A methodology for the construction of efficient PLC based low-power photovoltaic generation plants

The research of the operation of low-power photovoltaic generation plants used for self-contained electric power supply in Siberian climatic conditions is performed in this paper. It provides an analysis of the operation of individual units of an automated control system, and gives recommendations for the selection of hardware components. The article describes the operational principles, developed based on functional modules of the programmable logic controller, ensuring maximum possible use of solar energy in this continuous power supply system. The results of plant operation have been obtained, in the form of a power counter log, as well as data on the volume of solar energy produced in both overcast and in sunny weather, throughout the observation period. The article provides visual illustration of generated energy, which could be used to assess the efficiency and economic viability of the low-power photovoltaic plant. Authors would like to point out that examples of the proposed methodology for the construction of self-contained power supply systems can be found in existing industrial facilities, on which further scientific research can be based

A modified particle swarm optimization based maximum power point tracking for photovoltaic converter system

This thesis presents a modified Particle Swarm Optimization based Maximum Power Point Tracking for Photovoltaic Converter system. All over the world, many governments are striving to exploit the vast potential of renewable energy to meet the growing energy requirements mainly when the price of oil is high. Maximum Power Point Tracking (MPPT) is a method that ensures power generated in Photovoltaic (PV) systems is optimized under various conditions. Due to partial shading or change in irradiance and temperature conditions in PV, the power-voltage characteristics exhibit multiple local peaks; one such phenomenon is the global peak. These conditions make it very challenging for MPPT to locate the global maximum power point. Many MPPT algorithms have been proposed for this purpose. In this thesis, a modified Particle Swarm Optimisation (PSO)-based MPPT method for PV systems is proposed. Unlike the conventional PSO-based MPPT methods, the proposed method accelerates convergence of the PSO algorithm by consistently decreasing weighting factor, cognitive and social parameters thus reducing the steps of iterations and improved the tracking response time. The advantage of the proposed method is that it requires fewer search steps (converges to the desired solution in a reasonable time) compared to other MPPT methods. It requires only the idea of series cells; thus, it is system independent. The control scheme was first created in MATLAB/Simulink and compared with other MPPT methods and then validated using hardware implementation. The TMS320F28335 eZDSP board was used for implementing the developed control algorithm. The results show good performance in terms of speed of convergence and also guaranteed convergence to global MPP with faster time response compared to the other MPPT methods under typical conditions (partial shading, change in irradiance and temperature, load profile). This demonstrates the effectiveness of the proposed method

Floating solar panel park

Treball desenvolupat dins el marc del programa 'European Project Semester'.This Final Report is the culmination of a four month long design study on floating solar panel park feasibility in Vaasa, Finland. The Floating Ideas Team was tasked with coming up with a design that would not only work, but also make a profit. The team focused a lot of time on initial research, an iterative design process, and experiments to gather information that could not be found during the research phase. In this report, one can expect to find the major findings from research in many different areas such as location, panel design, flotation design, cooling techniques, and efficiency adding techniques. The first takeaway is that implementing floating solar parks in Finland would require adding efficiency techniques such as mirrors or concentrators. Second, how the panels are placed means a lot in a location so far north. Placing the panels far away from each other and horizontally will reduce the negative impact of shadows. And third, the rotation of the structure is important in increasing efficiency. Multiple axis tracking is not necessary, but tracking in the vertical axis can add a 50% increase in power generated. This research then lead into the defining of four initial designs which were eventually paired down into one. The largest factors leading to the change in design were the combination of rotation and anchoring methods, the flotation structure, and the structure required hold the panel modules together. In the end, the final design is a modular circular design with panels and mirrors to help add efficiency, approximately 37%. From there, an economic and environmental feasibility study was done and for both, this design was deemed feasible for Finland. With the design, detailed in this report, it would be possible to implement this and make a profit off of it, leading the team to believe that this should be implemented in places looking for alternatives for renewable energy production

New Trends in the Control of Grid-Connected Photovoltaic Systems for the Provision of Ancillary Services

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).The gradual displacement of conventional generation from the energy mix to give way to renewable energy sources represents a paradigm shift in the operation of future power systems: on the one hand, renewable technologies are, in general, volatile and difficult to predict; and on the other hand, they are usually connected to the grid through electronic power converters. This decoupling due to power converters means that renewable generators lack the natural response that conventional generation has to the imbalances between demand and generation that occur during the regular operation of power systems. Renewable generators must, therefore, provide a series of complementary services for the correct operation of power systems in addition to producing the necessary amount of energy. This paper presents an overview of existing methods in the literature that allow photovoltaic generators to participate in the provision of ancillary services, focusing on solutions based on power curtailment by modifying the traditional maximum power point tracking algorithm

Comparison of solar output of vertical and inclined solar panels in the high north

Abstract. This thesis presents a comprehensive literature review on the endurance of solar photovoltaic (PV) systems in high north conditions and the optimized setup parameters for maximizing their output. To simulate data, PV panels were considered in the high north region of Oulu, Finland, and their output was measured under various setup parameters. The data was then analyzed using SketchUp simulation with the Skelion plug- in to determine the optimized setup parameters that yield maximum output. The findings of the study highlight the importance of tilt angles and orientation for maximizing energy production. The evaluation of different tilt angles, including vertical panels, roof-mounted panels with varying tilt angles, and inclined panels on a carousel, revealed that tilt angles closer to 45 to 47 degrees contribute to improved solar PV performance. Inclined panels exhibited peak outputs during the summer months, while vertically mounted panels performed better during spring. The optimal tilt angle was determined to be 45–47 degrees, enabling effective energy generation throughout the year. The study also emphasized the significance of south-facing panel orientation, which consistently yielded higher energy production compared to other orientations. Furthermore, the thesis suggests future research directions, including the incorporation of complex weather variables, analysis of regional variation and temperature patterns, and the integration of advanced technologies into solar PV system simulations. Overall, this research contributes valuable insights for the design, installation, and optimization of solar PV systems in high north conditions, promoting the adoption of efficient and sustainable solar energy solutions

Photovoltaic Emulation System and Maximum Power Point Tracking Algorithm Under Partial Shading Conditions

In this thesis, a novel photovoltaic (PV) emulator and the state-of-art learning–based real-time hybrid maximum power point tracking (MPPT) algorithms have been presented. Real-time research on PV systems is a challenging task because it requires a precise PV emulator that can faithfully reproduce the nonlinear properties of a PV array. The prime objective of the constructed emulator based on integration of unilluminated solar panels with external current sources is to overcome the constraints such as the need for wide surrounding space, high installation cost, and lack of control over the environmental conditions. In addition, the proposed PV emulator is able to simulate the electrical characteristics of the PV system under uniform irradiation as well as partially shading conditions (PSC). Moreover, the application of MPPT technology in PV systems under PSC conditions is challenging. Under complex environmental conditions, the power-voltage (P-V) characteristic curve of a PV system is likely to contain both local global maximum power points (LMPPs) and global maximum power points (GMPP). The MPPT algorithm applied to a PV system should have minimal steady-state oscillations to reduce power losses while accurately searching for the GMPP. The proposed MPPT algorithms resolved the drawbacks of the conventional MPPT method that have poor transient response, high continuous steady-state oscillation, and inefficient tracking performance of maximum power point voltage in the presence of partial shading. The intended algorithms have been verified using MATLAB/Simulink and the proposed PV emulator by applying comparative analysis with the traditional MPPT algorithms. In addition, the performance of the proposed MPPT algorithms and control scheme is validated experimentally with the implementation of MATLAB/Simulink/Stateflow on dSPACE Real-Time-Interface (RTI) 1007 processor board and DS2004 A/D and CP4002 Digital I/O boards. The results indicate that the algorithm is effective in reducing power losses and faster in tracking the speed of the maximum power point with less oscillation under partial shading conditions. In addition, excellent dynamic characteristics of the proposed emulator have been proven to be an ideal tool for testing PV inverters and various maximum power point tracking (MPPT) algorithms for commercial applications and university studies

Performance Analysis Of Hybrid Ai-Based Technique For Maximum Power Point Tracking In Solar Energy System Applications

Demand is increasing for a system based on renewable energy sources that can be employed to both fulfill rising electricity needs and mitigate climate change. Solar energy is the most prominent renewable energy option. However, only 30%-40% of the solar irradiance or sunlight intensity is converted into electrical energy by the solar panel system, which is low compared to other sources. This is because the solar power system\u27s output curve for power versus voltage has just one Global Maximum Power Point (GMPP) and several local Maximum Power Points (MPPs). For a long time, substantial research in Artificial Intelligence (AI) has been undertaken to build algorithms that can track the MPP more efficiently to acquire the most output from a Photovoltaic (PV) panel system because traditional Maximum Power Point Tracking (MPPT) techniques such as Incremental Conductance (INC) and Perturb and Observe (P&Q) are unable to track the GMPP under varying weather conditions. Literature (K. Y. Yap et al., 2020) has shown that most AIbased MPPT algorithms have a faster convergence time, reduced steady-state oscillation, and higher efficiency but need a lot of processing and are expensive to implement. However, hybrid MPPT has been shown to have a good performance-to-complexity ratio. It incorporates the benefits of traditional and AI-based MPPT methodologies but choosing the appropriate hybrid MPPT techniques is still a challenge since each has advantages and disadvantages. In this research work, we proposed a suitable hybrid AI-based MPPT technique that exhibited the right balance between performance and complexity when utilizing AI in MPPT for solar power system optimization. To achieve this, we looked at the basic concept of maximum power point tracking and compared some AI-based MPPT algorithms for GMPP estimation. After evaluating and comparing these approaches, the most practical and effective ones were chosen, modeled, and simulated in MATLAB Simulink to demonstrate the method\u27s correctness and dependability in estimating GMPP under various solar irradiation and PV cell temperature values. The AI-based MPPT techniques evaluated include Particle Swarm Optimization (PSO) trained Adaptive Neural Fuzzy Inference System (ANFIS) and PSO trained Neural Network (NN) MPPT. We compared these methods with Genetic Algorithm (GA)-trained ANFIS method. Simulation results demonstrated that the investigated technique could track the GMPP of the PV system and has a faster convergence time and more excellent stability. Lastly, we investigated the suitability of Buck, Boost, and Buck-Boost converter topologies for hybrid AI-based MPPT in solar energy systems under varying solar irradiance and temperature conditions. The simulation results provided valuable insights into the efficiency and performance of the different converter topologies in solar energy systems employing hybrid AI-based MPPT techniques. The Boost converter was identified as the optimal topology based on the results, surpassing the Buck and Buck-Boost converters in terms of efficiency and performance. Keywords—Maximum Power Point Tracking (MPPT), Genetic Algorithm, Adaptive Neural-Fuzzy Interference System (ANFIS), Particle Swarm Optimization (PSO

Performance evaluation of the photovoltaic system

The various renewable energy source technologies, Photovoltaics (PV) transforming sunlight directly into electricity, have become standard practice worldwide, especially in countries with high solar radiation levels. PV systems have been developed rapidly over recent years, and many new technologies have emerged from different producers. For each type of PV module, manufacturers provide specific information on rated performance parameters, including power at maximum power point (MPP), efficiency and temperature factors, all under standard solar test conditions (STC) 1000 W/m2. Air. In addition, the mass (AM) of 1.5 and the cell's temperature was 25 ̊C. Unfortunately, this grouping of environmental conditions is infrequently found in outdoor conditions. Also, the data provided by the manufacturers are not sufficient to accurately predict the performance of photovoltaic systems in various climatic conditions. Therefore, monitoring and evaluating the performance of the off-site systems is necessary. This thesis aims to overview various photovoltaic technologies, ranging from crystalline silicon (c-SI) to thin-film CdTe and GiCs. The following are the main parameters for evaluating the external units' performance to describe the PV systems' operation and implementation. In addition, a review of the impacts of various environmental and operational factors, such as solar radiation, temperature, spectrum, and degradation

Understanding macroscale functionality of metal halide perovskites in terms of nanoscale heterogeneities

Hybrid metal halide perovskites have shown an unprecedented rise as semiconductor building blocks for solar energy conversion and light-emitting applications. Currently, the field moves empirically towards more and more complex chemical compositions, including mixed halide quadruple cation compounds that allow optical properties to be tuned and show promise for better stability. Despite tremendous progress in the field, there is a need for better understanding of mechanisms of efficiency loss and instabilities to facilitate rational optimization of composition. Starting from the device level and then diving into nanoscale properties, we highlight how structural and compositional heterogeneities affect macroscopic optoelectronic characteristics. Furthermore, we provide an overview of some of the advanced spectroscopy and imaging methods that are used to probe disorder and non-uniformities. A unique feature of hybrid halide perovskite compounds is the propensity for these heterogeneities to evolve in space and time under relatively mild illumination and applied electric fields, such as those found within active devices. This introduces an additional challenge for characterization and calls for application of complimentary probes that can aid in correlating the properties of local disorder with macroscopic function, with the ultimate goal of rationally tailoring synthesis towards optimal structures and compositions