Model Predictive Power Control Research Articles

Power allocation scheme for grid interactive microgrid with hybrid energy storage system using model predictive control

Grid-interactive microgrids have become a promising alternative to traditional centralized power systems as a result of the rising demand for reliable and sustainable energy solutions. DC microgrids (MG) based upon renewable energy sources (RES) are on the rise due to high energy efficiency and compact size than the AC microgrids. Microgrid equipped with hybrid energy storage system is sensible for a reliable and stable power supply to the system. Implementation of a potent control system makes sure of the system stability. A model predictive control (MPC) based control strategy is proposed due to its easy implementation approach and inclusion nonlinear dynamics & constraints of the controlled system. An efficient power allocation scheme is developed in this paper for a grid-interactive photovoltaic microgrid featuring a hybrid energy storage system (HESS). The system-level power allocation scheme (PAS) considers the real-time data of load demands, generation, market energy cost, and energy storage state-of-charge to actively manage the power flow within the microgrid and also with the utility grid. The proposed control scheme uses a sophisticated model predictive current control for the DC/DC bi-directional converters control and model predictive combined power and voltage control is used for bidirectional interlink converter. The proposed control approach provides faster DC bus voltage recovery. The HESS consisting of battery and supercapacitor is used as energy buffers for responding to the fluctuation of power generation and demand for smoothing the PV power. The potency of the proposed controller is verified using MATLAB/Simulink (2021b) environment. Also, the real-time implementation of the proposed approach is carried out using the OPAL-RT OP4510 simulator.

High Precision Model Predictive Power Control Method for Battery Energy-Stored Quasi Z-source Inverter

High Precision Model Predictive Power Control Method for Battery Energy-Stored Quasi Z-source Inverter

A Model Predictive Power Control Scheme for PV Inverter and Battery Energy Storage System for a Microgrid

In recent decades, there has been a substantial surge in the adoption of renewable energy systems (RESs), particularly photovoltaic systems (PVs). However, the increasing integration of PV systems into distribution networks has exposed limitations in traditional control methods, such as cascaded linear control with PID controllers. These limitations manifest in dynamic response, power regulation capability, and adaptability to varying operating conditions. Voltage fluctuations arising from intermittent PV output have become a significant concern. The primary aim of this study is to develop a Model Predictive Control (MPC) scheme capable of generating a control signal based on a cost function. The study also seeks to validate the effectiveness of MPC under diverse scenarios. Additionally, an energy storage system, represented by a battery, is incorporated to support the PV system during periods of low power output. The chosen methodology involves using a Dual Active Bridge converter for charging and discharging the batteries. Including an LCL filter in the design significantly reduces THD from 51% to 2.62%. When comparing the response to changing linear loads, MPC demonstrates faster performance than DQ control, with a response time ahead of 0.23 seconds.

Power-Compensated Triple-Vector Model Predictive Direct Power Control Strategy for Nonredundant Fault-Tolerant Rectifiers

A power compensated triple-vector model predictive direct power control (PCTV-MPDPC) strategy is proposed in this paper for non-redundant fault-tolerant three-phase rectifiers. The system fault tolerance ability and post-fault control performance are enhanced with low hardware complexity. By defining the negative conjugate of complex power as the control variable, a straight-forward pattern of power-voltage relationship analysis is obtained. Besides, a novel voltage vector selection method is put forward based on the location of the error vector between the reference and real control variables. The sector identification process can be omitted to derive a unified expression for duty ratio calculation. On top of that, an intuitive power compensation architecture is proposed to eliminate the dc-bus voltage deviation so that its utilization rate can be maximized. With the proposed PCTV-MPDPC strategy, the power ripples and input current harmonics can be significantly reduced. Superior steady-state and dynamic performance can be achieved compared with the conventional finite-control-set MPDPC (FCS-MPDPC) scheme and a recently proposed multiple-vector model predictive power control (MV-MPPC) method, along with excellent dc-bus voltage deviation suppression effect. Furthermore, simulation validation in Matlab/Simulink and experimental verification on a four-switch three-phase rectifier are carried out to demonstrate the effectiveness of the proposed PCTV-MPDPC strategy.

Performance Enhancement of a Variable Speed Permanent Magnet Synchronous Generator Used for Renewable Energy Application

The paper aims to develop an improved control system to enhance the dynamics of a permanent magnet synchronous generator (PMSG) operating at varying speeds. The generator dynamics are evaluated based on lowing current, power, and torque ripples to validate the effectiveness of the proposed control system. The adopted controllers include the model predictive power control (MPPC), model predictive torque control (MPTC), and the designed predictive voltage control (PVC). MPPC seeks to regulate the active and reactive power, while MPTC regulates the torque and flux. MPPC and MPTC have several drawbacks, like high ripple, high load commutation, and using a weighting factor in their cost functions. The methodology of designed predictive voltage comes to eliminate these drawbacks by managing the direct voltage by utilizing the deadbeat and finite control set FCS principle, which uses a simple cost function without needing any weighting factor for equilibrium error issues. The results demonstrate several advantages of the proposed PVC technique, including faster dynamic response, simplified control structure, reduced ripples, lower current harmonics, and decreased computational requirements when compared to the MPPC and MPTC methods. Additionally, the study considers the integration of blade pitch angle and maximum power point tracking (MPPT) controls, which limit wind energy utilization when the generator speed exceeds its rated speed and maximize wind energy extraction during wind scarcity. In summary, the proposed PVC enhanced control system exhibits superior performance in terms of dynamic response, control simplicity, current quality, and computational efficiency when compared to alternative methods.

Model predictive power control of a heat pipe cooled reactor

Heat pipe cooled reactor (HPR) has broad application prospects in deep space exploration, deep-sea submarine exploration, and other scenarios due to the small size, high inherent safety, and easy modularization and expansion. However, the HPR conducts thermal energy through evaporation and condensation of the working fluid inside the heat pipe. This feature makes the HPR a large time-delay system. If the power control system adopts the conventional PID algorithm, there will be a long settling time. Therefore, the model predictive control algorithm is proposed for the power control system to improve the control performance. The HPR linear model, which is developed by linearization of its nonlinear model, is chosen as the predictive model. The optimal control value is obtained by solving the optimization problem based on the predictive model and the electric power feedback value. The discrepancy between the predive model and the actual system response results in the presence of steady-state error. To solve this problem, an integral controller is added to eliminate the error. The appropriate control system parameters are tuned by the trial and error method. The proposed control system has satisfactory control performance, which can significantly shorten the settling time. The model predictive control can effectively overcome the influence of the large time-delay characteristic.

Computationally efficient model predictive power control with self-balancing capacitor voltages for grid-tied nested neutral point clamped inverter

ABSTRACT This study outlines an effective power control strategy based on a finite control set model predictive control (FCS-MPC) of grid-tie four-level Nested Neutral Point Clamped Inverter. A mathematical model is employed to predict the behavior of grid current and power that is used to evaluate the cost function. In contrast to the traditional FCS-MPC, the suggested method only evaluates the optimization loop with the feasible candidate switching states, resulting in a reduced computational burden. The main contribution of our study is to incorporate the selection of distribution of inverter voltage vectors and self-balance of flying capacitor voltage without weighting factor. The computational load of the optimization processes is not only greatly decreased by selecting the appropriate switching states among redundant voltage vectors but also remaining the balance of flying capacitor voltages. Moreover, the difficulty of selecting the optimal weighting factor is avoided with our research. A simulation study of a 4 MW power system with various operational statuses is performed using Matlab software. The comparison results of the suggested method, traditional FCS-MPC, and previous FCS-MPC have verified the significance and validity of the offered technique with regard to steady-state, transient-state operational conditions, and computational burden.

Multivector Model Predictive Power Control With Low Computational Burden for Grid-Tied Quasi-Z-Source Inverter Without Weighting Factors

Nowadays, a model predictive control has gained a lot of attractions for power converter. To overcome the defects of the conventional model predictive control and achieve better performance, this article presents a multivector model predictive power control (MV-MPPC) with low computational burden for the grid-tied quasi-Z-source inverter (qZSI). Compared with the conventional model predictive power control (MPPC) that adopts only one vector in one control period, the proposed MV-MPPC applies several vectors including two active vectors, one null vector, and one shoot-through (ST) vector to fulfill the expected performance for the grid-tied qZSI. By calculating the optimal duty ratios of the ST vector, the inductor current ripple can be greatly reduced. To eliminate the weighting factor, a modified sliding-mode control method for simultaneously controlling the capacitor voltage and the inductor current to their setting points is proposed. Furthermore, a quick and effective optimal sector selection method is given, which chooses the optimal sector through a look-up table instead of a bunch of complex calculations. Hence, the computational burden of the proposed MV-MPPC is decreased with significance. Both simulation and experimental results are shown to validate the effectiveness and the advantages of the proposed method.

Model predictive power control for grid-tied inverters under unbalanced and distorted grid conditions

Normally, a grid-tied inverter with model predictive power control (MPPC) possesses a fast dynamic response and outstanding static performance. However, under unbalanced and distorted grid conditions, such a grid-tied inverter is troubled by considerable unwanted grid-current distortion. Here, a modifying power reference MPPC (MPR-MPPC) is proposed to address the above issue. Considering the characteristics of the output power, the revised power references are derived to mitigate the grid-current distortion and achieve three alternative targets (minimization of active- and reactive-power oscillation and balancing of the grid currents). However, these corresponding power references only consider one of three targets, and the oscillation amplitudes of active and reactive power are inversely proportional. Then, the three-target power references are incorporated into a single reference with two parameters to regulate the magnitude of active/reactive-power oscillation comprehensively. Finally, an experimental platform is established to demonstrate the validity of MPR-MPPC and the results show that the MPR-MPPC meets expectations.

Three‐vector model predictive power control for three‐level T‐type rectifier based on dead‐beat control

This paper presents a three-vector model predictive power control with neutral-point voltage balance (TVMPPC-NPVB) and a dead-beat control for three-phase three-level T-type rectifier. Three vectors are adopted to reduce the current ripple, balance the neutral-point voltage and achieve unity power factor on the AC side simultaneously, while the conventional model predictive power control suffers from large current ripple, difficulty in designing the weighting factor and computational burden. In the proposed strategy, two cost functions are derived to select the optimal space vector sequence and reduce the computational burden. The action time of each vector is introduced in detail. The traditional PWM rectifier utilizes PI controller to regulate the output voltage. But PI controller tends to have a long transition time and big variation on output voltage when the load changes suddenly. The proposed dead-beat control is capable of solving these problems caused by conventional PI controller. Both simulation and experimental results are shown to confirm the effectiveness of the proposed method.

Robust model predictive power control for three-phase VSRs under unbalanced grid

The unbalanced grid voltage and circuit parameter uncertainty are two main obstacles for three phase voltage source rectifiers (VSRs) to achieve high performance in the practical applications. According to the instantaneous power model of the three-phase VSRs, six power components have to be well-regulated using only four available current manipulated variables, which is a typical underactuated problem. The model predictive control (MPC) provides a unified framework to regulate six power components simultaneously. However, how to balance the six power components control efforts is a challenge task. Meanwhile, the predictive model maybe inaccurate because of circuit parameters uncertainty, which degrades the performance of the MPC as well. In this paper, a robust model predictive power control (RMPPC) method is proposed for the three-phase VSRs to overcome above twice obstacles. The contributions of the work are: (1) The proposed method achieves the balance six power components control of the three-phase VSRs under unbalanced grid by using the off-line optimized weights; (2) a soft robust item with time variant boundary is proposed to achieve robust predictive model to deal with parameter uncertainty. Comparing with the existing voltage oriented control (VOC), direct power control (DPC) and model predictive control (MPC) methods, the proposed method achieves the best power quality in the sense of highest power factor and the lowest power oscillation in experiment, which verify the superiority of the proposed method.

Model Predictive Power Control for Bidirectional Series Resonant Isolated DC–DC Converters With Steady-State and Dynamic Performance Optimization

With more and more attention on dual bridge series resonant isolated dc–dc converters in power electronic transformer, various phase-shift optimization methods have been widely studied. In this article, a model predictive power control with a triple phase shift scheme is proposed to improve both the steady-state and dynamic performance of the adopted converter. In addition, the proposed method can realize the dynamic response improvement without the output current sensor, compared with the existing methods. With the current stress optimization model, the minimum current trajectory control can be achieved. The proposed model predictive power control can greatly improve dynamic performance compared with the traditional PI control and is not sensitive to the circuit parameters. The design procedure of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LC</i> resonant tank is given to ensure minimum current stress operation. Finally, comparative experimental tests in a 400 W hardware prototype are carried out to verify the effectiveness of the proposed method in both steady-state and dynamic performance optimization.

Model-Free Predictive Power Control for PWM Rectifiers under Asymmetrical Grids

Conventional model predictive power control (MPPC) features a simple concept and quick dynamic response. However, it relies heavily on the system model and its parameter accuracy. Furthermore, the steady ripples are still high due to the use of one voltage vector during one control period. Recently, model-free predictive current control (MFPCC) has been proposed in the current control of PWM rectifiers. Despite the strong parameter robustness, the principle of MFPCC cannot be directly applied to power control, because the relationship between power and converter voltage is more complex. This paper first proposes a basic model-free predictive power control (MFPPC), which successfully extends the principle of MFPCC to power control. Subsequently, an improved MFPPC is proposed, which uses an extended finite control set of voltage vectors to improve the steady-state performance. Furthermore, by using the online updated ultralocal model of PWM rectifiers, the problem of stagnant power updating in basic MFPPC is solved. The ideal three-phase grid voltages are symmetrical and sinusoidal, but the actual grids are usually unsymmetrical. In this paper, the proposed method is extended to asymmetrical power grids by adding an appropriate compensated power to the original power references. The proposed basic MFPPC and improved MFPPC are compared to conventional MPPC. The presented experimental results confirm the effectiveness of the proposed methods.

Simplified Three-Vector-Based Model Predictive Direct Power Control for Dual Three-Phase PMSG

The conventional model predictive power control suffers from heavy computational burden and large power ripple. To address the problem, this paper proposes a simplified three-vector-based model predictive direct power control, which can greatly optimize the control set. Firstly, a switching table is designed to exclude the redundant virtual voltage vectors (VVs). Only half of the candidate VVs are retained. Then, the effect of each VV on power is analyzed in detail. The deviation of reactive power is used as the evidence to further simplify the control set. In this way, the prediction behavior is reduced from 12 to 2, which effectively reduces the computational burden. In addition, the concept of power deadbeat is employed to determine the duty cycles of the two virtual VVs and one zero VV. Thus, the power ripples and current harmonics can be considerably suppressed. With this proposed method, the calculation complexity is reduced, and the steady-state performance is improved. The simulation and experimental results are given to verify the proposed method.

An Improved Active Disturbance Rejection Model Predictive Power Control with Circulating Current Reduction for Grid-Connected Modular Multilevel Converter

In this paper, a new combined control strategy based on model predictive control and active disturbance rejection control has been proposed to control a modular multilevel converter used for interfacing a floating photovoltaic system into the power grid. The proposed control consists of a combination of the active disturbance rejection controller for DC link voltage regulation and an optimized model predictive control strategy for power flow control and circulating current reduction for modular multilevel converter. The great number of submodules in the MMC converter increases the complexity of the MPC control and the calculation time for choosing the voltage vector to be synthesized according to the required cost function. In this paper, an optimized method is proposed to minimize the time of calculation and application of the vector voltage reference, this method is based on a local optimization for each elementary hexagon. A study is carried out in order to illustrate the performances of the proposed control strategy. A multiple case study approach is adopted to show the superiority of the active disturbance rejection model predictive control strategy in terms of rapid convergence, elimination of overshoot, and improved robustness under various disturbances and operating conditions.

Model predictive power control with Kalman filter for grid‐tied inverter with parameter variation

Model predictive power control is considered to be a promising control algorithm owing to its simple structure and fast dynamic response. This algorithm relies heavily on the predictive model and the parameter variation adds the difficulty to gain the accurate model, which affects the static performance of the grid-tied inverter. Here, a model predictive power control with Kalman filter (MPPCKF) is proposed to address above issue. On the basis of the grid-current predictive model, the mathematical model is gotten to estimate the AC-filter inductance and a Kalman filter is introduced to suppress the noise interference of calculating inductance. Finally, a simulation and an experimental platform are established to demonstrate the validity of the proposed approach. The results exhibit that the MPPCKF can effectively suppress the grid-current distortion and the active-/reactive-power ripple of grid-tied inverter caused by the mismatched predictive power model.

Optimised coordinated control of hybrid AC/DC microgrids along PV-wind-battery: a hybrid based model

This work presents a novel coordinated control approach for an MG with hybrid AC/DC loads and energy resources. As a result, power interactions between the DC and AC subgrids are appropriately enabled by distributing power fluctuations in a coordinated manner. Initially, a coordinated control scheme of distributed converters was presented. Then, a model predictive power and voltage control (MPPVC) technique is used for DC/AC interlinking converter that offers high-quality voltages and guarantees the optimal power transfer amongst ac and dc sub-grids. This paper aims to enhance the control structure of the energy storage system (ESS), where the optimised PI controller is insisted. Particularly, the gain of PI controller is optimally tuned by a new hybrid algorithm named as index-based fly-rider combined model (IF-RM) that incorporates both the concepts of rider optimisation algorithm (ROA) and firefly algorithm (FF). Finally, the betterment of the proposed work was validated over other existing models.

Continuous Control Set Model Predictive Control for an Indirect Matrix Converter

A continuous control set model predictive power control strategy for an indirect matrix converter is proposed in this paper. The load reactive power, the load active power, and the input reactive power are controlled simultaneously. This control strategy can obtain output waveforms with fixed switching frequency. Additionally, an optimal switching sequence is proposed to simplify the commutations of the indirect matrix converter. To suppress the input filter resonance, an active damping method is proposed. Experimental results prove that the proposed method features controllable input reactive power, controllable load active and reactive power, fixed switching frequency output waveforms, zero-current switching operations, and effectively suppresses input filter resonance.

Multivector Model Predictive Power Control for Grid Connected Converters in Renewable Power Plants

Model-based predictive power control (MPPC) is a well-known and useful technique for control of electric drives and renewable energy generation systems. However, this strategy relies on the knowledge of accurate system models and parameter values, and would otherwise lead to tracking errors in applications because of inevitable parameter uncertainties. Also, step delays in MPPC implementations have to be compensated to eliminate errors. This article proposes a predictive control application to control active and reactive powers exchanged between the grid and the grid side converter (GSC) interfacing renewable energy sources such as wind farms or photovoltaic. The proposed MPPC minimizes the power tracking error based on a proposed cost function and the control system output is the voltage reference for the electronic converter that is converted into switching pulses by the modulation stage. The proposed control strategy is designed to eliminate tracking errors and has fixed switching frequency while featuring a fast-dynamic response, low current THD, and low computational burden. The method has been evaluated using MATLAB/Simulink environment and an experimental 5-kW grid-connected voltage source converter. Finally, the proposed MPPC method has been critically compared to the previous MPPC approaches.

Model Predictive Power Control of Grid-Connected Quasi Single-Stage Converters for High-Efficiency Low-Voltage ESS Integration

The grid-connected quasi-single-stage converter (QSSC) provides a direct power flow path from low-voltage (LV) energy storage systems (ESS) to ac–dc converters, resulting in reduced power conversion stage for high-efficiency ESS integration. However, due to the dc-link voltage ratio between the two ports varies with the ESS state of charge, the voltage vectors in the ac side are unevenly distributed in the QSSC. This nonlinearity brings some challenges in control and modulation design for the ac–dc part of QSSC. In this article, a virtual two-level converter model considering the two ports as a single dc source is proposed, and model predictive power control (MPPC) based on the virtual two-level model is proposed to enhance the grid current performance. The duty cycle of each phase is calculated based on the active and reactive power tracking error minimization in the two-level converter frame, and the duty cycle for each switching pair is obtained by minimizing the power processed by the dc–dc converter. In this way, the complicated implementation of MPPC for a three-level converter is avoided, and the controller design of the QSSC can be simplified. The effectiveness of the proposed virtual two-level converter model and MPPC method are verified with experimental results.