Performance Analysis of a Hybrid Renewable-Energy System for Green Buildings to Improve Efficiency and Reduce GHG Emissions with Multiple Scenarios

Abstract

A hybrid system, such as solar and wind, may be more successful than nonhybrid systems in accelerating the transition from conventional to renewable power sources. However, these new energy sources have several challenges, such as intermittency, storage capacity, and grid stability. This paper presents a complete analysis and study of a hybrid renewable-energy system (HRES) to convert a facility into a green building and reduce its dependence on conventional energy by generating clean energy with near-zero greenhouse-gas (GHG) emissions. The proposed system aims to reduce the energy bill of a hotel in Petra, Jordan, by considering different sustainable energy resource configurations in a grid-connected hybrid renewable energy system (GHRES). The hybrid optimization of multiple energy resources (HOMER) grid software was utilized on the hybrid systems to study ways to improve their overall efficiency and mitigate GHG emissions from an economic perspective. The hybrid system components included in the simulation were a solar photovoltaic (PV) system, a wind turbine (WT) system, a diesel generator (DG), and a converter. Five scenarios (PV–Converter–DG–Grid, PV–Converter–Battery–DG–Grid, WT–DG–Grid, PV–WT–Converter–Battery–DG–Grid, PV–WT–Converter–DG–Grid) were considered. The optimal configuration had a USD 1.16 M total net present cost, USD 0.0415/kWh cost of energy, 15.8% effective internal rate of return, and an approximately 77% reduction in carbon emissions compared to the base case.

1. Introduction

Solar and wind energies are domestic and free energy sources and some of the best local solutions for increasing energy demand. According to the Fraunhofer Institute for Solar Energy Systems photovoltaics report, worldwide installed photovoltaic (PV) capacity increased to more than 515 gigawatts, supplying approximately two percent of global electricity demand [1]. In addition, 60.4 gigawatts of wind energy capacity were installed globally in 2019, a 19 percent increase [2].

Sustainable renewable energy is considered a green energy source. However, it is not a fully green source due to the production of renewable-energy system parts and components that are often manufactured in factories and facilities powered by nongreen energy. In addition, the transportation of these systems’ parts and components usually depends on conventional fuels. Moreover, greenhouse-gas (GHG) emissions are undesirable, causing pollution in the atmosphere. The primary greenhouse gases are water vapor (H₂O), carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and ozone (O₃). Conventional energy sources such as coal and other fossil fuels are significant sources of GHGs [3,4,5]. The burning of these fuels contributes to the well-known global-warming phenomena in which the earth’s atmosphere warms by absorbing solar energy and retaining it in the atmosphere [6].

Renewable energy is the best choice for providing clean, reliable, and sustainable energy. The electricity generated from clean sources improves quality of life and enhances economic growth. In addition, replacing conventional energy sources like fossil fuels with renewable-energy sources helps mitigate world climate change due to greenhouse-gas emissions. While renewable energy is a domestic source, it faces limitations due to high initial cost, intermittency, and geographic boundaries [7].

Annual energy consumption growth is 1% for developed countries and 5% for developing countries [8]. In Jordan, the need for electricity increases each year, with the primary energy sources for 2018 (the most recent data from the Ministry of Energy and Mineral Resources) reported as imported oil and natural gas, with a share of 87% of total energy consumption (9712 kt), followed by renewable energy at 7% [9,10]. The transportation sector consumes 49% of Jordan’s total national energy demands, while the second-highest share is 21.5% from residential consumption, according to the last data report from Jordan’s National Electric Power Company [11]. One of Jordan’s most significant energy challenges is that increasing energy costs contribute to slower economic growth since most of its energy is imported. Buildings in general use approximately 20–40% of the total energy demand in developed countries [12], where conventional energy sources are still the dominant sources. Among active solar solutions, photovoltaic systems are intensely studied thanks to the aesthetical and technical opportunities offered in the last years as well as to the publication of clear design criteria and recommendations [13].

Renewable-energy system electrical grids need to be more flexible and dynamic than conventional grids to increase their contribution to total energy generation and overcome the challenges of intermittency and varying availability at specific locations. Using decentralized power-generation concepts such as microgrids, smart grids, and standalone (off-grid) power systems can increase the share of renewable energy. Green buildings that depend on sustainable sources of energy such as solar and wind can help the environment and the consumer by replacing conventional energy sources with accessible, domestic, and green-energy sources [14].

The design of grid-connected hybrid energy systems, shown in Figure 1, involves and determines factors such as the most suitable components, their sizes, and power management (PM) while considering the location and load demand.

The main goal of this research is to design and simulate a hybrid renewable-energy system (HRES) consisting of a solar photovoltaic (PV) system, a wind turbine (WT) system, and a storage system. The main contribution of this study is to investigate and find applicable configurations for a hybrid system that can achieve the most effective hybrid configuration using a systematic methodology.

This HRES will provide improved total electrical efficiency for a green building and study the impact on the environment from GHG reduction by replacing part of the national electrical grid energy with renewable-energy sources.

The following are the research objectives:

• Design a hybrid renewable-energy system (solar, wind energy, and grid network) to supply power to a building that decreases dependence on the electrical grid.
• Analyze the resulting data for power, economic, and environmental output for hybrid systems from HOMER software simulations to investigate overall efficiency and define an optimal configuration.
• Evaluate the environmental impact of GHG reduction by the optimal hybrid renewable-energy configuration.

This study contains electrical, environmental, and economic analyses. When investigating the reliability and applicability of a new HRES, the starting point is that 85.1% of grid electricity (GE) is currently generated from conventional power, from sources such as fossil fuels. These conventional sources suffer from losses that can reduce electrical efficiencies, such as transmission losses, heat loss, and the use of lower-quality technologies, which can increase the final electricity costs for the distributor and the consumer [11,15]. Some of the generated electrical power is wasted in the transmission and distribution networks as voltage drops, so the additional power needed to recover these losses will increase the pollution of the climate. Therefore, increasing electrical efficiency is one of the best and most cost-effective ways to mitigate GHG emissions and save money. Figure 2 shows the CO₂ emission reduction potential by technology for the planned energy scenario (33 Gt in 2050), with 45% renewable energy, 26% energy efficiency, and the remainder from other technologies [16].

2. Literature Review

This section reviews the primary studies and reports that provide insight and understanding into the use of a hybrid renewable energy system (HRES) consisting of two or more renewable-energy subsystems. The review also reveals the challenges of renewable power research, problems associated with power fluctuations and remote generation, and possible solutions. Furthermore, previous studies introduced the advantages and disadvantages of an HRES, photovoltaics (PVs), wind turbines (WT), and economic cost effectiveness.

Addressing the design and simulation of hybrid renewable-energy systems, Suresh et al. [17] optimized an off-grid hybrid energy system through the control, sizing, and choice of components. They considered a cost-effective power solution for electrifying villages in the Kollegal block of the Chamarajanagar district in India. The aim was to reduce the system’s net present cost (NPC), cost of energy (COE), unmet load scenario, and CO₂ emissions, which were computed using a genetic algorithm and also using the HOMER Pro Software. The results of the two methods were compared for four HRES configurations. The simulations suggested that the combination of biogas, solar, wind, and fuel cells with battery was the optimal solution with zero unmet loads and the least energy cost at USD 0.163 per kWh.

Bagheri et al. [18] focused their research on proposing a systematic approach to optimal planning of hybrid renewable energy for neighborhoods. They studied the impact of system economics on the life-cycle cost analysis of the optimal proposed hybrid renewable systems for Vancouver, Canada. Wind energy was found to be an uneconomical choice, while biomass appeared to be the optimal hybrid system. System configurations, capital investment, and operating costs controlled the economic decision. The results showed that the optimized proposed systems’ net present costs (NPCs) were USD 59 M, USD 116 M, and USD 290 M. The study suggests a framework for local governments and planners to integrate neighborhood-scale hybrid renewable-energy systems in their jurisdictions. Therefore, introducing biomass waste as a renewable-energy resource can effectively enhance the techno-economic performance of urban regions’ hybrid renewable-energy systems, limit landfill waste, and create green jobs for the community. The authors also develop a new model of the hybrid energy system using the HOMER algorithm that optimizes techno-economic and environmental performance.

Olatomiwa et al. [19] suggest a solution for healthcare centers or clinics in remote areas using an off-grid hybrid renewable-energy system. They conducted an analysis of wind and solar sources for a selected rural location in Nigeria based on meteorological data with an optimal technical and economical design. Sizing hybrid renewable-energy system components such as wind, PV, battery, and inverter using the HOMER software, they studied each site’s solar and wind data. They showed that the PV–wind–diesel–battery hybrid system configuration was optimal for a health center in the rural Iseyin site, with an NPC of USD 102,949 and a COE of USD 0.311/kWh.

In an economic assessment of hybrid renewable-energy systems, Ali and Jang [20] proposed a model for an off-grid HRES for remote Deokjeok-do Island in South Korea using daily mean-load electricity-consumption data for one year (24,720 kWh). They collected the average annual wind speed and the daily solar radiation values of 3.6 m/s and 4.13 kWh/m², respectively. A total of 8760 simulations were needed to compute the hourly load demand using the HOMER software. The disadvantages found for this system were the surplus and electricity deficit. To find the most suitable HRES, the authors investigated criteria such as leveled cost of energy (LCOE) and net present cost (NPC). The total estimated cost for a wind-energy system was USD 11.3 M. However, the estimated cost was USD 17.6 M for a solar energy system, and its LCOE was USD 0.1594/kWh. Both systems consisted of one STX 93/2000 wind turbine and 2504 kW PV panels (for system one) and 3157 kW PV panels (for system two). They have also designed a pumped hydro storage (PHS) system to mitigate the surplus and deficit of electricity for the two systems and conducted an economic comparison between the PHS and a battery which indicated that the PHS could be a less expensive choice.

Odou et al. [21] also included a battery as a storage system and analyzed a case study of the village of Fouay in the Republic of Benin from a techno-economic perspective. They suggested a hybrid PV–diesel generator (DG)–battery system as the best choice to electrify the village. Their study showed that it has the lowest net present cost, provides a reliable power supply, and reduces battery storage cost since its battery requirement is only 30% of a standard PV–battery standalone system. The PV–DG–battery system has the shortest payback period of 3.45 years and a 33.3% internal rate of return (IRR). The results suggested that the off-grid hybrid PV–DG–battery system with a COE of USD 0.207/kWh was a suitable technology to sustainably electrify the village as projected in the county’s master rural electrification plan. The research recommends this system for electrification projects in Benin in the future.

The ability to use biomass energy depends on location and the available raw material, as discussed by Kasaeian et al. [22]. They conducted a techno-economic investigation to evaluate the optimal HRES consisting of a hybrid PV, biomass, and DG for Bandar Dayyer, Iran. The simulation results using the HOMER software showed that the total amount of electricity production by these systems was equal to 470,176 kW, with 22,409 kW produced by a PV–generic system and 447,767 kW produced by a PV–diesel–generic system. The economic analysis result implies that the NPC for the PV–wind system is USD 23,148.84, ensuring the benefits of using an HRES over a PV–diesel–generic system [22,23].

Karunathilake et al. [24] presented an optimized HRES at the building level to achieve the net-zero level goals of Canada and British Columbia. The study considers factors to develop an optimized model, such as minimizing energy system cost and life cycle environmental impacts and maximizing operational cost savings. The results indicated that ground sources such as heat pumps and solar sources such as photovoltaics were the optimal energy choices for multiunit residential buildings for the selected site location in British Columbia, Canada. The best energy system combination supplied 44% of the building’s energy demand through the renewable-energy system. However, reduced emissions and operational costs can also be achieved with an HRES. The overall life-cycle impacts could be reduced by 11.4%, and annual carbon emissions could be decreased by 21.70%. For the optimal solution, the levelled cost of energy does not vary more than ±0.05% from the standard grid electricity price, and the system will pay for itself within 22–23 years. The authors state that the proposed system’s per capita annual energy cost saving was USD 7. While renewable-energy systems can promote environmental and energy security, they carry an economic burden, particularly for small communities and neighborhood-level applications.

Another study on large buildings was conducted by Islam, Md Shahinur [25], who presented a techno-economic analysis on a grid-connected HRES for a large office building in France. The data was collected from the study location, and the HOMER simulation software was used to compute the technical, economic, and environmental parameters of the PV–Grid system. The economic evaluation parameters considered in the analysis were the NPC, COE, and energy payback time (EPT). The results show that the PV–Grid system was the most cost-effective system configuration and minimizes more than 90% of emissions compared to the existing emissions at the study site. Furthermore, the system performs reasonably well with the variation in electric load and solar insolation. The HRES will be most competitive during rising electricity prices with a COE of USD 0.087/kWh. Furthermore, the results also indicate the superiority of the HRES if a carbon tax is imposed in the future.

In literature related to mathematical modeling, Mayer et al. [26] studied the optimization of an HRES and indicated that the previous studies do not consider life-cycle environmental impacts other than GHG emissions. For factual life-cycle bases using the environmental footprint corresponding to the NPC as an objective function, the research includes the complete set of commercially available renewable-energy systems handling heating and electricity demand at the household level. Renewable energy was better than fossil fuels even when considering life-cycle impacts. However, renewable-energy systems are not emission-free and have some adverse environmental profiles, and there is no universal solution for reducing their effects. Their study used a genetic algorithm to solve and ecodesign the economic and ecological multisizing problem of a building-sized microgrid. The result of the life-cycle assessment for an HRES design provides a framework for practical applications such as supporting consumer decisions or policymaking.

The choice of an optimal system by cost effectiveness was studied by Wang et al. [27] using a two-stage optimal framework to solve the design problem for an HRES in a seaport area. They focus on finding the best renewable-energy subsystems with a specific installed capacity to ensure the lowest investment cost and minimize operation costs by considering stochastic characteristics of wind-energy production and energy demands. The result confirms the desirability of maintaining constraints such as power balances, capacity limitations, and emission regulations. They found that the choice of the optimal wind–storage–onshore hybrid power system for a container seaport with different emission limitations and wind speeds can be applied to achieve optimal installation capacity and cost effectiveness.

Fulzele and Daigavane [28] researched the design and optimization of an HRES consisting of a solar photovoltaic (PV), wind generator, and battery with a converter. They used an improved hybrid optimization genetic algorithm (IHOGA) simulation developed in the electric department of the University of Zaragoza. The effect of variables such as global solar radiation, wind speed, and PV panel cost was considered. The results showed that economic viability should be prioritized over technical considerations. In developing countries, poor economic conditions are a limitation for high-cost hybrid renewable energy.

Focusing on the environmental effects of a base system and a new system that they proposed, Jeong et al. [29] conducted a technical design and analyzed the use of renewable-energy sources to supply green buildings. They combined three types of buildings in different regions to find the optimal design for minimum cost and reduced carbon dioxide emissions compared with the standard conventional energy sources. The case study found that the most significant power consumption was from diesel generators, and the solar PV system works as a buffer for temporal balance. However, they suggested that if the building has enough area to install a wind turbine, it would be a promising solution for realizing the goal of green buildings. The estimated COE for the HRES was six times more than the electricity price, though the HRES system shows better environmental performance with less CO₂ emission than a conventional system. Although the system in this study was not economical, it can still be helpful as a guideline for stakeholders and the government.

Table 1. Literature review summary for hybrid renewable-energy systems.

Ref.	Configuration	Component	Location	Software	Result Summary
Suresh et al. [17]	Off-grid Hybrid	Biogas–PV–WT–Fuel Cell as Battery	India	Genetic Algorithm (GA) and HOMER	COE was at 0.163 USD/kWh illustrating the use of multisystems.
Bagheri et al. [18]	Off-grid Hybrid	Biomass–PV	Canada	HOMER	NPCs were 59, 116, and USD 290 M and illustrate three different places
Olatomiwa et al. [19]	Off-grid Hybrid	PV–WT–DG–Battery	Nigeria	HOMER	NPC USD 102,949 and the COE 0.311 USD/kWh illustrate high cost.
Ali and Jang [20]	Off-grid Hybrid	PV–WT–Pumped Hydro Storage–Battery	South Korea	HOMER	COE at 0.159 USD/kWh and NPC USD 11.3 M, illustrate an economic comparison between the PHS and a battery and indicate that PHS can be a cheaper choice
Odou et al. [21]	Off-grid Hybrid	PV–DG–Battery	Benin Republic	HOMER	Shows that an off-grid hybrid system was a suitable technology to sustainably electrify the village of Fouay with a COE of 0.207 USD/kWh.
Kasaeian et al. [22]	Off-grid Hybrid	PV–DG–Biomass	Iran	HOMER	The NPC USD 23,148.84 illustrates the benefits of using an HRE system with Biomass over a PV/DG system.
Kasaeian et al. [22]	On-grid hybrid system	PV–Biomass–DG	Iran	HOMER	Resulted in a comparative cost for the gid purchase with COE 0.13 USD/kWh and NPC USD 0.97 M
Karunathilake et al. [24]	Off-grid Hybrid	PV–Heat Pumps	Canada	ENERGY-PLUS	Paid back within (22–23) years. And the per capita annual energy cost saving with the proposed system was USD 7. RE energy systems feasible environmental and energy security.
Islam, Md Shahinur [25]	On-grid Hybrid	PV–Grid	France	HOMER	The result shows that the PV/Grid system was the most cost-effective system configuration and minimizes above 90% emission compared to the existing emission in the study site, with COE of 0.087 USD/kWh.
Chen and Wang [30]	Off-grid Hybrid	PV–WT–DG	China	(MATLAB) (SimPowerSystem)	Improve the system reliability from Loss of Power Supply Probability (LPSP) 5.08% to 0%.
Jung and Villaran, [31]	Microgrid Hybrid Systems	PV–DG–Battery	US	DER-CAM	Illustrate the efficiency improvement to a proposed hybrid system
Mavroyeoryos et al. [32]	Electrical Energy Demand	PV–WT	Greek	Exploratory data analysis	Illustrate a considerable correlation between energy consumption and temperature
Pérez-Navarro et al. [33]	On-grid Hybrid System	PV–WT–Biomass–Battery	Spain	LABDER laboratory	Illustrated the best feasibility by standard demand curve of the residential segment
Nurunnabi et al. [34]	On-grid off-grid Hybrid System	PV–WT–Grid	Bangladesh	HOMER	Illustrate cost-effective value with NPC USD 0.535 M and COE 0.099 USD/kW
Peerapong and Limmeechokchai [35]	On grid	PV–Grid	Thailand	RETscreen	For the residential rooftop and the integrated ground-mounted and rooftop with 0.48 USD/kW and 0.31 USD/kW
Phurailatpam et al. [36]	On-grid hybrid system	PV–WT–Biodiesel Generator	India	HOMER	Illustrate acceptable COE 0.269 USD/kWh and NPC of 0.613 MUSD
Dhundhara et al. [37]	On-grid hybrid system	PV–WT–Battery–Grid	India	HOMER	With COE of 0.225 USD/kWh and NPC of 1.12 MUSD and illustrate the high cost of using a storage system
Usman et al. [38]	On-grid hybrid system	PV–DG–Grid	India	HOMER Pro	NPC of USD 3.12 M and CEO of USD 0.120 illustrate solar energy penetration of 18% of the total load
Shafiullah, G.M. [39]	On-grid Hybrid	PV–WT–Battery–Grid	Australia	HOMER	Using the wind-solar hybrid system was more economical than using a PV grid-connected with a payback of 7.8 years for the hybrid system for a PV–Battery–Grid-connected system, NPC, COE, and RF are 16,347 USD/year, 0.240 USD/kWh and 0.77, respectively.
El Khashab and Al Ghamedi [40]	Off-grid Hybrid	PV, wind turbine, and fuel cell	Saudi Arabia	HOMER	The researchers imply that the energy cost for the three investigated systems was still high due to the high cost of imported RE systems without locally fabricated system parts with each system found to be 0.36 0.49 and 7.3 USD/kWh, respectively.
Adaramola et al. [41]	Off-grid Hybrid	solar panels, wind turbines, and diesel generators	Ghana	HOMER	The results show that the electricity costs for this hybrid system were 0.281 USD/kW h. and the result will be a guide to the government policy and investment in Ghana.
Ranaboldo et al. [42]	Off-grid Hybrid	wind and solar photovoltaic system	Cape Verde	HOMER	The results show that when using the hybrid renewable system of energy in demand points with microgrids it can save more than 30% of the initial investment comparing with individual generation configurations.

provides a comprehensive overview of the literature reviewed and a summary of each study configuration, components, location, and simulation software used. A similar framework for the proposed hybrid system can be used from these previous studies since they provide feedback to compare to the final result. At the same time, renewable-resource availability limits the use of certain types of renewable-energy sources. Each location has a different resource potential, as seen in many of the previous studies. In some areas, the optimal configuration was PV–wind–diesel–battery, while in other locations, the optimal configuration was biogas, solar, wind, and fuel cell with batteries.

Previous studies showed an HRES could be more efficient by many measures and in many locations, as the power output is more reliable and the cost of energy is also feasible and comparable to the cost of energy for a nonrenewable system. In addition, grid-connected hybrid renewable-energy systems are promising and can be the best option for advancing renewable technologies.

3. Methodology and Experimentation

3.1. Methods and Instruments

This study includes electrical, environmental, and economic analyses to investigate the proposed system’s reliability and applicability. The research methodology was to develop a framework to increase electrical energy efficiency and reduce greenhouse-gas emissions resulting from fossil fuel-based electricity generation by using a hybrid renewable energy system (HRES). An HRES consists of multiple renewable-energy technologies, including solar and wind energy, in contrast to conventional sources relying on fossil fuels. This change transitions an HRES-powered facility toward becoming a green building. Study data were collected for the load consumption profile, renewable-energy resources, and GHG emissions.

The system components of an HRES are optimized to deliver the needed power output using available renewable resources and to minimize the total NPC to achieve technical, economic, and carbon emissions requirements. In accordance with the design constraints, the design software chooses the most suitable system for various inputs. A systematic framework is proposed in this study to ensure that the optimal compatible HRES system using sustainable and clean energy sources is selected. The framework consists primarily of the five stages presented in Figure 3.

The first stage is the preliminary feasibility analysis, which is necessary to select candidate HRES technologies and create a proper system to meet the load demand. The meteorological data give feedback to the case study in the form of ambient environmental conditions such as temperature, wind speed, and climate. The renewable-energy (RE) resources at the site location are then evaluated to select an HRES based on their availability. Next, the load profile is needed to determine the number of kilowatts required from the proposed system. The load profile also consists of the load type (residential, commercial, or industrial) with daily and seasonal profile data showing the energy needed at what time of day and how the load differs in the summer and winter seasons. The current power supply is the case study system’s energy provided before the new proposed HRES is implemented. The HRES constraints associated with system design can limit the use of some HRES technologies and include geographical restraints (e.g., a limited area available for PV panels or WT structure obstacles).

The HRES components are the subsystems within the proposed system, potentially consisting of solar, wind, diesel generator, and electrical grid systems. Energy generation is the electrical energy delivered to the loads by the various subsystems comprising the HRES.

The simulation and optimization analysis stage consists of HRES-developed parameters, electrical efficiency, and economic and environmental studies. The simulation process collects the HRES parameters so that the optimization accurately represents the case study. The system’s efficiency is optimized to account for the type of energy generation and the distance from the load. An economical and environmental optimization performed in the simulation stage indicated an improvement to the overall output data.

The final stage in the systematic framework is the result stage. The proposed winning system HRES parameters and advantages are tabulated, and a comparison is made with the base-system configuration, as shown in Figure 4.

The device used to collect field measurement data for this study was the METREL MI 2892 Power Master power quality analyzer shown in Figure 5. This device was connected to the main distribution panel for the case-study hotel and measured the load consumption profile and the electrical load every 10 min for thirty days to obtain an accurate load profile from the electrical distribution company (EDCO). The hotel smart meter also gives the load profile for an entire year with daily measurements. The full load profile data was created by combining the field data and the meter log profile.

The advantages of the used methodology are:

• Consists of real and field measurements data collection;
• Using specialist software for this field of study;
• Several comparisons were done on the research results;
• The decision making was done by ordering the best results based on economic and environmental value.

3.2. Petra Marriott Hotel Case Study

The case study was designed to demonstrate the suggested improved framework for a five-star hotel in Petra, Jordan. The objective was to design and study a hybrid renewable-energy system to improve electrical efficiency by utilizing sustainable energy resources while limiting outages and power fluctuations. The proposed system improves economic performance and limits emissions by replacing electrical fossil fuel base production with an electrical green energy-based renewable output. The Petra Marriott Hotel, shown in Figure 6a, is located in southern Jordan, 230 km from Amman in the Ma’an district, as shown in Figure 6b [43]. Petra is a “Seven New Wonders of the World” city and is surrounded by the magnificent rose mountains of Petra near Wadi Musa. The hotel is 6 km from the carved rock Al-Khazneh temple, and the hotel is considered one of the central hotels in the area due to its unique location.

3.2.1. Renewable Resources Assessment for the Case Study

The case study location has ample solar irradiation of 5.5 kWh/m²/day per year [44] and average daily solar irradiation, as shown in Figure 7. The annual mean air temperature for the area is 17.7 °C, and the site elevation is 1303 m above sea level.

The hotel site also had a good wind resource potential as the mean power density is 574 W/m² from the global wind atlas, while the mean wind speed is 7.22 m/s [45]. The mean wind speed at 10 m elevation is 6.23 m/s, and the monthly average Petra Marriott Hotel location wind speeds are shown in Figure 8. The solar and wind resource data were used to conduct the HOMER software simulations.

The hotel consumes electricity through a local private transformer with a rated power of 630 kVA connected to the 33 kV distribution grid. The average electricity bill for this hotel is about USD 15,888, with an average consumption of 123 MWh per month, as shown in Figure 9, and 4126 kWh/day. Detailed consumption data for the selected month of April is shown in Figure 10. This data was used to choose a suitable hybrid system for the hotel to meet the load demand in every period.

3.2.2. Greenhouse-Gas Emission from Grid Electricity Production

Currently, 85.1% of grid electricity generation in Jordan is from the burning of conventional fuels, and about 15.1% is from renewable-energy resources [11]. A meeting was held with the head of the environmental assessment department of the Royal Scientific Society (RSS) to collect data, shown in

Table 2. GHG emissions factors.

Parameter	GHG Emission Factors (CO2/MWh)	Conversion Factors
Electricity	0.670	-
LPG	0.227	13.1 MWh/ton
Diesel	0.267	10 kWh/l

, for greenhouse-gas emissions resulting from electricity generation in Jordan. The number of mitigated emissions using the new system compared to the existing system using output data for the proposed HRES. Fuel consumption for electricity generation is shown in

Table 3. Fuel consumption for electricity generation (T.T.O.E).

Fuel	2016	2017	2018	2019	Percent
Heavy Fuel	344.6	454.2	120.0	15.1	87.4%
Natural Gas	3377.1	3340.9	3402.2	3337.9	1.9%
Diesel	13.6	9.4	4.2	1.8	57.1%
Total	3735.3	3804.4	3526.4	3354.8	4.9%

[11].

For every megawatt hour of electricity generated, 0.67 tons of CO₂ are produced. Therefore, if this emission factor is applied to the Petra Marriott Hotel with 1485.52 MWh yearly, then 995.29 tons of CO₂ are produced over one year (see Figure 11).

Economic data was collected from the local renewable-energy market, and a comparison was conducted at the simulation stage to show how the hybrid system can meet the desired applicability and cost-effectiveness requirements. The net present cost (NPC) can be calculated as follows [46]:

$$NPC=\frac{C_{Total}}{CRF\left(i.t\right)}$$

(1)

where t is the project lifetime (years), C_Total is the total annualized cost of the system (USD/year), i is the annual real interest rate (%), and CRF is the capital recovery factor. The following equation calculates the annual real interest rate:

$$i=\frac{i^{\prime }-f}{1+f}$$

(2)

where i′ is the nominal interest rate (%), equal to 1.77% in Jordan according to the Department of Statistics [47], and f is the annual inflation rate (%).

The capital-recovery factor can be estimated as follows:

$$CRF\left(i,n\right)=\frac{i{(1+n)}^n}{{(1+n)}^n-1}$$

(3)

and the COE can be computed using the following equation:

$$COE=\frac{C_{ann,Total}}{E_{Total}}$$

(4)

where $C_{ann,Total}$ is the total annualized cost of the system (USD) and $E_{Total}$ is the total electricity consumption per year (kWh/year). The annualized cost of a component is equal to its annual operating cost plus its capital and replacement costs annualized over the project lifetime.

The internal rate of return (IRR) is the discount rate for which the base case and current system have the same net present cost. The IRR is computed by determining the discount rate that makes the present value of the difference between the two cash-flow sequences equal to zero. The IRR can be calculated from the following equation [48]:

$$NPV={\displaystyle \sum }_{n=0}^N\frac{C_n}{{\left(1+IRR\right)}^n}$$

(5)

where NPV is the net present value of the system (USD), n is the period (years), N is the total number of periods, and C_n is the cash flow (USD).

Simple payback is the number of years for which the cumulative cash flow of the difference between the current and base case systems switches from negative to positive. The payback indicates how long it would take to recover the difference in investment costs between the current system and the base case system and may be calculated using the following equation:

$$\mathrm{Payback} \mathrm{Period}=\frac{Initial investment}{\mathrm{Cash} \mathrm{flow} \mathrm{per} \mathrm{year}}$$

(6)

3.3. Design Specifications and Technical Model

The general structure of the proposed HRES consists of solar PV, wind turbines, an electrical grid, and a power converter, as shown in Figure 12. The HRES components were selected as feasible preliminary technologies. The study location has a high solar irradiance (5.5 kWh/m²/day per year) and a good wind resource (mean wind speed of 6.23 m/s). Therefore, the solar PV system and wind turbine were determined to be the most efficient RE technology options for installation at the Petra Marriott Hotel. Furthermore, the electrical grid was included in the study to maintain the power balance between generation and consumption by exporting excess energy from the HRES as needed. A power converter (inverter) was added to the system to convert the direct current (DC) to alternating current (AC).

3.3.1. Solar Photovoltaic System

Photovoltaic panels collect solar irradiance and convert it into DC electrical power, which is then transformed into AC electrical power by the converter/inverter. Three types of solar PV panels were used in the simulation to determine the optimal configuration.

The first PV type used was the CanadianSolar HiKu5 Mono PERC CS3Y-475PV flat plate module with a 0.475 kW rated capacity, 21.2% efficiency, and a 25 year specified lifetime. The derating factor (DF) for these PV panels is 90%. The derating factor represents the difference due to losses and manufacturing deficiencies between power from field measurements and the rated power specified by the manufacturer. The nominal operating cell temperature (NOCT) is 45 °C. The PV panel is horizontally fixed at a slope of 20° and operates without a maximum power point tracking system. The capital and replacement costs were considered to be the same at USD 410/kW, and the operating and maintenance (O&M) cost is USD 12/kW/year [49], while the cost of the panels was obtained from the local market of various local solar companies. The capacity of the proposed PV system was determined through the HOMER software optimization. Some limitations were created by the electrical grid transformer and the hotel area and topographical terrain, whereas around 2000 m² of the total case-study area.

The second type of PV panel used in the simulations was the LONGiSolar lr4-72hph-440m flat plate module with a 0.440 kW rated capacity, 21.1% efficiency, and 25 year specified lifetime. The DF for these PV panels is 90%, and the NOCT is 45 °C. The PV panel is horizontally fixed at a slope of 20° and operates without a maximum power point tracking system. The capital and replacement costs were considered the same at USD 380/kW, and the O&M cost is USD 12/kW/year [50].

The SunPower SPR-E20-327 flat plate module with a 0.370 kW rated capacity, 20.4% efficiency, and a 25 year specified lifetime was the third type of PV panel used in the simulations. The DF for these panels was 90%, with a NOCT of 45 °C. The PV panel is horizontally fixed at a slope of 20° and operates without a maximum power point tracking system. The capital and replacement costs were considered the same at USD 320/kW, and the O&M cost is USD 12/kW/year [51].

The power supplied to the load by the PV panels, considering the effect of temperature, was calculated as follows [52]:

$$P_{PV}=f_{PV}\times Y_{PV}\times \frac{G_T}{G_s}\times \left. [1+\alpha \left(T_c-T_{c,ref}\right)\right]$$

(7)

where P_PV is daily energy produced by one PV module (kWh), f_PV is the PV derating factor (DF), Y_PV is the peak power output of the PV module, G_T is the global solar irradiance incident on the surface of the PV array (kWh/m²), G_s is the incident radiation at standard test conditions (STC), α is the temperature coefficient of power, T_c is the PV module temperature (°C), and T_(c,ref) is the PV module temperature at STC (°C).

3.3.2. Wind Turbine

The wind turbine can deliver sufficient power to the load from wind motion both in the daytime and at night. The Eocycle EO10 wind turbine model with a 10 kW rated capacity, a 16 m hub height, and a 20 year lifetime was used in the simulations. The Eocycle EO10 has cut-in and cut-off speeds of 2.7 m/s and 20 m/s, respectively. Capital and replacement costs were USD 56,000, while the O&M costs were assumed to be USD 200/year [53]. The output power from the wind turbine was calculated by determining the wind velocity at the hub height of the turbine using the following equation [54]:

$$P_{WT}=\frac{1}{2}\times {\rho }_a\times A\times {\upsilon }^3\times \eta$$

(8)

where ${\rho }_a$ is the air density (kg/m³), $A$ is the swept area of the wind-turbine blades (m²), $\upsilon$ is the wind speed (m/s), and $\eta$ is the wind-turbine total efficiency.

3.3.3. Power Inverter

DC electrical power generation from PV cells must be converted to AC electricity due to mainly AC power being used to drive electrical loads. A power converter (or inverter) was used to convert DC to AC electrical power and is a vital component in the system. This study used an ABB MGS100 converter with capital and replacement costs estimated to be USD 170/kW, a 15 year lifetime, and a 95% efficiency [55].

3.3.4. Electrical Network Grid

The system proposed is an on grid HRES, and the electrical distribution grid was included in the simulations and optimization. The operator of the distribution grid is the Electricity Distribution Company (EDCO). The case-study building (Petra Marriott Hotel) is connected to this grid through a transformer rated at 630 kVA at 33 kV rated grid voltage. Therefore, the grid power cost and sale price were the same at USD 0.13/kW and collected from EDCO (EDCO, 2021) as the grid is a net metering system.

Outages in the grid were defined within the simulation software using two parameters: resilience and reliability. Resilience is the ability of a system to respond to and recover quickly from an extended, multiday utility outage such as a natural disaster (e.g., hurricanes or wildfires). In such circumstances, only critical electrical loads should be serviced. The current study considered the resilience for outages of 0.02 days from grid data collected from the electrical distribution company. Reliability specifies grid outages as the failure frequency (outages per year) and their duration as the inputs. The HOMER grid simulation software generates the outage time series, displaying the outages and the mean repair time in a grid outages map (see Figure 13).

3.3.5. Diesel Generator

The diesel generator (DG) used in the simulations in this study was the existing diesel generator. This diesel generator has a 275 kW rated capacity, USD 44,000 capital and replacement costs, and O&M costs assumed to be USD 0.010/h. The minimum load ratio of the generator was 30%, the specified lifetime was 90,000 h, and the diesel fuel price was USD 0.7/L.

Hourly fuel consumption by the diesel generator is calculated as [56]:

$$F{C}_{Dgen}\left(t\right)=\alpha P_R+\beta P\left(t\right)$$

(9)

where $F{C}_{Dgen}\left(t\right)$ is the diesel generator fuel consumption (L/h), $P_R$ is the rated power of diesel generator (kW), $P\left(t\right)$ is the diesel generator power (kW), and $\alpha$ and $\beta$ are constants.

3.3.6. Electrical Energy Efficiency

Due to the mitigation of electrical energy losses within the transmission, distribution, and generation station auxiliary systems, energy efficiency will improve when transitioning from a traditional electrical energy source to a hybrid renewable and sustainable energy source. In 2019, transmission, distribution, and generating station auxiliary losses were 2.18%, 12.35%, and 2.07%, respectively, as shown in Figure 14 [11]. The proposed HRES has no transmission or distribution network due to the generation of renewable-energy resources near the load using domestic resources. In contrast, the national electric grid uses long transmission lines since the generating station’s location is far from the loads. The conventional generation process needs to be close to fossil-fuel storage, typically in a remote area. Therefore, an HRES will improve electrical energy efficiency by about 16% compared to conventional production.

3.3.7. Battery System

The storage system used in the simulations in this study was a battery bank. The battery type was BAE SECURA SOLAR 6 PVV 900 with a 1.89 kWh rated capacity, USD 795 capital and replacement costs, and assumed O&M costs of USD 0.010/year. The maximum capacity is 947 Ah, and the string size is one. Using a storage system in the on grid hybrid system can be inefficient due to the surplus electrical energy production from the renewable-energy systems that can be stored in the grid as net metering power. In addition, the diesel generator can limit the dependence on the storage system even in emergencies since the operation and maintenance cost of a DG is less than for a battery.

4. Results

This section presents the results of various scenarios considered in the current study.

Table 4. Summary of different optimized grid-connected HRES configurations ranked by NPC.

Rank	Configuration	System Component	Optimum Size	NPC (MUSD)	COE (USD//kWh)	RF (%)	Capital Cost (USD)	Replacement Cost (USD)	O&M (USD)	Salvage (USD)	Grid Consumption (USD)	GHG (kg/Year)
1	Fifth	PV WT Converter DG	573 KW 20 Qty. 317 KW 275 KW	1.16	0.0415	84.1	1.40 M	379,916	−403,238	−216,047	344,156	218,497
2	Fourth	PV WT Converter Battery DG	562 KW 20 Qty. 303 KW 2 strings 275 KW	1.17	0.0419	84.1	1.40 M	385,637	−396,753	−215,872	343,639	218,170
3	First	PV Converter DG	1050 KW 413 KW 275 KW	1.275	0.049	56.5	420,790	29,792	804,476	−9066	861,570	546,481
4	Second	PV Converter Battery DG	982 KW 403 KW 6 strings 275 KW	1.257	0.049	56	431,526	43,322	828,517	−16,501	865,027	549,777
5	Third	WT DG Converter Battery	18 Qty. 160 KW 490 KW 89 strings	2.097	0.0969	69.6	1.19 M	493,292	615,529	−201,586	507,666	321,993
6	Base case	DG Grid	275 KW	2.54	0.131	0	0	0	2.53 M	−3451	1,502,474	952,763

summarizes different simulation input parameters used to generate the results for the HRES scenarios. The data illustrate the economic and environmental values, including net percent cost (NPC), cost of energy (COE), and return of interest (ROI). Table 4 also illustrates the total system GHG emission values, mainly the carbon dioxide gas emissions, for each case. In Table 4, RF indicates the percentage of renewable-energy introduced into the design. The simulation software also gives the selected optimal capacities for each component in each configuration and the cost summary of items. Figure 15 shows an optimization plot of the total NPC against CO₂ emissions. In the simulation process, 5950 solutions, or system configurations, were simulated. One case has been selected from these configurations, the dot with the lowest CO₂ emissions in Figure 15, as the optimal solution (the winning scenario).

Table 5. Savings summary of different optimized grid-connected HRES winning configurations.

Component	Case 1	Case 2	Case 3	Case 4	Case 5	Unit
Annualized Utility Bill Saving	145,696	143,017	151,427	236,760	237,389	USD/year
Internal Rate of Return (IRR)	32.7	33.7	11.6	15.8	15.8	%
Payback Period	3.05	2.95	7.60	5.99	5.98	Year
Fuel Consumption	745	742	434	374	375	(L/year)

illustrates the money that an HRES can save. The annualized utility grid bill savings for the last-ranked system (Case 3) was USD 151,427/year and USD 237,389/year for the optimal winning system (case 5). The internal rate of return (IRR), the expected compound annual rate of return earned on a project, varies from 11.6% for the third configuration to 33.7% for the second configuration. This difference is because the IRR depends on the grid bill savings mainly through operating costs. There was some fuel consumption even for the optimal scenario (375 L/year) since, for some operating conditions and emergencies, the DG must be used to maintain the delivery of electricity to the load. The highest fuel consumption for any case was at 745 L/year.

In Table 4, the different designs were ranked by their net percent cost (NPC) value, with the lowest NPC as the first and winning configuration. In contrast, the highest NPC value was the least desirable configuration. The system components for each case are shown and compared to the base-case components (DG and grid). The highest NPC was USD 2.54 M due to the dependence on grid power and the DG during emergencies. The grid bill for the base case was highest at USD 1.5 M since the primary source of electrical power was from the national electrical grid (EDCO), so the cost of energy was the grid purchase cost (USD 0.13/kWh). The second and third configurations were ranked last due to their high energy cost compared to the winning configuration.

The last-ranked (fifth-ranked) case was the third configuration consisting of a wind turbine, DG, converter, and batteries. The NPC was USD 2.09 M, and the COE and renewable-energy fraction were USD 0.096 and 69.6%, respectively. Also, the replacement cost was considered high at USD 493 K due to the use of batteries and wind turbines. This configuration’s batteries and wind turbines also raised the capital cost to USD 1.19 M, salvage cost to around USD 201 K, and operating cost to USD 615,529. The grid consumption drops to a suitable value of about USD 507 K with 321,993 kg/year GHG emissions due to the use of the WT. The discounted payback was 10.1 years, and the simple payback was 7.60 years. From the optimization results, the proposed fifth winning scenario, HRES sizing configuration excluded PV arrays, 490 kW inverter, and included Eocycle EO10 wind turbines. In addition, the diesel generator was the 160 kW DG2 (CAT-200kva), with a storage battery of 89 strings (BAE SECURA SOLAR 6 PVV 900).

The second configuration of PV, DG, converter, and batteries was ranked fourth, with an NPC of USD 1.257 M and COE and RF values of USD 0.049 and 56%, respectively. The replacement cost was considered low at USD 43 K since wind turbines were not included. The capital cost was also low at USD 431 K, the salvage cost was around USD 16 K, and the operating cost was USD 828,517. The grid consumption drops to a value of around USD 865 K which is considered to be high. GHG emissions were reduced to 549,777 kg/year, though this is unremarkable and is due to the grid share of the system energy. The discounted payback was 3.35 years, and the simple payback was 2.95 years. From the optimization results, the fourth winning HRES configuration was proposed with 982 kW (SUNPOWER SPR-E20-327) PV arrays, 403 kW inverter, and excludes wind turbines. The generator was DG1 (275 kW CAT-C9 Prime DG), and a six-string storage battery (BAE SECURA SOLAR 6 PVV 900) was used.

The first configuration, including PVs, DG, and a converter, was the third-ranked choice, having an NPC of USD 1.275 M and COE and RF of USD 0.049 and 56%, respectively. Since this configuration is the same as the previous one, though without batteries, the replacement cost was the lowest at USD 29 K since wind turbines and batteries were excluded. The capital cost was also the lowest at USD 420 K, and the salvage cost was around USD 9 K, while the operating cost was USD 804,476. Grid consumption dropped to about USD 861 K, which is still considered high. GHG emissions showed a substantial reduction at 218,170 kg/year, while the discounted payback was 3.46 years, and the simple payback was 3.05 years. Therefore, this design was proposed with 1050 kW (SUNPOWER SPR-E20-327) PV arrays, 413 kW inverter, and a DG1 (275 kW CAT-C9 Prime) diesel generator, excluding wind turbines and batteries.

Ranked second was the fourth configuration consisting of PVs, WTs, DG, converter, and batteries. The NPC had a high cost of USD 1.17 M, and the COE and RF were USD 0.0419 and 84.1%, respectively. However, the replacement cost was considered high at USD 385 K due to the use of wind turbines and batteries. The capital cost was also high at USD 1.40 M, the salvage cost was around USD 215 K, and the operating cost was USD 396,753 due to the energy sold to the grid from excess production. The grid consumption dropped to around USD 343 K and is considered a low grid bill. GHG emissions showed an unremarkable reduction of 218,170 kg/year due to the grid energy share. The discounted payback was 7.60 years, and the simple payback was 5.99 years. This HRES configuration included 562 kW (SUNPOWER SPR-E20-327) PV arrays, a 304 kW inverter, 20 Eocycle EO10 wind turbines, a DG1 (275 kW CAT-C9 Prime) diesel generator, and a two-string storage battery (BAE SECURA SOLAR 6 PVV 900).

The winning design was the fifth configuration, including PVs, WTs, DG, and a converter, with a high NPC cost of USD 1.17 M. The COE and RF were USD 0.0415 and 84.1%, respectively. The replacement cost was considered high at USD 379 K due to the use of wind turbines. The capital cost was also high at USD 1.40 M, the salvage cost was around USD 216 K, and the operating cost was USD 403,238 due to the energy sold to the grid from excess production. Grid consumption drops to a low value of around USD 344 K. GHG emissions were reduced unremarkably to 218,497 kg/year due to the share of energy from the grid. The discounted payback of 7.59 years and the simple payback of 5.98 years were the lowest values of any case.

The cost summary for this winning configuration is provided in Figure 16a, and the overall cost is less than the base case. Figure 16b shows the cash flow for the optimal configuration by component over the project lifetime. The wind turbines dominate the cash flow due to their enormous capital cost and O&M, though by the end of the project life, the WTs save money.

Figure 17 shows the cumulative nominal cash flow for the base and winning (optimal) systems. While the base system cash flow becomes more negative over the project lifetime, the winning system cash flow gradually increases over time. The drop in cumulative nominal cash flow for the optimal system at around 20 years is due to the high replacement cost of the WTs.

The winning HRES configuration was proposed with 573 kW SUNPOWER SPR-E20-327 PV arrays from the optimization result. PV energy production in the summer was higher than in winter due to the higher solar irradiance and longer days. The average power was around 400 kW from the PVs, 317 kW inverter, and 20 Eocycle EO10 wind turbines. The energy production from the wind turbines was reasonably constant due to wind availability over most of the year. The generator used was a DG1 (275 kW CAT-C9 Prime) diesel generator, which already exists in the base case.

Three PV systems have been used in this study: CanadianSolar, SunPower, and LONGi Solar Technology. The optimal PV type was SunPower due to its low capital cost of USD 183,335, followed by LONGi Solar with USD 203,481, and CanadianSolar with USD 212,524, as shown in Figure 18. The electrical energy production for the SunPower PV was 955,599 kW/year, while for LONGi Solar and CanadianSolar, it was 1,047,159 kW/year and 930,882 kW/year, respectively.

In

Table 6. PV systems comparison.

Parameter	SunPower	CanadianSolar	LongiSolar	Unit
Rated power	0.327	0.475	0.44	kW
Size	573	518	535	kW
Capital cost	183,335	212,524	203,481	USD
O&M	88,877	80,412	83,068	USD/year
Efficiency	20.4	21	21.1	%
Production	1,047,159	930,882	955,599	kWh/year

, the different PV types are compared by their rated power, size, capital cost, O&M, efficiency, and energy production. Although some PVs had high-rated power, their cost was not as economical, so these factors combined to decide the optimal PV type to be used in the system.

In

Table 7. Optimized equipment sizes and cost results associated with each case studied.

Rank	Configuration	NPC Reduction (%)	COE Reduction (%)	O&M Reduction (%)	Grid Consumption Reduction (%)	GHG Reduction (%)
1	Fifth	54	68	116	76.92	77.1
2	Fourth	53	68	115	77.12	77.22
3	First	50	62	68	42.62	41.45
4	Second	50	62	67	42.41	42.32
5	Third	18	26	75	66.23	65.93

, a comparison is provided between each case and the base case in the last column. These data include the reduction in GHG emissions and the NPC, COE, and O&M costs for each case compared to the base case. The reduction percentages show how the different proposed configurations interacted and changed the cross-bonding value. The winning scenario had the largest reduction in NPC (54%), and the smallest reduction for any case was 18%. In addition, the O&M cost, which depends mainly on the grid consumption bill, was reduced by 116% for the winning case due to the excess energy sold to the grid. For comparison, the variations in economic and environmental emissions for the different scenarios are presented graphically in Figure 19.

As a summary of the optimal configuration, Figure 20 shows the power output profile for the PV, wind, DG, and grid purchase, grid sale, excess electrical production, and total load for a selected day in each month of the year. From the data provided in Figure 20, the PV output power was highest during the summer season at around 400 kW due to the length of the day and the solar irradiance level. In July, the PV power output was the most significant, while in December and May, the PV output had a lower value, around 230 kW, due to weather conditions and seasonal solar irradiance. However, wind power maintains its output for all months due to relatively constant wind availability. The maximum wind power was around 800 kW in January. The DG output power was limited since it was used only during emergencies and outages.

Sales were made to the grid network from excess electrical production every month, and the most significant months for grid sales were January and April. In contrast, grid purchases were limited in all months and concentrated in the morning and night due to the lower wind and solar resource availability. For example, in December, the grid purchase power was limited to the first few hours of the day. Despite the lack of solar power after sunset, wind availability at night essentially eliminates the need for grid power. The excess electrical energy after the primary load is served correlates with both solar and wind-energy availability. The maximum excess energy was in January and March, and wind power is the largest contributor to the excess electrical energy.

In

Table 8. Storage system comparison.

Parameter	Case 1	Case 2	Case 3	Case 4	Case 5	Unit
Using storage	No	Yes	No	Yes	No	Yes–No
Battery size	0	6	0	2	0	String
COE	0.0488	0.0508	0.0969	0.0419	0.0415	USD/kWh
NPC	1,252,733	1,293,578	2,097,264	1,173,615	1,165,217	USD
Battery annual throughput	0	2807	0	1289	0	kWh/year
Hybrid system production	1,921,655	1,797,014	1,172,200	2,329,636	2,348,993	kWh/year
Battery percentage throughput	0	0.15	0	0.05	0	%

, the storage system proposed in this study is summarized and compared for each case. This table shows that storage-system usage was limited to cases two and four with 0.15% and 0.05% battery percentage throughputs, respectively. The lack of need for a storage system is due to the use of a net metering grid system, as the surplus electrical energy can be sold back to the grid and credited. Hence, the grid works as a storage system with no capital or O&M costs.

The production summary for the optimal winning configuration is shown in

Table 9. Production summary for the optimal configuration.

Component	Production (kWh/Year)	Percent
SunPower SPR-E20-327	1,047,159	38.9
CAT-C9 Prime	1019	0.0378
Eocycle EO10	1,300,815	48.3
Grid Purchases	344,156	12.8
Total	2,693,150	100

. The wind share was the largest at 1300 MW/year (48.3% share), followed by the PV at 1047 MW/year (38.9% share). The grid-purchased power was reduced to 344 MW/year (12.8% share) due to the system’s dependence on renewable resources.

Excess electrical production for the optimal design is shown in Figure 21 with the primary load and the total renewable-energy output for a selected day of the year (17 April) to illustrate the change in power over an entire day. This figure shows that a large amount of excess energy was available during peak solar irradiance in the middle of the day. The PVs contribute a large portion of the excess energy, followed by the wind turbines. Protocols that limit grid sales to peak load consumption restrict the excess energy supplied to the grid for on-grid renewable-energy systems.

A summary of the power output from each optimal configuration component is shown in Figure 22 over a selected day to illustrate the variation between components. The solar PV contributed considerable power during the peak solar irradiance hours (near solar noon), while the wind-turbine output was relatively large and constant due to semicontinuous wind availability. The DG power output was zero due to the availability of PV, WT, and grid power and since there were no emergencies or outages on that day. For comparison, the energy purchased and sold to the grid for the same sampled day is shown in Figure 23.

The HRES reliability is the probability that the system will perform and meet its intended function adequately for a specified period.

Table 10. Reliability summary for the optimal configuration.

Quantity	During Outage	Unit
Diesel Generator DG run time	8.73	Hours/day
Diesel Generator DG O&M cost	0.0873	USD/day
Diesel Generator DG fuel consumption	409	L/day
Diesel Generator DG fuel cost	286	USD/day
Capacity-shortage hours	0	Hour

shows that the system’s reliability resulted from limited diesel generator operation, with 8.73 h/day running time. The operation and maintenance costs were USD 0.0873/day, and the fuel consumption was 409 L/day, costing USD 286/day. However, the main benefit of the proposed HRES was the elimination of shortage hours in the system.

4.1. Operational Performance of the Optimal System

The HOMER grid simulation results showed that the winning scenario was the case five configuration of PV–WT–Converter–DG–Grid, based on the lowest NPC and COE of USD 1.16 M and USD 0.0415/kWh, respectively. The monthly average electric power production from each system component of the optimal configuration is shown in Figure 24.

Solar and wind energy were considered as the primary sources to supply the proposed system with electrical demand. Due to adequate solar and wind availability, the DG can be regarded as a backup power source in most months and is only needed when the output power from the PVs and WTs is affected by weather conditions or when outages occur.

Table 11. Summary of electrical-energy production and consumption results for the optimal configuration.

Component	Production (kWh/Year)	Percent	Load	kWh/Year	Percent
SunPower SPR-E20-327	1,047,159	38.9	Electrical load	2,173,742	100%
CAT-C9 Prime	1019	0.0378	Electrical excess	491,576	18.3
Eocycle EO10	1,300,815	48.3	Unmeted load	0	0
Grid Purchases	344,156	12.8	Capacity shortage	0	0
Total	2,693,150	100

demonstrates the total annual electrical energy production and consumption for HRES case five from the various power sources. From this table, the WTs have contributed the highest energy generation of 1,300,815 kWh/year (48.4% share), the PVs contributed 1,047,159 kWh/year (38.9% share), and grid purchases contributed 344,156 kWh/yr. Figure 24 shows the monthly average electric production for the optimal configuration and that the WT and PV productions were the dominant energy sources.

4.2. Economical Performance of the Optimal System

The optimal and winning HRES case costs, including the capital, replacement, O&M, fuel, and salvage costs for the system components and the overall system, are summarized in

Table 12. Cost summary of the optimal configuration scenario.

Name	Capital	Operating	Replacement	Salvage	Resource	Total
CAT-C9 Prime	USD 44,000	USD 1.03	USD 0.00	−USD 10,517	USD 3,389	USD 36,873
EDCO	USD 0.00	−USD 543,826	USD 0.00	USD 0.00	USD 0.00	−USD 543,826
Eocycle EO10	USD 1.12 M	USD 51,710	USD 357,064	−USD 201,229	USD 0.00	USD 1.33 M
SunPower SPR-E20-327	USD 183,335	USD 88,877	USD 0.00	USD 0.00	USD 0.00	USD 272,212
System Converter	USD 53,861	USD 0.00	USD 22,852	−USD 4,301	USD 0.00	USD 72,412
System	USD 1.40 M	−USD 403,238	USD 379,916	−USD 216,047	USD 3,389	USD 1.17 M

. The resulting data indicate that the capital cost of the WTs (USD 1.12 M) is the highest of any system component and is approximately 80% of the entire system’s capital cost. However, worldwide capital costs for wind turbines are expected to fall soon due to increasing mass production.

The solar panel cost is considerable at USD 183,335, and the diesel generator also contributes to the total system cost of USD 44,000, while the system’s converter is USD 53,861. Compared to all other system components, the PVs were the most affordable technology in the proposed HRES. As a result of their nearly zero O&M cost and low capital and installation costs, PV technology costs become lower each year.

4.3. Environmental Performance of the Optimal System

The environmental impact of the optimal HRES design showed that the resulting GHG emissions were reduced significantly from 952,763 kg/year for the base case (zero renewable energy) to 218,497 kg/year with the high fraction (84.1%) of renewable energy in the system. This 77% reduction in emissions justifies the elimination of diesel generators, which decreases the dependence on fossil fuels, as demonstrated in Table 6. The total number of operating hours for the DG was reduced significantly from 23 h/year to 8 h/year. Therefore, the total amount of diesel fuel consumed decreased to around 37 L for the optimal case compared to 1210 L for the base case. As a result, this increases the dependence of the proposed optimal HRES on clean and renewable energy. A breakdown of the major gas emissions by the optimal design is provided in

Table 13. Major gas emission for the optimum case.

Quantity	Value	Unit
Carbon Dioxide	218,497	kg/year
Carbon Monoxide	0.604	kg/year
Unburned Hydrocarbons	0.0173	kg/year
Particulate Matter	0.0570	kg/year
Sulfur Dioxide	945	kg/year
Nitrogen Oxides	471	kg/year

and Figure 25.

4.4. Comparison with Other Studies Results

This study has defined a new HRES configuration that differs from other studies in component sizes, load requirements, and renewable resources. Therefore, an exact comparison cannot be made with those systems. However, a comparison using the systems’ economic configuration is an acceptable and appropriate way to compare them.

Table 14. NPC and COE of the optimal HRES in different regions worldwide.

Region	HRES Structure	NPC (MUSD)	COE (USD/kWh)	Simulation	Grid Type	Reference
Iran	PV–DG–BIO DG–Converter–Grid	0.970	0.130	HOMER Pro	On grid	Kasaeian et al. [22]
Bangladesh	PV–WT–Converter–Grid	0.535	0.099	HOMER Pro	On grid	Nurunnabi et al. [34]
Thailand	PV–Converter–Grid	N/A	0.280	RETscreen	On grid	Peerapong and Limmeechokchai, [35]
India	PV–WT–Converter–Grid	0.613	0.269	HOMER Pro	On grid	Phurailatpam et al. [36]
India	PV–WT–Converter–Battery–Grid	1.12	0.225	HOMER Pro	On grid	Dhundhara et al. [37]
India	PV–Converter–Grid	3.12	0.120	HOMER Pro	On grid	Usman et al. [38]
Jordan	PV–WT–Converter–DG–Grid	1.16	0.0415	HOMER Grid	On grid	Current study 2021

shows a comparison between the NPC and COE of this study of the Petra Marriott Hotel in Jordan and the other listed studies. In Table 14, the COE values are considered the primary comparison parameter due to the use of renewable energy. In addition, Figure 26 illustrates the NPC and COE values for the optimal HRES configuration in different regions worldwide.

5. Regulation Rule of Energy Storage

The main regulation rule of energy storage that limits the expands of the hybrid system size and refunds can be mentioned below [57]:

• Electrical loss rates of 6% are applied to the user as a result of using the transmission or distribution system or both electrical losses and USD 0.1/kWh;
• The generating capacity of the system of renewable-energy sources to be connected to the system licensed for transmission or licensed for distribution and retail supply shall be determined, so that the annual amount of energy generated from this system does not exceed the actual consumption for the last 12 months from the date of submitting the application for the beneficiary subscription, in addition to the amount of energy calculated to cover the losses. Electricity results from the transmission of electrical energy through the transmission system and distribution system. Regarding new subscriptions that have had no actual consumption for the last 12 months, the applicant estimates the generating capacity in the application;
• The user is not compensated for the annual surplus quantities if the amount of excess energy exceeds 10% of the energy consumed by the subscriber benefiting from the transit transportation process.

6. Conclusions

This research conducted a comprehensive analysis of a hybrid renewable energy system (HRES) to transform a facility into a green building with clean energy at near-zero greenhouse-gas (GHG) emissions. The HRES aims to reduce the energy bill by considering different sustainable energy resource configurations using a grid-connected hybrid renewable energy system (GHRES) for the case study of a hotel in Petra, Jordan.

The HOMER grid software was applied to hybrid systems to study ways to improve overall efficiency and mitigate greenhouse-gas emissions while maintaining an economic perspective. Data were collected using metrological data, field measurements using a power quality logging device, and power distribution company recorded data. The hybrid system components included in the simulation were a solar photovoltaic (PV) system, wind-turbine system, battery storage system, diesel generator (DG), and converter. With multiple configurations analyzed in the software simulations, a complete comparison was conducted to find the most suitable design using feasible component parameters. Three PV panel types and two DGs were compared within the simulation process.

The renewable-energy fraction (RF) was one of the more important selection parameters, while the economic parameters were also crucial in making the proposed new configuration feasible. The resulting values for the optimal winning configuration, out of five main scenarios, had a USD 1.16 M total net-present cost, a USD 0.0415 USD/kWh cost of energy, a 15.8% effective internal rate of return, and an approximately 77% reduction in carbon emissions compared to the base case. Since the proposed system was grid connected, a storage system for the excess electrical energy generated from the HRES was not used since the grid acts as an enormous storage system (though somewhat limited by policies based on grid stability and electricity prices).

The results from this study can give a comprehensive understanding of a hybrid system connected to a grid and some suggestions in the future and recommendations as follows:

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Using a hybrid renewable-energy system was more effective than using a single type of renewable-energy system, as it was more sustainable and cost effective;

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Policymakers should ease the rules and limitations on using renewable-energy and their share of the grid;

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Proposed a local fabricated system for parts production due to the energy cost still being high as the cost of imported RE systems is high;

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Using solar water heaters can improve the overall system’s cost effectivity by reducing energy to heat water by electricity heaters.