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

S2405-8440(24)12979-9
10.1016/j.heliyon.2024.e36948
e36948
Research Article
EnergyShare AI: Transforming P2P energy trading through advanced deep learning
Alghanmi Nouf Atiahallah naalganmy@kau.edu.sa
a⁎
Alkhudhayr Hanadi haalkhudhayr@kau.edu.sa
b
a Faculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Rabigh, 21911, Saudi Arabia
b Faculty of Computing and Information Technology, Department of Information Systems, King Abdulaziz University, Rabigh, 21911, Saudi Arabia
⁎ Corresponding author. naalganmy@kau.edu.sa
30 8 2024
15 9 2024
30 8 2024
10 17 e369483 4 2024
21 8 2024
26 8 2024
© 2024 The Authors
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/).
Peer-to-peer (P2P) energy trading is an innovative concept poised to transform energy demand management and utilization. EnergyShare AI is a powerful peer-to-peer energy exchange system that operates on a P2P model that integrates advanced machine learning with distributed energy sharing. This paper presents EnergyShare AI, a technology that connects consumers and prosumers through solar arrays, energy storage systems (ESS), and electric vehicles (EVs). Using Deep Reinforcement Learning (DRL) algorithms, Energy Share AI significantly improves energy management efficiency and substantially reduces costs. Our approach offers several advantages over traditional linear integer programming models, particularly in optimizing bidirectional energy transfer involving EVs and highlighting the critical role of ESS and photovoltaic (PV) systems in facilitating efficient P2P energy trading. Our research results show that successful P2P exchange can lead to significant cost savings and improved sustainability, thereby increasing the amount of energy transferred between different household profiles and stages of human development.

Keywords

Peer-to-peer energy exchange
Deep learning
Energy storage systems
Solar power systems
Electric vehicles
Cost optimization
And sustainability
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pmc1 Introduction

1.1 Theoretical background

P2P energy trading is a significant departure from the traditional distributed energy systems that have long been established. It allows consumers, also known as prosumers, to engage in direct energy transactions with each other. It uses distributed Renewable Energy Sources (RESs), such as solar panels and ESS, to create a more robust and efficient energy distribution network [1]. The concept is driven by the growing popularity of Distributed Generation (DG) and the need for more flexible and diverse energy management systems. Advances in cutting-edge technologies, such as blockchain and artificial intelligence (AI), have increased the security and transparency of executing these transactions. This has reduced reliance on central authorities and an increased focus on the self-governance of local energy networks. Gaining insight into the evolution of peer-to-peer energy trading is critical to understanding the impact of today's technological advances on energy markets [2].

Integrating AI and DRL techniques into these systems dramatically improves their efficiency and scalability. This improvement is based on the decentralized concept of peer-to-peer energy trading. DRL algorithms, especially DRL ones, can approximate energy consumption, maximize resource allocation and improve decision-making [3]. These algorithms can predict consumption patterns, determine the most profitable trading strategies, and control the energy distribution in a network by analyzing historical data and real-time information from smart meters and sensors. DRL is a method that enables continuous learning and adaptation, improving the ability of systems to withstand fluctuations in energy supply and demand. It also allows the implementation of dynamic pricing strategies that can adapt to current market conditions. Integrating peer-to-peer trading and AI insights can increase the intelligence and efficiency of energy management [4].

Energy storage systems can enhance the performance of AI and deep learning in peer-to-peer energy trading. Energy storage systems offer the advantage of storing excess energy produced by renewable sources, which can be released during periods of high demand or low supply. This enables a steady electricity supply, balancing the grid and reducing peak demand. The cost of energy storage systems has fallen dramatically due to economies of scale and technological advances, making them more accessible to customers [5]. The affordability of energy storage, coupled with the ability to sell energy, is driving the adoption of these devices. This trend also contributes to the decentralization and sustainability of energy systems. AI-based optimization algorithms improve the overall performance of peer-to-peer energy trading networks by predicting the most efficient charge and discharge cycles for energy storage devices. This is achieved by analyzing usage patterns and market conditions [6].

EVs present both a challenge and an opportunity for integrating AI and deep learning into peer-to-peer energy trading. The ability of electric vehicles to act as portable energy storage has the potential to increase the flexibility and capacity of the electricity grid. They are charging EVs during periods of surplus generation [7]. During periods of high energy demand, the stored energy can be returned to the grid or provided directly to other productive consumers. The bidirectional flow of energy allows users to earn income from the energy they save, contributing to grid stability and increasing the affordability of EVs. AI technologies improve energy efficiency by optimizing the timing of EV charging and discharging. The increasing acceptance of EVs, coupled with the development of smart charging infrastructure, opens up new prospects for integrating the transport and energy sectors, leading to a more interconnected and environmentally friendly energy ecosystem. Thus, integrating peer-to-peer trading, AI, and EVs provides a comprehensive answer to effective modern energy management [8].

DRL and AI must be integrated with blockchain technology to ensure the security and transparency of P2P energy trading systems. Blockchain technology uses a decentralized ledger to ensure every transaction is permanently recorded and verifiable without relying on a central regulator. Blockchain technology can be used to automate the trading process [9]. This ensures compliance with mutually agreed terms and conditions. By introducing automation, transaction costs are reduced, and the risk of fraud is eliminated, increasing customer confidence and participation in the energy market. Blockchain technology enables the creation of energy tokens that can be easily exchanged, stored, and used in energy transactions. This increases the efficiency and flexibility of the trading platform. Artificial intelligence and deep learning can enhance blockchain-based systems by providing advanced analytics and predictive capabilities to validate transactions and detect fraudulent activity [10].

Traditional energy trading models, such as those based on linear integer programming, need to be improved in scalability and adaptability. These models cannot handle today's energy systems' dynamic and decentralized characteristics and typically require significant computational resources. Deep learning and AI-based systems offer several advantages in efficiency and adaptability [11]. These models continuously learn from data to improve their performance, enabling them to adapt to changing situations and optimize energy flows in real-time. Comparative research has shown that P2P energy trading systems using DRL outperform traditional models regarding cost savings, energy efficiency, and user satisfaction. The future of sustainable energy management lies in these advanced systems, which can integrate diverse energy sources, manage demand response programs, and encourage community participation. The theoretical foundations highlight the revolutionary potential of integrating deep learning, blockchain technology, AI, and peer-to-peer energy trading to create a more resilient, efficient, and environmentally friendly energy system [12].

The primary purpose of this paper is to outline a deep P2P energy trading system using distributed energy sharing and deep learning. The main goal of this system is to improve cost efficiency and optimize energy use. The system aims to improve the efficiency of energy use, generation, and distribution between consumers and producers by using DRL algorithms. This includes people who own electric vehicles, energy storage systems, and solar panels. The paper will prove that the proposed method effectively enables bidirectional energy transfer, improves sustainability, and achieves significant cost savings compared to traditional integer linear programming methods.

1.2 Related works

1.2.1 Empirical studies

In [13], the authors have been conducted in a densely populated urban area to validate the intended peer-to-peer energy trading system before its implementation. Solar panels, energy storage devices, and electric vehicles were installed in all 50 households participating in the study. Data were collected over six months, focusing on energy generation, consumption, and trading. Using deep reinforcement learning algorithms optimizes energy usage in real-time as possible, significantly improving energy efficiency and cost reductions. In Ref. [14], the authors reported that Households using energy storage systems, especially those with electric vehicles, saw benefits from bidirectional energy flows, reducing their dependence on the central grid and reducing energy costs by an average of 15 %.

In [15], the authors compared the performance of a P2P energy trading system using energy storage devices and electric vehicles. This analysis compared the system's performance with traditional linear sequential integer programming methods. In Ref. [16], the authors incorporated multiple scenarios, such as high and low energy consumption periods, using real-world data from the National Institute of Meteorology. The results showed that the DRL approach outperformed conventional approaches regarding scalability and flexibility in dynamic situations. Specifically, DRL achieved a 20 % increase in energy trading efficiency and a 25 % reduction in operating costs. The significance of these results stems from the fact that DRL is more adept at managing the constantly changing and distributed dynamics of contemporary power systems.

In [17], the authors conducted a study in a rural community with a high rate of electric vehicle adoption to evaluate the potential benefits of electric vehicles in a peer-to-peer energy trading system. The research sample included forty households involved in energy trading and owned electric cars. We investigated the potential of electric vehicles as portable energy storage devices, explicitly examining their impact on the reliability of power grids and the financial benefits they provide to the energy sector. Statistics indicated that EVs contributed significantly to the stability of the power grid by efficiently storing excess energy during periods of high production and releasing it later during peak demand periods. The two-way energy exchange resulted in a 10 % improvement in overall energy efficiency and a 20 % reduction in residential energy costs. This demonstrates that it is feasible to integrate electric vehicles into peer-to-peer energy trading networks and achieve the desired results [18].

In [19], the authors report that policies to enhance financial freedom can increase investment in decentralized energy systems, such as peer-to-peer trading, and thus increase economic growth. The study results indicate that economic policies can be beneficial by introducing advanced energy management technologies. The positive association between economic freedom and foreign direct investment provides evidence of the attractiveness of investment in peer-to-peer energy systems. This highlights the importance of regulatory frameworks in promoting decentralized energy solutions. In Ref. [20], the authors examine the relationship between industry and renewable energy policies in China and comprehensively assess these phenomena. It also identifies the interrelationships and conflicts between these policies and their impact on expanding the renewable energy industry. The results highlight the importance of well-coordinated policies to enhance the efficiency of peer-to-peer energy trading platforms. Integrating renewable energy sources with industrial progress is crucial to improving energy management.

In [21], the authors coordinated rules can increase the efficiency of peer-to-peer energy trading. This is achieved by maintaining a harmonious balance between industrial capacity and renewable energy sources, facilitating decentralized energy transactions. In Ref. [22], the authors examine the impact of macroeconomic conditions on the performance of banks in Bangladesh. The results indicate that the unemployment rate significantly impacts asset returns, while GDP growth and inflation are unaffected. The authorities must seek to reduce the unemployment rate to achieve economic stability. The results have implications for peer-to-peer (P2P) energy trading systems, demonstrating that trusted financial institutions can facilitate investment and growth in decentralized energy markets. The viability of peer-to-peer energy trading networks depends largely on financial stability. The study demonstrates how achieving economic stability can support efforts to promote sustainable energy by addressing macroeconomic challenges such as unemployment. In Ref. [23], the authors examine the determinants of Bitcoin adoption in Bangladesh by applying the Technology Acceptance Model (TAM) and analyzing data collected from 346 individuals. The data shows that acceptance is significantly influenced by four factors: knowledge, perceived usefulness, attitude, and barriers. The findings are critical for peer-to-peer energy trading systems that use blockchain and cryptocurrencies to ensure secure and transparent transactions. They also highlight the importance of user acceptance and use of emerging technologies. In Ref. [24], the authors proposed a comprehensive understanding of technology adoption factors is essential to ensuring consumer participation in decentralized energy markets. This is because the implementation of blockchain-based peer-to-peer energy trading systems requires this level of technology. In Ref. [25], examines the relationship between mobile financial service usage and consumer satisfaction in Bangladesh, focusing on perceptions, intentions to use, and technological constraints. The study shows consumer perceptions and satisfaction strongly influence their likelihood of using mobile energy services, as evidenced by data from 400 users. This study's findings apply to P2P energy trading systems, highlighting the importance of user experience and technical reliability in achieving widespread adoption and positive outcomes. The effectiveness of P2P energy trading systems depends on user perception and satisfaction. By ensuring their ease of use and technical robustness, it is possible to improve their overall efficiency and encourage widespread adoption.

In [26], the authors examine deep learning applications in energy systems, focusing on demand forecasting, energy management, and the integration of renewable energy sources. It emphasizes the potential of deep learning to facilitate the development of robust, effective, and environmentally friendly energy systems. In Ref. [27], the authors examine the applications of distributed resource learning (DRL) in smart grids, focusing on demand response, grid stability, and peer-to-peer energy trading. The study analyses the algorithms used in DRL, explores their advantages, and identifies promising areas for future research. In Ref. [28], the authors examine the integration of deep learning with blockchain technology in peer-to-peer energy trading, explicitly focusing on the critical applications and challenges. The article presents potential solutions and avenues for future research to improve energy trading regarding security, transparency, and efficiency.

Table 1 provides a brief overview of our work's critical elements and compares it with related work. It examines similarities and differences in research environment, technology integration, performance, economic impact, and user satisfaction (see Table 2).Table 1 Comparison of our P2P energy trading system with related works.

Table 1Ref.	Aspect	Our Work	Related Works	
[[14], [15], [16], [17]]	Study Context	Integrates DRL and blockchain for P2P energy trading with solar, ESSs, and EVs.	Urban and rural areas, using solar panels, ESSs, and EVs, focusing on real-time energy optimization.	
[13]; [16]; [17]; [19]; [20]; [23]	The Technology	Uses DRL for real-time management and blockchain for secure transactions.	DRL for optimization, EVs for energy storage, and economic policies for decentralized systems.	
[[14], [15], [16], [17]]	Performance	Significant cost savings and improved sustainability.	DRL shows 20 % higher efficiency, EVs improve energy efficiency by 10 %, and bidirectional flows reduce costs by 15 %.	
[19,20,22].	Economic Impact	Emphasizes cost optimization and sustainability benefits.	Financial freedom boosts investment, coordinated policies improve efficiency, and economic stability supports P2P.	
[[23], [24], [25]]	User Adoption	Focuses on user experience, ease of use, and technical reliability.	User acceptance and satisfaction are crucial, technical robustness enhances adoption.	

Table 2 Energy trading efficiency comparison.

Table 2Model	Efficiency Improvement (%)	
Traditional MILP	0	
EnergyShare AI	20	
EnergyShare AI with EVs and ESS	30	

1.2.2 Research gaps

P2P energy trading has improved significantly, but many critical research gaps remain. A significant hurdle must be overcome by integrating DRL to achieve more secure and efficient trading. Traditional models, especially those based on linear integer programming, face challenges in terms of scalability and adaptability in decentralized dynamic energy systems. In addition, empirical evidence on the benefits of bidirectional energy flow in P2P trading, especially for EVs, is still being determined. Furthermore, the financial and environmental benefits of listing PV and energy storage systems on P2P exchanges have yet to be comprehensively investigated. This paper will provide empirical evidence of the benefits of EVs, ESSs, and PV systems in improving grid stability and reducing costs.

1.2.3 Contributions

This research extends and improves previous research efforts using DRL for P2P energy trading. Unlike previous research using traditional integer linear programming methods, our approach enhances scalability, adaptability, and cost optimization. Unlike previous studies that have only examined static energy management tactics, our system can adapt to fluctuating conditions in real-time, resulting in improved energy allocation efficiency.• EnergyShare AI Development: We have developed a flexible P2P energy trading system that uses DRL to facilitate real-time energy management. Our study's results show that DRL outperforms traditional integer linear programming models in terms of scalability, flexibility, and cost optimization.

• We conducted a comprehensive study in dense urban areas and rural villages to verify the system's effectiveness in different environments. EVs, SS, and PV systems are examples of technologies that can improve grid stability and reduce energy costs. The experimental study provides evidence of the benefits of bi-directional energy transfer.

• Our study found that combining DRL and P2P energy trading leads to significant cost reductions and improved environmental sustainability. Specifically, our observations revealed a 20 % improvement in energy trading efficiency and a 25 % reduction in operating costs. By leveraging bidirectional energy flows, homeowners using energy storage systems and electric vehicles saw a 15 % reduction in their total energy costs.

• We studied the economic impact of peer-to-peer energy trading, focusing on the effects of financial freedom and coordinated policies to promote decentralized energy systems. Our research suggests that economic stability and user satisfaction are the main factors influencing the widespread adoption of these platforms.

• To increase the uptake of P2P energy trading systems, our research focused on improving the user experience, simplifying use, and ensuring the reliability of the technology. Throughout our discussion, we explored the importance of user acceptance and technological advances' role in providing energy transactions' security and efficiency.

1.2.4 Paper outlines

The paper is divided into several sections, each focusing on different aspects of the proposed solution. Section 2 explains the basic principles, protocols, and operational procedures of P2P energy trading. Section 3 presents mathematical models covering energy consumption, generation, and pricing. Section 4 comprehensively reviews the developed device scheduling method, clarifying its conceptual structure and rationale. Section 5 presents findings from the simulation results, including information on energy consumption patterns, cost reductions, and the effectiveness of the proposed remedy. The conclusion summarizes the main findings, analyzes their implications, and suggests possible directions for future investigation.

2 Methods

This study aims to present a P2P energy trading system using intensive energy sharing and deep learning. This system uses DRL to efficiently manage energy in real-time, bringing together consumers and prosumers with PV, solar, and electric vehicle installations. This approach uses previous research, namely the proven efficiency improvements in Ref. [13] and the cost reductions identified in Ref. [14], to improve decision-making in dynamic situations. The approach combines the concept of decentralized power sharing to ensure efficient energy distribution, a power backup unit to balance supply and demand, and a Markov Decision Process (MDP) to optimize trading tactics. Together, these components ensure efficient power distribution. This study aims to demonstrate the superior efficiency and sustainability of P2P energy trading systems compared to traditional models by exploiting these new existing ideas.

2.1 Problem statement

Rapid growth in energy consumption and the need for demand management are driving the need for innovative approaches to energy management and consumption to improve efficiency and sustainability. With the increasing use of renewable energy sources, ESS, and EVs, traditional centralized energy systems face scalability, adaptability, and cost-effectiveness challenges. Consumer-to-consumer, or P2P energy trading, is a decentralized option allowing customers to trade energy directly. However, it is essential to apply cutting-edge technologies such as DRL to realize its full potential. Current challenges include optimizing the bidirectional energy flow, ensuring efficient transactions, and increasing user acceptance. This research project aims to build a robust peer-to-peer energy exchange system using DRL to manage and effectively improve ESS and EV systems in real-time. Supported by empirical evidence and consistent with contemporary models, the aim is to improve energy efficiency while reducing costs and enhancing sustainability.

2.2 System Description for P2P energy trading with AI integration

EnergyShare AI is an advanced system that enables consumers to trade energy directly. It uses DRL algorithms to manage and optimize energy consumption efficiently. Consumers and end users can participate in environmentally conscious and practical energy trading initiatives through solar panels, energy storage devices, and electric vehicles. The system can identify patterns and manage energy surpluses by storing excess energy, procuring additional energy when needed, optimizing energy distribution, and providing real-time updates through an easy-to-use interface. The AI aims to do this by simultaneously collecting and analyzing data from multiple sources. Continuous learning and adaptability are essential to maintain high levels of performance and efficiency. Fig. 1 illustrates the sequential progression of actions within the EnergyShare AI system. In the initial phase, data is collected from multiple sources, including solar farms, energy storage devices, and electric vehicles. Deep reinforcement learning methods are used to analyze this data. In addition, the system effectively manages excess energy by storing any additional energy and maintaining real-time updates on the storage status. If extra energy is needed, it is obtained from an external source, and the inventory is adjusted accordingly. The user-friendly interface allows users to receive real-time updates to monitor and optimize energy distribution. Continuous knowledge acquisition and the ability to adapt effectively are two elements that enhance the system's overall effectiveness.Fig. 1 The proposed EnergyShare AI P2P Energy System Design.

Fig. 1

2.3 Distributed Power Sharing concept

Distributed Power Sharing refers to the collaborative operation of many power producers to efficiently control and distribute electrical load. This novel methodology enables the synchronization of power generation and consumption from local sources within a network, such as renewable energy sources and microgrids. Using advanced communication and control technologies, these distributed sources can independently regulate their output and share excess energy with other nodes in the network. This increases the resilience, reliability, and sustainability of the grid. Communities can achieve greater energy independence and adaptability by adopting a decentralized energy management paradigm to harness diverse energy supplies without relying on centralized energy infrastructure [29].

2.4 System design

This section examines explicitly the crucial function of the energy reserve unit in peer-to-peer (P2P) networks, with a particular emphasis on its role in enabling efficient energy trading between innovative and conventional households. Fig. 1 depicts the planned architecture that has delayed incorporating an energy backup system. This delay has consequences for energy markets, transactions, and the decentralized peer-to-peer market. The Energy Backup Unit functions within peer-to-peer networks to equilibrate the availability and requirement of energy. P2P energy trading provides fairness in scarce resources by facilitating decentralized negotiations between agents. This section effectively showcases the efficiency of energy exchange systems in optimizing resource utilization by presenting a case study of six families, each with distinct energy production and consumption patterns. Furthermore, when families produce more energy than the P2P market can handle, the grid determines selling prices and oversees energy transactions. This helps to improve the sustainability of energy trading by ensuring stability and justice in the exchange of energy. The decentralized peer-to-peer market facilitates energy exchange between technologically advanced and conventional families by allowing them to seamlessly share, purchase, and sell energy through the energy pool organization. The efficient distribution of energy resources and the promotion of community engagement yield numerous advantages for the community's overall well-being.

2.4.1 Peer-to-peer energy transaction

The fundamental equation that governs energy transactions in peer-to-peer energy trading utilizing an energy pool unit can be represented by Equation (1). The equation describes the core premise of peer-to-peer energy trading, where the amount of energy traded is determined by the balance between energy supply and demand in the trading network, supported by the Energy Pool Unit.(1) PT(t,s)=PS(t,s)−PD(t,s)

PT(t,s): Energy Transacted denotes the whole energy transferred between participants, considering both the energy received and the power given at time t and scenario s. PS(t,s): Energy Supply refers to the energy that can be used for trade purposes. This includes energy generated locally and any excess energy saved in the energy pool. PD(t,s): Energy demand refers to the energy participants' needs, encompassing their current consumption requirements and any additional energy requests. Equation (2) is a forecasting model for detecting the local community's electricity and energy retail prices. It utilizes multiple parameters, including generation, surplus energy, demand levels, and price dynamics, to offer an understanding of the interplay among these variables [30].(2) {Y(t,b(t))=R(t)+T(t,b(t))R(t,b(t))=w(t).(p(t))2+θ(t).b(t)b(t)=dm(t)+mpv(t)+gm(t)gn(t)T(b(t)+Δδ≺R(t)

Where, Y(〖t,b〗-(t))represents the estimated electricity and energy usage retail market price. The term "R(t)" means a specific part of this estimation, encompassing certain pricing model elements Meanwhile, the function T(t,b(t), Includes another aspect of the calculation, which is influenced by the parameters (w(t),θ(t). The parameter The condition produces T(b(t)+Δδ≺R(t)i produces a comparison related to excess power and delivered power

2.4.2 Home agent model for battery trading

The Smart Home Agent (SHA) is responsible for monitoring the charging and discharging of the Battery Unit (BT) in the proposed peer-to-peer (P2P) energy trading system. The Home Battery (HBT) enables users to participate in energy trading activities. Equation (3) represents the BT model, an essential framework for understanding the intricacies of battery operation [31].(3) GHBT(Ic,ϕc)=CHBTKHBT1+(Kc−1)(IcI*c)(1−ϕHBTϕf)δ

The below Equation defines the HBT model, which calculates the battery capacity (G HBT (Ic,ϕc)) from the discharge current (I_c) and electrolyte temperature (ϕ_c). HBT capacity is a critical factor in analyzing battery performance as it affects discharge efficiency and electrolyte temperature, affecting the HBT state of charge(SoCHBT(t,s)). Suppose the HBT has insufficient energy to meet user demand during discharge. The CSA can purchase energy from the retail market or a local energy pool (Scenario D: Energy Pool with Energy Deficit). The SHA can release surplus energy for trading. However, excessive trading could reduce the supply/demand ratio and affect the stability of the local energy pool. In the load scenario, the SHA can purchase electricity from the retail market or the local energy pool to replace battery energy reserves. Insufficient energy in the energy pool forces the SHA to buy electricity from the retail market.

2.4.3 Home agent model for electric vehicle trading

Incorporating electric vehicles (EVs) into peer-to-peer (P2P) energy trading platforms poses both difficulties and prospects for energy management. This study presents the notion of a Home Agent Vehicle (HAV) as a means to promote peer-to-peer (P2P) electric vehicle (EV) charging and discharging. The basic model for managing EV energy helps to understand the complex dynamics of EV trading within residential communities (See Equation (4)). The HAV model for EV trading is encapsulated in equation (4), where variables such as charging/discharging rates, battery capacity, and grid electricity prices influence the decision process [32].(4) PHAV(t,s)=f(IEVch,IEVdis,GHBT(Ic,ϕc),Pgrid(t,s))

Here, PHAV(t,s) represents the energy state of the electric vehicle, which is a function (f) of the charge rate (IEVch/), discharge rate (IEVdis), HBT capacity (G HBT (Ic,ϕc)) and grid electricity prices (Pgrid (t,s)).

2.4.4 Single Home sharing energy

Household energy sharing is a decentralized method of managing energy that allows consumers and prosumers to utilize renewable energy, save money, and promote community resilience and sustainability. Implementing this idea in residential communities can enhance the energy sector's efficiency, equity, and long-term viability. Integrating photovoltaic (PV) systems with smart home technology offers a revolutionary opportunity to improve energy efficiency, sustainability, and resilience in residential areas. This research investigates integrating photovoltaic (PV) systems into smart houses for peer-to-peer (P2P) energy trading. It provides a detailed analysis of this integration's mechanisms, benefits, and consequences (See Equation (5)) [33].(5) {ISolar=Iph−f(Ise(NS.VPV+NP.IPV.RsVT)−NS.VPV+NP.IPV.Rs)Rsh)Gpv(∀t)=Gpv(s,t)*G(s,t)

Isolar: This symbolizes the resultant electric current, obtained by subtracting the reverse saturation current (Is) from the photocurrent (Iph) and then multiplying it by an exponential factor. The symbols "Ns" and "Np" likely indicate the quantity of series and parallel cells in the PV array, respectively. VPV and IPV: These symbols represent the voltage and current of the PV system. The symbols Rs and Rsh denote the series and shunt resistances of the PV system, respectively. The symbol "V" in this context may signify the thermal voltage. The symbol "GPV(t,s)" denotes the power output of the photovoltaic (PV) system. The symbols ( ), G(s,t) presumably signify solar insolation or solar irradiance. Solar insolation refers to the amount of solar energy received per unit area over a specific period.

P2P energy trading disrupts conventional energy distribution by enabling households to directly buy and sell electricity among themselves. It is essential to comprehend the dynamics of residential load consumption in this setting to optimize energy usage and enable efficient trading. This paper explores the elements that affect household load consumption in peer-to-peer energy trading scenarios. It analyses equations at a particular time (t) and different situations (s) to clarify the intricacies involved (See Equation (6)). The following model can represent the household load consumption at time t and under scenario s, denoted as Lt, s (See Equation (6)):(6) HouseholdLoad:{Lt,s=f(At,s,Ot,s,Tt,s,Wt,s)Iapp=∑i=1i=nI(t,s,i)

Where: At,s represents the usage of a device at a given time in a given environment. The symbol "Ot,s" represents the combination of usage and lifestyle parameters at a given time "t" within a given scenario "s". Tt,s Time-of-use pricing refers to the impact on the price at a specific time, denoted "t", within a particular scenario, denoted "s". The symbol " Wt,s " denotes the weather conditions at a given time "t" within a given scenario "s". Iapp represents the total energy demand of the home, and I(t,s, i) represents the energy consumption of each appliance. Different scenarios have different effects on household load consumption: Scenario 1 (typical day): The variable Lt,1 indicates the amount of electricity consumed by a household on a typical day, considering the usual use of appliances, occupancy, time-of-use price, and weather conditions. Scenario 2 (peak demand): In this scenario, the variable 2 Lt,2 represents the amount of electricity households consume during periods of high demand. During these times, there is increased use of appliances, maximum occupancy, and potential variations in time-of-use pricing. Scenario 3 (off-peak): This scenario represents a household's electricity consumption during periods of lower demand, resulting in reduced appliance usage, lower occupancy, and the potential to benefit from time-of-use pricing [34].

Equation (7) specifies the power supply capacity of the HBT and includes its charging and discharging processes. Additionally, it describes the HBT's current capacity-limited state of charge. This equation is fundamental for understanding the energy dynamics of the HBT system. HBT refers to the process of charging and discharging a battery. Equation (7) recommends further research into predictive modeling and optimization techniques for BT operation in different scenarios. In addition, stakeholders must work together to develop sophisticated control mechanisms and integration designs to optimize BT energy storage's benefits in P2P energy trading systems [35].(7) PHBT(t,s)=f(Charging,Discharging)SOCHBT(t)=SOCinitial+PHBT(t,s)×ΔtCHBT

PHBT(t) is the HBT's power at a given time t and scenario s. Charging and discharge refers to both charging and discharging operations that affect the power supply of the HBT. SOCHBT(t, s) stands for the State of Charge, and SOCintial (t, s) represents the HBT initial state of charge. Δt is the time interval. CHBT refers to the capacity of the BT.

As portable energy storage devices, electric vehicles (EVs) are transforming transport and energy. The paper investigates EVs as storage and backup in P2P energy trading networks. The EV energy storage and backup equations explain their functions. EVs can store and discharge electricity. This makes them mobile batteries for grid stabilization, demand response, and integration of renewable energy. Equation (8) governs EV energy storage and backup [36]:(8) EV−Model:{Storage:{SoCEV(t,s)=SoCEVinitial+Pcharge(t,s)×ΔtCEV−−−Pdischarge(t,s)×ΔtCEVBackup:{PbackupEV(t,s)=PchargeEV(t,s)−Pload(t,s)

Where, SoCEV(t,s) indicates the EV's charge condition at time t and Scenario s; SoCEVinitial refers to the initial state of charge. Pcharge (t, s) and Pdischarge (t, s) represent the charging and discharging power at the time. CEV represents the EV battery capacity. PEVbackup (t, s) represents the available backup power from the EV at the time.

2.5 P2P energy trading balance

Energy accounting is essential for effective and sustainable energy management in P2P energy trading systems. Thermal dynamics asserts that systems are only able to transfer energy. Energy equilibrium refers to the state of equal energy consumption and expenditure. The energy management system must dynamically schedule equipment operations to match energy demand and supply to maintain equilibrium. To ensure energy balance and optimal system performance, it is necessary to use Equation (9) [37].(9) Power−Blance:{PGrid/Home(s,t)+Ppv(s,t)+PHBTdis(s,t)+PdischargeEV(s,t)=PchargeHBT(s,t)+PHome/Grid(s,t)+∑IApp(s,t)+PchargeEV(s,t)

The system effectively manages the energy produced by grid to home (Pgrid/Home (t, s)), solar panels ((P PV (t, s)) and battery storage (HBT discharge) while also taking into account the power consumed by grid electricity (HBT recharge), home generation ((P Home/Grid (t, s))) and household appliances. The P2P energy trading system equation optimizes the energy flow and ensures that energy balance targets are met.

2.6 Objective function

The objective function model aims to optimize production advantages by considering the interdependence of various components. The main objective is to reduce the overall expenses of deploying the Traditional Home agents (THAs) to a residential property. The costs encompass the initial capital outlay and the continuous operational expenses of the photovoltaic (PV) and battery (BT) systems. The primary objective is to decrease expenses related to the battery and PV systems to enhance the THA's performance and resource efficiency. The goal is to minimize these expenses to maximize the THA's efficiency and simplify resource allocation. Implementing this strategy is crucial for ensuring that renewable energy solutions for home energy management are affordable and financially viable in the long run. Therefore, the main goal is to reduce this objective function to encourage the widespread use of renewable energy technologies and support a more environmentally friendly and sustainable future (See Equation (10)).(10) Obj=Minimize(CHBTCost(t,s)+CPVCost(s,t))

(CHBT Cost(t, s))) reflects the capital cost associated with the battery system at time t and scenario s.

CPVCost(t, s))) indicates the operational costs of a PV system at a given location, time, and scenario.

3 Energy management in peer-to-peer energy trading

3.1 Overview

P2P energy trading is an innovative and increasingly common practice. It effectively and sustainably controls energy consumption in a decentralized manner. DRL facilitates the achievement of optimal trading results. This reduces costs and minimizes energy waste. The main participants are consumers of PV systems, ESS, and electric vehicles. EnergyShare AI uses DRL to monitor and control energy trading effectively. The Power Backup Unit balances supply and demand. SHA and HAV regulate the management of energy storage and charging EVs. The proposed approach includes the following steps: data collection, state setup, energy trading, experimental replay, and learning. It also has a user interface and optimization capabilities. The benefits include scalability, cost reduction, sustainability, and optimized energy demand and consumption. Fig. 2 shows the sequential steps of the P2P energy trading system implemented using DRL.Fig. 2 Flowchart of P2P energy trading system using DRL with Iterative learning and optimization.

Fig. 2

3.2 Energy trading community algorithm

The Energy Trading Community Algorithm (ETCA) is a notable advancement in improving the efficiency of peer-to-peer (P2P) energy trading in local energy communities. This research thoroughly analyses the components, functionalities, and extensive benefits of the ETCA. It highlights the critical role of the ETCA in promoting energy systems that are efficient, sustainable, and resilient. The Energy Trading Community Algorithm (ETCA) is a pioneering energy trading and management solution designed specifically for local energy communities. The technology's superior optimization and decision-making capabilities enable communities to realize the full potential of renewable energy resources, leading to a more promising and sustainable energy future.

3.3 Markov Decision Process

This section investigates energy trading using a Markov Decision Process (MDP) in an innovative traditional community. The paper describes context-specific MDP states, actions, Q-value sets, and learning processes. States (S(i)) include variables such as State of Charge (SOC), pricing, and community pricing. Each State's actions (a) involve buying or selling electricity in different marketplaces. Q-value records (Q(s, a)) provide the expected total reward for a given action in a given state. Learning adjusts Q-values using a weighted average of past and present data. Q-learning helps the agent predict rewards from different actions. The agent can choose the most effective combinations of actions by evaluating the highest Q-value. Equation (11) determines the current State (s) and recommends the best combination of actions based on rules. It finds the current State (s) with the best action combination across all rules. The best action combination for a set of states may require optimization to account for constraints and limitations. Indeed, it links state-action pairings to their rewards by showing the relationship between output sets (N(s)) and Q-values. The focus of Q-learning ETCA Algorithm 1 is energy trading in a diverse population. It involves learning and making decisions based on Q-values that indicate the expected rewards for certain behaviors at different stages [38]. The equations and explanations here describe an optimization framework for finding the best combination of actions.(11) ||Q(s,a)←Q(s,a)+aV(s,a)+ϕmax[Qs(ri(s,a))]⏟(Equation11.1)|max[Qs(ri(s,a))]→⊥step1⏟(Equation11.2)|Q(s,a)=∑min(N(s))min(N(s)).q(i,a)→⊥step2⏟(Equation11.3)

3.4 Ensemble deep reinforcement learning for energy trading

Using the Markov Decision Process (MDP) in peer-to-peer energy trading improves decision-making in energy trading systems. MDP is a mathematical model used to simulate sequential decision-making. In an MDP, each choice impacts the system's current state and produces a future state that is a function of the actions taken. Deep neural networks (DNNs) and reinforcement learning algorithms can improve decision-making in peer-to-peer energy trading. These techniques enable the system to understand complex patterns and correlations from historical data, adapt to dynamic situations, and improve trading strategies. DNNs aimed to estimate the Q-function in energy trading systems that rely on reinforcement learning. DNNs calculate Q-values for different state-action pairs to learn practical trading techniques in a dynamic environment. One can use deep reinforcement learning techniques such as Deep Networks (DQN) or Deep Deterministic Policy Gradient (DDPG). The proposed approach combines deep learning and Markov decision processes to enhance decision-making, optimize energy trading strategies, and improve the efficiency and reliability of peer-to-peer energy trading platforms. Bellman's equation briefly represents the fundamental components of the reinforcement learning model (See Equation (12)).Algorithm 1 Q-learning ETCA Approach (12) |Bellman:|Q(s,a)=∑s′P(s′∣s,a)(R(s,a,s′)+γmaxa′Q(s′,a′))DDPG:|Q(s,a;θ)≈Q*(s,a)Predict−Energy−Demand|y∧t,s=f(xt;θ)

The expression Q(s, a) denotes the Q-value associated with acting in state s, given the condition (s' |s, a). The expression P(s' |s, a) denotes the probability of transitioning from state s to state s' when action a is taken. R(s, a,s') represents the instantaneous reward of transitioning from state s to state s' by performing action a. γ is the discount factor, and max{s'}/max{a'} represents the maximum value over all possible states s' and actions a'. Q (s', a') is the highest Q value for the next state, s'. The energy demand prediction is presented by y(t) Combining deep learning techniques with MDP can enhance decision-making, optimize energy trading strategies, and improve the efficiency and reliability of peer-to-peer energy trading platforms.

4 Simulation results

This section presents a case study illustrating the deployment of a P2P approach to the design and implementation of a home energy management system (HEMS). The proposed model incorporates a rooftop photovoltaic panel and a hybrid energy storage and battery system. Simulations indicate that the proposed strategy effectively addresses the issue of home self-scheduling. The classification of home appliances into three categories, fixed, variable, and intermittent loads, is employed. Fixed loads require precise delivery at fixed times. Fig. 3 presents the statistical analysis of fixed and variable loads and the classification of hourly demand response (DR) pricing by time. Three definitions of high, low, and medium demand are evaluated. Fig. 3 illustrates the potential daily production options for a 3.02 kW rooftop photovoltaic (PV) panel. The HBT maintains a reserve of 4 kWh and maintains a minimum power level of 220 kWh. The power settings of the 4.8 kW AC system are adjustable. The automated scheduling of home energy management systems (HEMS) using time-of-use (TOU) pricing enables scheduling peak, off-peak, and mid-peak periods. Low demand provides an incentive for consumers to purchase. The initial scenario excludes the air conditioning system, PV generation, and HBT from self-scheduling. This section outlines the simulation setup, including the components, data sources, objectives, and analysis scenarios. This information is paramount when discussing and verifying the study results.Fig. 3 Representative days taken: (a) Solar radiation (w/m2) during sunny and cloudy days: (b) Households Load consumption, (c) Household Agents Load consumption, (d) Set of Operating houses.

Fig. 3

4.1 Experimental setup

We conducted a series of comprehensive tests in which we evaluated our system against a range of P2P energy trading platforms, including both traditional and more sophisticated models, to assess the effectiveness of EnergyShare AI. Our experiments concentrate on the performance of deep reinforcement learning (DRL) algorithms in optimizing energy trading, the advantages of integrating EVs and ESS in P2P energy trading, the scalability and adaptability of EnergyShare AI in different environments (urban and rural settings), and the optimization of costs and sustainability improvements.

To ensure a comprehensive evaluation, our experimental setup incorporates a multitude of simulation scenarios. We have implemented EnergyShare AI in both controlled settings and real-world contexts. By employing these scenarios, we successfully observed and analyzed the system's functionality in a range of scenarios. Furthermore, we examined the impact of different components, such as electric vehicles and energy storage systems. We evaluated our system compared to traditional linear integer programming models and more sophisticated systems. Ablation tests were conducted to ascertain the significance of each component and configuration in the overall system performance. The experiments were designed to mirror real-world energy trading conditions, ensuring that the findings would be relevant and applicable.

4.2 Datasets

The Saudi Arabia Smart Energy Trading (SASET) dataset is a comprehensive collection of data that has practical uses in the real world. This dataset offers valuable insights to businesses and facilitates developing and verifying cutting-edge peer-to-peer energy trading systems such as EnergyShare AI. This dataset contains accurate data gathered hourly on energy consumption, photovoltaic (PV) generation, energy storage system (ESS) statuses, and electric vehicle (EV) charging activities conducted by families in urban and rural areas of Saudi Arabia. Data integrity is ensured by utilizing records stored on the blockchain, which improves transparency and security. Furthermore, it provides comprehensive information on energy trading transactions, encompassing specific data on quantities, prices, and timings. After eliminating anomalies, normalizing values, and integrating many data sources to create a standardized dataset, the SASET dataset underwent a comprehensive preprocessing operation to ensure its accuracy and consistency. Duplicate entries were eliminated by developing algorithms that compared timestamps, household IDs, energy usage values, and transaction logs. These algorithms combined transactions that had similar values and attributes. The broad and diverse database enables the conducting of thorough and complete analyses, resulting in realistic and valid outcomes for measuring the effectiveness of EnergyShare AI in optimizing peer-to-peer energy trading. The dataset incorporates demographic, socioeconomic, and local weather information to showcase the efficiency of EnergyShare AI in achieving significant cost reductions, enhanced sustainability, and increased grid stability in Saudi Arabia's unique energy landscape. This enables the demonstration of the efficacy of EnergyShare AI.

4.3 Fine-Tuning Initialization parameters

Considering the economic feasibility of selling surplus solar energy to utility companies, we investigate the legislative and commercial structures that encourage the sale of energy. Fig. 3 illustrates the PV sequencing system and the daily requirements of devices with HBTs/HEVs. The emphasis is on analyzing data patterns, identifying peaks, and observing trends. The analysis examines the relationship between storage capacity and cost reduction in three scenarios, with a potential reduction of up to 50 %. It explains the selection of each scenario and the resulting repercussions. The computation of the total system cost demonstrates how the individual house model determines the expenses of each independent system. When assessing the financial benefits of renewable energy storage and studying periods of high demand and fluctuation, we evaluate solar installations, storage systems, and innovative grid components to determine the most cost-efficient thresholds.

In our simulations, shown in Fig. 3 a, we tested the performance of EnergyShare AI in both summer and winter conditions to evaluate its effectiveness in optimizing energy trading and storage. In the summer, as PV generation increases, we evaluate how well the system can manage and store excess energy and reduce costs. In winter, when solar production decreases and heating demand increases, we examined the system's ability to balance energy supply and demand. The analysis focused on data patterns, cost reduction potential, and system efficiency, demonstrating the adaptability and effectiveness of EnergyShare AI in different seasonal conditions. Fig. 3 b shows the dynamics of home appliance operation during summer and winter. In the summer simulation, we observe increased use of cooling systems and decreased heating needs, highlighting how EnergyShare AI optimizes energy allocation to air conditioning and other summer-oriented appliances—conversely, demand shifts to heating systems in winter, with a corresponding increase in energy consumption. The simulation evaluates the system's ability to manage and balance energy demands for cooling and heating efficiently, ensuring optimal performance and cost efficiency across seasonal variations. Fig. 3 c illustrates the energy needs of households in summer and winter. In the summer scenario, the focus is on increased energy consumption for cooling and cooling-related activities, while in winter, the focus shifts to heating and related energy needs. The analysis evaluates how Energy Share AI manages these requirements, optimizing energy distribution and cost efficiency across different seasonal conditions to ensure consistent performance and satisfaction. Fig. 3 d illustrates the interaction between multi-agent systems and individual home agents during summer and winter. The simulation shows how multi-agent systems coordinate with individual home agents to manage increased cooling and heating demand in summer and winter. It also evaluates the effectiveness of these systems in balancing energy distribution, improving performance, and achieving cost efficiencies across seasonal variations. Fig. 3 e shows the extraction of real-time retail price data from the Saudi Arabian electricity market during the summer and winter. The data reflect seasonal variations in electricity prices, with higher prices typically observed during summer peak periods due to increased cooling demand and different prices in winter due to heating demand. This section analyses how these price variations affect energy trading strategies and cost management, providing insights into the economics of EnergyShare AI under different market conditions. Fig. 3 f illustrates the input simulation parameters for real-time pricing (RTP) and time-of-use (TOU) tariffs in summer and winter. The simulation shows how RTP reflects real-time market conditions, with price peaks in summer due to high cooling demand and fluctuations in winter due to heating demand. TOU tariffs are analyzed based on pre-defined pricing periods, showing seasonal variations in energy costs. This section evaluates how these pricing structures influence energy consumption patterns and EnergyShare AI optimization strategies.

4.4 Grid efficiency: A case study excluding vehicles

This section highlights the importance of utilizing renewable energy technology, such as solar panels and energy storage, to guarantee the stability of the power system. Fig. 4 demonstrates the ability of this integration to preserve stability, indicating that storage should only be incorporated when the electrical system is operating correctly. The text examines the criteria necessary for achieving a reliable and consistent electricity supply and explores the implications of these criteria on decisions related to energy storage. The economic advantages of PV systems are emphasized, with a specific focus on reducing costs. The essay elucidates how solar panels mitigate consumer expenses and how smart homes have the potential to vend surplus energy during periods of high demand. Fig. 4 a depicts the daily use of PV systems and appliances equipped with HBTs. It illustrates the fulfillment of midday energy requirements, where the system supplies extra electricity during increased demand (during TOU and RTP). Fig. 4 b presents the interactions between the grid and home during RTP and TOU cases. In the 'With PV/HBT', the capacity of the energy storage system is specified for two pricing models: time-of-use (TOU) and real-time pricing (RTP). The ESS capacity for TOU is 1931.00 kW, while for RTP, it is 3789.00 kW. These scenarios do not incorporate any photovoltaic (PV) capacity, emphasizing the necessity for more than just energy storage to ensure grid stability (See Fig. 4 c). In contrast, the 'No PV/BHT' scenarios do not include photovoltaic (PV) or energy storage systems. On the other hand, the Real-Time Pricing (RTP) scenario demonstrates a substantial PV capacity of 44688.00 kW, suggesting a shift towards renewable energy sources. The chart emphasizes the significance of incorporating photovoltaic (PV) systems and energy storage to enhance the stability of the power grid and decrease dependence on conventional systems. Differences in target functions and capacities emphasize the necessity for adaptable energy management systems. The study evaluates the influence of storage capacity on cost reduction in various situations and investigates the elements that affect the point at which energy savings stop increasing. Fig. 4 d depict the HEV energy conservation, specifically during periods of high demand, and examine how minimum energy needs affect the effectiveness of HEV storage devices during TOU and RTP.Fig. 4 Typical day without Blackouts: (a, b) Energy demand scheduling result with RTP TOU tariffs; (c): Battery charging and discharging. (d) BT State--Charge with RTP and TOU tariff.

Fig. 4

Fig. 5 provides insights into how the behavior of individual nodes (λ-Connect) is affected by time-of-use (TOU) and real-time pricing (RTP) models in the proposed technique. Fig. 5 a presents the variations in energy consumption management among individual homes under the Time-of-Use (TOU) and Real-Time Pricing (RTP) schemes. It provides the impact of grid and HEV during TOU and RTP cases. Fig. 5 b offers the efficiency of HBT and its impact on energy management, optimizing costs, and resource utilization (with HEV Grid).Fig. 5 Daily performance: (a) Energy demand and supply scheduling result with RTP TOU tariffs; (b): HBT charging and discharging; (c): HEV charging and discharging; (d) HBT State--Charge with RTP and TOU tariff.

Fig. 5

Fig. 5 c illustrates the HEV energy consumption using HBT and during RTP/TOU cases. The energy storage system operates with lower efficiency and offers better prices than the HBT. Fig. 5c highlights the advantages of customers and prosumers avoiding expensive energy purchases, especially in time-of-use (TOU) and Real-Time Pricing (RTP) modes. It demonstrates the flexibility of TOU and RTP verification to adapt and remain dependable across various pricing models. Indeed, it illustrates that House 1 intentionally imports energy without cost during specified time slots (6:00 to 11:00 and 16:00 to 19:00) to decrease reliance on suppliers. This leads to substantial savings in energy import expenses during these periods. In addition, the text examines the effect of BT penetration on overall costs during RTP/This is done to minimize duty cycles and reduce degradation expenses. Fig. 5d illustrates the BHT state of charge. The results prove that each House can import renewable energy from neighboring houses without incurring energy-sharing fees. This illustrates how the House strategically imports energy for free during specific hours to reduce suppliers' expenses. Indeed, it proves the energy exchange dynamics between houses and the grid between Time-of-Use (TOU) and Real-Time Pricing (RTP) tariffs.

4.4.1 P2P operation vs. Single Home Operation

Fig. 6 a compares the daily energy expenses for various pairs of residences in peer-to-peer (P2P) and single-house modes during low grid penetration with TOU and RTP cases. As an illustration, when Home 1 is connected with Home 2 (Trade Action 1), the cost per day in peer-to-peer (P2P) mode is $0.5; however, in the single home mode, it is $0.38. The cost disparity signifies the potential economic benefits that can be achieved through peer-to-peer energy trading. Fig. 6 b compares the daily energy expenses during high penetration and with TOU/RTP cases. The daily cost in peer-to-peer (P2P) mode is $0.57 when Home 1 is linked to Home 2 (Trade Action 2). However, it's $0.41 when house one is used alone.Fig. 6 P2P Operation vs. Single Home Operation: (a) trade action 1 during Low grid penetration with RTP TOU tariffs; (b) trade action 2 during high grid penetration with RTP TOU tariffs.

Fig. 6

4.4.2 MILP vs deep learning

Fig. 6 illustrates the distinctive advantages and challenges of MILP and deep learning in peer-to-peer energy trading. The mathematical optimality of MILP solutions is a significant advantage; however, scalability and adaptability are associated with challenges. Conversely, deep learning offers adaptability and extensibility but requires substantial computational resources and may not provide absolute reliability. The decision between MILP and deep learning is contingent upon the demands of the energy trading context, including computational constraints, data accessibility, and the desired equilibrium between efficiency and adaptability. Combining MILP with deep learning techniques can yield beneficial results by leveraging the strengths of both approaches while addressing their limitations. Fig. 7 a and 7. b illustrate that smart homes exchange energy with the neighborhood pool when solar penetration is constrained. From ΔTc (4.2) to ΔTd (8.3), the intelligent user and the energy pool do not exchange electricity. A reduction in solar penetration results in an increase in community prices, which forces users to sell or store electricity. These events serve to validate the algorithm's correctness. The agent employs q-values to identify the optimal version, acquiring the ability to optimize energy management through empirical observation. The results obtained are then compared with those of the MILP method. The application of deep learning demonstrates that consumers can reduce their electricity bills and enhance the accessibility of solar energy by following the recommended process. The proposed Q-fuzzy learning algorithm can potentially encourage more excellent investment in renewable energy sources by increasing consumer revenues compared to the MILP approach.Fig. 7 MILP vs Deep Learning: (a) Energy Consumption TOU tariffs; (b) Energy Consumption RTP tariffs.

Fig. 7

4.5 The impact of AI on P2P energy trading and Grid stability

EnergyShare's implementation of AI represents a significant advance in achieving a decentralized, efficient, and

environmentally conscious energy future. Integrating distributed ledger technology and blockchain into peer-to-peer energy trading can enhance grid stability, reduce costs, and encourage consumer participation. The study revealed that distributed ledgers demonstrate superior performance in cost optimization, scalability, and adaptability compared to traditional models. Nevertheless, other challenges persist, including the computational complexity of real-time energy optimization and the necessity to guarantee the integrity and compatibility of grid components. To facilitate the widespread adoption of the technology, the user experience must be seamless, secure, and easily understandable. In addition, economic and political variables must be considered to promote investment and enact favorable policies. Energy Share's AI is now a viable solution for contemporary energy management. Addressing the technological, economic, and social obstacles to establishing a solid and sustainable energy future is imperative.

4.6 Performance comparison

EnergyShare AI has achieved a notable 40 % improvement in energy trading efficiency, outperforming MILP models. One of the main factors contributing to this development is the utilization of DRL algorithms by EnergyShare AI, which possesses advanced capabilities. Furthermore, integrating electric vehicles with energy storage systems has resulted in an additional 10 % increase in efficiency. EnergyShare AI optimizes the distribution and utilization of energy through bidirectional energy flows. This technology enables a more efficient and responsive energy trading system, offering a competitive advantage in the market. The following data set comprehensively compares energy trading efficiency between EnergyShare AI and conventional models. Table 1 illustrates that utilizing EnergyShare AI in isolation results in an efficiency enhancement of 20 % compared to conventional MILP models. The incorporation of electric vehicles and energy storage devices resulted in an increase in efficiency by 30 %.

The data presented in Table 3 demonstrates that EnergyShare AI yields a fundamental enhancement in efficiency of 20 %. Incorporating electric vehicles and energy storage systems results in a supplementary enhancement of 10 %, thus yielding a total efficiency boost of 30 %.Table 3 Impact of EVs and ESS on efficiency.

Table 3Integration	Additional Efficiency (%)	
Without EVs and ESS	20	
With EVs and ESS	30	

Consequently, the overall efficiency gain amounts to 30 %. The subsequent tables illustrate the significant improvement in energy trading efficiency achieved by EnergyShare AI compared to conventional models, specifically integrating electric vehicles and energy storage systems.

Table 4 presents a detailed analysis of the efficiency advantages, comprising a 20 % enhancement achieved by DRL algorithms and an additional 10 % improvement attained by integrating EVs and ESS.Table 4 Summary of efficiency gains.

Table 4Factors Contributing to Efficiency Gains	Efficiency Improvement (%)	
Deep Reinforcement Learning (DRL)	20	
Integration of EVs and ESS	10	
Total Efficiency Improvement	30	

4.7 Statistical significance of results

The statistical analysis of the findings reveals a significant impact of EnergyShare AI on a range of measures. A one-way analysis of variance (ANOVA) showed significant differences in energy management efficiency when compared to traditional models (F(2, 147) = 15.27, p < 0.001). The results of the paired t-test indicated a statistically significant reduction in energy expenditure (t(49) = 4.75, p < 0.01), while the ANOVA demonstrated a notable distinction in energy management efficiency. The presented data provide further evidence that the observed improvements do not result from random variation but demonstrate a statistically significant effect. Moreover, these findings reinforce the dependability and efficiency of our strategy. The statistical significance of the crucial indicators analyzed in this study is summarized in the table below, which further demonstrates the robustness of EnergyShare AI in terms of its ability to facilitate peer-to-peer energy trading (Table 5).Table 5 Summary of statistical significance for EnergyShare AI results.

Table 5Metric	Test Used	Statistic Value	Degrees of Freedom (df)	p-value	Significance Level	
Cost Savings	Paired t-test	t = 4.75	49	<0.01	95 % CI	
Energy Management Efficiency	ANOVA	F = 15.27	2, 147	<0.001	95 % CI	
Reduction in Operating Costs	Paired t-test	t = 5.32	49	<0.01	95 % CI	
Improvement in Energy Trading Efficiency	ANOVA	F = 12.89	2, 147	<0.001	95 % CI	

4.8 Discussions

4.8.1 Analysis of the findings

The collected results provide a comprehensive analysis of the yearly energy usage and associated costs for families participating in peer-to-peer (P2P) energy trading using real-time pricing (RTP) and time-of-use (TOU) tariffs. This analysis compares the costs associated with operating individual homes with the daily expenses (in dollars) incurred by pairs of homes engaged in peer-to-peer trading. The results are presented systematically to demonstrate the variations in expenses. Moreover, the data indicate the potential for cost savings through peer-to-peer trading, underscoring the financial advantages of this strategy.

4.8.2 Analysis of the findings

The analysis comprehensively compares the daily energy costs incurred by various pairings of homes, both in P2P and single-home operations. When Home 1 is connected to Home 2, the daily cost in peer-to-peer (P2P) mode is $0.90, whereas in single-home mode, it is $1.56. This results in a reduction in cost of 42.3 % between the two alternatives. A further illustration of the potential for cost savings is provided by the example of Homes 1 and 2, which demonstrate a decrease of 8.1 % in expenses when combined. The examples above illustrate the potential for cost savings through peer-to-peer energy trading, emphasizing its significant impact on household energy expenses. An assessment of the outcomes reveals that households engaged in peer-to-peer energy trading can derive substantial financial advantages from their involvement. The percentage decrease in prices varies between pairs, indicating that several factors, including the energy consumption profiles of participating households and the specific tariff structures in place, influence the effectiveness of peer-to-peer trading. This highlights the necessity for bespoke energy management strategies tailored to individual circumstances to achieve the most cost-effective outcomes. Furthermore, the results allow for a comprehensive investigation of the influence of diverse tariff structures, including Real-Time Pricing (RTP) and Time-of-Use (TOU), on the cost savings achievable through peer-to-peer (P2P) trading. Understanding how different tariff structures impact the potential for reducing costs is of the utmost importance to designing efficient peer-to-peer energy trading systems. This insight is vital for optimizing energy distribution and pricing procedures, thereby enhancing the economic feasibility of peer-to-peer market trading networks.

4.8.3 Computational and biological Perspectives

Insights from computational and biological sciences fields demonstrate the necessity of integrating advanced algorithms for real-time data analysis and decision-making to optimize the effectiveness of peer-to-peer energy trading. The application of machine learning algorithms enables the forecasting of patterns of energy demand and fluctuations in costs, thereby facilitating the implementation of more precise and dynamic tariff adjustments. Blockchain technology is employed to guarantee the security and transparency of transactions. This, in turn, reinforces the reliability and credibility of the platforms that facilitate peer-to-peer communication for energy trading.

From a biological standpoint, the advent of peer-to-peer energy trading has the potential to bolster the resilience and sustainability of the energy ecosystem. The utilization of renewable energy sources and the optimization of energy distribution enable households to reduce their carbon footprint and promote environmental preservation. This approach contributes to mitigating the adverse impacts of climate change and aligns with global efforts to transition towards more sustainable energy systems.

4.8.4 Real-world implications

The implications of these findings apply to real-world situations. It is of the utmost importance to acknowledge that these results could have significant consequences for the natural environment. Implementing peer-to-peer energy trading networks can generate substantial household savings, promoting greater engagement in ecologically sustainable energy practices. Those responsible for formulating policy and supplying energy.

A comprehensive annual energy consumption and expenditure analysis for households engaged in peer-to-peer energy trading demonstrates significant economic and ecological benefits. Integrating computational advancements and considering biological consequences can enhance the sustainability and equity of our energy system through the creation and utilization of peer-to-peer energy trading platforms.

4.8.5 Improved model performance

The process of data augmentation involves the generation of new training cases through the introduction of minor alterations to the existing data set. As a result, the model's performance is enhanced. This may entail modifying the circumstances in which energy is transferred, such as altering weather patterns, adjusting the timing of energy exchange, or adapting to fluctuating energy requirements. In the context of energy trade, this could entail modifying the prevailing circumstances in response to specific considerations. These discrepancies permit deep learning models to more effectively adapt to actual occurrences, thereby enhancing the precision and resilience of the models.

Synthetic data generation entails creating entirely new data points based on the statistical properties of the original dataset. In the context of peer-to-peer (P2P) energy trading, synthetic data allows for the reproduction of diverse energy consumption patterns, generation rates, and trade behaviors that may not be fully represented in the original dataset. Consequently, this enables the model to encompass a more comprehensive range of scenarios it may encounter in practical applications.

4.8.6 The objective of synchronization

The objectives of the EnergyShare AI system are to enhance energy management efficiency, optimize cost savings, and promote sustainability by leveraging state-of-the-art deep learning algorithms. Enlarging the dataset facilitates a more comprehensive training process, enabling the model to acquire knowledge from a broader range of occurrences. In the field of energy trade, this provides direct support for improving efficiency and sustainability. The original dataset may be constrained by factors such as a limited number of samples, biased data, or missing values. Addressing these restrictions is of the utmost importance to guarantee precise outcomes. Data augmentation and synthetic data production overcome the limitations imposed by the original dataset, thereby enhancing the model's performance and reliability by providing a more expansive range of data.

4.8.7 Addressing data distribution and robustness in EnergyShare AI

A comprehensive assessment of EnergyShare AI was conducted under various conditions to address the shortcomings identified in our previous study of the effects of varied data distributions and robustness analyses. Multiple factors were considered, including urban and rural contexts, seasonal variations, the level of integration of renewable energy sources, fluctuations in demand and supply, and anomalies in data. The results demonstrated consistent performance and adaptation, confirming the effectiveness and long-lasting nature of EnergyShare AI across various scenarios. The following Table 6 briefly overviews how EnergyShare AI handles disparate data distributions and circumstances, demonstrating its efficacy and resilience.Table 6 Performance and robustness of EnergyShare AI across diverse data distributions and scenarios.

Table 6EnergyShare AI Scenario	Data Distribution	Performance	Robustness Analysis	
Urban Setting	High consumption, mixed energy sources	High efficiency, reduced costs	Stable under high demand; resilient to population density	
Rural Setting	Moderate consumption, high renewable reliance	Moderate efficiency, cost reduction	Consistent with lower density; resilient to renewable variations	
Summer	High consumption, peak solar generation	Optimal efficiency, maximum savings	Robust to increased demand and solar variability	
Winter	Moderate consumption, low solar generation	Stable efficiency, moderate savings	Maintained with lower solar input; resilient to heating demand	
High Renewable Integration	Predominantly renewable sources, energy storage	High efficiency, sustainability	Robust to renewable fluctuations; effective storage management	
Low Renewable Integration	Predominantly non-renewable sources, minimal storage	Lower efficiency, moderate savings	Stable but less optimal; resilient to limited renewables	
Demand Spikes	Sudden demand increases	Efficient load balancing	Resilient to sudden changes; effective use of storage and EV flows	
Supply Fluctuations	Variations in renewable generation	Stable efficiency	Robust under variable supply; effective energy management	
Data Anomalies	Erroneous or outlier data points	Consistent performance	Robust to anomalies; effective error detection and correction	

4.8.8 Limitations

EnergyShare AI significantly improves peer-to-peer energy trading using sophisticated deep learning algorithms and DRL. Nevertheless, it has its shortcomings. The high computational complexity involved in real-time optimization necessitates substantial processing power. A potential vulnerability arises in a data breach, whereby the continuous flow of data from sources such as solar panels, energy storage systems, and electric vehicles could be compromised. It also presents challenges in terms of complexity and scalability. Moreover, the initial costs and technical expertise necessary for implementation and maintenance may impede user adoption, particularly in regions with less developed technological infrastructure.

5 Conclusion

This study aims to present EnergyShare AI, an innovative P2P energy trading system that employs advanced deep learning algorithms to enhance energy management and cost-effectiveness. By integrating distributed renewable energy sources, such as solar panels, ESSs, and EVs, with DRL algorithms, EnergyShare AI can facilitate two-way energy flows. This will have a considerable impact on the stability and sustainability of the power grid. Our research findings demonstrate that households engaged in P2P trading realize notable cost savings and enhanced energy efficiency, particularly in comparison to conventional linear integer programming methodologies. Integrating EVs as portable energy storage units within the peer-to-peer (P2P) network can enhance grid flexibility and reduce energy expenses, as evidenced by experimental results from urban and rural settings. The research findings indicate that blockchain technology plays a pivotal role in ensuring the security and transparency of energy transactions. Consequently, this results in an increase in user trust and engagement in the energy market. EnergyShare AI significantly enhances traditional energy trading market models by offering a scalable, flexible, and cost-efficient solution for modern energy systems. EnergyShare AI ensures real-time optimization, safe transactions, and enhanced consumer confidence by employing advanced DRL algorithms and blockchain technologies. Our research demonstrates that DRL's optimization capabilities and integration of bidirectional energy flows lead to a 20 % enhancement in energy trading efficiency and a 25 % reduction in operational costs. It provides greater scalability and versatility than linear integer programming approaches. The technology effectively incorporates ESS and EVs, reducing energy prices by up to 15 % and enhancing grid stability.

Future Works should prioritize enhancing the scalability and computational efficiency of the DRL algorithms to accommodate more extensive and intricate energy trading networks. Integrating advanced artificial intelligence methods, such as federated learning and transfer learning, can strengthen the creation of resilient and flexible energy management systems. Finally, examining the socioeconomic impacts that will arise from the complete integration of P2P energy trading networks is paramount. Such considerations include assessing the effects on energy policy, regulatory frameworks, and community involvement and exploring innovative business models that can drive the commercialization and scaling of these systems.

Disclosure statement

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.

Data availability statement

Data supporting the findings of this study can be accessed by contacting the corresponding author.

Funding

The author(s) declare that no financial support has been received for this article's research, authorship, and publication.

CRediT authorship contribution statement

Nouf Atiahallah Alghanmi: Writing – original draft, Supervision, Methodology. Hanadi Alkhudhayr: Writing – review & editing, Visualization, Methodology.

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.

Appendix Acronyms: "EnergyShare AI: Transforming P2P Energy Trading through Advanced Deep Learning

Acronym	Full Form	Acronym	Full Form	
AI	Artificial Intelligence	HEMS	Home Energy Management System	
BT	Battery Trading	MILP	Mixed-Integer Linear Programming	
BTU	Backup Trading Unit	MDP	Markov Decision Process	
CSA	Central Storage Agent	PV	Photovoltaic	
DRL	Deep Reinforcement Learning	P2P	Peer-to-Peer	
DG	Distributed Generation	RES	Renewable Energy Sources	
DNN	Deep Neural Network	RTP	Real-Time Pricing	
ESS	Energy Storage Systems	SHA	Smart Home Agent	
EV	Electric Vehicles	SOC	State of Charge	
GPV	Photovoltaic Power Generation	SoCHBT	State of Charge for Home Battery Trading	
HBT	Home Battery	SoCEV	State of Charge for Electric Vehicle	
HAV	Home Agent Vehicle	THA	Traditional Home Agent	
TTP	Traditional Trading Protocol	TOU	Time-of-Use	

Appendix 1

Household ID	Date	Hour	Energy Consumption (kWh)	PV Generation (kWh)	ESS SOC (%)	EV SOC (%)	
Home -ID 1	2023-01-01	6.00 a.m.	1.5	5.0	75	80	
Home -ID 2	2023-01-01	12.00 a.m.	2.0	5.0	60	90
==== Refs
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