Grid-connected Photovoltaic Generation Research Articles

Novel five-level inverter based on DC power-capacitor series charge-discharge switching strategy

Photovoltaic power generation systems generally include four modules: solar cells, batteries, inverters and controllers. Among them, the inverter converts the direct current generated by the photovoltaic array into a power conversion device that meets the grid-connected requirements for industrial frequency alternating current, which is the key equipment for grid-connected photovoltaic power generation system. Most multilevel inverters in the market today are based on the Neutral-Point-Clamped (NPC) structure to generate voltages at different levels, and then control the magnitude and direction of the voltages through H-bridge circuits. This topology has been maturely used in three-level inverters, but if we want to build five-level, seven-level and more level numbers of inverters based on NPC, the number of capacitors used is increasing, and the circuit becomes more and more complex. Therefore, this paper proposes a five-level inverter topology based on single power supply-single capacitor series charging and discharging. (Power-capacitor series charge-discharge switching, PCSCDS). This inverter has a simple structure, comprehensible control strategy and requires only one DC power supply and one large capacitor to generate a five-level voltage. The topology is subsequently simulated and analyzed by MATLAB/Simulink simulation, which proves the effectiveness of the novel inverter.

Design Enhancement of Grid-Connected Residential PV Systems to Meet the Saudi Electricity Regulations

Distributed grid-connected photovoltaic (PV) generation explores several methods that produce energy at or near the point of consumption, with the aim of reducing electricity losses among transmission networks. Consequently, home on-grid PV applications have garnered increased interest from both scientific researchers and industry professionals over the last decade. Nevertheless, the growing installation of intermittent nature residential PV systems (R-PV) in low-voltage distribution networks is leading to more cautious considerations of technology limitations and PV design challenges. This conservative perspective arises from the standpoint of grid quality and security, ultimately resulting in the revocation of PV connection authorization. Hence, the design of R-PV systems should consider not only the specifications of the PV panels and load profiles but also the characteristics and requirements of the connected power grid. This project therefore seeks to enhance the design considerations of grid-connected PV systems, in order to help the end-users meet the grid codes set out by the Saudi Electricity Regulatory Authority (SERA). Since the maximum amount of generated power is essential for PV system optimization, the ratio of grid strength to maximum transmitted power was employed to ascertain the suitable capacity of the PV system, while the assessment of PV power output was utilized to specify the system size. Furthermore, a battery energy storage system (BESS) with a small size (~10% of the PV capacity) is employed to enhance the PV power quality for a dependable grid interconnection. The BESS is equipped with a versatile power controller in order to achieve the designed objectives. The obtained results show an essential advancement in terms of power quality and reliability at the customer’s connection point. Moreover, with the design assessment process, the low-voltage ride-through (LVRT) and power factor requirements can be met, in addition to the total harmonic distortion (THD) and frequency transient limitations. The proposed solution assists end-users in efficiently designing their own R-PV systems while ensuring quality and sustainability for authorized grid interconnection.

Energy enhancement in grid-connected photovoltaic generation systems using adaptive control technique

This research paper presents an innovative adaptive control technique for enhancing energy efficiency in grid-connected photovoltaic (PV) generation systems. By integrating an advanced high-gain DC-DC converter with adaptive and non-linear control methods, the proposed system ensures optimal power absorption from various renewable resources and maintains robust power transfer efficiency and reliability. Key results from the implementation at the Union Territory of Puducherry substation demonstrate significant improvements: the adaptive control system dynamically adjusts to changes in source voltage and load conditions, maintaining a constant output voltage with minimized steady-state error and low overshoot. The feedback controller effectively responds to fluctuations in duty cycle and output load, ensuring stable operation under varying environmental conditions. These enhancements contribute to a reliable and efficient integration of renewable energy sources into the power grid, addressing critical challenges in renewable energy technology.

Photovoltaic grid-connected monitoring technology considering power quality

Abstract With the continuous expansion of grid-connected photovoltaic power generation, monitoring the power quality fluctuations in intelligent photovoltaic grid-connected systems has become a key research direction. Addressing the issues of low accuracy in voltage and current monitoring, as well as insufficient monitoring of SOE events, a comprehensive power quality-oriented detection technology for photovoltaic grid integration is proposed. This technology enables automatic monitoring of power quality fluctuations by collecting signals such as voltage deviation and frequency deviation. The study compares techniques involving a determination of input voltage and current amplitudes, power, phase difference, and SOE event design. The results demonstrate that the power quality monitoring technology for photovoltaic grid integration proposed in this study exhibits high accuracy in monitoring the imbalance of three-phase voltage and current.

Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters

In the static stability analysis of the grid-connected photovoltaic (PV) generation and energy storage (ES) system, the grid-side is often simplified using an infinite busbar equivalent, which streamlines the analysis but neglects the dynamic characteristics of the grid, leading to certain inaccuracies in the results. Furthermore, the control parameter design does not consider the coupling relationships among parameters, resulting in arbitrary values and the inability to achieve overall optimality. To address these issues, this paper presents a comprehensive parameter optimization method for the oscillation characteristics of grid-connected PV generation and ES systems in various frequency ranges. Firstly, a detailed modeling of the grid-connected PV generation and ES system is conducted, resulting in the derivation of the system’s small-signal model. This study investigates the impact of parameters related to PV arrays, ES units, and the virtual synchronous generator (VSG) on the system’s characteristic roots using participation factors, sensitivity analysis, and eigenvalue root trajectories. Subsequently, based on the analysis of system root trajectories and the Particle Swarm Optimization (PSO) algorithm, a holistic parameter optimization design method for the system’s oscillation modes is proposed, and the parameter optimization results are obtained. Finally, the accuracy of the theoretical analysis is validated through perturbation testing using both Matlab/Simulink models and the small-signal model.

Asynchronous co-simulation of photovoltaic power generation system based on FPGA-CPU

In order to realize the real-time electromagnetic transient simulation of grid-connected photovoltaic power generation system with small time step, a heterogeneous multiplexed parallel real-time simulation method based on field programmable gate array (FPGA) and central processor is studied. According to the simulation accuracy requirements of the high and low frequency decoupling module, a differentiated simulation step size is used to solve the problem, and a 1us/50us FPGA-CPU multi-rate real-time simulation platform is constructed, in which the FPGA simulation part meets the real-time requirements of small-step transient simulation through the optimization of the response matching method and the EMTP algorithm, the offline simulation results are compared with Simulink, and the model before and after optimization is analyzed, the real-time electromagnetic transient simulation results are compared with Simulink, and the model before and after optimization is analyzed. By comparing the offline simulation results with Simulink and analyzing the FPGA resource consumption before and after model optimization, the accuracy and real-time performance of the co-simulation method for real-time simulation of grid-connected photovoltaic systems are verified.

Enhanced Multiobjective Optimization Algorithm for Intelligent Grid Management of Renewable Energy Sources

Optimal scheduling of microgrids (MGs) is a crucial component of smart grid optimization, playing a vital role in minimizing energy consumption and environmental degradation. However, existing methods tend to consider only a single optimization and do not consider the multiobjective optimization problem of MGs in a comprehensive and integrated way. This study proposes a comprehensive multiobjective optimal scheduling methodology for renewable energy MGs, incorporating demand-side management (DSM) considerations. Initially, a DSM multiobjective optimization model is formulated, focusing on the load shifting of controllable devices within the MG to refine the electricity consumption structure. This model contemplates the renewable energy consumption of the MG, customer electricity purchase costs, and load smoothness. Subsequently, a multiobjective optimization model for grid-connected MGs, encompassing wind and photovoltaic power generation, is constructed with the dual objectives of economic and environmental optimization for the MG. Ultimately, a multimodal multiobjective optimization algorithm, amalgamating a local convergence index and an environment selection strategy, is proposed to solve the model. The experimental results show that compared with other methods, the proposed method in this paper can reduce the integrated cost by 32.6% and 38.9% in summer and 19.4% and 40.2% in winter. This stands out as a unique contribution in the field of MG optimization, as it integrates DSM considerations into a multiobjective optimization model. This methodology achieves a balance between minimizing energy consumption and environmental degradation while also enhancing economic efficiency.

Application analysis based on solar grid-connected photovoltaic power generation and intermittent energy storage system

Solar photovoltaic power generation as a building electrical professional in the energy use of important technical means, through the realisation of photovoltaic conversion, can be a large amount of light energy transmission to the building application, once the electrical energy surplus is too much, but also can be transmitted to the power grid use. Based on this, the article takes the solar photovoltaic power generation system as the basis, comprehensively analyses the operation principle and basic characteristics of solar photovoltaic power generation, summarizes the main influencing factors of grid-connected solar photovoltaic power generation, as well as the impact of grid-connected solar photovoltaic power generation on the grid, and researches on applying automation technology to solar photovoltaic power generation, such as photovoltaic buildings, DC inverters, reactive power compensation, etc., so as to comprehensively improve the grid-connected solar photovoltaic power generation efficiency and provide a constant source of energy for buildings. Solar thermal power generation technology is another kind of solar power generation technology besides photovoltaic power generation. It is a renewable energy generation method that integrates solar thermal conversion power generation, large-scale heat storage and the characteristics of power grid synchronmachine. According to the influence of different intermittent heat load fluctuation characteristics on the design of solar heat collection and heat storage system, the design method of heat collection and heat storage of intermittent solar heating system is established. The operation characteristics of solar energy system in three typical intermittent heating modes are compared and analyzed. It provides the design method and operation optimization strategy for the efficient utilization of solar heating system.

Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation

Adaptive Regulated Sparsity Promoting Approach for Data-Driven Modeling and Control of Grid-Connected Solar Photovoltaic Generation

Power System Stability Impact of 2 MW Grid-Connected Photovoltaic Generation in Iraq-Sulaymaniyah

Power System Stability Impact of 2 MW Grid-Connected Photovoltaic Generation in Iraq-Sulaymaniyah

A novel MIMO SMC for comprehensive unified control of the cascaded DC‐DC and DC‐AC converters in grid connected photovoltaic systems

SummaryIt is well known that DC‐DC boost converters are cascaded with DC‐AC inverters for grid connection of the photovoltaic (PV) systems. In the traditional control approaches, the mentioned DC‐DC and DC‐AC converters are controlled separately to facilitate the controller design problem. However, from the controller design viewpoint, the overall structure of the grid connected PV generator is a multi‐input multi‐output (MIMO) system. The duty cycle of the DC‐DC converter and inverter modulation index are the control inputs and, on the other hand, generated photovoltaic DC power and exported power to the grid are control outputs. Moreover, the inverter DC link voltage should be stabilized by the closed‐loop controller as well as an internal control output. If controllers are designed separately, it means that the interaction between DC‐DC and DC‐AC controllers isn't considered accurately, and since the isolated models of DC‐DC converter and DC‐AC inverter are extracted based on some approximated assumptions, separate controller design cannot guarantee stability and robustness of the whole system in a wide range of operation. To cope with these problems, in this paper, a novel MIMO sliding mode controller (SMC) is developed for comprehensive closed‐loop control of the DC‐DC boost converter cascaded with a single‐phase DC‐AC grid connected photovoltaic inverter. In the proposed approach, the dynamic model of whole system is developed comprehensively at first, and then, a unique MIMO controller is designed to control both DC‐to‐AC and DC‐to‐DC converters together. To cope with the nonlinear characteristic of the system and uncertainty of model parameters in a wide range, a fixed‐frequency SMC is developed using the comprehensive state space model of the closed loop system. In the proposed MIMO‐SMC controller, the AC power (which is exported to the grid) and operating point of the PV source are controlled via inverter modulation index and duty cycle of the DC‐DC boost converter, respectively. Another major advantage of the proposed system is mitigating the non‐minimum phase characteristic of the boost converter through the indirect control of inverter DC link capacitor. To evaluate the performance of the designed control system, simulation results are compared with a standard linear PI controller. It is shown that the developed system has zero steady‐state error and enjoys faster dynamic response during the start‐up and step changes of AC and DC current references. Moreover, it can maintain the stability of closed‐loop systems in a wide range of operations.

A hybrid control topology for cascaded H-bridge multilevel inverter to improve the power quality of smart grid connected system: NBO-RERNN approach

This manuscript proposes a hybrid control technique for the grid-connected photovoltaic (PV) generation system with a cascaded multilevel inverter (CMLI). The proposed hybrid approach integrates the Namib beetle optimization (NBO) and recalling-enhanced recurrent neural network (RERNN) approach, known as the NBO-RERNN technique. The cascaded multilevel inverter (MLI) is intended to increase the optimum control signal using the proposed controller. The cascaded multilevel inverter (MLI) is designed by using the minimum count of switches. The key purpose of the proposed approach is to improve power regulation or maximal solar energy conversion system (SECS) and to achieve good power quality (PQ) of the system. The proposed NBO technique generates the optimum control signal dataset offline. Based on the fulfilled dataset, the RERNN generates the best CMLI control signals inonlinemanner. The resultant control signals are used to regulate cascaded multilevel inverter insulated gate bi-polar switches (IGBT). For this purpose, the proposed NBO-RERNN control topology defines converter switching states by modeling generation system operating modes. The parameter variations of the system and external disturbances are mitigated, and the load demands are fulfilled optimally by using this control technique. The NBO-RERNN control topology is implemented in MATLAB, and its performance is compared with existing approaches. The simulation reveals that the proposed method has lower THD than the existing one.

A Matching Scheduling Control Strategy Based on Modulation Wave Reconstruction for the Single-Phase Photovoltaic Cascaded Multilevel Inverter

The single-phase cascaded multilevel inverter (CMI) becomes an attractive solution for grid-connected photovoltaic (PV) power generation owing to its several advantages, such as the scalable modularity, the ability to reach the component level maximum power point tracking (MPPT), and the multilevel output voltage. However, the PV power of each H-bridge (HB) is uneven due to unequal illumination and partial shading of the PV panels, which may lead to overmodulation and further deteriorate the power quality of the grid-connected current. Existing methods to address this issue have not considered the effects on various operating conditions, and their application scope is limited. Therefore, this paper proposes a matching scheduling control strategy based on modulation wave reconstruction (MSMR). The relatively optimal modulation waveform of each HB is reconstructed scheduledly to match its PV power by detecting the operating conditions. Meanwhile, the PV power of each HB is adjusted to improve the ability to cope with the power unevenness. Finally, the proposed strategy is validated on a down-scaled experimental test bench.

A Dual Quasi Z-Source Based T-Type Five-Level Inverter With Improved HERIC Structure for Photovoltaic Applications

A new single-stage, T-type five-level inverter, which is based on a dual quasi Z-source, and an improved HERIC structure has been proposed in this paper. It is envisaged that the proposed power converter could find applications in the area of grid-connected PV generation. The conventional topologies, which are based on two-stage conversion are bulky and expensive. The proposed single-stage system overcomes these disadvantages. Furthermore, the issue of leakage current, which is of paramount interest in PV systems is addressed by the adaptation of a PWM scheme along with the employment of passive filters. It is shown that the leakage current is effectively suppressed by the elimination of the high-frequency transitions across the parasitic capacitance due to the combined effort of the PWM scheme and the filter. The proposed power converter also displays the capability of supporting reactive power, which is one of the essential requirements for the present-day PV inverters. The steady-state and the dynamic performances of the proposed power converter are assessed with simulation studies and are then experimentally validated. A comparative study of the proposed power converter with the existing quasi-Z-source-based power converters is also undertaken, which reveals the advantages of the proposed configuration.

Agricultural Grid Connected Photovoltaic System Design and Simulation in Egypt by using PVSYST Software

Agricultural Photovoltaic Systems are a key technology to achieve sustainable development goals by reducing competition between land for food and electricity. In addition, Agricultural Photovoltaic Systems are at the heart of the link between power generation, crop production and irrigation water conservation. The main ecophysiological constraint on crop production under photovoltaics is the reduction of light. It is difficult to recommend shade tolerance for some plant varieties due to insufficient information on shading conditions for most plants. The use of shading panels (photovoltaic panels) requires more crop-specific research to determine the optimal percentage of panels and their placement that will not reduce agricultural yields. Crop yield variation versus field shading and availability to maximize the system require extensive research. This study aims to develop a standard procedure for designing an agricultural grid-connected photovoltaic power generation system for solar power generation in an agricultural area in Bahteem, Egypt. The technical and annual performance of the grid-connected PV system was simulated using PV Syst software. The paper started with a pre-feasibility study of a grid-connected photovoltaic system using PV Syst. Software with an extensive database of meteorological data, including global daily horizontal solar irradiance, and a database of various renewable energy system components from different manufacturers. In this work, a comprehensive literature review of agricultural solar photovoltaic systems is conducted, with a particular focus on grid-connected systems, followed by a design procedure for grid-connected solar photovoltaic systems. The planned photovoltaic system will generate a total of 400 KWp of electricity. This generated electricity can drive down electricity prices by exporting excess electricity to the national grid. In addition, solar power systems are fuel-efficient and have a low environmental impact.

Architecture design of grid-connected exploratory photovoltaic power generation based on Internet of Things and construction of power marketing system

Abstract Solar energy, as a prominent clean energy source, is increasingly favored by nations worldwide. However, managing numerous photovoltaic (PV) power generation units via wired connections presents a considerable challenge. The advent of the Internet of Things (IoT) and cloud service technologies has facilitated the creation of an efficient and convenient PV grid-connected management system. This paper investigates IoT technology and PV grid-connected systems, integrating wireless sensor network technology, cloud computing service platforms and distributed PV grid-connected systems. We propose a Zigbee wireless network featuring ad hoc network functionality and Narrow Band Internet of Things (NB-IoT) smart gateway with multi-protocol and multi-device support. This system enables the collection and uploading of PV grid-connected system data to cloud service platforms, addressing daily operation and maintenance as well as intelligent management of distributed PV power stations. Furthermore, it expedites the aggregation and analysis of PV power generation data, streamlining power marketing across various regions.

Research on the joint optimal allocation of hydropower, wind energy and photovoltaic power generation in the basin

To improve the constancy of hydropower, wind energy and photovoltaic hybrid power generation systems for power furnish to the grid, the joint optimal allocation of hydropower, wind, and photovoltaic is studied. Firstly, the goal of the highest load-matching degree of hydropower, wind energy, and photovoltaic power is put forward. Considering the constraints such as the maximum total power generation ability of hydropower, wind energy, and photovoltaic power generation and the water balance of the reservoir, a joint optimal allocation model of hydropower, wind energy, and photovoltaic power generation in the basin is created, and solved by a progressive optimization algorithm. An example is given to illustrate the influence of source load matching degree on optimal allocation, and the validity of this approach is verified. The simulation results indicate that during grid-connected operation, hydropower, wind energy and photovoltaic complementary power generation can ensure high electricity furnish reliability and deliver more stable power to the grid.

Small-signal oscillatory stability of a grid-connected PV power generation farm affected by the increasing number of inverters in daisy-chain connection

The daisy-chain connection of inverters is one of the basic configurations of the power collecting network in a grid-connected photovoltaic (PV) power generation farm. In this study, the total impact of a cluster of M similar inverters in daisy-chain connection in the PV farm is examined in the following two aspects: 1) aggregated representation of the cluster of inverters is derived for stability study based on the dynamic equivalence. The derivation confirms the rationality of representing the cluster of inverters by an aggregated inverter connected to the external system via an equivalent reactance, which is the maximum eigenvalue of the matrix of daisy-chain connection defined in the article. 2) Analysis is conducted to indicate that the risk of oscillatory instability may be collectively induced by all the inverters in the daisy-chain connection in the cluster. This explains why the increasing number of inverters may imply the possible instability risk of a PV farm. An example of a power system with a grid-connected PV power generation farm is presented in the article to demonstrate and evaluate the analytical conclusion obtained.

Agricultural Grid Connected Photovoltaic System Design and Simulation in Egypt by using PVSYST Software

Agricultural Photovoltaic Systems are a key technology to achieve sustainable development goals by reducing competition between land for food and electricity. In addition, Agricultural Photovoltaic Systems are at the heart of the link between power generation, crop production and irrigation water conservation. The main ecophysiological constraint on crop production under photovoltaics is the reduction of light. It is difficult to recommend shade tolerance for some plant varieties due to insufficient information on shading conditions for most plants. The use of shading panels (photovoltaic panels) requires more crop-specific research to determine the optimal percentage of panels and their placement that will not reduce agricultural yields. Crop yield variation versus field shading and availability to maximize the system require extensive research. This study aims to develop a standard procedure for designing an agricultural grid-connected photovoltaic power generation system for solar power generation in an agricultural area in Bahteem, Egypt. The technical and annual performance of the grid-connected PV system was simulated using PV Syst software. The paper started with a pre-feasibility study of a grid-connected photovoltaic system using PV Syst. Software with an extensive database of meteorological data, including global daily horizontal solar irradiance, and a database of various renewable energy system components from different manufacturers. In this work, a comprehensive literature review of agricultural solar photovoltaic systems is conducted, with a particular focus on grid-connected systems, followed by a design procedure for grid-connected solar photovoltaic systems. The planned photovoltaic system will generate a total of 400 KWp of electricity. This generated electricity can drive down electricity prices by exporting excess electricity to the national grid. In addition, solar power systems are fuel-efficient and have a low environmental impact.

Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics.

Along with the increasing prominence of energy and environmental issues, solar energy has received more and more extensive attention from countries around the world, and the installed capacity of photovoltaic power generation, as one of the main forms of solar energy development, has developed rapidly. Solar energy is by far the largest available source of energy on Earth, the use of solar power photovoltaic system has the advantages of flexible installation, simple maintenance, environmentally friendly, etc., by the world's attention, especially the grid-connected photovoltaic power generation system has been rapid development. However, photovoltaic power generation itself is intermittent, affected by irradiance and other meteorological factors very drastically, and its own randomness and uncertainty are very large, and its grid connection affects the stability of the entire power grid. Therefore, the short-term prediction of photovoltaic power generation has important practical significance and guiding meaning. Multi-input deep convolutional neural networks belong to deep learning architectures, which use local connectivity, weight sharing, and subpolling operations, making it possible to reduce the number of weight parameters that need to be trained so that convolutional neural networks can perform well even with a large number of layers. In this paper, we propose a multi-input deep convolutional neural network model for PV short-term power prediction, which provides a short-term accurate prediction of PV power system output power, which is beneficial for the power system dispatching department to coordinate the cooperation between conventional power sources and PV power generation and reasonably adjust the dispatching plan, thus effectively mitigating the adverse effects of PV power system access on the power grid. Therefore, the accurate and reasonable prediction of PV power generation power is of great significance for the safe dispatch of power grid, maintaining the stable operation of power grid, and improving the utilization rate of PV power plants.