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]

This paper presents a methodology for enhancing community resilience through optimal renewable resource allocation and load scheduling in order to minimize unserved load and thermal discomfort. The proposed control architecture distributes the computational effort and is easier to be scaled up than traditional centralized control. The decentralized control architecture consists of two layers: The community operator layer (COL) allocates the limited amount of renewable energy resource according to the power flexibility of each building. The building agent layer (BAL) addresses the optimal load scheduling problem for each building with the allowable load determined by the COL. Both layers are formulated as a model predictive control (MPC) based optimization. Simulation scenarios are designed to compare different combinations of building weighting methods and objective functions to provide guidance for real-world deployment by community and microgrid operators. The results indicate that t... [more]

Control of AC/DC pulse-width modulation (PWM) power electronic converter, referred to as “AC/DC PWM converter”, is vital to the efficient regulation of power flow between AC and DC parts of a hybrid microgrid. Given the importance of such converters in AC/DC microgrids, this paper investigates the design of fault-tolerant control for AC/DC PWM converters in the presence of microgrid faults. In particular, two novel fault-tolerant schemes based on fuzzy logic and model predictive control are proposed and implemented in an advanced hybrid microgrid benchmark in MATLAB/Simulink environment. The considered hybrid microgrid consists of dynamic loads and distributed energy resources including solar photovoltaic arrays, wind turbines, and battery energy storage systems. The proposed schemes especially target the fault effects due to common power-loss malfunctions in solar photovoltaic arrays in the presence of microgrid uncertainties and disturbances. The effectiveness of proposed fault-toler... [more]

A switching sequence model predictive direct torque control (MPDTC) of IPMSMs for EVs in switch open-circuit fault-tolerant mode is studied in this paper. Instead of selecting one space vector from the possible four space vectors, the proposed MPDTC method selects an optimized switching sequence from two well-designed switching sequences, including three space vectors, according to a new designed cost function of which the control objectives have been transferred to the dq-axes components of the stator flux-linkage under the maximum-torque-per-ampere control. The calculation method of the durations of the adopted space vectors in the optimized switching sequence is studied to realize the stator flux-linkage reference tracking. In addition, the capacitor voltage balance method, by injecting a dc offset to the current of fault phase, is given. Compared with the conventional MPDTC method, the complicated weighting factors designing process is avoided and the electromagnetic torque ripples... [more]

Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on trai... [more]

Due to the large-scale renewable energy connected to the power grid by power electronic converters, the inertia and stability of the power grid is declining. In order to improve the inertia and support the grid recovery, the three-phase converter works as a virtual synchronous generator (VSG) to respond to the frequency and voltage changes of the power grid. This paper proposes a model predictive control for the virtual synchronous generator (MPC-VSG) strategy, which can automatically control the converter output power with the grid frequency and voltage changes. Further consideration of fault-tolerant ability and reliability, the method based on improved voltage vector selection, and reconstructed current is used for MPC-VSG to ensure continuous operation for three-phase converters that have current-sensor faults, and improve the reconstruction precision. The proposed method can respond to the frequency and voltage changes of the power grid and has fault-tolerant ability, which is eas... [more]

In this paper, a polymer electrolyte membrane fuel cell (PEMFC) stack control study is presented. The goal is to track the transient power demand of a real fuel cell (FC) vehicle while ensuring safe and efficient operation. Due to the dynamically changing power demand, fast transients occur in the internal states of the fuel cell (e.g., pressure, humidity, reactant mass) leading to degradation effects (e.g., high/low membrane overpressure, reactants starvation) which are avoided by imposing safety constraints. Efficiency is considered in terms of internal voltage losses minimization as well as minimization of the power of the compressor used to pressurize the cathode. For solving the optimization problem of power demand tracking, adhering to safety constraints, and maximizing efficiency, model predictive control (MPC) has been chosen. Due to the nonlinearity of the FC system, a successive linearization based MPC (SLMPC) is used to control the FC throughout its operating region. Simulat... [more]

As much wind power is integrated into the power grid through power electronic equipment, the use of wind power is increased rapidly. Wind power system makes the power grid lack inertia and damping, thereby reducing power grid stability; in severe cases, it may even be disconnected. virtual synchronous generator (VSG) has been put forward to enhance the anti-disturbance performance of power grid. However, conventional VSG adopts an outer power loop and inner-current loop control. The inner-current loop control needs a pulse width modulation (PWM) module and proportion integration (PI) parameter settings. In order to reduce the parameter settings and simplify control structures, in this study, model predictive control (MPC) is used instead of inner-current loop control. At the same time—for the overall stability and control flexibility of the back-to-back system—we further propose to use outer-voltage loop control (OVLC) and MPC to stabilize direct current (DC) voltage on the machine-sid... [more]

With the rapid development of urban rail transit systems and the consequent sharp increase of energy consumption, the energy-saving train operation problem has been attracting much attention. Extensive studies have been devoted to optimal control of a single metro train in an inter-station run to minimize the energy consumption. However, most of the existing work focuses on offline optimization of the energy-saving driving strategy, which still needs to be tracked in real train operation. In order to attain better performance in the presence of disturbances, this paper studies the online optimization problem of the energy-saving driving strategy for a single metro train, by employing the model predictive control (MPC) approach. Firstly, a switched-mode dynamical system model is introduced to describe the dynamics of a metro train. Based on this model, an MPC-based online optimization problem is formulated for obtaining the optimal mode switching times with minimal energy consumption fo... [more]

This paper proposes a multi-objective model predictive control (MPC) designed for the power management of a multi-stack fuel cell (FC) system integrated into a renewable sources-based microgrid. The main advantage of MPC is the fact that it allows the current timeslot to be optimized while taking future timeslots into account. The multi-objective function solves the problem related to the power dispatch at time that includes criteria to reduce the multi-stack FC degradation, operating and maintenance costs, as well as hydrogen consumption. Regarding the scientific literature, the novelty of this paper lies in the proposal of a generalized MPC controller for a multi-stack FC that can be used independently of the number of stacks that make it up. Although all the stacks that make up the modular FC system are identical, their levels of degradation, in general, will not be. Thus, over time, each stack can present a different behavior. Therefore, the power control strategy cannot be based o... [more]

Power distribution systems nowadays are highly penetrated by renewable energy sources, and this explains the dominant role of power electronic converters in their operation. However, the presence of multiple power electronic conversion units gives rise to the so-called phenomenon of Constant Power Loads (CPLs), which poses a serious stability challenge in the overall operation of a DC micro-grid. This article addresses the problem of enhancing the stability margin of boost and buck-boost DC-DC converters employed in DC micro-grids under uncertain mixed load conditions. This is done with a recently proposed methodology that relies on a two-degree-of-freedom (2-DOF) controller, comprised by a voltage-mode Proportional Integral Derivative (PID) (Type-III) primary controller and a reference governor (RG) secondary controller. This complementary scheme adjusts the imposed voltage reference dynamically and is designed in an optimal fashion via the Model Predictive Control (MPC) methodology b... [more]

This paper proposes a new cascaded multilevel converter topology based on three-phase H bridge cells with a common DC-link structure. The proposed multilevel converter topology main advantages, compared with literature renowned multilevel converters topologies, are discussed in the paper, such as modularity, construction, implementation cost, and DC voltage ripple mitigation. Despite presenting an elementary structure and easy implementation, the use of classic PWM switching strategies is not feasible for this topology, causing the appearance of several short-circuit states between its capacitors. Thus, a graph theory algorithm combined with a model predictive control is also proposed in this work to identify and avoid the new cascaded multilevel converter short-circuit switching states and, concomitantly, guaranteeing the converter output power quality. In order to validate the presented topology applicability, a low voltage synchronous static compensators (STATCOM) with an optimal sw... [more]

We present a novel methodology for the control of power unit commitment in complex ship energy systems. The usage of this method is demonstrated with a case study, where measured data was used from a cruise ship operating in the Caribbean and the Mediterranean. The ship’s energy system is conceptualized to feature a fuel cell and a battery along standard diesel generating sets for the purpose of reducing local emissions near coasts. The developed method is formulated as a model predictive control (MPC) problem, where a novel 2-stage predictive model is used to predict power demand, and a mixed-integer linear programming (MILP) model is used to solve unit commitment according to the prediction. The performance of the methodology is compared to fully optimal control, which was simulated by optimizing unit commitment for entire measured power demand profiles of trips. As a result, it can be stated that the developed methodology achieves close to optimal unit commitment control for the con... [more]

This paper presents a finite control-set model predictive control (FCS-MPC) based technique to reduce the switching loss and frequency of the on-grid PV inverter by incorporating a switching frequency term in the cost function of the model predictive control (MPC). In the proposed MPC, the control objectives (current and switching frequency) select an optimal switching state for the inverter by minimizing a predefined cost function. The two control objectives are combined with a weighting factor. A trade-off between the switching frequency (average) and total harmonic distortion (THD) of the current was utilized to determine the value of the weighting factor. The switching, conduction, and harmonic losses were determined at the selected value of the weighting factor for both the proposed and conventional FCS-MPC and compared. The system was simulated in MATLAB/Simulink, and a small-scale hardware prototype was built to realize the system and verify the proposal. Considering only 0.25%... [more]

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and a... [more]

Recently, model predictive control (MPC) methods have been widely used to achieve the control of two-level voltage source inverters due to their superiorities. However, only one of the eight basic voltage vectors is applied in every control cycle in the conventional MPC system, resulting in large current ripples and distortions. To address this issue, a dual-vector modulated MPC method is presented, where two voltage vectors are selected and utilized to control the voltage source inverter in every control cycle. The duty cycle of each voltage vector is figured out according to the hypothesis that it is inversely proportional to the square root of its corresponding cost function value, which is the first contribution of this paper. The effectiveness of this assumption is verified for the first time by a detailed theoretical analysis shown in this paper based on the geometrical relationship of the voltage vectors, which is another contribution of this paper. Moreover, further theoretical... [more]

The absorption process of CO2 by ethanolamine solution is essentially a dynamic system, which is greatly affected by the power plant startup and flue gas load changes. Hence, studying the optimal control of the CO2 chemical capture process has always been an important part in academic fields. Model predictive control (MPC) is a very effective control strategy used for such process, but the most intractable problem is the lack of accurate and effective model. In this work, Aspen Plus and Aspen Plus Dynamics are used to establish the process of monoethanolamine (MEA) absorption of CO2 related models based on subspace identification. The nonlinear distribution of the system under steady-state operation is analyzed. Dynamic tests were carried out to understand the dynamic characteristics of the system under variable operating conditions. Systematic subspace identification on open-loop experimental data was performed. We designed a model predictive controller based on the identified model c... [more]

In this work, the optimal management of the water grid belonging to a pilot agro-industrial district, based on greenhouse cultivation, is analyzed. Different water supply plants are considered in the district, some of them using renewable energies as power sources, i.e., a solar thermal desalination plant and a nanofiltration facility powered up by a photovoltaic field. Moreover, the trade with the water public utility network is also taken into account. As demanding agents, a greenhouse and an office building are contemplated. Due to the different water necessities, demand profiles, and the heterogeneous nature of the different plants considered as supplier agents, the management of the whole plant is not trivial. In this way, an algorithm based on the energy hubs approach, which takes into account economic terms and the optimal use of the available resources in its formulation, is proposed for the pilot district with a cropping area of 616 m2. Simulation results are provided in order... [more]

This article studies the exchange of self-produced renewable energy between prosumers (and with pure end consumers), through the discrete trading of energy packages and proposes a framework for optimizing this exchange. In order to mitigate the imbalances derived from discrepancies between production and consumption and their respective forecasts, the simultaneous continuous trading of instantaneous power quotas is proposed, giving rise to a time-ahead market running in parallel with a real-time one. An energy management system (EMS) based on stochastic model predictive control (SMPC) simultaneously determines the optimal bidding strategies for both markets, as well as the optimal utilisation of any energy storage system (ESS). Simulations carried out for a heterogeneous group of agents show that those with SMPC-EMS achieve savings of between 3% and 15% in their energy operation economic result. The proposed structures allows the peer-to-peer (P2P) energy trading between end users with... [more]

Modern agriculture represents an economic sector that can mainly benefit from technology innovation according to the principles suggested by Industry 4.0 for smart farming systems. Greenhouse industry is significantly becoming more and more technological and automatized to improve the quality and efficiency of crop production. Smart greenhouses are equipped with forefront IoT- and ICT-based monitoring and control systems. New remote sensors, devices, networking communication, and control strategies can make available real-time information about crop health, soil, temperature, humidity, and other indoor parameters. Energy efficiency plays a key role in this context, as a fundamental path towards sustainability of the production. This paper is a review of the precision and sustainable agriculture approaches focusing on the current advance technological solution to monitor, track, and control greenhouse systems to enhance production in a more sustainable way. Thus, we compared and analyze... [more]

This paper proposes a microgrid central controller (MGCC) solution to the energy management problem of a renewable energy-based microgrid (MG). This MG is a case study from the Brazilian energy market context and, thus, has some operational particularities and rules to be obeyed. The MGCC development was based on a hybrid model predictive control (HMPC) strategy using the mixed logical dynamic (MLD) approach to deal with logical constraints within the HMPC structure, which results in a mixed integer programming (MIP) problem. The development of the solution is done through economic and dynamic modeling of the MG components; furthermore, it also takes into account the energy compensation rules of the Brazilian energy market and the white energy tariff. These conditions are specified through a set of MLD constraints. The effectiveness and performance of the proposed solution are evaluated through high-fidelity numerical simulation.

Nowadays, uninterruptible power supplies (UPS) play an important role in feeding critical loads in the electric power systems such as data centers or large communication hubs. Due to the increasing power of these loads and frequent need for expansion or redundancy, UPS systems are frequently connected in parallel. However, when UPS systems are parallel-connected, two fundamental requirements must be verified: potential circulating currents between the systems must be eliminated and the load power must be distributed between the systems according to UPS systems availability. Moreover, a high-quality load voltage waveform must be permanently ensured. In this paper innovative control strategies are proposed for paralleled UPS systems based on Finite Control Set Model Predictive Control (FCS-MPC). The proposed strategies simultaneously provide: controlled load power distribution, circulating current suppression and a high-quality load voltage waveform. A new dynamic converters deactivation... [more]

Home energy management systems (HEMS) are a key technology for managing future electricity distribution systems as they can shift household electricity usage away from peak consumption times and can reduce the amount of local generation penetrating into the wider distribution system. In doing this they can also provide significant cost savings to domestic electricity users. This paper studies a HEMS which minimizes the daily energy costs, reduces energy lost to the utility, and improves photovoltaic (PV) self-consumption by controlling a home battery storage system (HBSS). The study assesses factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policy. Two management strategies have been used to control the HBSS; (1) a HEMS based on a real-time controller (RTC) and (2) a HEMS based on a model predictive controller (MPC). Several methods have been developed for home demand energy forecasting and PV generation forecasting and their impact o... [more]