A strategic review: the role of commercially available tools for planning, modelling, optimization, and performance measurement of photovoltaic systems

1.1 Novelty of the paper

The novelty of this systematic review is that:

1. It provides a comprehensive dataset that will aid scholars in identifying the latest trends regarding the modelling approaches used in solar system sizing.

2. This in-depth analysis will demonstrate the developments in tools, modelling, and studying PV behaviour.

Additionally, the innovative, motivating, and inspired arguments are what models and software are available for designing PV systems? How have these software, techniques, and models changed throughout time? What performance metrics are produced by various tools and software? Which tool or software is better equipped to meet my necessities?

2.1 Model of array performance based on five parameters

Based on the 4-parameter one-diode model, the 5-parameter model is recommended. Wisconsin Solar Energy Laboratory’s original one-diode model has been upgraded with the help of the new proposal. As a means of improving PV power forecasts under STC, the newly suggested model was implemented into TRANSYS (De Soto et al. 2006). The five-parameter model can successfully forecast the I-V curve’s form if the idealization factor coefficient, diode reverse saturation current, light current, series resistance, and shunt resistance are all known quantities. These settings need to be suitable for the amount of radiation striking a given cell and its internal temperature (Boyd et al. 2011).

2.2 Solar advisor model (SAM)

The SNL and the NREL worked together to create the “Solar Advisor Model” (SAM) software. There is a vibrant user network and a steady stream of updates and enhancements to the model, making it suitable for usage in a stand-alone system. Before the entire model was released, a paper by discussed the idea of developing a comprehensive modelling tool under the name PVSunVisor (Mehos and Mooney 2005). This programme is used to do a technical assessment of the PV and a cost-benefit analysis. The programme can also optimize and do sensitivity analyses. Attempts to use the SAM approach to define a configuration for a demo power station faced challenges. Power cycles are predetermined by some of SAM’s settings, which may or may not be realistic. Adjusting the power cycle of the plant may be difficult because precise, well-specified data is required for making reliable projections about the plant’s actual output from the power output. Station factors such as solar radiation intensity and storage hours are prioritized while working with the SAM system. These variables allow one to deduce why the LCOE and total station output are what they are. In order to increase a project’s profitability, it is important to lower the LCOE values by increasing plant production while decreasing costs (Ezeanya et al. 2018).

2.3 Sandia photovoltaic array performance model (SPAPM)

The SNL created a model and software to assess the efficiency of PV arrays based on their 12 years of experience with PVSS, PVForm, and PVSIM. The suggested SPAPM model accounts for PV modules’ optical, thermal, and electrical properties. The model utilised hourly weather observations. The programme may be used to create, optimize, and assess photovoltaic installations (Cameron et al. 2008). The programme also generates a variety of comparisons based on the measured data. To directly calculate the output of PV modules under their working circumstances, climatic data is commonly analysed using SPAPM (Madaeni et al. 2012).

2.4 Sandia inverter performance model (SIPM)

In order to assess PV systems that are linked to the grid in a way that accounts for inverter characteristics, the SNL created SIPM in a similar vein to SPAPM (King et al. 2004). The inverter’s modelling equations and coefficients have been developed. SAM and PV DesignPro, which include the SIPM and SPAPM, will be explored in the following sections. The nonlinear behaviour of inverters may be analysed using the SIPM model, leading to greater efficiency and a larger market share at high power outputs (Petrana et al. 2018).

2.5 Evans and Facinelli model (E&FM)

With funding from SNL, researchers at the Arizona State University developed the “Evans and Facinelli” model in 1970 to evaluate the effectiveness of PV installations (Hottel and Woertz 1942). At first, the model was used once a month to evaluate the MPPT efficacy of the energy stored in a PV array or battery packs. TRNSYS was used for the model’s implementation, and it has since been improved upon for more functionality and precision. The accuracy and number of assessment results of the offered equations from the model have been enhanced with the introduction of several new features (Florschuetz 1979). The outcomes are also presented in a wider variety of formats, including numerical data, graphical representations, etc. However, SNL is not actively using, updating, or otherwise maintaining the model.

2.6 Aurora solar software

Aurora Solar creates solar PV-enabled cloud-based software. Aurora Solar, the manufacturer of the system, markets it primarily to businesses and homeowners. The energy may be calculated with the help of this program. The Aurora Solar Design Application Site evaluation, shading analysis, system design, performance estimates, and cost-benefit analysis are all included in one single solar design application.

2.7 REopt software

Since 2007, NREL has been working on the REopt software in order to better find and select the most cost-effective renewable energy projects across a portfolio of locales. Investigators at NREL use the REopt techno-economic decision support platform to improve the efficiency of energy systems in structures of all kinds, including buildings, universities, communities, microgrids, and more. REopt suggests the best combination of renewable energy, conventional generation, and energy storage technologies to maximize efficiency, reliability, carbon reduction, and power output.

The amount of shadow-free land on the site is being calculated. Aurora Solar creates cloud-based applications that facilitate solar PV. Aurora Solar produces the Aurora system, which is popular in both the commercial and residential markets. Users are able to compute energy with the help of this programme. Solar power system design software: Aurora Solar.

2.8 BlueSol PV software

BlueSol is an international software company that designs solar systems. From the first feasibility analysis through the finalization of the project documents, it facilitates the whole process of building a PV system. BlueSol is an easy-to-use programme that maintains every aspect of your PV system. It was developed using a normal Microsoft interface.

2.9 CPE software

To help customers do a cost-benefit analysis of PV systems before making purchases or contacting installers, CPRC created the CPE. Because of this, the simulator requires as few user actions as possible. The study generates parameters for cash flow, payback time, and other forms of cost analysis (Perez et al. 2003).

2.10 SolarPro software

To aid in the process of designing and assessing PV systems, the Kyoto, Japan-based Laplace System published its SolarPro software simulation tool in 1997. The system accommodates a variety of PV setups, including home and business installations, freestanding arrays, and tracking mechanisms (Thula et al. 2017). In addition, the effect of shadowing from trees, surrounding buildings, or PV arrays was taken into account throughout the design process. What’s more, the I–V curves for the PV modules are created automatically by the program. Shadow impact assessment, I–V slope assessment, cost assessment, and system strength assessment are the four primary features of SolarPro. The most precise design of the photovoltaic module may be achieved by analysing the shadowing effect (Turcotte, Ross, and Sheriff 2001). If you want to make sure your PV modules are working properly, this programme will examine their I–V curves quickly and correctly. This programme can precisely predict both the power output and the cost of the system by factoring in the position of the PV modules (in terms of latitude and longitude) and the local meteorological conditions (Ishaque et al. 2011). With the use of SolarPro Software’s modelling, we can easily identify the optimal placement and shading configurations for the arrays thanks to a thorough understanding of how solar radiation is distributed across them. The programme can calculate the reflector dispersion and provide visualizations. SolarPro now has the capability of using animations to show things like moving shadows, ongoing computations, hourly system performance, etc. (Lalwani et al. 2010).

2.11 INSEL software

In 1991, the German firm Doppelintegral GmbH created a piece of software called INSEL (Integrated Simulation Environment Language) to simulate the effects of switching to renewable energy sources. This programme is used in conjunction with MATLAB’s capabilities to develop and evaluate solar thermal and photovoltaic systems. Any group with an interest in renewable energy systems, whether students, scientists, engineers, or businesses, may benefit from using this programme. It is important to note that INSEL has its own meteorological data. In order to accurately predict solar output, the programme relied on German-specific solar irradiance data. When assessing the PV system’s technical and financial merits, INSEL uses a simple one-diode model. The application may be used to design renewable energy systems by determining which parts of the system need to be assembled. This programme includes weather data from over 2000 locations around the world. Also included is a library of PV and solar thermal systems currently in operation. Hourly measurements of sun radiation, temperature, dampness, and wind velocity are also recorded and kept by INSEL at these locations. Every one of these locations may have its monthly and annual averages retrieved by the programme in either way. Ever since its debut, this program’s database and capabilities have been the focus of ongoing development.

2.12 Polysun software

Polysun, a PV system optimization and analysis programme, was created and published in 2009 by Swiss firm Vela Solaris. The programme simulates many photovoltaic (PV) technology variants, including a-Si, c-Si, CdTe, c-Si, CiS, Ribbon, and HIT. The programme not only provides a technical examination but also an in-depth economic analysis (Witzig et al. 2010). For the first time ever, the application has been designed so that users may create their own systems. The software continues to grow its collection of renewable energy system models while keeping its high standard of quality. Several new features, including support for heat pumps (Lacoste 2009), thermography (Witzig et al. 2010), PV (Lacoste 2009), PVT (Witzig et al. 2010), and solar cooling (Rezaei and Witzig 2009), have recently been included in the programme.

An advantage of this programme is that it may reuse the same simulation kernel across several contexts. The numerical model’s dependability and steadiness are improved by this addition. One other perk of the programme is that it facilitates online collaboration between the homeowner and the program’s designers (in the form of both the architect and the designer). The ability to include cutting-edge technology like thermoelectric systems into the software is another recent addition (Witzig et al. 2010).

2.13 PVSol software

The German software company Valentin Energy Software released PVSol in 1998 for use in designing and assessing PV installations. Solar PV system design and economic assessment software. The planned PV module system’s location, needed load, and yearly consumption are all input by the user into the programme. Details of the planned inverter are included, as are the specifications of the solar modules that will be employed (including all modules, such as kind, position, and slant angle). The PVSol programme suggests a new, better system, down to the exact module and inverter placement. provides information such as the PV plant’s yearly output of electricity, its performance ratio, and the solar portion (Kumar et al. 2017).

2.14 RETScreen

In order to determine whether or not a renewable energy (RE) scheme is financially and ecologically viable, experts utilize RETScreen, a programme developed by Natural Resources Canada (Yimen and Dagbasi 2019). The software has several intriguing uses, including evaluating the effectiveness of solar systems in different regions of the world. This programme can calculate the financial and ecological implications of different RE techniques throughout a substantial area of the globe. Both standalone and interconnected systems, such as WTs and water pumping systems (WPSs), are within the scope of the program’s analysis capabilities. The software uses NASA’s meteorological information to make forecasts. Adding technical, economic, and environmental considerations to RETScreen has made it more appealing to engineers and academics studying renewable energy systems. A unique feature of this application is that it includes weather reports from more than 6000 different locations on the ground. The statistics range from wind maps to the monthly intensity of solar radiation to yearly temperatures. Data about the electricity output of solar cells and wind turbines is also included in the software. The ability to use a wide variety of live languages is a defining feature of the software (more than 30 dialects). Researchers claim that software failure to account for the impact of solar panel temperature on PV system performance led to the negative findings. The issue of data sharing after retrieval is also present (retscreen.net).

2.15 PV F-Chart software

When it comes to modelling and evaluating PV systems, nobody does it better than the folks at the University of Wisconsin with their innovative PV F-Chart. Various solar energy systems, including those that are connected to the grid, those that operate independently, and those that rely on batteries, may all be analysed with the help of the PV F-Chart. This software also analyses mono- and dual-axis monitoring systems. The programme also calculates the model’s monthly mean performance for each hour of the day (Fayaz et al. 2018) from a technical and economic standpoint. More than 300 sites across the world’s weather are included in this software, and more may be easily added. Multiple battery-powered PV system models are available in this programme. Unique aspects of the software include how quickly changes can be made, how much information can be downloaded at any one time of the month, and how much things cost to buy and sell. The software can do these computations for both stationary and mobile PV systems, on either one or two axes of movement, respectively. The programme may collect information on how the planned stations can be used to reduce emissions of greenhouse gases. The software is compatible with both centralized and decentralized networks (Chen et al. 2011).

2.16 IPSYS software

With the help of the C++ programming language, the developers of IPSYS have created a hybrid energy model simulator that may be used to improve the efficiency of energy systems in outlying regions (Kosek et al. 2014).IPSYS can work with a wide variety of energy sources, including PV, WT, shale gas, fuel cells, and storage mechanisms like hydro dams and battery packs.

2.17 HySys software

The Spanish research organization CIEMAT has developed a programme called HySys (an acronym for “Hybrid Power System Balance Analyser”). The programme is quite similar to IPSYS, except it was written specifically for use inside MATLAB. The programme in question can examine the useful life of hybrid systems and provide estimates of their size. Many modern technologies, such as photovoltaic (PV) arrays, WTs, and DGs, are designed to function autonomously, meaning they don’t need to be connected to the national grid.

2.18 Modelica/Dymola software

Germany’s “Fraunhofer Institute for Solar Energy” (ISE) created the OOP language-based Modelica/Dymola to simulate hybrid energy models. When compared to IPSYS and HySys, the Modelica/Dymola environment is quite comparable. Hybrid systems may be modelled using this programme (consisting of PVSs, WTs, DGs, FCs, and BTs). The weather and average daily sunlight hours are used as inputs in this model. The yearly energy cost and the total cost of ownership of the hybrid system may both be determined with the help of this programme (Aronson et al. 1981).

2.19 HySim software

HySim was developed by SNL in 1987 to look at the potential for using solar modules, batteries, and a standard DG in tandem. HySim wants the greatest possible result and places extra weight on the financial factors. HySim computes monetary metrics such as life cycle cost, fuel cost, energy level cost, O&M expenses, and configuration-specific cost comparisons. After 1996, when HybSim (Kendrick et al. 2003) was created, this programme stopped being used.

2.20 HybSim software

Hybrid Simulation Model is SNL’s newest renewable energy programme (HybSim). The new method was used to compare many HRES and identify the one that had the most technological and economic benefits. DGs with PVSs and BTs are only one example of the hybrid architectures that may be simulated using HybSim. The energy of hybrid systems, such as those in remote areas that rely on solar panels, diesel backup generators, and batteries for backup, needs meteorological data to be collected and analysed. In this case, the software was sufficiently reliable for the system to be used (Sinha and Chandel 2014).

2.21 RAPSIM

Australian researchers at Murdoch University have created a hybrid energy system similar to HybSim and Hysim (Ochacker and Tamer Khatib 2014). RAPSIM can first calculate the power flow by calculating the amount of power produced by each source.

2.22 SOMES

Similar to RAPSIM, the “Simulation and Optimization Model for Energy Systems (SOMES)” was developed at Utrecht University in the Netherlands to analyse the performance of HRES. RAPSIM relies heavily on solar irradiance and ambient temperature for its calculations. RAPSIM, on the other hand, offers the best layout for a variety of energy system setups that may include PV, WT, battery, and DG. Additionally, the programme supplies data for a feasibility evaluation of the proposed system (Ochacker and Tamer Khatib 2014). Because of its intended application in educational settings, RAPSim has a user-friendly graphical interface. In addition to providing customers with specialized algorithms to manage their PV stations, the application also provides users with readily expandable features that allow them to integrate the specified PV system models.

2.23 PVSYST software

The energy lab at Switzerland’s University of Geneva created the PVSYST programme to study solar arrays by using weather records. The software’s accessibility and straightforward graphical user interface make it useful for both professional design engineers and academic settings (Mermoud 1995). Evolving the photovoltaic system’s perspective, shading effect, mismatch, etc., is just one example of how PVSYS’s adaptability can be put to use in the design process (Kumar et al. 2017). Also cites PVSYS as the most cutting-edge software available for modelling and analysing PV systems across all of their many uses (SA or GC-WPS). The application has capabilities to help maximize the potential of the intended PV station by considering factors like shadowing. Existing versions of this programme have a large database that lets users do in-depth economic analyses (Kumar 2017).

2.24 TRNSYS software

The energy modelling programme UW-Hybrid, a new simulation model in TRNSYS, is used to study hybrid energy systems that combine renewable and conventional sources of power, including solar, wind, diesel, and batteries. It is quite similar to the Hybrid2 software, but in a new setting. When calculating solar irradiance, UW-Hybrid relied on a straightforward isotropic sky model (Kazem et al. 2013a). TRNSYS may be used to model the behaviour of transient systems and is a graphically-based programme with a lot of customization options. TRNSYS is often used for heating and cooling purposes in solar systems. There has been a lot of recent work evaluating hybrid photovoltaic and thermal energy systems using TRNSYS, so it is important to realise this.

2.25 PVSIM software

Specifically dubbed PVSIM (King et al. 2004), SNL’s model and software for simulating large-scale solar systems were established in 1996. The new software incorporates the assessment of PV modules inside a PV array. PVSIM checks for overshoot as well as undershoot, overheating, unit heating (from 25 °C to 50 °C), cell mismatch, and unit block diodes. Solar cells were simulated in the computer programme as a pair of similar diode circuits (Van Dyk et al. 2005).

The PV system’s current-voltage characteristics may be determined with adequate precision using PVSim-GUI, despite the fact that it only uses basic methodologies. Series resistance (Bissels et al. 2014), shunt resistance (Saleem and Karmalkar 2009), idealization factors (Bashahu and Nkundabakura 2007), and fill factor (Silvestre and Castan 2002) are only some of the important characteristics of the PV device model that may be calculated by the programme. Using this programme, you can test out how your BV system would function in a number of hypothetical scenarios and evaluate how well your plant would perform under each scenario. This programme is helpful for training purposes and may be used for analysis and simulation of PV systems (Shekoofa et al. 2015).

2.26 PVWatts software

Using the SNL PVForm model as inspiration, the NREL built PVWatts to mimic a grid-connected PV system. The performance evaluation of the proposed model incorporates models of solar radiation and power temperature coefficients. Scientists, students, and engineers worked together to make the PVWatts web app as user-friendly as possible. Many of the processes and models used to predict the PV system’s performance are built into PVWatts, but they are not exposed to the user. This programme hides the site’s interface by rendering it as a drawing, and it also lets the user sketch out the solar array installation zones (Kumar et al. 2017). Users enter their position, solar radiation statistics for said region, PV system details and scale, PV technology, installation choices, slope angle (in 0–90°), and azimuth angle (in 0–360°). The software models the PV system’s size and runs the numbers to calculate the annual energy production, monthly energy yield, and monthly and yearly electricity pricing.

2.27 PVToolbox

The “PVToolbox” hybrid system model was developed using Matlab Simulink and is tailored to the climate of Canada. This model was developed by the Energy Technology Center at Natural Resources Canada.

2.28 PVForm software

SNL’s PVForm (Menicucci 1985) is yet another solar energy simulation and program. Errors in the irradiance prediction are accounted for using the new approach. To make the model accessible on desktop and server systems, it has been included in a stripped-down application. The PV system was modelled in the software so that technical and financial considerations could be made. The programme also supports both off-grid and grid-tied PV installations (Menicucci and Fernandez 1989). PVForm was designed to run on PCs using the command-line operating system MS-DOS (Ropp et al. 1997) (as distinct from the graphical operating system Windows).

2.29 PV DesignPro software

The PV DesignPro programme was developed via a joint effort between SNL and the MSESC. Models, algorithms, and databases created with and for SNL are all part of the programme. For the purpose of designing and evaluating PV arrays using various technologies, several researchers have utilised the programme (Perez et al. 2004). PV-DesignPro does a year-long simulation of a solar station’s operation and provides hourly statistics for this operation (Automation et al. 2008). The planned system type and climatic data are both important considerations when compiling this information. This software comes in three different variations: stand-alone PV systems with battery banks may be modelled with PV-DesignPro; interconnected PV systems without storage (battery packs) can be modelled with “PV-DesignPro-G;” and WPSs can be simulated with “PV-DesignPro-P” (Tiba and Barbosa 2002).

PV-DesignPro may be used to analyse and plan PV installations. In addition to accounting for cost changes associated with the planned system installation, the software also provides realistic estimates of the system’s potential output and the energy produced by the plant over the course of a year of operation (Deshmukh et al. 2006; Lalwani et al. 2010).

2.30 CPF software

Clean Power Finance (CPF) is a piece of software that was created in 2007 by Energy Matters LLC specifically for the purpose of designing photovoltaic (PV) systems. Both technical and financial considerations are handled by the programme.

2.31 Solar Estimate software

Solar Estimate, another tool created by Energy Matters LLC, is used for estimating solar resource use in industrial and household settings. The Solar Estimate is a web-based programme that helps you figure out the design parameters and costs by letting you put in information about your location and your utility company.

2.32 OnGrid software

Northern California saw the 2005 debut of OnGrid, a programme designed for use throughout the United States in calculating the performance of photovoltaic (PV) installations. Furthermore, OnGrid relied on PVWatts models when figuring out how much energy a PV system would produce. As grid-connected PV systems become more commonplace in the United States, however, financial incentives have been included in the estimate.

2.33 PVOptimize software

KGA Associates created PVOptimize, a PV system design and assessment programme based on the PVWatts program. There is just one version of the programme available, and it only works in California.

2.34 PVSS software

Solar System Simulation Program (PVSS) is a FORTRAN simple code developed by SNL that may be used to simulate either SA or GC PV systems for the purposes of sizing, designing, and evaluating their overall performance. This programme employs a simple diode model and is one of the first efforts at a tool for PV design, dating back to 1978 (Goldstein and Case 1978). The software’s use of elementary model equations allowed it to simulate 16 different PV setups. However, neither a cost-benefit nor an impact study analysis has been provided. This programme uses a model with a single diode to determine voltage and current. When it comes to the technical implementation of PV systems from the perspective of electrical behaviour, PVSS became the first group to attempt to formulate a code. PVSS is now a suite of control programmes running on both Linux and Microsoft Windows workstations. The operating system and any necessary security updates are taken care of by these applications (Young and Yung 2001).

2.35 HOMER software

NREL created and published free software in 2005 for the public to use called HOMER. It is a time-step simulator for evaluating renewable energy systems, using as inputs the hourly demand and environmental data; it helps find the best configuration of renewable energy sources, given a set of limitations and sensitivity factors.

Two of HOMER’s most impressive qualities are its capacity to find the appropriate solution based on cost evaluations and its potential to do an impact competency study in order to comprehend tradeoffs among various technology upgrades and economic concerns (Alwaeli et al. 2019). The programme may take into account a wide variety of system architectures as well as a wide range of battery options. KiBaM is the code that HOMER uses to show how much juice it has left in the battery. The model may combine the following subsystems: photovoltaic cells, hydropower, wind power, batteries, conventional power generation, an ac-to-dc converter, an electrolyzer, a hydrogen storage tank, and a reformer. In its probes, HOMER employs a tried-and-true module style. Climate and insolation data for HOMER may come from either the TMY2 database or from user-supplied data.

2.36 SOLSTOR software

For the purpose of enhancing the systems integration and cost assessment of HRES, SNL developed a second software programme in the mid-1970s. SOLSTOR focuses on PV, WT, power conditioning, and energy storage. Additionally, SOLSTOR assesses both off-grid and grid-connected renewable energy installations. SOLSTOR may generate system performance characteristics in addition to finding the best possible design and minimizing LCC. This program’s strengths lie in its simplicity and its ability to analyse available backup power sources. The programme can calculate the average off-grid power cost and the recurrent energy cost of the system. Additionally, time of day (ToD), load profile, and electricity prices could be adjusted.

2.37 SOLCEL software

In 1977, the SOLCEL programme was created to optimize the scale, design, and layout of PV systems (Linn 1977).

The programmes were created using the FORTRAN programming language. Both stand-alone and grid-connected PV systems may benefit from using the software. The simulation made use of hourly data. Both technical and financial considerations are made. We took into account how temperature affects PV performance. The programme takes into account a year’s worth of weather predictions and chooses the best layout for the budget. The programme has three distinct releases. Additionally, SOLCEL software is very effective at assessing how shade and sun exposure will affect PV installations (Yoo 2011). In order to determine where on a solar panel shadows will fall, the programme employs a technique described in (Quaschning and Hanitsch 1998). In order to get reliable data, it is necessary to conduct a survey of the region surrounding the solar stations using facilities like a fish-eye webcam. Using SOLCEL software to design enormous PV energy plants to satisfy the requirements of a town, for example, is not feasible due to the strategy’s single control point applicability (Wang et al. 2014).

2.38 Hybrid2 software

The investigators from the University of Massachusetts Amherst developed the Hybrid2 programme to enhance HRES. Using probabilistic methods, Hybrid2 allows for the creation and improvement of hybrid systems that include RESs like WT and solar PV, fossil fuel power sources like DGs, and storage solutions like battery packs. The versatile Hybrid2 software platform allows for in-depth, long-term profitability analysis of a variety of different hybrid power stations.

The optimizer may model various electrical components, including photovoltaic (PV) panels, wind turbines, diesel generators, batteries, and loads. We compare cSi, CdTe, CiS, and aSi PV modules. The solar irradiance of the Hybrid2 is a mystery. The latest version of Hybrid has been enhanced with a number of new features and fixes to user complaints from earlier versions. For instance (Manwell et al. 2005), there’s the problem with making the solar radiation information slope more visually appealing, the inaccuracy in the light load model, etc. HYBRID 2 can run simulations of the future for as long as 1 h. It can model up to three different systems, including ones with renewable energy sources like wind and solar power, conventional power sources like a diesel generator, and storage options like batteries (Green and Manwell 1995). With this updated edition, the user may quickly and simply setup projects in the programme and validate their work for any input problems. GRI (Sinha and Chandel 2014) additionally allows users to add graphical annotations to the output data.

2.39 REPS.OM software

REPS.OM is a software tool that helps with the technical and financial evaluation of potential PV system designs. Even if you are not an expert in the subject, you should have no trouble using the program. Basically, the programme just needs some basic information from the user, including the kind of PV module, battery, and system, as well as the location. The efficacy of a hybrid model may be optimized by a software program, despite the size of the solar PV array, the slope angle, the battery storage capacity, the air velocity, or the power of the DG. It is important to note that the Liu and Jordan model was only used to calculate energy coefficients and was not used to simulate PV. A model developed by researchers Liu and Jordan was used to determine the optimal inclination. Every year, the programme computes technical behaviour and cost analysis. A MATLAB GUI was used to create the original REPS.OM code (Kazem et al. 2022).

2.40 iHOGA software

The University of Zaragoza’s Electric Engineering Department developed a C++ programme for optimization and simulation called HOGA (Spain). It might be put to good use within hybrid energy systems (HESs) that produce power (DC and AC), hydrogen, and/or pump water (either alone or in combination).

Renewable, nonrenewable, and HESs may all be created and assessed with the optimizer’s help. The optimizer may plan a wide variety of systems, such as those based on photovoltaics (PV), diesel generators (DG), wind turbines (WT), and batteries (SB like HOMER and REPS.OM). This optimizer’s output is a comprehensive feasibility assessment that takes into account technological, economic, and environmental factors. iHOGA might be employed to figure out the best design of a HES that incorporates RE-producing methods such as SPV, WT, HTs, FCs, H2 tanks, electrolyzers, and BTs. Through the deployment of a net metering system (NMS), this plan makes it easier to purchase and sell power to the grid (Sinha and Chandel 2014).

2.41 HelioScope

Folsom Lab USA has unveiled HelioScope, a new tool for designing solar systems that combines certain aspects of PVSyst with AutoCAD design capability to help designers create a whole design in a single application. HelioScope mainly needs the location’s address, array arrangement, PV module, and inverter specifications. By using this programme, one may calculate the amount of energy that would be produced while taking weather and climate-related losses into consideration. Analysis of shading, wiring, component efficiency, panel mismatches, and ageing is also possible to provide recommendations for equipment and array architecture. When displaying the results of simulations, this tool also provided yearly production, weather data sets, performance ratios, and other system metrics. As a web-based tool, HelioScope may be used from any connected computer and requires no downloading of software.

2.42 Solarius PV

A professional solar PV calculator called Solarius PV was created by the Italian business ACCA Software to make it simple to construct photovoltaic systems and find the optimum practical and financial outcome. Using input such as weather information, modules, inverters, batteries, etc., this model generates technical and economical analyses for system setup. The overall performance of the solar system may be determined by Solarius PV, together with its profitability and amortisation period (total yearly output with a timetable for hourly production). This programme enables testing the impacts of shade projected onto the PV modules by close-by obstructions like antennas and chimneys and visually viewing shadow interferences.

2.43 SOLARGIS pvPlanner

In 2010, SolarGIS released pvPlanner, a programme used to simulate PV systems. SOLARGIS pvPlanner is an online modelling application that uses high-performance algorithms and high-temporal and spatial-resolution climatic and geographic data to design and optimize solar systems, accompanied by maps. With this programme, you can quickly and easily simulate the effects of different PV technologies and mounting choices on energy output, making it a useful tool for site prospection. The SolarGIS interface allows the user to do a search for a certain site, pick a PV system configuration (including system capacity, module type, inverter specs, mounting system, azimuth, inclination angle, etc.), and then perform the corresponding simulations. These models use as input information from the likes of 19 geostationary satellites stationed in 5 main locations, as well as atmospheric and meteorological models run by the ECMWF and NOAA meteorological data centres. Long-term monthly and annual readings of global horizontal irradiation (GHI), global in-plane or tilted irradiation (GTI), diffuse horizontal irradiation (DIF), reflected irradiation (RI), and temperature (TEMP) are the primary outputs of the simulation procedure.

2.44 iGRHYSO

The GRHYSO is a C++ software that optimizes hybrid renewable energy systems connected to the grid; the iGRHYSO (improved Grid-connected Renewable Hybrid Systems Optimization) is an upgraded version of the GRHYSO. We regret that this programme is only available in Spanish. Modelling and optimising storage battery systems for renewable energy (solar, wind, small hydro, etc.) and alternative energy (battery chemistry, hydrogen, etc.) is the focus of iGRHYSO. You can get this application from the NASA website, and it is great for plugging in your own irradiation, wind, and temperature readings. The application may also be used to study how temperature affects the production of renewable energy sources like solar cells and wind turbines. This programme can handle a wide range of electricity sales and purchases from the grid. The internal rate of return (IRR) may be calculated as a measure of success. It is possible to export simulation data to an Excel file and examine it there.

2.45 SOLSIM

SOLSIM (Ibrahim et al. 2011) was created by researchers at Germany’s Fachhochschule Konstanz to simulate the performance of hybrid renewable power plants that combine solar panels, wind turbines, diesel engines, batteries, and bioenergy systems to produce electricity and heat. With its few configuration settings, this programme may do basic economic analysis (e.g. photovoltaic panel tilt angles). Using SOLSIM, you may input a significant quantity of granular data to fine-tune your simulation. Each simulation generates a vast quantity of data, but the program’s graphical user interface makes it simple to understand and use by displaying the data in increments of an hour, a day, a week, or a month (Schaffrin et al. 1998). That programme is now unavailable.

2.46 Hybrid Designer

Hybrid Designer (Schaffrin et al. 1998) was made possible by the South African Department of Minerals and Energy and was created by the Energy and Development Research Centre (EDRC) at the University of Cape Town. In Africa’s climate, this technique is most useful for applications that don’t need constant access to the power grid. This genetic algorithm–based software is intuitive and can compare alternatives for achieving the lowest possible total cost of ownership during their lifetime. A full solution, including technical considerations and life cycle costs, may be generated by simulating many sources in Hybrid Designer, including photovoltaics, wind generators, batteries, and engine generators.

Application-wise, software may be broken down into the categories shown in Figure 2. Each category’s software may be further segmented into photovoltaic (PV) and hybrid systems.

Figure 2:

PV system design classifications.

4.1 A unified methodology for the design of PV systems

Two independent approaches, classical and artificial, exist for optimising the same algorithm. The literature provides a wide range of indicators for PV system evaluation.

Table 3 shows that the most important metrics are reliability (RL), economics (EC), and environment (EN). Tables 2 and 3 provide comparisons between traditional and artificial systems in terms of dependability, cost, and impact on the environment. Each indicator has its own unique set of key performance indicators (KPIs), some of which measure reliability (LPSP, LOPL, TED, DPSP, EENS, LOLR, ELF, D, P(R), REP), some measure cost effectiveness (NPC, TIC, COE, LCC), and still others measure the impact on the environment (TCO2E, EE, LCA). Measures the technological, economic, and environmental (TEE) sustainability of a system using a predefined metric. Some of these programmes and models can even construct unique setups, such as completely off-grid, completely hybrid, completely tracking, and completely grid-connected systems.

A few key points may be drawn from Tables 3 and 4, including:

1. The software is adaptable to a wide range of power generation situations, such as SPV, WT, DG, FC, H2 tanks (HG), BM, and BG. Solar PV systems may be designed and evaluated on their own or in conjunction with other energy sources as a part of a hybrid system.

2. Most programmes and models include an algorithm that can mimic PV, WT, and DG.

3. It has been revealed that battery storage (BS) and hydrogen tanks are the two most common methods of energy conversion (HT). However, compared to HT, BS has a lot of support from models and software.

4. To a large extent, electrical converters are used to effect the energy transformation (CO). Power electronics circuits like these converters are used to transform electrical quantities into other forms. Nonetheless, converters are classified into four types: direct current (DC) to alternating current (AC) to direct current (DC), direct current (AC) to direct current (DC), and direct current (AC) to direct current (DC).

5. Most algorithms use a synthetic, objective-independent approach. Recent literature has seen just a trickle of investigations using the traditional method.

6. The efforts of the algorithms are directed at reducing waste, expenses, and emissions. Algorithms are tested on a wide variety of KPIs, including LCOE, TCO, E, TCC, EE, GHG, TAOC, TAC, NPC, and TDP. The system was fine-tuned after careful consideration of technological feasibility, economics, ecology, and other factors. When it comes to finances, the total cost (TC) and the total annual cost (TAC) are the most crucial key performance indicators (KPIs).

7. The best PV system design is determined by software, models, and algorithms that take into account optimal technical design parameters while minimizing costs and emissions.

8. Some examples of available methods include: linear programming (LP), non-dominated sorting genetic algorithms (NDS-GA), genetic algorithms (GA), mixed-integer linear programming (MI-LP), adaptive genetic algorithms (AGA), mine blast algorithms (MBA), controlled elitist genetic algorithms (CE-GA), particle swarm optimization (PSO), mu-swarm optimization (MSO), and multi-objective particle swarm optimization (MOPSO), etc. In the past, “graphical user interface” (GUI) has been employed in certain approaches, such as (Gan et al. 2015)’s technique for finding the best possible layout for a wind and solar power installation. It is important to note that annual averages for wind speed and sun irradiation have been utilised.

Table 4:

Outlining a unified methodology optimization layout.

Techniques	Energy sources							Energy conversion		Energy storage		Method	Minimized KPI	Analysis	Reference
	PV	WT	DG	FC	HG	BM	BG	CO	EL	BS	HT
LP												Classical	TC	EC & RL	Eduardo et al. (2014)
MILP												Classical	LOCE	EC	Castro et al. (2015)
GA												Artificial	LCC, E, D	EC, RL & EN	Ogunjuyigbe et al. (2016)
AGA												Artificial	IC	EC & RL	Chen (2013)
NSGA-II												Artificial	TC, DPSP	EC & RL	Sheng et al. (2014)
Controlled elastic GA												Artificial	LCC, EE, LPSP	EC & RL	Fathy (2016)
MBA												Artificial	TAC	EC & RL	Sanchez et al. (2014)
PSO												Artificial	TC	EC & RL	Hassan et al. (2015); Naseem et al. (2021)
MPSO												Artificial	LCC, LOCE	EC	Baghaee et al. (2016)
MOPSO												Artificial	TIC	EC & RL	Shi and Yan (2017)
MLUCA												Artificial	TAC, GHG	EC & EN	Suhane et al. (2016)
ACO												Artificial	TC, TAC	EC & RN	Shi et al. (2015)
PICEA												Artificial	ACS, LPSP, GHG	EC, RL & EN	Gupta et al. (2015)
IFOA												Artificial	ATC, E	EC & EN	Maleki and Askarzadeh (2014a)
BBO												Artificial	COE	EC	Singh et al. (2016)
ABSO												Artificial	TAC	EC & RL	Gharavi et al. (2015)
ABC												Artificial	TC	EC & RL	Gharavi et al. (2015)
ICA												Artificial	NPC	EC, RL & EN	Sanajaoba and Fernandez (2016)
CS												Artificial	TC	EC & RL	Maleki and Askarzadeh (2014b)
DHS												Artificial	TAC	EC & EN	Chang and Lin (2015)

4.2 Hybrid-methodology-based PV system design

The preceding part focused on a single technique that outperformed artificial methods in terms of providing optimal design with quick computing. As the use of design-related software, models, and studies has increased with the worldwide proliferation of PV systems, however, more sophisticated optimization and precise design have become imperative. To bridge this gap, hybrid algorithms were developed, each of which combines elements of many separate algorithms (both artificial and classical). Literature evaluations show that complex PV system difficulties are more common in the outer suburbs, including islands and rural areas, and hybrid methodologies gave the most accurate design for these situations. The various hybrid algorithms are compared in Table 5.

From Table 5, we may draw a few conclusions, including the following:

1. The software can be used with many different types of energy sources, but PV, WT, and DG are the most common

2. As opposed to HT, most programmes and models provide energy conversion through BS.

3. Electrical converters are primarily used to effectuate the energy transformation (CO).

4. Most existing algorithms are optimized for planning off-grid PV systems and have limited functionality when it comes to creating grid-connected systems.

5. The algorithms are designed with efficiency in mind, with a goal of reducing wasteful activity and associated costs and environmental impact. Algorithms examine a wide range of KPIs, including total cost, levelized cost of ownership, levelized capital cost, energy intensity, distribution, distribution cost, energy efficiency, energy production, total installed cost, total installed cost per kW hour, greenhouse gas emissions, annualized cost per kW hour, and net present cost per kW hour. The TC and TAC, however, are the most crucial KPIs.

6. The programmes, models, and algorithms used to find the most efficient and cost-effective layout for a PV system.

Table 5:

Outlining a hybrid methodology optimization layout.

Techniques	Energy sources							Energy conversion		Energy storage		Method	Minimized KPI	Configuration		Analysis	Reference
	PV	WT	DG	FC	HG	BM	BG	CO	EL	BS	HT			SA	GC
HBB-BC												Artificial	TPC			EC, RL	Ahmadi and Abdi (2016)
TLBO												Artificial	TAC, LPSP, FC			EC, RL	Cho et al. (2016)
Hybrid GA												Artificial	TC			EC, RL	Tito et al. (2016)
IPF												Artificial	TC			EC, RL, SL	Mukhtaruddin et al. (2015)
MESCA												Artificial	TAC			EC, RL	Zahboune et al. (2016)
MOEA												Artificial	NPC			EC, RL	Dufo-lopez et al. (2016)
Hybrid GA, ANN-MCS												Artificial	NPC			EC, RL, SL	Lujano-rojas et al. (2013)
SA-TS												Artificial	TAC			EC, RL	Katsigiannis et al. (2012)
Markov based GA												Artificial	TC			EC, RL	Hong et al. (2012)
DCH-SSA												Artificial	TAC			EC, RL, EN	Askarzadeh (2013)
HSMCS												Artificial	LCC			EC, RL	Maleki et al. (2016b)
Hybrid Iterative-GA												Artificial	TC			EC, RL	Khatib et al. (2012)
SA-PSO												Artificial	LCC			EC, RL	Zhou and Sun (2014)
NS-PSO												Artificial	LPSP, LCC, LEP, KI			EC, RL	Ma et al. (2016)
PSO based MCS												Artificial	TAC			EC, RL	Maleki et al. (2016b)
FPA-SA												Artificial	LPSP			EC, RL	Tahani et al. (2015)
HOMER												Artificial	COE, NPV			EC, RL, EN	Zahboune et al. (2016); Baneshi and Hadianfard (2016)
REPS.OM												Artificial	COE, NPV			EC, RL, EN	Kazem and Khatib (2013b); Kazem et al. (2014)
iHOGA												Artificial	COE, NPC			EC, RL, EN	Dufo-lópez et al. (2011); Fadaeenejad et al. (2014)