Enabling Intelligent Internet of Energy-Based Provenance and Green Electric Vehicle Charging in Energy Communities

Abstract

With the gradual shift towards the use of electric vehicles (EV), electricity demand is expected to increase especially in energy communities. Therefore, it is important to investigate how energy is generated as the provenance of electricity supply is directly linked to climate change. There are only a few studies that investigated the internet of energy and energy provenance, but this area of research is important to prevent the rebound effect of CO₂ emission due to the lack of a transparent approach that verifies the source of electricity consumed for charging EVs. The energy system is a complex network, which results in difficulty verifying the source of electricity as related to the generation of energy. Identifying the provenance of electricity is challenging since electricity is a non-physical element. Moreover, the volatility of a Renewable Energy Source (RES), such as solar and wind power farms, in relation to the complex electricity distribution system makes tracking and tracing challenging. Disruptive technologies, such as Distributed Ledger Technologies (DLT), have been previously adopted to trace the end-to-end stages of products. Likewise, artificial intelligence (AI) can be adopted for the optimization, control, dispatching, and management of energy systems. Therefore, this study develops a decentralized intelligent framework enabled by AI-based DLT and smart contracts deployed to accelerate the development of the internet of energy towards energy provenance in energy communities. The framework supports the tracing and tracking of RES type and source consumed for charging EVs. Findings from this study will help to accelerate the production, trading, distribution, sharing, and consumption of RES in energy communities.

1. Introduction

According to the report from the Intergovernmental Panel on Climate Change (IPCC), economic activity related to the use of energy is a key contributor to greenhouse gas (GHG) emissions [1]. The International Energy Agency (IEA) reported that, in the operational phase for companies, almost 50 percent of the energy supplied is used for cooling purposes and space heating in “The Organisation for Economic Co-operation and Development” (OECD) countries [2]. Furthermore, apart from the energy consumed in electric vehicles (EV), the energy consumed in buildings contributes to approximately 30 percent of the yearly global greenhouse gases emission [3]. Thus, the use of Renewable Energy Sources (RES) for electricity, supplied to both residential for domestic purposes and non-residential buildings for related activities such as cooling and heating, is necessary to prevent a rebound effect of CO₂ emission [4]. RES comprises non-fossil-based energy generation such as wind energy, solar energy, biomass energy, hydropower, and possibly nuclear energy. RES is a green, clean, and low-carbon energy source, which is important for protecting the natural environment and improving energy structure, as a response to climate change, and moving towards achieving sustainable social and economic development [5].

Findings from the literature revealed that road transportation contributes to greenhouse gas emissions by up to 70 percent in comparison to other modes of transport. Likewise, the energy needed for road transportation is also higher than other modes of transport. To contribute to reducing greenhouse gas emissions, EVs are being adopted to replace fossil fuel-based vehicles, which inevitably emit CO₂ [6]. Since EVs require charging to be operational, it is now important to investigate the sources of electricity consumed by charging EVs, as electricity is generated from diverse energy sources which have different impacts on climate change. For instance, renewable energy sources from hydropower, solar PV, etc., have fewer negative effects on the environment [7]. For the internet of energy, explainable energy tracing and tracking are valuable as they provide a transparent energy footprint to prevent a possible rebound effect, which may occur in the consumption of electricity, particularly that of EVs, when energy from fossil fuels is used to charge EVs. Thus, electricity can be traced from the source of generation to the final energy consumer via an internet of energy approach. However, research that explores the types of energy and sources of electricity generated are few. But, owing to the complexities associated with the distribution of electricity generated from diverse energy sources, it is challenging to effectively verify the provenance or source of electricity [7].

Thus, studies that investigated the internet of energy as related to the provenance of electricity in energy communities remain scarce [4]. With disruptive technologies, such as Distributed Ledger Technologies (DLT), Internet of Things (IoT), and artificial intelligence (AI), digitalization, decarbonization, and decentralization of the energy system is becoming more attainable [8]. Digitalization, in the use of disruptive technologies, can contribute to decarbonizing future energy systems, such as in energy management systems and tracing EV consumption. Findings from the literature revealed that a few studies have examined energy tracking. One such study was carried out by Petrusic and Janjic [9], where the authors proposed an innovative charging system to track the source of energy used for charging EVs in multiple systems utilizing multicriteria algorithm. However, there are fewer studies that employed disruptive technologies such as DLT to support electricity tracking, possibly due to the fact that electricity is naturally difficult to trace [4]. DLT provides distributed trust, data integrity, anonymity, and availability. DLT can benefit energy communities based on its distributed nature and its intrinsic security structures by design. Thus, DLT is absolutely a promising technology to be deployed in achieving a future energy system [10].

DLT has been previously applied to energy trading as it ensures non-tamperability of transactions, high transparency, and traceability [11]. In energy policy, DLT has been applied to improve energy trading, green certificates for EV charging, renewable energy promotion, demand response, etc. Overall, DLT can be employed to facilitate local renewable energy trading, accelerating clean energy production, supporting the energy supply to improve grid stability, and aiding residents to track and trace the sources or provenance of electricity usage [12]. Likewise, AI can be utilized to support predictions and recommendations by analyzing and learning from available energy related historical, synthetic, and real-time data produced from IoT devices, such as smart meters and sensors, to help energy consumers and prosumers alike with optimal decision support. AI can support energy communities with the smart monitoring of power infrastructure, operation, and maintenance of RES, offering more resilient system operations, and design of innovative energy trading schemes [13]. AI has been previously adopted to perform data-driven tasks, such as forecasting, controlling, and efficient energy system operations [14]. Accordingly, this study aims to explore the following research questions:

• How can DLT facilitate real-time internet of energy tracking and tracing of RES from generation and distribution to consumption in energy communities?
• How can AI support the decision-making process for citizens for green EV charging in energy communities?
• How can AI and DLT support the realization of energy sharing and what strategies can be adopted to unlock the transition towards internet of energy in energy communities?

Therefore, this study contributes beyond the state of the art by developing a decentralized intelligent framework grounded on AI and DLT to accelerate the development of internet of energy for energy provenance within the electric power system. Innovation from this study is that AI and DLT are employed to support decentralized green energy vehicle charging. Findings from this study present the organizational and legal aspects that need to be clarified to support the adoption of DLT and AI to support the development of the internet of energy for energy tracking and tracing in contributing towards the energy transition. Additional innovation from this article describes how the implementation of disruptive technologies, for example, DLT, can help by tracking and tracing the source of renewable energy; this is aimed at increasing the transparency of electricity consumed by energy consumers towards accelerating a behavioral shift away from the consumption of fossil-based production. The remainder of the article is as follows: Section 2 is the theoretical background and Section 3 is the methodology employed. Section 4 presents the findings, Section 5 highlights the discussion and implications, and, finally, Section 6 concludes the study.

2. Theoretical Background

Over the last decade, a few studies have examined issues related to either energy provenance or electric vehicles charging by applying different technologies, systems, and approaches. Among these studies, Zhang et al. [5] researched the traceability system of RES trading, centered on the use of DLT-based blockchain. The authors presented data on a process and data management model traceability approach based on a digital fingerprint cryptography algorithm by employing blockchain to achieve mutual trust. Wan and Huang [4] researched energy tracing using the blockchain to ascertain the provenance of electricity supply, since it is directly linked to climate change. Furthermore, the study identified the current state-of-the-art centered on energy tracing via blockchain from a research and commercial perspective. Augello et al. [15] explored the tracing of battery usage based on the second life market by leveraging blockchain. The study described the system design, based on blockchain, for sharing important information among the stakeholders involved in grid service. Hao [16] designed an analytical framework for tracing electricity conservation and emission decrease in the transportation sector by facilitating energy and emission policies.

Additionally, Petrusic and Janjic [9] designed renewable energy optimization and tracking in a hybrid EV charging station that was connected to a small-scale PV system and battery, with energy storage that was planned to eliminate the negative effects of uncontrolled EV charging, based on an exact calculation of the RES supplied from power stored in the battery. Wessel et al. [17] researched the possibility of providing intelligent tracing and tracking to data mining and data-driven application for lithium-ion battery manufacturing. Yang et al. [11] contributed by providing a blockchain-based energy tracing approach for EV charging. The researchers presented a blockchain-based RES tracing method to track EV charging utilization centered on RES type and source. Yu et al. [18] proposed big data-based energy battery recycling for intelligent transportation optimization and sharing platform for new EV information. Also, the study focused on the challenges faced in the actual transaction of decommissioned energy batteries and the difficulties faced, such as trace and recovery.

Furthermore, Fonseca et al. [19] investigated the tracking of energy in networked-embedded systems. A network-wide energy and time profiler was introduced for embedded network devices to help map how time and energy are spent across nodes and within a network. As reviewed in this section, there are a few studies that have explored research related to energy provenance and green EV charging in energy communities. Most studies are either concerned with the tracing and tracking of energy sources or are more concerned with optimizing EV charging based on renewable energy. Also, even fewer studies have employed disruptive technologies, such as DLT, blockchain, AI, and smart contracts, and none of these studies have integrated these technologies to improve electricity tracking, tracing, and green EV charging. Therefore, this study adds to the body of knowledge by investigating the technological convergence of AI, DLT, and smart contracts to enable internet of energy-based provenance and green energy vehicle charging in energy communities.

3. Methodology

The systematic literature review, as suggested in the literature [20,21,22], was carried out in this study to guide the research method following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [23]. Thus, systematic review and meta-analysis was employed to synthesize data to investigate the implementation of AI, DLT, and smart contracts for sustainable energy provenance and green electric vehicle charging in energy communities. The systematic literature review was carried out based on the four primary phases described below.

3.1. Identification of Studies

A search was carried out using the following online databases, Web of Science, Scopus, and Google Scholar, to identify the relevant literature in the study area. The search was employed using different keywords aimed at systematically searching, synthesizing, and selecting existing work on the application of DLT, AI, and smart contracts for sustainable energy provenance and green EV charging in energy communities. The keywords, as seen in

Table 1. Employed search string.

RQs	Search String
1	((“Artificial Intelligence” OR “Machine Learning” OR “AI” OR “Machine Learning Algorithm” OR “Machine Learning Models”) AND (“Internet of Things*” OR “IoT*” OR “Sensors*” OR “Physical Infrastructure *AND (“Distributed Ledger Technologies*” OR “Distributed Ledger*” OR “DLT*” OR “Blockchain*”) AND (“Convergence*” OR “Integration*”) AND (“Energy Tracking*” OR “Energy Tracing*” OR “Energy Communities*” OR “Renewable Energy Sources*”))
2	((“Artificial Intelligence” OR “Machine Learning” OR “AI models” OR “AI Algorithms” OR “AI systems”) AND (“EV Charging*” OR “Green EV Charging *” OR “Green Electric Vehicle Charging*”) AND (“Decision-making*” OR “EV Fleets *”) AND (“Citizens*” OR “Residents*” OR “Energy Prosumption*” OR “Energy-based-Communities*”))

, were derived based on the research questions being examined, as presented in the introduction section of this paper. The search was carried out within 2022 and was last updated in the first quarter of 2024.

3.2. Review of Studies and Inclusion/Exclusion Criteria

This step selects suitable sources to be used in the study based on specified criteria as stated below:

• Be a journal article, conference paper, technical reports, book, or chapter published between the years 2000 and April 2024 to be included (inclusive criteria). This study chose to select sources from 2000 as a starting year as energy communities research started to pick up from early 2000.
• Thesis reports, web links, and unpublished works were considered if they discussed the application of disruptive technologies to accelerate sustainable energy provenance and green electric vehicle charging in energy communities.
• Mainly focused on the application of AI, IoT, and DLTs, such as blockchain, for sustainable energy provenance and green electric vehicle charging.
• Must be grounded in a conceptual, theoretical, or empirical paper.
• Published in the English language.

Studies that did not match these criteria, for instance, examined other issues in energy communities and were not related to sustainable energy provenance and green electric vehicles charging, were excluded.

Figure 1 depicts the source selection steps employed to select studies in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis, analogous to prior previous studies [24,25]. Thus, Figure 1 reveals that 117 potential studies were retrieved from online databases using the search terms or keywords stipulated in Table 1. 14 articles were found that were duplicates and were removed. This resulted in 103 studies, in which the abstract and titles were assessed. After a preliminary check, 37 papers were excluded which were not in line with the study area, resulting in 66 studies. Next, after checking the remaining articles against the research questions being investigated, no articles were removed. Following this, the highlighted 66 sources were then assessed against the stipulated inclusion and exclusion criteria (as discussed previously). Next, five papers were removed which were not in line with the inclusion criteria. This resulted in 61 sources. Finally, three papers that aided in the systematic literature review process were included (Webster and Watson, 2002 [22]; Kitchenham, 2004 [20]; Kitchenham et al., 2009 [21]). These studies provide guidelines and best practices on how SLR can be carried out. The inclusion of these 3 studies brought the total to 64 studies.

3.3. Quality Assessment

To ensure that the included studies possess a level of scientific quality, each study is checked to ensure that the research aim and problem to be addressed are specified. Also, the study methodology, scope, data collection approach, and results are checked as to whether they are well-presented and in line with the conclusions. Additionally, the quality of the included studies was individually rated by the author(s) by verifying that more than half of the studies included in this current research are indexed in Scopus and/or Web of Science online databases.

3.4. Qualitative Data Analysis

Qualitative statistical analysis was performed using Microsoft Excel for meta-analysis as well as descriptive statistical analysis. The findings from secondary data are presented using frequency and percentage based on the data distribution, and were analyzed as seen in the subsequent section. Furthermore, data related to the year of study publication, disruptive technologies adopted, methodology employed, country of published authors, and domain investigated were extracted from each study. The information is synthesized in a Microsoft Excel sheet. Next, data extraction, and coding of the secondary data, was manually carried out in the Microsoft Excel sheet to ensure accuracy and reliability of the data analysis.

4. Findings

4.1. Meta-Analysis of Secondary Data

The frequency of the studies published annually (Figure 2) indicate that works related to the application of AI, DLT, and smart contracts for sustainable energy provenance and green electric vehicles charging in energy-based-communities have risen over the years, as highlighted in Figure 2.

Figure 2 illustrates the number of studies included in this research from 2002 to 2024. The result shows a fewer number of studies were published from 2002 to 2016 and 2018. The result further indicates that the rate of published sources in the study area has increased to three studies in 2017 and then five studies in 2019. Surprisingly 14 studies were published in the study area in 2020, and 12 studies in 2012. In 2022 and 2023, eight studies are recorded, respectively, and four studies were recorded in 2024. This publication trend highlights that the interest in the adoption of disruptive technologies in energy communities has grown over recent years. This awareness and adoption of these disruptive technologies can be explained by the general attractiveness of these technologies in the energy systems and management domains.

Findings from Figure 3 illustrate the categories of disruptive technologies implemented to improve sustainable energy provenance and green electric vehicle charging in energy communities. Among the results, Figure 3 indicates that 19 sources used “blockchain”, a type of DLT, within the study area, whereas 18 studies employed other IoT devices. Then, seven studies employed big data systems and DLT, respectively, whereas four studies employed general ICT, and two studies employed IoT, big data, and AI individually. The remaining studies each adopted AI and blockchain, AI and big data, DLT, digital twins, IoT, and AI, IoT, AI, and blockchain and machine learning. Evidence from this result suggests that there are fewer studies that employed AI, IoT, and DLT reported in the literature. This suggests a need for research in employing these disruptive technologies in energy communities.

Figure 4 depicts the research methodology adopted in the studies included in this research. Generally, this result suggests that the literature review was the most employed research methodology following the studies that employed simulations. Then, case studies and experiments were the most employed methodology. In summary, the literature review was the most employed research methodology regarding the application of AI, DLT, and smart contracts for sustainable energy provenance and green electric vehicle charging in energy communities. This is based on the fact that technology convergence in the area of energy-based communities is still evolving. Hence, there is need for more studies that provide practical implementation of disruptive technologies for internet of energy-based provenance and green energy vehicle charging in energy communities. Findings from Figure 5 show the distribution of all the countries of the selected sources included in this study. The results suggest that the published sources included a total of 29 countries. Most of the published authors are in China, Norway, the United States of America, the United Kingdom, and France. In summary, the results reveal that the sources were published in Asia, North America, and Europe.

Evidence from Figure 6 captures the domain areas explored by the selected studies. The result suggests that most of the studies examined digitalization and energy, consumption of renewable energy, secure smart grid and power distribution, interoperable decentralized technology, sustainable energy industry, and tracing energy.

Additionally, the result reveals that most studies examined electric mobility as a service, energy tracing methods for electric vehicle charging, intelligent energy management, power battery recycling for new energy vehicles, smart grid operations, control and management, sustainable energy prosumption, sustainable energy utilities, and tracking and tracing. But studies that examined internet of energy-based provenance and green energy vehicle charging in energy communities are limited. As such, there is a need for research that can contribute to this gap in knowledge.

4.2. Background of Internet of Energy in Energy Communities

The internet of energy (IoE) has evolved as a prevalent topic in the energy sector by mixing diverse types of energy. The internet of energy term was proposed by Jeremy Rifkin in 2011, who harmonizes several electric utilities using the Internet to improve energy efficiency and reliability by managing and distributing the collected electricity data across the entire network actors [26,27]. The development of the IoE supports energy production, distribution, and consumption while addressing the energy demand of energy consumers via automated and smart tools that ensure secure data exchange among different actors in decentralized energy prosumer-based communities [28]. Figure 7 depicts some of the key goals of IoE technology in energy communities.

Using the Internet, the IoE collects, manages, organizes, and optimizes the electricity network data from discrete devices to achieve a decentralized intelligent energy infrastructure [29,30]. The IoE is linked to EVs using smart energy sensors, energy metering devices, and communication protocol-generated data are utilized to predict energy demand and supply by energy consumers and prosumers/suppliers, respectively. Presently, IoE is now shifting towards decentralized and distributed solutions due to the development of RES, EVs, smart grid, Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G), and Vehicle-to-Everything (V2X) technology [30,31,32]. The IoE employs disruptive technologies, such as IoT, computing technologies, big data, and AI, in decentralized and distributed energy management systems aimed at optimizing the efficacy of the existing energy installations [33].

IoE exploits deployed smart energy meters in energy communities to assess the quantity of energy produced, and this information is communicated across the distributed network to manage energy demand. IoE simplifies the collaboration of RES, microgrids, smart grids, EVs, billing centers, and control centers, which connects to enhance energy efficiency, supportability, and flexibility [34]. Hence, IoE aids toward shifting from a centralized producer-concentrated one-way electricity approach towards an on-demand, decentralized two-way energy management system [30]. Accordingly,

Table 2. Applicable benefits of internet of energy across energy communities.

Application	Possible Scenarios	Intended Benefits	Limitations
Microgrid management and monitoring	Smart energy metering devices and sensors embedded with intelligence to coordinate and monitor the state of the microgrid parameters and associated entities. Providing data analytics to identify energy consumption patterns. Using available data to forecast and plan energy prosumption services.	Automated fault detection and diagnostics. Supports grid stability for congestion management and power. Decentralized microgrid control and management. Optimization of overall microgrid operations.	Needs real-time communication protocols. Throughput and scalability. Big data analysis, processing, and storage. Highly computationally intensive. Interoperability and standardization of interface.
Data security and Privacy protection	Data protection from cyberattacks. Achieving authentication, authorization, certification, and encryption.	Energy systems are secure. User privacy and confidentiality are preserved.	Achieving seamless data exchanges and improved computation for data-driven energy systems in a decentralized way. Securing data collected from smart energy metering devices and sensors.
Energy tracking and tracing	Employing smart energy devices to orchestrate and guarantee the provenance of RES from generation, distribution, and transmission to consumption.	A decentralized approach that provides stakeholders with access to the lifecycle of RES in a sustainable way.	Throughput and scalability. General Data Protection Regulation (GDPR) and other legal compliance.
Distributed energy exchange	Decentralized energy marketplaces for Peer-to-Peer (P2P)-based RES sharing and trading in a sustainable manner.	Energy prosumers share and sell energy directly with energy consumers. Local distribution, optimization, and balancing, of microgrid to reduce strain on the entire power networks. Provide incentivization of energy prosumers and decrease in electricity costs.	Possible energy market disturbance and grid defection. Efficient balancing of load. Fair and transparent pricing and billing. Improving energy consumers’ experience.

shows the potential applications, possible scenarios, benefits, and challenges faced in the deployment of internet of energy across energy-based communities.

4.3. Background of Green Energy Vehicle Charging in Energy Communities

The use of cars makes the mobility of people more convenient but also contributes to greenhouse gases when fossil energy is consumed, resulting in environmental problems. The EC is committed to cut its greenhouse gas emissions to 40 percent lower by 2030 and 80–95 percent lower by 2050 [13,35]. Presently, road transportation is one of the major contributors of CO₂ emission, and, as such, there has been a shift towards the adoption of EVs, which are associated with little to zero CO₂ emission [29,36]. As such, EVs have contributed as an integral component of energy communities due to the fact that EVs produce less CO₂ emission. Also, EVs provide energy storage for balancing power supply when integrated with solar and wind power plants to reduce the use of fossil fuels [37,38].

Notably, based on the penetration of distributed renewable resources, bidirectional energy trading can be deployed for EVs, i.e., Grid-to-Vehicle (G2V) for charging EVs, Vehicle-to-Grid (V2G) for the discharging of energy to be used from EVs, and V2X, where energy from the EVs is used to provide energy to other infrastructures within energy communities [30,39]. The application of V2G can stabilize the energy load throughout peak-hours of electricity usage using a charging infrastructure that is equipped with a bidirectional charger which enables the buying and selling of electricity via P2P manner [40]. V2G technology aids EVs in acting as a clean energy storage mode to share energy back to the power grid for balancing load during peak hours. EV users, in return, receive incentivization, such as financial gains, for supplying electricity to the power grid from their charged EVs, changing the mode of the electricity market from a central network towards a distributed and decentralized network [30].

4.4. Challenges and Recommendations for Green Electric Vehicles Charging

To contribute to reducing climate change, environmental pollution, and overall global energy policy, the adoption of EVs has increased in society, especially in European countries [9]. EV users are faced with a few challenges in energy communities, one of which is the limitations of charging stations, which inhibit the usage of EVs, as well as how to address EV users’ anxiety of battery power running down amidst a long drive [12]. As such, in some countries, manufacturers of EVs are faced with uneven distribution and availability of charging stations. Thus, there is a need for accessible charging as one key strategy for increasing EV use in energy communities. A notable challenge is the availability of inadequate infrastructure (such as quick and mobile charging), as it is expensive to install EV charging facilities. Also, there is a need to consider transparent and fair pricing mechanism for charging EVs, data privacy, and security protection issues associated with the public charging infrastructure [41]. But presently, data sharing in energy communities is limited resulting in data silos. Thus, there is a need to promote data integration and to foster planning, coordination, and collaboration [28].

Moreover, with the increased use of EVs in energy communities, the conventional charging station sharing system is no longer able to address issues related to data security and privacy required for the resilient charging of EVs [42]. The existing shared charging infrastructure employs outdated systems that result in a waste of resources, high maintenance cost, and particularly low returns for investors. Thus, there is a need for a safe and efficient approach for managing the charging station sharing for EVs [43]. Prior studies, such as [28], aimed to achieve the traceability of the energy being consumed while enabling optimal energy management, protecting end-user privacy, and aiding controllers to assess the identity of different energy devices deployed in smart grid networks. Green electric vehicle charging is also faced with technological setbacks, such as the lack of solutions that support the shift from traditional demand response to decentralization-based models [44]. Also, most users, as energy prosumers, are faced with regulatory and economic barriers due to existing business models which provide reduced returns to users. Also, there is a series of changes for existing energy policies and regulations related to energy sharing. More importantly, there are reports of inadequate prosumers or a lack of community engagement, as many prosumers are less motivated to be involved in a decentralized P2P vehicle charging sharing market [44].

Another challenge relates to safety concerns faced during the charging transaction process. Thus, there is a need to deploy a distributed EV-based charging scheme, which is based on DLT. Nevertheless, the adoption of a distributed charging mechanism also requires a fair and transparent pricing scheme, as well as a privacy protection process, during energy sharing and trading. Data leakage of some personal information, such as the driving route of EV users and their preferred charging station, is a breach to the users’ personal information [41]. Moreover, the development of standardization and interoperability is crucial to enabling EVs to interact with the energy system, such as G2V, V2G, and V2X [31,45]. Also, the incentives provided for V2G are low when compared to the degradation of the battery lifespan due to charging and discharging operations. In comparison to a traditional-based charging platform, which is centrally operated, the DLT-based platform can ensure data privacy and security of EV charging when using charging stations provided by private owners [12]. This requires the implementation of disruptive technologies such as Distributed Ledger Technologies (DLT) and smart contracts. A report published by IEA [35] highlighted the use of DLT for supporting P2P charging of EVs, emphasizing DLT as a key technology for mitigating climate change. DLT can provide a seamless solution to promote the adoption of EVs by supporting the sharing of private charging stations owned by private residents, by which EV owners can have flexibility to charge their EV at different locations and times.

DLT and Smart Contracts for Green Energy Vehicle Charging

There has been an increasing involvement of different actors and processes in the generation, transmission, and distribution of electricity networks, including new infrastructures into the power grid such as EV and RES from photovoltaics and wind. As such, disruptive technologies are being employed into existing energy businesses and operational models in energy communities [12]. One of these technologies that is gaining momentum in the field of energy systems is DLT. As such, there has been rapidly increasing interest in DLT-based systems as a technology for enabling clean energy transitions, with decentralized applications such as P2P EV charging, energy trading, demand response, and accountable energy exchange, etc. [12]. Hence, DLTs have previously been adopted in different spheres of applications, such as in healthcare, finance, agriculture, manufacturing industry, supply chain, energy, smart cities, etc. [46]. DLT is considered as one of the disruptive technologies for the deployment “of next generation” systems across different domains, e.g., healthcare, finance, real estate, education, energy systems, etc. DLT employs a transparent, immutable, and fully replicated ledger of data or transactions without needing a centralized governance to orchestrate the network towards verifying added transactions and data [47].

DLT is based on peer-to-peer networks, which involve a self-organizing decentralized system of independent and equal participation [47]. In the context of this current study, DLT, smart contracts, and cryptocurrencies offer some remarkable features to foster the development of energy communities. Accordingly, DLT can be leveraged since this technology has some immutability, automation, irreversibility, consensus mechanism, decentralization, etc., which can address some of the challenges faced in existing centralized energy systems [48]. Similarly, DLT smart contracts are based on sets of pre-programmed functionalities deployed within the distributed ledger network. As such, each node user within the distributed ledger network has access to, and can run, the smart contract code. Smart contracts are executed when a pre-determined event or transaction occurs, then another pre-set action will inevitably take place. Thus, smart contracts are executed autonomously without the need for any human intervention, as the program checks each business step, requesting to execute what was assigned. Smart contracts cannot be modified at runtime or when deployed within DLT, thus ensuring a further assurance of security [46]. By employing smart contracts, DLT enables P2P energy trading, automated data exchange, demand response management, complex energy transactions, etc. DLT can critically contribute to the evolution of energy communities for distributed renewable resources and the re-enforcing of data security and privacy within the electricity grid network [30].

As the energy communities are transforming towards involving different numbers of participants, it is important to enable energy consumers and prosumers to exchange energy. In energy communities, this suggests that each resident could actively partake in the trading of energy, autonomously, to any intermediaries, such as power suppliers or utilities, towards achieving a low carbon energy transition [47]. This interaction, among these actors and the correlated processes, requires standardization and governance which can be enabled by DLT. The utilization of DLT for energy sharing in energy communities can lead to the monetization of energy excess, the exclusion of brokers, and the future development of energy communities. These intermediary parties and brokers are typically needed for verifying, or for safeguarding, the trustworthiness of information across different actors, and can be substituted by a more automated DLT platform. As DLT delivers a high level of data protection and security for different systems reinforced by a transparent ledger that saves all data and transactions, it thereby eliminates the need for third-party verification [8]. The decentralized design nature of DLT makes this technology more suitable for enabling RES into the current energy systems in terms of transaction payment and proof of origin for energy generation, and it is, as well, an enabler for P2P energy sharing in energy communities [47]. But the use of DLT is faced with some limitations, such as scalability, due to minimal transaction throughput. Another challenge is the technical limitations and existing rules and regulations which impede the widespread adoption of DLT [47].

Overall, it ensures energy data security and user privacy techniques, such as consensus mechanisms, asymmetric encryption algorithms, and mutual trust, are employed to secure the transmission of energy related data among different actors involved in energy as a service value chain [24]. Smart contracts are employed to improve the transparent business execution towards achieving credible energy data generation and transmission of required credentials across energy community [11]. Accordingly, Yang et al. [11] researched renewable energy, tracing for EV charging and leveraging the technical capabilities of DLT to improve resource sharing and mutual trust. The authors used information of energy-trading places, vehicle networking applications, social operators, and energy-dispatching centers to trace the consumed energy type and the district of RES for green certification of the energy. The study aimed to promote the use of green energy in charging EVs and thereby encouraged energy consumers participation in the use of RES. In order to resolve the needs of EV charging and RES consumption, and to lessen load peak to fill valleys in energy communities, this current article advocates for the use of DLT and smart contracts for tracing and tracking the RES consumed to charge EVs in energy communities. As pointed out by Yang et al. [11], this will help to promote the provenance of RES from production to distribution, and then to the consumption of clean energy. Thereafter, the type and source of energy consumed by the EVs can be traced by considering the price, quantity, and time of usage for charging.

Furthermore, according to the EV user’s charging patterns, the charged energy can be matched with local distributed and cross-region traded RES. Also, smart contracts can produce confirmed green energy certification that authenticates that green energy is used for charging. This green energy certification comprises a unique digital identification, and the charged energy type, source, and amount of RES [11]. Using the unique digital identification, generated by a smart contract, the green energy can be traced back to the original source. Additionally, the use of DLT and smart contracts can help EV users to select the most appropriate charging location based on the cost, distance, source of energy, etc. [41]. Evidence from the literature suggested that some initiatives have been published on EV charging; one such study is the Share and Charge case (https://www.electrive.com/2020/03/03/sharecharge-launches-open-charging-network/, accessed on 15 July 2025), which is a DLT-based P2P system that provides seamless and intelligent sharing of charging stations. The Share and Charge approach employs a mobile platform which is to be used by EV drivers to access the locations of available and accessible charging stations which are nearby. By using the platform, EV users can receive real-time price information for charging, which also enables owners of privately owned charging stations to earn money when they are sharing their idle charging stations. Similarly, another charging station sharing services is provided by Chargemap (https://chargemap.com/pass, accessed on 15 July 2025), where EV users can be assured of consuming verifiable 100 percent clean energy from RES such as solar, wind, etc.

4.5. Electricity Tracing and Tracking in Energy Communities

The concept of tracing and tracking electricity flow from generation to consumption has been examined from as early as in the 1990s. Traceability is an essential factor to be considered in quality management. According to ISO 9000:2015 (https://www.iso.org/standard/45481.html, accessed on 15 July 2025), quality management system standard traceability is defined as the ability to identify the history, location, or application of an object [17]. In the digital age, data traceability has become important as it helps to ascertain the origin and evolution of data content according to the life cycle flow path. Conventional data traceability approaches mainly include the reverse query method and the annotation method [49]. Today, several entities claim to only distribute green energy sources to generate energy. An issue is how do all participants within the energy community confirm, track, and trace the source of electricity being consumed [4]. Unlike physical product tracking, such as with parcels, packages, etc., electricity is a non-physical element, making it complex to trace its source of origin [7]. The idea of tracking the provenance of physical items is easier as, in the existing approach, the item is assigned a unique identifier, but that is not applicable for tracking and tracing electricity from the source of generation, distribution, and consumption by energy consumers, as electricity flow is mainly dynamic.

There is a need to track energy volumes for individual consumer groups. Although, in energy communities, existing smart energy metering and measurement devices and systems make it possible to accurately determine the amount of energy from each generation source and, based on balance methods, estimate consumption by consumer groups. These systems do not provide information on the type of energy or where the electricity being consumed is generated from, either from renewable or non-renewable sources. Hence, research related to electricity tracing and tracking in energy communities is important to achieve the United Nations Sustainable Development Goals (SDG) 7—affordable and clean energy, 11—sustainable cities and communities, and 12—responsible consumption and production in society. The case for electricity is slightly different from other physical or digital goods exchanges [5], as electricity is an unconventional product that is transmitted to the end user through grid lines. Thus, it is challenging to track and trace its source or origin. Another notable challenge in tracing the source of electricity is attributed to the fact that electricity that is distributed and consumed is generated from a mix of energy sources, and is then delivered to energy consumers in order to provide adequate electricity. Hence, it is challenging to evaluate electricity generation from renewable sources and other sources of generation. For example, if you mix water collected from different sources it is difficult to know the source of the water after mixture, which is similar to electricity.

Although, in countries such as Norway, almost 100 percent of the electricity is generated from RES as reported by statistics Norway (https://www.ssb.no/en/energi-og-industri/energi/statistikk/elektrisitet, accessed on 18 July 2025). In other countries, there is usually an energy mix from different electricity generation sources, e.g., in the United States of America (USA) almost 80% of the electricity is generated from fossil-based fuels and about 11 percent is produced from RES. RES is a better substitution as compared to fossil-based fuels, which contribute to the greenhouse emission. As such, this volatile supply of electricity mostly contributes to increasing complexity, which makes the tracking and tracing of electricity challenging [4]. In energy communities, RES goes through a process of energy generation to energy transmission, to energy transformation, to electricity distribution and then to electricity consumption, where the electricity reaches the energy consumer after which the electricity is exchanged (shared and traded), as captured in Figure 8. But during the generation to the transmission phase, although electricity can be quantified, it is challenging for electricity to be located or tagged, tracked and traced in real-time, like other traditional services [5].

In the generation stage of RES, the power grid company dispatching platform tracks and saves the real-time actual energy generation output data of RES power plants. In the energy transmission and electricity distribution stages, the electricity-dispatching platform and the Geographic Information System (GIS) system take note of the geographic location and network structure connection with the main infrastructure of “RES” electricity transmission for energy transformation, and then electricity is distributed to electricity consumption (energy consumers) [5]. In the electricity consumption phase, the electricity consumption information is collected by an integrated dual-core system, and smart energy-metering devices record and read real-time electricity consumption information. The RES electricity transaction data is recorded in the decentralized energy trading platform, which stores the energy consumer’s energy purchase and transaction contracts, trade commitments, and energy supply contracts. Using this energy supply with contract information and data, the transaction date/time, transaction subject, transaction energy amount, transaction process, and other energy-related information of the RES energy transaction can be obtained the in case of electricity exchange (electricity sharing and trading).

Presently, green certification is a known approach which is being employed in the energy market to verify that the source of electricity is from a RES. The green certificate is simply a digital or electronic certificate issued by a government agency to companies who utilize RES to generate electricity [41]. The issue of the green certificates is mostly based on the quantity of electricity generated by a RES. The generation of RESs has twofold value of green energy and environment value, which is represented by the green certificate. The green certificate offers a means to monetize the environmental value of a RES [43] and is generally linked to the circulation power of the existing green certificate market.

Use of DLT for Electricity Tracing and Tracking in Energy Communities

DLT has been applied to tracking applications mostly in supply chain management, as well as in tracking carbon trading, green certificates, and plastic-offsets by creating a fair, transparent, and simple marketplace, and thereby streamlining transactions between sellers and buyers. Similarly, DLT has been adopted in other sectors, such as in pharmaceutical, food, battery, etc., to ensure end-to-end tracking and traceability to ensure the integrity of products [4]. But research and application of DLT in the energy domain has received continuous consideration, mainly focused on enhancing data sharing capabilities and improving business efficiency. Other researchers have focused on the deployment of DLT in managing renewable energy scenarios and in supporting transaction and green certification scenarios [5]. For example, Power Ledger (https://www.powerledger.io/, accessed on 18 July 2025), an application type of DLT, has cooperated with the Midwest Renewable Energy Tracking System to develop a DLT-based platform that could facilitate the sales of thermal energy and renewable energy credits, whereby energy credits can be traced and tracked. Due to the immutable nature of DLT, researchers have advocated that DLT can be employed to store, track, and trace the green certificate, which helps to guarantee that the source of electricity generated and distributed to energy consumers is from a green energy source. Furthermore, from a provenance perspective, DLT has the capability to provide trustworthy traceability of goods or products throughout the whole value chain operation. Specifically, when DLT is combined with IoT, the historical information regarding the products’ life cycle can be enhanced [46]. But only a few studies have focused on the application of DLT for traceability of RES [5]. Accordingly, this technology can be intrinsically employed for tracking and tracing the source of energy.

DLT can be employed to track and trace the provenance RES. Provenance is one of the characteristics of DLT that enables this technology for tracking traded assets via tokenization once the asset is created within the distributed ledger [44]. The main information for each process, for the RES life cycle as seen in Figure 8, is entered into the DLT platform to capture the key information of the whole RES electricity supply process. Each stage is recorded and stored within the distributed ledger, that is maintained by smart contracts, which efficiently maintains data security and safeguards the credibility and authenticity of key information throughout the entire RES life cycle process [5]. Through the calculation and verification of the energy generation phase to the final electricity consumption side, and the available energy transaction information, the traceability and tracking of RES in energy communities is possible. The incorporation of DLT can provide a unique and uniform identification for RES by enabling a trustworthy and transparent sharing of data of RES from energy generation, transmission, transformation, distribution, consumption, and exchange to support the tracking and tracing of electricity. During the transmission of energy, DLT can provide real-time monitoring of RES status. DLT can provide decision support to energy consumers and prosumers for predicting RES supply based on demand, thereby offering trustworthy and scalable energy as a service ecosystem that enables collaboration and coordinated energy sharing and trading services among different stakeholders in energy communities.

But presently, there is inadequate knowledge about how technologies such as DLT can be used to track and trace the provenance of electricity in energy communities. Providing information on the source of energy is important as various equipment and things are being electrified. Thus, it is vital for energy consumers to know the degree of greenness of the electricity they use in different tasks such as charging EVs [4]. Additionally, smart contracts can be used to enhance energy consumers’ and prosumers’ privacy by securing connectivity with IoT devices, such as the smart meters, and by offering a supporting traceability log to detect any malicious activities. DLT can support a publish/subscribe mechanism by enabling publishers and subscribers to govern the access control usage of energy-related data in a privacy-preserving, transparent, and traceable manner [8]. But, in the literature, only a few studies have examined the traceability of unconventional commodities using smart contracts and DLT, such as blockchain [5]. As such, there is need to develop a tracing and tracking mechanism based on DLT that provides new ideas for tracking and tracing the sources of unconventional commodities.

Recent microgrid initiatives are being developed, such as the Brooklyn microgrid, in New York, that employed DLT for managing energy transactions to provide insights as to where new energy systems are located based on distributed generation, enabling energy trading between neighbors and offering a novel role for distribution service utilities. DLT enabled energy prosumers to sell their excess solar energy to the power grid or to citizens of New York City who choose to consume clean energy in place of fossil fuel. The Brooklyn microgrid supports distributed renewable generation using an integrated market platform, and environmentally mindful energy consumers can also track the source of the energy that they consume. Also, by using AI-based energy systems energy prosumers could forecast the energy-demand profile of consumers so that the DLT-based trading platform can better adapt to the low-carbon energy demands of consumers [12]. But, since electricity is highly dynamic, the use of green certificates to specify the source or origin of electricity is still difficult. Presently, there are a few approaches being employed to ensure that the energy consumed is from a green source. One such approach is by trading green certificates.

4.6. DLT-Based Demand Response for Green EV Charging in Energy Communities

With increasing penetration of RES in energy communities, there is a need for better management of energy demand and supply for the stability of the power grid. Customer demand response is one of the most cost-effective strategies employed to provide balancing to the power grid [12]. Demand response (DR) is a type of demand-side management (DSM) that aims to reduce energy demand during specific times, or it involves a shift in energy demand across time. In energy communities, DR can refer to payment for energy consumers, either based on their motivation to change or an actual change in energy usage from the expected levels (https://www.energyknowledgebase.com/topics/demand-response-dr.asp/, accessed on 18 July 2025). Although, a conventional demand-response approach requires the centralized management of demand response among energy consumers, which can be time-consuming and less efficient, and 20 percent of energy consumers opt out of this type of traditional demand-response approach due to poor user experience [12]. Overall, the change in energy usage may also be controlled by the energy customer or pre-defined into the energy customer’s amenities and controlled by a demand-response aggregator or a utility. This change may be based on the system operator’s scalability request or based on a price signal.

Within energy communities, DR programs are operated in both dedicated wholesale markets enabled by Independent System Operators (ISOs), whereas in retail markets DR are aided by utilities. Moreover, in energy communities, DR may include emergency demand response where energy customers agree to minimize energy demand to support the power grid reliability amidst extreme events such as power outages or blackouts. As highlighted in the literature, Lucas et al. [50], DR services have the capability to enable large penetration of RES by regulating load consumption, hence providing balancing support to the power grid. Also, DR provides economic demand response where energy customers are given financial incentives to decrease energy demand, during times when the energy price is lower to minimize demand, rather than to purchase additional units of electricity supply. This also encompasses shifting the energy usage to times when there is ample renewable supply available for energy consumers (https://blog.enerdynamics.com/, accessed on 18 July 2025).

This can be likened to behavioral demand response where energy customers utilize DR capability as a means of managing their electricity bills. An example of DR in energy communities ranges from an electric car that can be controlled remotely to charging an electric car for an hour during peak energy demand periods. In this example, the charging station installed within the residential area can be controlled remotely to specify EV charging, thereby minimizing electricity consumption during peak hours. Another example is associated with an electric heater, where the energy consumer controls the time when the heater warms a residential apartment. In addition to adjusting energy consumption, DR can also supplement other system needs; for instance, ancillary services within the electricity markets. As such, DR services can have a substantial impact on minimizing peak loads by shifting loads to times with abundant supply or surplus RES, limiting price spikes in competitive energy markets, and also providing capacity resources that decrease associated system capacity costs (https://blog.enerdynamics.com/, accessed on 18 July 2025).

In energy communities, prosumers can utilize DR as a medium for bill management by reducing electricity usage amidst periods when electricity price is high by scheduling and managing peak demand, thereby controlling energy demand charges (https://blog.enerdynamics.com/, accessed on 18 July 2025). Although the realization of load flexibility provided by prosumers in energy communities, and its integration into the electricity markets, will rely on the adaptation and redesign of the current interactions among participants within the distributed electricity network. Additionally, new challenges are certain to surface regarding flexibility, with the large-scale contribution by smaller assets. This also involves other issues such as the validation of delivery of the DR provision, the dispatch coordination, and the subsequent settlement of energy transactions and contracts, while guaranteeing safe and secure data access among actors involved in the energy prosumption services [50]. Accordingly, DLT can be employed to securely track and trace DR services by enhancing the validation of transactions, re-enforcing data integrity between the relevant stakeholders (including Distribution System Operators (DSOs), Transmission System Operators (TSOs), Balance Responsible Parties (BRP), aggregators, and prosumers).

In a DLT-based system, any resident can become an energy trader and offer energy related services or products to potential consumers. DLT, therefore, has the prospect to leverage the benefits of decentralized energy systems towards enabling an environment where citizens can trade, share, pay, and even exchange green energy to others to recharge their EVs [8]. DLT can support the provision of economically based energy communities, leveraging the power of the Internet to implement energy as a service ecosystem [24]. DLT may provide an innovative method for autonomous demand response by efficiently leveraging the flexibilities amongst EV-charging scheduling, energy customer loads, energy storage, and the utilization of distributed energy resources [12]. Leap, a California-based company, developed a system called “Distributed Energy eXchange”, which could combine building management systems, building heating, ventilation, and air conditioning (HVAC) systems, and EV chargers for peak load minimization [51].

The participating user nodes within this platform can provide information on when they can change and distribute their energy loads. Based on the anticipated energy prices, energy load reductions are then executed through the communications interface between each participating node [51]. Additionally, DLT can enable EVs to absorb surplus RES generated from distributed resources as grid-to-vehicle (G2V) and then feed the electricity back into the power grid as vehicle-to-grid (V2G) to support demand-response programs and reduce large investments for dedicated battery storage, while benefiting from the distributed sources of RES [13]. For example, an innovative demonstration was provided by eMotorWerks (San Carlos, CA, USA) where smart EV chargers were used to balance up to 30 megawatts (MW) of RES in California, minimizing the on-peak demand of non-renewable energy and supporting energy communities towards the consumption of green energy for EV charging [52].

4.7. AI as Enabler in Energy Communities

AI are computer systems that aim to simulate human intelligence on some level to solve specific problems [46]. The concept of AI was first proposed in 1956. AI is seen as an emerging multidisciplinary area involving technology, methodology, theory, and system applications that integrates computer science, neurophysiology, cybernetics, mathematical logic, and informatics to simulate and extend human intelligence [53,54]. The main goal of AI is to equip computers to reason and act as efficiently as humans and ultimately support human intelligence. AI is specifically capable of sorting across data to find patterns, and thereby makes predictions [24,53]. Recently, AI techniques, such as fuzzy logic, artificial neural networks, and genetic algorithms, have also been adopted to solve different energy management challenges. But the use of AI is faced with issues such as how to manage data-based modeling for complex energy systems. AI can help to match local energy generation and consumption via smart control of flexible loads to manage the forecasting of inflexible consumption and EV-charging scheduling [55].

One of the first AI techniques was expert systems, which encompasses expert knowledge about an explicit domain to provide recommendations to solve identified problems using a rule-based inference mechanism. By employing real-time data, these rule-based inferences can help to generate conditioning-based systems that help to optimize energy generation for EV charging [46]. Figure 9 captures some key benefits derived from employing AI in energy communities. Therefore, with the use of real-time data produced from IoT, such as smart sensors and metering devices in energy communities, AI-based solutions can be employed to support optimization, simulation, and prediction to improve decision-making in energy communities, enabled by different technologies such as DLT [46].

AI-based solutions can identify unknown, and possibly useful, information patterns produced by data from energy metering devices. The information derived from these IoT devices can be transformed into actionable knowledge and outcomes to support improved decision-making for EV users [56]. For example, by processing and analyzing the data from smart metering devices deployed by prosumers, AI can provide information on forecasting and segmentation on load curves, compute pattern recognition within load curves, and carry out predictive modeling. AI can aid real-time analytics in a reliable and fast way in order to control complex systems, such as power grid resource efficiency and timeliness, towards enhancing the quality of life of citizens [57]. The information provided by AI can help to provide improvements in operations and predictive maintenance in real time based on the data produced from energy consumers, EV assets, charging stations, and infrastructures.

4.8. The Integration of AI and DLT as Enablers in Energy Communities

Conventional energy transactions are usually governed via a centralized organization. It is projected that the electricity market in energy communities will become more complex as large numbers of energy prosumers provide RES. As such, energy trading between energy prosumers and energy consumers will simultaneously become more complex [41]. The adoption of disruptive technologies has the potential to considerably improve energy supply, trade, and sharing [14]. Thus, the emergence of innovative decentralized energy sharing and trading approaches based on DLT will change the role of conventional power corporations in the electricity market, reducing the need for energy companies to invest high costs in installing infrastructure management facilities for energy prosumers and consumers, and providing energy consumers with more alternatives to energy purchasing based on a feasibly low-cost energy exchange environment.

The decentralized nature of DLT improves energy data availability and transparency. Also, DLT can store related data about energy products and services immutably, providing authentication and identification for actors. Using consensus mechanisms and smart contracts, DLT can support the addition of data intermediately, thereby fostering trust among participants in energy communities. This transparent energy-related data can be utilized by all actors for decision-making support [46]. Likewise, AI can support the integration of RES into the power grid to autonomously control and optimize decision-making operations [14]. Findings from prior research reveal that the implementation of AI techniques in electrical systems outperforms the traditional approaches in big-data handling, controllability, computational efficiency, mitigating cyberattacks, optimization of energy efficiency, and predictive maintenance control [58].

AI can support energy prosumers by planning energy generation as well as forecasting of energy consumption. Evidently, use of AI techniques will play an influential role in future energy communities [14]. Accordingly, the convergence of AI and DLT-based solutions are key to achieving cross-energy exchange as needed in energy communities. AI and DLT enable a self-organizing market, trusted logging for energy exchange, and fosters the optimization of rule-based algorithms [55]. In energy communities, the integration of DLT can help energy prosumers to set up decentralized energy markets and AI can help to ensure the market schemes comply with existing energy market design requirements. For example, the adoption of AI and DLT can advance the transparency of the energy trading procedure, improve the operationalization of the power grid in the development of point-to-point energy exchange, and optimize demand response, automated transaction, and billing process, and, lastly, re-enforce data security and privacy protection [41]. Use of AI and DLT can support operational stability by matching energy demand and supply to energy consumers to balance energy supply across the distribution grid. Thus, a connection to the large-scale power grid exists [59].

Developed Decentralized Intelligent Framework

This study develops a decentralized intelligent framework towards assuring tamper-proof trustworthiness and decentralized energy provenance and green EV charging in energy communities. The framework employs DLT, smart contracts, and AI to enable the tracking and tracing of energy flexibility in near real time for green electric vehicles charging in energy communities. The framework is developed and grounded based on recommendations posited from prior studies [30,60], as seen in Figure 10.

Figure 10 depicts the developed decentralized intelligent framework for sustainable energy provenance and green EV charging in energy communities. The framework provides service interfaces related to energy usage of energy consumers, including gateway access services, authorization and authentication, session management, and visualization to support decision-making [5]. Moreover, the framework enables the energy service providers to control the electricity flow, and is responsible for electricity distribution, operation, and sharing/trading. The energy consumer, or customers, consumes electricity, and also uses different infrastructures and equipment such as smart appliances, smart sensors, smart objects, Advanced Metering Infrastructures (AMI), demand response, automation stations, supervisory control, and data acquisition (SCADA), EVs, and home energy management systems [28]. To improve green EV charging-related services, data is collected from EV fleets using the On-Board Diagnostics (OBD) device, energy metering devices, smart sensors, etc. [15]. Then, DLT, smart contracts, and AI-based machine learning are employed to enable energy provenance in near real time for green electric vehicle charging services. This is carried out using the traceability data of RES stored within the distributed ledger triggered by smart contracts [5].

DLT can be used to develop distributed application (DApp) platforms that can be used in mobile phones, allowing different users to join the DLT platform. Using the DApp, energy consumers or energy prosumers can check the current balance of their energy via the digital wallet after selling energy to the power grid or buying energy from the power grid. Also, using the DApp, energy consumers and energy prosumers can also track and trace their electricity source and usage by viewing all the exchanged data transactions with the power grid [10]. AI is employed to optimize the hourly scheduling of EV-battery charge and discharge towards optimizing the energy and time needed to charge an EV. The AI-based optimization is grounded in the minimization of energy supply costs, maximization of RES for EV charging, and extending the longevity of EV battery life for the minimization of the battery wear [9]. Using AI, the scheduling is typically optimized by employing 24 h-ahead forecasts of RES, e.g., PV plant energy generation, dynamic load demand of other energy consumers, and estimated arrivals of EV fleets that need to charge [9,24]. Smart contracts based on Solidity (https://docs.soliditylang.org/en/develop/, accessed on 20 July 2025) were employed within the framework to support DTL and AI processes. The smart contracts can be written as energy-deal contracts and verification contracts, as suggested in the literature [8].

The energy-deal contract enables sellers and buyers to share and purchase RES, including solar, wind, hydro, and geothermal. The verification contract provides an audit-trail procedure for tracking the energy deals between sellers and buyers in order to identify any possible violation [8]. To ensure data security and privacy, a private- or consortium-based DLT is deployed for reading the energy consumption by energy consumers, and calibrated using smart metering devices which have a private key that is securely stored within the physical device. The smart metering devices calculate electricity consumption and sign it with their private key before transferring the data to other parties (e.g., the energy consumer) within the private DLT. Hence, the stakeholders are assured that the data has been signed by smart metering devices. There is also an energy-reading contract generated by smart contracts that only permits the subscriber (energy consumer, EV user), supplier (energy supplier, charging station provider), and authority (TSO, etc.), to send their requests to view the amount of electricity used [8]. As such, data requests are sent to the DLT, which authenticates the requester and then sends a request to the smart metering devices for access to the saved energy related data. The smart metering devices receive the request, sign the calculated used energy data, and later send this data to the requester [8].

4.9. Case Study on Use of AI and DLT to Improve EV Charging in Energy Communities

Currently, the existing charging management system employs centralized approaches to store and manage user data as well as the information of the shared charging stations, which results in the existence of data islands, or silos, in charging services [43]. This has also resulted in a lack of trust, data security, and privacy for users, creating barriers for charging station operators and greatly reducing the frequency of users utilizing shared charging stations [61]. Based on the distributed and decentralized configuration of DLT, this technology can decrease data leakage for users of charging stations and the data generated from shared charging stations. The introduction of DLT for EV-charging systems could reduce the need for any centralized intermediary. DLT can provide a trusted mechanism for all parties, creating a P2P EV charging network that combines energy sharing and EV-charging features, and enabling private charging owners to share their charging stations with other users and receive remuneration from other electric car drivers. Overall, the use of DLT and AI can enable an ecosystem where both private charging stations and charging operators of open charging stations can be connected and shared [43].

AI-based ML can forecast and suggest electricity prices to private owners of charging stations, or they can set their own costs for users to charge their EVs. DLT can help carry out identity management to detect users and to enable charging stations to supply electricity after confirming the identity authentication process of EVs. Then, a smart contract is triggered based on an existing agreement between the power grid company and the electric car owner made in advance [43]. The smart contract calculates the charging fee, which is presented to the electric car owner. Concurrently, the payment channel between the electric car owner, the power grid operator, and the charging stations is established [62]. Based on the calculated charging charge, the energy-sharing transaction is completed, and the transaction, or data record, is stored within the distributed ledger. The sharing of privately owned EV charging stations can lessen the shortage of EV-charging infrastructure, reduce investments needed to install new EV-charging facilities, achieve optimal allocation of social resources, and democratize EV-charging facilities [43].

5. Discussion and Implications

5.1. Discussion

The transportation sector is one of the fastest consumers of fossil-based energy and is among the highest emitters of greenhouse gases. As such, many European countries are advocating to use EVs as a green substitute for internal-combustion vehicles. The large-scale use of EVs will significantly lessen greenhouse gas emissions and also reduce the fuel cost incurred by drivers of combustion vehicles [41], although this is based on the availability of charging station and lower energy prices generated from renewable sources [24]. Moreover, there is a need to improve the reliability, safety, and efficiency for green EV-charging services via the promotion of renewable energy generation, transmission, transformation, distribution, and improved the efficiency of renewable energy consumption for society. The study adds to the existing body of knowledge by providing more insight into the impact of disruptive technologies as enablers for sustainable energy provenance and green electric vehicle charging in energy communities towards furthering the energy transition. Accordingly, in this study, a decentralized intelligent framework was developed that leverages DLT to store energy-related data collected from IoT devices, such as smart meters and energy sensors, in a tamper proof manner; this framework is enabled by AI and smart contracts, which programmatically define the predicted energy flexibility for each prosumer and the associated incentives. DLT has been adopted in the energy sector due to several benefits such as transparency, security, and low-cost operation solutions.

Nonetheless, research that focuses on the use of DLT for green energy tracing and tracking remains very limited. Also, there are few studies and industrial works that focus on energy tracing and tracking [4]. The current method of green energy tracking, by trading green energy certificates, may not truly reflect the source of green energy provenance as electricity is highly dynamic. Therefore, this study focuses on green energy tracing and tracking by employing DLT, leveraging the traceability capability of this technology to reduce the complexity and also open up new energy services such as transparent green energy for EV charging in energy communities. Analogous to the literature [8], this study suggests that DLT supports distributed demand-side management, which can support the matching of energy production and demand for green electric vehicle charging in energy communities. Findings from this article present an approach that can be employed for tracing the entire chain of renewable energy from the electricity generation side to the electricity consumption side. The suggested approach uses the encryption and anti-tampering capabilities of DLT, which is connected to digital energy traceability and certification verification using smart contracts to ensure the credibility and authenticity of traceable data and to improve supervision capabilities and provenance.

More importantly, findings from this study describe how RES (solar, hydro, wind, and thermal) from energy prosumers can be effectively harnessed, stored, shared, transmitted, and traded among energy consumers within the same electricity network. As suggested in the literature [8], using DLT, the developed decentralized intelligent framework, identifies explicit rules in relation to definite goals and sets participation policies and possible requirements imposed on all users within energy communities. Thus, by leveraging DLT and AI, the decentralized intelligent framework provides a novel approach to enable energy transaction within residential microgrids, and further provides a mechanism, using smart contracts, for settling payment for energy used for charging EVs based on a set of energy transactions executed between user nodes. Energy transactions happen in a given time frame, thereby tracking and tracing the electricity flows and enabling the physical monitoring of the community grid [63,64]. Findings from this study are similar to findings from Yang et al. [11], where a decentralized intelligent framework is developed as a technical foundation for guiding sustainable energy provenance and green electric vehicle charging, reinforcing energy communities’ capabilities to dispatch RES towards promoting green energy production and consumption.

5.2. Research and Practical Implications

Green transport policies and carbon neutral policies can potentially help to reduce emission for sustainable development [16]. Thus, to reduce CO₂ emissions, there is a need to regulate and reduce carbon emissions and initiate policies that aim to promote green energy use in the transportation sector. Disruptive technologies, such as DLT, are enabling innovative business models in the energy sector. Likewise, AI is being employed for load and generation forecasting and modeling of distributed and integrated energy systems. Also, there has been prior research on the use of AI for automated control and optimization to improve conventional control methods [55]. However, existing approaches are designed for centralized energy systems, and legal and organizational concerns need to be improved to support the adoption of DLT and AI for energy provenance and green electric vehicle charging in energy communities.

To positively contribute towards renewable energy initiatives and policies, and towards strengthening the decentralized dispatch of green energy across energy communities, this study develops a decentralized intelligent framework for energy tracking and tracing of green EV charging based on DLT and AI technologies to optimally allocate energy resources and actively engage EV users to participate in green energy consumption. Implications from this study advocate for the integration of RES to strengthen EV consumption and storage of green energy, enabling the intelligent interaction of the power grid with EVs, thereby realizing low-cost electricity to promote sustainable energy transformation. The developed framework provides an approach that employs DLT-based smart contracts as enablers to use data to trace and track energy generation, transmission, transformation, distribution, consumption, and exchange of electricity. DLT helps to ensure the credibility and authenticity of the provenance data of RES to improve the utilization rate of green energy in society.

6. Conclusions

Disruptive technologies, such as distributed ledger technology, internet of things, artificial intelligence, etc., have been proposed for tracking and monitoring products and analyzing data generated from IoT devices to support decision-making across different domains, such as smart cities. However, there are few studies that have explored the integration of these technologies, especially to achieve a reliable demand and supply of RES for green electric vehicle charging, peer-to-peer energy sharing, and RES certificate management, making renewable energy tracking and tracing more efficient and reliable in energy communities. Accordingly, this study investigates how energy communities can leverage the capabilities of AI, DLT, and IoT to aid the provision of energy flexibility services to energy consumers for green EV charging; at the same time, energy consumers and prosumers can trace and track the details of their energy production and consumption in near real time. Then, this article develops a decentralized intelligent framework based on AI, DLT, and smart contracts for sustainable energy provenance and green electric vehicle charging in energy communities. Additionally, this study examined how DLT-based smart contracts can facilitate real-time internet of energy tracking and tracing of RES from generation and distribution to consumption in energy communities. This study also examines how AI can support the decision-making process of citizens for smart EV charging in energy communities and, lastly, how AI and DLT can support the realization of energy sharing and what strategies can be adopted to unlock the transition towards internet-of-energy in energy communities.

More importantly, findings from this study provide a discussion of “internet of energy” in energy communities, a background of green EV charging in energy communities, challenges and recommendations for green EV charging, the application of DLT and smart contracts for supporting green EV charging, and a review of how electricity can be traced and tracked. Moreover, the discussion centered on the use of DLT for electricity tracing and tracking, the use of DLT-based demand response for green EV charging, and AI as an enabler in energy communities; this study then explored how the convergence of AI and DLT can act as key enablers in energy communities. Further findings from this study present a case study on the use of DLT and AI to enable EV charging in energy communities.

Limitations and Future Works

The current study is mainly grounded on secondary data, and the proposed framework was not empirically validated with primary data. Future work will examine the effectiveness of the developed decentralized intelligent framework through empirical research. This will involve the development of a DLT and AI-based prototype to be implemented for energy provenance from energy generation, energy transmission, and electricity distribution, energy transformation, electricity distribution, electricity consumption, and electricity exchange (electricity sharing and trading). The prototype will be enabled by DLT-based smart contracts to support electricity the provenance operation and dynamic pricing mechanism (by analyzing the load data of various electricity sources in comparison with the current spot price), for energy trading transactions. Research related to the energy consumption issue of DLT (such as the energy consumption of the blockchain consensus mechanism) and a sustainability analysis of DLT and AI will also be explored. The impact of policy differences among various regions will be considered as policy is a significant factor in experimental data. Thus, by comparing the differences in energy policies among different regions (such as Europe and Asia), the adaptability challenges of the analysis framework can be further addressed. Lastly, practical application data (such as user scale and emission reduction effect) will be employed to enhance the persuasiveness or applicability of the developed decentralized intelligent framework for energy communities.