Photovoltaic Generation System Research Articles

Distribution Grid Hosting Capacity Improvement using Reactive Power Control Optimization Algorithm of Inverter-based Photovoltaic Generation System

This paper uses a reactive power control optimization algorithm to present an inverter-based photovoltaic generation system for increasing the distribution grid host capacity. The main objective of this study is to regulate bus voltages by providing sufficient minimal reactive power consumption, which is obtained by a Particle Swarm Optimization-based optimization algorithm. A modeling study of the system network is developed using DIgSILENT PowerFactory, and the control algorithm is implemented in MATLAB. The proposed approach is efficient for various scenarios of the IEEE 13-bus test system and practical distribution network. The analysis results are compared with outcomes from other control methods, including technical and economic assessments. The performance of the proposed optimization-based power system has shown that the proposed algorithm method yielded significantly superior mitigated voltage rises and increased hosting capacity compared to those achieved by existing control methods of a specific distribution network.

Design Guidelines for Building and Infrastructure Integrated Photovoltaic Modules

The demand for renewable energy is increasing as efforts to decarbonize energy sources continue. Photovoltaic (PV) generation systems are the main contributor to the growth of renewable energy, but limited land availability in countries such as Belgium and Netherlands poses a challenge to their deployment. Integrated PV (IPV) can be a promising solution, but requires special consideration regarding electrical and fire safety, efficiency, durability, cost, and environmental impact in the design process. This study seeks to assist designers of IPV products by guiding the selection of materials, technologies, mechanical designs, and production methods for PV semifabricates (SF). It provides a comprehensive list of general design criteria, each offering various options in terms of bill of materials and production technologies. These options come with their own set of advantages and disadvantages, which are enumerated and quantified wherever feasible. The general design guidelines are validated based on the building‐integrated PV and infrastructure‐integrated PV demonstrators (in this case a noise barrier) being developed in the Solar Energy Made Regional (SolarEMR) project.

Supervisory control strategy for dual battery assisted solar water pumping using a 3-stage interleaved boost converter

This article proposes effective power management and battery protection strategies for a solar photovoltaic (SPV) powered dual-battery supported standalone water pumping approach propelled by a permanent magnet brushless direct current (PMBLDC) motor. A drift-avoidance perturb and observe MPPT algorithm is employed in conjunction with a 3-stage interleaved boost converter (TSIBC) and proposes a switching control scheme to enable double-stage solar photovoltaic generation system (SPGS) to operate at its maximum accessible power point (MPP) irrespective of any variation in climatic condition. The proposed dual battery control scheme (DBCS), which integrates a primary battery with an auxiliary battery, is connected to the 310 V DC link. This dual battery setup utilizes a parallel-active configuration facilitated by two bidirectional DC-DC converters (BDCs). The core idea behind dualization aims to maintain the state of charge (SoC) of the primary battery within upper and lower limits with the help of an auxiliary battery unit and regulate the DC link voltage at 310 V during fluctuations generated in the protection period. This scheme significantly extends the battery's lifespan, achieving an estimated 800 to 1000 cycles by maintaining its depth of discharge (DoD) at a level of 70 %. A centralized power handling unit (CPHU) is integrated for both the BDCs to facilitate the operating mode selection and regulate DC link voltage by alternatively charging/discharging both batteries. This approach enhances overall system efficiency by eliminating the need to oversize the solar array by up to 46 %, which would have been necessary to fully charge both batteries simultaneously. Previously, the DC link voltage regulation relied solely on MPPT control, resulting in significant deviations (up to ±38.43 %) during battery overcharging/deep-discharging (SoC ≥ 90 %/SoC ≤ 20 %). This work proposes a solution using an auxiliary battery controller within the DBCS, achieving near-zero (±0.5 %) DC link voltage deviation under all operating conditions. The effectiveness of the suggested DBCS controller is validated through a time-domain simulation conducted in MATLAB/Simulink, alongside real-time hardware-in-the-loop (HIL) digital simulator using the OPAL-RT platform. The acquired results corroborate enhanced performance for the standalone water pumping system (WPS) and demonstrate a seamless shift from the normal operational mode to battery protection mode, while ensuring steady regulation of the DC link voltage.

Spectrum Splitting-Based Performance of Combined Photovoltaic Thermoelectric Generator System

Spectrum Splitting-Based Performance of Combined Photovoltaic Thermoelectric Generator System

Based on CNN-LSTM for photovoltaic output prediction of multi-scale method

Abstract To maximize power generating efficiency, accurate forecast of generation power is the very important job for the photovoltaic generation system. In this paper, photovoltaic production is forecasted using a understanding way called multi-scale based on convolutional neural network-long short-term memory network (CNN-LSTM) for photovoltaic output prediction of multi-scale method. However, existing methods are often challenged by the multi-scale nature of the data when dealing with PV output prediction, so a more efficient method is needed to overcome difficulties. Therefore, this paper put forward an idea of a multi-scale method based on CNN-LSTM, which combines CNN and LSTM to accurately predict photovoltaic output at different time scales. The CNN-LSTM model can achieve the precision of 0.9097 for 15 minutes, the prediction accuracy of 0.8750 for 1 hour, and the prediction accuracy of 0.7987 for 4 hours, which can prove that the model can meet the scheduling rules.

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

Sizing a Renewable-Based Microgrid to Supply an Electric Vehicle Charging Station: A Design and Modelling Approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius.

Photovoltaic model parameters identification using diversity improvement-oriented differential evolution

Fast and accurate parameter identification of the photovoltaic (PV) model is crucial for calculating, controlling, and managing PV generation systems. Numerous meta-heuristic algorithms have been applied to identify unknown parameters due to the multimodal and nonlinear characteristics of the parameter identification problems. Although many of them can obtain satisfactory results, problems such as premature convergence and population stagnation still exist, influencing the optimization performance. A novel variant of Differential Evolution, namely, Diversity Improvement-Oriented Differential Evolution (DIODE), is proposed to mitigate these deficiencies and obtain reliable parameters for PV models. In DIODE, an adaptive perturbation strategy is employed to perturb current individuals to mitigate premature convergence by enhancing population diversity. Secondly, a diversity improvement mechanism is proposed, where information on the covariance matrix and fitness improvement of individuals is used as a diversity indicator to detect stagnant individuals, which are then updated by the intervention strategy. Lastly, a novel parameter adaptation strategy is employed to maintain a sound balance between exploration and exploitation. The proposed DIODE algorithm is applied to parameter identification problems of six PV models, including single, double, and triple diode and three PV module models. In addition, a large test bed containing 72 benchmark functions from CEC2014, CEC2017, and CEC2022 test suites is employed to verify DIODE’s overall performance in terms of optimization accuracy. Experiment results demonstrate that DIODE can secure accurate parameters of PV models and achieve highly competitive performance on benchmark functions.

Energy efficiency in medium-scale airports: Two Mexican airports as case of study

Reducing energy consumption in the operation of airports has been identified as one of the approaches to achieve the commitments of the countries in reducing their greenhouse gas (GHG) emissions. The first step in this approach is the development of an energy diagnostic. However, multiple practical aspects remain unresolved when applying the existing methodologies to perform energy diagnostics, especially in the case of small and medium-scale airports. Seeking to address these issues, this work presents energy diagnostics of two Mexican international airports so that it can be used to carry out energy diagnostics in other airports with similar characteristics. Emphasis is given to identifying and prioritizing, from a sustainable point of view, the strategies to reduce energy consumption and GHG emissions. The Ciudad del Carmen Airport (CME) is located in a nearshore region with high ambient temperatures (27 °C) and humidities. It was found that in 2019, the CME airport consumed 123 MWh with an average of 577 Wh per passenger, with the HVAC system being the primary energy consumer. Critical strategies for the CME airport include photovoltaic systems and HVAC renovation. In contrast, the Puebla airport (PBC) is located in a region with comfortable ambient conditions (16 °C). In 2019, the PBC airport consumed 61.31 MWh/year and 442 Wh per passenger. The main strategies for PBC include expanding its photovoltaic energy generation system, employee awareness programs, and renewing the vehicle fleet with electric vehicles.

Planning a Hybrid Battery Energy Storage System for Supplying Electric Vehicle Charging Station Microgrids

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction.

Integrating a dimensional perturbation module into exponential distribution optimizer for solving optimization problems

Exponential distribution optimizer (EDO) is a recently proposed optimization technique that is based on the exponential probability distribution model. As most swarm intelligence algorithms (SIAs), EDO is very good at handling common optimization problems. However, when addressing multimodal and center-bias problems, it exhibits limitations like imbalanced global and local search capabilities and poor solution accuracy. Building upon this, we introduce an improved version of exponential distribution optimizer that incorporates a dimensional perturbation module (DPM) called EDO_DPM. By testing the impact of dimensional perturbation, the K-dimensional evolution strategy is adopted in our approach. Concurrently, to modulate the usage frequency of DPM, an adjustment parameter, named evolutionary state factor (ESF), is designed contingent on the population’s evolutionary state. Moreover, to alleviate the deficiency of non full-dimensional perturbation, a bounce out operation is embedded in the algorithm. The efficacy of EDO_DPM has been substantiated through testing on 20 different types of functions and CEC2017 benchmark suite. Comparative analyses with the state-of-the-art algorithms have demonstrated a marked enhancement in EDO_DPM’s capability to manage multimodal problems and its proficiency in resolving center-bias problems. Meanwhile, it can achieve excellent results on CEC2017 by comparing excellent improved variants. Furthermore, EDO_DPM is applied to the problem of parameter estimation for photovoltaic (PV) generation systems. Comparison results show that EDO_DPM has good application capability.

Analysis of the Behavior of the Electromagnetic Frequency Regulator (EFR) Used in Hybrid Wind-Solar Photovoltaic Generation Systems

Within the context of integrating renewable energy sources, the Electromagnetic Frequency Regulator (EFR) presents a promising technology, particularly in hybrid distributed generation systems. This paper conducts a comprehensive analysis of pole pair configurations for both the EFR and the induction generator within such systems to enhance overall performance. An algorithm developed in Scilab is utilized for steady-state analyses considering various configurations. The methodology involves repetitive computations to evaluate the angular velocity of both the EFR and the generator, as well as the transformation ratios of the speed multiplier for different pole pairs. Scenarios with varying combinations of pole numbers are explored, demonstrating the impact on EFR velocity and the drive frequency of the EFR. Results showcase the efficacy of the proposed algorithm, providing insights into the selection of the most appropriate pole pairs to improve system performance.

A Generalized Load Model Considering the Fault Ride-Through Capability of Distributed PV Generation System

Considering the voltage stability problem brought by large-scale distributed PV access to the distribution network, this paper proposes a generalized load model that considers the fault ride-through capability of distributed PV. Firstly, the detailed model of the distribution network is established, and the detailed model is calibrated based on the measured data, the simulation errors are below 1%. And then establish a generalized load model considering distributed PV high and low voltage traversal ability. The sensitivity analysis results are used to rank the parameters to be identified, and the parameters with higher sensitivity are identified. The parameters are obtained from the detailed model and measured data, and four sets of parameters are identified and simulated under different PV penetration rates and fault conditions. The calculated fitting errors are less than 1%. The results show that the generalized load gray box model of the distribution network with distributed PV high and low voltage ride-through capability can reflect the dynamic characteristics of the distribution network well.

An enhanced jellyfish search optimizer for stochastic energy management of multi-microgrids with wind turbines, biomass and PV generation systems considering uncertainty

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively.

An avenue for providing the integration of distributed PV generation in low-income dwellings in Brazil

The purpose of this study is to analyze the impact on the income of families benefiting from the “Minha Casa Minha Vida” Brazilian Social Housing Program through the integration of photovoltaic (PV) generation systems into housing financing, as outlined by Law 14,620 enacted in July 2023. Given the novelty of this legislation, there is no existing database for analysis. Consequently, simulations of PV generation were carried out for the typical dwellings provided by the program across the five macro regions of Brazil. The simulation takes into account maintenance and equipment replacement costs, tariff rules, and standard adjustment rates in the country. Across all examined scenarios, the installation of residential rooftop PV systems yielded favorable outcomes, resulting in a reduction in electricity costs for the low-income population. Making provisions for a Sinking Fund in the program was identified as crucial to provide sustainability, long-term operation, maintenance and replacement. Additionally, it was observed that civil construction companies play a crucial role in popularizing the proposal. In conclusion, legislation incorporating PV generation into the national housing program represents merely the initial step toward realizing the widespread adoption of this energy generation technology in the country.

Frequency control enhancement for hybrid microgrid using multi-terminal multi-function inverter

Renewable energy sources (RESs) are considered a crucial energy transformation to reduce carbon emissions, so more RESs are being integrated into contemporary power systems. Power electronic converters are extensively utilized to connect power grids with renewable generators to manage the fluctuations and unpredictability of these renewable energy sources. This paper introduces a multi-terminal multi-function inverter (MT-MF) designed for a battery energy storage system (BESS) to maintain the frequency stability of a hybrid microgrid (MG). The MG comprises a photovoltaic generation system, a diesel generator, BESS, and two loads: one constant load and the other variable, fed through a medium-voltage radial feeding system. An introduced approach involves utilizing a model predictive control controlled virtual synchronous generator (MPC-VSG) for BESS. This method offers inertia support during transient states and improves the dynamic characteristics of system frequency. In addition, it enables the connection of multiple batteries, provides individualized control for each, and supports the injection of reactive power into the MG. The required power from the BESS is shared between the two batteries using the low pass filter technique. The simulation outcomes affirm the proposed control strategy’s effectiveness and underscore the MT-MF inverter approach’s potential in integrating extensive RESs. This paper also explores how the proposed technique outperforms other methods in improving frequency stability.

Using Urban Building Energy Models for the Development of Sustainable Island Energy Systems

This study evaluates the use of City Energy Analyst, an urban building energy modelling tool, to design zero-carbon energy communities in low-industry isolated island settings. The research aims to test the effectiveness of the software during the development of sustainable energy systems in isolated microgrids and compares it with the widely used tool EnergyPLAN. The goal of the study focused on making a community self-sustainable, considering the rooftop area available in the populated settlements to install photovoltaic systems and distributed storage capacity. With this purpose in mind, the evaluated tool estimated the energy consumption of each building and the respective total annual consumption of Corvo Island, a location that is naturally isolated and dependent on fossil fuels. The results demonstrated that City Energy Analyst is an innovative tool to estimate energy consumption and potential energy generation of photovoltaic systems in a remote location, providing additional features to a traditional model and motivating further development of the associated plug-in. However, it requires initial time-consuming efforts to build a reliable model. As a complement, EnergyPLAN can be used to enhance the design, with the integration of the local existing and potential generation sources and to confirm the stability of the overall energy system. This tool introduced additional wind capacity and centralized storage into the model, testing the balance of the system. Therefore, the study proposes a framework combining the strengths of both tools to measure island energy systems, as they can complement each other, to build a strong analysis model.

Numerical modelling and performance investigation of inorganic Copper-Tin-Sulfide (CTS) based perovskite solar cell with SCAPS-1D

Perovskite solar cells (PSCs) are a favorable option for the upcoming generation of photovoltaic systems due to their simplicity and high energy conversion efficiency. The third iteration of thin-film solar cells including copper-tin-sulphide (CTS) is easily accessible on Earth, possesses excellent optoelectrical properties, and does not include any harmful substances, making it environmentally sustainable. Using the solar cell capacitance simulator (SCAPS), this study examines the performance of a PSC based on copper oxide (Cu2O) and zinc selenide (ZnSe). The study investigates the factors that influence the performance of CTS-based SCs, such as absorber layer thickness, absorber defect density, interface defect densities, doping densities, and working temperature on the performance of the PSC. The results show that 30.37 % perovskite cell efficiency (PCE) increased from 20.94 % after optimization with 0.9403 V of open circuit voltage, 37.2682 mA/cm2 of short circuit current and 86.67 % of fill factor.

Multi-mode monitoring and energy management for photovoltaic-storage systems

The integration of photovoltaic generation systems and variable demand can cause instability in the distribution network, due to power fluctuations and the increase in reactants, particularly in the industrial sector. In response, photovoltaic units have been equipped with local storage systems, which eventually absorb power fluctuations and improve installation performance. However, during this procedure other functionalities that energy storage could provide are neglected. Consequently, this study provides a multi-mode energy monitoring and management model that enables voltage regulation, frequency regulation and reactive power compensation through the optimal operation of energy storage systems. With this objective, a smoothing control algorithm is developed that interacts with parameters of the electrical grid at the common connection point and also allows the compensation of reactive power based on an industrial demand profile. This strategy uses the Long short-term memory neural network of historical demand data prior to energy consumption with a relatively low RMSE of 1.2e-09. The results are previously validated in a development environment using a real-time OPAL-RT simulator and tests in the electrical Microgrid laboratory at the University of Cuenca. This configuration allows establishing a demand forecasting model that improves the supervision, automation and analysis of daily energy production. A series of results are provided and analyzed that demonstrate that the new tool allows taking advantage of the provision of multimode functionalities, achieving optimal voltage regulation and improving power quality by reducing the total harmonic distortion THD (V) and THD (I) indices by 0.5. % and 2 % respectively.

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.