Grid-connected Renewable Energy Systems Research Articles

Performance Analysis of Photovoltaic and Wind Turbine Grid-Connected Systems under LVRT Conditions

The integration of grid-connected renewable energy systems has gained significant attention and introduces several challenges and considerations. One of these challenges is ensuring the reliable and stable operation of these systems under various grid conditions. For example, faults at the grid could lead problems such as DC-link over-voltage and AC over-current that may cause disconnection or damage to inverter. This paper presents a comprehensive analysis of the performance of photovoltaic (PV) and doubly-fed induction generator (DFIG) wind turbine grid-connected systems under low voltage ride-through (LVRT) conditions. The study aims to investigate their behavior, and stability during LVRT events and provide insights for enhancing their grid integration capabilities. The PV and DFIG systems are modelled and simulated using MATLAB Simulink under three difference conditions, with and without using reactive current injection and DC chopper circuit. Various performance parameters, including grid voltage, grid current, DC-link voltage, active power, and reactive power, are analyzed to assess the system's behavior and compare their responses. The principal results reveal distinct performance characteristics of the PV and DFIG systems. The PV system shows higher overshoot currents, over-voltage, and significant drops in active power during fault occurrences, while the DFIG system exhibits lower overshoot currents and better stability in the DC-link voltage. Reactive power responses differ between the systems, with the PV system demonstrating a higher capability for support. The implementation of DC chopper shows more effective in the reduction of DC-link voltage and overshoot grid current in the PV system compared to the DFIG system.

Evaluation of LVRT capability and stability analysis of VSC based advanced control approach for grid connected PV system under grid fault conditions

Short circuit faults are a prevalent issue in power systems, causing disruptions to the grid's normal operation. Dynamic behaviours of the conventional power systems during short circuit faults have been extensively studied and understood. The bulk of ongoing research and development are focusing on the dynamic performance of grid-connected renewable energy systems under these fault conditions, due to changes in the grid code and a decrease in system inertia. The development of effective control strategies to enhance the system's reliability during fault conditions is of paramount importance. In this paper, a two-stages grid-connected photovoltaic system (GCPV) having a rated power of 2 MW was created in the MATLAB/Simulink environment. The dynamic behaviour of the presented system was evaluated in two scenarios: steady state conditions and short circuit faults. A line-to-ground short circuit fault was created at the grid side, and its effect on the PV system's operation was observed. An advanced control system was designed to maintain stability during fault conditions. The results demonstrated the efficiency of the designated control system in minimizing the effects of short circuit faults on the GCPV system's function, and restoring the system promptly after the fault was cleared. Furthermore, considering modifications in grid regulations, the low voltage ride through (LVRT) capability of the designed system was analysed and validated according to the UK standards. The Total Harmonic Distortion (THD) level at the common coupling point was also analysed for voltage and current, remaining below the acceptable level of 5% as specified in the IEEE Std. 519.

Dual-Stage Optimization Scheduling Model for a Grid-Connected Renewable Energy System with Hybrid Energy Storage

To operate the grid-connected renewable energy system economically, this study presents a dual-stage optimization scheduling model for grid-connected systems with hybrid energy storage, including day-ahead and intra-days stages. In the day-ahead stage, an economically optimal scheduling model is developed, considering the price peak-to-valley difference. This model aims to enhance the economic efficiency of the system by utilizing hybrid energy storage. In the intra-day stage, more accurate renewable energy forecasts with a shorter time scale are considered. The objectives are to minimize the curtailment rate of renewable energy and to track the day-ahead scheduling outcomes. The NSGA-II algorithm is employed for multi-objective optimization, achieving equilibrium solutions considering multiple optimization objectives. Compared to other published works, the proposed model achieves a balance between different optimization objectives, enabling the system to operate economically and stably. It provides a comprehensive approach to optimize the scheduling of grid-connected systems with hybrid energy storage by considering both economic and operational aspects. Overall, this proposed dual-stage optimization model presents a viable approach to improve economic efficiency and mitigate renewable energy curtailment in grid-connected systems. By effectively integrating renewable energy sources and optimizing their utilization, this model contributes to enhancing the sustainability and optimal operation of the power grid.

Power control of hybrid grid-connected renewable energy system using machine learning

This article addresses the crucial challenge of maintaining a reliable power supply in integrated electric systems that combine solar power and energy storage. It focuses on optimizing key parameters for remote photovoltaic, wind, and battery energy storage within the grid, with a primary goal of sustaining 24-hour load demand. The study introduces a comprehensive approach that integrates fuel price dynamics and battery depletion costs into the optimization model for hybrid grids. This approach seeks a delicate balance between sustainable energy usage and cost-effectiveness. To achieve this balance, the research employs genetic algorithms, a robust computational technique. These algorithms play a vital role in calculating discharge-charge cycles and assessing battery health, addressing concerns about battery longevity and performance. Additionally, the study explores a machine learning framework, specifically nested learning, to further enhance the optimization process. Nested learning allows for the development of a sophisticated target function that considers various grid operation parameters and constraints. This machine learning approach enhances the system's ability to dynamically monitor power consumption and generation. Within this machine learning framework, the TD Lambda learning algorithm takes a prominent role in identifying the optimal function. This algorithm is well-known for its efficiency, particularly in non-Markovian scenarios. Its quick convergence enhances the overall performance of the hybrid grid, ensuring efficiency in changing conditions. The research also evaluates the impact of weight factors on various aspects, including battery charging status, algorithmic decisions, and their consequences on optimal energy pricing within the hybrid grid. This comprehensive assessment deepens the understanding of how these weight factors influence grid performance and economic viability.

Online Inertia Allocation for Grid-Connected Renewable Energy Systems Based on Generic ASF Model Under Frequency Nadir Constraint

Frequency security is one of the challenges for the power grid penetrated by renewable energy systems (RESs). Virtual inertia provision from the RESs is considered to be efficient support for frequency security enhancement. How to estimate the frequency nadir rapidly and accurately is crucial for the online inertia allocation for the RESs. This paper develops a generic average system frequency (G-ASF) model for the power grid to estimate the frequency nadir at first. The low-order generic model of the speed governing system of the SG is proposed to enhance the accuracy and flexibility of the G-ASF model. Then, based on the G-ASF model, an online inertia allocation strategy for the grid-connected RESs under frequency nadir constraint is proposed. With the pre-identified generic models of the speed governing systems, the G-ASF model of the entire power grid is periodically updated according to the grid operation state. Next to the model updating, the critical inertia of the power grid is periodically calculated under the pre-defined contingencies for frequency security verification. Then, the virtual inertia provision tasks are determined and allocated to the RESs periodically, and the grid frequency security is effectively ensured. The simulation results validate the proposed model and strategy.

Artificial Intelligence-Enabled Techno-Economic Analysis and Optimization of Grid-Tied Solar PV-Fuel Cell Hybrid Power Systems for Enhanced Performance

The incorporation of energy from renewable sources into the power grid is crucial for achieving sustainable and environmentally friendly power generation. This study proposes an artificial intelligence (AI)-enabled methodology for the analysis & optimization of “grid-tied solar photovoltaic (PV)-fuel cell hybrid power systems.” The research aims to demonstrate how AI techniques can assist in decision-making, improve system performance, and achieve higher levels of energy efficiency and financial viability. The study presents the results of a project focusing on a renewable energy system that feeds into the grid and powers a university building. The hybrid power system’s performance and cost were evaluated using unified approaches to modeling, simulation, optimization, and control. The findings indicate that the AI-optimized “solar PV-fuel cell hybrid system connected to the grid” offers excellent performance, meeting 74% of the building’s energy needs through renewable sources. The system also achieved a low levelled price for energy and minimise CO2 emissions, further enhancing its environmental sustainability. The proposed AI-enabled approach proves to be a promising solution for creating grid-connected renewable energy systems with significant benefits for energy efficiency, cost-effectiveness, and environmental impact.

Grid-connected renewable energy systems flexibility in Norway islands’ Decarbonization

In recent decades, investing in renewable and eco-friendly energy technologies, such as replacing clean energy systems instead of traditional ones and equipment management, is an interesting and practical topic in all sectors. This research analyzes the optimization of a hydro plant, wind turbines, and photovoltaic (PV) panels with a careful examination of three scenarios in the Hinnoya region, Norway. Three consumption scenarios—including an industrial/domestic load scenario, transportation load, and household load alone—for this region are considered. HOMER software is used to simulate and analyze the techno-economic performance of solar panels/wind turbines/grid/batteries and converters. The results of this research show that using renewable and eco-friendly systems in accordance with the region's potential leads to a lower cost of electricity generation. The COE production is at least 50% less than the normal sales price of the electricity grid. The use of electric grid exchanges results in energy modification at night. The potential for the use of onshore wind turbines is more than offshore turbines. The results also indicate that using renewable systems in the household field can reduce the COE by nearly 70% (0.0296 €/kWh), and in other energy fields (transportation and industrial) can diminish the COE by nearly 50% (0.055 €/kWh). Thus, increasing the percentage of employing renewable and eco-friendly energy systems leads to reduce greenhouse gas (GHG) emissions (particularly carbon dioxide).

Power controlled by Two Type Direct Power Control Quality Improvement of PV Grid-tied based on Two Parallel Inverters

Due to the growing demand for photovoltaic power generation systems for grid-connected renewable energy systems, the high-quality energy and the good efficiency are necessary. For this reason, two-level parallel inverters are used in grid-connected PV systems connected to standard AC and DC connections are widely utilized. This structure is often relied on to raise the quality of the power and increase the power conversion efficiency. This article proposes a grid-tied energetic system composed of a photovoltaic source connected to the grid by the means of DC-DC boost converter controlled by hell clamping algorithm MPPT to improve the harvesting of the PV power, and of two VSIs connected in parallel interfaced by a coupling filter and controlled by two methods namely, classic DPC-PI and improved DPC-ST. An extensive simulation analysis was carried out to validate the proposed control approach by the comparison with the conventional method base on PI regulator. The proposed technique introduced a superior performance in terms of lower voltage stress at the DC-link, the THD of the injected current, and the ripples of the active and reactive power.

Harmonic reduction of grid-connected multilevel inverters using modulation of variable frequency carriers

Abstract Multilevel three-phase inverters are increasingly popular due to their ability to generate high-quality output voltage with harmonic distortion lower than traditional inverters. They are used in various applications, including grid-connected renewable energy systems, motor drives, and power transmission systems, to improve efficiency and reduce costs. The control quality of grid-connected multilevel inverters depends on various factors such as the modulation technique, switching frequency, and control strategy. A good control system can achieve a balance between output current harmonics and switching losses, improving the efficiency and performance of the inverter. This paper suggests a technique for reducing current harmonics of grid-connected multilevel three-phase inverters using variable frequency carriers, without any corresponding increase of the number of switching commutations. The effectiveness of the suggested method has been confirmed through simulation results, which were compared to those obtained from the method of phase opposite disposition modulation using fixed frequency carriers.

Theoretical Design and Experimental Implementation of a Three-Phase Two-Level Inverter with an Adapted Gate Driver Based on Bootstrap Circuit for Grid-Connected Renewable Energy Systems

Theoretical Design and Experimental Implementation of a Three-Phase Two-Level Inverter with an Adapted Gate Driver Based on Bootstrap Circuit for Grid-Connected Renewable Energy Systems

Sequence extraction-based low voltage ride-through control of grid-connected renewable energy systems

Various faults can cause voltage sag in the power grid at different voltage levels across the network. Balanced or unbalanced voltage sags lead to grid instability by tripping off a large number of wind or solar power plants from the electric power network. This is particularly problematic to maintain the stability of renewable energy-rich converter-dominated modern power systems. To mitigate the adverse effects of voltage sag, grid-connected converters (GCCs) need to be capable of operating in self-healing and fault-tolerant mode by embedding low voltage ride-through (LVRT) capability into the control system of GCCs. In order to facilitate the implementation of LVRT capabilities for unbalanced faults, fast and accurate frequency-adaptive sequence extraction of grid voltages and currents is essential. This motivated the present work of making a systematic comparison of adaptive observer-based sequence extraction techniques to provide LVRT capabilities into the control system of GCCs. In order to show the effectiveness of each observer, various comparative analyses were performed through Matlab-based numerical simulation. Different observers were benchmarked by the dynamic performance improvement during the low-voltage fault period. Experimental results using a laboratory-scale prototype GCC show that adaptive observers are a suitable choice of sequence extractors for LVRT operation of grid-connected converters in unbalanced and distorted grids. The results obtained in this work will contribute to enhancing the stability of modern power systems that are getting more and more converter-dominated.

Robust Cascade MRAC for a Hybrid Grid-Connected Renewable Energy System

Hybrid grid-connected renewable energy systems have gained significant importance in sustainably responding to an increased electrical energy demand. These are time-varying nonlinear dynamical plants, where the value of their parameters depends on changing weather conditions and the alternating grid voltage with randomly fluctuating amplitude. This paper proposes a robust cascade MRAC for nonlinear plants representing a class of these systems, which includes n renewable energy converts and a DC/AC single-phase full bridge inverter. The proposal reduces commissioning time by avoiding linearization and knowledge of the plant parameters. Moreover, it includes specific formulas for tuning the controller parameters that decrease their adjustments based on trial and error. Finally, it uses a direct adaptive method with adaptive laws having σθ modification and an inner loop at least five times faster than the outer loop. The proposition validation includes the theoretical stability proof based on the Lyapunov stability method and Barbalat’s Lemma. Furthermore, it presents comparative simulation results with quoted cascade PI controllers for a monophasic system, including two renewable energy sources and injection. Both techniques effectively track setpoint changes of the energy sources’ currents and direct current bus voltage, showing the proposal similar or reduced ripple. At the same time, both ensure robustness against decreased photovoltage panels irradiance, increased fuel cells voltage, and grid voltage amplitude random fluctuations. However, the proposal does these things while avoiding prior linearization and unknowing the plant parameters.

Performance Enhancement of Grid-Connected Renewable Energy Systems Using UPFC

No one denies the importance of renewable energy sources in modern power systems in terms of sustainability and environmental conservation. However, due to their reliance on environmental change, they are unreliable systems. This paper uses a Unified Power Flow Controller (UPFC) to enhance the reliability and performance of grid-tied renewable energy systems. This system consists of two renewable sources, namely photovoltaic cells (PV) and wind turbines (WTs). The UPFC was selected for its unique advantage in both active and reactive power control. The UPFC is controlled with an optimized Fractional Order Proportional–Integral–Derivative (FOPID) controller. The parameters of this controller were tuned using an Atomic Search Optimization (ASO) algorithm. Simulation results confirm the efficiency of the suggested controller in supporting the reliability and performance of the hybrid power system during some disturbance events including voltage sag, swell, and unbalanced loading. In addition, power quality can be improved through reducing the total harmonic distortion. It is worth mentioning that two maximum point tracking techniques had been included for the PV and WT systems separately. MATLAB/SIMULINK 2021a software was used to model the system.

Hybrid Deep Learning-Based Grid-Supportive Renewable Energy Systems for Maximizing Power Generation Using Optimum Sizing

The hybrid electricity production idea is a technology designed to produce and use electrical energy from many sources as part of an integrated setup. The combined size and power maximization technique for grid-connected renewable energy systems is presented in this research. This study optimizes three distinct geographic locations with various wind and sun renewable energy input potentials. Convolutional neural networks with long short-term memory are designed to maximize electricity production while taking consumer load, demand, and weather conditions into account. The established technique emphasizes the significance of taking into account the concurrent optimization of sizing and power management. To find the hybrid power system building option with the optimum cost–benefit ratio, the Shuffled Shepherd Optimization Technique is used. The optimization analysis uses annual demand data, solar irradiation, and wind turbine power output with a 10-min precision. Investigations have been done into how the system’s various parts behave. The amount of PV panels, wind turbines, battery banks, and the capacity of the diesel generator, together with the error rate, are the best choice factors shown by simulation results for sizing and producing the electricity for hybrid energy system. The results support the proposed strategy’s potential for producing hybrid renewable power.

Solar-driven Dish Stirling System for sustainable power generation in Bangladesh: A case study in Cox’s Bazar

In recent years, the power sector of Bangladesh has seen a major development in terms of generation capacity. But as before, it is heavily dependent on fossil fuels overlooking the potential of renewable energy resources. The scope for grid-connected renewable energy systems has not been explored too far and in terms of solar thermal energy and concentrating solar power (CSP), it is even less. This study focuses on assessing the techno-economic feasibility of solar-driven Dish Stirling system for large-scale grid-connected power generation in Bangladesh. Detailed modeling and optimization of a 100 MW Dish Stirling power plant have been carried out in Cox’s Bazar, Bangladesh, a location suitable for solar energy harnessing due to favorable climatic conditions. The modeling parameters and weather data have been collected from relevant literature, various solar data providers, and specific plant parameters have been optimized for the Bangladeshi climatic condition. Simulation of the modeled plant carried out by the System Advisor Model (SAM) shows that, it can supply 129.856 GWh electricity annually operating at an overall efficiency of 24.91% which is much higher than the values reported in similar literature for the South-Asian regions. The levelized cost of electricity (LCOE) has been determined to be 10.18 cents/kWh, which is highly competitive and promising. The insights obtained from this study can be a perfect starting point for the policymakers and concerned authorities of Bangladesh to further explore the viability of this technology for renewable and sustainable power.

Energy Management Strategies and Control of a Hybrid Grid-Connected Renewable Energy System with Three Modes Controlled DFIG based Wind Turbine

This paper deals with three novel Energy Management Strategies (EMS) for a Grid-Connected Hybrid Renewable Energy System (GCHRES) that utilizes a photovoltaic solar generator (PVG) and three modes controlled Wind Turbine system based on DFIG, along with a storage system based on battery. The CCP serves as the connection point between the Utility Grid (UG) and a residential AC load, primarily powered by the Renewable Energy Sources (RES). The UG acts as a backup source, and to limit the subscription power exceeding, the battery is designed to perform the peak shaving. Given the high vulnerability of batteries, the primary goal of all proposed EMSs is to maintain a continuous power supply while also protecting the battery from overcharging and deep discharge. Additionally, these EMSs are designed to lower the monthly energy bill for UG customers. The specific features of these EMSs vary depending on the metering types. And to maintain balance within the system, All components powers are controlled. The performances of the GCHRES were evaluated using simulation in MATLAB/SIMULINK environment. Results concerning the continuous ability to meet demand, stability of the system, tracking of references, and also wind turbine system control are discussed in this article. Keywords—Control and regulation, DFIG, Energy Management Strategies, Grid-Connected, Peak shaving, Photovoltaic,Pitch control, Renewable Energy, Wind Turbine

Performance Analysis Using Multi-Year Parameters for a Grid-Connected Wind Power System

One of the most crucial solutions to the issues of climate change and global warming is clean energy. However, creating intelligent, resilient, and sustainable systems is a worldwide problem, particularly for grid-connected Renewable Energy Systems (RES). Therefore, it is important to investigate how prospective changes in electricity pricing, renewable energy sources, and load demand could affect system performance during the projects. This paper presents a techno-economic analysis of a grid-connected wind energy system located in the Al-Jouf region in Saudi Arabia. To this end, the potential of renewable energy sources was assessed using Hybrid Optimization of Multiple Electric Renewables (HOMER) software, that also carried out the technical and economic study utilizing multi-year parameters. The novelty of this study is that it is the first-ever investigation of a grid-connected wind farm system in Saudi Arabia that considers the impact of multi-year parameters such as the grid price, system fixed operation, maintenance cost, and the AC electric load. The results showed that the proposed system in the chosen area recorded a very low Levelized Cost of Energy (LCOE) of around 0.06 USD/kWh compared to other systems. Also, running a multi-year model showed that the considered parameters have an impact on the system’s performance, and this reflects the importance of considering these parameters in any such system that will increase the study’s accuracy.

A Hybrid PWM Technique to Improve the Performance of Voltage Source Inverters

Due to the rapid advancement of power semiconductor devices, the use of voltage source inverters (VSIs) has gained widespread acceptance. As a consequence, the performance of the voltage source inverter has emerged as a critical aspect that is highly reliant on the modulation strategy. The pulse width modulation (PWM) technique is the most widely utilized method of controlling power semiconductor switches of VSI. Power quality is always considered as an industrial concern for VSI-based power system such as grid-connected renewable energy systems and industrial motor drives which largely depends on the PWM technique used for switching. However, the existing PWM schemes for VSIs suffer from high total harmonic distortion (THD) and power loss problems. To mitigate the THD and power loss of VSI, a hybrid PWM technique has been proposed in this paper. The proposed hybrid PWM technique introduces a modified modulating signal along with newly shaped carrier signal. A two-level VSI is used to evaluate the performance of the proposed hybrid PWM technique both for with and without filter conditions against RL load. The proposed hybrid PWM technique offers 0.89% filtered voltage THD and 0.69% filtered current THD which are lower than that of existing PWM techniques. On the contrary, at without filter condition, the proposed hybrid PWM technique shows THDs of 45.77% and 12.50% for inverter output voltage and current, respectively which are also lower than those of existing PWM techniques. Apart from these, the proposed hybrid PWM technique reduces the switching and conduction power losses of the VSI as compared to existing PWM techniques. The simulation works are carried out in MATLAB/Simulink environment and a reduced scale experiment is conducted in laboratory to evaluate the performance of the proposed hybrid PWM technique.

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit. Renewable energy sources are playing a significant role in the modern energy system with rapid development. In renewable sources like fuel combustion and solar energy, the generated voltages change due to their environmental changes. To develop energy resources, electric power generation involved huge awareness. The power and output voltages are plays important role in our work but it not considered in the existing system. For considering the power and voltage, Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier (GPIC-MDCNNC) Model is introduced for the grid-connected renewable energy system. The input information is collected from two input sources. After that, input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model. Hidden layer 1 is transferred to hidden layer 2. Gaussian activation is employed for determining the output voltage with help of the controller. At last, the output layer offers the last value in GPIC-MDCNNC Model. The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages. GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works. The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

Mitigation of power oscillations for energy harvesting capability improvement of grid-connected renewable energy systems

The increasing penetration of renewable energy sources in the power system raises several challenges from the power quality and output power fluctuations point of view. However, the output power oscillations and power quality problems caused by the low order harmonic oscillations at the second-order frequency of the utility grid on the dc-link capacitor are emerging as major issues for the efficient operation of power converters. In this manner, a novel method based on modification in the control algorithm to alleviate the output power oscillation and reduce the output current total harmonic distortion is proposed. The proposed control approach seems as a promising solution for the satisfactory grid operation of renewable energy sources regarding not requiring any additional circuit and increment in the capacity of the dc-link capacitor. Moreover, mitigation of oscillations enhances the active power reference tracking capability of the controller that enables the overall efficiency improvement of the system. The performed case studies reveal that the overall efficiency rate of the system is increased by 1.3%. On the other hand, the output current harmonic distortion is decreased below 3% even under low power operating conditions. Also, the complexity level of the control algorithm is quite low for real-time implementation.