal of this work is the optimal exploitation of energy produced and stored in multi-node microgrids, and the reduction of auxiliary energy sources. A small-scale multi-node microgrid was used as a basis for the mathematical modelling and real data were used for the model validation. A number of operation scenarios under different weat... [more]

Electrification of transportation is an effective way to tackle climate change. Public transportation, such as electric buses, operate on predetermined routes and offer quiet operation, zero local emissions and high energy efficiency. However, the batteries of these buses are expensive and wear out in use. The battery ageing is expedited by fast charging and power spikes during operation. The contribution of this paper is the reduction of the power spikes and thus a prolonged battery lifetime. A novel hybrid energy storage system for electric buses is proposed by introducing a flywheel in addition to the existing battery. A simulation model of the hybrid energy storage system is presented, including a battery ageing model to measure the battery lifetime. The bus was simulated during its daily driving operation on different routes with different energy management strategies and flywheel configurations. These different flywheels as well as the driving cycle had a significant impact on th... [more]

To improve the handling stability of automobiles and reduce the odds of rollover, active or semi-active suspension systems are usually used to control the roll of a vehicle. However, these kinds of control systems often take a zero-roll-angle as the control target and have a limited effect on improving the performance of the vehicle when turning. Tilt control, which actively controls the vehicle to tilt inward during a curve, greatly benefits the comprehensive performance of a vehicle when it is cornering. After analyzing the advantages and disadvantages of the tilt control strategies for narrow commuter vehicles by combining the structure and dynamic characteristics of automobiles, a direct tilt control (DTC) strategy was determined to be more suitable for automobiles. A model predictive controller for the DTC strategy was designed based on an active suspension. This allowed the reverse tilt to cause the moment generated by gravity to offset that generated by the centrifugal force, th... [more]

The integration of multi-energy systems to meet the energy demand of buildings represents one of the most promising solutions for improving the energy performance of the sector. The energy flexibility provided by the building is paramount to allowing optimal management of the different available resources. The objective of this work is to highlight the effectiveness of exploiting building energy flexibility provided by thermostatically controlled loads (TCLs) in order to manage multi-energy systems (MES) through model predictive control (MPC), such that energy flexibility can be regarded as an additional energy source in MESs. Considering the growing demand for space cooling, a case study in which the MPC is used to satisfy the cooling demand of a reference building is tested. The multi-energy sources include electricity from the power grid and photovoltaic modules (both of which are used to feed a variable-load heat pump), and a district cooling network. To evaluate the varying contri... [more]

Stability and reliability are of the most important concern for isolated microgrid systems that have no support from the utility grid. Interval predictions are often applied to ensure the system stability of isolated microgrids as they cover more uncertainties and robust control can be achieved based on more sufficient information. In this paper, we propose a probabilistic microgrid energy exchange method based on the Model Predictive Control (MPC) approach to make better use of the prediction intervals so that the system stability and cost efficiency of isolated microgrids are improved simultaneously. Appropriate scenarios are selected from the predictions according to the evaluation of future trends and system capacity. In the meantime, a two-stage adaptive reserve strategy is adopted to further utilize the potential of interval predictions and maintain the system security adaptively. Reserves are determined at the optimization stage to prepare some extra capacity for the fluctuation... [more]

Power converters have turned into a critical and every-day solution for electric power systems. In fact, the incorporation of renewable energies has led towards the constant improvement of power converter topologies and their controls. In this context, over the last 10 years, model predictive control (MPC) is positioned as one the most studied and promising alternatives for power converter control. In voltage source inverters (VSI), MPC has only been applied in the inner current control loop, accelerating and improving its dynamic response, but as mentioned, has been limited only to the current control loop. The fastest of the MPC techniques is the Deadbeat (DB) control, and in this paper, it is proposed to employ DB control on the entire system, therefore accelerating the time response not only for the current loops, but also for voltage loops. At the same time, this avoids overshoots and overpower in order to protect the power converter, leading to the fastest dynamic response accord... [more]

This paper presents the four-quadrant operation modes of bidirectional chargers for electric vehicles (EVs) framed in smart car parks. A cascaded model predictive control (MPC) scheme for the bidirectional two-stage off-board chargers is proposed. The controller is constructed in two stages. The model predictive direct power control for the grid side is applied to track the active/reactive power references. The model predictive direct current control is proposed to achieve constant current charging/discharging for the EV load side. With this MPC strategy, EV chargers are able to transmit the active and reactive powers between the EV batteries and the power grid. Apart from exchanging the active power, the vehicle-for-grid (V4G) mode is proposed, where the chargers are used to deliver the reactive power to support the grid, simultaneously combined with grid-to-vehicle or vehicle-to-grid operation modes. In the V4G mode, the EV battery functions as the static var compensator. According t... [more]

Innovative heating networks with a hybrid generation park can make an important contribution to the energy turnaround. By integrating heat from several heat generators and a high proportion of different renewable energies, they also have a high degree of flexibility. Optimizing the operation of such systems is a complex task due to the diversity of producers, the use of storage systems with stratified charging and continuous changes in system properties. Besides, it is necessary to consider conflicting economic and ecological targets. Operational optimization of district heating systems using nonlinear models is underrepresented in practice and science. Considering ecological and economic targets, the current work focuses on developing a procedure for an operational optimization, which ensures a continuous optimal operation of the heat and power generators of a local heating network. The approach presented uses machine learning methods, including Gaussian process regressions for a repe... [more]

Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and imple... [more]

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates).

The growing demand for various types of paper highlights the importance of optimizing the kraft pulping process to achieve desired paper properties. This work proposes a novel multiscale model to optimize the kraft pulping process and obtain desired paper properties. The model combines mass and energy balance equations with a layered kinetic Monte Carlo (kMC) algorithm to predict the degradation of wood chips, the depolymerization of cellulose, and the spatio-temporal evolution of the Kappa number and cellulose degree of polymerization (DP). A surrogate LSTM-ANN model is trained on data generated from the multiscale model under different operating conditions, dealing with both time-varying and time-invariant inputs, and an LSTM-ANN-based model predictive controller is designed to achieve desired set-point values of the Kappa number and cellulose DP while considering process constraints. The results show that the LSTM-ANN-based controller is able to drive the process to desired set-poin... [more]

Model Predictive Control (MPC) predictive’s nature makes it attractive for controlling high-capacity structures such as thermally activated building systems (TABS). Using weather predictions in the order of days, the system is able to react in advance to changes in the building heating and cooling needs. However, this prediction horizon window may be sub-optimal when hybrid geothermal systems are used, since the ground dynamics are in the order of months and even years. This paper proposes a methodology that includes a shadow-cost in the objective function to take into account the long-term effects that appear in the borefield. The shadow-cost is computed for a given long-term horizon that is discretized over time using predictions of the building heating and cooling needs. The methodology is applied to a case with only heating and active regeneration of the ground thermal balance. Results show that the formulation with the shadow cost is able to optimally use the active regeneration,... [more]

With the increasing penetration of distributed photovoltaics (PVs) in active distribution networks (ADNs), the risk of voltage violations caused by PV uncertainties is significantly exacerbated. Since the conventional voltage regulation strategy is limited by its discrete devices and delay, ADN operators allow PVs to participate in voltage optimization by controlling their power outputs and cooperating with traditional regulation devices. This paper proposes a decoupling rolling multi-period reactive power and voltage optimization strategy considering the strong time coupling between different devices. The mixed-integer voltage optimization model is first decomposed into a long-period master problem for on-load tap changer (OLTC) and multiple short-period subproblems for PV power by Benders decomposition algorithm. Then, based on the high-precision PV and load forecasts, the model predictive control (MPC) method is utilized to modify the independent subproblems into a series of subprob... [more]