Mohammad Nasrinasrabadi, Maryam A. Hejazi, Arefeh Jaberi, Hamed Hashemi‐Dezaki · 5 authors
Cryptocurrencies utilize blockchain technology to ensure transparency, decentralization, and immutability in financial transactions. It is expected that blockchain applications will significantly impact renewable energy markets. However, there is a lack of studies addressing the energy requirements of digital currencies. This research proposes optimizing a hybrid energy system consisting of distributed renewable and non-renewable energy sources, focusing on cryptocurrency mining. Although previous studies have not yet addressed energy system optimization considering cryptocurrency mining farms, the increasing prominence of such farms highlights the growing need for research in this area. The primary renewable sources in the proposed hybrid system include photovoltaic (PV) panels and wind turbines. We employ diesel generators as backup systems to compensate for the intermittent nature of solar and wind energy production. Besides meeting the demands of urban loads, cryptocurrency mining devices will be considered a major energy consumer. In this article, the optimal configuration of the energy system will be determined based on technical and economic indicators. Additionally, economic evaluations will be conducted to assess the income generated from cryptocurrency mining farms, and appropriate approaches will be identified from both technical and financial perspectives, focusing on return on investment (ROI).
This critical review examines decentralised renewable energy (DRE) systems as game changers for sustainable energy access in Sub-Saharan Africa (SSA). Although rich in renewable resources, over 570 million people in rural communities lack electricity. Traditional energy models, shaped by colonial histories and marked by inefficiencies, have failed to meet the continent's diverse energy needs. DRE systems provide flexible, community-focused solutions that promote energy equity, foster economic growth, and enhance climate resilience. Using Critical Juncture Theory and the Rational Choice Model, this study examines factors influencing DRE adoption. Analyses show how DRE encourages energy democracy, local ownership, and aligns with Sustainable Development Goals, including SDG 7 (Clean Energy) and SDG 13 (Climate Action). However, these systems face obstacles like fragmented policies, insufficient funding, technical gaps, and governance issues. Case studies from Kenya, Nigeria, South Africa, and Ethiopia demonstrate implementation strategies, revealing supportive environments and challenges. This review synthesises policy discussions, highlights innovations like pay-as-you-go financing and digitalisation and outlines an integrated energy planning roadmap. Recommendations include regulatory reforms, blended financing models, capacity-building initiatives, and regional cooperation. This paper argues that decentralisation should be viewed not as a temporary measure but as a foundation for energy strategies. With visionary leadership, collaborative governance, and targeted investments, decentralised systems can transform Sub-Saharan Africa's energy future, prioritising equity, resilience, and sustainability. • Decentralized renewable energy (DRE) is paving the way for fair energy access across Sub-Saharan Africa. • ii. DRE systems are all about empowering communities, promoting energy democracy, and building resilience against climate change. • iii. Unfortunately, there are policy, financial, and technical hurdles that hold back the widespread adoption of DRE in the area. • iv. Various case studies showcase a range of DRE strategies and creative financing solutions. • v. For a successful shift to sustainable energy, integrated policy reforms and regional collaboration are essential.
The increasing integration of renewable energy into smart grids introduces challenges of demand-supply imbalance, peak load stress, and cyber-physical vulnerabilities. Existing demand response (DR) frameworks often lack scalability, privacy-preserving data sharing, and secure transaction mechanisms, which limit user participation and grid resilience. To address these challenges, this study proposes GridSyncNet, a blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response. The framework integrates federated learning to enhance decentralized forecasting accuracy, blockchain consensus to ensure transparent and tamper-proof energy trading, and actor–critic based DRL agents to dynamically optimize load scheduling and energy dispatch across prosumers. Extensive simulations demonstrate that GridSyncNet outperforms benchmark models such as OD-CNN, D-FCAS, and USTCF. Specifically, it achieves a 98.2 % demand response efficiency, 30.6 % reduction in carbon emissions, and 97.4 % forecasting accuracy. Comparative analysis with multi-agent DRL (MADRL) approaches further confirms that GridSyncNet provides superior scalability, privacy, and security in decentralized environments. The proposed framework contributes to the design of secure, resilient, and sustainable energy management systems, offering practical insights for accelerating the transition toward net-zero energy communities. By combining blockchain, federated learning, and multi-agent reinforcement learning, GridSyncNet establishes a comprehensive pathway for trustworthy and adaptive smart grid operations. • A multi-agent deep reinforcement learning framework for adaptive DR in smart grids. • Decentralized peer-to-peer energy trading to transparent, secure energy trading. • Renewable energy utilization 89 %, CO 2 reduction 30.6 % & forecasting accuracy 92.4 %. • Federated learning & improved system resilience against cyber threats for DSM. • Optimize load balancing, peak shaving & cost efficiency for distributed grid agents.
The exponential growth of the electric vehicle (EV) industry, driven by decarbonization goals and energy transition policies, has intensified the need for sustainable and transparent supply chains. Lithium-ion batteries (LIBs), the cornerstone of EVs, pose complex life cycle challenges related to ethical sourcing, environmental degradation, traceability gaps, and inefficient end-of-life (EOL) management. Addressing these multifaceted issues requires an integrated technological approach. This study proposes a unified framework leveraging Digital Product Passports (DPPs) and blockchain technology to enable real-time, tamper-proof tracking of battery materials, components, and performance metrics throughout their lifecycle.The paper further integrates machine learning, with a focus on reinforcement learning (RL), to optimize logistics and predictive maintenance based on dynamic supply chain data. To ensure privacy and regulatory compliance in data sharing, the framework incorporates zkSNARKs—a zero-knowledge proof system that preserves confidentiality while maintaining verifiability across distributed networks. This triadic approach promotes lifecycle transparency, supports circular economy goals through efficient material reuse and recycling, and reduces the total cost of ownership (TCO) for EV stakeholders.The proposed solution addresses critical industry challenges—such as counterfeit components, low recycling efficiency, and supply chain opacity—while offering scalable applications in adjacent sectors like consumer electronics and renewable energy. The integration of DPPs, blockchain, and AI-based optimization establishes a resilient, interoperable infrastructure that enables enhanced sourcing, sustainability, and collaborative innovation in the evolving EV ecosystem.
The increasing integration of Distributed Energy Resources (DER) into modern power systems requires more flexible and decentralized approaches to improve operation and ensure system resiliency. In this scenario, the approach to provide ancillary services has a significant impact. Traditional centralized control schemes for ancillary services provision are increasingly challenged by issues of limited scalability, lack of transparency, and slower responsiveness. To address these challenges, this paper proposes a conceptual approach for the decentralized provision of ancillary services supported by Distributed Ledger Technology (DLT) and smart contracts. The proposed framework enables secure, transparent, and automated coordination among distributed assets without reliance on centralized intermediaries. This enables small-scale assets to access future ancillary service markets, supporting automated functions such as capability verification, streamlined access control, and instant verification and payment processes. The framework is fully implemented in a laboratory environment and validated using a Redox flow Battery Energy Storage System (BESS) and a DC charging station for Electric Vehicles (EVs). The experimental results highlight the feasibility and effectiveness of the DLT-based approach for Frequency Containment Reserve (FCR), laying the groundwork for deployments and extensions to additional ancillary services.
Type of the article: Research Article AbstractDecentralization and renewable energy have gained significant global attention due to their potential to enhance energy security, promote sustainability, and democratize energy access. This study aims to provide a comprehensive bibliometric analysis of research trends, key contributors, and thematic developments in the field of the decentralization of energy sources and their renewability. The research methodology involves a bibliometric analysis based on data extracted from the Scopus database, covering publications from 1973 to 2025. The analysis reveals exponential growth in research output, particularly after 2014, with over 3,700 publications recorded in 2023 alone. Citation trends indicate that foundational studies on decentralized microgrids and distributed energy systems remain highly influential, while recent works on blockchain-based energy trading and AI-driven energy management are gaining prominence. The study identifies China (11.7% of total publications), the United States (6.5%), and India (5.7%) as the leading contributors, with significant research activity also observed in European countries. Additionally, journals such as Applied Energy, Renewable Energy, and Energies serve as the primary publication platforms in this domain. Thematic analysis highlights a shift from bioenergy and land-use studies toward smart grids, energy storage, artificial intelligence, and decentralized finance for energy markets. Furthermore, co-authorship and international collaboration have increased significantly, with 25% of papers involving multi-country research efforts. Keyword analysis indicates growing research interest in emerging topics such as hydrogen energy, demand-side management, and digitalization in decentralized energy systems. These findings underscore the increasing interdisciplinary nature of decentralized energy research, integrating technological, economic, and policy dimensions. AcknowledgmentThis study was prepared as part of the project IZURZ1_224119/1 (Swiss National Science Foundation).
K Sujit, Komala Chowdenahally Ramaswamy, Siva Ramkumar M, Jayant Giri · 5 authors
Research and development in the vehicle industry have emphasized the potential for advancing electric transportation that is highly efficient, secure, and sustainable. The electric vehicle (EV), powered by renewable energy sources and equipped with high-efficiency electric motors and controls, offers a practical, dependable, and ecologically friendly urban transportation system. EVs operate using a battery that is equipped onboard. Practical and dependable system operation relies heavily on managing and monitoring batteries. Nevertheless, the market for electric vehicles has experienced a decline in growth due to their limited lifespan and high price. To enhance the system's efficiency and lifespan, substantially improving the battery management aspect is imperative. In this research, the Internet of Things (IoT), machine learning (ML), and Blockchain (BC) technologies are used to develop an energy-efficient EV battery management system (BMS). The IoT sensors are attached to the electric vehicles to collect data such as the charging level, the distance that must be driven, and the position of the electric vehicles. This information was saved and processed by a database, then inputted to the LightGBM classifier to determine the cost of charging. After that, it was processed by the power scheduling approach (PSA) to determine the space and time of charging that is closest to a particular electric vehicle and the charging site. At last, this information is saved in blocks to prevent electric vehicles from being misrouted and ensure that pricing transactions between users and charging stations are conducted securely using BC. The results demonstrate that the research model provided enhanced EV-BMS with an accuracy rate of 96.52% and that it retains a communication overhead that is 12% lower compared to the other models.
The current hike in electricity demand, deterioration of electrical grids, and climatic conditions have necessitated the push for technology to enhance energy efficiency, optimize energy usage, and minimize greenhouse gas emissions. The Transactive Energy System (TES) is a highly favoured technology designed to provide solutions for optimizing energy usage since it incorporates economic and dynamic control mechanisms to balance the amount of energy generated and supplied. Cost savings present a clear advantage of TES for consumers, translating to reduced bills, and the platform enables customers with Distributed Energy Resources to trade their excess energy, transforming consumers into prosumers. However, energy trading in TES comes with challenges such as maintaining a dynamic balance between supply and demand, as well as issues of privacy, trust, and resilience. Blockchain Technology (BT)-based TES can address these challenges due to its reliability, transaction transparency, and robust encryption methods. However, BT has its shortcomings that need to be addressed. Therefore, this research analyzes the opportunities, limitations, challenges, and complexities of implementing blockchain-based energy trading platforms within a decentralized TES. This review adopted a systematic approach, known as the Preferred Reporting Items for Systematic reviews and Meta-Analyses, to provide in-depth insights into the review purpose, methodology, findings, recommendations, and future research directions. It was observed from the review that certain challenges underscore the necessity for standardization in BT-based TES implementation. Moreover, it was discovered that decentralizing the TES energy trading infrastructure promotes energy democracy and that adopting fast computing techniques will facilitate digital and intelligent operations in TES. It was also found that the Directed Acyclic Graph-based distributed ledger may soon replace generic blockchain, as it can simultaneously process large micro-transactions in P2P networks. It is observed that implementing a peer rating mechanism in the energy trading network will enhance participants' commitment to their reputational standing in the market, while adapting analytical modelling for performance evaluation of this energy solution could equally be encouraged.
With the growing prevalence of electric vehicles (EVs), electrical grids face increasing strain due to heightened demand and potential overload during charging. This paper proposes a tokenized Ethereum-based framework that enables charging point operators (CPOs) and stations (CSs) to manage EV charging requests while ensuring grid stability through time flexibility (adjustable durations) and power flexibility (dynamic load modulation). Smart contracts automate peer-to-peer trading of charging parameters like energy needs and time limits, shifting loads to off-peak hours and adjusting prices based on real-time grid capacity. Simulations reveal that EV users who adopted time- and power-flexible charging experienced a 42% increase in participation compared to those using rigid, fixed-rate systems. Two scenarios were tested: 1) requests every 15 minutes on a 33 kW grid, where smart charging achieved a 71% efficiency improvement over uncontrolled charging and increased acceptance rates from 38% to 70%; and 2) consecutive requests to the same CSs, where acceptance rates rose from 23% to 43%, with smart charging reducing peak-to-valley load differences by 43–50%, flattening demand profiles. The system, developed on Ethereum using Remix IDE and MetaMask and tested on the Sepolia testnet, demonstrates higher electricity sales, improved grid stability, and enhanced flexibility in time, power, and cost. Tokenization incentivizes participation through rewards, allows users to bid for priority slots via proof-of-stake (PoS), and ties reputation metrics to token costs. Practical Byzantine Fault Tolerance (PBFT) ensures fault tolerance, while dynamic pricing and monetized flexibility create scalable EV-grid synergy, balancing supply-demand mismatches and attracting investors.
Silvio Meneguzzo, Nicolò Bertozzi, Marco Sacchet, Lucio Rocco Inglese · 5 authors
This paper introduces the EU-DREAM initiative, which combines Blockchain/Distributed Ledger Technology (DLT) and Digital Twin (DT) to achieve secure, transparent, and consumer-centric energy services. We discuss the high-level system architecture, emphasising how DLT ensures data integrity and how DT enable real-time scenario and historical analysis with DLT integrity-certified data for users. We describe the core platform layers, detail the blockchain–digital twin integration, and present application scenarios focusing on community-scale energy trading, demand flexibility, and microgrids. We conclude with a discussion of future development steps and the challenges of ensuring privacy, scalability, and compliance with EU data regulations.
Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations.
Meselu Tegenie Mellaku, Yibeltal T. Wassie, Pernille Seljom, Muyiwa S. Adaramola
The economy of East Africa (EA) is striving for a structural transformation with a strong focus on expanding the manufacturing sector. However, challenges related to modern and reliable energy supply have hindered the sector's growth performance across the region. This systematic review explores the potential, opportunities, and challenges to integrating decentralized renewable energy solutions to bridge the energy supply-demand gap in the EA's manufacturing sector. It also provides up-to-date insights into the extent of integration of decentralized renewable energy technologies in the EA manufacturing sector. Relevant data and information for the review were retrieved from 46 references, including databases and web-based sources. The findings highlight that the EA region possesses abundant untapped solar, wind, and bioenergy resources that can close the sector's energy supply-demand gap. The review also reveals that renewable energy solutions are becoming increasingly techno-economically competitive with conventional energy sources for hybrid and stand-alone applications in the manufacturing sector. However, several challenges impede the integration of decentralized renewable energy technologies in the sector, including a lack of enabling regulatory frameworks, limited financing options, limited access to renewable technologies , and a lack of skilled labor. Nonetheless, international initiatives aimed at supporting developing countries in combating climate change can help overcome the region's financial and technological constraints by facilitating technology transfer, capacity building, and offering affordable financing options. Furthermore, the ambition of East African nations to expand their manufacturing sectors presents a stimulating opportunity to accelerate the integration of decentralized renewable energy technologies into the sector.
This research presents an innovative blockchain-based solution for the charging and energy trading of electric vehicles (EVs). By combining the strengths of two prominent consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), the proposed system balances security, decentralization, and energy efficiency. PoW secures the blockchain, while PoS enhances energy efficiency and scalability, key factors in meeting the growing demand for EV infrastructure. The system’s decentralized nature allows for EV owners, charging stations, and stakeholders to interact and transact transparently, without relying on centralized entities. The research conducts a comprehensive simulation to assess the performance of the proposed hybrid blockchain model, demonstrating significant improvements in cost-effectiveness, scalability, and energy management. Additionally, dynamic pricing mechanisms within the blockchain enable real-time energy trading, optimizing charging times and balancing grid demand efficiently. Through the use of smart contracts, automated pricing adjustments, and incentive-driven user behaviors, the proposed system paves the way for more sustainable, cost-effective, and efficient energy solutions in the future.
The demand for electric vehicles (EVs) is influenced by the location of charging stations and the management of renewable energy production. Efficient control of renewable energy can optimize resource usage, cost reduction, and enhance operator’s profitability, whereas mismanagement adversely impacts economic and energy efficiency. This study proposed an optimal location management system for distributed charging stations with renewable energy. A reward mechanism has been designed to support the electrical vehicle charging station (EVCS) operators based on uncertain demand and location. The unexpected arrival of EV demand is catered with the proposed response model by considering the price, time, and charging. The Honey Badger Algorithm (HBA) is used for the optimal operation of the charging operator for EV response. The smart contract is deployed between the transmission and charging operators to decide incentives for handling congestion and voltage stability. The experiment results demonstrate that the proposed model can reduce the overproduction of variable renewable energy with proper location management of charging stations for EVs.
Electric vehicles (EVs) are the foundation of sustainable mobility, yet effective maintenance tracking and performance monitoring is still difficult, particularly in decentralized ecosystems. To fill these gaps, this study suggests a Sustainable Energy-Driven Peer-to-Peer (P2P) Blockchain System that combines blockchain technology, IoT-enabled EV systems, and renewable energy sources. The system lowers operating costs and minimizes the greenhouse effect by using sustainable energy sources like wind and solar to power blockchain nodes, EV charging stations, manufacturing, distributors and, service centers. The decentralized P2P blockchain network guaranteed transparent, immutable tracking of EV performance indicators and maintenance logs. The Istanbul Byzantine Fault Tolerance (IBFT) consensus method provides safe, effective, and energy-efficient transaction validation. EVs’ IoT sensors continually gather performance data, including mileage and battery health, which is then stored on the blockchain for computationally intelligent predictive maintenance. The Ethereum Smart contracts improve dependability and reduce administrative burdens by automating warranty monitoring, maintenance schedules, insuring renewal and, service provider communications. Furthermore, by offering verified service records, the blockchain makes ownership transfers easier and increases market trust for used EVs. In line with the objectives of smart cities and green technology, this integrated strategy promotes adopting sustainable energy and improves the lifetime, efficiency, and accountability of EVs.
Seelammal Chinnaperumal, Sekar Kidambi Raju, Amal H. Alharbi, Subhash Kannan · 8 authors
The research is aimed at filling the gap regarding the development of long-lasting, secure technologies that help build decentralized systems. Other consensus models, such as the Proof of Work (PoW), prevailing in cryptocurrencies, are known to be expensive in terms of energy, hence the development of enlightened models like Proof of Lightweight Hash, whereby while developing the model, an emphasis is placed on energy efficiency without compromising on security. At the same time, new technologies such as battery storage and electric vehicles are disrupting consumer habits where renewable energy is favored, and a decentralized energy market is promoted. It hails the aspect of fine access control provided by blockchain in addition to decentralization; a permission system is vital for any entities that require strict access control due to the nature of the data they hold. Blockchain in IoT and AI makes strategies innovative, adaptable, large-scale, and inclusive to make unique changes that benefit different industries and need scalability. Due to this combining of energy innovations and digital technologies, both energy and data networks become nearer to consumers, advocating sustainable, efficient urbanism. Altogether, these improvements will lead toward the emergence of systems that, aside from being technologically innovative, are also environmentally sustainable and protected. So the interaction of technology, ecological stability, and viable security provides the basis for a cleaner, stronger, de-centralized future as applied to advanced technologies, thus inculcating an equilibrium and stronger society.
Pratyush Kumar Patro, Raja Jayaraman, Adolf Acquaye, Khaled Salah · 5 authors
The aviation industry's carbon emissions are forecast to rise to 22% by 2050, posing a significant challenge to the goal of achieving Net Zero Emissions by the same year. Regardless of the structural or agentic strategies implemented to reduce these emissions, ensuring effective traceability of emissions in airline operations is crucial, as it enables the development of effective mitigation measures. Existing systems fall short of effectively providing end-to-end traceability of emissions within an effective carbon accounting framework. Indirect emissions and the complexities associated with emissions tracking throughout the extended aviation sector also exacerbate carbon accounting and offsetting difficulties. In this paper, we present a blockchain-based framework to address these plausible challenges. The proposed work categorises both direct and indirect emissions under Scope 1, 2, and 3 classifications. A blockchain-based collaborative platform, also provides data transparency across all stakeholders, ensuring traceability and security in a decentralised and reliable manner. A prototype model of a blockchain-based system is therefore developed using Ethereum smart contracts. The paper presents a cost and security analysis of the system, while highlighting the challenges and opportunities for the development of sustainable aviation operations. The smart contract is made publicly available on Github for verification.
Vittorio Capocasale, Maria Elena Bruni, Guido Perboli
Purpose Blockchain and distributed ledger technologies are increasingly prominent, yet their adoption remains complex. This paper addresses the common misalignment between blockchain technology and actual needs, often leading to project failure. It introduces a decision-making framework focused on the technological aspects of blockchain adoption. Design/methodology/approach We designed the framework by analyzing key decision drivers from existing literature and applied it to a real-world use case in the electric vehicle supply chain. The blockchain solution was tested with live production data. Findings Blockchain is beneficial for use cases requiring decentralized governance, but it often needs to be supplemented with additional technologies in industrial applications. Originality/value The framework provides a set of managerial-level questions that simplify the decision-making process for those without deep technical expertise, helping determine when blockchain is appropriate, valuable and superior to other technologies.
EVs live and die by their batteries. To keep drivers safe and confident in their vehicles, we need efficient, accurate, and private ways to track each battery's SoH. But, EV labelled data is scarce, sharing raw data raises privacy flags, and big models strain on-board hardware. This thesis tackles all three problems through a two-step remedy in one shot. 1. Learn data representations without needing labels: Each car trains a small autoencoder to reconstruct its own collected sensor data after randomly hiding parts of the signal. 2. Share knowledge, not data: Instead of uploading the raw collected data, every car sends only its trained model parameters to a remote cloud server. The server aggregates parameters from all cars and sends the improved model back. Four simple questions guide our work: 1. Does this usage of unlabelled data improve the model's performance? 2. How much of the signal should be hidden to get the best representation learning? 3. What is the optimal strategy for incorporating the limited labelled data available into the model? 4. Does this aggregation of separately trained models hurt accuracy compared with a fully centralized approach? Our experiments show a 17% lower average MAE, with up to a 60% improvement in the best cases, when we make use of the available unlabelled data versus training exclusively on labelled data. Hiding 30-40% of signals strikes the balance between challenge and clarity. Finally, aggregation of models on average stays within 0.05Ah of centralized training, virtually no loss, with zero raw-data exposure. This thesis incorporates cloud computing, SSL, and FL to present a light, privacy-friendly pipeline for fleet-wide SoH estimation, evidence that unfrozen fine-tuning outshines frozen variants, the first systematic look at how masking ratio shapes battery time-series representation learning, and practical proof that sharing model weights instead of data keeps accuracy basically untouched and privacy intact.
Arvind Singh, R. Seshu Kumar, K. Reddy Madhavi, Faisal Alsaif · 6 authors
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.