Trends in Hybrid Renewable Energy System (HRES) Applications: A Review

Abstract

Microgrids and hybrid renewable energy systems play a crucial role in today’s energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings.

1. Introduction

Currently, crude oil, coal, and natural gas are used as conventional energy sources to meet about 70% of the world’s energy demand [1]. Energy demand is soaring in response to the world’s growing economy and population. Consequently, the consumption of fossil fuels is also increasing significantly. Stocks of conventional fuels are limited and rapidly diminishing, requiring immediate action and long-term solutions to avoid a potential energy disaster in the coming years. In addition, fossil fuels are potential sources of dangerous emissions, such as greenhouse gases, which are major contributors to global warming [2].

The increase in these gases (especially CO₂), which has occurred since approximately 1990 from the excessive use of fossil fuels and electricity production [3], has caused an increase in droughts and seasonal mismatches (short winters, long summers) [4]; for this reason, the governments of a large number of countries have seen the need to take action to mitigate this problem, being fundamental to the sustainability of the environment, and have opened fiscal opportunities and incentives for energy investment, giving way to renewable energy as part of the solution to this global problem.

According to the World Economic Outlook 2020, some 940 million people (13% of the world’s population) live without electricity [5], while it is estimated that the world population will reach approximately 9.3 billion by 2050, which is a rapid increase and is expected to increase global energy demand by 1.5 to 3 times [6].

Hybrid renewable energy systems (HRES) have become an option for stand-alone or grid-connected models [7]. This design of different renewable energies can temporarily harmonize the demand for electricity with the availability of renewable resources [8]. Researchers’ interest in HRES has grown tremendously in the last two decades [9]. Furthermore, as a reaction to the accelerated growth of energy consumption, they have emerged as an option for reducing the harmful effects derived from the use of fossil fuels in traditional power plants and their harmful consequences for the natural environment, and with advances in power–electronics interfaces, a better integration of renewable energies has been achieved [10,11,12]. HRES are, therefore, generating important transformations in the paradigm of conventional energy systems. The traditional unidirectional flow of energy between power plants and users has evolved into a bidirectional flow, improving the energy transition [13,14].

Renewable energy sources (RES), such as solar, wind, and biomass, have been developed as an option with which to mitigate global warming, alleviate energy security issues, create new business opportunities, and provide other benefits. Thus, a hybrid system integrating multiple energy resources is more efficient and cost-effective [15].

Data have been reported in the literature on global electricity generation by sub-regions, for Latin America and the Caribbean, 1551 TWh (5.79%); North America, 4930 TWh (18.41%); Africa, 844 TWh (3.15%); the Middle East, 1265 TWh (4.72%); Asia and Australia, 12,919 TWh (48.25%); Europe 3871, TWh (14.46%); and the Commonwealth of Independent States, 1397 TWh (5.22%) [16]. That is why hybrid renewable energy systems play an important role in the energy transition process; in addition, the signing of the Paris Agreement in 2015 has encouraged nations to seek sustainable strategies to ensure energy security, techno-industrial development, decarbonization, and to reduce energy costs [17].

The integration of renewable energy storage devices and power electronic devices in hybrid systems has led to optimization processes using traditional, hybrid, or artificial intelligence methodologies to find better results for their design and cost analysis. In this analysis process, it is important to classify the objective functions of cost, air quality, technical aspects, supply reliability, grid autonomy, and whether it is an isolated configuration, grid connected, or both [18].

According to what has been provided in the research, the software tools are compared considering the input data (load requirement, details of supplies, details of components, among others) and output data (optimal sizing, evaluation of technological aspects environmental review, financial assessment, among others) required for an HRES analysis. Figure 1 shows the proportion of these requirements that these tools have. It can be seen that Homer Pro is an outstanding tool in certain aspects [19].

HOMER software performs three main functions: simulation, optimization, and sensitivity analysis. During the simulation, HOMER models the operation of a specific configuration of an energy microsystem for each hour of the year in order to determine its technical feasibility and life cycle cost. During the optimization process, HOMER evaluates multiple system configurations with the objective of finding the one that meets the technical requirements at the lowest possible cost over its lifetime. For the sensitivity analysis, HOMER performs various optimizations by varying the input assumptions to examine how uncertainty or changes in the input data may affect the results [20].

A hybrid electrification system in a remote area with multiple components is a complex system that requires careful planning. To produce a reliable and cost-effective system, the concept of optimal planning is critical. A renewable hybrid system is the most cost-effective way to store and utilize natural energy without interruption. Because of its reliability and cost-effectiveness in supplying energy to rural and remote areas, researchers have increasingly focused their attention on HRES integrated with ESS [21].

Several studies have examined resource utilization and techno-economic performance. The most common schematic diagram of an HRES plant is shown in Figure 2 below, in which the load is powered mainly by solar and wind generators, with the biogas generator serving as the backup. The battery ensures the balance of the energy flow in the system, as well as optimization [22].

As described, when building a hybrid renewable energy system, the most pointed elements to consider are cost and reliability; however, these variables are related to emissions and technological challenges. In addition, it is necessary to consider the different categories of target functions. Nowadays, most researchers focus on the central parameters in the study of HRES; these parameters are shown in Figure 3 [23].

The financial targets include the net present cost (NPC), levelized cost of energy (LCOE), total annual cost (TAC), simple payback period (SPP), and internal rate of return (IRR or IRR). One study described the NPC for a diesel generator, where it was calculated by summing all the current capital, maintenance, replacement, recovery, and fuel consumption costs. The capital recovery factor is multiplied by the NPC over the annual energy consumption of the system to determine the LCOE. To calculate the TAC, annual construction and maintenance costs are combined with annual fuel prices. The SPP measures how long it will take for annual earnings to cover component capital expenditures. The discount rate at which the net present value (NPV) of all future cash flows is zero is known as the IRR [25,26].

Some of the most common measures and target functions for the reliability of an HRES system described by [27] are as follows:

• Loss of power supply probability (LPSP).
• Expected energy not supplied (EENS).
• Loss of load expectation (LOLE).
• Loss of energy expectation (LOEE).
• System average interruption frequency index (SAIFI).
• System average outage interruption duration index (SAIDI).
• LPSP is the probability of an unmet load over the entire energy demand of a stand-alone or grid-connected hybrid renewable energy system.
• EENS is the energy that a hybrid renewable energy system is supposed to provide.
• LOLE is also known as loss of load probability.
• LOLP is the number of hours per year that energy exceeds the capacity of the HRE generation system.
• The LOEE represents the total energy not delivered by the grid-connected or stand-alone hybrid renewable energy system.

Estimating the hybrid system component sizes reduces system costs and increases system reliability. Oversizing can increase system cost, while under sizing can lead to power failure or inadequate power being supplied to the load [28].

The purpose of this document is to review the current state regarding the trend of hybrid renewable energy systems with the use of Homer pro software. This paper analyzes work related to sizing, optimization, and sensitivity analysis with algorithmic methods and the use of this software, and it seeks to obtain information on the trend of applications in industry, rural areas, commerce, and education.

2. Method

The techno-economic–environmental analysis of hybrid renewable energy systems is a fundamental part of the decision-making process for optimization and sizing. It evaluates parameters, such as technical characteristics, operating costs, maintenance, and meteorological and geographic data, in order to obtain comparative data that can deliver the high reliability of integrated systems, low costs in sizing or optimization, the capacity to cover the electrical load demand, and improve energy management or energy dispatch.

Recent works have mentioned the challenges faced by HRES in relation to energy management, system sizing and demand response [29]. A variety of researchers have performed HRES analyses with different methodologies, with various renewable energy configurations, in different areas or geographical zones, for cases of the integration of storage and backup components, stand-alone or grid-connected systems, and with various optimization methods, including MOPSO (Multi-Objective Particle Swarm Optimization), MOGA (Multi-Objective Genetic Algorithm), Fuzzy satisfaction, NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOSADEA (Multi-Objective Shuffled Differential Evolution Algorithm), IFOA (Invasive Footprint Optimization Algorithm), NSGA-II (Non-dominated Sorting Genetic Algorithm II), and PSO (Particle Swarm Optimization), where certain objective functions are mentioned [18].

Sensitivity analysis studies have been reported, such as in Morocco, with variations in wind speed and solar radiation in the meteorological input, and the interest rate and fuel cost in the economic input, and have applied modeling and optimization algorithms, including HHO (Harris Hawks Optimization), AEFA (Adaptive Enhanced Firefly Algorithm), EO (Estimation of Distribution Algorithm), and energy management strategies [30].

The literature search and review were performed for different scientific social networks, such as academia.edu and researchGate.net; we also searched in journals published in Elsevier, IEEE, MDPI, and SpringerLink. This query was based on the keywords Homer Pro, techno-economic–environmental analysis, and Smart Grid with the HRES approach published from 2019 to 2023. Figure 4 shows the comparison of the results for the number of articles by keyword.

Figure 5 shows the block diagram of the methodology. The results were filtered by scientific research specialty in the area of energy, for 3074 articles, and were subsequently delimited by the period from 2019 to 2023, and to those that exclusively presented a techno-economic and environmental analysis with the Homer tool. Using the Google Scholar search engine and research social networks, such as Academia and ResearchGate, the filtered papers were downloaded, for 110 in total.

Subsequently, repeated articles were filtered out from the database obtained from Science Direct, Elsevier, IEEExplore, MDPI, and Springer Link. The 110 articles, which are examined in this HRES study with the use of Homer, are detailed by year in

Table 1. Number of publications per year.

Year	Quantity	Percentage
2019	11	10%
2020	17	15%
2021	29	26%
2022	33	30%
2023	20	18%
Total	110	100%

; considering these data as percentages, the publication behavior can be observed as a bar graph, as shown in Figure 6.

The architectures of the technologies involved were identified for the photovoltaic panel (PV), wind turbine (WT), biomass (BM), diesel generator (DG), batteries source (BS), hydrogen tank (HT), biodiesel (BD), nuclear generator (NG), converter (Conv), hydrogen pump source (HPS), hydropower system (HS), grid system (GS), hydrogen reformer (HR), fuel cell (FC), and electrolyzed (EL).

From the literature surveyed, the publications were organized by location, with 31 countries reporting works on sizing, optimization, and sensitivity analyses. Figure 7 shows the geographical description, and shows India as one of the countries where studies are being applied to these systems with the help of Homer Pro.

According to the articles investigated, the general perception of the application of these systems is to cover the lack of energy for residential use in rural areas. Another specific application that represents an energy demand is in educational and industrial areas. Figure 8 shows the trend in applications of HRES systems. Given the important challenge of bringing electricity to remote and difficult-to-access areas, these systems are presented as an ideal solution for this purpose.

Another important point is to know the type of scenario regarding the hybrid system configurations; for this study, the information was ordered based on the most reported configurations. Figure 9 shows the percentage of each configuration in the case studies of the analyzed articles. The most used configuration is the one with solar energy, a diesel generator, battery bank, and converter (PV/DG/BS/CONV).

On the other hand, information was obtained about the storage, backup, and power electronics technologies used with these systems. Figure 10 shows a bar chart comparing the times which the use of these technologies was mentioned.

The Homer Pro financial indicators are determined relationships that are intended to be compared; they are born from the information that is collected from investments in a project, and allows one to approximate the current value of a project and its future projection. The financial measures allow one to know the financial situation of an entity, but should be categorized and taken into account only by those that can best evaluate them, since the use of many indicators negatively affects the functionality of the models, since it requires financial, administrative, and legal information about the entity that is not always available to users of the information. Therefore, it is necessary to consider the categories of indicators that allow one to evaluate the financial situation of companies, in order to have an idea about what are considered the vital signs of the financial health of a project, that is to say, about liquidity, profitability, and indebtedness.

3. Results

Table 2. HRES and Homer Pro publications about applications used by educational institutions.

Ref	Year	Configurations	Electrical Data	Country	COE USD/kW·h	NPC USD	O&M USD/kW/a	C.I. USD	RF (%)
[31]	2019	PV/BM/GS/CONV	4443.15 kWh/d 2005.11 kW	India	6.43 M	168 M	6.43 M	85 M	87
		PV/BM/GS/CONV			6.92 M	169 M	6.92 M	79.6 M	85
		PV/BM/BS/GS			6.47 M	170 M	6.47 M	86.3 M	87
[32]	2019	PV1/PV2/BS/CONV	5005.95 kWh/d 967 kW	Bangladesh	0.216	5.11 M		3.3 M
		PV1/PV2/GS/BS/CONV			0.203	5.39 M		2.21 M
		PV3/DG/BS/CONV			0.157	3.62 M		825,625
		PV4/DG/GS/BS/CONV			0.153	3.63 M		850,125
[33]	2019	PV/DG/CONV	30,629 kWh/d 2838.34 kW	Egypt	0.251	35.9 M		21.58 M	2.6
		PV/DG/BS/CONV			0.2	28.5 M			50.9
[34]	2019	PV/WT/DG/BS/CONV	1.5 kWh/d 0.47 kW	Argentina	4.65	597,256	3881	95,500	91.5
[35]	2020	PV/DG/BS/CONV	6.87 kWh/d 3.3 kW	Bangladesh	0.125	6191		2450	82.5
		PV/WT/DG/BS/CONV			0.216	10,696		7451	88.7
[36]	2020	PV/BS/GS/CONV	11.27 kWh/d 2.39 kW	India	3.03	21,166	3366		99
		PV/GS/CONV			3.60	27,238	17,784		60
		GS			7.50	39,883	30,852		0
		BS/GS/CONV			9.87	52,495	32,862		0
		PV/GS/BS/CONV			3.16	214,695	3418		99
		PV/GS/CONV			3.71	277,002	18,037		58
		GS			7.5	398,935	30,852		0
		BS/GS/CONV			9.87	524,952	32,869		0
[37]	2020	PV/GS/CONV	48,194.08 kWh/d 3731.56 kW	Malaysia	0.179	56,633,560	1,576,152	36,485,045
		PV/BS/GS/CONV			0.181	56,941,060	1,756,303	35,929,942
		GS			0.434	97,535,030	7,629,846	0
		BS/GS/CONV			0.438	98,479,890	76,680,556	456,394
[38]	2021	PV/DG/GS/BS/CONV	13,830.6 kWh/d 420.7 MWh/a 5048.2 MWh/a 1488.52 kW	India	4.37		234,514,437.4	525,520,706.4	17
		PV/DG/GS/BS/CONV			2.3		229,285,321.1		17
[39]	2021	PV/WT/DG/BS/CONV	11,335.51 kWh/d 1769.87 kW	India	0.1266	28.94480	256,761.50	14.7531469	99.9
		PV/DG/BS/CONV			0.1268	28.9811403	256,590.00	14.7989397	99.9
		PV/WT/BS			0.1338	0.589540	278,395.30	15.2021237	99.9
		PV/BS			0.1338	30.601110	278,866.30	15.1876606	99.9
[40]	2021	PV/GS/CONV	77.6 kWh/d 20.06 kW	Indonesia	382.78	145 M	3.1 M	105 M
		PV/GS/BS/CONV			487.58	183 M	4.66 M	123 M
[41]	2021	GS	4696.98 kWh/d 579.50 kW	India	6.35
		PV/GS			5.57
		WT/GS			5.4
		PV/WT/GS			4.71
[42]	2021	PV/WT/BM/BS/CONV	256.33 kWh/d 71.37 kW	India	0.159	184,687	6154	106,015
[43]	2022	PV/GS	45 kW	Morocco	0.41
[44]	2022	PV/DG/BS/CONV	633 kW	Colombia	≈0.55	≈500,000	52,111	329,400
[45]	2022	PV/WT/GS/BS/CONV	96.97 kWh/d 15.00 kW	Pakistan	0.0344	13,510		15,700
		PV/GS/BS/CONV			0.0384	1427		11,146
		WT/GS/BS/CONV			0.0365	3351		855
[46]	2022	PV/DG/BS/CONV	95.32 kWh/d	Ecuador	0354	159,659.7	8752.753	46,508.33
		PV/DG/CONV			0.796	358,191.8	26,703.06	12,987.5
		DG			0.871	392,089.5	29,943.07	5000
		DG/BS/CONV			0.880	396,020.1	30,049.22	7558.333
[47]	2022	PV/DG/BS/CONV	1.823 kWh/d 1.821 kWh/d	Colombia	0.54	190	4974.51	111,737.24
		PV/BS/CONV			0.84	405	2113.81	196,815.6
[48]	2022	GS/PT	643.00 kWh/d 69.00 Kw 5789 kWh/d 1588.86 kW	Romania	0.115	396,397	21,020
		PV/GS/PT/WT/mCHP/CONV
		PV/GS/PT/mCHP/CONV
[49]	2022	PV/BS/CONV	11.27 kWh/d 2.39 kW	India	0.66
		PV/WT/GS/BS/CONV			0.0895
		PV/DG/BS/CONV			0.439
		WT/BS/CONV			5.98
		WT/DG/BS/CONV			0.716
		PV/WT/DG/BS/CONV			0.434
[50]	2023	DG/GS	334.750 kWh/d 23.70 kW	Malawi	0.01397	116,853	7752.3	9500
		PV/DG/GS/CONV			0.1244	104,064	5771.75	24,137
		PV/BM/GS/CONV			0.09508	79,511	3985.9	24,319
		PV/WT/BM/GS/CONV			0.103	86,099	4061.33	29,858
		BM/GS			0.1054	88,954	5701.53	10,000
		PV/GS/BS/CONV			0.1428	118,475	4515.19	55,949
[51]	2023	PV/GS/CONV	200 kWh/d 52.95 kW	Iraq	0.058	77,680	1460	59,018
		PV/DG1/DG2/BS1/BS2/CONV
[52]	2023	GS	2594 kWh/d 196.22 kW	Romania	0.2	2190	189.362	0
		WT/GS			0.201	2200	188.836	15.000
		PV/GS/CONV			0.184	2020	165.954	100.000
		PV/WT/GS/CONV			0.185	2030	165.428	115.000

shows the 22 publications reported chronologically from the last five years in which Homer Pro software was applied to cases involving educational institutions. The focus of most of these publications was a techno-economic analysis to reduce the cost of billing and the environmental impact. The dimensioning for rural schools was recorded to meet the academic need. The percentages of the configurations reported in the literature for this type of case study are shown in Figure 11. It can be seen that the technology combination of two renewable sources, such as solar and wind, used by academic institutions, most often tends to be the PV/DG/BS/CONV configuration.

As can be seen, studies on India have reported an outstanding amount of work related to their application to educational organizations; also, a high load demand of 48,194.08 kWh/d has been reported in Malaysia, and a minimum of 1.5 kWh/d. Homer Pro has been instrumental in these cases for the cost results (COE, NCP, O&M, and initial cost); their prices are tabulated in each country’s currency and, in some cases, the cost-effective fraction is reported. The graph presented in Figure 12 shows 10 combinations of technologies used to cover the electric load demand, according to the investigated works. The highest demand is covered with the combination of CV/GS/CONV.

In

Table 3. HRES and Homer Pro Publications for rural applications.

Ref	Year	Configurations	Electrical Data	Country	COE USD/kW·h	NPC USD	O&M USD/kW/a	C.I. USD	RF (%)
[53]	2019	PV/WT/BS	50.50 kWh/d 13.9 kW 100.23 kWh/d 17.5 kW 17.53 kWh/d 5.0 kW	India	0.288 M	0.228 M	4994	0.166 M
		PV/BS			0.302 M	0.242 M	5480	0.176 M
		WT/BS			0.746 M	0.591 M	10880	0.450 M
[54]	2019	PV/BS/CONV	10.28 kW 0.77 kW	Pakistan	0.199	9650.0	332.39	5353
[55]	2019	DG/BS/CONV	84 kWh/d 14 kW	Malaysia	0.511		25,607	32,000
[56]	2019	WT/GS	2687.54 kWh/d 394.98 kW 1521.37 kWh/d 233.4 kW	Bangladesh	0.037	1,877,869			89.4
		WT/GS			0.43	2,049,735			89
		PV/GS			0.006	1,850,822			68
		WT/GS			0.053	1,690,032			96.8
		PV/GS			0.071	1,884,952			73.5
[57]	2019	PV/DG/BS/CONV	30.00 kWh/d 5.05 kW	Argentina	0.345	32.880	1.536	19.524	88.2
		PV/DG/BS/CONV			0.307	29.179	1.330	17.615	90.6
		PV/DG/BS/CONV			0.017	120.750	1.777	100.000	74.4
[58]	2019	PV/WT/BS/CONV	197.74 kWh/d 27.87 kW	Pakistan	0.137	127,345	4.522	68,882	100%
		PV/BS/CONV			0.15	140,048	5.640	67,132	100%
[59]	2020	DG	5416.6 kWh/d	USA	0.644	658,092		87,136	0
		PV/DG/BS1			0.229	234,219			93
		PV/DG/BS2			0.304	310,362			90.3
		WT/DG/BS/			0.18	184,253			78.7
		PV/WT/DG/BS			0.16	164,048			83.1
[60]	2020	PV/WT/DG/BS	170 kWh/d	India	0.24932	199,850.8	11,081.2		64.8
		PV/DG/BS			0.3982	319,414.8	13,700.6		57.8
		WT/DG/BS			0.5296	424,570.3	21,335.1		28.8
		PV/WT/BS			0.1293	103,661.7	1635.06		100
		PV/BS			0.1240	99,427.02	1758.8		100
		WT/BS			0.7273	583,120.8	9893.3		100
		DG/BS			0.4266	342,131.3	25,224.1		0
		DG			0.6263	502,348.3	37,969.2		0
[61]	2020	DG	63. 81 kWh/d 21.86 kW 166.92 kWh/d 22.81 kW 453 kWh/d 50.42 kW 722. 85 kWh/d 72.4 kW	Bangladesh	0.449 0.3 0.34	135,337 235,953 769,966	15,000 92,749 203,420	9309 11,078 91,413
		PV/DG/BS
		PV/BS
		DG
		DG/BS
		PV/DG
		PV/DG/BS
		PV/BS
		DG
[62]	2020	DG1/DG2	3853 kWh/d 421.89 kW	Nigeria	0.1055 0.106 0.119 0.271 0.921	1 M 100,799 1.14 M 2.58 M 8.70 M	383,820	91,339 183,377 112,686 344,390 464,975
		PV/DG1/DG2/BS
		PV/WT/DG1/DG2/BS
		PV/WT/DG1/DG2/HT/BS
[63]	2020	PV/WT/BM/BG/EL/BS/FC/CONV	724.83 kWh/d 149.21 kW	India	0.163 0.425	890,013 856,013
		PV/WT/BM/BG/EL/FC/CONV
		PV/WT/BM/BG/EL/BS/CONV
		PV/WT/BM/BG/EL/CONV
[64]	2020	PV/HT/BS/CONV	24,861 kWh/d 3000.9 kW	Cameroon	0.1666	26.39 M	11.7 M	14.1 M
[65]	2020	GS	13.93 kWh/d 0.73 kW	Bulgaria:	0.1
		PV/GS/BS/CONV	13.93 kWh/d 0.73 kW	Vidin	0.218				67.8
				Montana	0.211				68.8
				Vratsa	0.215				68.1
				Pleven	0.211				68.7
				Lovech	0.212				68.6
				Sofia	0.220				68.8
				Pernik	0.210				68.9
				Kyustendil	0.212				68.6
				Pazardzhik	0.212				68.8
				Gabrovo	0.216				68
				Veliko	0.313				68.6
				Ruse	0.195				71.1
				Stara	0.217				68
				Plovdiv	0.217				68
				Haskovo	0.212				68.7
				Yambol	0.217				68
				Kardzali	0.213				68.5
				Smolyan	0.219				67.7
				Silistra	0.209				69.1
		PV/WT/GS/BS/CONV	13.93 kWh/d 0.73 kW	Blagoevgrad	0.212				75.4
				Razlog	0.219				67.7
				Targovishte	0.198				78.6
				Shumen	0.197				79
				Sliven	0.201				78
				Burgas	0.216				79.4
				Varna	0.196				77.3
				Dobrich	0.198				78.6
[66]	2020	DG	189.800 kWh/d 13.989 kW	Indonesia	0.1968	283,965	13.63 M
		PV/DG/BS/CONV			0.1154	224,233	8.1 M
		PV/WT/DG/BS/CONV			0.1555	224,334	8.1 M
[67]	2020	PV/BD/CONV	3.00 kWh/d 0.36 kW	India	0.637	9009	85.11	7909
		PV/WT/BS/CONV			1.19	16,830	188.23	14,397
		WT/BS/CONV			2.86	40,470	589.91	32,844
[68]	2021	PV/BS	255.6 kWh/d	China
		PV/HPS
		PV/BS/PS
		WT/BS
		WT/HPS
		WT/BS/HPS
		PV/PV/WT/BS
		WT/HPS
		PV/WT/BS/HPS
[69]	2021	PV/WT/DG/BS/CONV	234.00 kWh/d 25.6 kW	Indonesia	0.276	414,951		152,664
		PV/DG/BS/CONV			0.284	426,966		144,142
		PV/WT/BS/CONV			0.322	482,468		200,129
		PV/BS/CONV			0.326	488,567		196,824
		DG			0.499	749,792		24,500
[70]	2021	PV/WT/BS	13,68 kWh/d 2,16 kW	Iran	0.322	24,662		18,381	100
		PV/WT/FC			0.617	47,233		32,727	100
		PV/WT/DG			0.286	21,913		6895	28.3
		PV/WT/BS/FC			0.403	30,854		24,170	100
		PV/WT/DG/BS			0.151	11,576		6930	72.2
		PV/WT/DG/FC			0.306	23,388		14,370	59.8
		PV/WT/DG/BS/FC			0.231	17,648		12,127	66.1
[71]	2021	PV/WT/DG/BS/CONV	110 Kwh/d 11.04 kW	India	0.321	166,400	9692	41,100
		PV/WT/DG/BS/CONV			0.326	169,461	9911	41,335
		PV/WT/DG/BS/CONV			0.295	153,131	8732	40,253
		PV/WT/DG/BS/CONV			0.345	178,815	10,305	45,603
		PV/WT/DG/BS/CONV			0.289	149,990	8683	37,736
		PV/WT/DG/BS/CONV			0.266	138,197	8085	33,674
[72]	2021	PV/DG/BS/CS	183.68 kWh/d 37.81 kW 40.38 kWh/d 6.75 kW	Ghana	0.399	296,552	20,569	109,846	40
		DG/BS/CS			0.523	388,358	35,458	66,500	0
		PV/BS/CS			0.782	580,170	16,252	435,646	100
		DG			0.902	669,394	70,992	25,000	0
		PV/DG/CONV			0.998	740,800	71,516	91,650	0
[73]	2021	PV/WT/BS/CONV	30 kWh/d 1.6 kW	Iraq	0.117	14,800	541	8590
					0.118	14,988	539	8810
[74]	2021	PV/FC/HT/EL		China			133,000	2,180,000
[75]	2021	PV/WT/BS/CONV	197.74 kWh/d 27.84 kW	Pakistan	0.137	127,345	4522	68,882	100
		PV/BS/CONV			0.15	140,048	5640	67,132	100
[76]	2021	PV/WT/BS	11.27 kWh 2.39 kW	Turkey	0.521	94,705	974.3	48,750	100
					0.495	89,992	929.51	46,150
		CONV			0.420	76,542	809.71	38,350
					0.409	74,436	800.06	36,700
[77]	2021	PV/GS/BS/CONV	542.60 kWh/d 58.17 kW	India	1.77	9.22 M	124,389	7.68 M	66.8
		GS			5.66	17 M	1.31 M	0	0
[78]	2021	PV/DG/BS/CONV	346.43 kWh/d 68.9 kW 80.87 kWh/d 7.44 kW	India	0.223	449,574	89,659	208,988	91.6
		WT/DG/BS/CONV			0.410	827,473			1.06
		PV/WT/DG/BS/CONV			0.223	449,573			91.6
		PV/WT/BS/CONV			0.361	727,327			100
[79]	2021	PV/WT/DG/BS/CONV	135 kWh/d 18 kW	Iran	1.058	284,724	14,583	205,000	64
		WT/DG/BS/CONV			1.072	288,338	29,695	126,000	29
		PV/DG/BS/CONV			1.079	290,343	24,391	157,000	37
		DG			1.18	317,394	45,253	70,000	0
		WT/DG			1.308	351,877	46,622	97,000	12
		PV/WT/BS/CONV			1.338	360,023	5309	331,000	100
		PV/DG/CONV			1.444	388,569	44,187	147,000	27
		PV/BS/CONV			1.478	397,453	4656	372,000	100
[80]	2021	PV/WT/BS/CONV	14.53 kW 8.09 kW 6.4 kW	Malawi	0.635	325,509	6219	228,700
		PV/BS/CONV			0.625	167,213	3470	113,200
		PV/BS/CONV			0.734	185,611	4170	120,700
[81]	2021	PV/GS/BS/CONV	1.26 kWh/a 1537 kWh/a	Honduras Zambia	0.06	256,133	4.45	181,733
					0.48	564,697	160	429,400
[82]	2021	PV/WT/BS/CONV		Malesia		221,329.97	294,156
[83]	2022	PV/DG/BS/CONV	5.3 kWh/d 0.78 kW 5 kWh/d 0.78 kW	Indonesia	122,237		4675 M
		MH/DG			19,715		2885 M
[84]	2022	PV/DG/BS/CONV	22 kWh/d 2.5 kW	Iran	371	27,020	333	13,582
		PV/WT/DG/BS/CONV			379	2728		14,492
		PV/BS/CONV			536	33,972		23,900
		PV/WT/BS/CONV			547	34,652		24,525
[85]	2022	PV/WT/BS/CONV	980.76 kWh/d 99.02 kW	Pakistan	0.0446	206,161	2813	169,800	100 100
		PV/WT/DG/BS/CONV			0.0416	192,353	2913	154,690
		PV/WT/BS/HG/FC/CONV			0.0489	226,420	2997	187,670
[86]	2022	PV/BS/CONV	530.00 kWh/d 55.66 kW	Bangladesh	6476	478,008	336,463
		PV/DG/BS/CONV			10,900	565,690	336,463
		PV/WT/BS/CONV			11,909	569,914	319,453
		PV/WT/DG/BS/CONV			13,355	658,652	377,786
[87]	2022	PV/BS/CONV	3.40 kWh/d 1.26 kW	Nigeria	0.25	4003			100
		PV/DG/BS/CONV			0.258	4146			97.1
		DG/BS/CONV			0.672	10,785			0
		PV/DG/CONV			3.18	51,093			---
[88]	2023	PV/DG/BS/CONV	11.27 Kwh/d 2.39 kW	Congo	0.11
		PV/BS/BV			0.89
[89]	2023	PV/WT/GS	13.26 kWh/d 6.20 kW	Turkey	0.01 0.051	2540 8951		3379.7 8140.1	40 87
		PV/WT/DG/BS			0.198 0.346	23,372 40,858	341	1552.1 6312.5	20 91
[90]	2023	PV/DG/BS/CONV	50.5 kWh/d 9.28 kW	Colombia	0.442	104,270	1385	21,700	32
[91]	2023	PV/DG/BS	1312.0 kWh/d 144.0 kW	Amdjarass Am Timan Ari Bagrai Biltine Bol Fada Goz Beida Koumra Lai Mao Massakory Massenya Mongo Moussoro Pala Arabia	0.389	2.52 M	439	1.63 M	99.2
		PV/DG/BS			0.367	2.38 M	2.40	1.53 M	100
		PV/BS			0.380	2.46 M	0	1.6 M	100
		PV/WT/DG/BS/CONV			0.416	2.69 M	646	1.7 M	97.6
		PV/BS/CONV			0.38	2.46 M	0	1.61 M	100
		PV/DG/BS/CONV			0.389	2.52 M	439	1.63 M	99.2
		PV/WT/DG/BS/CONV			0.406	2.63 M	692	1.63 M	97.3
		PV/DG/BS/CONV			0.375	2.43 M	2.10	1.8 M	100
		PV/BS/CONV			0.370	2.40 M	0	1.56 M	100
		PV/BS/CONV			0.373	2.42 M	0	1.56 M	100
		PV/BS/CONV			0.397	2.57 M	0	1.71 M	100
		PV/DG/BS/CONV			0.379	2.45 M	1.8	1.59 M	100
		PV/BS/CONV			0.375	2.43 M	0	1.55 M	100
		PV/DG/BS/CONV			0.373	2.42 M	2.10	1.56 M	100
		PV/DG/BS/CONV			0.388	2.51 M	439	1.63 M	99.2
		PV/WT/DG/BD/CONV			0.369	2.39 M	2.40	1.54 M	100
[92]	2023	PV/WT/GS/BS/CONV	12,742.40 kWh/d 1821.05 kW 15,928 kWh/d 2276.28 kW	Indonesia	1.241	74.7 B		5.36 B
					1.264	95.0 B		5.36 B
[93]	2023	PV/DG/BS/CONV	21,589.04 kWh/d 3209.49 kW	India	9.77	995 M	128.41 M	679 M	90
					9.86	1 B			90

, there are 44 publications sorted by year; most of these publications performed an analysis for an off-grid system. The most mentioned configurations are PV/DG/BS/CONV, PV/BS/CONV, and PV/WT/DG/BS/CONV, as can be seen from Figure 13. The trend of HRES configurations was determined using Homer Pro. In this scenario, there is one paper that reported the highest demand of 24.61 kWh/d, in which the PV/HT/BS/CONV combination was used, see Figure 14. It is also possible to see the average daily load demand for the 10 systems, which reported the highest amounts for this application in rural areas. It can be seen that the combination of PV/WT/GR/BS/BS/CV was applied twice to cover this high demand.

In

Table 4. HRES and Homer Pro Publications for industry applications.

Ref	Year	Configurations	Electrical Data	Country	COE USD/kW·h	NPC USD	O&M USD/kW/a	C.I. USD	RF (%)
[94]	2019	PV/WT/DG/BS/CONV	250 kWh/d 16 kW Max	Spain	0.404	473,013	17,993	243,000	96
					0.408	560,247	23,448	260,000	92
[95]	2020	PV/BS/CONV	32.43 kWh/d 4.44 kW 60.11 kWh/d 4.55 kW	Canada	0.425	64,969	47,932	1318	100
		PV/WT/BS/CONV
		WT/BS/CONV			0.585	89,512	70,563	1466	100
		PV/BS/CONV
		PV/WT/BS/CONV			1.03	157,555	133,474	1863	100
		WT/BS/CONV
[96]	2020	PV/DG/BS/CONV	300 kWh/d 43.98 kW 48.70 kWh/d 13.11 kW	Palestine	0.438	636,150			84% 87%
		PV/BS/CONV			0.521	731,927
		PV/DG/CONV			0.568	820,902
		DG			0.609	962,084
		DG/BS/CONV			0.666	10.7 M
[97]	2021	PV/DG/BS/CONV	54.00 kWh/d 15.07 kW	India	0.655	165,137
		PV/BS/CONV			0.813	197,152
		PV/DG/CONV			0.364	91,676
[98]	2022	HYD/BS	1500 kWh/d 205 kW	Ghana	0.06	509,202	18,318	272,391 849,298
		HYD/HPS			0.10	787,523	32,185
		PV/HRY/BS			0.14	1.14 M	22,606
		PV/HYD/HPS			0.16	1.34 M	32,296
		PV/HPS			0.31	2.53 M	85,700
[99]	2022	PV/HYD/FC/CONV	432 MWh/d 816 MWh/d 888 MWh/d 432 MWh/d 744 MWh/d	Pakistan	0.266	575 M	40.8 M	14.8 M
		DG			0.248	1010	61.1 M	175 M
		PV/BS/CONV			0.248	1100	66.1 M	190 M
		PV/DG/CONV			0.49	540 M	32.5 M	94.5 M
					0.248	923 M	55.6 M	159 M
[100]	2022	PV/BM/BS/CONV	27,523.34 kWh/d 4602.2 kW	Nigeria	0.4128	116.73 M	2.17 M
[101]	2022	PV/DG/BS/CONV	1006.0 kWh/d 112.84 kW	Argentina	0.329	2.42 M		902,460	93.5
		PV/BS/CONV			0.517	1.31 M		902,460	100
[102]	2022	PV/EL/FC/TH/CONV	47 kWh/d 5.4 kW	Argerlia	0.259	64,384	19.26	35,850
[103]	2023	PV/DG/GS/BS/CONV	18 MW 34 MW 37 MW 18 MW 32 MW	Pakistan	0.24	519.6 M
						981.4 M
						1 B
						519.6 M
						894.8 M

, only publications with an HRES and Homer Pro analysis focused on industrial applications, such as water purification, cement plants, field irrigation, and commerce, were listed. For this case, the configurations that were reported most frequently in the analysis of the papers are as follows: PV/BS/CONV, PV/WT/BS/CONV, and WT/BS/CONV. The highest load demand was 27,523.34 kWh/d with an HRES technology of PV/BM/BS/CONV. Figure 15 shows a pie chart, where we can see the proportions of the applications of the technologies involved that covered the needs of industry or commerce. The average daily load demand is also described; for the cases in which the system had a greater supply than load, see Figure 16.

Table 5. HRES and Homer Pro Publications for general research applications in urban residential areas for optimization.

Ref	Year	Configurations	Electrical Data	Country	COE USD/kW·h	NPC USD	O&M USD/kW/a	C.I. USD	RF (%)
[104]	2020	PV/GS/CONV	823.25 kWh/d 106.14 kW	Brazil	0.469	3.74	161,253	567,023	55.6%
		PV/WT/GS/CONV			0.548	4.38	163,626	116 M	56.6%
[105]	2020	PV/WT/GS/CONV	24,000 kWh/h 1833.4 kW	Colombia	0.2	11.8 M	94,410	9.3 M
[106]	2020	PV/GS	14,887 kWh/d 1310.47 kW	Bangladesh	5.3		15 M	23.2 M
[107]	2021	PV/DG/BS/CONV	112.49 kW/d 26.88 kW	Ecuador	0.83	183.52	12.51	37.32
		PV/BS/CONV
					0.67	319.69	13.92	157.05
					0.85	406.16	15.06	230.2
		PV/GS/BS/CONV			1.72	824.71	30.65	466.44
					0.09	44.74	3.74	1.03
					0.32	271.91	10.55	148.64
[108]	2021	PV/DG/BS/CONV	14,767.33 kWh/d 1294.20 kW	India	0.3965	309,432.90	70,361.33	255,549.5
[109]	2021	PV/GS/BS/CONV	5333.93 kWh/d 514.05 kW	Brazil	0.1000	1.81 M
					0.0999	1.82 M
					0.0999	1.82 M
[110]	2021	PV/WT/BS/CONV	50.77 kWh/d 10.45 kW	Canada	0.48	34,149.8	9578.77	23,064.72	100
[111]	2021	PV/WT/GS/BS/CONV	165.44 kWh/d 20.46 kW	Pakistan	0.3	180,026	18,116
[112]	2021	PV/WT/GS/BS/CONV	13.93 kWh/d 0.73 kW	Bulgaria		20,800			69
[113]	2021	DG	165.44 kWh/d 47.57 kW	Bangladesh	3.94	3.08 M	23,5621	31,800	0
		PV/DG/BS/CONV			1.07	833,844	44,235	261,991	86
		PV/WT/DG/BS/CONV			1.01	791,531	39,866	276,164	88.5
[114]	2021	PV/DG//BS/CONV	23 kWh/d 3 kW	Nigeria	0.258			11,000
		PV/WT/BS/CONV			0.45
		DG/BS/CONV			0.30
		PV/BS/CONV			0.37
		DG			0.41
[115]	2022	PV/GS/CONV	24,961.08 kWh/d 1461.40 kW	Saudi Arabia	0.115	14.20	1 M	3 M	44.6
		PV/GS/BS/CONV			0.117	15.30			48.6
		GS/BS			0.163	19.10			0
		GS			0.163	19.20			0
[116]	2022	PV/WT/DG/BM/EL/BS/FC/CONV	2426.44 kWh/d 405.71 k	India	0.138	1.58 M	182,039	940,932
[117]	2022	DG/CONV	25.55 kWh/d 2.9 kW 105.00 kWh/d 8.03 kW	Ghana	0.487	150,486	22,292	59,986
		PV/WT/DG/CONV			0.39	118,788	12,658
[118]	2022	PV/WT/DG/BS	21 kWh/d5.9 kW	Iraq	0.225	22,302	3448	10,520	100
[119]	2022	PV/WT/DG/BS/CONV	241,022.37 kWh/d 2105.55 kW 908.91 kWh/d 70.18 kW	Hungry	3.22		29,403
[120]	2022	PV/DG/GS/BS/CONV	11.26 kWh/d 2.09 kW	Malaysia	0 0.377 0.051	7256.74 −299,762.16 −637,870.28 −642,247.46	−8689.23 −522,416.71	17,564 203,121.5
		PV/WT/GS/DG/BS/CONV1
		WT/DG/GS/BS/CONV
		PV/WT/GS/DG/BS/CONV2
[121]	2022	PV/GS/CONV	18.00 kWh/d	Turkey	0.562		38,310.04	10,645.88
[122]	2022	PV/WT/BS/CONV		China		129,765	≈2905.4	94,198
		PV/BS/CONV				211,083	3383	174,974
		DG/BS/CONV				3.84 M	3468	19,484
		PV/WT/DG/BS/CONV				140,836	4182.6	90,122
		PV/DG/BS/CONV				227,898	4864.2	206,448
[123]	2022	DG	61.33 kWh/d 5.76 kW	India	58.55	16.9 M	280,320	83,200	0
		PV/DG/CONV			44.77	13 M	1.52 M	188,512	11.1
		PV/BS/CONV			10.99	3.18 M	2.5 M	0	100
		PV/DG/BS/CONV			9.29	2.69 M	2 M	5120	97.5
		PV/WT/DG/BS/CONV			7.94	2.3 M	1.71 M	4672	98.2
		PV/GS/BS/CONV			1.65	1.65 M	1.97 M	----	80.1
[124]	2022	PV/GS/CONV	11.26 kWh/d 2.09 kW	India	6.4			44.4
		PV/HT/CONV			4.15			100
[125]	2022	PV/DG/DG1/DG2/BS/CONV	19,424.65 kWh/d 2734.84 kW	India	18.06	1.65 B	182.39 M	12.93 B	73
[126]	2022	DG	843.29 kWh/d	Malawi	0.541	515,071	23,628	0
		PV/WT/DG/BS/CONV			0.198	188,814	4907	81,854
		PV/DG/BS/CONV			0.209	198,969	4774	94,892
		PV/WT/BS/CONV			0358	340,809	3410	266,462
[127]	2023	PV/GS/CONV	11.26 kWh/d	Ecuador	1.828
[128]	2023	PV/HG/BM/BS		India	0.106			17 M
		PV/HG/BM/GS/BS
[129]	2023	PV/WT/DG		China	0.164	10,015
		PV/WT/GT			7.89	244.9 M
		PV/BG			0.094	20,184
		PV/DG			0.178	98,911
		WT/NG			0.142	87.7 M
		HT/DG/EL			0.07	92,441
		PV/HT/DG			0.087	179,741
		PV/CONV			0.67	5 B
		PV/HT			0.092	3.2 M
[130]	2023	PV/WT/DG/BM/BS/CONV	300 kWh/d 12.5 kW	Iraq	0.1192		32.01	2875
[131]	2023	PV/GS/BS/CONV	2139.7 kWh/d 163.44 kW	Oman
[132]	2023	PV/DG/BS/HT	2426.45 kWh/d 405.71 kW	Bangladesh	0.271	3.11 M	62,297	2.3 M
		/CONV/HR
		PV/DG/BS/HT
		/CONV/EL/HR
		PV/DG/BS/HT			0.273	3.13 M	62,327	2.32 M
		/CONV/HR/HT
		PV/DG/BS/HT			0.273	3.13 M	62,307	2.33 M
		/CONV/EL/HR/HT
		PV/WT/DG/BS			0.275	3.15 M	61,855	2.35 M
		/HT/CONV/HR
		PV/WT/DG/BS			0.278	3.18 M	60,899	2.41 M
		/HT/CONV/HR
[133]	2023	PV/GS/BS/CONV	200 kWh/d 21.9122 kW	Saudi Arabia	0.00257	10.4 M	−8.37 M	108 M	88.6
[134]	2023	PV/DG/BS	3250 kWh/d 240 kW 570 kWh/d 71.25 kW	Bangladesh	0.0445	3,464,268			80.1
					0.0291	2,301,523			79.5
					0.0198	1,539,620			78.2
					0.0512	3,717,828			80.9
					0.0449	3,463,741			76.7
					0.283	10,354,990			78.1
[135]	2023	PV/WT/GS/BS/CONV	1940 kWh/d 424.80 kW 645 kWh/d 147.70 kW	Bangladesh	0.0714	1.82 M	815,883		54.3
		PV/GS/BS/CONV			0.0720	1.81 M	788,618		54.2
		PV/WT/DG/GS/BS/CONV			0.0812	1.81 M	569,575		42.7
		PV/DG/GS/BS/CONV			0.082	1.81 M	588,011		42.3
		PV/BS/CONV			0.269	5.56 M	1.81 M		100

shows articles on the simulation, sizing, and optimization of HRES with the use of Homer Pro for residential applications, lighting in squares, laboratories, health centers and others; the most reported configurations in these documents are PV/GS/BS/CONV, PV/DG/BS/CONV, PV/GS/CONV, and PV/WT/DG/BS/CONV. The importance of the grid power system energy source for this scenario is observable, as seen in Figure 17.

On the other hand, the maximum load demand recorded was 24,022.37 kWh/d, and its renewable energy and fossil source arrangement was PV/WT/DG/BS/CONV. The countries that reported the highest number of publications were India and Bangladesh, see Figure 18.

The predominant objective function of the researched literature was the cost of energy (COE) or levelized cost of energy (LCOE). The cost of electricity can be applied using the net present cost (NPC). Figure 19 visualizes the trend of four economic parameters in an operation analysis with Homer, for this review of the last 5 years.

In

Table 6. Summary of financial indicator equations in Homer Pro [136].

Financial Indicators	Equation	Description
Net Present Cost	$NP{C}_t=NP{C}_C+NP{C}_O$	Where $NP{C}_C=\mathrm{Net} \mathrm{Present} \mathrm{Cost} \mathrm{of} \mathrm{Components}$ $NP{C}_O=\mathrm{Net} \mathrm{Present} \mathrm{Cost} \mathrm{of} \mathrm{Operation}$ $P{C}_C=\mathrm{Caplex} \mathrm{Present} \mathrm{Cost}$ $P{C}_b=\mathrm{Present} \mathrm{Cost} \mathrm{Opex}$ $P{C}_m=\mathrm{Maintenance} \mathrm{Cost}$ $P{C}_r=\mathrm{Replacement} \mathrm{Cost}$ $C_m=\mathrm{Fixed} \mathrm{Annual} \mathrm{Maintenance} \mathrm{Cost}$ $C_r=\mathrm{Annual} \mathrm{Fixed} \mathrm{Replacement} \mathrm{Cost}$ $M=\mathrm{Component} \mathrm{Life} \mathrm{Time}$ $i=\mathrm{Interest} \mathrm{Rate}$ $n=\mathrm{Project} \mathrm{Life} \mathrm{Time}$ $r=$ Real Interest Rate, after taking into account the inflation rate, over the interest rate of the fuel and energy supply cost $C_0=\mathrm{Annual} \mathrm{Cost} \mathrm{Connected} \mathrm{or} \mathrm{off}-\mathrm{grid}$ $RP=\mathrm{Retail} \mathrm{Price}$ $FiT=\mathrm{Feed} \mathrm{Rate}$ $P_i=\mathrm{Power} \mathrm{Imported} \mathrm{to} \mathrm{the} \mathrm{Grid}$ $P_e=\mathrm{Power} \mathrm{for} \mathrm{Export} \mathrm{to} \mathrm{Grid}$ $FC=\mathrm{Fuel} \mathrm{Cost}$ $P_g=\mathrm{Power} \mathrm{Output}$ $T=8750h, \mathrm{represents} \mathrm{the} \mathrm{annual} \mathrm{operation} \mathrm{of} \mathrm{the}$ $\mathrm{system}$
	$NP{C}_C=P{C}_C+P{C}_b$
	$P{C}_b=P{C}_m+P{C}_r$
	$P{C}_r=C_m\frac{{\left(1+i\right)}^M-1}{i{\left(1+i\right)}^M}$
	$P{C}_r=C_r{\displaystyle \sum _{t=1}^{t<M}}\frac{1}{{\left(1+i\right)}^t}$
	$NP{C}_0=\frac{{\left(1+r\right)}^n-1}{r{\left(1+r\right)}^n}$
	$r=\frac{i-e}{1+e}$
	$C_0{\displaystyle \sum _{t=1}^T}(RP\left(t\right).P_i\left(t\right)-FiT\left(t\right).P_e\left(t\right))$
	$C_0{\displaystyle \sum _{t=1}^T}(FC\left(t\right).P_g\left(t\right))$
Levelized cost of energy	$LCOE=\frac{NP{C}_t.CRF}{E_1}$	Where $CRF=\mathrm{Capital} \mathrm{Recovery} \mathrm{Factor}$ $E_1=$ Annual Energy Demand $d=\mathrm{Discount} \mathrm{Rate}$
	$CRF=\frac{{d(1+d)}^n}{{\left(1+d\right)}^n-1}$

, a summary of the equations that Homer Pro integrates for the calculation of financial indicators, such as the net annual cost and the levelized cost of energy, are shown.

The net present cost (NPC or NPV (net present value)) of an HRES system is the present value of all capital expenditures (Capex), the maintenance and operating expenses (Opex) of the system over the life of the project, plus the present value of imported energy (if the HRES is grid-connected), minus the present value of all exported energy over the life of the project. The NPC operation is the energy balance with the grid in the grid-connected mode, or the fuel consumption cost in the stand-alone mode. The equations for calculating NPC are shown below.

The levelized cost of energy (LCOE) is the ratio of annual net payment to annual net electricity consumption, calculated based on the electricity component data and NPC.

The cost of energy (COE), as already mentioned, is an important metric for the economic study of renewable energy projects. Figure 20 shows the average of this value for different countries where more techno-economic–environmental analysis documents have been reported.

4. Energy Dispatch Strategies

A dispatch strategy is a set of rules that are used to control generator and storage bank operation whenever there is insufficient renewable energy to supply the load.

Load following: Under a cyclic load strategy, whenever a generator is required, it runs at full capacity and the surplus energy charges the battery bank. Cyclic charging (CC) tends to be optimal in systems with little or no renewable energy.

Cycle load (LF): When a generator needs to serve as the primary load, it operates at full power. Surpluses are directed to lower priority targets, such as deferral charging, charging the storage bank, and serving the electrolyze.

Homer Pro MATLAB Link (ML): Homer Pro MATLAB Link allows you to write your own dispatch algorithm for Homer Pro using MATLAB. Homer interacts with MATLAB R2023a software to execute MATLAB functions during simulation.

Combined dispatch: Uses the cycle load dispatch strategy when the net load is low and the following load dispatch strategy when the net load is high.

Predictive dispatch: Homer Pro knows the upcoming electrical and thermal demand, as well as the upcoming availability of solar and wind resources. It will often produce results with lower system operating costs compared to other dispatch strategies. Homer Predictive has a 48 h forecast and uses this knowledge to operate batteries economically.

Generator order: Homer Pro follows a defined order of generator combinations and uses the first combination in a list that meets the operating capacity. It only supports systems with generators, PV, wind turbines, a converter, and/or storage components. It does not run systems that include thermal or CHP components, hydrogen components, the grid, the hydroelectric component, or the hydro kinetic component [137].

Table 7. Reported sensitization analyses.

Ref.	Year	Dispatch	Sensitivity Analysis
[31]	2019	LF	Based on wind and irradiation profiles.
[94]	2019	LF	To study the impact of diesel price only on the optimal system design and also on the TNPC. Interest rate of 6%. Five values were considered: 0.31 USD/L, 0.50 USD/L, 0.7 USD/L, 0.8 USD/L, and 0.9 USD/L.
[56]	2019	CC	The model defined the results according to the initial costs, NPC, COE, capacity shortage, dispatch types, and penetration and fraction of renewables.
[59]	2020	CC	The life cycle cost (LCC). It consists of all maintenance and operating costs, including installation and the initial capital cost over the life of the system. Different scenarios were considered.
[60]	2020	LF	A comparative analysis of various combinations of energy sources.
[61]	2020	CC	Changes in annual wind speed and biomass fuel prices.
[63]	2020	CC	Two types of scenarios. During daytime hours with a certain load and the other scenario was interrupted.
[64]	2020	LF	The greenhouse gas emissions of the system in single-family homes were investigated, comparing the difference with the dispatch strategy when using different work strategies, depending on the greenhouse gas emissions and choosing the adequate dispatch strategy adequate.
[105]	2020	LF	Daily load profile and renewable resource with an existing system of 13 generators.
[106]	2021	LF	System cost changes with a fluctuation in solar radiation, wind speed, diesel price, operation and maintenance costs, capital costs, and replacement costs.
[68]	2021	ML	The effect of changes in average solar radiation and average wind speed on the cost of energy and CO2 emissions.
[69]	2021	CC	Examined the effect of the cost of diesel fuel, the intermittent nature of solar and wind energy, and the nominal discount rate, and determined the average scaled load per day.
[73]	2021	LF	For different values of annual average solar radiation, average temperature, oscillation in average WS, rise and fall of fuel prices, and changing multiplication value of capital costs, the RC and O&M costs of photovoltaic systems were realized.
[74]	2021	CC	The model defined the results according to the initial costs, NPC, COE, capacity shortage, dispatch and penetration types, and fraction of renewable energy.
[75]	2021	CC	Summer and winter load profile.
[78]	2021	CC	The sensitivity of the output systems was tested by varying wind speed and diesel pump rates.
[82]	2021	CC	The details of the system’s battery storage status and energy flow were analyzed through the energy balance of various system configurations. This analysis showed the operating cost, fuel cost, COE, fuel consumption, and renewable fraction.
[113]	2022	LF	PV size sensitivity versus O&M costs.
[84]	2022	LF	Sensitivity to evaluate the behavior of the proposed system when the scaled annual average energy consumption per day is increased by 10% and 20%
[118]	2022	LF	Sensitivity based on net current cost and lowest electricity cost.
[85]	2022	LF	The lowest cost system can also be modified by adjusting the sensitivity settings.
[121]	2022	CC LF	Microgrid optimization based on the dispatch strategy.
[48]	2022	CC	Microgrid optimization based on the dispatch strategy.
[122]	2022	CC LF LF CC	Prices change, load grows, or technology improves, and how a system design might adapt to different markets.
[138]	2022	CC	The sensitivity variables for the simulation were the operating time of the diesel generators and the cost of fuel.
[124]	2022	LF CC	Wind speed (m/s) and fuel consumption were considered as sensitivity variables.
[101]	2022	CC LF	Sensitivity analysis was performed based on the abundance of renewable resources, such as solar irradiance and wind speed.
[131]	2023	LF CC	Lowest and highest NPC values for each region.
[51]	2023	CC	Fuel prices of USD0.51/L and USD1.02/L.
[89]	2023	LF CC	It was carried out taking into account the different values of the inflation rate and the useful life of the project.
[90]	2023	CC	Simulation of renewable energy configurations.
[91]	2023	LF	The NPC, the system COE, and only diesel were considered for the sensitivity analysis.

shows the type of dispatch and sensitivity analyses that are mentioned in the literature; the main uses are noted as cycle load and load following. On the other hand, the literature reports little information on comparative dispatch analysis between load following and cycle loading with Homer Matlab Link.

5. Conclusions

In this bibliographic review, it was found that most of the case studies (47%) focused on localities which did not have electricity, suffered from electrical resilience or, in other cases, had very high tariffs. Therefore, sizing, optimization, design, and decision-making studies were developed based on the information on technologies, economics, and social and environmental impact. In addition, energy policies vary from country to country. On the other hand, 14% of the analyzed works focused on the industrial area. The trend of this work shows the impact of HRES sizing on isolated communities, and how the software facilitates simulation and financial analysis, for different loads and climatic conditions.

In general, the publications analyzed reported their techno-economic–environmental analysis with Homer Pro, considering financial indicators such as COE, NCP, O&M, and initial capital to be of greater importance.

Important configurations of renewable energies, fossil sources, storage, and backup were found, depending on the application to which the study was directed (for educational institutions, in rural areas, industry, and others). It was also observed that DC- and LF-type energy dispatches were the most reported, in approximately 50% of the publications; while ML dispatch is one of the least reported, it could be a relevant area of study.

The sensitivity analyses of 40 articles reported with Homer Pro works, from 2019 to 2023, and that were analyzed here, tended to be performed for the parameters of environmental profiles, load profiles, interest rate, isolated and connected costs, dispatch type, schedule type, price fluctuation, and probability of loss of energy supply; the most reported were analyses with a variation in solar irradiance and wind speed.

It was observed that there was the dimensioning for a PV/WT/DG/BS/CONV configuration, with the economic objective functions and a sensitivity study modifying the NCP based on the wind and solar profiles, together with the meteorological data for the locality based on local meteorological stations. In addition, to be able to find the profitable fraction, the penetration percentage of each technology and the pollution levels of the diesel generator need to be taken into consideration.

To cover an electrical load demand of 48,194.08 kWh/d, applied to an educational area, the technologies of photovoltaic panel systems connected to a grid and converter were combined. In the case of a rural area with a load of 24,861 kWh/d, the technologies of photovoltaic panel systems, a hydrogen tank, backup batteries, and a converter were combined. On the other hand, an application in industry reported a load of 27,523.34 kWh/d with a combination of photovoltaic panel systems, biomass, battery systems, and a converter. In the case of an urban application, a load of 24,961.08 kWh/d was reported, with a combination of photovoltaic panels and interconnected to the electrical system network; it was noted that for this case, there was a demand of 24,022.37 kWh/d with photovoltaic panels, a wind generator, diesel generator, storage batteries, and a converter.

The research on Homer Pro is a valuable tool in HRES analysis, and allows for an analysis of its current importance in the creation of detailed models of energy systems, such as microgrids, with a variety of renewable energy sources, energy storage and load profiles, and under different conditions and scenarios. It also concentrates the results data in publications that have simulated the operation of HRES under various conditions, such as changes in energy demand, interest rates, availability of renewable resources, among others, helping to evaluate the sizing, design optimization, and financial and environmental aspects.

In summary, Homer Pro plays a crucial role in techno-economic environment analysis by providing advanced tools to model, simulate, optimize, and evaluate the economic performance of these complex distributed energy infrastructures.