Small-scale Photovoltaic Systems Research Articles

Integrated Active and Reactive Power Control Methods for Distributed Energy Resources in Distribution Systems for Enhancing Hosting Capacity

Recently, there has been a significant increase in the integration of distributed energy resources (DERs) such as small-scale photovoltaic systems and wind turbines in power distribution systems. When the aggregated outputs of DERs are combined, excessive reverse current may occur in distribution lines, leading to overvoltage issues and exceeding thermal limits of the distribution lines. To address these issues, it is necessary to limit the output of DERs to a certain level, which results in constraining the hosting capacity of DERs in the distribution system. In this paper, coordination control methodologies of DERs are developed and executed to mitigate the overvoltage and overcurrent induced by DERs, thereby increasing the hosting capacity for DERs of the distribution system. This paper proposes three coordinated approaches of active and reactive power control of DERs, namely Var Precedence, Watt Precedence, and Integrated Watt and Var Control. The Var and Watt Precedence prioritizes reactive power for voltage (Q–V) and active power for current (P–I) to address network congestion, thereby enhancing hosting capacity. Conversely, the Integrated Var and Watt Precedence propose a novel algorithm that combines four control indices (Q–V, P–V, Q–I, and P–I) to solve network problems while maximizing hosting capacity. The three proposed methods are based on the sensitivity analysis of voltage and current to the active and reactive power outputs at the DER installation locations on the distribution lines, aiming to minimize DER active power curtailment. Each sensitivity is derived from linearized power equations at the operating points of the distribution system. To minimize the computation burden of iterative computation, each proposed method decouples active and reactive power and proceeds with sequential control in its own unique way, iteratively determining the precise output control of distributed power sources to reduce linearization errors. The three proposed algorithms are verified via case studies, evaluating their performance compared to conventional approaches. The case studies exhibit superior control effectiveness of the proposed DER power control methods compared to conventional methods when issues such as overvoltage and overcurrent occur simultaneously in the distribution line so that the DER hosting capacity of the system can be improved.

Experimental analysis of a photovoltaic self-consumption system for the offices of a local company

The article analyzes the prospect of a solution for the mutual interaction of a photovoltaic generator and the local electrical grid, within the context of electrifying the offices of a hydrocarbon storage facility. The idea is to examine the operational performance of a system that enables the maximum self-consumption of energy generated by a small-scale photovoltaic system installed on the facility's site. Such a system could prove beneficial for multiple consumers operating in a complex energy environment. The issue is scrutinized based on experimental data from a real case in Dakar, Senegal. An installation with a photovoltaic system of approximately 17 kWp peak power is monitored for a year to assess the reliability of the self-consumption photovoltaic system for a broader application. The data encompasses both the functioning of the photovoltaic system and its interaction with the electrical grid. The potential use of this solution in the context of promoting self-consumption policies to reduce electricity consumption expenses is discussed and analyzed, revealing a relatively high level of interaction with the electrical grid. Despite maintenance costs, the photovoltaic system proves to be cost-effective.

Performance Assessment User Interface to Enhance the Utilization of Grid-Connected Residential PV Systems

The share of renewable energy resources in modern electrical power networks is increasing in order to meet environmental and technical targets. Consequently, energy researchers and power providers have been focusing on optimizing the integration of renewable energy into existing power grids. One of the most significant growing applications of renewable energy resources is residential photovoltaic (PV) systems; therefore, this paper discusses a new methodology to enhance the utilization of small-scale and medium-scale PV systems. For this purpose, this study proposes a user-friendly interface to help novice users optimally design their own PV projects with the highest possible utilization of the installed panels. Unlike the commercially available design tools, the proposed interface in this paper provides a higher degree-of-freedom computational process, as well as the option of improving the generated power quality, while maintaining the simplicity of the required tools and inputs. The proposed methodology mainly relies on a deep mathematical analysis considering different generation and consumption aspects, such as the load profile, time of usage, ambient temperature, PV system specifications and location. Furthermore, the mechanism of integrating a small portion of Energy Storage Systems (ESSs), to improve the quality of the extracted power, is also discussed in this study. The user interface provides the ability to estimate optimal ESS usage versus the estimated price when energy is urgently required. The case study was conducted in Riyadh, Saudi Arabia, and the results showed an essential improvement in the efficiency, solar fraction and power quality of the studied PV project, which can be extended to other home and distributed generation (DG) scales.

Accelerating Convergence for the Parameters of PV Cell Models

Small-scale photovoltaic (PV) systems are essential for the local energy supply. The most commonly known PV cell is configured as a large-area p–n junction made from silicon, but PV systems today include PV cells of various manufactures and origins. The dependence relationship between current and voltage is nonlinear, known as the current–voltage characteristic. The values of the characteristic equation’s parameters define the working regime of the PV cell. In the present work, the parameter values are iteratively obtained by nonlinear regression for an explicit model. The acceleration of the convergence of these values is studied for an approximation simplifying the iterative calculation in the case of perpendicular offsets. The new estimations of parameters allow for a much faster estimate of the maximum power point of the PV system.

Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery

The photovoltaic (PV) industry boom has accelerated the need for accurately understanding the spatial distribution of PV energy systems. The synergy of remote sensing and artificial intelligence presents significant prospects for PV energy monitoring. Currently, numerous studies have focused on extracting rooftop PV systems from airborne or satellite imagery, but their small-scale and size-varying characteristics make the segmentation results suffer from PV internal incompleteness and small PV omission. To address these issues, this study proposed a size-aware deep learning network called Rooftop PV Segmenter (RPS) for segmenting small-scale rooftop PV systems from high-resolution imagery. In detail, the RPS network introduced a Semantic Refinement Module (SRM) to sense size variations of PV panels and reconstruct high-resolution deep semantic features. Moreover, a Feature Aggregation Module (FAM) enhanced the representation of robust features by continuously aggregating deeper features into shallower ones. In the output stage, a Deep Supervised Fusion Module (DSFM) was employed to constrain and fuse the outputs at different scales to achieve more refined segmentation. The proposed RPS network was tested and shown to outperform other models in producing segmentation results closer to the ground truth, with the F1 score and IoU reaching 0.9186 and 0.8495 on the publicly available California Distributed Solar PV Array Dataset (C-DSPV Dataset), and 0.9608 and 0.9246 on the self-annotated Heilbronn Rooftop PV System Dataset (H-RPVS Dataset). This study has provided an effective solution for obtaining a refined small-scale energy distribution database.

Optimization of Feed-in Tariff mechanism for residential and industrial photovoltaic adoption in Hong Kong

Due to the late introduction of Feed-in Tariff (FiT) Scheme to Hong Kong's context, its effectiveness, efficiency, and optimization have merely been investigated yet. Therefore, this study proposes two FiT mechanisms according to the policy characteristics and structure elucidated from existing literatures (i.e., payoff, tariff, and contract structures) (i) Mechanism 1 (i.e., M1): follows the current policy characteristics with a short-term contract structure, which terminates in 2033; and (ii) Mechanism 2 (i.e., M2) introduced a widely adopted policy characteristic with a long-term contract structure, which allows to sell all electricity generated to the power grid. The proposed FiT rate under M1 and M2 guarantees the profitability of the solar photovoltaic (PV) installers with different scales. For residential PV installers (i.e., small-scale PV systems with installed capacity less than10 kW), 6–10 years of discounted payback period (DPB) is considered as the optimization objective. As for industrial PV installers (i.e., large-scale PV systems with installed capacity of 200–1000 kW), 8–12% of internal rate of return (IRR) is considered as the optimization objective. Results indicate that the current FiT rate is slightly high for residential PV installers, and it is recommended to be lowered after 2024 to fit the proposed FiT range, and then levered up in 2028 to ensure the profitability of late participation under M1. Meanwhile, the current FiT rate is found significantly low for industrial PV installers, and it is suggested to be levered up to the proposed FiT range. Furthermore, policy reform is highly recommended altering from M1 to M2 when current policy expires (i.e., 2033). In terms of the techno-economic analysis based on the calculation of levelized profit of electricity (LPOE) where self-consumption of PV system-generated electricity is considered as energy bill savings under M1, the long-term profitability for industrial PV installers under M1 is found to be higher than M2, whereas the opposite is observed for residential PV installers. The contribution of this study is attributed to the flexibility and applicability of the optimized results, the context-specific investigation of the market-independent renewable energy development, and the comprehensive parameters considered in the model development. The results of this study are believed to shed light on policy implications, grid parity, and investment recommendations for the government, utility companies, and PV installers in the long run.

A Sustainable Fault Diagnosis Approach for Photovoltaic Systems Based on Stacking-Based Ensemble Learning Methods

In this study, a novel technique for identifying and categorizing flaws in small-scale photovoltaic systems is presented. First, a supervised machine learning (neural network) was developed for the fault detection process based on the estimated output power. Second, an extra tree supervised algorithm was used for extracting important features from a current-voltage (I–V) curve. Third, a multi-stacking-based ensemble learning algorithm was developed to effectively classify faults in solar panels. In this work, single faults and multiple faults are investigated. The benefit of the stacking strategy is that it can combine the strengths of several machine learning-based algorithms that are known to deliver good results on classification tasks, producing results that are more precise and efficient than those produced by a single algorithm. The approach was tested using an experimental dataset and the findings show that it could accurately diagnose faults (a detection rate of around 98.56% and a classification rate of around 96.21%). A comparison study with different ensemble learning algorithms (AdaBoost, CatBoost, and XGBoost) was conducted to evaluate the effectiveness of the suggested method.

Day-Ahead Prediction of Distributed Regional-Scale Photovoltaic Power

Day-ahead forecasts are required by electricity market investors to make informed decisions on the trading floor. Whereas it is relatively easier to predict the performance of a few large-scale photovoltaic (PV) systems, a large number of small-scale PV systems with a wide geographical spread poses more challenges because they are often not metered for real-time monitoring. This paper proposes an artificial neural network (ANN)-based model to achieve regional-scale day-ahead PV power forecasts based on weather variables from numerical weather predictions (excluding solar irradiance) as inputs. The model was first implemented by dividing a region into clusters and selecting a representative site for each cluster using data dimension reduction algorithms. Solar irradiance forecasts were then generated for each representative PV system and the corresponding PV power was simulated. The cluster power output was obtained using a linear upscaling model and summed to produce regional-scale power forecasts. The model’s accuracy is validated using power generation data of several distributed systems in California. The results show at least a 29-percent root mean square error reduction over the benchmarking models.

Techno-economic feasibility analysis of grid-connected residential PV systems in Algeria

Small-scale photovoltaic (PV) power systems have been proven to be successful in generating electricity, conserving fossil fuels, and reducing greenhouse gas emissions in the residential sector, which is one of the largest consumers of energy. In Algeria, to reduce energy consumption in this sector, the authorities are considering implementing a policy that would encourage grid-connected residential PV systems. This paper presents a techno-economic assessment of grid-connected residential PV systems in four climate zones in Algeria. This work was performed using HOMER software for two different PV system configurations, grid/PV and grid/PV/battery. The technical performances of the considered systems were evaluated through the assessment of the self-consumption and self-sufficiency, while the net present value (NPV), internal rate of return (IRR), profitability index (PI), and discounted payback period (DPBP) were used to determine their feasibility. A sensitivity analysis was carried out to evaluate the effects of feed-in tariff (FiT), battery costs, and PV array capacity on the profitability of the systems. The results revealed that the grid/PV systems are technically and economically feasible in all of the four climate zones. For the grid/PV/battery systems, the grant of battery costs and the development of a regional FiT system are recommended. This article provides a tool for policymakers to assess the technical and financial performance of residential solar PV systems to develop adequate policy supports and tariff structures for Algeria.

Global Flexible Power Point Tracking in Photovoltaic Systems Under Partial Shading Conditions

Flexible power point tracking (FPPT) aims to regulate the output power of photovoltaic (PV) systems to a predefined value to enable grid support functionalities, such as virtual-inertia provision. Even though partial shading is a common condition in PV systems, especially in small-scale residential PV systems, the current literature, only investigates FPPT control under uniform shading conditions. This article proposes a novel global FPPT (GFPPT) algorithm to provide two functionalities in PV systems under partial shading conditions: first, constant power control (CPC), in which the PV power is regulated to predefined reference value, and second, power reserve control (PRC), under which a constant power reserve is kept in the PV system under steady state to enable grid support under transients. The conventional global maximum power point tracking (GMPPT) algorithm is also modified to adapt with the proposed GFPPT algorithm. The aim of such modification is to obtain faster dynamics after the detection of any environmental changes. Experimental results under dynamically changing partial shading conditions verify the effectiveness of the proposed algorithm in providing CPC or PRC functionalities in PV systems.

Evaluation of solar tracking systems applied to small-scale photovoltaic systems: a review

This work evaluates solar tracking systems in application to small-scale photovoltaic systems. To do this, these systems are divided into two subsystems: one-axis solar tracking subsystem and two-axis solar tracking subsystem. Each is explored for its suitability as a suitable technology for small-scale application, defined by meeting a set of seven attributes. A specialized literature review process was developed specifically to present the potential of each subsystem evaluated, through an evaluation matrix. The analysis of solar tracking technologies and attributes was carried out through the formal concept analysis methodology. To facilitate the processing of information, a free access tool called concept Explorer is used. The analysis carried out shows that two-axis solar tracking systems are the most promising option as they meet a greater number of attributes than single-axis tracking systems. The applied methodology has proven to be useful for the evaluation of renewable technologies and serves as a reference, to carry out analyzes that group and link different alternatives as a model for evaluating a deterministic set of attributes.

Low-cost pole and wire photovoltaic racking

Although solar photovoltaic (PV) module costs have been declining aggressively there has been only modest capital cost improvements for the balance of systems (BoS) in general, and for racking in particular. For small-scale PV systems racking can make up the majority of the capital expenditures. To overcome this challenge, in this study a novel parametric mobile low-cost open source racking system is designed using open source software and validated with numerical modeling. The system is designed to use readily accessible vertically-mounted conduit and steel cables and be built with common hand tools so that it is easily replicable in remote areas and the developing world. It can be assembled and deployed in half an hour and easily dissembled and flat-packed for transport. The system can be fabricated for less than commercial racks and is completely customizable in terms of tilt angle and size.The racking system is designed to resist light to moderate wind loads consistent with everyday breezes rather than those commonly used for structural design which are based on extreme events; as it is assumed the racking system will be disassembled and stowed prior to an extreme wind event.

Investigation of Small-Scale Photovoltaic Systems for Optimum Performance under Partial Shading Conditions

Not only are small photovoltaic (PV) systems widely used in poor countries and rural areas where the electrical loads are low but they can also be integrated into the national electricity grid to save electricity costs and reduce CO2 emissions. Partial shading (PS) is one of the phenomena that leads to a sharp decrease in the performance of PV systems. This study provides a comprehensive performance investigation of small systems (consisting of ten modules or fewer) under all possible shading patterns that result from one shading level (300 W/m2 is chosen). The most common configurations are considered for which a performance comparison is presented. Five small systems of different sizes are studied under PS. A new simplifying method is proposed to identify the distinct PS patterns under study. Consequently, the number of cases to be studied is significantly reduced from 1862 to 100 cases only. The study is conducted using the MATLAB/SimulinkÒ environment. The simulation results demonstrate the most outperformed configuration in each case of PS pattern and the amount of improvement for each configuration. The configurations include static series-parallel (SP), static total-cross-tied (TCT), dynamic switching between SP and TCT, and TCT-reconfiguration. The study provides PV systems’ owners with a set of guidelines to opt for the best configuration of their PV systems. The optimum recommended configuration is TCT reconfiguration, rather than dynamic switching between SP and TCT. The less recommended option, which enjoys simplicity but is still viable, is the static TCT. It outperforms the static SP in most cases of PS patterns.

Modeling predictive suitability to identify the potential of wind and solar energy as a driver of sustainable development in the Red Sea state, Sudan.

This paper investigated the potential and economic validity of wind and solar energy at 17 selected locations in the Red Sea state, Sudan, for the first time. To this aim, the NASA database was utilized. The results demonstrated that vertical axis wind turbines would be a good solution for electricity generation for building in the selected locations. Additionally, it is found that the chosen areas are suitable for installing photovoltaic (PV) systems due to the high-value solar radiation. Moreover, the economic viability of small-scale wind and PV systems for rooftop buildings in the selected regions is investigated. For a financial analysis of wind turbines, the performance of different characteristics of vertical axis winds was evaluated based on the determination of capacity factor and energy production cost. For the economic validity of installing PV systems, RETScreen Expert software was used. The results indicate that the annual production energy from wind turbines and solar power is within the range of 158.50-29,063.93kWh and 6648-15,533 kWh, respectively. This amount of energy output would reduce the effect of global warming and enhance the sustainable technological development of the country. Moreover, the results indicate that model#9 (Vertical Axis Wind Generator-V) with a capacity of 5kW has the lowest cost value (0.08703-0.01025 $/kWh) compared to the other selected turbines for the studied locations. Besides, the average energy production cost is within the range of 0.036-0.049 $/kWh for PV systems. In the end, it is concluded that using small-scale renewable energy systems will help reduce the electricity bills and the dependency on fossil fuels, the effect of global warming, and enhance the country's sustainable technological development.

Autonomous Dual Active Power-frequency Control in Power System with Small-scale Photovoltaic Power Generation

Active power control of the photovoltaic (PV) power generation system is a promising solution to regulate frequency fluctuation in a power system with high penetration of renewable energy. This paper proposes an autonomous active power control of a small-scale PV system for supporting the inertial response of synchronous generators and power-frequency control. In the proposed control approach, an effective grid frequency regulation scheme is realized using slow- and fast-frequency responses. A low-pass filter based frequency measurement is used for slow-frequency response, while direct frequency measurement is used for fast-frequency response. The designed dual droop characteristic-based control is shaped to achieve a smooth transition between slow- and fast-frequency responses. The performance of the proposed control approach is demonstrated for serious disturbance scenarios, i. e., considerable power-load imbalance and generation trip. In the power-load imbalance test scenario, the proposed control approach works properly within the normal frequency deviation region even when the frequency deviation exceeds that region occasionally. In the generation trip test, the frequency deviation is mitigated quickly, and the employed droop control is smoothly transferred from the slow- to fast-frequency responses.

Takagi–Sugeno State Delayed Feedback and Integral Control for PV Systems: Modeling, Simulation, and Control

The integration of the large-scale photovoltaic systems has experienced significant growth, which is similarly expected to occur with small-scale photovoltaic systems. Since small-scale systems must be simple in cost-effective components, control strategies must be implemented in low complexity circuits. However, current maximum power point tracking (MPPT) algorithms are generally complex and require electronic components to support variable control gains for different irradiance conditions, preventing simple MPPT implementations suitable for small-scale photovoltaic systems. This paper proposes a new control strategy to tackle the power tracking problem of the power systems. First, a dynamic model of the photovoltaic system is described and converted into a Takagi–Sugeno (T-S) model. Then, an MPPT scheme is proposed in series with a fixed integral and a fuzzy gain state delay feedback controller, which avoids the need for a variable control gain, simplifying the electronic implementation of the control strategy. New delay-dependent stabilization conditions based on the Lyapunov-Krasovskii functional are proposed in terms of a convex optimization problem, where the delayed feedback and integral gains are designed simultaneously. Simulation results using Matlab and Simulink are used to validate the proposed method.

A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays

In this paper, a novel fault detection and classification method for photovoltaic (PV) arrays is introduced. The method has been developed using a dataset of voltage and current measurements (I–V curves) which were collected from a small-scale PV system at the RELab, the University of Jijel (Algeria). Two different machine learning-based algorithms have been used in order to detect and classify the faults. An Internet of Things-based application has been used in order to send data to the cloud, while the machine learning codes have been run on a Raspberry Pi 4. A webpage which shows the results and informs the user about the state of the PV array has also been developed. The results show the ability and the feasibility of the developed method, which detects and classifies a number of faults and anomalies (e.g., the accumulation of dust on the PV module surface, permanent shading, the disconnection of a PV module, and the presence of a short-circuited bypass diode in a PV module) with a pretty good accuracy (98% for detection and 96% classification).

Optimal Sizing and Cost Minimization of Solar Photovoltaic Power System Considering Economical Perspectives and Net Metering Schemes

In this paper, economic feasibility of installing small-scale solar photovoltaic (PV) system is studied at the residential and commercial buildings from an end-user perspective. Based on given scenarios, the best sizing methodology of solar PV system installation has been proposed focusing primarily on the minimum payback period under given (rooftop) area for solar PV installation by the customer. The strategy is demonstrated with the help of a case study using real-time monthly load profile data of residential as well as commercial load/customers and current market price for solar PVs and inverters. In addition, sensitivity analysis has also been carried out to examine the effectiveness of net metering scheme for fairly high participation from end users. Since Saudi Arabia’s Electricity and Co-generation Regulatory Authority (ECRA) has recently approved and published the net metering scheme for small-scale solar PV systems allowing end users to generate and export energy surplus to the utility grid, the proposed scheme has become vital and its practical significance is justified with figures and graphs obtained through computer simulations.

Socioeconomic Factors Influencing the Prosumer’s Investment Decision on Solar Power

This paper identifies socioeconomic factors that are supposed to impact the investment decision of private households in Germany regarding small-scale solar PV (photovoltaic) systems. In 2022, the last nuclear power plant will phase out and the end of coal-fired power plants is fixed for 2038. Thus, the legislator is mandated to foster the addition of renewable energy capacities to close the gap fossil fuels and nuclear power leaves. Some share of the renewable energies could be from private households that mainly invest in small-scale solar PV systems. To stimulate investments, it is necessary to identify factors that are important for the investment decisions of private households. Within this paper, secondary socioeconomic data for the period from 2009–2018 was compiled. In order to identify the latent variables, a factor analysis was conducted. The results state five factors that are supposed to impact the investment decisions of the prosumer in Germany.

Design of a Novel Remote Monitoring System for Smart Greenhouses Using the Internet of Things and Deep Convolutional Neural Networks

To support farmers and improve the quality of crops production, designing of smart greenhouses is becoming indispensable. In this paper, a novel prototype for remote monitoring of a greenhouse is designed. The prototype allows creating an adequate artificial environment inside the greenhouse (e.g., water irrigation, ventilation, light intensity, and CO2 concentration). Thanks to the Internet of things technique, the parameters controlled (air temperature, relative humidity, capacitive soil moisture, light intensity, and CO2 concentration) were measured and uploaded to a designed webpage using appropriate sensors with a low-cost Wi-Fi module (NodeMCU V3). An Android mobile application was also developed using an A6 GSM module for notifying farmers (e.g., sending a warning message in case of any anomaly) regarding the state of the plants. A low-cost camera was used to collect and send images of the plants via the webpage for possible diseases identification and classification. In this context, a deep learning convolutional neural network was developed and implemented into a Raspberry Pi 4. To supply the prototype, a small-scale photovoltaic system was built. The experimental results showed the feasibility and demonstrated the ability of the prototype to monitor and control the greenhouse remotely, as well as to identify the state of the plants. The designed smart prototype can offer real-time remote measuring and sensing services to farmers.