A New Toolkit for Energy Planning for Isolated Microgrids

Rural areas in various locations are facing electricity shortages and are compelled to heavily rely on non-renewable and ecologically harmful fossil fuels as their primary source of energy. To address this issue, microgrids were proposed as a solution to provide energy to these areas. An IEEE working group, the SESDC Working Group, was established to investigate the feasibility of implementing isolated microgrids as solutions in these communities. However, it has been identified that a proper software tool for microgrid planning is needed to accurately analyze the optimal microgrid configuration. Thus, a user-friendly and secure web-based system for microgrid planning was proposed. This tool was developed using the Django web development framework and Python programming language. It was then validated with users to ensure that the data entered is calculated correctly and produced the expected results.


I. INTRODUCTION
Electricity is critical for poverty reduction, economic progress, and higher living standards [1].
In the early twentieth century, bringing the advantages of electricity to farms and rural regions was primarily a dream. Many challenges had to be addressed before widespread rural electric power use became feasible [2]. These conditions have significantly changed, and actually 87% of the population has access to electricity. However, there is still 13% of the worldwide population, which represents 770 million inhabitants [3], that do not have access to electricity. This has to be addressed with proper policies, investments, and planning programs.
Some of the causes for failure of electrification programs include high capital and fuel costs, and the remote and The associate editor coordinating the review of this manuscript and approving it for publication was Antonio Piccinno . sparsely populated areas in which most impoverished people live [4]. To address these issues, there has been a promotion of small-scale off-grid energy systems, usually known as isolated microgrids, for governmental and nongovernmental organizations (NGOs) alike [5]. As per [6], a microgrid is defined as a group of loads, distributed generators, and energy storage systems that work together to reliably deliver electricity. It can connect to a host power system at the distribution level through a single point of connection or operate independently from the main grid. Community isolated microgrids are established in areas distant from transmission infrastructure; they cannot connect to the primary grid, and must operate continuously in standalone mode.
Microgrid energy concerns include economic, technical, logistical, social, and environmental issues that must be addressed collaboratively. Economically, the high energy rates are a direct result of the challenges that microgrids are now facing in delivering electricity to consumers. Microgrids can be difficult to operate considering the low load and the possible uncertainties depending of the generation (e.g.: wind and solar fluctuations).
In terms of logistics, technicians need to travel to remote communities for both installation and maintenance, and the required materials must also be transported to these locations. From a societal perspective, limitations on energy can hinder the growth of a community when the community's electricity demand approaches the current system's limits of power generation capacity -if the community even has access to electricity. Lastly, environmental concerns include the significant pollution emissions associated with the reliance on fossil fuels, such as microgrids based on diesel [7].
Thus, when implementing an isolated microgrid, it is crucial to first conduct proper planning. A conventional technique often entails an investigation of the available energy sources (primarily irradiance and wind speeds), and since these vary by location and season, the outcomes are not easily transferable from case to case [8]. Thus, it is crucial to have an appropriate planning and analysis tool to consider the specific data inputs that a location or village could have.
In the last several years, there has been a growing interest in designing open-source tools to facilitate research in engineering. These tools include topics such as CO 2 capture [19], multi-scale integrals [20], information theory [21], meanfield-type games [22], and muscle artifacts [23]. In electric power systems, tools are also available for power system analysis [24] and home energy management systems [25].
From a techno-economic point of view, the planning problem of isolated community microgrids consists of determining the combination of generation and storage and their corresponding specific designs, thus optimizing the trade-off between cost and reliability of supply to some pre-specified ensemble of consumers, subject to any constraints that may exist [26]. To simplify the hybrid system design process and optimize the use of renewable resources, a variety of software tools have been created to analyze the technical and economic possibilities of various hybrid renewable technologies [27]. However, to the knowledge of the authors, there is not an open-source tool that perform the techno-economic analysis for isolated community microgrids that allows for comparison with zero existing load and capacity, while also incorporating load and capacity growth projections.
The Sustainable Energy Systems for Developing Communities (SESDC) Working Group aims to promote the use of renewable energy systems through access to these services in developing communities around the world. As one step in achieving this goal, the Working Group has established an analysis tool that will help organizers and planners conduct a preliminary assessment of the feasibility of community-led off-grid microgrids, which can be accessed at the link [28].
The main contributions of this paper are highlighted as follows: • The toolkit uses a clear, easy to use front end interface that organizes and simplifies entry of project data.
• It allows for the management of multiple projects, which are easily accessible from the user's login screen.
• Data presentation is enhanced by incorporating interactive charts that display relevant information for analysis, while the application takes care of all necessary calculations and estimations in the background.
• The portability of the tool is a significant advantage, as it enables projects to be accessed from any internet-connected computer or mobile device. Also, multiple users can be given access to a common project. This eliminates the need to transfer files to team members, enhancing productivity.
• It is free to use. No subscription is required, only a registration for login and managing project files. The rest of this paper is organized as follows: Section II presents the background for this proposed toolkit. Section III describes the microgrid pre-feasibility toolkit. The results are discussed in Section IV. Finally, Section V is devoted to the conclusions.

A. MOTIVATION
Microgrid investment interests for such projects are often directly driven by expected beneficial outcomes like electricity access, education attainment, improved health, and improved equality. A challenge with small-scale projects is that many sector specific Non-Governmental Organization (NGO) and respective partnering communities lack the low-cost tools and expertise to appropriately design off-grid electrical systems [29]. Yet, the initial design is practically difficult to produce, due to cost of the software tools and lack of expertise of project partners. At the same time, conducting robust investment grade feasibility studies is typically restricted to well funded country led projects that cost tens or hundreds of thousands of dollars. Instead of this, small-scale projects must instead rely on low cost local estimates that may only have limited data support -these studies subsequently can be easily overlooked by potential funders.
This challenge justifies further development of low-cost tools to be provided to these projects with the objective of increasing their access to larger sums of investments. The key features are: • Improved accessibility for NGOs and communities. • Moderately robust forecast of desirable outcomes.
The key features of such tools are listed as follows: Accessibility is composed of: a lower learning curve for users, compatibility with common software, open-access and open-source, and low to no-cost. As a simplification of software design is needed to lower the learning curve, it must be recognized that doing so will reduce the precision of the outputs. In this setting, a middle ground between precision is justified in order to provide users with an improved forecast of the project impacts versus the status quo.
An additional requirement is that the forecasted outputs reflect targeted outcomes that are relevant to potential funders. Existing off-the-shelf software tools for off-grid design are typically restricted to technical and financial outputs -but do not extend to local or global outcomes.
Achieving universal access to electricity from the Sustainable Development Goal #7 supports targeting a wide range of project developers. Small-scale NGO led projects have historically been at the forefront of providing electricity access in the most remote (and costly) areas that also have the lowest access rates. With improved tools, they can accelerate their efforts by reducing costs for the process of pre-feasibility design, and acting as a catalyst for securing investment funds.

B. SIMILAR SOFTWARE TOOLS FOR MICROGRID PLANNING
In the market, there are multiple software tools for microgrid design and optimization, including HOMER, RETScreen, Hybrid2, GridLAB-D, TRNSYS, among others. Each one has its specificities, main objectives, advantages, and limitations.
In this paper, HOMER [30] and RETScreen [31] were selected for comparison with the new software tool as they are considered the most well-regarded among the available software tools for microgrid design and optimization.
HOMER (Hybrid Optimization of Multiple Energy Resources) is a software tool developed by the National Renewable Energy Laboratory (NREL) in the United States for microgrid design and optimization. It allows users to model and optimize hybrid energy systems that integrate various renewable and conventional energy sources, energy storage systems, and demand-side management strategies. HOMER uses a sophisticated optimization algorithm to determine the optimal configuration of a microgrid system based on various inputs, such as energy demand, renewable energy resources, energy storage capacity, and system cost. It also allows users to simulate and analyze the performance of the microgrid system under different operating conditions and scenarios. HOMER is widely used by researchers, engineers, and policymakers around the world for microgrid design and optimization, particularly in remote and off-grid areas where access to reliable electricity is limited.
However, HOMER does have some limitations that may affect its application in certain contexts. One of the main limitations is its complexity, which may make it challenging for non-experts to use and interpret the results. The optimization algorithm used in HOMER involves a large number of variables and constraints that must be carefully defined and calibrated. This requires a high level of technical expertise and knowledge of microgrid design and optimization. Another limitation of HOMER is its cost. While a free version of the software is available, it has limited features and capabilities. The full commercial version of HOMER can be expensive, making it less accessible to small businesses and organizations with limited budgets. The cost of HOMER may also be prohibitive for researchers and policymakers in developing countries, where the need for microgrid design and optimization is particularly acute.
RETScreen is a software tool developed by Natural Resources Canada for renewable energy and energy efficiency analysis. It provides a wide range of modules and tools for designing, analyzing, and optimizing renewable energy systems, such as photovoltaic systems, wind turbines, and hybrid systems. One of the main advantages of RETScreen is its user-friendly interface, which makes it accessible to a wider range of users with varying levels of technical expertise. It also offers a free version that includes most of its features, making it an affordable option for small businesses and organizations with limited budgets.
However, RETScreen also has some limitations that may affect its application in certain contexts. One limitation is its limited scope, as it is primarily focused on renewable energy systems and does not include as many features for energy storage systems or demand-side management. Additionally, the accuracy of the tool may be impacted by the quality of the input data used, and RETScreen's default values may not always accurately reflect local conditions, which may require customization. Another limitation of RETScreen is its lack of support for complex optimization algorithms. This may make it less suitable for applications where precise optimization is required, such as microgrids in remote and off-grid areas.
As a consequence of the limitations of HOMER and RETScreen, the software being discussed emerges as a viable alternative. With its user-friendly interface and open-source nature, it fills the gap for users who lack the resources to invest in commercial tools. By providing a simpler and more accessible option for microgrid design and optimization, it offers a promising solution to address electricity shortages and promote the integration of renewable energy in rural areas.

C. IEEE TASK FORCE ON MICRO-GRIDS PRE-FEASIBILITY TOOLKIT
The SESDC working group, a component of IEEE, is dedicated to promoting knowledge and understanding of energy systems, including electricity, in developing communities worldwide. The group seeks to educate technical experts, policymakers, government officials, and organizations on these issues [32].
The SESDC working group works closely with stakeholders to ensure that their recommendations for sustainable energy system technologies and solutions are aligned with social norms, traditions, and cultures. Their goal is to promote environmentally-safe, cost-effective, and measurable solutions that can enhance economic and social viability, as well as the overall quality of life for people in developing communities. VOLUME 11, 2023

III. MICROGRID PRE-FEASIBILITY TOOLKIT A. PLANNING METHODOLOGY
In the realm of microgrid planning, financing options play a crucial role in determining the feasibility and success of new projects. Using the sponsor's balance sheet is the conventional method for corporate finance in new projects. Opting for project finance involves setting up a new entity, resulting in additional time and significant transaction costs for financing structuring. Since project creditors lack recourse to the sponsor's debt, evaluating project cash flows requires meticulous scrutiny, involving legal, technical, and insurance advisors, as well as meticulous negotiations of contract terms among all parties [33], [34], [35].
Moreover, in the evaluation of microgrid projects, environmental metrics play a significant role, particularly in assessing the sustainability and impact on carbon emissions. Carbon emissions are a commonly used metric to measure the environmental footprint of microgrid systems. By analyzing and minimizing carbon emissions, microgrid planners aim to promote cleaner and more sustainable energy solutions. The consideration of environmental metrics, such as carbon emissions, is crucial for ensuring that microgrid projects align with global sustainability goals and contribute to reducing greenhouse gas emissions [36], [37].
These metrics play a vital role in enabling project managers and investors to evaluate the potential profitability and associated risks of a given microgrid project [38], [39]. By quantifying these metrics, stakeholders can make informed decisions regarding investment opportunities. In the following section, we will describe the formulas utilized to calculate these essential metrics, providing a comprehensive understanding of their calculation methodologies.
To calculate the estimation of CO 2 emission savings by fuel, the tool uses the following formula: The total carbon footprint of the project is calculated by multiplying the average CO 2 emissions from residences by the number of years of project duration and adding this to the product of the average CO 2 emissions from businesses and the number of years of project duration.
To calculate the estimation of CO 2 emission savings by microgrids, the tool uses the following equation: where GES are the Grid Emission Savings [Ton/yr], and CE t the Carbon Emission for year t [Ton/yr] where E D,T is the total energy demand, and CF the carbon factor.
The estimation of CO 2 emission savings is obtained by summing the CO 2 emissions resulting from the total energy demand of residences and businesses. This sum is calculated by multiplying the total energy demand by the CO 2 factor, which represents the amount of CO 2 emitted per unit of energy generated by the microgrid system.
The tool generates an economic summary that includes subsidies, which is calculated using the following formula: where LCOE stands for Levelized Cost of Energy ($/kWh), C T for Total Cost (Discounted), and E P,T for Total Energy Produced The total cost discounted variable is the sum of the energy produced in each period of expansion plus the capital expenditure, and the total energy produced variable is the sum of the discount of each expansion period according to the discount rate in that period.
where NPV pre−tax is the pre-tax net present value, CF t the net cash flow at year t, and CAPEX the capital expenditures where R T is the total revenue, OPEX t the operating expenses at year t, C E the expansion cost, and r the discount rate The total revenue variable is the fixed cost of residences multiplied by the number of residences plus the fixed cost of businesses multiplied by the number of businesses. The yearly OPEX variable is equal to the yearly operational expenditure multiplied by the operation and maintenance cost variable and multiplied by the operating expense subsidy. The expansion variable is sum of all costs within an expansion multiplied by the subsidy rate.

B. ARCHITECTURE
The web application was developed using the Model-View-Controller (MVC) software design pattern. This pattern divides the system into distinct parts, each with a specific function. The screenshots accompanying this text were taken from a model built using data from an existing real-world system in Darewadi, a village in the Nagar Taluka of Ahmednagar District in Maharashtra State, India.
The first component is the view layer, which contains everything related to the user interface's structure. This is where the application's different pages are designed, enabling the user to interact with the system's functionalities such as creating new projects, entering data, and presenting dynamic graphs necessary for evaluating data results.
The second component of the design pattern is the controller layer, which is the system's main layer as it acts as a connector between the view layer and the model layer. Its task is to manage interactions received from the user through the view and manipulate the data in the model layer according to the request.
The last component of this design pattern is the model layer, responsible for handling everything related to reading and writing information in the database. For the toolkit, this component helps to load information when creating a project and entering the required data. Additionally, it facilitates storing the information, making it possible to extract data for generating results. Each queried data from the database is processed through functions containing the formulas established in the pre-feasibility calculation model.
The workflow of the web application is illustrated in Figure 2. It begins with user interaction through the web interface, which initiates the processing of the request. The application adds the information to the database, makes calculations as required, and generates a new interface that displays the response to the user.

1) LOGIN
The login page is depicted in Figure 3. It is the initial page of the web application [28]. To access all software functions, users must authenticate their credentials. From this screen, users can create an account, required to use the application. To create an account, the user must enter a valid email address for password recovery and username retrieval. It also contains provisions if the user needs to recover their login credentials and reset their password.

2) MAIN PAGE
In the next section of the application, buttons are available for navigating to the project list, changing the account password, accessing the main page, and logging out, as seen in Figure 4.
The ''Projects'' button allows users to access a list of all the projects they have created. Clicking on the button redirects them to the projects list, where they can view only their own projects. This is explained in more detail in Section III.
The ''Main Page'' button is located on the ''Microgrids Toolkit'' title. Clicking on this button redirects users to the main page and allows them to navigate back to the other interfaces.   The ''Logout'' button is located in the navigation bar section of the page and can be clicked on at any time. Clicking on this button signs the user out of the app and exits it.

3) PROJECTS MANAGEMENT
The projects management section contains the functionality to create, edit and delete projects, as illustrated in Figure 5. Each user has their own projects, and no one can view projects that other users have created. Each project contains a button that redirect to the project details page where a user can insert all the information about that project. The ''Create New Project'' button redirects the user to a new page where they can build a new project. The user will enter a name and description for the project. After entering the information, the user must click the ''Save'' button to store it and will then be redirected to the projects management page. Additionally, there is a button next to this option for the user to return to the main page.
Each project on the list has three buttons. The ''Edit'' button redirects the user to the edit page to change the name and description of the selected project. The ''Details'' button redirects users to the project page. From here, the user can enter additional information about the selected project. The information that can be entered on this page is described in more detail in Section IV.
The ''Delete'' button allows the user to remove the project from their account. Clicking this button will trigger a confirmation message asking the user to confirm the delete process.
The Carbon results screen of the microgrid planning toolkit provides an overview of the estimated greenhouse gas emissions associated with the proposed microgrid configuration.

4) PROJECT DETAILS
The project details section is divided in two parts, the input pages and the results pages, as illustrated in Figure 6. The input pages are those where the user can enter all the information about the proposed system. The results pages the user can see the project summary and the results of the financial analysis. On the General tab, the user can enter information about the location, demographics, average income, and willingness to pay for both residential and commercial consumers. The user can also specify the current fuel used in the settlement, the discount rate that applies to the project's expenses during the project period, the projected life of the system, and the World Bank tier of the community, which enables the user to set an economy classification for analytical purposes.
In the Plant Expenditure tab, the user can enter information about the size of the system and anticipated expenses for the project. This includes the size of the electrical plants and batteries, the capital or initial expenditure for the beginning of the project (or CAPEX ), the operational expenditure during the project (or OPEX ), the replacement expenditure for the maintenance of the project (REPEX ), and any subsidies that may be applied against these expenditures.
The Revenue and Electricity Consumption tab allows users to enter information about the average electricity consumption in both residential and commercial properties, which is illustrated in Figure 7. It also allows users to estimate annual growth in demand. On the revenue side, it provides two functions to better project long-term project income. • Users can model three different types of revenue streams: a variable pricing model based on kilowatthours consumed; a fixed or subscription model based on a monthly charge; or a hybrid of the two, with a lower monthly charge accompanied by a monthly usage cap.
• It also allows user to factor in periodic tariff increases. In the CO 2 tab users can enter information about current carbon emissions according to the type of fuel used, the CO 2 produced per liter of fuel used, and the value of emissions from an average car to aid in demonstrating potential reductions, which is depicted in Figure 8. Figure 9 illustrates the 'Project Overview' page, which is the first of three tabs showing the resulting calculations from the information entered previously. The page is divided into three sections. The first section contains key scenario information about the settlement where the project is going to be implemented. The next section displays information about the number of residential and commercial properties in the settlement and the ones that will be connected to the microgrid. Finally, the page has a chart section that shows information about the residential and commercial properties in the settlement.
On this page, users can see a summary of basic information about the project, such as the name of the settlement, the region and country where the microgrids will be installed, the starting year and projected years for installation, and the discount rate to be applied to the project. Additionally, there are a couple of charts that show the number of residential and commercial properties included in the project. The Carbon results page presents information about CO 2 emissions, including the reduction in emissions compared to the current fuel used by the community and the savings between fuel and microgrid emissions. Users can see a comparison of the carbon emissions from the old fuel-based electricity installation and the new microgrid installation using renewable energy methods, as shown in Figure 10. This information is displayed in both tabular and graphic format to aid understanding and explanation.
The economic results page shows the project revenues and expenses, as depicted in Figure 11. The first tab shows a summary of the expenditures sorted by the CAPEX , OPEX and REPEX . It also includes a chart to facilitate understanding and analysis. The second tab provides more detailed information regarding the CAPEX . This is of vital importance to the project because it has the most significant impact on financial feasibility. It includes both direct and indirect costs for items such as materials, transportation, and land use. The intent is to capture a high level or ''10,000-meter'' view while also encouraging decision-makers to pursue a deeper analysis using advanced tools such as HOMER or RETScreen.
The third tab compares the three revenue options for the project. Based on the projected expenditures and tariff pricing, it calculates the projected revenue (or loss) generated over the life of the project in three ways: as annual cashflow, net present value (NPV ), and discounted internal rate of return (IRR). The results are presented both numerically and graphically, providing for an easy comparison between the pricing options. This tab can help organizers and developers select the pricing plan that best suits the economic conditions of the community and the preferences of external investors. VOLUME 11, 2023

A. CASE STUDY
The case study used for demonstrating the application is based on an existing and operational system, located in the Indian village of Darewadi, Nagar Taluka, in the Ahmednagar District of Maharashtra State.
The data collected for this case study is divided into three groups, corresponding to the data called for in the input screens as previously described.
In order to validate the calculations and outputs of the Economic Toolkit, three model systems were created. These models were derived from existing systems by using published literature and making realistic assumptions to fill in gaps in information that could not be obtained from public sources.
The three model systems selected were: • The village of Dharnai, Bijar province, India [40], [41] • The village of Myin Chi Niang, Myanmar [42] • A health clinic and school in the village of Sapra, Jharkand, India [43] Each of the three model systems was analyzed using the Economic Toolkit, as well as two commonly used publicly available tools, HOMER and RETScreen. The economic outputs of HOMER and RETScreen were then compared to those of the Toolkit. To simplify the validation process, four common economic metrics were selected: net present value (NPV ), internal rate of return (IRR), annual cash flow, and cumulative cash flow. The workgroup determined that if the Economic Toolkit outputs were within 10 percent of the outputs from HOMER and RETScreen, then the results would represent a reasonably approximate prediction of economic performance, and thus could serve as a solid ''first draft'' prefeasibility assessment.
• IRR: As constructed, there is a significant difference between the IRRs as calculated by the Toolkit and by  RETScreen. This is due to a structural difference to how each app determines IRR. [44] -The Economic Toolkit incorporates a discount rate when calculating IRR. This reflects what the rate of return is projected to be in relation to what is desired by the funders. For example, if a project's base IRR is 10%, and investors are looking for a rate of return of 8%, then the discounted IRR is 2%. -Using the free version of RETScreen, evaluators were unable to determine how to incorporate the discount rate when calculating IRR. However, when comparing the IRR when calculated without including the discount rate, The Toolkit and RETScreen provided outputs within 1% in each of the three scenarios.
• Annual and cumulative cashflow: The Economic Toolkit and RETScreen both allow for modeling of tariff increases. However, there is a difference in approaches: -The Toolkit allows for five discrete tariff increases over the life of the project. -RETScreen allows for a fixed annual increase.
• When no tariff increase is included, the Toolkit and RETScreen results match perfectly. If a tariff increase is included, there is a divergence in annual and cumulative cashflow. However, this divergence is based on a predictable geometric progression resulting from trying to emulate an annual increase using discrete steps. Using an exponential formula to determine the effective annual percent increase based on the discrete-step increase in the Toolkit, divergence in the results was greatly minimized, and cashflow results were within 2% of each other across all three scenarios. HOMER: -Two separate runs of the Dharnai model, one that included a fixed tariff and another that incorporated a tariff that grew by a fixed rate each year, resulted in NPV variances less than 0.65%. -Two separate runs of the Myin Chi Niang model, one with a fixed tariff and one with no tariff, resulted in NPV variances of less than 1.5%. -Three runs of the Sapra model resulted in NPV variances between 2.86% and 5.57%.
• IRR: It was not feasible within the scope of this testing to construct a model that enabled a valid comparison of IRRs between HOMER and the Toolkit. The models were constructed to represent a solitary system design, with the alternative being ''do-nothing'' or no electrical service. Conversely, HOMER looks to provide design comparisons and optimization between a base system and an optimized system. Testers were unable to find a way to calculate the IRRs in HOMER for a solitary design that was not necessarily optimized, where the base system is a null set (no generation or load).
• Annual and cumulative cashflow: -Dharnai: In each test run, the average difference in annual and cumulative cashflow was less than 0.6%. -Myin Chi Niang: In each test run, the average difference in annual and cumulative cashflow was less than 0.5%. -Sapra: In each test run, the average difference in annual and cumulative cashflow was less than 2.0%.
• Fluctuations in year over year cashflow calculations: When incorporating growth in tariffs over the life of the project, a divergence was observed similar to that in the comparisons with RETScreen. This divergence is from the same difference in approaches to how tariff growth is entered; HOMER uses an annual growth rate, where the Economic Toolkit uses discrete step increases. As with RETScreen, the divergence is a predictable geometric progression, which is simple to compensate for. In the test cases modeled, when the geometric progression was accounted for, the annual cashflow never differed by more than 1.2%, and the cumulative cashflow never differed by more than 0.8%.

C. ANALYSIS
The results of the Economic Toolkit compare very favorably to those of both HOMER and RETScreen when used on identical models. The economic calculations agreed perfectly in several instances. Where a divergence in results was observed, the divergence was easily understood and still within the 10 percent variance parameters set by the Working Group.
HOMER is truly the gold standard of microgrid modeling, and RETScreen is a powerful modeling application in its own right. The Economic Toolkit, as it currently stands, does not provide functions such as HOMER's component optimization, or the sensitivity analysis and thermal analysis that RETScreen provides. It assumes that the user already has a technical solution in mind, and has already researched the technical performance and financial aspects of that solution.
However, there is an observed need to conduct economic modeling on smaller systems, by people and groups that do not have the resources to access these more robust and complex tools. Certain features of both HOMER and RETScreen may not lend themselves to modeling of these smaller systems by people that are not experienced users, or have the wherewithal to hire experienced users. In short, this Toolkit is envisioned as a compliment to these more powerful tools, serving as an ''entry-level'' model that can be used to justify a deeper analysis with more robust packages.
Further testing by field users and system developers is called for in order to aid users in fully exploiting the capabilities of the Economic Toolkit. The Toolkit provides users a way to determine carbon emissions avoidance, but this feature needs to be tested and its results compared with HOMER (see Table 4) and RETScreen(see Table 5).  While both HOMER and RETScreen are incredibly powerful and well-developed design and analysis tools in their own right, several challenges were encountered with each in trying to assess the economic characteristics of small, off-grid systems such as the ones that are the focus of the Economic Toolkit. GENERAL: • Many existing tools are well suited and optimized for larger systems, and so determine emissions reductions on the scale of tons of carbon dioxide equivalent. The projects this toolkit would support are much smaller, and would have reductions on the order of kilograms of carbon dioxide equivalent. Presenting emissions reductions of that scale in the larger tools can result in rounding down, to the point where a comparison between alternatives may not exhibit any reduction. VOLUME 11, 2023 • For many of the communities that would benefit from this Toolkit, a chief source of lighting is kerosene lanterns. An inexperienced user may have difficulty reflecting the use of these lanterns in a baseline model in larger tools, given that they neither consume electricity, nor consume a common fossil fuel used in electricity generation (such as gasoline or diesel). HOMER: • The financial analysis appears to be intended to compare a proposed design with an existing base case with a pre-determined load. It was not readily apparent how novice or infrequent users could construct and compare economics using a null set (no generation and no load) as a base case.
• Modeling of different rate tariffs is not straightforward in HOMER PRO. To accomplish this in the simulations for validation, it was necessary to treat it as if it were a negative annual O&M cost. While this procedure did result in valid cashflow analyses, it was cumbersome and required additional calculation steps outside of the software. It also prevented the direct use of this field to represent annual O&M expenditures. It is possible to model both financial aspects in this field simultaneously, but this would require conducting several sets of calculations outside of the model. While it appears to be possible to model these aspects separately in HOMER Grid, the model then relies on having the grid as a resource. This tends to interfere with modeling of a completely off-grid system.
• A subscription to HOMER can be very costly for a distinct user who may only need it for a couple of projects. While HOMER Legacy is free to use, it cannot be downloaded from the HOMER website, and its support has been discontinued. RETScreen: • RETScreen is designed to assess the energy needs for a single building or facility at a time. To simulate a village, it was necessary to treat all village buildings (homes, shops, etc) as a single structure.
• RETScreen assumes by default that excess electricity is exported to a grid.

D. PERFORMANCE EVALUATION
When evaluating the performance of a Django application, there are several metrics that can be used to measure its effectiveness.
While the proposed Economic Pre-feasibility Toolkit shows great promise in enhancing energy planning for remote areas, there is still a need to evaluate its performance in realworld scenarios. For future work, as the Economic Toolkit is circulated and users provide feedback, certain metrics can be collected and reported. This includes: • The accuracy of the application in order to measure the percentage of projects that are correctly processed by the application.
• Scalability: as the number of users or amount of data increases, the application should be able to scale up without significant decreases in performance.
• A combination of load testing, performance monitoring, and user feedback will be used over time to identify areas where the application may be slowing down, and future improvement of the application from a user experience point of view.

E. DISCUSSION
It is important to note that while the Toolkit [28] has several advantages, it also has some limitations when compared to existing tools such as HOMER and RETScreen. One of the main limitations of the toolkit is that it does not have the same level of optimization capabilities as HOMER. HOMER uses a sophisticated optimization algorithm to determine the optimal configuration of a microgrid system based on various inputs, such as energy demand, renewable energy resources, energy storage capacity, and system cost. This allows users to design microgrid systems that are highly optimized for their specific needs, resulting in more efficient and cost-effective systems.
Another limitation of the Toolkit is that it does not have the same financial analysis and project evaluation capabilities, or sensitivity analysis, as RETScreen. RETScreen allows users to conduct detailed financial analysis and project evaluation for their microgrid projects. This is important for ensuring the financial viability and sustainability of microgrid projects, as it allows users to identify potential financial risks and evaluate the economic benefits of different system configurations. Moreover, it also provides a range of pre-built templates and modules for different types of energy systems.
Despite these limitations, the Toolkit offers several unique features and advantages that make it a valuable tool for microgrid design and optimization, particularly for low-income investors and users with limited technical expertise. One key advantage is that the Toolkit is completely free and opensource, making it accessible to a wide range of users who may not have the resources to invest in commercial tools such as HOMER or RETScreen. Its user-friendly interface and streamlined workflow also make it easy for non-experts to use and generate optimized microgrid designs. Additionally, its open-source nature allows for customization and adaptation to specific user needs and preferences. Overall, the Economic Toolkit provides a valuable addition to the existing suite of microgrid design and optimization tools, offering a simpler and more accessible option for users with limited resources and technical expertise.
Future research and improvement of the presented Toolkit could focus on expanding its capabilities to include more advanced optimization algorithms for microgrid design and control. Additionally, the integration of financial analysis and project evaluation features could further enhance the usefulness of the toolkit for stakeholders and decision-makers in the renewable energy industry. Further research could also explore the development of more user-friendly interfaces and intuitive visualizations to facilitate the use and interpretation of results. Finally, the inclusion of machine learning techniques and artificial intelligence algorithms could potentially improve the accuracy and speed of microgrid optimization and control, paving the way for more efficient and sustainable energy systems.

V. CONCLUSION
This paper presents a new economic pre-feasibility toolkit for energy planning for isolated microgrids. It is a valuable tool for designing and optimizing microgrids, particularly for low-income investors and users with limited technical expertise.
Its user-friendly interface and open-source nature make it accessible to a wide range of users who may not have the resources to invest in commercial tools such as HOMER or RETScreen. However, the system has some limitations compared to these tools, such as its lack of advanced optimization algorithms and sensitivity analysis features. Future research and improvement could focus on expanding these capabilities and incorporating machine learning techniques for more efficient and sustainable energy systems. Overall, the system provides a simpler and more accessible option for microgrid design and optimization, offering a promising solution to address electricity shortages and promote renewable energy in rural areas.

JUAN-FERNANDO POLANCO was born in
Quito, Ecuador, in 1997. He received the degree in software engineering from Universidad de las Américas, Quito, in 2022. He is currently a software developer of web apps, specifically on logistics applications for food transportation. His current research interests include data analytics and cybersecurity.
ALEXIS ESPARZA was born in Caracas, Venezuela, in 1998. He received the degree in software engineering from Universidad de las Américas (UDLA), Ecuador, in 2022. He is currently a software developer of web and local app's, specifically in the banking area. His current research interests include cyber security for software systems and web development with new technologies.
JASPREET SINGH was born in Aurangabad, India, in 1991. He received the B.Eng. degree (Hons.) in electrical and electronics engineering (with industry) from the University of Leicester, U.K., in 2013, and the M.Phil. degree in engineering for sustainable development from the University of Cambridge, U.K., in 2015. His current research interests include energy strategy, technoeconomic modeling, renewable energy (power and heat), energy storage, energy transition, and business investment case development. He became a Chartered Engineer (C.Eng.), in 2022, and has been a member of the Institution of Engineering and Technology (MIET), since 2015. Furthermore, he completed the CFA Investment Foundation Course, in 2021, and the Certified Expert in Climate and Renewable Energy Finance Course (developed by the Frankfurt School-UNEP Collaborating Centre for Climate and Sustainable Energy Finance), in 2022.