Sustainable Growth in the Telecom Industry through Hybrid Renewable Energy Integration: A Technical, Energy, Economic and Environmental (3E) Analysis

Abstract

In response to escalating concerns about climate change, there is a growing imperative to prioritize the decarbonization of the telecom sector and effectively reduce its carbon emissions. This study presents a thorough techno-economic optimization framework for implementing renewable-dominated hybrid standalone systems for the base transceiver station (BTS) encapsulation telecom sector in Pakistan. It is noted that from the results obtained from 42 BTS sites overall, 21 BTS sites had a feasible combination of a photovoltaic battery system, having a diesel generator as a backup source with an average LCOE of 0.1246 USD/kWh to 0.2325 USD/kWh. Thus, seven BTS sites had an optimal combination of biomass, with photovoltaic and battery storage systems and with a varied LCOE of 0.1175 USD/kWh to 0.1318 USD/kWh. Moreover, due to the high flow of hydro water in the north region, five BTS sites presented an ideal configuration of a hydro system coupled with a photovoltaic, wind, and battery storage system, with a varied LCOE of 0.04547 USD/kWh to 0.07419 USD/kWh. Wind energy systems are dominant in the southern region; therefore, five BTS sites presented an ideal combination of a wind energy system coupled with a photovoltaic battery storage system, having DGs as backup sources for sustainability and with a varied LCOE of 0.1096 USD/kWh to 0.1294 USD/kWh. In addition, 02 BTSs had an optimal combination of photovoltaic systems coupled with hydro and wind systems, with diesel generators having a varied LCOE of 0.07618 USD/kWh to 0.04575 USD/kWh. The remaining 02 BTS sites had a feasible combination of wind–hydro-battery and diesel generator–photovoltaic–hydro-battery systems, with an LCOE of 0.7035 USD/kWh and 0.1073 USD/kWh, respectively. Finally, an environmental analysis based on carbon emissions, as well as sensitivity analyses based on different uncertainties, i.e., wind speed, solar irradiance, inflation rate, discount rate, and load demand, was performed to evaluate the behavior of the proposed systems. The optimization of these systems and comparative study findings indicate that the hybrid BTS system is the best option, better than conventional diesel-operated BTS systems in terms of cost-effectiveness, environmental friendliness, and sustainability.

1. Introduction

A critical factor in economic progress and growth is electricity. Therefore, the capacity to consume electricity is a prerequisite for a country’s development. Electricity is necessary for the telecom industry to deliver dependable services to prospective customers. COVID-19 has fueled a major uptick in wireless communication networks, as seen in the rise of remote work, online education, and virtual services. Because many businesses and institutions now require employees to work from home or attend classes online, wireless communication has become more crucial in today’s society. Efforts must be made to reduce greenhouse gas (GHG) emissions in light of mounting evidence of the effects of climate change on various parts of the globe. Accelerating the global transition to clean energy and achieving “net-zero” emissions as soon as feasible are urgently needed.

The sustainable development goals (SDGs) have recently prompted much-needed debates and research initiatives, and the energy industry plays a crucial role in achieving these SDGs. Energy should be accessible, dependable, sustainable, and modern because it is a crucial enabler for reaching the SDGs. Remote area development, especially on islands, relies on a reliable power supply for sustainable growth where grid connection is challenging. The only other option for electrifying these regions is the traditional system, which uses diesel generators. However, storing fossil fuels in these areas is troublesome in addition to environmental issues. Additionally, diesel generators have higher expenses and only provide electricity for a short period. Globally, utilizing local renewable energy (RE) for island electrification is becoming more and more popular [1].

The growth of technology, especially the development of mobile communications systems, has resulted in the operation of numerous mobile communications businesses to serve many customers. Cellular network operators must build more telecommunication towers for superior transmission and comprehensive coverage to fulfill the rising demand for telecommunications services [2]. In remote areas with inconsistent grid power, telecom providers encounter challenges. High-demand periods force reliance on diesel generators (DGs), contributing to CO₂ (GHG) emissions and global warming. Using green energy like solar, wind, biomass, hydro, and tidal power is crucial for powering telecom towers [3]. Diesel fuel, which constitutes 80% of total energy use, is the main expense for off-grid tower sites. Efficient design, maintenance, and technology evolution are vital for the optimal return on investment, considering factors like energy efficiency, emissions, and operational scenarios [4].

Despite the general populace’s growing acceptance of mobile telephony, taking advantage of its services might be more challenging due to powering base transceiver stations. Mobile telecom operators then place DGs nationwide as a source of uninterruptible power to decrease the unbarred power supply to BTSs. Using conventional diesel generators raises money, logistics, and climate issues [5]. The predominance of using conventional energy sources has caused several mountain regions in South Asian nations like Pakistan to become warmer and the country’s inflation rates to increase [6], which eventually leads to regional poverty and worldwide warming. The current energy mix in Pakistan is 5.4% from renewables (solar and wind), as depicted in Figure 1a [7]. In a similar vein, Pakistan’s NEPRA proposed the IGCEP 2022–31, which aims to raise the on-grid capacity of renewable energy generation by 22% by 2030 and is presented in Figure 1b [7]. Since a few years ago, the consumption of renewable energy resources (RERs) has increased worldwide to address related issues. This is due to increased environmental consciousness to reduce emissions and increase fuel’s cost, which increases energy costs [8]. World Energy Outlook 2017 projects a 30% increase in global energy consumption by 2040, and green energy is expected to fulfill 40.1% of the demand [9]. Pakistan has been facing severe power outages, the worst since 2008, due to low generation capabilities and transmission losses. This has led to a decline in GDP from 8.0% to 2.0% over the past ten years [10].

A typical day includes 8 to 10 h of solar sunshine. Different cellular BTS-powering alternatives were investigated. A techno-economic study revealed that hybrid systems are the best solution for cities, and these include PV, wind power, diesel, and batteries. Additionally, these minimize CO₂ emissions and ensure pollution-free operation [11]. The power consumed by a BTS load is directly obtained from solar, wind, and DG power.

A storage battery, along with a charge controller, is integrated for combined wind and solar power on a BTS site. The battery primarily stores energy for use during nighttime or when solar energy is minimal. A DG serves as a backup since solar and wind energy are not consistently reliable. This becomes the primary power source during emergencies, due to their intermittent nature. All BTS sites rely solely on DGs with battery storage devices. This paper explores how solar, wind, biomass, hydro, and diesel generators, along with a battery storage system, are integrated at 42 specific BTS sites. BTS sites do not generate cost-effective energy for their base loads and are environmentally unfriendly, with raised carbon emissions from high diesel usage. Instead of inefficient standalone DG systems, this study explores the techno-economic viability of HRESs (PV, W, H, BG, biomass, and battery).

Utilizing a wide range of assessment indicators, such as the NPC, IRR, ROI, LCOE, operating cost, PBP, and GHG emissions, the techno-economic effect of HRESs was quantified. Numerous experiments using different HRESs were conducted to develop the ideal SEM. Inclusive, this research defined the reach intended to provide energy for everybody through HRESs, following SDG-7, and presented the vital aspects of the energy in question, such as its cost-effective, flexible, ecofriendly, and modern attributes.

Study Motivation and Objectives of the Proposed Study

Due to the surging energy demand, environmental concerns, depleting fossil fuel reserves, volatile energy charges, and the requirement to power off-grid systems, interest in HRESs has grown. A sustainable HRES design reflects economic, societal, ecological, and technological factors. Creating a successful dispatch strategy for HRESs requires a comparison of various procedures in terms of their economic, environmental, technical, and social aspects. Besides reducing stakeholder costs for meeting load demand, this study aims to enhance social standing and reduce environmental pollution. The authors were motivated to delve deeper into research and share findings on the complete design of HRESs, considering technology, economics, the environment, and social aspects, with an appropriate dispatch approach for the telecom sector in Pakistan.

The main findings/objectives of this proposed study are outlined below:

• A framework for decision making that combines precise sustainability evaluation with techno-economic optimization should be established.
• To account for various potential power outage situations in the telecom sector, hybrid systems (PV, wind, hydro, biomass, and battery) should be used to maximize a system’s capacity to meet the energy demands of telecom towers.
• After initial screening, superior outcomes should be attained through the utilization of component models and storage technology. The optimization process must take into account various losses associated with wind turbines to offer a realistic perspective.
• Hybrid systems powered by solar PV, wind power, hydropower, biomass, and diesel with a battery storage system for telecom towers should be compared and contrasted with the conventional options presently on the market.

This study is structured as follows: an introduction and a review of previous work with their significant contributions in Section 1 and Section 2; methodology in Section 3; system modeling in Section 4; and results and discussion in Section 5. Thereafter, Section 6 covers policy with future work, and Section 7 covers the conclusion.

2. Literature Review

The mobile communication sector is Asia’s deep-rooted industry, with impressive development having been made. Due to its reliable and firm development over the previous limited years, Pakistan’s telecom market is one of the fastest-growing industries. Numerous communication firms can meet the massive demand in the country; nevertheless, the expenses and fuel consumption are unsettling [5]. Despite the population’s sharply growing acceptance of mobile communications, BTSs struggle to receive a steady source of power, which could prevent users from reaping the full benefits of the service. Therefore, telecom providers opt for installing diesel generators nationwide to address the unstable power supply to BTSs. However, using conventional diesel generators poses logistical, economic, and environmental challenges [12].

The northern and western regions, in particular, hold promise for solar, hydroelectric, wind, and biomass energy. Similar to the north region, the southwest exhibits a good photovoltaic profile, making it a perfect site for constructing large-scale solar systems for homes and businesses. The existing literature highlights crucial techno-economic analyses aimed at resolving power disparities. Therefore, in a study performed in Sousse, Tunisia, by author [13], the ideal off-grid MG was explored, factoring in autonomous days using solar irradiance, temperature, and wind speed. This method relies on the DPSP for system reliability assessment. The EC and NPC are employed to analyze system cost, comparing two configurations: (PV, battery) and (PV, wind, battery). This study illustrated the impact of autonomous days on system performance and cost. Diesel generators are effective in terms of costs and meeting load demand and can serve as a source of backup power. A comparison study suggests that a large storage capacity could raise a MG system’s initial capital investment. Globally, efforts are underway to electrify rural areas, leveraging solar, wind, and limited hydropower as energy sources.

It is evident from a study of the literature that there has yet to be an investigation into the techno-economic evaluation of integrating renewables with BTS sites across Pakistan. To the authors’ knowledge, previous studies concentrated on just two or three development variables, mainly the NPC and LCOE. These studies predominantly focused on areas with comparable geographic and climatic conditions. Prior research has been restricted to a few regions, lacking comprehensive study at the national level that considers the diverse nature of the sites.

Table 1. Current investigations into the techno-economic investigation of HRESs and the significance of this current study.

Ref.	Location	Analysis Type			OGI	Method	Technical Characteristics Components					Objective Functions and Variables				Load Type
		T	Ec	E			PV	WE	Hyd	BM	BA	LCOE	IRR	ROI	SA
[14]	AUS	Y	Y		N	HMR	Y	Y	Y		Y	Y	Y		Y	ILS
[15]	S.A				N	HS,PSO	Y	Y	Y		Y	Y	Y		Y	DOMS
[16]	IND		Y	Y	Y	HMR	Y				Y				Y	RSD
[17]	Iran	Y	Y		N	IGOA	Y	Y			Y	Y			Y	COM
[18]	CHI	Y	Y		N	HMR		Y	Y	Y	Y	Y	Y	Y	Y	IND
[19]	Nigeri	Y	Y		N	HMR	Y	Y			Y	Y			Y	DOMS
[20]	IND		Y	Y	N	HMR	Y	Y			Y	Y			Y	AGR
[21]	IND	Y	Y		N	HMR	Y	Y	Y		Y	Y	Y		Y	RSD
[22]	Egypt		Y	Y	N	HMR	Y	Y				Y				COM
[23]	BANG	Y	Y		N	HMR	Y	Y	Y		Y	Y	Y		Y	RSD
[24]	IND		Y	Y	N	HMR	Y	Y	Y		Y	Y	Y		Y	DOMS
[25]	CAM		Y	Y	N	HMR	Y	Y	Y			Y	Y			DOMS
[26]	S.A	Y	Y		N	HMR	Y		Y	Y	Y		Y	Y	Y	DOMS
[27]	IND		Y	Y	N	HMR	Y			Y	Y			Y	Y	DOMS
[28]	IND	Y	Y		N	HMR	Y				Y				Y	COM
[29]	Europe	Y	Y		N	GA	Y	Y			Y	Y			Y	DOMS
[30]	Thailand	Y	Y		N	HMR	Y		Y	Y	Y		Y	Y	Y	ILS
[31]	Iraq	Y	Y		N	HMR	Y			Y	Y			Y	Y	RESD
[32]	S.A	Y		Y	Y	HMR	Y	Y		Y		Y		Y		COM
[33]	BANG	Y		Y	N	HMR	Y		Y	Y	Y		Y	Y	Y	DOMS
[34]	IND		Y	Y	N	HMR	Y	Y	Y	Y	Y	Y	Y	Y	Y	DOMS
[35]	S.K		Y	Y	N	HMR	Y		Y	Y	Y		Y	Y	Y	COM
[36]	S.A	Y	Y		N	HMR	Y	Y		Y		Y	Y	Y	Y	IND
[37]	CHI	Y	Y		N	HMR	Y	Y		Y	Y	Y		Y	Y	ILS
[38]	IND		Y	Y	N	GA	Y	Y	Y		Y	Y	Y		Y	AGR
[39]	PAK	Y	Y		N	HMR	Y	Y		Y	Y	Y		Y	Y	DOMS
[40]	Turkey		Y	Y	N	HMR	Y	Y			Y	Y	Y	Y	Y	RESD
[41]	Namibia	Y	Y		N	HMR	Y		Y	Y	Y		Y	Y	Y	RESD
[42]	Malaysia		Y	Y	N	HMR	Y			Y	Y			Y	Y	DOMS
[43]	Colombia	Y	Y		Y	HMR	Y	Y	Y	Y	Y	Y			Y	DOMS
[44]	S.K		Y	Y	N	HMR	Y	Y		Y	Y		Y	Y		COM
[45]	Turkey	Y	Y		N	HMR	Y		Y	Y	Y		Y	Y	Y	RESD
[46]	S.A		Y	Y	Y	HMR	Y	Y		Y	Y	Y		Y	Y	DOMS
[47]	USA	Y	Y		N	HMR		Y	Y		Y	Y		Y	Y	RESD
[48]	IND		Y	Y	N	HMR	Y									DOMS
[49]	Chile		Y	Y	Y	HMR		Y		Y		Y		Y		RESD
[50]	PAK	Y	Y	Y	Y	HMR	Y	Y	Y		Y		Y		Y	RESD
[51]	S.A	Y	Y		N	PSO	Y	Y			Y	Y			Y	DOMS
[52]	AUS		Y	Y	N	PSO	Y	Y				Y				DOMS
[53]	Yamen	Y	Y		N	HMR	Y	Y	Y	Y		Y	Y	Y		DOMS
[54]	Malaysia		Y	Y	N	HMR	Y		Y	Y	Y		Y	Y	Y	DOMS
[55]	Iran				N	HMR	Y	Y	Y	Y	Y	Y	Y	Y	Y	IND
[56]	Nigeria	Y	Y	Y	N	MATLAB	Y	Y				Y			Y	RESD
[57]	PAK	Y	Y		Y	HMR	Y					Y				RESD
[PS]	PAK	Y	Y	Y	Y	HMR	Y	Y	Y	Y	Y	Y	Y	Y	Y	TELEC

Note: T: Technical; E_c: Economical; E: Environmental Y: Yes; N: NO; OGI: On-ground inputs; S.A: Saudi Arabia; S.K: South Korea; BANG: Bangladesh; IND: India; CHI: China; PAK: Pakistan; AUS: Australia; CAM: Cameroon; HMR: HOMER Pro PV: Photovoltaic; DG: Diesel Generator; WE: Wind energy; H_yd: Hydro energy; SA: Sensitivity analysis; BM: Biomass; BA: Battery; ILS: Load of Island; DOMS: Domestic; IND: Industrial; AGR: Agricultural; COM: Commercial; RESD: Residential; PS: Proposed study; TELEC: Telecom BTS load.

thoroughly compares the contributions and novelty of the suggested inquiries in previous studies. In past research, site selection often relied on random analysis, using single energy sources for every location. Most studies opted for common components or the trial-and-error method, aiming for near-ideal but not yet commercially feasible results. Conversely, the application of storage technology varied based on different climate zones in each location under study. It is observed that prior studies did not evaluate the proposed analysis under ideal conditions.

The innovative aspect of this study lies in its simultaneous evaluation of several decision characteristics to determine an optimal solution. In this proposed study, the decision factors that were used for assessment include the NPC, LCOE, PBP, ROI, and capacity shortfall percentage (CSF), along with unmet load demand. Another original aspect is the use of real input datasets, incorporating discount and inflation rates from the fiscal year 2022–2023 to reflect the country’s current condition. This study focuses on areas with diverse topographical and climatic characteristics, utilizing local wind farms with business installations to calculate losses. In conclusion, this proposed study is novel, as it tackles all the identified issues in the existing literature.

3. Research Methodology

Using BTS sites around Pakistan, a suggested study was carried out. A complete visual map depicting the Pakistani-nominated locations is shown in Figure 2. The region was separated into three sections, referred to as Northern, Central, and Southern, according to variables related to geography, climate, and society. This study incorporated discount and inflation rates for 2022–2023, reflecting the country’s real situation, including fluctuating policy rates and high inflation from the State Bank of Pakistan.

Table A2. Final optimization outcomes for both existing and proposed selected BTS sites.

Site No.	Selected Areas	Proposed Structure										Existing Infrastructure
		Confg.	DG	PV	WT	BM	Hydro	BA	Con.	DGH	DGFC	Confg.	DG	BA	Con.	DGH	DGFC
			(kW)	(kW)	(No.)	(kW)	(kW)	(No.)	(kW)	(No.)	(L/yr.)		(kW)	(No.)	(kW)	(No.)	(L/yr.)
Northern Zone
BTS-01	N-Chakwal	PV-BM-B	0	19.3	0	5	0	66	3.14	0	0	DG-B	3.1	11	0.104	8760	7304
BTS-02	N-Islamabad	DG-PV-B	8	59.6	0	0	0	165	8.00	522	895	DG-B	8	11	0.229	8760	18799
BTS-03	N-Jhelum	DG-PV-B	3.5	25.5	0	0	0	77	3.41	489	366	DG-B	3.5	11	0.104	8760	8120
BTS-04	N-Rawalpindi	DG-PV-B	6.9	51.4	0	0	0	143	6.88	523	774	DG-B	6.9	11	0.208	8760	16193
BTS-05	N-Talagang	PV-BM-B	0	15.5	0	5	0	55	2.6	0	0	DG-B	2.6	11	0.083	8760	6026
BTS-06	N-Taxila	DG-PV-B	6.6	50.2	0	0	0	143	6.64	507	726	DG-B	6.6	11	0.208	8760	15633
BTS-07	N-Bajaur	DG-PV-B	9.5	70.9	0	0	0	198	9.48	524	1062	DG-B	9.5	11	0.271	8760	22427
BTS-08	N-Dir	PV-W-B-HYD	0	2.02	1	0	5.49	11	0.146	0	0	DG-B	4.4	11	0.146	8760	10421
BTS-09	N-Mardan	PV-W-B-HYD	0	1.21	1	0	5.15	11	0.083	0	0	DG-B	2.7	11	0.026	8760	6281
BTS-10	N-Chitral	DG-PV-B-HYD	9.3	26.4	0	0	5.15	44	2.69	3210	3430	DG-B	9.3	11	0.087	8760	21916
BTS-11	N-Swat	W-HYD-B	0	0	2	0	5.15	11	0.542	0	0	DG-B	5.7	11	0.053	8760	13331
BTS-12	N-Kohat	DG-PV-B	4	31.8	0	0	0	88	3.96	518	443	DG-B	4	11	0.125	8760	9398
BTS-13	N-Nowshera	DG-PV-B	4	31.0	0	0	0	88	4.01	466	407	DG-B	4	11	0.125	8760	9398
BTS-14	N-Buner	PV-W-B-HYD	0	2.27	1	0	5.15	11	0.146	0	0	DG-B	5	11	0.047	8760	11748
BTS-15	N-Peshawar	DG-PV-B	4	31.0	0	0	0	88	3.98	471	409	DG-B	4	11	0.125	8760	9398
BTS-16	N-Abbottabad	DG-PV-B	4	31.2	0	0	0	88	4.01	581	504	DG-B	4.1	11	0.125	8760	9654
BTS-17	N-Kohistan	PV-W-B-HYD	0	2.46	1	0	5.49	11	0.167	0	0	DG-B	5.4	11	0.053	8760	12771
BTS-18	N-Mansehra	PV-W-B-HYD	0	2.1	1	0	5.49	11	0146	0	0	DG-B	4.7	11	0.047	8760	10981
BTS-19	N-Gilgit	DG-PV-B	2.59	21.3	0	0	0	44	2.59	3687	2017	DG-B	2.6	11	0.083	8760	6026
BTS-20	N-Mingora	DG-PV-B	3.8	27.1	0	0	0	77	3.74	545	436	DG-B	3.8	11	0.040	8760	8887
BTS-21	N-Malakand	DG-PV-W-HYD	6.6	5.29	2	0	5.49	0	0.677	0	0	DG-B	6.6	11	0.067	8760	15633
BTS-22	N-Kamri	DG-PV-W-HYD	4.3	3.43	2	0	5.15	0	0.406	0	0	DG-B	4.3	11	0.040	8760	10165
BTS-23	N-Mirpur	DG-PV-B	2.2	17.1	0	0	0	44	2.2	268	0.734	DG-B	2.2	11	0.016	8760	5210
BTS-24	N-Muzaffarabad	DG-PV-B	5.9	44.8	0	0	0	121	5.84	840	2.3	DG-B	5.9	11	0.053	8760	13843
Central Zone
BTS-25	C-Lahore	DG-PV-B	6.4	48.9	0	0	0	132	6.41	511	708	DG-B	6.4	11	0.188	8760	15121
BTS-26	C-Sheikhupura	PV-BM-B	0	25.4	0	5	0	88	4.94	0	0	DG-B	4.6	11	0.146	8760	10272
BTS-27	C-Bhakkar	PV-BM-B	0	25.0	0	5	0	77	3.73	0	0	DG-B	3.7	11	0.104	8760	8631
BTS-28	C-Khushab	DG-PV-B	3.7	29.4	0	0	0	77	3.64	561	448	DG-B	2.2	11	0.083	8760	5210
BTS-29	C-Mianwali	DG-PV-B	3.1	24.6	0	0	0	66	3.14	552	374	DG-B	3.1	11	0.104	8760	7304
BTS-30	C-DG Khan	PV-BM-B	0	15.3	0	5	0	44	2.27	0	0	DG-B	2.2	11	0.083	8760	5210
BTS-31	C-Layyah	PV-BM-B	0	15.8	0	5	0	44	2.22	0	0	DG-B	2.2	11	0.083	8760	5210
Northern Zone
BTS-32	N-Karachi-I	DG-PV-W-B	9.5	54.2	1	0	0	154	9.47	473	775	DG-B	9.5	11	0.271	8760	22427
BTS-33	N-Karachi-II	DG-PV-W-B	4.1	16.2	1	0	0	55	4.18	589	407	DG-B	4.1	11	0.125	8760	9654
BTS-34	N-Badin	DG-PV-W-B	2.9	10.4	1	0	0	33	2.94	670	329	DG-B	2.9	11	0.083	8760	6793
BTS-35	N-Hyderabad	DG-PV-W-B	3.8	15.7	1	0	0	55	3.75	522	344	DG-B	3.8	11	0.125	8760	8887
BTS-36	N-Mirpur Khas	DG-PV-B	2.2	17.6	0	0	0	44	2.2	561	269	DG-B	2.2	11	0.083	8760	5210
BTS-37	N-Ghotki	DG-PV-B	3.8	30.1	0	0	0	77	3.76	630	513	DG-B	3.8	11	0.125	8760	8887
BTS-38	N-Rajan Pur	DG-PV-W-B	9.5	66.8	1	0	0	176	0.129	592	1116	DG-B	9.5	11	0.271	8760	22427
BTS-39	N-Rahim Yar Khan	PV-BM-B	0	21.4	0	0	0	66	3.22	0	0	DG-B	3.2	11	0.104	8760	7560
BTS-40	N-Sukkur	DG-PV-B	2.2	17	0	0	0	44	2.23	536	257	DG-B	2.2	11	0.083	8760	5210
BTS-41	N-Gawadar	DG-PV-B	2.5	20	0	0	0	55	2.43	522	277	DG-B	2.5	11	0.083	8760	5770
BTS-42	N-Quetta	DG-PV-B	3.5	23.9	0	0	0	77	3.42	425	313	DG-B	3.5	11	0.104	8760	8120

provides a summary of essential variables such as desired inputs, datasets, assumptions, and other critical factors. This research focused on RESs-based battery–wind–photovoltaic–biomass–hydro hybrid microgrid systems, considering likely resource availability at each site. Because of its sustainability and consistent 24 h reliability, a DG served as the backup energy source.

HOMER Pro was utilized for its techno-economic effect for sustainability analysis. This tool has many advantages such as complex configurations, dependability, and hourly time step simulation. GHG emissions were used as the basis for environmental evaluation to achieve sustainability analysis goals. With the help of tools like Global Wind and World Solar Atlases, along with the Energy Sector Management Assistance Program (ESMAP), the World Bank conducts initial assessments for renewable energy potential. This involves estimating solar irradiation and wind speeds throughout a region. To verify the presence of locations with strong resource potential, NASA’s database was used. Overall, 42 BTS sites were initially chosen for this study, as illustrated in Figure 2. Out of these 42 BTS sites, 24, 7, and 11 sites were selected from the north, central, and south region, respectively.

Table 2. Data on chosen BTS sites in Pakistan, including their load demands.

Sr No.	Zone—Selected Site N: North; C: Central; and S: South	Load of BTS Site		Coordinates
		ED (kWh)	ED (kWh/day)
01	N-Chakwal	2.8	67.2	32.9328° N, 72.8630° E
02	N-Islamabad	7.2	172.8	33.6844° N, 73.0479° E
03	N-Jehlum	3.1	74.4	32.9425° N, 73.7257° E
04	N-Rawalpindi	6.2	148.8	33.5651° N, 73.0169° E
05	N-Talagang	2.3	55.2	32.9172° N, 72.4081° E
06	N-Taxila	6.0	144	33.7463° N, 72.8397° E
07	N-Bajaur	8.6	206.4	34.7865° N, 71.5249° E
08	N-Dir	4.0	96	35.1977° N, 71.8749° E
09	N-Mardan	2.4	57.6	34.1986° N, 72.0404° E
10	N-Chitral	8.4	201.6	35.7699° N, 71.7741° E
11	N-Swat	5.1	122.4	35.2227° N, 72.4258° E
12	N-Kohat	3.6	86.4	33.5889° N, 71.4429° E
13	N-Nowshera	3.6	86.4	34.0105° N, 71.9876° E
14	N-Buner	4.5	108	34.3943° N, 72.6151° E
15	N-Peshawar	3.6	86.4	34.0151° N, 71.5249° E
16	N-Abbottabad	3.7	88.8	34.1688° N, 73.2215° E
17	N-Kohistan	4.9	117.6	35.2611° N, 73.2765° E
18	N-Mansehra	4.2	100.8	34.3313° N, 73.1980° E
19	N-Gilgit	2.3	55.2	35.8819° N, 74.4643° E
20	N-Mingora	3.4	81.6	34.7717° N, 72.3602° E
21	N-Malakand	6.0	144	34.5030° N, 71.9046° E
22	N-Kamri	3.9	93.6	34.7212° N, 74.9489° E
23	N-Mirpur	2.0	48	33.1480° N, 73.7537° E
24	N-Muzaffarabad	5.3	127.2	34.3551° N, 73.4769° E
25	C-Lahore	5.8	139.2	31.5204° N, 74.3587° E
26	C-Sheikhupura	4.1	98.4	31.7167° N, 73.9850° E
27	C-Bhakkar	3.3	79.2	31.6082° N, 71.0854° E
28	C-Khushab	2.0	48	32.2955° N, 72.3489° E
29	C-Mianwali	2.8	67.2	32.5839° N, 71.5370° E
30	C-DG Khan	2.0	48	30.0489° N, 70.6455° E
31	C-Layyah	2.0	48	30.9693° N, 70.9428° E
32	S-Karachi-I	8.6	206.4	24.9148° N, 66.8888° E
33	S-Karachi-II	3.7	88.8	24.8458° N, 66.9794° E
34	S-Badin	2.6	62.4	24.6459° N, 68.8467° E
35	S-Hyderabad	3.4	81.6	17.3850° N, 78.4867° E
36	S-Mirpur Khas	2.0	48	25.5065° N, 69.0136° E
37	S-Ghotki	3.4	81.6	28.0271° N, 69.3235° E
38	S-Rajan Pur	8.6	206.4	29.1044° N, 70.3301° E
39	S-Rahim Yar Khan	2.9	69.6	28.4212° N, 70.2989° E
40	S-Sukkur	2.0	48	27.7244° N, 68.8228° E
41	S-Gawadar	2.2	52.8	25.1313° N, 62.3250° E
42	S-Quetta	3.1	74.4	30.1798° N, 66.9750° E

displays the names and coordinates of each site. For the detailed research methodology, refer to Figure 3.

3.1. Decision Parameters

In our proposed study, we utilized HOMER Pro to perform analyses using both a proprietary derivative-free algorithm (PDFA) and an original grid search algorithm (OGSA). These algorithms optimize design by considering factors such as the NPC and LCOE while promoting the integration of renewable sources for improved diversity. The NPC is calculated based on total cash inflow over a specified term, “t”, initial capital investment, and discount rate (“r”). The LCOE represents the life-cycle cost of energy per unit [7]. Objective functions are expressed as follows:

$$O{F}_{\left(1\right)Mn}\because { \mathrm{NPC}}_T={{\displaystyle \sum }}_{t=1}^T{(1+r)}^{-t}\left(C_t-C_o{(1+r)}^{+t}\right)$$

(1)

Equation (2) [58] illustrates,

$$\min \left\{\left(\mathrm{m} {. \mathrm{NPC}}_{\mathrm{PV}}\right)+\left(\mathrm{n} {. \mathrm{NPC}}_{\mathrm{WT}}\right)+\left(\mathrm{o} {. \mathrm{NPC}}_{\mathrm{BG}}\right)+\left(\mathrm{p} {. \mathrm{NPC}}_{\mathrm{hyd}}\right)+\left(\mathrm{q} {. \mathrm{NPC}}_{\mathrm{B}}\right)\right\}$$

(2)

$$LCC=C_{fuel}+C_{cap}+C_{O\&M}+C_{fuel}-C_{salvage}$$

(3)

$$C_{salvage}={\displaystyle \frac{1}{T}}\left(C_{rep}\times R_T\right)$$

(4)

$$O{F}_{(2)Min}\because LCO{E}_T(\$/\mathrm{k}\mathrm{W}\mathrm{h})={\displaystyle \sum _{t=1}^T\left({\left(1+R\right)}^{-t}\left(\frac{C_t-C_o{\left(1+R\right)}^{+t}}{E_p\times {\left(1+R\right)}^{-t}}\right)\right)\times C_{RF}(r,T)}$$

(5)

$$C_{RF}={\displaystyle \frac{rT+r^{2}T}{rT+r^{2}T-1}}$$

(6)

Equation (3) [58] illustrates,

$$\min \left\{m.LCO{E}_{PV}+n.LCO{E}_{WT}+o.LCO{E}_{BG}+p.LCO{E}_{hyd}+q.LCO{E}_B\right\}$$

(7)

$$O{F}_{(3)Min}\because R.Di{v}_{inj}\left\{PV,WT,H,BM\right\}$$

(8)

E_p is used in Equation (5) to represent each site’s load. CRF, short for capital recovery factor [7], is a factor that is used to compute the coefficients m, n, n, o, p, and q that are used in Equation (7).

3.2. Performance Indicators

In this research study, the ideal decision minimizes the LCOE and NPC. As a result, these financial factors also have a strong relationship with performance. The payback period (P_BP) is the time (Yrs.) necessary to recoup a project’s investment costs. Equation (9) [58] contains the calculation method for the P_BP.

$$P_{BP}=C_{in}+\left[\left({\displaystyle \sum _{t=1}^T{B}_{an}\left(t\right)\left(\frac{T}{{\left(1+r\right)}^t}\right)}\right)\times \left({\displaystyle \sum _{t=1}^T{C}_{an}\left(t\right)\left(\frac{T}{{\left(1+r\right)}^t}\right)}\right)\right]$$

(9)

The IRR, a key financial metric, assesses investment profitability. Its discount rate makes the net present values of cash flows zero in a discounted cash flow analysis, as shown in Equation (10) [7]. In Equation (11), the ROI is the yearly change in nominal cash flows divided by the difference of capital costs. It signifies long-term cost savings compared to initial investment.

$$IRR (\%)=\left({\displaystyle \frac{C.flow}{{\left(1+r\right)}^t}}-I_{inv}\right)\times 100$$

(10)

$$ROI (\%)={\displaystyle \frac{1}{\left(R\ast C_{cap}-R\ast C_{cap,ref}\right)}}\left({{\displaystyle \sum }}_{i=0}^R{C}_{i,ref}-C_i\right)\times 100$$

(11)

The project lifespan is 25 years, and Equation (11) [7] indicates performance evaluation (CF), highlighting the normal control segment having the highest influence on the theoretical yield.

$$C{F}_A(\%)=\left({\displaystyle \frac{E_{a,yr}}{E_{r,elect}\times 24\times 365}}\right)\times 100$$

(12)

Equation (13) [7] is the mathematical expression for system efficiency, representing the total efficiency of all RE systems.

$${\eta }_{sys}(\%)=\left[\left({\eta }_{PV}+{\eta }_{WT}+{\eta }_B+{\eta }_{BG}+{\eta }_{hrd}\right)\times {\eta }_{conv}\right]\times 100$$

(13)

3.3. Site Selection with Load Assessment

Energy usage is one of the most expensive costs for the telecom sector. One of the major energy-consuming parts of cell sites is the base transceiver station. Over 80% of the expenses for off-grid and BTS locations are attributed to diesel fuel used in generators. In Pakistan, BTS locations are expanding across the north, south, and central regions. This study focused on 42 selected BTS sites to create HRESs, depicted in Figure 2. The Pakistan Telecommunication Company (PLC) estimated the load for all the chosen locations of BTSs. Figure 4 and Table 2 display the comprehensive load evaluation of all the BTS installations.

3.4. Proposed Design Framework for BTSs

In a BTS, a diesel generator is the only power source that is currently operational. Diesel generators are, therefore, both expensive and unfriendly to the environment. As a result, these locations must incorporate renewable energy resources, i.e., solar and wind energy [4].

This study indicates the widespread use of accessible renewable energy sources like solar, wind, tidal, and ocean energy. As a result, conventional energy sources (such as diesel, etc.) have been replaced by renewable ones because they are clean, green, and advantageous to the environment [59]. Due to the gradual advancement in the integration of hybrid renewable energy during the preceding five years, the installation has doubled. Modern technology encourages the creation of distributed energy. In Figure 5, the suggested BTS model is displayed.

4. Modeling of the System

The IEEE states that a microgrid is essentially a connected system. It brings together various elements like energy sources (solar PV, wind, biomass, tidal, geothermal), energy storage systems (battery, fuel cell), and loads. These components operate together as a unified and controllable entity within a larger power grid [60].

4.1. Resource Assessment of Selected System Components

The redesigned BTS sites in this research proposal have HRESs, such as PV, WD, and DGs, as backup systems to achieve sustainability. As a result, a BTS model resource evaluation and an in-depth modeling of each component used in this proposed research are explained below.

4.1.1. Photovoltaic System

Global horizontal irradiance (GHI) is among the most important factors when picking a location to install PV systems. Because on-ground data are not accessible for all selected sites, hourly PV data based on a NASA built-in record were used. Using an integrated HOMER Pro [6], Equation (14) used a clearance index to calculate and synthesize hourly data depending on the GHI profile. In Equation (14) [7], solar radiation (H_x,avg) at the top of the earth’s atmosphere is expressed in kWh/m²/day, and solar radiation (H_m,avg), which represents the monthly average, is expressed at the top of the plane surface of the earth’s atmosphere.

$$K_T={\displaystyle \frac{H_{m,avg}}{H_{x,avg}}} \& H_{x,avg}={\displaystyle \frac{{\displaystyle \sum _{n=1}^{365}{G}_o}}{365}}$$

(14)

Figure 6 displays the shortlisted BTS sites’ monthly average solar radiation profiles. As illustrated in Figure 6, Mingora in northern Pakistan experienced its highest solar irradiation in June, which was 7.879 (kWh/m²/day). Conversely, even in June, Gilgit undergoes minimal sun irradiation, which is 6.790 (kWh/m²/day). In Pakistan’s central region, Lahore saw the highest average sun irradiance in June (7.261 kWh/m²/day), whereas Layyah experienced the lowest solar irradiance (6.180 kWh/m²/day). Last but not least, Pakistan’s southern region receives slightly less average sun irradiation than its northern and central regions. Quetta had the southern region’s highest average sun irradiation in June at 7.611 (kWh/m²/day), while Karachi and Badin had the lowest at 6.40 (kWh/m²/day). In this investigation, a flat-plate solar module named LONGi Solar LR6-72PE was employed. It uses a specific optimal slope and azimuth with a 370 W_p rated capacity. This PV module has high efficiency, easy availability in the wholesale market, high affordability, and a good adaptability to space-constrained sites.

Table 3. Selected solar module (LONGi Solar LR6-72PE): cost (USD/kWh) and technical parameters [58].

Capital Cost	Efficiency	O&M Cost	Replacement Cost	Lifetime	Rated Capacity	Derating Factor	Operating Temperature	Temp. Coefficient	Ground Reflectance
(USD)	(%)	(USD/year)	(USD)	(year)	kWp	(%)	(°C)	(%/°C)	(%)
259.0	19.10	3.70	185.0	25.00	0.370	80.00	47.0	−0.38	20.00

[7] lists the price and appropriate technical specifications for the chosen PV module.

The PV module’s output power only comes from solar irradiation. As a result, the PV module uses Equation (15) to generate the output power. This study also includes the impact of temperature on the PV module when modeling the derating factor. In Equation (15), total solar radiation at the STP is denoted by H_T, STC, and P_r.

The temperature of cell (T_C) is determined using the Duffie and Beckman technique [61] by HOMER Pro, using the dimensions of solar transmittance (k) and absorptance (θ), and are correlated by Equations (17) and (20) [7].

$$P_{PV}(t)=\left[1+\left({\tau }_{pv}\times T_c\right)-\left({\tau }_{pv}\times T_{c,STC}\right)\right]\times \left({\displaystyle \frac{P_r{D}_{PV}\times H_T}{H_{T,STC}}}\right)$$

(15)

$${\tau }_{pv}={\displaystyle \frac{V_{OC}}{V_{xp}}}$$

(16)

$$v\theta H_T=h_t{T}_c-h_t{T}_a+{\eta }_c{H}_T$$

(17)

$$T_C=T_z+H_T\left(1-{\displaystyle \frac{{\eta }_c}{v\theta }}\right)\left({\displaystyle \frac{k\theta }{h_t}}\right)$$

(18)

$${\displaystyle \frac{k\theta }{h_t}}={\displaystyle \frac{T_{c,NOCT}}{H_{T,NOCT}}}-{\displaystyle \frac{T_{z,NOCT}}{H_{T,NOCT}}}$$

(19)

$$T_C=H_T\left(1-{\displaystyle \frac{{\eta }_c}{k\theta }}\right)\left({\displaystyle \frac{T_{c,NOCT}-T_{z,NOCT}}{H_{T,NOCT}}}\right)+T_a$$

(20)

$${\eta }_{mppt}=\left[1+\left({\tau }_{pv}\ast T_C\right)-\left({\tau }_{pv}\ast T_{C,STC}\right)\right]\times {\eta }_{mppt,STC}$$

(21)

4.1.2. Battery

To account for climate in less-explored regions, this study employed a generic li-ion model for battery storage. Temperature effects were simulated in the li-ion model to imitate efficiency losses. A PV module with a 40 V nominal rated voltage served as the energy source, connected to a DC bus. Thereafter, battery string size was set to 11 to maintain a consistent voltage of 40 V on the DC busbar.

Table 4. The selected battery storage model’s cost and technical parameters.

Capital Cost	O&M Cost	Minimum State of Charge	Nominal Capacity	Replacement Cost	Ground Reflectance	Degradation Limit	Trip Losses	Initial State of Charge
(USD)	(USD)	(%)	(kWh)	(USD)	(%)	(%)	(%)	(%)
200.0	10.00	20.00	1.02	200.0	20.00	30.00	8.00	100.0

[7] provides examples of technical details and prices. Storage capacity is expressed mathematically in Equations (22) and (23) [7].

$$E_{cap}(t)=\left(E_{TD}\times B_B\right){\eta }_{conv}\times DDB\times {\eta }_{bat}$$

(22)

$$B_B={\displaystyle \frac{\left(24h/d\right)\times N_b\times \left(1/100\right)-\left(SO{C}_{\min }/100\right)\times V_{nom}\times E_{nom}}{P_{avg,prime,load}(1000 \mathrm{Wh}/\mathrm{kWh})}}$$

(23)

The power present in the battery bank at the given time “t” can be expressed using the given Equation (24). The formula for calculating the battery’s SOC and DOD is given in Equations (25)–(27) [7].

$$E_{b(t)}=\left[E_{(t-1)}+E_{(t)}\right]\times \left({\eta }_{x,ch}\times {\eta }_{ch}\right)$$

(24)

$$SO{C}_{\max (t)}\ge SO{C}_{(t)}\ge SO{C}_{\min (t)}$$

(25)

$$SO{C}_{\min (t)}=1-DD{B}_{(t)}$$

(26)

$$DD{B}_{\max (t)}\ge DD{B}_{(t)}\ge DD{B}_{\min (t)}$$

(27)

Equations (28) and (29) [7] show charging and discharging rates through a battery over a precise time.

$$P_{\max }^{+}={\displaystyle \frac{k\left[\left[E_{(t)}\times \left(c-\frac{c}{e^{k\Delta t}}\right)\right]+\left[\left(Et-E\right)\left(\frac{1}{e^{k\Delta t}}\right)\right]-\left(c\times E_{\max (t)}\right)\right]}{1-e^{-\Delta tk}+ck\Delta t+c{e}^{-\Delta tk}-c}}$$

(28)

$$P_{\max }^{-}={\displaystyle \frac{\left(kEt{e}^{-\Delta tk}-kE{e}^{-\Delta tk}\right)+\left(E_{(t)}\times \left(ck\right)\right)-\left(E_{(t)}\times \left(ck\right)\ast e^{-\Delta tk}\right)}{1-e^{-\Delta tk}+ck\Delta t+c{e}^{-\Delta tk}-c}}$$

(29)

$$P_{\max ,aa}^{-}={\displaystyle \frac{\left[\left(e^{-k\Delta t}\right)\left(kEt-kE\right)\right]+\left(E_{(t)}\times -kc\times E_{\max (t)}\right)}{1-e^{-\Delta tk}+ck\Delta t+c{e}^{-\Delta tk}-c}}$$

(30)

$$P_{\max ,bb}^{-}={\displaystyle \frac{\left[E_{\max }\left(1-e^{-{\alpha }_c\Delta t}\right)-E(t)\left(1-e^{-{\alpha }_c\Delta t}\right)\right]}{\Delta t}}$$

(31)

4.1.3. Wind Turbine

The selection and positioning of wind turbines should be influenced by wind speed. Wind turbines transform mechanical energy produced by wind’s kinetic energy into electrical energy. Because there was a shortage of on-ground data, HOMER Pro used the NASA-provided built-in profile to carry out the recommended study. Using base-line data, HOMER Pro applied the Power Law to calculate scaled wind speed statistics while accounting for the numerous hub heights of selected wind turbines, as shown in Equation (32) [62]. µ_ane and µ_hh in Equation (32) stand for the wind speed at anemometer and hug height, respectively. To represent the wind system in HOMER Pro, specific variables were stated as the Weibull distribution function, as given in Equations (33) and (34) [7]. The variables “c”, “k”, and “v” represent the Weibull scale, Weibull shape, and wind speed (m/s), respectively. As a result, Figure 6 displays the average monthly wind speed of the shortlisted BTS sites. The average wind speed in Pakistan’s southern region is higher than in its northern and central parts, at 8.0 m/s.

$${\mu }_h={\mu }_{ane}{\left({\displaystyle \frac{H}{H_o}}\right)}^R$$

(32)

$$f(u)=exp((-{(u/c)}^k)\ast (k/c){(c/u)}^{(1-k)}$$

(33)

$$v=c\varphi \left({\displaystyle \frac{k+1}{k}}\right)$$

(34)

There were four wind turbine models, named Bergey Excel 6-R, Ecocycle EO10, Bergey Excel 10, and Xzeres 7.2, evaluated in this study. In these four wind turbine models, Bergey Excel 6-R had a rated capacity of 6 kW, while all the others had a rated capacity of 10 kW. Figure 7 displays their distinct power curves. The Bergey Excel 10 wind turbine was ultimately chosen as the best option because of its max cutoff speed of 20.0 m/s, min cut-in wind speed of 3.0 m/s, and its compensations in transportation and fitting.

Table 5. The Bergey Excel (10 kW) wind turbine’s cost and technical parameters with losses.

Capital Cost	Rated Capacity	(Ta) Effect	O&M Cost	Replacement Cost	Lifetime	Temp. Coefficient
(USD/kW)	(kW)		(USD/year)	(USD/kW)	(year)	(%/°C)
1500	10	Yes	1250	1250	20.00	−0.38
Loss type	Electrical loss	Miscellaneous loss	Availability loss	Balance of plant loss	Wake effect loss
	(%)	(%)	(%)	(%)	(%)
value	10	10.54	5.18	1.7	5.0

[7] displays the cost of a wind turbine with its technical specifications and losses. Equation (35) [7] explains power density (E), while Equation (36) [7], shows the method for calculating the wind’s power output and describes the procedure for power density (E).

$$E(w/m^2)=\varphi \left({\displaystyle \frac{k+3}{k}}\right)\times c^3\times f(v)$$

(35)

$$P_{wind}={\displaystyle \frac{1}{2}}\rho A{V}^3$$

(36)

$$P_{WT}=P_{WT,STP}\times \left({\displaystyle \frac{\rho }{{\rho }_a}}\right)$$

(37)

It is noted that the output power only relies on the cross-sectional area (A) at the STP, wind speed (v), and air density (ρ) [7]. HOMER uses the air density ratio, which compares the actual air density to the ideal air density, to calculate the power output of a wind turbine at a specific altitude. Equation (41) [7] expresses the power characteristics of a wind turbine. It is important to emphasize that, in order to accurately represent real-world installations, this research takes into account the various losses associated with wind turbines. These losses were accrued due to several licenses NEPRA granted to municipal and private firms to construct wind farms with varying capacity levels, as depicted in Table 5 [7].

$$\rho ={\displaystyle \frac{P}{R}}\left({\displaystyle \frac{1}{T}}\right)$$

(38)

$${\displaystyle \frac{\rho }{{\rho }_O}}=\left({\displaystyle \frac{T_o}{T_o-Bz}}\right)\times {\left(1-{\displaystyle \frac{Bz}{T_o}}\right)}^{(g/RB)}$$

(39)

$$P_{WT,STP}=C_p\times {\displaystyle \frac{1}{2}}\rho A{V}^3$$

(40)

$$\left\{\begin{array}{l}{P}_{rated}=P_{WT} if v_{cut-out}\ge v\ge v_{rated}\\ P_{WT}=P_{rated}\left({\displaystyle \frac{v^3}{{v^3}_{rated}-{v^3}_{cut-in}}}\right)-{v^3}_{cut-in}\left({\displaystyle \frac{P_{rated}}{{v^3}_{rated}-{v^3}_{cut-in}}}\right)if v_{rated}\ge v\ge v_{cut-in}\\ P_{WT}=0 if v_{cut-in}>v>v_{cut-out}\end{array}\right\}$$

(41)

$$L_{overall}={\displaystyle \prod _{i=1}^{n}1}+{\displaystyle \frac{L_i}{100}}$$

(42)

4.1.4. Power Converter

A power converter is used to transform electrical energy, altering voltage or frequency. It can convert a current from an alternating current (AC) to a direct current (DC) or any combination. In this setup, an AC generator and a bi-directional power converter connected the AC and DC buses for two-way power conversion. The rating of the converter, typically determined based on the power wattage of the connected energy, was expected to have an efficiency of 85%. For a solar PV energy source on the DC bus, Equations (43) and (44) [7] were used to calculate the converter’s efficiency and capacity. Technical stipulations and the converter’s value are detailed in

Table 6. Selected converter: cost and technical parameters.

Cap. Cost	Rep. Cost	Relative Capacity	Efficiency	Lifetime
(USD/kW)	(USD/kWh)	(%)	(%)	(Yrs)
150	150	100	95	15

[7].

$$C_{power.conv}(\mathrm{kW})={\displaystyle \frac{100}{85}}{P}_{array}$$

(43)

$${\eta }_{power.conv}(\%)={\displaystyle \frac{Outpu{t}_{conv}}{Inpu{t}_{conv}}}\times 100$$

(44)

4.1.5. Hydel Power System

Khyber Pakhtunkhwa, in the northern zone, holds significant hydropower potential at 30,000 MW [44]. However, due to variable hydel flow in winter and geographical instability, relying solely on this source raises reliability concerns. To address this, standby options like DGs are maintained. The region already hosts cost-effective micro and minor hydroelectric plants, serving rural villages. Utilizing these for BTS load requirements is suggested. By investing in existing hydro infrastructure, the telecom sector can access environmentally friendly electricity. Data from previously established hydro plants by organizations like WAPDA and PEDO were assessed, with potential hydel resources indicated in

Table 7. Hydel systems for each selected BTS site: technical and financial parameters.

Sr. No	BTS Site	Capital Cost	Replacement Cost	O&M Cost	Design Flow Rate	Net Head	Pipe Head Losses	Lifetime
		(USD/kWh)	(USD/kWh)	(USD/kWh/year)	(m3/s)	(m)	(%)	(yrs.)
1	Dir	1589	1191.75	39.726	1.250	30	15	25
2	Mardan	863	647.25	21.576	1.950	32	15	25
3	Chitral	801.746	601.308	20.044	1.750	39	15	25
4	Swat	2831	2123.25	70.776	1.850	34	15	25
5	Buner	1387.5	1040.626	34.688	1.760	36	15	25
6	Kohistan	2744	2058	68.6	1.790	37	15	25
7	Mansehra	1104	828	27.6	1.760	33	15	25
8	Malakand	744	558	18.4	1.500	35	15	25
9	Kamri	1580	1185	39.5	1.350	35	15	25

. Monthly water flow rates for selected sites are depicted in Figure 8, considering peak flow rates during turbine selection. Dir has the highest annual flow rate of 15.86 m³/s, while Kohistan has a lower rate of 1.69 m³/s. Most locations experience an avg flow rate of 0.50 m³/s from November to March, significantly increasing in the summer months. From the 42 BTS sites, this project focused on 9 sites, where the integration of hydropower was feasible in microgrids. Generic 5-kW rated capacity hydro turbines were selected based on the design flow rate and net head availability at each site, with an efficiency of 80%, aligning with BTS load profiles. These turbines boast a 25-year lifespan, assuming a 15% pipe head loss. Costs vary by site (see Table 7). HOMER Pro calculates the max and min allowable flow rates using monthly average values from the design flow rate, as shown in Equations (45) and (46) [7].

$$L_{allow(\max )}=L_{design}\times w_{\max }$$

(45)

$$L_{allow(\min )}=L_{design}\times w_{\min }$$

(46)

HOMER calculates this value in each time step using these conditions: Equations (47) and (48) [7].

$$\left\{\begin{array}{l}{L}_{turb}=0 if L_{allow(\min )}>L_{avail}\\ L_{turb}=L_{avail} if L_{allow(\max )}\ge L_{avail}\ge L_{allow(\min )}\\ L_{turb}=L_{allow(\max )} if L_{allow(\max )}<L_{avail}\end{array}\right\}$$

(47)

$$Q_{avail}=Q_{str}-Q_{res}$$

(48)

The ideal net head is a crucial factor in the layout and design of a hydro turbine. Table 7 summarizes HPPs’ available net heads for each site. Using Equation (49), HOMER Pro determines the effective net head, employing the available net head, where “f_h” is the pipe head loss (15%) and “h” is the available head (m). HOMER determines the hydro turbine’s power output (P_hyd) as indicated in Equation (50) [7].

$$h_{net}=h-h{f}_h$$

(49)

$$P_{hyd}={\displaystyle \frac{\mathrm{kW}}{1000\mathsf{W}}}\times \left({\eta }_{turbine}\ast {\eta }_{hyd}\ast {\rho }_{water}\ast g.h_{net}\right)$$

(50)

4.1.6. Biogas System

Biomass is an abundant and long-standing energy source of organic material, including agricultural, wood, animal, and human waste. It is a mixture of gases produced by microorganisms, including carbon dioxide (CO₂) and methane (CH₄) [7]. In this study, only animal waste, specifically from cows and buffaloes, was considered. Monthly average manure production, assumed constant year-round, varies by site based on animal density. Data on animal availability for each site were sourced from PBS Livestock Census 2006 and were projected for 2022, accounting for a growth rate of 1.13% (1986–2006). Equations (51) and (52) were used to express the process for calculating the number of animals for the chosen area. Equation (53) [7] was used to compute the total annual manure output (M) from animals.

Table 8. Characteristics of biogas and process of generating power using produced manure at selected BTS sites.

BTS Sites	Village/Area Near BTS Site	No. of Houses Covered by BTS Sites	Variable nh	Total Number of Animal (N)	Total Manure (ton/year)	Biogas Production (m3) KDMc = 0.18	Energy (kWh)	Estimated Power (kW)
				G.R = 1.13%		KOMc = 0.86		Thours = 10
Chakwal	Dhoda	57	3.44	196.08	751.4766	1962	42.283	7.120
Talagang	Mamdot	66	3.44	227.04	870.1308	2272	48.959	8.159
Sheikhupura	Bhadroo	80	2.370	189.60	726.6420	1897	40.885	6.814
Bhakkar	Daryai	50	5.80	290.00	1111.425	2901	62.536	10.42
DG Khan	Allahabad	49	6.44	315.56	1209.387	3158	68.048	11.34
Layyah	Noor Abad	44	5.92	260.48	998.2896	2606	56.170	9.361
R.Y. Khan	Chak 152P	61	4.56	278.16	1066.048	2783	59.983	9.997

estimates the production of manure and electricity. Equation (54) [7] was used to determine the volume of biogas (VBG) produced from animal dung.

$$N_{region}=N{H}_{region}\times n_h$$

(51)

$$n_h={\displaystyle \frac{N{d}_{istrict}}{N{H}_{district}}}$$

(52)

$$M=m_a\times N$$

(53)

$$V_{BG}=V_{BG}\times M\times k_{DMc}\times k_{OMc}$$

(54)

This study used monthly average biomass resources to generate electrical power using a custom-sized generic biogas generator. Since local biomass supplies were accessible, the cost of biogas fuel (USD/kg) was assumed to be zero.

Table 9. Biogas generic genset (5 kW): cost and technical parameter.

Capital Cost	Replacement Capacity	O&M Cost Per kW	Minimum Load Ratio	Organic Matter Content	Dry Matter Content
(USD)	(USD)	(USD/op. hours)	(%)	(KOMc)	(KDMc)
855	570	0.10	50	0.86	0.18
Lifetime	Lower heating value	Carbon content	Density	Slope	Intercept coefficient
(Hours)	(MJ/kg)	(%)	(kg/m3)	(kg/hr./kW output)	(kg/hr./kW output)
20,000	5.5	5.0	0.720	2.0000	0.10000

[7] provides a biogas generator’s price and other technical details. Equation (55) was used to compute biogas energy (E_BG) produced from animal dung, while Equation (56) was used to estimate electric power (P_BG) produced from available manure [46]. In this study, 07 BTS sites were used for the study of biomass. The load profile of the BTS sites was not greater than 6 kW.

Therefore, villages/areas with average animal measurements were used for this study to produce 7 kW to 10 kW power so that the base load could be met.

$$E_{BG}=e_h\times V_{BG}$$

(55)

$$P_{BG}={\displaystyle \frac{1}{k}}\left({\displaystyle \frac{e_h\times V_{BG}}{T_{hours}}}\right)$$

(56)

5. Analysis and Discussion of Results

The following explanation of the results is presented and conducted in four portions. The Section 1 covers a techno-economic analysis and examination of objective functions. The Section 2 provides a comparative analysis between the proposed and existing BTS sites. The Section 3 introduces an environmental analysis focusing on greenhouse gas (GHG) emissions. The Section 4 presents a sensitivity analysis, exploring various uncertainties such as solar irradiance, the DR, the IR, wind speed, project life, and load demand.

5.1. Techno-Economic Optimization

The aim of techno-economic assessment is to extend system components in a way that is both cost-effective and ensures the reliability and performance of the system. Therefore, 42 distinct BTS sites were simulated by HOMER Pro while considering site-mandatory variables. Thus, the suggested BTS sites were adapted to the region based on the NPC and LCOE.

Table 10. Optimized arrangement for selected BTSs with optimal decision pointers.

BTS Site No.	Project Area	Optimal BTS Configuration	Dispatch Strategy	Cost with Objective Parameters				Financial Parameters			Capacity Shortage	Excess Energy	Unmet Load
				LCOE	ICC	OPC	NPC	IRR	ROI	PBP
				(USD/kWh)	(Million USD)	(USD/yr.)	(Million USD)	(%)	(%)	(yr.)	(%)	(%)	(%)
Northern Zone
BTS-01	Chakwal	PV-BM-B	L.F	0.1241	0.0314	1589	0.0658	25.7	21.2	3.83	0	20.4	0
BTS-02	Islamabad	PV-DG-B	L.F	0.1268	0.0798	4304	0.1728	25.8	21.2	3.81	0	31.5	0
BTS-03	Jehlum	PV-DG-B	L.F	0.1296	0.0354	1878	0.0760	25.0	20.5	3.92	0	29.2	0
BTS-04	Rawalpindi	PV-DG-B	L.F	0.1270	0.0690	3704	0.1491	25.7	21.2	3.83	0	31.4	0
BTS-05	Talagang	PV-BM-B	L.F	0.1263	0.0264	1320	0.0550	25.1	20.7	3.91	0	19.2	0
BTS-06	Taxila	PV-DG-B	L.F	0.1278	0.0679	3573	0.1451	25.0	20.6	3.92	0	30.8	0
BTS-07	Bajaur	PV-DG-B	L.F	0.1266	0.0953	5131	0.2062	25.8	21.2	3.82	0	31.8	0
BTS-08	Dir	PV-W-B-HYD	L.F	0.0529	0.0265	624	0.0400	50.4	46.7	1.96	0	46.7	0
BTS-09	Mardan	PV-W-B-HYD	L.F	0.0742	0.0223	525	0.0337	35.1	30.9	2.89	0	66.1	0
BTS-10	Chitral	DG-PV-B-HYD	L.F	0.1073	0.0369	6216	0.1706	66.9	62.6	1.49	0	29.3	0
BTS-11	Swat	W-HYD-B	L.F	0.0703	0.0464	995	0.0679	36.0	31.9	2.82	0	25.5	0
BTS-12	Kohat	PV-DG-B	L.F	0.1322	0.0424	2206	0.0900	24.0	19.6	4.08	0	32.9	0
BTS-13	Nowshera	PV-DG-B	L.F	0.1296	0.0418	2149	0.8833	24.4	20.0	4.00	0	32.8	0
BTS-14	Buner	PV-W-B-HYD	L.F	0.0454	0.0257	601	0.0387	59.8	56.4	1.67	0	38.5	0
BTS-15	Peshawar	PV-DG-B	L.F	0.1297	0.0418	2152	0.0883	24.4	20.0	4.00	0	32.8	0
BTS-16	Abbottabad	PV-DG-B	L.F	0.1338	0.0420	2277	0.0912	24.0	19.6	4.07	0	33.3	0
BTS-17	Kohistan	PV-W-B-HYD	L.F	0.0532	0.0326	773	0.0493	50.1	46.5	1.97	0	35.0	0
BTS-18	Mansehra	PV-W-B-HYD	L.F	0.0457	0.0242	564	0.0364	59.6	56.1	1.68	0	45.8	0
BTS-19	Gilgit	PV-DG-B	L.F	0.2325	0.0253	3511	0.1012	16.9	12.7	5.70	0	44.6	0
BTS-20	Mingora	PV-DG-B	L.F	0.1258	0.0368	2043	0.0809	26.6	21.9	3.72	0	31.0	0
BTS-21	Malakand	DG-PV-W-HYD	L.F	0.0457	0.0408	516	0.0519	49.9	46.6	1.98	0	30.1	0
BTS-22	Kamri	DG-PV-W-HYD	L.F	0.0762	0.0425	636	0.0425	29.8	25.9	3.37	0	49.3	0
BTS-23	Mirpur	PV-DG-B	L.F	0.1290	0.0221	1234	0.0488	25.6	20.9	3.84	0	32.7	0
BTS-24	Muzaffarabad	PV-DG-B	L.F	0.1384	0.0593	3374	0.1323	25.2	20.6	3.90	0	32.3	0
Central Zone
BTS-25	Lahore	PV-DG-B	L.F	0.1272	0.0647	3466	0.1396	25.6	21.0	3.84	0	31.2	0
BTS-26	Sheikhupura	PV-BM-B	L.F	0.1175	0.0403	2351	0.0911	29.8	25.0	3.36	0	9.91	0
BTS-27	Bhakkar	PV-BM-B	L.F	0.1237	0.0377	1831	0.0773	25.4	20.9	3.87	0	20.9	0
BTS-28	Khushab	PV-DG-B	L.F	0.1315	0.0383	2029	0.0821	24.4	20.0	4.00	0	32.4	0
BTS-29	Mianwali	PV-DG-B	L.F	0.1310	0.0324	1713	0.0695	24.4	20.0	4.01	0	31.8	0
BTS-30	DG Khan	PV-BM-B	L.F	0.1318	0.0241	1194	0.0499	23.4	19.0	4.19	0	21.1	0
BTS-31	Layyah	PV-BM-B	L.F	0.1308	0.0244	1159	0.0495	23.1	18.8	4.23	0	23.9	0
Southern Zone
BTS-32	Karachi-I	DG-PV-W-B	L.F	0.1096	0.0898	4098	0.1783	28.5	24.1	3.50	0	31.3	0
BTS-33	Karachi-II	DG-PV-W-B	L.F	0.1111	0.0399	1754	0.0778	27.6	23.1	3.60	0	38.5	0
BTS-34	Badin	DG-PV-W-B	L.F	0.1180	0.0307	1264	0.0580	25.1	20.6	3.92	0	48.8	0
BTS-35	Hyderabad	DG-PV-W-B	L.F	0.1171	0.0394	1664	0.0754	25.6	21.1	3.85	0	43.2	0
BTS-36	Mirpur Khas	DG-PV-B	L.F	0.1303	0.0225	1240	0.0493	25.1	20.5	3.90	0	31.2	0
BTS-37	Ghotki	DG-PV-B	L.F	0.1333	0.0389	2169	0.0858	24.7	20.1	3.97	0	30.7	0
BTS-38	Rajan Pur	DG-PV-W-B	L.F	0.1294	0.1030	4979	0.2106	23.8	19.4	4.13	0	30.9	0
BTS-39	Rahim Yar Khan	PV-BM-B	L.F	0.1245	0.0328	1641	0.0683	25.3	20.9	3.88	0	19.3	0
BTS-40	Sukkur	DG-PV-B	L.F	0.1282	0.0221	1224	0.0485	25.7	21.1	3.81	0	29.6	0
BTS-41	Gawadar	DG-PV-B	L.F	0.1355	0.0266	1380	0.0564	23.4	18.0	4.18	0	31.6	0
BTS-42	Quetta	DG-PV-B	L.F	0.1246	0.0343	1795	0.0731	26.1	21.7	3.77	0	28.6	0

provides all the BTS sites and the corresponding ideal system forms and decision factors in this preliminary research. While establishing the most optimal system arrangement, designing components and cost parameters, the key considerations include the probability of different energy resource availability at the selected site and the overall regional electricity demand. For each area under study, an ideal BTS site design category was selected in order to quantify the analysis. Section 5.1.1, Section 5.1.2, Section 5.1.3, Section 5.1.4, Section 5.1.5, Section 5.1.6 and Section 5.1.7 examine the techno-economic enhancement of a selected BTS site design, considering itemized component sizing with relevant structures. Therefore, using a radar chart, Figure 9 and Figure 10 present a comparative study of hybrid configurations involving more than two energy resources, considering parameters such as the IRR, ROI, NPC, PBP, LCOE, and excess electricity.

5.1.1. DGs with PV and Battery Storage Systems (DG-PV-B)

Twenty-one of the forty-two BTS locations that were examined for optimization results showed an ideal configuration based on PV-B-DG. Table 10 demonstrates that most northern regions have PV-B-DG configurations since solar energy is more prevalent there and wind energy is less prevalent. Therefore, a PV system with battery storage is the ideal choice for the northern region. Among the northern sites, Bajaur BTS-07 is the most economical, with an LCOE of 0.1266 USD/kWh and an NPC of 0.2062 million USD. Conversely, Gilgit BTS-13, facing higher electrical demand and lower solar irradiance, exhibits the maximum LCOE in the northern region at 0.2325 USD/kWh, with an NPC of 0.1012 million USD. Temperature variance is a significant factor, with Gilgit’s avg. annual temperature of 3.76 °C contrasting with Bajaur’s temperature of 17.71 °C. Gilgit’s low avg. temperature, particularly in the winter, limits solar irradiation due to cloudy days, which lowers PV performance and efficiency. By contrast, Lahore BTS-25 was determined to be the best site in the central area, offering an NPC of 0.1396 million USD at the lowest cost of 0.1272 USD/kWh. Khushab BTS-28 reported having a maximum LCOE of 0.1315 USD/kWh and an NPC of 0.0821 million USD, simultaneously. Out of eleven sites in the south, five BTS sites have the PV-B-DG arrangement that works best. Quetta BTS-42 has a minimum LCOE (0.1246 USD/kWh) and NPC (0.0731 million USD) because of the higher temperature and sun radiation there, while Gawadar BTS-33 has an NPC of 0.0564 million USD and a maximum LCOE of 0.1355 USD/kWh. Figure 9 and Figure 10 compare analyses on the IRR, ROI, NPC, PBP, LCOE, and excess energy of the selected BTS sites.

5.1.2. PV-Wind and Battery with Hydro Energy (PV-W-B-HYD)

It can be observed from Table 10 that among the 42 BTS sites, only 5 (BTS-08 Dir, BTS-09 Mardan, BTS-14 Buner, BTS-17 Kohistan, and BTS-18 Mansehra) were optimized for PV-W-B-HYD configuration. Of these, BTS-14 Buner stands out as the most viable site with the lowest LCOE at 0.04547 USD/kWh and an NPC of 0.0387 million USD. By contrast, BTS-17 Kohistan has a maximum LCOE of 0.05321 USD/kWh with an NPC of 0.0493 million USD. The comparison analysis is presented in Figure 9 and Figure 10. Table 7 shows the technical and financial parameters that demonstrate that in five BTS sites, there is a dominance of hydro energy with solar and wind energy. This also happens due to the temperature effects of these selected sites, as shown in Figure 6.

5.1.3. DG-PV and Wind with Battery Storage System (DG-PV-W-B)

Table 10 shows that five BTS sites in the south area have the ideal DG-PV-W-B arrangement. The wind speed is the only factor that matters here; these five sites are BTS-32 Karachi-I, BTS-33 Karachi II, BTS-34 Badin, BTS-35 Hyderabad, and BTS-36 Rajanpur. Figure 6 shows the most significant wind speed of the BTS sites. BTS-32 Karachi-I has the lowest LCOE of 0.1096 USD/kWh and an NPC of 0.1783 million USD in the southern region. Conversely, BTS-38 Rajan Pur has an NPC of 0.21096 million USD and a maximum LCOE of 0.1294. Because of how fast the wind blows at these two locations, the LCOE varies. BTS-38 Rajanpur has a wind speed of 4.61 m/s, compared to BTS-32 Karachi-I’s annual average wind speed of 5.96 m/s. Figure 9 and Figure 10 compare analyses of the IRR, ROI, NPC, PBP, LCOE, and excess energy of the selected BTS sites.

HOMER Pro employs two methods of dispatch. The primary method is called the load-following dispatch, and the secondary one is called the cycle charging (CC) dispatch. A DG only runs at maximum capacity in a CC dispatch; any further energy is regarded as excess energy needed to replenish the battery. When there is little to no penetration of renewable energy, this approach is most likely to be effective. When using a load-following dispatch technique, which only supplies the electricity that is required, a DG is required. When several heterogeneous sources are introduced to the system and there is substantial utilization of renewable energy, this approach performs best [7]. As a result, the Quetta BTS-42 site was used to assess the effectiveness of two dispatch options, as shown in

Table 11. Comparison among dispatch strategies employed in this present study.

Dispatch Strategy	LCOE	Payback Time	Operating Cost	Excess Electricity
	(USD/kWh)	(Year)	(USD)	(%)
Load Following (LF)	0.1246	3.77	1795	28.6
Cycle Charging (CC)	0.3150	8.94	7282	64.6

. The LF method outperformed the CC method, indicating a higher index. The better asset management standards and power regulation used by the LF system, which satisfies load demand, are the causes of these gains.

5.1.4. DG-PV and Wind with Hydro Energy (DG-PV-W-HYD)

Only two BTS sites have shown this ideal configuration of DG-PV-W-HYD in all the selected BTS sites. These two BTS sites are BTS-21 Malakand and BTS-22 Kamri. These two sites have an LCOE of 0.04575 USD/kWh and 0.07618 USD/kWh, respectively. The NPCs of these sites is 0.0519 million USD and 0.0425 million USD, respectively. These two sites, Malakand and Kamri, belong to the northern region of Pakistan. These are purely dependent on hydro, photovoltaic, and wind speed as well. In addition, these two sites have the least operating cost of all the other selected sites. The operating cost of Malakand and Kamri is 516 USD/yr. and 636 USD/yr., respectively. The LF dispatch technique was used, and all the objective functions and financial parameters are revealed in Table 10. Figure 9 and Figure 10 compare analyses for the IRR, ROI, NPC, PBP, LCOE, and excess energy of the selected BTS sites.

5.1.5. PV and Biomass with Battery Storage System (PV-BM-B)

Pakistan has a high potential for biomass resources. Therefore, the entire district was not considered, but areas/villages near the BTS sites were considered, as shown in Table 8. The optimization results show that seven BTSs using biomass production have optimal configurations of PV-BM-B. These seven BTSs are BTS-01 Chakwal, BTS-05 Talagang, BTS-26 Sheikhupura, BTS-27 Bhakkar, BTS-30 DG Khan, BTS-31 Layyah, and BTS-39 Rahim Yar Khan. In addition, BTS-26 Sheikhupura reflects the most feasible sites with an LCOE of 0.1175 USD/kWh and an NPC of 0.0911 million USD. By contrast, BTS-30 DG Khan has a maximum LCOE of 0.1318 USD/kWh and an NPC of 0.0499 million USD. Figure 9 and Figure 10 compare analyses for the IRR, ROI, NPC, PBP, LCOE, and excess energy of the selected BTS sites. The battery system’s initial SOC is set at 100% during modeling, relying on available autonomy until reaching its maximum discharge. During surplus electrical output periods, such as off-peak hours or peak PV operation, the battery charges at its maximum rate. The biogas generator consistently uses an LF dispatch strategy, ensuring it supplies sufficient power for immediate battery charging. Table 10 provides dimensions and technical details for the recommended configuration.

5.1.6. Wind and Hydro with Battery Storage System (W-HYD-B)

Due to its low operating costs, hydel electricity is a commonly used energy source and the primary energy source in most nations, including Pakistan. Only one BTS site named BTS-11 Swat has an optimal configuration of W-HYD-B, which can be seen in Table 10. BTS-11 Swat Chitral has an LCOE of 0.07035 and an NPC of 0.0679 million USD. The operating cost of BTS-11 Swat is 995 USD/yr. Figure 9 and Figure 10 illustrate a comparison of analyses for the IRR, ROI, NPC, PBP, LCOE, and excess energy of the selected BTS sites.

5.1.7. DG-PV and Battery with Hydro Energy (DG-PV-B-HYD)

Northern regions typically experience fewer hours of sunshine throughout the winter than the south and northwest. But at the same time, throughout the winter, most of the northern regions experience temperatures below a specific level, which results in the freezing of water bodies. Additionally, snowfall at various sites harms the PV system’s performance, creating a serious capacity shortfall and reliability problem. To address these concerns, a diesel generator (DG) was used in this study. This can be seen from Table 10; only one site named BTS-10 Chitral has an optimal configuration of DG-PV-B-HYD with an LCOE of 0.1073 and with an NPC of 0.1706 million USD. The monthly energy production of all nominated BTS sites is displayed in Figure A1.

5.2. Comparative Analysis

5.2.1. Levelized Cost of Energy

The LCOE of current and planned BTS (Figure 11) sites were used to compare the findings of this research study shown in Figure 11. DGs are the only power source for BTS sites’ current infrastructure. The present BTS sites have a higher LCOE as a result of rising diesel prices. It was noted that the LCOE of conventional BTS sites ranges from 0.3774 to 0.3916 USD/kWh. The integration of PV, wind, hydro, and biomass sources with BTS sites resulted in a reduced LCOE across all locations, aligning with the primary objective of our study. The avg. LCOE for all suggested sites is 0.14 USD/kWh. All of the sites were found to have a lower LCOE after integrating PV, wind, hydro, and biomass sources with BTS sites, which was the main goal proposed in this study. Thus, the avg. LCOE of all the proposed sites is 0.14 USD/kWh. Additionally, DG-PV-W-HYD configuration sites at the planned BTS sites have a marginally lower LCOE than all the other configuration sites. Sites with more substantial reliance on hydro and wind energy may be more practical because hydro and wind energy efficiency is higher than solar efficiency. In addition, the optimal configuration of DG-PV-B has a slightly higher LCOE than all the other configurations in the proposed BTS sites because depending on solar energy only is not enough due to the lower efficiency levels of solar energy. Research has shown that the proposed study performed far better than current BTS sites regarding economic aspects.

5.2.2. Operating Costs and Net Present Cost

Figure 12 illustrates the net present and operating costs for both proposed and current BTS sites. Integrating renewable energy has significantly reduced these costs, mainly due to the decreased reliance on DGs. This shift makes renewable energy the primary energy source, leading to lower DG hours and fuel consumption. The NPC drops from an avg. of 0.4 million USD in a conventional BTS to 0.1 million USD in the proposed BTS, reflecting reduced fuel usage dependence. Solar energy emerges as a more cost-effective and technically superior option compared to DGs. Wind and hydro energy, known for their higher efficiency and environmentally friendly production, are gaining dominance. Biomass also contributes to economically sound energy production, collectively reducing overall costs.

Table A1. Essential variables such as desired inputs, datasets, assumptions, and other critical factors.

Inputs	Value	Source
Resource Factors
Temperature	HOMER NASA data	Location dependent
Solar radiations	HOMER NASA data	Location dependent
Hydel related parameters	HPPs	PEDO and WAPDA [7]
Speed of wind	HOMER NASA data	Location dependent
WT losses	LF: 21.02702%	Source: [7]
Biomass resources	tons	KPK livestock census 2006
System economics
DR	9.75%	Pakistan State Bank [7]
IR	8.50%	Pakistan State Bank [7]
ACSF	0.00%	Not Applicable
Project life	25 yrs.	Not Applicable
Minimum renewable fraction	0.00%	Not Applicable
Electrical load
kWh and kWh/day	Site-dependent	Pakistan Telco
Timestep and day-to-day variability	0–1%	Not Applicable
Load profile	Commercial (constant)	HOMER dependent built in
Fuel and System costs
Diesel fuel price	1.03 USD/L	Market value: Oct 05,2023
Cost parameters	USD/kW	Previous studies
Biogas fuel price	0.00 USD/kg	Not Applicable
Optimization settings
Optimization timestep	60 min	Not Applicable
Focus factor	50	Not Applicable
NPC precision	0.0100	Not Applicable
System design precision	0.0100	Not Applicable
Battery Autonomy	Less than 2 h	Not Applicable
Maximum simulation per optimization	10,000	Not Applicable
Operating reserve
Load in current time step	10.00%	Not Applicable
Wind power output	50.00%	Not Applicable
Solar power output	80.00%	Not Applicable

provides details on PV (kW), capacity in kilowatts, wind turbines, distributed generation (kW) capacity in kilowatts, DG hours, and fuel consumption along with associated carbon emissions.

5.3. Environmental Analysis

Environmental, social, and economic factors are the three main pillars that sustainability assessments include. This study specifically focused on environmental assessment, employing a life cycle perspective spanning 25 years, to analyze greenhouse gas (GHG) emissions from each BTS site. The primary emphasis in this analysis is on Co₂ emissions, as they exert the most significant influence on the overall GHG emission factors. Nonetheless, the computation of carbon emissions includes a part related to fuel consumption and system modeling. A mathematical notation for calculating carbon emissions is found in Equation (57). “E_fc” stands for emission factor, “A_r” stands for activity rate, and “η_ER” stands for overall emission reduction efficiency (%).

$$E=E_{fc}\times A_r\times {\displaystyle \frac{\left(1-{\eta }_{ER}\right)}{100}}$$

(57)

Figure 13 shows a graphical representation of the comparison of all proposed and existing BTS sites’ carbon emissions. Due to the diesel generator’s high fuel consumption at the BTS sites in operation now, carbon emissions are significantly greater. As a result, Bajaur, Rajan Pur, and Karachi have increased annual carbon emissions by 58.705 (tons/Yr). After incorporating renewable resources, the best option for each of the chosen BTS locations was found. The energy sources used at the proposed BTS locations are solar, wind, biomass, and hydro energy. As a result, a diesel generator’s demand for diesel fuel decreases, and it emits fewer greenhouse gases. As a result, the Dir and Mardan BTS site’s most negligible carbon emission is 0.00016 (tons/Yr) of the proposed BTS sites. By contrast, the Chitral BTS site has higher carbon emissions at 8.978 (tones/Yr). Therefore, it can be said that these suggested BTSs are both technically and economically more effective and environmentally beneficial.

5.4. Sensitivity Analyses

Finding out how a perfect system might react to different factors, like changes in load, wind speed, IR, DR, and solar irradiance, is made simpler with the help of sensitivity analysis. This study selected the BTS-38 Rajanpur (DG-PV-W-B) site to analyze the sensitivity of the variables’ effects.

5.4.1. Wind Speed and Solar Irradiance

Sensitivity analysis is performed by varying GHI and wind speed by ±1.0% around reference values of 5.02 kWh/m²/day and 4.61 m/s, respectively. In Figure 14, the sensitivity analysis shows an inverse relationship between the LCOE and NPC with changes in solar radiation and wind speed.

Therefore, increasing wind speed from 3.6 m/s to 5.61 m/s led to a decrease in the NPC and LCOE from 0.22106 to 0.19236 million USD and from 0.136 to 0.118 USD/kWh, respectively. Conversely, raising solar irradiance from 4.05 to 6.05 kWh/m₂/day resulted in a decrease in the LCOE and NPC from 0.139 to 0.122 USD/kWh and from 0.22661 to 0.19834 million. This sensitivity analysis highlights a more pronounced impact of changes in wind speed and solar irradiation on the NPC and LCOE.

5.4.2. Inflation and Discount Rates

Sensitivity analysis is performed, by varying IR and DR by ±1.0% around reference values of 9.75% and 8.5%, respectively. In Figure 14 (middle), the LCOE drops from 0.145 to 0.115 USD/kWh, whereas the NPC rises from 0.18643 to 0.23772 million USD due to a rise in the inflation rate from 6.5% to 10.5%. This substantial reduction in the LCOE is attributed to increased government funding given to power companies in response to the nation’s higher inflation rate. Conversely, Figure 14 shows that when discount rates increase from 7.75% to 11.75%, the LCOE rises from 0.115 to 0.145 USD/kWh, and the complete NPC decreases from 0.23791 to 0.18999 million USD.

5.4.3. Load Variation and Project Lifetime

Future telecom companies may see an increase in electrical load as a result of growing customer numbers. Therefore, sensitivity is assessed by altering electrical demand with an expected fluctuation of 20 kWh/day, using a 206.4 kW load as reference. In Figure 14 (bottom), a complete graphical picture is presented. The link between the NPC and LCOE and load variation can be seen to be direct or inverse, respectively. The NPC grew from 0.17609 to 0.25251 million USD by raising the load from 166.4 to 246.4 kWh/day, while the LCOE decreased from 0.134 to 0.13 USD/kWh. This is due to the fact that when the supply of any particular commodity decreases, the price per unit rises. Therefore, by increasing the project lifetime, the NPC increases and the LCOE decreases. The NPC improved from 0.06034 million USD to 0.21073 million USD. However, the LCOE decreased from 0.166 USD/kWh to 0.129 USD/kWh.

The recommendations set by this study for NEM-related strategies and policies aim to create an energy surplus through internal development that is both dependable and sustainable. Emphasizing equality and job creation can enhance the social and environmental aspects of the area in question. A hybrid energy system, incorporating diverse energy sources, ensures security and reliability. The region under study may benefit greatly from this research in meeting its targets for a sustainable energy mix set by governing bodies, corporate power, and energy groups.

6. Policy Recommendations and Implications for Future Research

Pakistan possesses significant potential for harnessing renewable energy from natural sources. This study highlights the finding that the central and northern regions of the country predominantly utilize solar radiation, hydropower, and biomass. Consequently, BTS locations in these areas demonstrate substantial opportunities for adopting solar, hydro, and biomass energy, with diesel generators serving as backup. Despite the southern region experiencing strong winds, certain locations still rely on wind energy. The ideal solution for telecom division to transition its load entirely to renewable resources for sustainability varies by region, incorporating a combination of solar, biomass, wind, and hydropower, supported by battery storage. Despite the favorable winds in the south, some locations continue to rely on less sustainable diesel generators. This research can guide policy initiatives, advocating for financial support to encourage the telecom industry’s adoption of renewable energy, addressing both economic and environmental concerns associated with diesel generators.

In line with aims to conduct the recommended study in an efficient way, the following policy recommendations are given, and the obtained results corroborate these recommendations.

• Handling the centralized grid risks, long-term market reservations, and financial support programs related to hybrid BTS installations in Pakistan calls for a comprehensive policy. Effective cost-recovery procedures, bearable procedures, and regulatory obligation management are all required in a regulatory framework.
• To identify loss-making BTS regions and assess the possibility for renewable energy, a simple method called the geospatial analysis methodology should be applied. Additionally, long-term premeditated planning for linking to the national grid can benefit from the use of this research. This information can also serve as a future base point for on-grid hybrid BTS stations, helping to optimize efficiency. It can assist various electricity supply firms and relevant authorities in achieving a consistent addition of renewable energy into Pakistan’s infrastructure and telecommunications sector.
• Lowering the initial capital expenditure from the investor’s perspective should be given priority in financial structures needed for the employment of energy-efficient projects in order to increase energy efficiency. Energy projects aiming to increase energy efficiency should include promising terms for debt financing, tariff control, financial leasing, and public–private and municipal–private collaborations.
• In order to achieve year-round power supply and help achieve Sustainable Development Goal 7 (SDG-7), it is necessary to evaluate the environmental effects in different areas where diesel generators are mixed with renewable energy sources (RESs) in a ratio that prefers RESs.
• An extra comprehensive sensitivity investigation is needed by taking the viewpoints of academics, investors, and customers with respect to the system’s levelized cost of energy (LCOE) and net present cost (NPC). Therefore, it makes sense to begin industry privatization efforts to maintain parameters that will hold for the foreseeable future.
• In further research, BTSs will offer local charging stations, facilitating the conversion of gasoline-powered cars to electric ones. It is necessary to amend policies regarding the duty tax on electric vehicles in order for customers to easily purchase electric cars and have access to BTS locations for charging them.

Future research can expand on this analysis by incorporating additional renewable energy sources, such as tidal and geothermal energy. Net metering can be integrated with the national grid, and the integration of electric vehicles into the telecom industry can provide a reliable and sustainable means of meeting the electricity demand.

This research has taken into account the technical feasibility of the proposed models by considering factors such as efficiency, reliability, and scalability, which were validated through simulations and experiments of the selected BTS sites. To validate the proposed models, complete models were simulated using HOMER by selecting multiple cities in Pakistan, and resource data were used on a ground reality basis. The HOMER platform is widely used due to the superiority of its optimization algorithms to find the optimized system configuration based on user-defined objectives (e.g., minimizing cost and maximum reliability). In short, the simulation results validated the proposed models with a consistency of findings across technical, energy, economic, and environmental dimensions.

7. Conclusions

In Pakistan, existing base transceiver stations (BTSs) primarily depend on diesel generators or the conventional grid for power. However, rising international fuel costs pose challenges like load shedding, power outages, and escalating expenses. To address this, this study assessed the viability and sustainability of hybrid systems, focusing on renewable energy, in 42 autonomous BTS sites across north, central, and south Pakistan. Optimization findings show that specific areas in the north are more suitable for solar, wind, biomass, and hydropower. Configurations like PV-BM-B, DG-PV-B, PV-W-B-HYD, DG-PV-B-HYD, and W-HYD-B are efficient for northern BTS locations. For instance, Buner BTS-14 exhibits the lowest LCOE at 0.0454 USD/kWh, with an NPC of 0.0387 million USD and an annual operating cost of USD 601, providing a 38.5% energy surplus. By contrast, Gilgit BTS-19 in the same region displays a maximum LCOE of 0.2325 USD/kWh, with an NPC of 0.1012 million USD and an annual operating cost of USD 3511, offering a 44.6% energy surplus. In Pakistan’s central region, PV and biomass are prevalent, leading to varying LCOE values. For example, BTS-26 in Sheikhupura exhibits the lowest LCOE of 0.1175, while BTS-30 in DG Khan shows a higher LCOE of 0.1318, with energy surpluses of 9.91% and 21.1%, respectively. In the south area, the DG-PV-W-B configuration is suitable for five BTS sites. BTS-32 in Karachi-I displays the lowest LCOE of 0.1096 USD/kWh, an NPC of 0.1783 million USD, and an impressive excess energy production of 31.3%. By contrast, BTS-41 in Gawadar with the same configuration exhibits a higher LCOE of 0.1355 USD/kWh, an NPC of 0.0731 million USD, and a 28.6% energy surplus. The LF dispatch method outperforms CC, with a 39.55% lower LCOE score. A sensitivity analysis indicated an inverse correlation between LCOE and the inflation rate, directly proportional to the discount rate, wind speed, solar radiation, and the increasing load profile demand.

Finally, the upgrade of BTS sites presents multiple benefits to Pakistan’s telecom industry, encompassing technical, financial, and environmental aspects. Future analyses will explore real-time meteorological data and the telecommunications sector’s load demand to further enhance these findings and solutions.

To enhance the impact of this proposed study on sustainable growth in the telecom industry through hybrid renewable energy integration, we emphasize the practical implications and applications of this study’s findings. Here are some suggestions to achieve this:

• Scalability: The findings can be scaled up for broader implementation across different telecom networks or regions. The integration of renewable resources like solar, hydro, biomass and wind technologies that are adaptable to varying geographical and operational conditions should be implemented.
• Cost-effectiveness: This proposed study concludes that shifting conventional BTS sites from diesel generators to renewable generation, as well as implementing the proposed hybrid renewable energy solutions, can lead to cost savings over time. The potential financial benefits or cost reductions compared to traditional energy sources can be quantified, showcasing economic feasibility.
• Environmental Impact: The techno-economic-environmental assessment of BTS sites emphasizes the environmental benefits due to lesser carbon emission. Dispensing with diesel generators lead to reducing reliance on fossil fuels, and integrating renewable energy sources can contribute to carbon footprint reduction and environmental sustainability.
• Technological Innovation: The majority of this research focused primarily on techno-economic-environmental analysis; not a single study has used economic resources and provision methodologies to support energy productivity. Accurate sustainability assessment was combined with technological, economic, and environmental optimization in a decision-making paradigm.
• Policy Implications: New regulatory frameworks or policy incentives could facilitate the adoption of hybrid renewable energy solutions in the telecom industry on a larger scale.
• Community and Stakeholder Engagement: Across their networks, telecom operators are essential to the implementation of hybrid renewable energy solutions. They can make investments in modernizing infrastructure and combining conventional energy systems with renewable energy sources like biomass, solar, and wind energy. Operators can lower costs and improve network reliability by managing energy use and implementing energy-efficient devices. By funding the creation of innovative technology for the integration of renewable energy, operators may stimulate innovation. In order to create telecom equipment that is energy-efficient, they might also work with equipment makers. By emphasizing these practical implications and applications, this proposed study effectively conveys the broader significance of research beyond technical and economic analyses, thereby enhancing its impact on both academia and industry stakeholders.