A review on state of art development of model predictive control for renewable energy applications (original) (raw)
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Predictive Control of Power Electronics Converters in Renewable Energy Systems
Energies, 2017
Predictive control has attracted much attention and has been widely used in power electronics and electric drives. However, further developments for applications in the field of renewable energy systems are still under investigation. In this paper, the principles of predictive control are studied with a focus on model predictive control (MPC) and vector-sequence-based predictive control (VPC). Based on these techniques, two control strategies for flexible power supply are developed. They are implemented in the most promising renewable energy systems, namely solar photovoltaic (PV) systems and wind generators, respectively. The experimental results based on a laboratory prototype show that the active and reactive powers supplied by the PV and wind generator can be controlled flexibly with excellent steady-state and transient performance. As the penetration level of the renewable energy sources in electricity network continues to rise, predictive control tends to be an attractive and powerful technique for power electronics converters in renewable energy systems.
Enhanced performance of PV power control using model predictive control
Solar Energy, 2017
This paper focuses on the use of model predictive control (MPC) to control a DC/DC boost converter in order to regulate the PV power. When integrated with the grid, the PV system must deliver maximum power most of the time; however, if a voltage sag occurs, new grid codes demand that the control system should limit the PV power generated to avoid over current conditions and, consequently, a grid disconnection. Maximum and reduced power modes are implemented following the MPC strategy to achieve high-performance and stable operation in the system. First, the system is modeled in Matlab/Simulink and PLECS to understand its operation and to evaluate the effectiveness of the proposed algorithm. Secondly, experimental results are verified using the control hardware-in-the-loop (CHIL) approach on the Real Time Digital Simulator (RTDS).
Electronics, 2022
In recent times, Microgrids (MG) have emerged as solution approach to establishing resilient power systems. However, the integration of Renewable Energy Resources (RERs) comes with a high degree of uncertainties due to heavy dependency on weather conditions. Hence, improper modeling of these uncertainties can have adverse effects on the performance of the microgrid operations. Due to this effect, more advanced algorithms need to be explored to create stability in MGs’. The Model Predictive Control (MPC) technique has gained sound recognition due to its flexibility in executing controls and speed of processors. Thus, in this review paper, the superiority of MPC to several techniques used to model uncertainties is presented for both grid-connected and islanded system. It highlights the features, strengths and incompetencies of several modeling methods for MPCs and some of its variants regarding handling of uncertainties in MGs. This survey article will help researchers and model devel...
An Application of Model-based Predictive Control for Renewables-intensive Power Distribution Grids
IFAC-PapersOnLine, 2020
in recent years, growing penetration of renewable-energy-based distributed generation into power distribution grids has been compromising operational constraints. In this paper, a model-based predictive control (MPC) strategy is proposed for demand/supply balance and voltage regulation in a power distribution grid with prolific distributed generation using flexible assets (water tower and biogas plant). Then, the impact that errors of photovoltaic (PV) power generation and grid load forecasts have on its performance is examined. Results show that the proposed control scheme is efficient and resilient to forecasting errors.
Two cases studies of Model Predictive Control approach for hybrid Renewable Energy Systems
AIMS Energy, 2021
This work presents a load frequency control scheme in Renewable Energy Sources(RESs) power system by applying Model Predictive Control(MPC). The MPC is designed depending on the first model parameter and then investigate its performance on the second model to confirm its robustness and effectiveness over a wide range of operating conditions. The first model is 100% RESs system with Photovoltaic generation(PV), wind generation(WG), fuel cell, seawater electrolyzer, and storage battery. From the simulation results of the first case, it shows the control scheme is efficiency. And base on the good results of the first case study, to propose a second case using a 10-bus power system of Okinawa island, Japan, to verify the efficiency of proposed MPC control scheme again. In addition, in the second case, there also applied storage devices, demand-response technique and RESs output control to compensate the system frequency balance. Last, there have a detailed results analysis to compare the two cases simulation results, and then to Prospects for future research. All the simulations of this work are performed in Matlab /Simulink .
IEEE Transactions on Industry Applications, 2015
This paper presents a model predictive direct power control strategy for a grid-connected inverter used in a photovoltaic system as found in many distributed generating installations. The controller uses a system model to predict the system behavior at each sampling instant. The voltage vector that generates the least power ripple is selected using a cost function and applied during the next sampling period; thus, flexible power regulation can be achieved. In addition, the influence of a one-step delay in the digital implementation is investigated and compensated for using a model-based prediction scheme. Furthermore, a two-step horizon prediction algorithm is developed to reduce the switching frequency, which is a significant advantage in higher power applications. The effectiveness of the proposed model predictive control strategy was verified numerically by using MATLAB/Simulink and validated experimentally using a laboratory prototype.
Model Predictive Current Control of Grid Connected PV Systems
Indonesian Journal of Electrical Engineering and Computer Science, 2016
This paper deals with the design and simulation of an efficient solar photovoltaic system with a maximum power point tracking system (MPPT). Maximum power point (MPP) is obtained by using Perturb and Observe (P&O) algorithm. The output from solar panel is fed to the DC-DC (Boost) converter which steps up the output voltage. It is then fed to a 3-phase inverter. The inverter used is a 3-phase two-level inverter implemented with a Model Predictive Control strategy. Model of the system is considered in order to predict the control variables. Optimum switching state is selected by minimizing the cost function for each sampling period. This is achieved through modelling and MATLAB simulation of various stages that constitute the overall system.
Model Predictive Control for Microgrid Functionalities: Review and Future Challenges
Energies , 2021
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 its applications in microgrids are described and summarized.
Predictive Control of PV/Battery System under Load and Environmental Uncertainty
Energies
The standalone microgrids with renewable energy resources (RERs) such as a photovoltaic (PV) system and fast changing loads face major challenges in terms of reliability and power management due to a lack of inherent inertial support from RERs and their intermittent nature. Thus, energy storage technologies such as battery energy storage (BES) are typically used to mitigate the power fluctuations and maintain a power balance in the system. This paper presents a model predictive control (MPC) based power management strategy (PMS) for such standalone PV/battery systems. The proposed method is equipped with an autoregressive integrated moving average (ARIMA) prediction method to forecast the load and environmental parameters. The proposed controller has the capabilities of (1) effective power management, (2) minimization of transients during disturbances, and (3) automatic switching of the operation of the PV between the maximum power point tracking (MPPT) mode and power-curtailed mode...
Predictive Power Control for PV Plants With Energy Storage
This work presents a model predictive control (MPC) approach to manage in real-time the energy generated by a grid-tied photovoltaic (PV) power plant with energy storage (ES), optimizing its economic revenue. This MPC approach stands out because, when a long enough prediction horizon is used, the saturation of the ES system (ESS) can be advanced by means of a prediction model of the PV panels production. Therefore, the PV+ES power plant can modify its production so as to manage the power deviations with regard to that committed in the daily and intraday electricity markets, with the objective of reducing economic penalties. The initial power commitment is supposed in this work to be given by a higher level energy management operator. By a proper definition of its objective function, the predictive control allows to economically optimize the PV+ES power plant performance. This control strategy is tested in simulations with actual data measured for different days with varying meteorological conditions. Results provide a good reference on the economic benefits which can be obtained thanks to the MPC introduction.