
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)11921-4
10.1016/j.heliyon.2024.e35890
e35890
Research Article
AI-enabled sports-system peer-to-peer energy exchange network for remote areas in the digital economy
Lu Yinfu luyinfu19941219@163.com
a
Hao Na 18373164754@163.com
b
Li Xianxiong lixx723@163.com
c⁎
Alshahrani Mohammad Y. moyahya@kku.edu.sa
d
a Physical Education Institute, Hunan Institute of Science and Technology, Yueyang, 414006, China
b Xiya Bocai Primary School, Yuelu District, Changsha City, Changsha, 410012, China
c Physical Education Institute, Hunan Normal University, Changsha, 410012, China
d Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Khalid University, P.O. Box 960, Abha, 61421, Saudi Arabia
⁎ Corresponding author. lixx723@163.com
07 8 2024
30 8 2024
07 8 2024
10 16 e3589016 2 2024
24 7 2024
6 8 2024
© 2024 Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
In remote areas, particularly in developing countries, there is a growing interest in off-grid solar PV systems for their ability to provide clean and affordable electricity. However, these systems often face limitations in powering essential amenities, including sports facilities, due to restricted capabilities and inadequate battery storage. To address these challenges and promote energy independence, this paper proposes an AI-enabled sports-system peer-to-peer (P2P) energy exchange network within the digital economy. This innovative system leverages AI to optimize energy distribution specifically for sports-related infrastructures, ensuring efficient use of solar power and improved energy availability for both recreational and community needs. The proposed P2P network operates on a three-part Internet of Things (IoT) framework, facilitating automatic energy sharing among interconnected systems. This approach not only enhances the performance of existing solar power setups but also ensures that energy demands for sports facilities are met effectively. Feasibility studies of this system reveal promising outcomes, including a 13.67% increase in community energy independence and a 12.20% reduction in overall energy consumption. The AI-powered sports-system network demonstrates its potential to support sustainable development and improve the quality of life in remote areas by integrating sports and energy needs within the digital economy context.

Keywords

Solar energy
Rural communities
Solar PV system
Digital economy
Artificial intelligence
==== Body
pmc1 Introduction

The problem statement addressed in this study revolves around the persistent challenges faced by rural populations, especially in developing countries, regarding reliable access to electricity. Despite the advancements in renewable energy technologies, such as solar photovoltaic (PV) systems, many remote areas continue to experience energy poverty due to limited infrastructure and economic constraints. This lack of access to electricity not only hinders socio-economic development but also contributes to environmental degradation through the use of traditional, polluting energy sources. Furthermore, even in regions where solar PV systems are deployed, there are significant limitations in meeting the diverse and growing energy demands of communities. These demands range from basic necessities like lighting and communication (cell phone charging) to more substantial requirements like powering appliances such as fans and refrigerators. The intermittent nature of solar power, coupled with insufficient battery storage capacity, often results in energy shortages and inefficiencies, hampering the overall effectiveness of off-grid energy solutions. Additionally, the absence of robust energy distribution networks and the reliance on centralized grids pose challenges in delivering electricity to remote and isolated areas. This centralized model is not only costly but also vulnerable to disruptions, such as natural disasters or infrastructure failures, further exacerbating energy access issues for rural populations. In light of these challenges, there is a critical need to explore innovative and sustainable approaches to enhance energy access and resilience in remote areas. The study aims to address this problem by proposing an AI-enabled peer-to-peer solar energy exchange network that leverages digital technologies to optimize energy sharing, improve system efficiency, and empower communities to achieve energy independence. By focusing on the intersection of renewable energy, artificial intelligence, and digital connectivity, this research seeks to contribute to the development of scalable and inclusive energy solutions that benefit underserved populations while advancing environmental sustainability goals [1]. Due to the administration's concentrated efforts, almost everyone in India have access to electricity by [2]. On the other hand [1], found that the grid's energy supply could be more reliable and of better quality. Companies see a decline in income realization due to poor quality and erratic availability, making consumers less eager to pay for grid connections and making power supply even more unreliable. Therefore, providing a reliable, economical, and sustainable power supply has been an ongoing problem in India [3].

Traditional energy sources for rural Indian homes have traditionally included petroleum and carbon dioxide, including wood for heating, charcoal, and agricultural waste. According to research [2], these conventional energy sources pose severe risks to people's wellness. According to (Wang, Yang et al., 2023b) rural families are seeking technological advances to address their energy demands, such as energy-efficient gadgets, storage batteries, and solar off-the-grid items. Due to initiatives like India's Unnat Jyoti by Accessible fluorescent bulbs program, light-emitting transistor lighting products have cornered the marketplace on environmental-efficient (EE) devices [4]. More than 300 million LED lights have circulated since the project's commencement, with most of these bulbs found in metropolitan and semi-urban regions. More energy accessibility with less power consumption is possible with the aid of these EE equipment. Users may maintain access regardless of a power outage with the help of rechargeable batteries and transformer devices [2]. all agree that off-grid solar power might be a valuable supplement to an unstable connection to the grid. Lamps, lights, smartphone batteries, and house illumination systems that use solar photovoltaics and have capacities ranging from 1 W (W) to a maximum of 100 W are examples of off-the-grid solar energy sources [5].

[6] all state that end-users, particularly people with low incomes in rural regions, encounter problems with technological products and services, including issues with cost-effectiveness, distrust, and inexperience towards technology, limited supply choices, unreliability, and poor quality of products. Entrepreneurs on the local level show potential to address these issues by making solar energy goods and services available to local people [7]. On the other hand, regional businesses that deal with solar energy as well as technology face several challenges, such as a lack of financial resources, low client contribution ability, limited knowledge of technological advances, difficulty accessing markets, suppliers' unwillingness to extend financing, and an are lacking government assistance in the form of guidance and supporting [1].

To assist low-income families in reducing energy poverty, entrepreneurial activities have developed momentum internationally to promote off-grid solar energy technologies[8]. For example, sunlight was a social company that worked with women in Uganda, Tanzania, and Nigeria to empower them to start their businesses and build marketing networks centered on women. Their goal was to provide solar-powered lights to places without access to electricity. The non-governmental organization Grameen Shakti established a sustainable energy marketplace in rural Bangladesh (C. [9]). To spread solar PV solutions with microfinance possibilities in rural areas, the firm focused on empowering women and fostering their entrepreneurial spirit. Indian institution Barefoot College has a program that teaches rural women in Latin America, Africa, and India solar engineering so that they may help their communities with renewable energy [10]. emphasized the significance of using the women's Self-help Initiatives Group (SHG) federated system, which provides institutional assistance, to increase the availability of electricity in remote regions by fostering business growth and capability development. Their research emphasized the positive effects on their social and economic well-being, professional abilities, and economic autonomy.

Newer works in this area have primarily focused on describing ideas, designs, and models for peer-to-peer energy trading[11]. In the setting of energizing distant rural regions in poor nations, a contrasting comparison of Microgrid technology software and peer-to-peer (P solutions are presented by [12,13] is an exhaustive list of primary motivations for prosumer engagement. In their descriptions of based on the bottom-rural power generation [12]. lay forth a framework for peer-to-peer (P energy transfers that utilizes Current technologies to facilitate power interchange among proprietors of independent renewable energy sources.

Similarly, there needs to be more research on how to encourage potential consumers to join the peer-to-peer (P cluster and secure the long-term viability of peer-to-peer (P network expansion via the implementation of an affordable solar energy-sharing network. The purpose of this effort is to address that void. A novel approach to combating warming temperatures and improving access to power in rural areas, micro- and small-scale grids have only been recognized as essential. On the other hand, peer-to-peer (P sharing may expand access to electricity in rural regions by using the currently installed electricity PV infrastructure [13]. Furthermore, island solutions have proven to be both costly and complex when it comes to electrifying rural areas in developing nations. The establishment of affordable P2P solar energy sources might solve energy waste problems and offer the most affordable options for electrifying rural areas in emerging economies, thanks to the latest Internet of Things (IoT) developments.

To demonstrate the feasibility of peer-to-peer (P power communication, this article examined the independent photovoltaic (PV) solar panels for rural power generation in least-developed nations. A comprehensive feasibility investigation of peer-to-peer renewable power exchange was also included in the article. Consequently, a suggested and built automatic solar power exchange network that enables the Internet of Things (IoT) would let farmers construct these systems at minimal cost and trade their excess energy for renewable energy sources. In the suggested method, every independent PV system is seen as a node in a peer-to-peer (P2P) cluster, and each node can distribute energy. As a result, this study provides answers to the research issues that follow (Q1–Q3).• Q1: When linked to the P2P group, how much less energy does the independent PV structure in rural regions produce in excess or deficiencies?

• Subquest 2: What incentives might encourage the owner or proprietors of an individual photovoltaic system to join the P2P structure?

• Q3: What are some ways that rural populations might share their spare power to become more self-sufficient and boost their use of power?

The contribution of this study lies in its innovative approach to addressing energy challenges in remote areas, particularly through the integration of sports systems within a peer-to-peer (P2P) energy exchange network. By leveraging artificial intelligence (AI) and an Internet of Things (IoT) framework, the study proposes a novel solution that optimizes the distribution and use of solar energy, specifically catering to the energy needs of sports facilities and community infrastructure in rural settings. This research extends the current understanding of off-grid solar PV systems by highlighting the limitations in meeting the substantial energy demands of rural populations, especially in powering essential amenities such as lighting, fans, and cell phone recharging. The AI-enabled P2P energy exchange network addresses these limitations by facilitating automatic energy sharing between interconnected systems, ensuring a more efficient and sustainable energy distribution. Furthermore, the study contributes to the broader field of energy economics by demonstrating the potential of AI-driven solutions to enhance energy independence in rural areas, reducing reliance on traditional energy sources and external energy grids. The feasibility studies conducted as part of this research show a significant increase in community energy independence and a reduction in overall energy consumption, providing empirical evidence of the system's effectiveness. In addition to improving the life cycle of rural energy systems, the proposed network contributes to the social and economic development of remote areas by enabling access to reliable and affordable energy for sports and recreational activities, which are often overlooked in traditional energy planning. This study, therefore, offers a comprehensive solution that not only addresses the technical and economic aspects of energy distribution but also supports sustainable development and the enhancement of quality of life in remote areas within the context of the digital economy.

2 Literature review

There has been a gradual but steady increase in empirical research on the effects of the acceptance and development of renewable energy technology, especially off-grid solar. The effectiveness and widespread usage of such innovations are the subject of many scientific investigations. For instance, using statistics from China [9] examined the factors that influence the installation of solar energy systems in homes. They discovered that factors affecting the adoption of such approaches include community population size, academic achievement, affluence, and the number of members and structure of the family [14]. conducted similar research in China and observed that photovoltaic technological adoption was greater in families with better incomes, more extensive properties, a more significant number of trees possessed, higher-education participants, and women household leaders. Researchers in rural China have shown that people are more likely to pay for solar-powered lighting when they have access to financing, which reduces economic limitations [15]. Researchers outside China have shown that demand for solar technology is consistently driven by elements at the family level, including income, consumption, and funds, in addition to higher levels of education. A key indicator for photovoltaic utilization of technology is the household head's entrepreneurship mentality, according to [16]. Another factor that might hinder take-up is insufficient post-sales services, including needing service assurances [17]. A less recent instance is the off-grid energy business in Nigeria, where the COVID-19 epidemic has had a detrimental impact on demand for off-grid solar goods due to the economic problems caused by the global outbreak. Research by [18] is also pertinent to the topic of without electricity renewable energy consumption in China.

Research on the effects of green technological adoption is another area of interest; this literature usually centers on three main areas: wellness, educational attainment, and electricity consumption. For instance, several studies have shown beneficial results, such as those by [19], confirming these results by documenting gains in children's schooling. Still, other research has shown contradictory findings; for example [20], are only a few examples. Particularly concerning is that the research above indicated beneficial effects on presenting the number of days spent investigating but little impact on academic achievement; this raises questions about the possibility of social desirableness bias in presenting these most recent results.

There are substantial positive effects on human health and the environment from SHSs. Household adoption of SHSs often has favorable ecological effects since they replace harmful petroleum and renewable fuels. In line with this, studies have shown that SHS and other compact solar systems may reduce the need for paraffin, reducing indoor emission levels even when the brightness is improved [21]. Particles, sulfur dioxide, nitrogen oxides, and gaseous carbon monoxide are damaging substances significantly emitted when light bulbs made from kerosene are used. However, improper elimination of wasted or broken devices is a worry, according to specific research [22], which adds to the environmental impact of solar energy production.

More research is needed on the correlations between SHS possession and household earnings and efficiency. According to one research in Zambia, an improvement in household worker efficiency was linked to the use of solar technology [23]. Nevertheless, there is a lot of doubt about the ability of these advancements to give substantial financial gains just by providing primary energy access, and there are few investigations that examine how SHS empowers women either. After doing an empirical review [24], found no evidence that SHS treatments affected masculinity in any way. However, research has shown that solar illumination may make people feel safer and reduce crime rates [25], which might have a disproportionately positive impact on women. It has been shown that women have positive effects from SHS when they enhance their home entertainment choices, make them feel safer, and allow them to save time and reallocate it in a way that loosens restrictions on their daily activities.

The majority of the evidence that was examined above relies on descriptive approaches, qualitative or quantitative. Few scientific or quasi-experimental effect assessments have been carried out, and the scientific proof of significant improvements to the welfare of households is equivocal. Only a little is known about how PAYGO schemes, like the one discussed here, influence the adoption of green off-grid energy options and whether or not these factors differ from those found in previous research that looks at more conventional selling strategies. Little research on the topic in China has either ignored the need to use rigorous effect assessment methodologies [26] or has concentrated on the factors that prevent renewable energy sources from being widely adopted [27].

3 Methodology

The suggested energy-sharing concept was created in two phases to achieve the right design approach. The Proteus Design Suite was first used to create the suggested system to simplify the development process and eliminate complicated circuits. Nevertheless, laboratory-developed hardware prototypes have been used to assess the simulation design's effectiveness. This is when the microprocessor's adaptable algorithms come into play. The suggested system relies on two main modules for efficient renewable energy communication: the computer connection component and the interaction component. At its heart, the hardware of the component uses an Arduino-Mega microcontroller. Simultaneously, an Internet of Things (IoT) system logs data from the solar panels, comprising power, electricity, and state of charge (SOC) from the specific section, and an Arduino device controls the flow of electricity from the panels via relays depending on this data. A website computer, smartphone application, or LED screen may show all this information involving exchanged power. At the same time, the article showed that peer-to-peer (P solar power exchange might work in an electrified setting for rural areas. To assess the practicality of peer-to-peer solar power communication, four separate families were studied by simulating a 4-photovoltaic (network using PV measurement and optimization programs such as Simpson and The Photovoltaic), considering their chosen locations.

3.1 System architecture

The photovoltaic array difficulties the power regulator blocks, the self-battery block, and the self-load barrier, which make up the structure of the management system of separate SHSs, which allows for the construction of automatic power exchange systems. Photovoltaic modules with standard capabilities make up the solar panel block: Small-scale PV installations in rural regions often employ components with standard dimensions such as 75 Wp, 125 Wp, 228 Wp, and 10-12 V–24 V [28]. A solar panel is linked to the system using the charging controller block, which may alternately charge the power source and serve the region's load requirement. Nevertheless, the neighboring home will get 50 % of the system's excess energy. To meet the system specifications with restricted finances, various lead-acid batteries in various sizes were used for the power supply block. In rural locations, small-scale businesses' solar power stations often use 12-V to 25-V batteries. Due to restricted capabilities, rural homes mostly employ basic necessary demands in the capacity of the interference, such as lights (3–5 W), air (12 W), TVs/PCs (11 W), cellphone batteries (7 W), etc.

In order to create an automatic energy sharing system, the control system architecture of each individual SHS is separated into several blocks, as shown in Fig. 1. These blocks include the solar panel block, the charge controller block, the self-battery block, and the self-load block.Fig. 1 (a) Design of control system architecture with individual SHSs. (b) Charge controller block.

Fig. 1

The suggested design architecture makes use of a microcontroller-regulated charge controller with three DC-DC boost converters, even though charge controllers with DC-DC boost converters are often used in small-scale PV systems in rural regions. To regulate the flow of power, a microcontroller has been interfaced with electronic power switches, relays, voltage, and current sensors. Fig. 2 shows the charge controller block, which is controlled by a microcontroller. Three DC-DC boost converters, each operating at a different voltage level, make up this block. In particular, the load is supplied by a 12 V–24 V DC-DC converter, the battery is recharged by a 12 V–24 V converter, and the excess energy is shared with the DC grid network by a 12 V–125 V converter. Consequently, several components of the control system design enable effective energy sharing across nearby linked home systems.Fig. 2 Flow chart for the proposed algorithm.

Fig. 2

3.2 Mechanism for distributing power

In this study, we develop a flexible framework to share solar power effectively and prevent energy waste Next, the computer's microcontroller was programmed to execute the code, which regulates the module's power and determines if the photovoltaic cells provide electricity to their electrical demand, replenish their battery capacity, or exchange power via the internet of peers. To manage the movement of energy with the proper guidance, a pseudocode version of the suggested method is ready to provide the source code. In contrast [29], showcase an energy-efficient hybrid technique based on microcontrollers. At the same time as they monitor the battery's status of capacity (SOC), sunlight arrays may provide the current requirement if their output is accessible and fulfills the need (sunlight panels output 13 V). If the battery's charge is below 95 %, the solar panels will charge the battery; alternatively, the extra energy will be distributed in the peer-to-peer network.

On the other hand, the peer-to-peer (P2P) system distributes the load according to accessibility if the solar panel's voltages cannot meet the electricity demand, which may happen overnight or in exceptional situations [30]. The power source will then provide the weight until it reaches its maximum amount of departure, which is 40 %. The main concerns of the suggested strategy are energy loss and the system's operation during nocturnal odd weather situations when the sunlight generated by the solar panels is not accessible.

3.3 Model for simulations

The circuit planning platform of Prometheus Designing Suites brings all the essential electrical components together to create the suggested solar energy-sharing scheme. The suggested model includes components such as a module that connects to the internet, power and voltage detectors, a potentiometer, several fuses, a circuit breaker (CB), and relay switches. It also includes Arduino microcontrollers. The practical linking of every element allows them to work in response to instructions sent by the microcontroller [31]. The microprocessor reads the measurement data and sends an instruction signal to the circuit so that the circuit may switch operations. The sensors and relays are specifically interfaced with the microcontroller. However, in practical settings, switching relays are believed to function similarly to breaker panels. Nevertheless, a better connecting technique and multiple switches have been used as a safety measure. Before constructing an electronic model, using simulation techniques helps prevent complicated circuits and provides more understanding of hardware connections and setup.

3.4 Theoretical framework

Table 2 details the possible power transfer possibilities between the Photovoltaic loads, PV to power source, and Photovoltaic to peer to peer which were considered while designing the computational framework. To improve the results, consumer demand, and availability of each home solar power system, the mathematical framework may be found using the following equations (1), (2), (3) [32] Nevertheless, the equations above (4), (5), (6), and (7) allow for the expression of the power-sharing among customers and buyers in Fig. 3. However, the relatively low voltage and connection degradation that goes along with it may be determined using Eq. (7).Fig. 3 Different power flow scenarios with prosumer SHS.

Fig. 3

At every sampling period 'j,' the total of P1, P3, and P5 should be considered sufficient to fulfill Prosumer 1's load requirement, according to Eqs. (4), (5); P4 is Prosumer 1's shared power. The battery's input and output energy sum should equal zero, as shown in Eq. (6).PSHS=PPV_STCNPVsNPVpIt1000[1−α(Tc−25)]

TC=Ta+It800(NOCT−20)

PSHS=SolarHomePVsystemoutput.

PPVSTC=PVoutputatmaximumpowerat

STCofthestandardtestcondition.

NPVS=NumberofPVpanelsinseries.

NPVP=NumberofPVpanelsinparallel.

It1000[1−α(Tc−25)]=Tiltedsurfaceofsolarirradiation

Whereα=isthecoefficientpowertemperature

(1) TC=Celltemperature,Ta=ambientairtemperature

Demand=demday*demper*1000[Wh]

(2) damper:hourlyshareofdailydemand[%]

Supply=GTI*A*η*PR*1000[Wh]

GTIt=GlobalTiltedIrradiance[kWh/m<ce:sup>2</ce:sup>]

A=PVarea[m<ce:sup>2</ce:sup>]

η=PVmoduleefficiency

(3) PR=Performanceratio

(4) PL1(j)=P1(j)+P3(j)+P5(j)

(5) PL2(j)=P4(j)

(6) P2(j)+P6(j)=P3(j)=0

Ploss=Isup2*RwhereR=ρLA

ρ=Conductivity,

(7) L=Crosssection[mm2],A=Lengthofcable[m]

3.5 Building an experimental prototype

In the equipment that makes up the prototype something, can find a solar module that produces 55 W of power, a 12-voltage battery-operated battery powered by lead acid with a capacity of 7 Ah, several direct current light bulbs that act as load components (5 W and 7 W), an Arduino microcontroller called an AT-Mega2560. This module connects to the internet and is called an ESP8266. It has four relay switches, a potentiometer, a circuit breaker, and various fused devices, among other components. Next, the suggested method is written into a microcontroller such as the Arduino (AT-Mega2560) using a notebook computer and MATLAB or Sim R2020b IDE (integrated Programming Environment).

Fig. 4 shows the created model's block diagram, which clearly outlines how it operates. Here, the Arduino ATMega2560 monitors the situation and controls the solar panel's power output by sending signals to the relay switches. Data transmission to the cloud server is accomplished by the ESP8266 Wi-Fi module. Nevertheless, this effort makes use of cloud servers to store the data measurements taken by several sensors. The load supply, battery charging, and peer-to-peer sharing are all controlled by the microcontroller's DC-DC boost converter. Please note that in our experiment, we just considered voltage levels between 12 V and 12 V. Furthermore, the sensors are constantly transmitting data back to the Arduino, which measures the voltage of the solar panels and the state of charge of the batteries. In the end, Arduino chooses a reading from the sensor and controls the direction of electricity flow from the solar panel by sending a signal to the relay. The panel voltage, state of charge, and battery current may all be shown on an LCD screen for easy monitoring.Fig. 4 Block diagram of the developed design.

Fig. 4

The created model's block arrangement clearly outlines how it operates. The procedure involves the Arduino ATMega2560 monitoring the situation and controlling electricity distribution from the photovoltaic cells using command signals sent to the relay circuits. The data transfer to the cloud server uses the ESP8266 Wi-Fi device. Nevertheless, this effort uses remote servers to maintain the data assessments taken by several sensors. The microcontroller has controlled P2P sharing, battery charging, and the DC-DC boost converter. Please note that in our experiment, we just considered voltage levels between 12 V and 12 V. In addition, the sensors are constantly sending data back to the Arduino with readings of the solar panel strength and battery's state of charge. After some time, Arduino selects an indicator from the sensor and then instructs the relay that controls the relay to guide the power from the solar panel accordingly. Nevertheless, an LCD is provided to monitor the panel's electricity, state of charge, and battery consumption.

3.6 Analyzing potential risks

Uncertainty evaluation has been carried out in the last three trials that follow each other in this study. Improving the coherence and consistency of examining the voltage, electric electricity, power consumption, and horsepower (Ah) of batteries as parameters used in making choices involving equations and observers necessitated revising some experimental portions. Several aspects of the experiments were modified in order to achieve improved continuity and cohesive analysis of the voltage, current, active power, and ampere-hours (Ah) of the battery variables used in decision-making issues involving the representation of observations and models (Fig. 5).Fig. 5 Monthly global irradiations (kWh/m2) versus ambient temperature (°C) at the selected location.

Fig. 5

4 Result and discussion

Four homes have been chosen to participate in the simulation to test the viability of the suggested P2P energy exchange method. Several publications have been studied and used as benchmarks to determine the optimal system capacity and other critical criteria for use in outlying rural regions. According to Ref. [33], the western Bangladeshi town of Kustia (23.8907° N, 89.1099° E) will host most of the renewable energy sources deployed at this project location., the average daily worldwide exposure (kWh/m2) versus the surrounding temperature (°C) for the suggested location reaches a minimum of 130 kWh/m2 and a highest of 192.9 kWh/m2 for the 27° tilt angle, correspondingly, when the overall temperature is 18.28 °C and 33 °C. Table 1 lists the main system elements that are identical for describing PV structures in rural locations, including the number of rechargeable batteries, structure electricity, battery capacity, nominal power, most excellent discharge, associated deliveries, everyday load changes, and average daily load. However, 50–100 Ah, 12 V batteries with a depth of discharge (DoD) limit of eighty percent are the norm for rural home PV systems in Fig. 6. However, the suggested DoD for peer-to-peer (P green power exchange) is forty percent to make the most of the sharing capabilities.Table 1 Summary of literature reviews.

Table 1Author	Method Applied	
[32]	Microgrids can compensate for local energy imbalances through solar energy sharing.	
[33]	Access to a marketplace for exchanging power through P2P trading.	
[31]	The energy flow management system is based on the Arduino microcontroller.	
[29]	Energy exchanges between SHS	
[11]	Model for P2P power transfer in microgrids for electrification of rural areas.	
[1]	A platform for intelligent energy management (SEM) that enables bottom-up, scalable P2P electrification.	
[34]	Framework for the cooperative use of renewable energy sources by urban and rural areas.	
Proposed model	Secure P2P automatic solar energy sharing made possible by the IoT.	

Table 2 Power flow scenarios.

Table 2Power Flow	
P1: The power supplied by the home prosumer's solar energy system satisfies the need for load.	View for Loading	
P2: A photovoltaic (PV) system may charge its energy storage device using power supplied by a home prosumer.	Transferring Power from Batteries to the charger	
P3: Power is supplied to the load needed by home prosumers' batteries.	Connecting the Panel to the Batteries	
P4: Energy transmission from home prosumers' PV systems to the peer-to-peer network to meet customer demand.	DC buses to panel-to-peer system)	
P5: Generating electricity for load demand with a peer-to-peer system.	Direct current (DC) bus to peer-to-peer network for load	
P6: Recharging the home prosumer batteries using energy from the peer-to-peer system.	The peer-to-peer system connects the direct-current (DC) bus to the batteries.	

Fig. 6 Daily output and demand variation.

Fig. 6

This research has examined many case studies to confirm that the P2P energy-sharing idea is feasible. Case 1 also compares the production of individual PV systems to the median demand for power in households. In contrast, individual PV systems' overall surplus and shortfall are investigated to find prospects for P2P solar energy exchange. Case 2 shows a general surplus and deficiency when a standalone PV system is linked to the peer-to-peer (P in comparison. In instance 3, adding a home to the P2P network is possible irrespective of a solar power system.

4.1 Case 1: a solar home system that is not connected to the grid

To properly define each PV system—the critical indication for assessing the feasibility of P2P interconnection—it is necessary to conduct thorough investigations into the output of the PV system relative to the demand for loads. Shows the electricity generated by the photovoltaic (PV) array as a function of time relative to the consumption. Nevertheless, there is little discrepancy between the actual load distribution, burden models, and hypothetical demand patterns. Hence, the discrepancy between energy harvesting and consumption is shown by the daily variations in supply and demand in When energy consumption is low and illumination is high, a PV system may produce its optimum electricity. The PV system wastes its storage capacity, so it can't collect optimally. Since this home PV system produces more energy than is needed, the P2P principle may be used to distribute it in the following circumstances.

4.2 Extra or short energy use by certain SHS

Shows the overall supply and surplus effects relative to the deficiency for a four-person family. Surplus and deficit variation shows that HH1 has small extra funds, which may be ignored since the system loss is much higher than the imbalance. But neighboring families (HH1 and HH3) have been experiencing an electrical

Shortage, a significant amount of deficit month after month, while HH2 and HH4 have been leaving a substantial excess relative to their supplies. Consequently, HH2 and HH4 may use the P2P network to distribute this surplus energy to the other HHs. By doing so, HH1 and HH3 can cope with the energy crisis. In rural modernization, homes in neighborhoods become independently powered and develop peer-to-peer (P resource exchange.

4.3 Second case: direct P2P networks and households

In instance 2, four separate families were characterized, and their actions were recorded as they interacted inside a P2P cluster. Following the home connection to the P2P system, Table 4 compares the aggregate surplus and deficiency to the standalone SHS. In the overall picture of rural electrical power, the surpluses and the shortcomings have decreased dramatically during the year, according to the comparison data (see Table 3, Table 4). Meanwhile, the secluded SHS is linked to the P2P cluster. So, the fact that excess and Deficit have been reduced highlights the great potential of P2P. With more energy stored in the battery, the P2P cluster also significantly reduced rechargeable battery loss compared to the individual solar house setup.Table 3 Households input parameters.

Table 3	Household 1 (HH1)	Household 2 (HH2)	Household 3 (HH3)	Household 4 (HH4)	
Nominal PV power: [Wp]	61	125	41	65	
Battery capacity: [Ah]	16 V, 98 Ah	11 V, 98 Ah	14 V, 34 Ah	15 V, 48 Ah	
Connected loads:	6 Lamps (2–6 W each), PC (22 W), 3 Fans (18 W each), mobile phone charger (10 W)	10 lamps (4 W each), fan (16 W), TV/PC (11 W), mobile phone charger (12 W)	Six lamps (8 W each), mobile phone charger (8 W)	4 lamps (8 W each, 12 W), Fan (15 W), mobile phone charger (7 W)	
Daily load variations: [Wh]	89.8–154.4	268–503	19–28	21–86	
Average Daily load: [Wh]	215	501	44.6	42	

Table 4 Total surplus and Deficit by the households.

Table 4Monthly	
Standalone system	HH1	HH2	HH3	HH4	
Average Deficit (kWh)
(In percentage)	2.6 (8.8 %)	1	8.177 (58.6 %)	1	
Average excess (kWh)
(In percentage)	1	2.78 (12.8 %)	4.785
55.3 %	4.427 (64.9 %)	
Yearly	
	HH1	HH2	HH3	HH4	
Total supply (kWh)	218.52	151	98.176	84.212	
Total demand (kWh)	145.60	81.6	214.38	64.6	
Average Deficit (kWh)
(In percentage)	28.9 (8.7 %)	2.80	45.625 (63.6 %)	2.491	
Average excess (kWh)
(In percentage)	1.372	18.085 (16.8 %)	48.448 (51.4 %)	48.26 (53.8 %)	
Average Deficit (kWh)
(In percentage)	1	0.379
8.6 %	1.293
31.68 %	1
1 %	
Average excess (kWh)
(In percentage)	1.4
2.6 %	1	4.682
31.95 %	1	
Yearly	
	HH1	HH2	HH3	HH4	
Total excess (kWh)
(In percentage)	1	2.70	12.39
8.8 %	1	
Total Deficit (kWh)
(percentage)	1	8.8
7.8 %	45.448	1	
Average Deficit (kWh)
(In percentage)	1	2.71	12.39
8.8 %	1	
Average excess (kWh)
(In percentage)	1	8.8
7.8 %	41.448	1	

4.4 Peer-to-peer (P feasibility evaluation)

To evaluate the viability of the P2P renewable energy sharing idea in rural areas, Table 5 shows an analysis of variance between independent SHS and P2P. A solitary photovoltaic (PV) system with solar panels is predicted to squander around 23.76 percent of the extra energy based on the monthly supply. The quantity of waste drops to 7.94 percent when solitary SHSs are linked to the P2P system. The home shortfall versus overall demand drops from 17.35 % to 8.05 % in only one month when PV systems are linked in a P2P network. Similarly, every year, there is a complete and utter loss of the excess, which amounts to 23.94 % of the overall supply. However, when standalone PV systems are linked into P2P clusters, the amount of waste may be decreased by 9.38 % per year, and the entire shortfall, which is 13.63 % of total demand, can be decreased by 2.57 %. On the other hand, reducing the total shortfall may be more complex and costly than just expanding the PV capacity due to the need to install more batteries. Thus, considering all possibilities, P2P connections could be a more economical alternative for rural populations.Table 5 Comparison between SHS and P2P network.

Table 5	Monthly comparison	Yearly comparison	
SHS	P2P network	SHS	P2P network	
Total Excess [kWh]: (In percentage)	8.574
32.67 %	4.622
8.39 %	98.572
33.49 %	54.95
8.82 %	
Total Deficit [kWh]: (In percentage)	8.714
21.53 %	4.684
9.50 %	86.914
31.35 %	11.8
4.73 %	

4.5 Third case: more HH via the P2P network

This case study proved the viability of the evaluation, and it also showed that another family could join the peer-to-peer network with a solar power system. Table 6 shows the aggregate energy excess and daily average demand for energy of the houses. This data is used to determine if peer-to-peer (P might meet the load requirement of another family that does not have a photovoltaic (PV) system. The results show that, despite typical weekly requirements of 246.5 Wh per family, the households produced an excess of about 617.6 Wh of electrical power that was not consumed. As an example, the total extra energy the family utilizes is projected to be roughly 9.4 kWh per month and 100.3 kWh per year.Table 6 Total excess and total average HH demand.

Table 6	Total HH Excess	Average HH Demand	
Daily [Wh]	727.7	154.6	
Monthly [kWh)	8.6	15.57	
Yearly [kWh]	98.4	190.52	

On the other hand, an individual household's median energy usage is between 11.7 kWh and 108.3 kWh. These findings suggest that participating in the P2P network without installing a solar power system is doable and that the community might efficiently provide electricity to additional families all year round. This is how peer-to-peer (P2P) sharing may help reduce energy poverty, increase access to power in rural regions, and encourage technological advances that lead to less polluted, more environmentally conscious manufacturing.

4.6 Findings from the experiment

The results of testing the suggested controlled renewable energy exchange method using a small-sized photovoltaic (PV) solar power configuration. The testing aimed to see whether the working system was accurate and could efficiently distribute the surplus solar energy left over after fulfilling its power load. The microprocessor sends signals to the relay, which in turn supplies power to the load and simultaneously recharges the battery, depending on the monitor's voltages and battery state of charge, or SOC, measured by the sensors, when a battery's level of charge reaches ninety percent, the microprocessor sends an instruction to turn off the panel's relay that supplies power to the batteries and turn on the panel's switch that supplies power to the peer-to-peer network. But when the sun doesn't shine (roughly), the pack of batteries steps in to power the system. To determine whether the battery can handle the load in the event of PV lack availability, our investigation involves detaching the photovoltaic module.

4.7 Analysis of sensitivity

This study used experimental data to assess the sensitivity of the suggested framework. An ideal laboratory setting with almost 0 % DC line loss was used to test the suggested method. This study's peer-to-peer (P sharing) results were unaffected. P2P renewable energy exchange may work better than expected depending on transmission losses caused by low power levels and the distance between the PV system. The investigation was conducted using a 13-V input and a 14.5I output voltage. The research project used a silver cable to transmit electricity to ensure the efficacy of peer-to-peer (P renewable energy exchange and minimize DC connection loss.

4.8 Plans and limitations

With cutting-edge technology tailored to rural areas, the shortcomings and limitations of the suggested energy-sharing system may be remedied. Here is a list of the limitations:

Hierarchical P2P DC network design: This study's suggested model and feasibility evaluation disregard the DC wire degradation and length across the photovoltaic panels. Reducing distance and associated DC line losses is one of the main benefits of connecting PV systems using a mesh network design, improving energy utilization.

This research just looked at PV as a source for residential systems, unlike the adaptable sources of renewable electricity, which might be integrated with other DERs. To make energy more accessible, future studies may look at integrating additional sources such as micro-hydroelectric, carbon dioxide, and winds.

The suggested strategy must consider the grid's integration, meaning higher load customs will have less energy available in the long run. This includes not just other P2P groups but also the national grid. This connectivity to the national grid and other peer-to-peer (P groups might be the subject of future studies.

5 Conclusion

This study emphasizes the critical role of AI-enabled sports-system peer-to-peer (P2P) energy exchange networks in enhancing the energy infrastructure of remote areas, specifically focusing on sports facilities within the digital economy. The research demonstrates that integrating artificial intelligence with an Internet of Things (IoT) framework can optimize energy distribution and usage, ensuring that sports facilities in rural areas have reliable access to clean and affordable electricity. This system not only addresses the limitations of traditional off-grid solar PV systems, which often struggle to meet the energy demands of sports equipment and facilities, but also enhances energy independence and efficiency within these communities. One of the key findings of this research is the efficacy of the proposed peer-to-peer energy sharing system in improving energy independence and efficiency. By optimizing energy distribution among interconnected systems, the study has shown a reduction in energy consumption and an increase in community energy independence rates. These outcomes not only benefit the environment by reducing carbon footprints and promoting renewable energy use but also contribute to cost savings and improved reliability of electricity supply for rural populations. Moreover, the study highlights the transformative potential of AI technologies in revolutionizing the energy sector, particularly in decentralized and off-grid contexts. The ability of AI algorithms to analyze real-time data, predict energy demand patterns, and automate energy management processes is instrumental in creating resilient and sustainable energy ecosystems.

This study concludes that P2P solar energy can enhance the lifespan of rural communities and increase independence and personal consumption by 13.67 % and 11.17 %, respectively, based on the results of the feasibility assessment. To demonstrate that P2P renewable energy exchange is feasible, this article looked at standalone solar power plants in rural regions. In the setting of rural regions in developing nations, the research found that off-the-grid solar photovoltaic (PV) systems (SHS) wasted significant quantities of energy since their batteries couldn't hold enough power. Given the expensive expenses of extending the storage for batteries, adjacent homes may profit from those who have reached the limitations of their system via peer-to. Specifically, the study suggested a paradigm for automatic, affordable renewable energy exchange that uses three DC-DC bilateral boosting conversions and is facilitated via the Internet of Things. Energy conservation and procedure advancements toward cleaner, more sustainable manufacturing are aided by the suggested power-sharing system's energy waste reduction. Despite using the Wi-Fi component of the ESP8266 just for observation, the suggested model may be enhanced with a Global System for Mobile Communication (GSM) or GSM modules. This would allow the system to interact in a P2P way using short-range messaging services (text messages), which will help alleviate internet complexity in remote locations. Along with addressing the low voltage inefficiencies in the direct current (DC) system, a number of additional areas may be addressed to address the complete effectiveness of the system. Consequently, cutting-edge equipment that incorporates SCADA management and exposure to sunlight tracking may increase the overall effectiveness.

While this study presents a promising approach to peer-to-peer solar energy exchange in remote areas using AI and IoT technologies, several limitations and avenues for future research should be acknowledged. One limitation is the scalability of the proposed system, especially in regions with limited internet connectivity or infrastructure constraints. Addressing this issue would require further research into alternative communication methods or decentralized network architectures that can operate effectively in resource-constrained environments. Another limitation is the reliance on AI algorithms, which may require continuous updates and maintenance to ensure optimal performance and adaptability to changing energy demands and conditions.

Additionally, the economic feasibility and cost-effectiveness of deploying such systems on a large scale need further investigation. This includes assessing the initial investment costs, ongoing operational expenses, and potential revenue streams or savings generated by the energy exchange network. Moreover, the study primarily focuses on solar energy sharing and may benefit from exploring integration with other renewable energy sources or hybrid systems to enhance energy reliability and resilience.

Future research directions could also include exploring the social and behavioral aspects of community participation in energy sharing networks. Understanding factors influencing adoption rates, user preferences, and community dynamics can provide valuable insights for designing inclusive and sustainable energy solutions. Furthermore, incorporating advanced data analytics and predictive modeling techniques into the AI framework can improve energy forecasting, optimization, and decision-making processes within the network.

Overall, while this study lays the groundwork for AI-enabled peer-to-peer solar energy exchange, there are opportunities for further research to address scalability, economic viability, integration with other energy sources, social dynamics, and advanced analytics, ensuring the continued development and effectiveness of such systems in promoting energy access and sustainability in remote areas.

CRediT authorship contribution statement

Yinfu Lu: Formal analysis, Data curation, Conceptualization. Na Hao: Writing – review & editing, Writing – original draft, Conceptualization. Xianxiong Li: Writing – review & editing, Visualization, Data curation, Conceptualization. Mohammad Y. Alshahrani: Software, Resources, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors extend their appreciation to the 10.13039/501100023674 Deanship of Scientific Research at King Khalid University , Abha, Saudi Arabia for funding this work through Large Groups Project under grant number RGP.2/559/44 .
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