Optimizing photovoltaic model parameters for simulation

Different types of photovoltaic (PV) cell technologies have been commercially used for various applications. These cell technologies can be categorized as multi-crystalline, mono-crystalline and thin film. Due to the differences in PV cell technologies, a single PV model is unable to model all the output characteristics. The single and double diode PV models have been widely used for modeling the output characteristic of a PV module. This paper introduces a generalized multi-dimension diode PV model which can be used to select the most suitable dimension for a particular PV cell technology. The results confirm that the single diode PV model is suitable for the multi-crystalline and mono-crystalline PV modules. For the thin film PV module, a multi-dimension diode PV model is required to achieve a low modeling error.

Particle swarm optimisation with adaptive mutation strategy for photovoltaic solar cell/module parameter extraction

The increasing demand for electrical energy has made it inevitable to bring forth an environment-friendly energy resource which has to be robust as well as economic. Solar energy fulfils these criteria significantly, and to cultivate such energy, a reliable photovoltaic (PV) model is required. But most of the typical PV system undergoes a low energy conversion ratio because of improper choice of PV parameters. To build a proficient PV model, the estimation of precise and accurate parameters is mandatory. This paper portrays the Sine Cosine Algorithm (SCA) for the estimation of photovoltaic (PV) module parameters. Unknown parameters of the PV model of a single diode PV module are estimated under the standard test condition (STC). PV parameter estimation using SCA has shown a significant minimum value of the fitness function hence maximizing the convergence. A comparative study has been done between SCA and other existing popular techniques named as the nonlinear least square (NLS) method and the modified Newton–Raphson (N-R) method. From the power–voltage (P-V) and current–voltage (I-V) characteristics, it’s found that the SCA model matches more accurately with the datasheet values.KeywordsRenewable energyPhotovoltaicSingle diode modelSine cosine algorithmParameter estimation

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

Random learning gradient based optimization for efficient design of photovoltaic models

This study presents a comparative analysis of various optimization algorithms—Differential Evolution (DE), Particle Swarm Optimization (PSO), Arithmetic Optimization Algorithm (AOA), and Hippopotamus Optimization Algorithm (HOA)—for parameter identification in photovoltaic (PV) models. By utilizing RTC France solar cell data, we demonstrate that accurate parameter estimation is crucial for enhancing the efficiency of PV systems, ultimately supporting sustainable energy solutions. Our results indicate that DE achieves the lowest root mean square error (RMSE) of 0.0001 for the double-diode model (DDM), outperforming other methods in terms of accuracy and convergence speed. Both the HOA and PSO also show competitive RMSE values, underscoring their effectiveness in optimizing parameters for PV models. This research not only contributes to improved PV model precision but also aids in the broader effort to advance renewable energy technologies, thereby fostering a more sustainable future.

Advanced three-stage photovoltaic system phasor model for grid integration dynamic studies

Parameters identification of photovoltaic (PV) models based on measured current-voltage characteristics curves is significant for the simulation, evaluation and control of PV systems. To accurately and reliably identify the parameters of different PV models, a novel optimization algorithm, multi-strategy success-history based adaptive differential evolution with linear population size reduction (MLSHADE), is proposed. MLSHADE mainly divides evolutionary process into two phases during every generation. According to the definition of class probability variable, the population individuals of first phase are assigned to different two populations for exploration and exploitation, respectively. The novelty of MLSHADE algorithm lies primarily in three improvements: (i) a new weighted mutation strategy is used to enrich the population diversity of later iterations for differential evolution population in the first phase; (ii) inferior solutions search (ISS) technique is presented to avoid falling into local optimum for covariance matrix adaptation evolution strategy population in the first phase; and (iii) Eigen Gaussian random walk strategy is proposed to help maintain effectively the balance between the global exploration and local exploitation abilities in the second phase. The experiments on CEC 2018 test suite illustrate that the proposed MLSHADE exerts the better performances against the stat-of-the-art algorithms in terms of accuracy, reliability and time consumption. The proposed MLSHADE is used to solve the parameters identification problems of different PV models including single diode, double diode, and PV module. Comprehensive experiment results and analyses indicate that MLSHADE can obtain a highly competitive performance compared with other state-of-the-art algorithms, especially in terms of accuracy and reliability.

Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm

Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. The ICO algorithm’s search phase is controlled based on the leader’s position. The step length is adjusted following the sorted population. As a result of this updated operator, the algorithm can perform global and local searches. Furthermore, the interaction factor during the attack phase is adjusted based on the position of the prey, and a random value controls the turning factor. Single-, double-, and PV module models are investigated to test the ICO’s parameter estimation performance. Statistical analysis uses the minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in the test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared with other state-of-the-art optimization algorithms, the CO and ICO algorithms are proven to be highly reliable and accurate when identifying PV parameters. According to the results, the ICO and CO obtained the first- and second-best sum ranking results for the studied PV models among 12 applied algorithms. Despite this, the ICO algorithm reduces the CO’s computation time by 40% on average. Additionally, ICO’s convergence speed is high, reaching an optimal solution in less than 25,000 function evaluations in most cases.

Photovoltaic (PV) technology is gaining much interest as a clean, sustainable, noise-free source of energy. However, the non-linear behavior of PV modules and their dependency on varying environmental conditions require thorough study and analysis. Many PV modeling techniques have been introduced in the literature, yet they exhibit several complexity levels for parameter extraction and constants estimation for PV power forecast. Comparatively, a simple, accurate, fast, and user friendly PV modeling technique is proposed in this paper featuring the least computational time and effort. Based on function representation of PV curves’ available in PV datasheets, an empirical mathematical equation is derived. The proposed formula is considered a generic tool capable of modeling any PV device under various weather conditions without either parameter estimation nor power prediction. The proposed model is validated using experimental data of commercial PV panels’ manufacturers under various environmental conditions for different power levels. The obtained results verified the effectiveness of the proposed PV model.

The paper presents grid connected Photovoltaic (PV) models for three-phase distribution load flow analysis. The models comprises of single-diode PV model with and without the effect of the series and parallel resistances, I-V nonlinear question, I-V database and single-phase PV modeled as singlephase real power injection. The three-phase load flow program with Photovoltaic models has been tested using IEEE 13 node feeder. The solution of the base case is compared with the radial distribution analysis package (RDAP) before used to analyze distribution networks. The analysis is carried out with various photovoltaic mathematical models. The simulation results show that the grid-connected three-phase photovoltaic system in distribution network can improve the voltage profile as well as reduce the total system losses. However, single-phase PV DG model does not always guarantee voltage improvements.

Parameters identification of photovoltaic models using niche-based particle swarm optimization in parallel computing architecture

Long term supporting schemes for photovoltaic (PV) system installation have led to accommodating large numbers of PV systems within load pockets in distribution grids. High penetrations of PV systems can cause new technical challenges, such as voltage rise due to reverse power flow during light load and high PV generation conditions. Therefore, new strategies are required to address the associated challenges. Moreover, due to these changes in distribution grids, a different response behavior of the distribution grid on the transmission side can be expected. Hence, a new equivalent model of distribution grids with high penetration of PV systems is needed to be addressed for future power system studies. The thesis contributions lie in three parts. The first part of the thesis copes with the PV modelling. A non-proprietary PV model of a three-phase, single stage PV system is developed in PSCAD/EMTDC and PowerFactory. Three different reactive power regulation strategies are incorporated into the models and their behavior are investigated in both simulation platforms using a distribution system with PV systems. In the second part of the thesis, the voltage rise problem is remedied by use of reactive power. On the other hand, considering large numbers of PV systems in grids, unnecessary reactive power consumption by PV systems first increases total line losses, and second it may also jeopardize the stability of the network in the case of contingencies in conventional power plants, which supply reactive power. Thus, this thesis investigates and develops the novel schemes to reduce reactive power flows while still keeping voltage within designated limits via three different approaches: 1. decentralized voltage control to the pre-defined set-points 2. developing a coordinated active power dependent (APD) voltage regulation Q(P) using local signals 3. developing a multi-objective coordinated droop-based voltage (DBV) regulation Q(V) using local signals In the third part of the thesis, furthermore, a gray-box load modeling is used to develop a new static equivalent model of a complex distribution grid with large numbers of PV systems embedded with voltage support schemes. In the proposed model, variations of voltage at the connection point simulate variations of the model’s active and reactive power. This model can simply be integrated into load-flow programs and replace the complex distribution grid, while still keeping the overall accuracy high. The thesis results, in conclusion, demonstrate: i) using rms-based simulations in PowerFactory can provide us with quite similar results using the time domain instantaneous values in PSCAD platform; ii) decentralized voltage control to specific setpoints through the PV systems in the distribution grid is fundamentally impossible due to the high level voltage control interaction and directionality among the PV systems; iii) the proposed APD method can regulate the voltage under the steady-state voltage limit and consume less total reactive power in contrast to the standard characteristic Cosf (P) proposed by German Grid Codes; iv) the proposed optimized DBV method can directly address voltage and successfully regulate it to the upper steady-state voltage limit by causing minimum reactive power consumption as well as line losses; v) it is beneficial to address PV systems as a separate entity in the equivalencing of distribution grids with high density of PV systems.

This study deals with an approach for photovoltaic (PV) panel monitoring based on extended Kalman filter (EKF). For detection of anomalous condition, I–V and P–V characteristics derived by the PV model in normal condition (named reference model) are compared with the corresponding ones extracted from the PV model in the test condition (named test model). Parameters of the reference model are obtained from the standard test condition. Generally, maximum power point (MPP) tracking algorithms deviate operating point of PV systems to find the global MPP. In the proposed approach, the measured operating points of the PV system are passed through the EKF. In fact, the EKF is an adaptive low-pass filter for estimation of the operating MPP dictated by the MPP tracker. During a predefined inspection time interval, parameters of the test model are derived from the estimated MPP. The performance of the proposed method is evaluated using some simulations and experiments in different normal and anomalous conditions. The results confirm desirable accuracy and reliability of the proposed method for detection of the PV panels’ anomalous condition.