Techno-economic optimization of hybrid energy systems for zero energy buildings in remote communities: a case study from Turkey

Othman J. Alhayali
1
,
Mehdi Mehrtash
2,*
*Correspondence to: Mehdi Mehrtash, Energy Systems Engineering, Atilim University, Ankara 06830, Turkey, E-mail: mehdi.mehrtash@atilim.edu.tr
J Build Des Environ. 2024;2:36334. 10.70401/jbde.2024.0002
Received: June 25, 2024Accepted: August 02, 2024Published: September 28, 2024

Abstract

This study evaluates the economic efficiency and viability of optimizing hybrid renewable energy systems (HRES) for zero-energy buildings (ZEBs) in remote communities, with a specific focus on Ankara, Turkey, in response to the increasing demand for renewable energy driven by concerns over fossil fuel scarcity, environmental sustainability, and rising conventional energy costs. Using the Hybrid Optimization Model for Multiple Energy Resources (HOMER) program, known for its advanced algorithms that accurately model and optimize hybrid systems by considering factors such as weather data, load profiles, and equipment specifications, we perform a comprehensive techno-economic analysis. We explore five different HRES configurations, combining photovoltaic (PV) panels, wind turbines (WT), diesel generators (DG), and battery storage systems, to determine the most cost-effective and reliable solution for powering approximately 30 rural households. The analysis reveals that the optimal configuration includes 107 kW of PV, three 10 kW WT, a 10 kW DG, and 45 units of 7.15 kWh batteries, demonstrating a net present cost (NPC) of $568,431 and a cost of energy (COE) of $0.257/kWh. This setup achieves significant annual energy production of 165,068 kWh from PV, 96,329 kWh from WT, and 27,100 kWh from DG. This configuration maintains a high state of charge (SoC) in the battery storage, ensuring system stability and extending the battery lifespan. The system's ability to consistently meet load demands with minimal reliance on the DG highlights its superior techno-economic synergy compared to other scenarios. Sensitivity analysis reveals that a doubling of fuel prices increases COE by 14% and NPC by 13%, while a 40% reduction in PV and WT capital costs decreases COE and NPC by approximately 16% and 18%, respectively. Furthermore, declining expenses associated with PV and WT installations emphasize the ongoing affordability of renewable energy solutions. These results provide valuable insights for the deployment of cost-effective and reliable HRES in similar remote locations, contributing to the broader goal of sustainable energy solutions for ZEBs.

Keywords

Techno-economic analysis, hybrid renewable energy systems, zero-energy buildings, off-grid systems

1. Introduction

The global energy landscape is experiencing a significant transformation, driven by increasing energy demands, the necessity to reduce dependence on fossil fuels, and the urgent need to address environmental degradation. At the forefront of this transformation is the burgeoning interest in zero-energy buildings (ZEBs) and the deployment of renewable energy sources to cover their energy demands sustainably. Buildings account for a significant portion of global energy consumption and carbon emissions, making them a focal point for efforts to achieve energy efficiency and decarbonization[1]. As such, the development of ZEBs, which generate as much energy as they consume over a specified period, represents a pivotal strategy in the transition to a more sustainable built environment.

In parallel, the electrification of remote and off-grid communities presents a unique set of challenges and opportunities. Approximately 1.5 billion people in the world have no access to electricity, with many residing in remote or underserved areas[2]. Traditional grid extension efforts are often economically impractical or environmentally unsustainable in such regions, highlighting the need for alternative energy solutions. Furthermore, interconnecting WT, PV, and DG systems to fulfill electricity load demand reduces fossil fuel usage and has environmental, economic, and social benefits[3]. In this context, hybrid renewable energy systems (HRES) emerge as a promising approach to electrifying remote communities by harnessing the abundant and diverse renewable resources available in these areas.

HRES is the subject of several research projects that combine two or more global sources. Prasad et al.[4] looked at how much energy a building uses and how it may become ZEB by combining various renewable energy sources. The findings showed that ZEB would be best served by renewable energy technologies. The economic viability of using two systems consisting of a single source for the first system and several sources for the second system to supply energy to a remote location in India was investigated by Nag and Sarkar[5]. The outcome showed that renewable energy from several sources is more dependable than electricity from a single source. Off-grid PV/Battery systems for supplying power to a single dwelling in Faisalabad, Pakistan, were explored by Ghafoor and Munir[6]. System design took into account the load profile and solar energy availability. The findings showed that, in the analyzed region, the cost of energy (COE) suggested by the system of PV/Battery is more economical than a conventional grid. Makhdoomi and Askarzadeh[7] optimized an off-grid HRES with PV as a source and DG and battery as a backup. PV/DG, PV/DG/Battery, and PV/DG/pumped hydro storage systems were used to minimize the COE. Simulation findings showed that the system with PV/DG/ pumped hydro storage has a lower COE of 0.198 $/kWh compared to other systems. Forrousso et al.[8] designed an off-grid system comprising PV, building integrated photovoltaic (BIPV), and battery energy storage installations for ZEB blocks in six different cities in Morocco. Particle Swarm Optimization (PSO) was used to determine the system with the lowest Net Present Cost (NPC). The results showed that the minimum COE in Quarzazate city was around 0.366 $/kWh compared with the maximum COE in Ifrane city with around 0.664 $/kWh. The techno-economic study of PV/WT/DG/Battery to deliver electrical power for a dwelling was examined by Kharrich et al.[8]. The goal of the case study was to determine the best economic system in Baghdad and Rabat. The results indicated that, at the same load, the micro-grid hybrid system in Rabat is more feasible and cost-effective than in Baghdad, owing to Rabat's abundant renewable resources. The temperature impact that lowers PV production was not taken into account in their investigation. Güğül[9] designed PV/WT/DG/ Battery HRES for hotel buildings, animal hospitals, and faculty of technology using Swarm NonLinear Solver. The objective was to maximize the renewable fraction with the lowest COE. The findings demonstrated that, in both on- and off-grid settings, HRES with PV and WT reduced power prices to 0.18 $/kWh and 0.321 $/kWh. A genetic algorithm was used by Hazem Mohammed et al.[10] to optimize an off-grid WT/Tidal/PV/Battery HRES in France. The goal was to have the best economical HRES system, resulting in the WT/Battery system being identified as the optimal system for the area. The same authors[11] later used the PSO algorithm to determine the optimal standalone system combining PV/WT/Tidal/Battery in Brittany/France to cover the load demand using an improved PSO algorithm aimed to enhance system reliability with minimum costs. The results indicated that, because of the high wind speed in that area, WT/Battery was the best solution in the examined area. Shezan et al.[12] examined the NPC differences across power systems utilizing conventional and renewable energy sources. The off-grid WT/PV/DG/Battery HRES was examined using the hybrid Optimization Model for Multiple Energy Resources (HOMER) program, finding that the optimized system could reduce NPC by 29.65%. Kharrich et al.[13] explored the feasibility of implementing renewable energy technologies at six sites in Morocco, employing PSO to ascertain the optimal scale and economic viability of HRES. According to the results, the low tidal speed makes the tidal energy inappropriate in all areas examined. In comparison to the other combinations, the WT/PV system proved to be the most economical, particularly in Dakhla. Due to the high solar radiation, PV panels were identified as a promising component for electricity production in Morocco. Utilizing Hybrid Optimization Model for Multiple Energy Resources (HOMER) software, Lozano et al.[14] conducted a techno-economic study of PV/DG and PV alone to determine the best solution for supplying Gilutongan Island with power. Findings showed that, in comparison to PV alone, the PV/DG system can lower the COE by around 68-70%. Aykut et al.[15] designed an HRES combining PV and biomass to optimize energy for a university in Turkey, emphasizing economic efficiency with a focus on COE and NPC. The study found that due to low wind speeds, WT were not feasible for the area. Similarly, Kelly et al.[16] conducted an economic and technical evaluation of a PV/Diesel/Wind/Battery system for isolated regions in Chad, demonstrating that COE ranged from $0.367 to $0.529/kWh, with varying economic feasibility across different locations. Odetoye et al.[17] optimized an off-grid system using PV, concentrating solar power, hydropower, and batteries in Nigeria, revealing a substantial reduction in electricity costs compared to diesel-based systems. Youssef et al.[18] evaluated eight HRES configurations, including PV, WT, biomass, and batteries, for a school in Cairo, Egypt, finding that the PV/Wind/Biomass/Battery system achieved the lowest COE and NPC. Ladu et al.[19] analyzed the feasibility of HRES for rural electrification in South Sudan, identifying PV/Diesel/Battery as the most economical option. Kiani et al.[20] developed a PV/WT/Diesel/Battery hybrid system, optimizing it with HOMER to reduce COE and NPC and assess the impact of rising fuel prices, confirming the economic advantages of renewable sources in areas with high fossil fuel costs.

While numerous studies have explored the integration of HRES in various contexts, they often do not address the specific needs and unique meteorological conditions of remote rural communities. Additionally, existing research frequently lacks a comprehensive comparison of multiple HRES configurations tailored for such regions. Our study addresses these shortcomings by utilizing localized meteorological data to design and optimize HRES for a remote area near Ankara, specifically targeting ZEB standards. We conduct a detailed techno-economic analysis to compare five different HRES configurations, providing a nuanced understanding of their cost-effectiveness and reliability. Moreover, we incorporate a sensitivity analysis to assess how fluctuations in fuel prices and capital costs impact system performance and economic viability. These innovations provide a robust and adaptable energy solution tailored to the unique challenges faced by rural communities, contributing to the broader goal of sustainable energy solutions for ZEBs.

2. Methodology

The case study was conducted in Ankara, Turkey to optimize an HRES capable of powering 30 ZEBs. Designing such a system requires meticulous analysis of meteorological data specific to the target location and the characteristics of system components. This section explores several key aspects, including the studied site, load demand, meteorological data, system components specifications and prices, and the essential mathematical calculations required to develop an optimal system tailored to cover the load demand with the lowest costs for achieving ZEBs in the specific remote place of Ankara.

2.1 Selected site

The study focuses on 30 small, adjacent houses in a rural and remote area near Ankara, situated at Latitude: 39° 55' 11.53" N and Longitude: 32° 51' 15.37" E. Each comprises a kitchen, dining hall, one room, and a bathroom. It features a single-story layout with an approximate floor area of 120 m². The structure comprises reinforced concrete foundations, brick walls, and a sloped roof with ceramic tiles. Designed to accommodate an average family size of 4-5 members. These houses were selected based on typical community sizes in the region and the specific energy needs identified through preliminary surveys. The energy demand profile of the building is characterized by seasonal variations, with higher loads in winter due to heating requirements and in summer due to cooling needs

The area faces significant challenges, including limited grid connectivity and high energy costs, making it an ideal candidate for implementing HRES. The region's challenging terrain complicates the installation of electric towers and electricity delivery, leading to frequent maintenance issues and service interruptions. Given Turkey's commitment to reducing fossil fuel usage and increasing reliance on renewable energy, an HRES that operates independently from the grid can effectively address these challenges. By focusing on this specific community, we aim to address the unique energy needs of remote locations and demonstrate the potential benefits of HRES in enhancing energy access and reducing costs. The chosen site for implementing the HRES is shown in Figure 1.

Figure 1. The selected study site.

Given Turkey's commitment to reducing fossil fuel usage and increasing reliance on renewable energy, this study examines the economic feasibility of renewable energy sources to fulfill the electricity requirements of households. The climate of the chosen area is mostly continental, with cold, wet winters and hot, dry summers. Temperature fluctuations between night and day, as well as seasonal variations, are significant. Notably, July and August register as the hottest months, while January is the coldest.

2.2 Environmental factors and load profile analysis

The electrical load requirement of the selected location, solar radiation levels, ambient temperature, and wind speed all influence the design and sizing of the HRES. The system is designed to be able to meet the energy requirements of a range of domestic appliances, including lights, TVs, fans, refrigerators, water pumps, air coolers, kettles, and small geysers, as listed in Table 1.

Table 1. Energy consumption by device.
Item Power (W) Number in use Operating hours Total load (Wh)
Living room lamp4019360
lamps244161,536
TV25013750
Room fans503131,950
refrigerator3501248,400
Water pump75011750
Air cooler3501124,200
Kettle40011400
geyser1,500111,500

With peak and average loads of 30 kW and 24.8 kW respectively, the hourly load demand per residence indicates an average daily energy usage of 595.2 kWh/day. One important factor whose impact on the system performance is carefully calculated is sun radiation. Obtained from the National Aeronautics and Space Administration (NASA), the solar radiation data for the selected site reveals an average annual and maximum solar radiation of 4.39 kWh/m²/day and 7.16 kWh/m²/day, respectively. The ambient temperature, averaging 11.99°C annually, is a critical factor influencing the performance of PV panels and WTs by affecting air density and subsequently power output. The site under consideration has an average yearly wind speed of 4.6 m/s.

2.3 Hybrid system components

The standalone system, utilized in this study, proves crucial for electrifying rural areas due to its reliability, efficiency, and cost-effectiveness. The proposed system comprises five major components: PV, WT, DG, battery, and inverter. The schematic representation of the proposed HRES is depicted in Figure 2.

Figure 2. Proposed system configuration.

Various components with distinct properties and costs are employed in every system. The system parameters, specifications, and costs are as follows:

PV:

• Model: Polycrystalline panels with a capacity of 325 W,

• Capital Cost: 1,500 $/kW,

• Operation and Maintenance (O&M) Cost: 5 $/kW/year,

• Lifetime: 25 years.

WT:

• Nominal Capacity: 10 kW,

• Cut-in Wind Speed: 2.75 m/s,

• Cut-off Wind Speed: 20 m/s,

• Capital Cost: 30,000 $,

• O&M Cost: 1,000 $/year,

• Lifetime: 25 years.

DG:

• Capacity: 10 kW,

• Capital Cost: 5,500 $,

• Replacement Cost: 5,000 $,

• O&M Cost: 0.3 $/hour,

• Lifetime: 15,000 hours.

Battery and Converter:

• Battery Type: Vented Lead Acid (LA) with a maximum capacity of 3,570 Ah,

• Capital Cost: 722 $,

• Replacement Cost: 665 $,

• O&M Cost: 180 $/year,

• Lifetime: 10 years,

• Converter Capital Cost: 550 $/kW,

• Lifetime: 15 years.

HOMER is the software used for designing HRES, combining various sources like WT, PV, and DG. It models on/off-grid systems, optimizing configurations for cost-effectiveness. HOMER performs simulation, optimization, and sensitivity analysis, evaluating system options based on loads and renewable resources to find the most economical design.

HRES requires efficient power management since it combines traditional and renewable energy sources with battery backup. Balancing the supply from renewable sources, battery charge/discharge cycles, and the activation of the DG when necessary is essential for optimal performance. The power management strategy can be summarized in three key points:

• The renewable energy covers the load and the excess energy is transferred to the battery.

• When renewable energy is insufficient to cover the load, the battery discharges to bridge the gap.

• If renewable sources are insufficient and the battery reaches its minimum SoC, the DG is activated to cover the load.

The power management strategy is visually depicted in Figure 3.

Figure 3. Power management strategy used in HOMER. HOMER: Hybrid Optimization Model for Multiple Energy Resources.

2.4 The Scenarios used in this study

This research analyzes five scenarios to determine the best HRES that can handle load demand while maintaining reliability and an acceptable price for power, as shown in Figure 4.

Figure 4. Scenarios used in this study.

In the first scenario, PV and battery systems are utilized, whereas in the second scenario, WT and battery systems are employed. PV operates to meet the daytime load and WT production operates whenever the wind speed is greater than the cut-in speed and lower than the cut-out speed.

An electric generator, the DG, is introduced to the system in the third scenario (PV/DG/Battery). The fourth scenario (WT/DG/Battery) connects WT, DG, and battery for analysis. All the elements from the previous scenarios, namely PV, WT, DG, and battery, are considered in the fifth scenario (PV/WT/DG/Battery).

2.5 Mathematical representation

2.5.1 PV model

PV power output can be calculated by[21]:

P P V = Y P V F P V × G G R E F × [ 1 + K t ( T c T c , S T C ) ]

where, YPV is the PV capacity, FPV is the derating factor of PV, G is the solar radiation (W/m2), GREF is the standard radiation (1,000 W/ m2), TC,STC is the standard ambient temperature (25 °C), Kt is the temperature coefficient (%/°C), and TC is the PV surface temperature.

2.5.2 WT model

The hub height wind speed can be calculated using:

U hub = U anem ln ( Z hub / Z 0 ) ln ( Z anem / Z 0 )

where Uanem is the wind speed at anemometer height (m/s), Zhub and Zanem are the WT height and the anemometer height (m), respectively, and Z0 is the surface roughness length (m).

The power output of a WT can be calculated using:

P W T = P W T , S T ( ρ ρ 0 )

where, PPV is the PV power generated, PWT is the WT power generated, PDG is the DG output, P S o C M I N is the minimum SoC of the battery and PLOAD is the load demand.

2.5.3 Battery model

The lifetime of the batteries, which are affected by the number of times they are charged and discharged, is calculated by the following equation[22]:

R batt = M I N ( N batt x Q lifetime Q thrpt , R batt , f )

where Rbatt is the storage bank lifetime (year), Qlifetime is the lifetime throughput of single storage (kWh), Qthrpt is the annual storage throughput (kWh/year), Nbatt is the number of batteries in the storage bank, and Rbatt,f is storage float life (year).

The battery wear cost refers to the expense associated with cycling energy through the storage system. When the storage characteristics indicate that its lifespan is limited by throughput, the system is presumed to need replacement once its total lifetime throughput is reached. Therefore, with each kWh of energy cycled, the storage system approaches the point of replacement. The battery wear cost is determined by:

C b w = C rep,batt  N batt  x Q lifetime  x η r t

where Crep,batt is the replacement cost of the storage bank (USD), Nbatt is the number of batteries, Qlifetime is the single storage lifetime throughput (kWh), and ƞrt is storage roundtrip efficiency (fractional).

2.5.4 DG model

Hourly fuel consumption and efficiency are two features of any DG that should be considered while developing an HRES. Fuel consumption can be calculated by[23]:

Q ( t ) = a P ( t ) + b P r

where Pr is the rated capacity of the generator, P(t) is the produced power in kW, and Q(t) is the fuel consumption in L/h. The coefficients a and b represent constants for fuel consumption and are approximated as 0.0165 l/h/kW and 0.267 l/h/kW, respectively[24].

The DG efficiency is calculated by:

η G = 3600 x P G ρ D X F D X L H V D

where ρD is the density of DG (kg/l), 𝐹𝐷 is the fuel consumption (l/h), LHVD is the lower heating value of DG (kJ/kg).

2.6 Economic and reliability criteria

2.6.1 Net present cost and cost of electricity

The NPC is a key metric used in economic analysis to assess the cost-effectiveness of different energy systems or projects. The NPC lists the fuel expenses for the DG during the lifespan of the project, in addition to the capital, replacement, and O&M costs. The NPC can be calculated by[25]:

N P C = C t o t CRF ( i , n )

Where Cto is the total cost per year ($/year) and CRF (i,n) is the Capital Recovery Factor, which accounts for the time value of money over the system's lifetime and is calculated using:

C R F = i ( 1 + i ) n ( 1 + i ) n 1

where i is the interest rate and n are the lifetime of the system.

Another important parameter in the analysis of HRES is the COE which stands for the cost per kWh of energy and can be calculated by:

COE = C t o t E t o t

where Etot is the annual consumption of the total electricity (kWh/year). The COE provides a standardized way to compare the costs of different energy projects or technologies, making it easier to evaluate their relative economic efficiency. Lower COE values indicate more cost-effective energy generation.

2.6.2 Reliability

The reliability of an HRES is crucial to ensure uninterrupted operation, as electricity production relies on variable factors such as wind speed and solar radiation throughout the day. As a result, fluctuations in electrical supply also have an impact on the system reliability[26]. Different methods are employed for reliability evaluation, including loss of energy expected, equivalent loss factor, and loss of power supply probability (LPSP). Most hybrid systems employ the LPSP approach, which can be computed by:

L P S P = ( P P V + P W T + P D G + P S o C M I N ) P L O A D

3. Results and Discussions

The findings from the techno-economic study conducted on several situations are presented. A total of five scenarios are examined to determine the best HRES while maintaining reliable power at reasonable prices. The lifespan of the project is set at 25 years, and the lifetimes of PV and WT are considered equivalent to this lifespan across all scenarios, eliminating the need for replacements. The findings of each scenario study are discussed below.

3.1 Scenario 1: PV/battery system

The optimization results indicate that a system with 245 kW of PV capacity paired with 104 battery units as shown in Table 2 is the most cost-effective configuration in this scenario. This setup achieves an NPC of $730,166 and a COE of $0.335/kWh. While this configuration generates 377,929 kWh/year, accounting for 100% of the total renewable energy production, it incurs high costs due to the reliance on a large battery storage system. Since PV energy generation occurs only during daylight hours, the batteries must compensate for energy needs during the night, resulting in substantial capital and replacement costs for the batteries. The monthly average electric generation over one year is depicted in Figure 5.

Figure 5. Monthly electric production for scenario 1. PV: photovoltaic.

Table 2. Optimal system sizes for scenario 1.
Scheme PPV(kW) NWT PDG(kW) NBattery COE ($) NPC ($) LPSP
PV/Battery245--1040.336730,1669.1%

NPC: net present cost; COE: cost of energy; LPSP: loss of power supply probability.

The expenses related to capital, replacement, and O&M are illustrated in Table 3. It is notable that although the capital cost of PV exceeds that of batteries, PV has a significantly longer lifetime compared to batteries, which require replacement costs over the project's lifespan.

Table 3. Prices of system components.
Component Capital ($) Replacement O&M ($)
PV34,425.130.001,224.94
battery7,034.154,821.9318,720
converter1,766.35556.83171.41
system43,225.645,378.7620,116.35

The output energy of the system is depicted in Figure 6. It shows the load demand, PV power output, and SoC throughout the two weeks of January. The results show that the PV energy is sufficient to meet the load while simultaneously charging the battery. During nighttime, when solar radiation is absent, the battery discharges to meet the load and begins recharging on the subsequent day. Since there is no DG in this configuration, the renewable fraction is 100%. However, despite its high renewable fraction, this system may not be cost-effective due to the significant PV energy required to meet both the load demand and battery charging needs simultaneously.

Figure 6. System power output for scenario 1. SoC: state of charge; PV: photovoltaic.

3.2 Scenario 2: WT/Battery

The optimal configuration in this scenario comprises 13 units of WT and 150 units of batteries, as detailed in Table 4. The WT generates a total energy of 417,426 kWh/year. The COE and NPC are 0.48 $ and 1,040,000 $, respectively. The costs are elevated due to the WT’s variable energy production, which is dependent on high wind speeds. When wind speeds are low, the system relies heavily on batteries, which require a substantial investment and incur high replacement costs. This leads to a higher NPC and COE, as the system must be equipped with a larger battery capacity to handle periods of low wind.

Table 4. Optimal system sizes for scenario 2.
Scheme PPV(kW) NWT PDG(kW) NBattery COE ($) NPC ($) LPSP
WT/Battery-13-1500.481,040,0009%

NPC: net present cost; COE: cost of energy; LPSP: loss of power supply probability.

Figure 7 displays the monthly average energy production for this configuration. The power generation reaches its minimum level during May and June. This seasonal dip in energy production highlights the need for robust energy storage solutions or supplementary power sources to ensure a consistent and reliable power supply throughout the year.

Figure 7. Monthly electric production for scenario 2. WT: wind turbines.

Table 5 outlines the costs associated with each component, including capital costs, replacement costs, and operation and maintenance (O&M) costs. Despite the high initial capital cost, the ongoing O&M expenses are also significant, primarily attributed to the numerous moving parts associated with WT requiring continuous maintenance. Since the operational lifespan of the system is set at 25 years, there are no replacement costs associated with the WT.

Table 5. Net prices of system components for scenario 2.
Component Capital ($) Replacement O&M ($)
WT390,0000.00138,772.09
battery108,30067,604.73288,218.96
converter37,613.4211,857.323,650.14
system535,913.4279,462.05430,641.18

Figure 8 illustrates the energy output of the system. Given the variability of wind speed, WT operation may extend for more hours compared to PV, resulting in reduced reliance on battery storage. The SoC of the battery is preserved while the system efficiently satisfies the load demand. However, the system incurs high capital and O&M costs, despite its environmental suitability.

Figure 8. System power output for scenario 2. SoC: state of charge; WT: wind turbines.

3.3 Scenario 3: PV/DG/battery

The system in this scenario combines PV with batteries and a DG. The optimal configuration of this system comprises 176 kW PV, a 10 kW DG, and 80 units of batteries, as detailed in Table 6. With a COE of 0.295 $ and NPC of 644,896 $, the PV contributes 93% of the total energy production, generating 273,176 kWh/year, while the DG contributes 6.9%, producing 20,477 kWh/year. The high costs in this scenario are primarily attributed to the large number of batteries required and the high fuel costs associated with the DG. The combination of these factors results in increased capital and operational expenses, leading to a higher NPC and COE. Monthly average electrical production is depicted in Figure 9, revealing peak generator operation from September to April and minimal operation in June and August, corresponding to high solar radiation levels. Notably, DG usage is unnecessary in July due to abundant solar radiation.

Figure 9. Monthly electric production for scenario 3. PV: photovoltaic; DG: diesel generators.

Table 6. Optimal system sizes for scenario 3.
Schemes PPV(kW) NWT PDG(kW) NBattery COE ($) NPC ($) LPSP
PV/DG/Battery176-10800.295644,8969.6%

NPC: net present cost; COE: cost of energy; LPSP: loss of power supply probability.

Table 7 presents the system components prices over the project's lifetime. Notably, the cost of DG replacement exceeds its initial capital price, indicating multiple replacements during the project's lifespan due to its shorter lifespan relative to the project duration. Despite its low initial cost, the high O&M expenses result from the DG's moving parts and fuel requirements.

Table 7. Prices of system components for scenario 3.
Component Capital ($) Replacement ($) O&M ($)
PV265,616.580.009,451.33
DG5,5007,100.647,913.21
battery57,76056,098.23153,716.78
converter22,8507,203.292,217.45
system351,726.6270,402.15173,298.76

In the presence of DG, Figure 10 shows the load demand, PV output, and SoC. It is evident that during certain hours, PV-generated power falls short of covering the load, resulting in the battery reaching its minimum SoC and causing electricity shortages. With the addition of the DG to the system, it kicks in to meet the load when both the PV and the battery fail to supply electricity. Thus, the DG enhances system reliability by mitigating electricity shortages.

Figure 10. System power output for scenario 3. SoC: state of charge; PV: photovoltaic; DG: diesel generators.

3.4 Scenario 4: WT/DG/battery

Similar to Scenario 3, this scenario also includes a combination of PV, batteries, and DG.

The optimal system configuration includes 10 units of WT, a 10 kW DG, and 94 battery units, as outlined in Table 8. The WTs are the primary source of electricity, producing around 321,098 kWh per year, while the DG contributes 22,069 kWh per year. This represents 93% and 7% of the total electricity production, respectively. The COE is calculated at 0.386$, and the NPC is 835,713$. The high costs are due to the significant capital and replacement costs of the batteries, in addition to the fuel costs for the DG. The reliance on the DG further elevates operational costs, contributing to a higher NPC and COE.

Table 8. Optimal system sizes for scenario 4.
Schemes PPV(kW) NWT PDG(kW) NBattery COE ($) NPC ($) LPSP
WT/DG/Battery-1010940.3868357139.9%

NPC: net present cost; COE: cost of energy; LPSP: loss of power supply probability.

Figure 11 displays the average monthly electricity generation, with the lowest levels occurring in May and June. This highlights potential seasonal challenges and underscores the importance of optimizing energy storage and management strategies during these months to ensure a consistent power supply.

Figure 11. Monthly electric production for scenario 4. WT: wind turbines; DG: diesel generators.

Table 9 details the costs associated with capital, replacement, and O&M for this scenario. Systems utilizing WT and DG incur significant O&M costs due to fuel usage and the presence of moving components. Notably, the replacement cost of the DG surpasses that of the PV/DG/battery scenario, indicating more extensive operational hours for the DG.

Table 9. Prices of system components for scenario 4.
Component Capital ($) Replacement ($) O&M ($)
WT3000000.00106747.76
DG55007905.888954
battery6859042816.33182538.67
converter41237.8612999.894001.86
system415327.8663722.11302242.30

The output of the system for energy generation in this scenario is demonstrated in Figure 12. It reveals that the DG operates for fewer hours compared to the PV scenario, primarily between 200 and 250 hours. This limited operation is due to the battery being maintained above its SoC throughout the 14 days, indicating that the WT energy efficiently covers the load, with the DG required only to enhance system reliability for brief intervals.

Figure 12. System power output for scenario 4. SoC: state of charge; WT: wind turbines; DG: diesel generators.

3.5 Scenario 5: PV/WT/DG/battery

The PV/WT/DG/Battery configuration in this scenario is identified as the most cost-effective and reliable system, comprising 107 kW PV, 3 units of WT, 10 kW DG, and 45 units of batteries, as detailed in Table 10. PV accounts for 57.2% of the total energy generated, producing 165,068 kWh/year, while WT contributes 33.4% with 96,329 kWh/year, and DG provides 9.39% with 27,100 kWh/year. The high output from PV and WT effectively meets the majority of the load demand. The COE was determined to be 0.257 $ and the NPC is 568,431$. The low NPC and COE are attributed to the high wind speeds and solar radiation at the study site. The combined energy production from PV and WT reduces the dependency on the DG, lowering fuel and maintenance costs. Additionally, the battery storage system ensures a stable power supply, further enhancing system reliability. This configuration benefits from fewer batteries compared to other scenarios, as the PV and WT provide a more consistent energy supply, making it the most economical and environmentally friendly option.

Table 10. Optimal system sizes for scenario 5.
Scheme PPV(kW) NWT PDG(kW) NBattery COE ($) NPC ($) LPSP
PV/WT/DG/Battery107310450.257568,4319%

NPC: net present cost; COE: cost of energy; LPSP: loss of power supply probability.

This detailed analysis underscores the advantages of Scenario 5, where the synergy between PV, WT, and battery storage leads to significant cost savings and operational efficiency, compared to other configurations that rely more heavily on DG or have higher battery costs.

The monthly energy generated by the HRES over one year is illustrated in Figure 13. The power output from PV and WT is influenced by solar radiation and wind speed, with both technologies playing a significant role in meeting the load demand. This indicates that the meteorological conditions of the selected location are conducive to the installation of renewable energy systems. Furthermore, the figure illustrates that PV power generation exceeds that of WT, primarily due to the greater PV capacity within the system.

Figure 13. Monthly electric production for scenario 5. PV: photovoltaic; WT: wind turbines; DG: diesel generators.

The cost breakdown of system components is shown in Table 11. By incorporating WT and PV energy into the system, the need for batteries has been significantly reduced, resulting in improvements to both the COE and NPC. This approach demonstrates considerable cost savings and efficiency enhancements.

Table 11. Prices of system components for scenario 5.
Component Capital ($) Replacement ($) O&M ($)
PV160,5000.005,711.01
WT90,0000.0032,024.33
DG5,50010,007.2210,760.17
battery32,49035,113.8586,465.69
converter19,2506,068.401,868.09
system307,74051,189.48136,829.28

To observe how renewable sources affect battery charging, Figure 14 illustrates the output of renewable sources and the SoC over 10 days in January. It shows that DG functions when the battery reaches its minimum SoC and additional power is required from renewable sources to maintain power levels. Thus, the proposed PV/WT/DG /Battery HRES effectively meets the electrical load demand.

Figure 14. System power output for scenario 5. SoC: state of charge; PV: photovoltaic; WT: wind turbines; DG: diesel generators.

3.6 Optimization result

In each scenario, the system components used have consistent properties and operate with identical meteorological data. All five scenarios are capable of supplying the load demand without an electrical shortfall, meaning that all the systems are technically reliable and balanced, and no one system can be considered the absolute best. However, with the primary goal of designing a reliable system at minimal cost, the emphasis is on identifying a solution capable of efficiently meeting load demands with low COE and NPC. As demonstrated in Figure 15, in scenario 5, the PV/WT/DG/Battery HRES exhibits the lowest COE and NPC, attributed to the high wind speed and solar radiation at the proposed site.

Figure 15. NPC and COE of all five scenarios. NPC: net present cost; COE: cost of energy.

In addition to cost metrics, the technical performance of each scenario was evaluated based on energy production reliability and capacity to meet load demands. Scenario 5 outperformed others by ensuring a consistent energy supply throughout the year, with minimal reliance on the DG. The battery storage system in scenario 5 maintained a high SoC, ensuring grid stability and extending the battery lifespan. Comparatively, scenarios with higher DG dependence showed increased fuel consumption and maintenance requirements, highlighting the superior synergy of techno-economic factors in scenario 5. The HRES identified in the fifth scenario is deemed optimal for covering the load to achieve ZEB requirements for approximately 30 households in a rural remote area near Ankara.

3.7 Sensitivity analysis on system costs

Sensitivity analysis investigates how changes in prices and specifications of certain system components impact the overall system cost. This study specifically examines variations in fuel prices and fluctuations in the capital costs of PV and WT components.

3.7.1 Impact of fuel price

The fuel price for the DG is subject to fluctuation due to global market conditions. To assess its impact, nine different prices ranging from 0.75 $ to 1.55 $ are considered in this study. The selected fuel price range of $0.75 to $1.55 is determined through a detailed analysis of historical data and market trends. Specifically, $0.75 per liter reflects the fuel price in Turkey for 2021, while $1.55 per liter represents the projected price for 2024. This range is chosen to encompass both historical price levels and anticipated future increases. It allows for a comprehensive sensitivity analysis by accounting for potential fluctuations due to market volatility, geopolitical factors, and inflationary pressures. By incorporating this broad range, our analysis aims to better evaluate the system's performance under varying future conditions, thereby providing a more robust assessment of its economic viability. The results, illustrated in Figure 16, indicate that both the COE and NPC of the system rise with an increase in fuel price. Specifically, doubling the fuel price leads to a roughly 14% increase in COE and a 13% increase in NPC.

Figure 16. Effect of variation of fuel price on the system cost. NPC: net present cost; COE: cost of energy.

3.7.2 Impact of PV and WT costs

The costs of renewable energy generation have significantly declined in recent years due to technological advancements, economies of scale, improved competitiveness in the supply chain, and increased expertise among developers. To assess the impact of declining prices on system costs, the capital costs of PV and WT are scaled down by factors of 0.6, 0.7, 0.8, and 0.9 in this study. Table 12 illustrates that when the capital costs are reduced by a factor of 0.6, the COE and NPC decrease by approximately 16% and 18%, respectively.

Table 12. The COE and NPC of the system with 5 multipliers.
System Capital multiplier COE ($) NPC ($)
PV/WT/DG/Battery10.25568,431
0.90.246543,381
0.80.234518,331
0.70.223493,281
0.60.212468,231

NPC: net present cost; COE: cost of energy.

3.8 Comparative analysis with existing studies

To contextualize our findings, we compared them with results from various studies, acknowledging that differences in load characteristics, weather conditions, and design constraints across regions can make direct comparisons challenging. However, COE remains a crucial indicator for evaluating the economic feasibility of renewable energy systems. Our study identifies the PV/WT/DG/Battery configuration as optimal, with a COE of $0.257/kWh, which is competitive compared to other systems reported in the literature. For context, Table 13 summarizes the COE of various systems from different studies.

Table 13. Comparison of the designed systems with existing literature.
Region of study System Configurations System Type Optimization Technique COE ($/kWh) Ref.
Kutubdia Island, BangladeshPV/Wind/Diesel/BatteryOff-gridHOMER0.236 [27]
Rajshahi, BangladeshPV/Diesel/BatteryOff-gridHOMER0.28 [28]
GhanaPV/Diesel/BatteryOff-gridHOMER0.435 [29]
Shyamnagar, IndiaPV/Battery/Diesel/BiogasOff-gridHOMER0.28 [30]
Pasni, Balochistan, PakistanPV/Electrolyzer/Wind/Hydrogen Tank/BatteryOff-gridHOMER0.3 [31]
Monaragala, Sri LankaPV/Diesel Generator/Wind/BatteryOff-gridHOMER0.3 [32]

COE:cost of energy; HOMER: Hybrid Optimization Model for Multiple Energy Resources.

For instance, the PV/WT/DG/Battery system in our study achieved a lower COE compared to the systems in Ghana and Monaragala, Sri Lanka, which had COE values of $0.435/kWh and $0.3/kWh, respectively. Our study’s innovation lies in its use of localized meteorological data to tailor the HRES for the Ankara region, offering a solution that is specifically optimized for local conditions. This approach contrasts with more generalized studies, providing a more accurate representation of feasibility for the area.

Despite these strengths, our study has limitations. It relies on current fuel price projections and capital cost estimates, which may fluctuate over time. Additionally, the study does not incorporate other potential renewable sources such as biomass or hydro, which could enhance system performance. Future research should address these limitations by integrating additional renewable technologies and performing more extensive sensitivity analyses to improve the robustness and adaptability of the energy systems.

4. Conclusion

The study tackles the pressing challenges of escalating energy demand, diminishing fossil fuel reservoirs, and environmental repercussions by advocating off-grid HRES as a viable remedy. Utilizing PV, WT, DG, and battery technologies, the research endeavors to fulfill the electricity requirements of 30 households in a remote area near Ankara. Employing HOMER software, five system configurations underwent comparison, revealing the optimal setup to be a blend of PV, WT, DG, and battery storage. The chosen HRES configuration includes 107 kW PV, 3 units of 10 kW WT, a 10 kW DG, and 45 units with 7.15 kWh battery units, yielding 165,068 kWh/year from PV, 96,329 kWh/year from WT, and 27,100 kWh/year from DG. The NPC and COE are calculated as 568,431 $ and 0.257 $, respectively. Sensitivity analysis reveals that COE and NPC escalate with fuel price increases. Moreover, reducing the capital costs for WT and PV by 40% could potentially decrease the COE by approximately 16% and the NPC by 18%. These results are likely to benefit government agencies, researchers, and engineers working in the fields of renewable energy and standalone system development. Future studies are advised to broaden renewable energy integration nationwide, explore supplementary renewable sources, and conduct extensive sensitivity analyses to refine system efficiency and accuracy.

Author contribution

Alhayali OJ: Methodology, software, investigation, data curation, writing-original draft, visualization.

Mehrtash M: Conceptualization, supervision, formal analysis, writing-original draft, reviewing and editing.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Not applicable.

Funding information

Not applicable.

Copyright

© The Author(s) 2024.

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Alhayali OJ, Mehrtash M. Techno-economic optimization of hybrid energy systems for zero energy buildings in remote communities: a case study from Turkey. J Build Des Environ. 2024;2:36334. https://doi.org/10.70401/jbde.2024.0002