Optimization of Grid-connected Pv Systems: Balancing Economics and Environmental Sustainability

doi:10.21203/rs.3.rs-4886066/v1

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Optimization of Grid-connected Pv Systems: Balancing Economics and Environmental Sustainability

https://doi.org/10.21203/rs.3.rs-4886066/v1

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The increasing harmful gas emissions from industries and institutions pose a significant risk of extreme global warming and climate change, while unreliable power remains a major challenge in Nigeria requiring urgent attention. To address these issues, this study presents a cost-effective and optimized design for a grid-connected PV system at the Department of Electrical Engineering, Ahmadu Bello University Zaria, Kaduna, Nigeria. Utilizing HOMER for optimization, the research examines three distinct optimization scenarios, each offering different insights into energy management approaches. The First scenario was designed consisting only grid, relying solely on grid connectivity, with high annual costs and vulnerability to price volatility and environmental degradation, particularly from coal power plants, resulting in an annual energy cost of $2,838 with no renewable energy contribution. The second scenario was designed consisting of grid and solar PV energy sources integrated resulting in lower grid dependence and improved cost-effectiveness though not fully optimized by dint of economic and ecological issues at play, with the annual energy cost being $2,714 with 1867KW h supplied by the solar PV system. With initial higher capital expenditures offset by lower energy purchasing costs and surplus energy sales, this third scenario option turns out to be economically advantageous in the long run. With 29684KW provided by solar PV and $521 sold back to the grid, the yearly energy cost comes to $1,043. The system is expected to recover the initial installation cost in two years, saving about $20,000 over the course of the system's 20-year lifespan. These findings support solar PV domination as a viable way to handle energy concerns in the examined setting and highlight the importance of taking economic and environmental factors into account when designing energy systems.

Electrical Engineering

Optimization

Renewable energy sources

Grid

Environmental impacts

Cost effectiveness

Any country's ability to advance is largely dependent on its availability to electricity [1]. Nigeria, a developing nation, has long worked to improve the standard of living for its people by guaranteeing consistent access to electricity in both urban and rural areas. With its large population and ambitions to be among the world's top economies, Nigeria needs to make significant changes to its electrical infrastructure in order to draw in industrial establishment investments and grow the country's economy. As was noted in [2] 80% of Nigeria's rural communities are considered to be impoverished. This is mostly because the electrical sector has not advanced enough, which is necessary to allow rural entrepreneurs to run their small businesses.

The majority of Nigeria's energy sources so far have been derived from thermal, hydroelectric, and fossil fuel sources. This has made it important to investigate renewable energy options to generate power that is cleaner and more reliable [3–5]. Since it is not practical to connect rural towns to the national grid due to financial constraints, employing off-grid renewable energy sources is becoming more and more important as a way to address these places' electricity shortages. As a result, a lot of research has been done to look at these renewable energy sources' (RES) potential for producing independent and hybrid energy while taking into consideration their unique availability in various locations [6–9]. Many rural settlements in developing nations—particularly in Africa—remain disconnected from the national grid because of the substantial cost barriers involved [10, 11]. For cooking, heating, and lighting, the inhabitants resort to alternative energy sources including wood and diesel generators [12, 13]. However, the use of fossil fuels contributes to environmental contamination, exacerbating concerns about global warming. The need for innovative solutions in the power sector has increased due to worries about energy security, environmental sustainability, and the world's rising energy consumption. Grid-connected hybrid systems, which integrate grid electricity with renewable energy sources, have become a feasible remedy for these issues [14]. These systems offer better reliability, efficiency, and cost-effectiveness than standalone renewable energy installations [15]. In this context, there has been a lot of interest in developing and optimizing grid-connected hybrid systems using sophisticated optimization techniques such as the Hybrid Optimization Model for Multiple Energy Resources (HOMER).

Since the restoration of democracy in Nigeria in 1999, numerous initiatives have been undertaken to improve the socioeconomic activity of the country's rural inhabitants by increasing their access to electricity [16]. Unfortunately, significant progress has been hampered by a variety of problems, including a lack of political will and corruption in the electric power sector. Nigeria has a great opportunity to harness its plentiful renewable energy resources—such as wind, solar, biomass, and hydropower—to address its electrical issues. The country receives 5.25 KWh/m2/day of yearly average solar radiation due to its estimated 6.25 hours of sunshine each day [18, 19]. This shows the range of solar radiation, which is between 3.5 and 7.0 kWh/m³/day, across the country, from the northern borders to the coastal regions [20]. Nigeria's potential energy production from sunshine alone is estimated to be over 4000 times greater than its crude oil production and 13000 times greater than its natural gas production [17]. Several studies have recommended substituting RES (Renewable Energy Sources) for electricity in Nigeria's rural electrification projects [21–24]. Both standalone RES [24–26] and hybrid RES [22, 27–29] are supported by these studies. Standalone renewable energy sources (RES) solely employ one energy source to generate power, as opposed to hybrid RES, which combines many RES for energy production. Increasingly, the integration of many Renewable Energy Sources (RESs) is being used to achieve an optimum energy system.

Combined with the inclusion of a diesel-powered generator, this tactic increases overall system efficiency, particularly in terms of mitigating the effects of irregular weather patterns. Many designs of hybrid renewable energy systems have been thoroughly studied and implemented. Intricate combinations such as sun-wind-battery-diesel [32–34] and the integration of wind, solar, and storage batteries [29–31] are examples of these setups. These designs have undergone a great deal of testing and have consistently proven to be viable solutions in the specific regions where they have been used. The combination of advanced control strategies, forecasting methodologies, and energy management systems enhances grid-connected hybrid systems. HOMER assists stakeholders in making educated decisions and promotes the construction of sustainable energy infrastructure by providing information on optimal designs, component sizes, and economic viability. The transition to cleaner energy can be accelerated by using HOMER to optimize grid-connected hybrid vehicles [35–36]. Research all emphasize HOMER's importance in configuration optimization and financially sensible system design.

This study significantly advances the examination of how to best balance economic and environmental considerations when implementing grid-connected hybrid photovoltaic systems to electrify Zaria Local Government in Kaduna State, Nigeria. Although the viability of hybrid renewable energy systems (RES) in Nigeria has been investigated in the past, Zaria has mostly gone unnoticed. The economic viability of renewable energy sources (RES) has received most of the attention in the literature, with technical and environmental factors receiving less attention. To close this gap, this study has integrated optimization analyses of grid-connected hybrid PV systems that take into account technological, economic, and environmental factors. Through the integration of technological, environmental, and economic optimization analyses of grid-connected hybrid PV systems specifically for Zaria, this study bridged the gap by utilizing the city's abundant solar resources for electrification—not just for the university but also for the larger rural community. For feasibility studies and optimization, meteorological data was downloaded from NASA's website and used with the Hybrid Optimization Model for Electric Renewable (HOMER) Pro program. For this study, HOMER Pro makes it easier to identify the best RES settings.

This paper explores the integration of technological, environmental, and economic analyses for grid-connected hybrid photovoltaic system optimization. In particular, Zaria is the intended case study location, and it focuses on using a grid-connected hybrid renewable energy system (HRES) to provide electricity in a rural Nigerian community. The grid, solar PV, and batteries are all optimization elements that are taken into account. System analysis was performed using HOMER optimization software, which allowed designers to compare the technical, financial, and environmental aspects of different configurations and designs. Power systems and harmonic analyzers were also used to test power electronics, inverters, motors and drives, lighting, home appliances, office equipment, power supplies, and industrial machinery, among other various electrical power-related applications. Furthermore, the power system analyzer played a crucial role in gathering load data for the study location.

Methodologies employed in this study are;

Homer software

Load profile

Research Location

Model input parameter

Grid Connected Hybrid System Modelling

2.1. Homer software

The HOMER Pro Microgrid Analysis Tool Version 3.11.2 (Pro Edition) software was employed in this study to design, sizing and maximization of the Hybrid Renewable Energy System (HRES) which gives a wide array for decision analysis. This includes weighing financial risks, environmental policies, renewable energy resources and what types of renewables are most abundant in the selected region. Daily load demand profile with seasonal variations, local meteorological data and site-specific conditions required for effective design and planning of HRES must be known also as the current power production capabilities of the system. HOMER Software uses simulation, optimization and sensitivity analysis to determine the least cost effective design for Hybrid Renewable Energy System. The approach is used to evaluate parameters like Net Present Cost (NPC), Levelized Cost of Energy (LCOE), and initial and maintenance cost. HOMER also performs a techno-economic analysis to identify the most affordable HRES configuration. Sensitivity analysis is used to determine how financially feasible the system is with changing operating parameters [39]. For this research, the HOMER software is used to develop the most suitable HOMER model to the load profile of the chosen project location. This description can be divided into four procedures: a load profile of the location of the area is analyzed, and the solar profile and wind profile data is collected. Afterward, the system is built by optimizing the component and taking into consideration size, and cost of components in building the system. Finally, an optimization is carried out to determine how cost-effective the HRES model quoted will be. Figure 1 shows the method that the HOMER model in this research follows.

2.2. Load profile

A power system analyser attached to the Distribution board is shown in Fig. 1, indicating that energy audit processes are in progress. With this configuration, trends of energy use within the study site was measured and analysed.

The research site's daily load profile, as shown in Fig. 2, shows that, over the course of a year, energy consumption averaged 92.73 kWh per day, with a peak demand of 18.06 kW spread throughout the day. In order to properly satisfy the energy demands, the hybrid renewable energy system must be designed and optimized with this load profile in mind.

2.3. Research Location

Furthermore, Fig. 3 shows the study site's coordinates, which are (110 9.6'N, 70 38.3'E) and indicate that it is located in Ahmadu Bello University Zaria's Electrical Engineering building. As shown in Table 1, these coordinates are crucial input for calculating solar radiation levels using information from NASA's surface meteorology and solar energy database.

Table 1

Solar radiation data.
Month	Solar irradiance (kWh/m2/day)	Solar irradiance (kWh/m2/day)	Solar irradiance (kWh/m2/day)	Solar irradiance (kWh/m2/day)	Average Daily radiation (kWh/m2/day)
Month	2020	2021	2022	2023	Average of the years
January	5.50	5.60	5.55	5.65	5.575
February	5.9	6.1	6.0	6.0	5.999
March	6.3	6.4	6.35	6.35	6.348
April	6.45	6.5	6.4	6.55	6.474
May	6.4	6.5	6.45	6.52	6.455
June	6.0	6.1	6.05	6.2	6.086
July	5.3	5.4	5.35	5.36	5.352
August	5.1	5.2	5.15	5.16	5.154
September	5.75	5.9	5.8	5.8	5.812
October	6.2	6.3	6.3	6.35	6.285
November	6.1	6.2	6.05	6.2	6.13
December	5.75	5.8	5.8	5.85	5.801

The monthly sun irradiance data for 2020–2023 is shown in Table 1, where average daily radiation levels show a strong potential for solar energy utilization at Ahmadu Bello University Zaria's Department of Electrical Engineering. The data indicates that solar irradiance is constant over the course of the year, peaking in April at 6.474 kWh/m²/day and falling in August to 5.154 kWh/m²/day on average. This implies that a solar photovoltaic system may be used efficiently all year round.

2.4. Model 1nput Parameter

The components are chosen from HOMER to perform the simulations shows that grid connected with the PV array, load, converter and battery. For economic analysis the following values are used:

2.4.1. PV array:

The simulation makes use of a Generic flat plate PV array, which has a 20-year lifespan and an efficiency of roughly 80%. This high efficiency, which is good for flat plate PV panels and implies strong performance, means that 80% of the sunlight collected by the PV array is transformed into electrical energy. The PV array's 20-year lifespan indicates that it will operate efficiently for an extended length of time, decreasing the need for replacements and maintenance disruptions, and improving the system's economic viability. In terms of economics, the PV array's extended lifespan and high efficiency, which primarily generate electricity and lessen reliance on grid power, contribute to a reduced cost per kWh during the system's lifetime, even though its initial investment cost is substantial.

2.4.2. Storage:

A Generic 100kWh li-on battery with an 80% efficiency and a ten-year lifespan serves as the storage component as adopted in this study. It is essential to maintain an efficient and cost-effective storage system so that 80% of the energy stored can be recovered, as indicated by the 80% round-trip efficiency. Given lithium-ion technology, the battery's 10-year lifespan is feasible; nonetheless, it will eventually need to be replaced, raising the total cost. Although the storage system is more expensive initially and needs to be replaced every ten years, it makes efficient energy management possible by storing excess energy produced by the PV array for use during periods of low solar generation. This maximizes resource utilization and lessens dependency on the grid.

2.4.3. Converter:

In the proposed design, the system converter has a 15-year lifespan and a 95% efficiency. The PV array and battery create DC, and there is only 5% energy loss during the conversion to AC, which is used by the grid and loads. This high efficiency guarantees that the maximum amount of generated and stored energy is accessible for use. Because replacements will be needed less frequently over the project's 15-year lifespan, the economic analysis will benefit from lower replacement costs. Economically speaking, the converter's extended lifespan further improves the system's total economic efficiency by lowering maintenance and replacement costs. Its high efficiency also ensures efficient exploitation of generated and stored energy.

2.4.4. Grid:

The grid performs two functions in the simulation: it absorbs surplus power and acts as a backup power source. In instances where the power generated by renewable energy sources (RES) such as photovoltaic arrays is insufficient to fulfill load demand, the grid steps in to ensure a steady supply of power. On the other hand, if the PV array produces more electricity than is immediately required, it can be absorbed by the grid and stored or sold back to the utility, contingent on grid laws. Economically speaking, using the grid as a backup reduces the danger of power outages, provides dependability and continuous power supply, and has the potential to earn cash by selling excess power back to the grid, which can offset certain operating costs. Increased dependability and the possibility of getting returns from surplus power sales typically outweigh any costs associated with connecting to the grid, including connection fees and tariffs.

Table 2

Components specification
Components	Details	Efficiency (%)	Lifetime (year)	Economic Analysis
PV Array	Generic flat plate PV	80	20	Long lifespan, high efficiency, and high initial cost all contribute to lower replacement frequency and lower cost per kWh.
Storage	Generic 100kWh Li-on	80	10	Effective energy management lowers reliance on the grid by lowering initial investment and recurring replacement costs.
Converter	Generic system converter	95	15	Long lifespans save replacement costs, limit energy loss, and improve economic efficiency.
Grid	Backup power and excess power absorber			Provides a steady supply of electricity, the possibility to make money by selling extra energy, and expenses such as tariffs and connection fees.

2.5. Grid Connected Hybrid System Modelling

The hybrid system examined in this study comprises grid connection, solar PV panels, batteries and converter. Modeling and optimizing the system's energy were performed using HOMER software. The solar PV system, batteries, converter and grid were all modeled and optimized. Figure 2 illustrates the hybrid design created using HOMER software.

2.5.1. Solar PV modelling:

In a solar setup, power is produced by a series or parallel arrangement of interconnected solar panels. The amount of power generated by solar panels is measured using Eq. (1) [37].

$$\:{P}_{pv-out}={P}_{pv-rated}\times\:\left(\frac{G}{{G}_{ref}}\right)\times\:\left[1+\alpha\:\left({T}_{C}-{T}_{ref}\right)\right]\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$

The rated power at a reference situation is represented by $\:{P}_{pv-rated}$ in this equation, whereas the output power of the photovoltaic (PV) cells is written as $\:{P}_{pv-out}$. The solar radiation at a standard temperature is denoted as $\:{G}_{ref}$ (where Gref = 1000 W/m2), and the solar radiation is represented as $\:G$ (measured in W/m2). The temperature of the cell under standard conditions is indicated by Tref (25°C). The ambient temperature is denoted by Tc, while the temperature coefficient is expressed as α.

2.5.2. Battery Modelling:

The battery is a part of the system design that stores energy in case the weather makes it impossible for renewable energy sources (RES) to generate power. The battery's stored energy powers the load when solar irradiance is not enough to power it, preventing the need for backup generators throughout the night. Numerous factors, such as battery capacity, rate of charge and discharge, and state of charge (SOC), affect this utilization. The formula for figuring out the battery storage capacity is given by Eq. (2) [38].

$$\:{\:\:\:\:\:\:\:\:\:\:\:\:\:C}_{wh}=\frac{{E}_{L}\times\:AD}{{\eta\:}_{inv}\times\:{\eta\:}_{batt}\times\:DOC}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$

Where $\:{E}_{L}$ stands for daily average daily energy, $\:AD$ signifies the numbers of days of autonomy, $\:DOC$ represents depth of charge, $\:{\eta\:}_{inv}$ represents inverter efficiency, $\:{\eta\:}_{batt}$ is the battery efficiency.

2.5.3. Grid connected modelling:

The relationship between the power generated by the renewable energy system and the electricity supplied by the grid is essentially illustrated by the grid equation in the HOMER program. While there isn't a single, exact equation that defines grid modeling in HOMER, Eq. (3) provides a straightforward representation of the grid interaction:

$$\:{P}_{grid}={P}_{demand}-{P}_{RES}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$

Where:

Power taken from (or supplied to) the grid is referred to as the $\:{P}_{grid}$.

$\:{P}_{demand}$ is the total amount of electricity that the system needs to run.

The electricity produced by the renewable energy system is represented as $\:{P}_{RES}$ .

Equation (3) shows how the quantity of electricity generated by renewable energy sources and the total amount of electricity required by the system interact, influencing how much power is exported or taken from the grid. The extra electricity generated by the renewable energy system can be sent to the grid. On the other hand, more power can be taken from the grid to meet the demand if it exceeds the renewable supply.

The optimization utilizing HOMER software produced three different configurations, each having advantages in terms of economy, technology, and the environment. The outcomes of this optimization are shown in this part. The benefits and downsides of each design were thoroughly discussed and examined, taking into account aspects like environmental impact, system reliability, and cost-effectiveness. Following a thorough analysis, the design that performed best overall was determined by looking at all of these factors combined.

3.1. Results of the First Scenario

As depicted in Table 3, the first optimization result was grid only connected without contribution from any renewable energy source. While this configuration has minimal capital cost as the electricity is just purchased from the grid, it suffers a numerous draw back.

Table 3

Grid connected to the load without renewable source
Month	Energy consumed(kW)	Energy Purchased (kW)	Energy charge($)
January	3,276	3,276	279
February	3,044	3,044	259
March	3,593	3,593	305
April	3,633	3,633	310
May	3,572	3,572	304
June	3,388	3,388	288
July	3,016	3016	256
August	2,559	2,560	213
September	2,476	2,476	210
October	2,666	2,666	227
November	1,075	1,075	92
December	1,110	1,110	95
Annual	33408	33408	2,838

The data displays a consistent pattern of energy usage, with monthly consumption ranging from 1,075 kW in November to 3,633 kW in April, totaling 33,408 kW annually. Similarly, monthly energy purchases from the grid mirror these consumption figures, totaling 33,408 kW annually. The associated energy charges for purchases vary from $92 in November to $310 in April, accumulating to an annual charge of $2,838. This result highlights a consistent dependence on grid energy to fulfill the load requirements throughout the year, emphasizing the significance of renewable energy integration to potentially mitigate reliance on conduction.

3.2. Results of the Second Scenario

The outcome of the second scenario emphasizes how, to achieve optimal performance, the solar PV system must be linked with the grid. Due to the costs associated with the solar PV system, battery, and inverter, this arrangement may have higher initial costs, but it offers substantial long-term advantages. The operational parameters of the solar PV system and converter in this arrangement are displayed in Table 4. It demonstrates that 6,134 hours a year are spent by the solar PV system. During this period, 2,428 kWh of energy are produced annually by the solar PV panels that go via the system converter. After that, 2,307 kWh of energy are delivered to the load annually by the converter. However, the system experiences 121 kWh of losses.

Table 4

Annual energy generation by the PV System
Quantity	Value	Units
Hours of Operation	6,134	hrs/yr
Energy Out	2,307	kWh/yr
Energy In	2,428	kWh/yr
Losses	121	kWh/yr

Table 4. Shows PV/battery/grid connected hybrid system for the second optimization result throughout the year.

Table 5

PV/battery/grid connected hybrid system for the second optimization result
Month	Energy consumed(kW)	PV power generated (kW)	Energy Purchased (kW)	Energy sold(kW)	Energy charge($)	Energy sold ($)
January	3,276	106	3,244	73.6	278	6
February	3,044	123	2,954	33.7	251	2.9
March	3,593	166	3,464	36.4	294	3
April	3,633	199	3,457	26.8	293	2.1
May	3,572	212	3,383	23.1	288	1.8
June	3,388	207	3,197	15.8	272	1.3
July	3,016	202	2,829	14.7	240	1.2
August	2,559	158	2,417	15.7	205	1.3
September	2,476	154	2,347	24.6	199	2.0
October	2,666	139	2,552	25.1	217	2.0
November	1,075	103	1,025	53.0	87	4.2
December	1,110	98	1,063	50.5	90	4.0
Annual	33408	1867	31,932	393	2,714	31.8

The performance metrics of a PV/battery/grid connected hybrid system for the second scenario result over a twelve-month period are shown in Table 5, Figs. 5 and 6. It shows the energy dynamics of the system and shows that the monthly energy consumption ranges from 1,075 kW to 3,633 kW, with an annual total of 33,408 kW. The PV system exhibits variable power generation, with 1,867 kW of annual output and monthly outputs varying from 98 kW to 212 kW. The monthly energy acquired from the grid varies from 1,025 kW to 3,464 kW, for an annual total of 31,932 kW. On the other hand, monthly energy sold back to the grid varies from 15.8 kW to 73.6 kW, totalling 393 kW annually.

Table 6

Energy produced annually by the PV and the grid purchase.
Component	Production (kWh/yr)	Percent
Generic flat plate PV	1867	5.52
Grid purchase	31,932	94.48
Total	33,799	100

The distribution of annual energy production between grid purchases and a standard flat plate photovoltaic (PV) system is shown in Table 6 and Fig. 5. The annual energy production from the PV system is 1,867 kWh, or around 5.52% of total energy production. The remaining 31,932 kWh come from grid purchases, accounting for 94.48% of the total energy production. When these sources are combined, 33,799 kWh of energy are produced annually. In comparison to the renewable energy produced by the PV system, this breakdown shows a considerable dependency on grid purchases. This emphasizes the necessity for additional integration of renewable sources to lessen reliance on conventional grid electricity.

Table 7

Energy consumed by the AC primary load and grid energy sales.
Component	Consumption (kWh/yr)	Percent
AC Primary Load	33408	98.84
DC Primary Load	0	0
Grid sales Total	393 33,801	1.16 100

As displayed in Fig. 7 and Table 7. With an annual total of 33,408 kWh, or roughly 98.84% of the total consumption, the AC primary load is the source of the majority of energy use. The DC main load uses no energy at all. 393 kWh, or roughly 1.16% of the total energy consumption, are sold back to the grid by the system. The overall annual energy usage of the system is 33,801 kWh.

Table 8

Annualized cost of the energy of the second optimization result
	Capital ($)	Operating ($)	Replacement ($)	Total ($)
Generic flat plate PV	224	35	200	459
Generic Battery 100kWh	250	10	215	485
System Converter	153	15	153	321

The annualized expenses of the generic flat-plate PV, grid, generic 100 kWh battery, and system converter are shown in Table 8. The annualized total cost of the generic flat plate PV is $459, which includes capital, operational, and replacement costs. The grid component has no initial capital costs, however, it does have substantial yearly operational costs of $2,714 to pay. The annualized cost of the common 100 kWh battery is $485, which includes capital, operating, and replacement expenditures. Finally, the system converter has an annualized cost of $321 which includes operating, replacement, and capital costs.

3.3. Results of the Third Scenario

The third scenario result includes PV, battery, and grid-connected hybrid, just like the second scenario result. However, in this result, a significant portion of the energy delivered to the load comes from the PV system. This configuration has a larger initial cost than the other two, but over time, the difference is made up for and a profit is made. Table 9 provides an overview of the operational parameters of the converter and the solar photovoltaic (PV) system. It demonstrates that the solar PV system operates for 6,134 hours a year, and that during that time, 29,982 kWh of energy are generated by the system and pass through the system converter. It also shows that the energy that is delivered to the load from the converter is 29,682 kWh annually, with 298 kWh of losses occurring within the system.

Table 9

Annual energy generation by the PV System
Quantity	Value	Units
Hours of Operation	6,134	hrs/yr
Energy Out	29,684	kWh/yr
Energy In	29,982	kWh/yr
Losses	298	kWh/yr

Table 10

PV/battery/grid connected hybrid system for the third optimization result
Month	Energy consumed(kW)	PV power generated (kW)	Energy Purchased (kW)	Energy sold(kW)	Energy charge($)	Energy sold ($)
January	3,276	2,011	1640	375	139	30
February	3,044	2,593	794	343	67	28
March	3,593	2,815	1142	364	294	29
April	3,633	3,003	1157	527	98	42
May	3,572	3,047	1147	622	97	50
June	3,388	3,029	1073	714	91	57
July	3,016	3,023	650	659	55	53
August	2,559	2,361	730	543	62	45
September	2,476	2,110	753	445	64	36
October	2,666	2,002	896	367	76	30
November	1,075	1,895	0	804	0	65
December	1,110	1,795	0	703	0	56
Annual	33,408	29,684	9,982	6,466	1,043	521

The performance metrics of a PV/battery/grid connected hybrid system for the third optimization outcome during a twelve-month period are shown in Table 10, Figs. 8 and 9. With a monthly energy consumption range from 1,075 kW to 3,633 kW and an annual total of 33,408 kW, it depicts the energy dynamics of the system. With an annual total of 29,684 kW, the PV system exhibits fluctuating electricity generation, peaking at 3,003 kW in April. Energy sold back to the grid fluctuates from 343 kW in February to 804 kW in November, with an annual total of 6,466 kW. Energy purchased from the grid ranges from 0 kW in November and December to 1,640 kW in January.

Table 11

Energy produced annually by the PV and the grid purchase
Component	Production (kWh/yr)	Percent
Generic flat plate PV	29,684	74.83
Grid purchase	9,982	25.17
Total	39,666	100

The annual energy production breakdown for a system with a standard flat plate photovoltaic (PV) setup and grid purchases is shown in Table 11 and Fig. 10. The average flat plate photovoltaic system produces 29,684 kWh of electricity per year, or roughly 74.83% of the total energy produced. On the other hand, grid purchases account for 9,982 kWh each year, or roughly 25.17% of the total energy produced. When these sources are combined, 39,666 kWh of energy are produced annually. This breakdown reveals a substantial reliance on renewable energy from the PV system, which provides the majority of the energy produced. The remaining energy needs are met via grid purchases.

Table 12

Energy consumed by the AC primary load and the grid sales
Component	Consumption (kWh/yr)	Percent
AC Primary Load	33408	83.78
DC Primary Load	0	0
Grid sales Total	6,466 39,874	16.22 100

The annual energy usage breakdown for a system's grid sales and AC primary load is shown in Table 12. The yearly energy consumption of the AC primary load is 33,408 kWh, or around 83.78% of the total energy consumption. The DC main load uses no energy at all. 6,466 kWh of energy are sold back to the grid by the system, making up approximately 16.22% of the total energy used. When these sources are combined, the annual energy usage comes to 39,874 kWh. This breakdown shows how much energy the AC primary load uses, and that some of that energy is sold back to the grid. This suggests ways to optimize energy use and better integrate renewable energy sources to reduce reliance on the grid.

Table 13

Annualized cost of the energy of third optimization result
Name	Capital ($)	Operating ($)	Replacement ($)	Total ($)
Generic flat plate PV	1,230	60	1,028	2,318
Grid	0.00	1,043	0.00	1,043
Generic Battery 100kWh	451	20	415	886
System Converter	287	20	287	634

The yearly cost breakdown for the energy system components of the third optimization outcome is shown in Table 12. The annualized total cost of the generic flat plate PV system is $2,318. This cost is made up of capital, operating, and replacement costs. On the other hand, the grid component requires no initial capital expenditure but has significant ongoing expenses of $1,043 per year. The annualized cost of the common 100 kWh battery is $886, which includes capital, operating, and replacement expenditures. In addition, the system converter has a yearly cost of $634 which includes operating, replacement, and capital costs.

3.4. Discussion of Results

The first scenario result focused only on grid connectivity without integrating any renewable energy sources due to the annual energy consumption, which was recorded by a power analyzer in the Electrical Department of Ahmadu Bello University Zaria and shown in Table 1 and Fig. 4. This energy consumption was 33,048 kW on average. The system successfully met all of its energy needs through the grid alone, negating the need to install a generic flat plate PV, generic 100kWh lithium-ion battery, and system converter. Nevertheless, even if the system effectively meets the demand at a yearly cost of $2,838—an apparent short-term optimality—this technique ultimately proves to be unsuccessful due to rising energy costs. The main source of grid electricity, coal, has erratic prices, which could eventually result in higher energy costs and worsen financial strains. Furthermore, frequent power outages are a major problem in Nigeria and have the potential to interfere with university teaching and learning. The system is unstable, rigid, and contributes to increased greenhouse gas emissions because of its reliance on grid electricity that is mostly derived from coal plants. This highlights the need for a more resilient and sustainable energy solution.

The results of the second scenario indicated the following distribution of energy supply, as shown in Table 5, Fig. 5, and Fig. 6: A solar PV system produced 1,867 kW, 31,932 kW were taken from the grid, and 393 kW were excess that was sold back to the grid per year. $2,714 was spent on energy that was purchased, and only $31.8 was made from the sale of energy. With 94.48% of the energy supply in this configuration coming from the grid, the Generic flat plate PV system only contributed 5.52%. The Generic flat plate PV system was found to have $687 in capital and operating costs, and a $568 replacement cost. It is vital to acknowledge that a considerable amount of Nigeria's electricity comes from coal thermal power plants. This means that the system is susceptible to price swings and significant environmental implications, such as water pollution and greenhouse gas emissions. This system is vulnerable to price volatility and environmental effects due to its dependency on the grid.

The Generic flat plate PV system is relatively inexpensive to install, but its dependency on imported electricity makes it less efficient financially. The results of the third scenario, which is referred to as the solar PV dominant system, show a notable change in the distribution of energy supplies. The annual energy demand of 33,048 kW is effectively met by the 29,684 kW supplied by the solar PV system, 9,982 kW obtained from the grid, and 6,466 kW surplus energy sold back to the grid. The cost of energy consumed is $1,043, however the proceeds from energy sales come to a mere $521. With 74.83% of the total power generated in this setup coming from the solar PV system, the grid provides only 25.17% of the total energy. The generic flat plate PV system's capital and operating expenses were determined to be $2,038 in this case, with a $1,730 replacement cost.

The third scenario emphasizes the domination of solar photovoltaic systems, thereby reducing reliance on the grid and indirectly mitigating the effects on the environment, including greenhouse gas emissions. In contrast, the first optimization—which is entirely dependent on the purchase of grid electricity at a high yearly cost of $2,838—proves to be non-optimal because of its high cost, instability, and lack of flexibility, which are made worse by the dependency on coal power plants and the resulting environmental deterioration. In addition, even though the second optimization plant (grid dominant) has a lower initial capital outlay of $687, the third optimization plant falls short of being optimal due to both economic and environmental concerns because it has an annual energy purchasing cost of roughly $2,714 and minimal revenue, totalling around $2,682 annually. On the other hand, the greater $2,038 initial capital cost of the solar PV-dominated system is gradually offset because only $1,043 is used to import energy from the grid, and $521 is made from sales of excess energy, leaving a net yearly expenditure of $522. With a projected PV system lifetime of 20 years, it is anticipated that within two years, the system would recoup the initial installation cost, subsequently saving approximately $20,000 over the 20-year period.

In conclusion, this study addresses the urgent need for sustainable and cost-effective energy solutions in Nigeria by optimizing a grid-connected PV system at the department of Electrical Engineering, Ahmadu Bello University Zaria, using HOMER software. Through the analysis of three scenarios, the research highlights the economic and environmental drawbacks of a grid-only system, which incurred an annual energy cost of $2,838 with no renewable energy contribution. The second scenario, combining grid and solar PV, reduced the annual energy cost to $2,714 with 1,867kWh supplied by PV. The third scenario, prioritizing solar PV dominance, demonstrated the lowest annual energy cost of $1,043, with 29,684kWh supplied by the PV and $521 worth of surplus energy sold back to the grid. This scenario proved economically favorable with the system expected to recoup its initial installation cost within two years and save approximately $20,000 over a 20-year period. These findings advocate for prioritizing solar PV dominance to mitigate energy challenges. Future research should focus on integrating advanced energy storage solutions and smart grid technologies to further enhance system efficiency and resilence.

ACKNOWLEDGEMENT

I would like to express my sincere gratitude to the Department of Electrical Engineering at Ahmadu Bello University, Zaria, Kaduna State, Nigeria for their invaluable support throughout the duration of this research work. Special thanks are extended to the faculty members for their supervision, guidance, and expertise, which greatly facilitated the completion of this work. Additionally, I am grateful for access to the high-speed computers provided by the department, which were instrumental in conducting the necessary simulations and analyses. This research would not have been possible without their unwavering support and encouragement.

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Optimization of Grid-connected Pv Systems: Balancing Economics and Environmental Sustainability

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Abstract

Figures

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Homer software

2.2. Load profile

2.3. Research Location

2.4. Model 1nput Parameter

2.4.1. PV array:

2.4.2. Storage:

2.4.3. Converter:

2.4.4. Grid:

2.5. Grid Connected Hybrid System Modelling

2.5.1. Solar PV modelling:

2.5.2. Battery Modelling:

2.5.3. Grid connected modelling:

3. RESULTS AND DISCUSSION

3.1. Results of the First Scenario

3.2. Results of the Second Scenario

3.3. Results of the Third Scenario

3.4. Discussion of Results

4. CONCLUSION

Declarations

ACKNOWLEDGEMENT

References

Additional Declarations

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