The transition of Micro, Small, and Medium Enterprises (MSMEs) toward decentralized rooftop solar is critical for sustainable industrial growth in emerging economies, yet commercial adoption remains sluggish despite grid parity. This study empirically investigates MSME preferences for solar financing architectures using a Choice-Based Conjoint (CBC) experiment grounded in Random Utility Theory. Primary data were collected from 100 MSMEs in India’s National Capital Region, generating 1,000 discrete choice observations under strictly controlled load conditions (50–60 kW). A Conditional Logit Model was employed to estimate part-worth utilities across capital structures, tariff mechanisms, and performance risk allocation. Contradicting standard market assumptions, the aggregate choices revealed a 77.6% rejection rate of standard solar offerings. The econometric results demonstrate severe utility penalties for upfront capital and fixed repayment obligations . Crucially, the requirement for firm-assumed maintenance risk generated perfect separation , acting as an absolute barrier to adoption. However, market simulations isolating an optimized financing package—combining zero-upfront OPEX, pay-per-unit tariffs, and developer-assumed risk—resulted in the adoption rate increasing to 76.3%. The findings indicate that the current stagnation in commercial solar diffusion is driven primarily by suboptimal risk allocation and product mismatch, rather than a lack of underlying economic viability. To accelerate deployment, policymakers and financial institutions must pivot from capital-subsidy models toward standardizing and de-risking third-party "Energy-as-a-Service" frameworks.
Madina Konyrova, Katipa Chezhimbayeva, Abdul Razaque, Dina S.M. Hassan
The integration of renewable resources and prosumers into smart grids poses challenges related to scalability, transparency, and transmission efficiency. Centralized routing frequently depends on expensive technology and experiences significant losses. This study presents a blockchain-based smart contract system (BSCS) that reduces transmission losses while guaranteeing secure and decentralized energy transfers. The smart grid is represented as a weighted directed graph, with edges denoting actual power losses. Dijkstra’s shortest path algorithm generates optimal paths from the generator to the consumer with minimal loss. These optimal pathways are permanently documented and regulated by permissioned blockchain smart contracts, ensuring tamper-proof and verifiable energy settlement. Validation is performed using an enhanced IEEE 58-bus test system, which is based on the standard IEEE 57-bus network, by incorporating an additional synthetic consumer node (Bus 58) linked to Bus 12 to simulate a flexible prosumer load of 1.5 MW + 0.5 Mvar. Additionally, the synthetic consumer node employs Ganache, Truffle, and Solidity for its implementation. This modification facilitates the assessment of dynamic energy routing and decentralized transaction settlement in extended topology scenarios. The proposed BSCS demonstrates substantial enhancements compared to baseline blockchain systems. Active power losses in transmission lines are diminished, gas consumption declines by approximately 12%, latency is enhanced by as much as 21%, and throughput increases by more than 30%. The rapid deployment and execution of smart contracts within sub-second intervals validate the system's appropriateness for real-time grid operations. The proposed technique combines graph-theoretic optimization with blockchain governance to provide a safe, scalable, and hardware-independent framework for decentralized energy markets.
Michele Fabi, Viraj Nadkarni, Leonardo Leone, Matheus V. X. Ferreira
<div> We develop an axiomatic theory for Automated Market Makers (AMMs) in local energy sharing markets and analyze the Markov Perfect Equilibrium of the resulting economy with a Mean-Field Game. In this game, heterogeneous prosumers solve a Bellman equation to optimize energy consumption, storage, and exchanges. Our axioms identify a class of mechanisms with linear, Lipschitz continuous payment functions, where prices decrease with the aggregate supply-to-demand ratio of energy. We prove that implementing batch execution and concentrated liquidity allows standard design conditions from decentralized finance-quasi-concavity, monotonicity, and homotheticity-to construct AMMs that satisfy our axioms. The resulting AMMs are budget-balanced and achieve ex-ante efficiency, contrasting with the strategy-proof, expost optimal VCG mechanism. Since the AMM implements a Potential Game, we solve its equilibrium by first computing the social planner's optimum and then decentralizing the allocation. Numerical experiments using data from the Paris administrative region suggest that the prosumer community can achieve gains from trade up to 40% relative to the grid-only benchmark. </div>
Efficient energy sharing among solar-based microgrids was crucial for enhancing grid reliability, scalability, and sustainability in modern energy systems. This research presents a novel blockchain-powered decentralized energy trading framework that integrates Raspberry Pi 4, IoT-driven real-time monitoring, and Ethereum-based smart contracts to facilitate seamless and secure peer-to-peer (P2P) energy exchange. The proposed system enables real-time data acquisition and transmission of critical energy parameters, including current, voltage, and power generation, from five interconnected solar microgrids. Raspberry Pi 4 serves as the centralized edge computing node, aggregating and transmitting real-time energy data to the ThingSpeak IoT platform, where advanced AI-driven analytics optimize grid efficiency. Blockchain technology, specifically Ethereum with Ganache, was employed to create a tamper-proof, transparent, and trustless energy marketplace, eliminating reliance on centralized energy intermediaries. The incorporation of Solidity-based smart contracts automates transactions, ensuring secure, immutable, and fair energy trading while enabling dynamic pricing models based on real-time demand-supply conditions. Python, integrated with Web3.py, facilitates seamless interaction between Raspberry Pi 4 and the blockchain network, ensuring low-latency transaction execution and verifiable trade settlements. Through the integration of IoT-enabled smart grids, blockchain-based energy transactions, and AI-driven predictive analytics, the proposed system offers a scalable, autonomous, and energy-efficient solution for decentralized energy management. Experimental validation confirms the system's effectiveness, demonstrating its ability to achieve real-time energy balancing, seamless P2P trading, and enhanced security through blockchain immutability. This cutting-edge approach significantly advances the adoption of renewable energy sources, optimizes microgrid autonomy, and reinforces the resilience of next-generation smart power networks, paving the way for a sustainable and decentralized energy economy.
The integration of renewable energy sources (RES) into modern power grids has enabled decentralized energy generation at the community level, fostering peer-to-peer (P2P) energy trading among prosumers and microgrids. Accurate forecasting of household energy consumption and photovoltaic (PV) generation is critical for optimizing energy flows, enhancing grid reliability, and enabling cost-effective trading decisions. This paper presents an intelligent energy trading platform that integrates machine learning-based forecasting, battery-aware decision-making, and blockchain-enabled transactions to facilitate secure and efficient local energy exchange. Using historical smart meter and weather data from London households, multiple forecasting models including GRU, LSTM, Random Forest, and XGBoost were trained and evaluated. The GRU model achieved superior performance in predicting energy consumption, while Random Forest produced the most accurate PV generation forecasts. These predictions were combined with household battery levels to dynamically determine next-day operational roles: Buyer, Seller, Store, or Use Battery. Unlike conventional fixed-threshold approaches, the framework supports user-defined variable battery thresholds, allowing personalized energy management strategies. The proposed decision-making model achieved an accuracy of 90.72 % for one random block, and extended simulations across 29 different random household blocks confirmed its robustness with an average accuracy of 88.69 % (95 % CI: 87.9–89.6 %). In the trading phase, households participate in a decentralized energy trading platform powered by blockchain and smart contracts. Based on the next-day forecasts, a linear programming-based optimization algorithm matches buyer requests and seller offers to minimize the total system cost while ensuring fairness and efficient energy allocation. To assess its performance, the proposed optimization approach was compared against a greedy matching algorithm where sequential matching is done without a cost optimization and a grid baseline scenario where no storage/sharing of energy takes place. The optimized matching consistently achieved substantially lower trading costs across all households demonstrating superior efficiency, fairness, and scalability compared to the benchmark methods. All transactions are executed securely and transparently on the blockchain through Ethereum-based smart contracts, which automate energy trading, pricing, and settlement. A user-friendly web interface was developed to allow participants to monitor and interact seamlessly with the platform. Overall, this battery-aware, community-driven trading framework showcases how intelligent energy forecasting, cost-optimized decision-making, and blockchain-enabled trading can collectively enhance energy autonomy, cost savings, and renewable energy utilization at both the household and community levels.
Virtual Power Plant (VPP) trading mechanisms confront unprecedented challenges from behavioral complexities and technological uncertainties that conventional rational choice models inadequately address. This research develops an integrated framework combining prospect theory-driven decision modeling with evolutionary smart contracts and multi-stage negotiation protocols to enhance trading effectiveness in cross-regional energy markets. We establish mathematical foundations incorporating loss aversion, probability distortion, and reference-dependent preferences into VPP decision-making, while developing adaptive contracts capable of autonomous evolution responding to market changes. Through composite game-theoretic analysis examining nested interactions between contract evolution and negotiation dynamics, we validate the framework across three comprehensive scenarios: emergency dispatch under extreme weather, renewable energy integration, and cross-regional collaboration. Simulation results demonstrate 15–25% negotiation efficiency improvements compared to traditional mechanisms, with behavioral models capturing significant heterogeneity in loss aversion coefficients (2.1–3.4) across VPP configurations. The evolutionary contracts successfully adapt within 72-hour windows to policy changes and technological developments, while maintaining system stability. Cross-regional analysis reveals how cultural distance and information asymmetries influence trading outcomes, with the framework achieving superior market integration despite these barriers. These findings establish new paradigms for behaviorally-informed energy market design, offering transformative implications for renewable integration and decentralized electricity systems.
The integration of renewable energy sources (RES) and distributed energy resources (DER) into local energy markets is transforming modern power grids toward a decentralized architecture. To enhance the efficiency of decentralized energy trading, blockchain technology has been widely adopted in constructing peer-to-peer energy trading platforms, providing incentives for renewable energy generation and utilization. However, the rapid growth of small-scale suppliers and intermittent DERs introduces significant challenges to grid stability, including supply–demand imbalances and voltage fluctuations. To address these challenges, we propose a blockchain-based energy trading system architecture designed to enable a self-regulating, sustainable, and resilient grid. The proposed system architecture achieves grid stability through three key components: (i) precise endpoint control via AI Agents with lightweight forecasting models integrated into existing hardware systems, (ii) flexible distributed control through an efficient incentive mechanism, named Proof of Prediction, based on a blockchain-based automated trading process, and (iii) macro-level coordination via global regulation roles. We implemented a prototype of the proposed architecture on the Ethereum Blockchain and applied it to a microgrid-scale distributed automated trading environment. Our evaluation results show that using the architecture we proposed achieves a peak-shaving rate of up to 29.6%, while maintaining the overall supply–demand deviation of around 5% on average, demonstrating its strong potential as a foundation for building stable and modern power grids.
Ihunanya Udodiri Ajakwe, Victor Ikenna Kanu, Simeon Okechukwu Ajakwe, Dong‐Seong Kim
The Korean Emission Trading Scheme (K-ETS) is vital for reducing carbon emissions in South Korea. However, issues in transparency, security, and computational overhead limit its effectiveness. This work proposes an energy-efficient blockchain-based framework (eBCTC) to enhance the system with a decentralized blockchain architecture, Purechain. The framework leverages an improved consensus mechanism, the Proof of Authority and Association (PoA 2 ). This is to address key challenges in the current K-ETS, such as centralization, lack of transparency, and high energy consumption. The PoA 2 significantly reduces gas usage, with experimental results showing a 22 % reduction in gas consumption compared to traditional Proof of Work (PoW) and Proof of Authority (PoA) mechanisms. Also, PoA 2 recorded a ×6 and ×2 reduction in gas price compared to PoW and PoA. The system also achieves faster transaction finality and lower computational costs, with transaction costs reduced by up to 83 % across the key K-ETS activities, including emissions reporting, credit allocation, and trading. Also, the system achieved moderate throughput, high latency, doubling scalability, high reliability, and a high success rate compared with DPoS and PBFT based on transaction stress validation tests. With an improved smart contract, intelligent automation of key functions, the system achieved a high energy gain for improved incentives. The proposed framework not only enhances the scalability and transparency of K-ETS but also aligns with South Korea's carbon neutrality goals by minimizing the environmental impact of blockchain operations. This study provides a solid foundation for sustainable carbon trading systems and an accountable carbon economy, contributing to global efforts to combat climate change in achieving the 2050 net-zero carbon emissions goal. • Purechain PoA2 enables secure, low-energy carbon trading in K-ETS. • 22 % less gas usage and 83 % lower costs than PoW and PoA. • Smart contracts automate K-ETS compliance and incentives. • Improves scalability, transparency, and network reliability. • Supports South Korea's 2050 net-zero carbon goal.
The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively.
The hydropower fleet in Austria is ageing and needs to be modernised to adapt to changing conditions in national and international energy systems. The financial viability of hydropower repowering projects remains a challenge because of high investment costs and long payback periods. A part from additional revenuestreams, a Bitcoin mining operation has the potential to be used as a flexible demand source also for curtailment and grid stability services. This thesis provides quantitative evidence on whether a Bitcoin mining operation can serve as an additional revenue stream to improve the investment metrics of a hydro repowering project in Austria, using a dynamic investment calculation and sensitivity analysis.The results show that Bitcoin mining can improve the financial performance especially for run-of-river plants with higher full load hours. These positive effects are sensitive to the volatility of the Bitcoin price and the network hash rate, making long-term returns difficult to predict.
Maneesha Papireddygari, Xintong Wang, Bo Waggoner, David M. Pennock
Automated Market Makers (AMMs) are used to provide liquidity for combinatorial prediction markets that would otherwise be too thinly traded. They offer both buy and sell prices for any of the doubly exponential many possible securities that the market can offer. The problem of setting those prices is known to be #P-hard for the original and most well-known AMM, the logarithmic market scoring rule (LMSR) market maker [Chen et al., 2008]. We focus on another natural AMM, the Constant Log Utility Market Maker (CLUM). Unlike LMSR, whose worst-case loss bound grows with the number of outcomes, CLUM has constant worst-case loss, allowing the market to add outcomes on the fly and even operate over countably infinite many outcomes, among other features. Simpler versions of CLUM underpin several Decentralized Finance (DeFi) mechanisms including the Uniswap protocol that handles billions of dollars of cryptocurrency trades daily. We first establish the computational complexity of the problem: we prove that pricing securities is #P-hard for CLUM, via a reduction from the model counting 2-SAT problem. In order to make CLUM more practically viable, we propose an approximation algorithm for pricing securities that works with high probability. This algorithm assumes access to an oracle capable of determining the maximum shares purchased of any one outcome and the total number of outcomes that has that maximum amount purchased. We then show that this oracle can be implemented in polynomial time when restricted to interval securities, which are used in designing financial options.
Rooftop solar (RTS) represents a critical component of India’s clean energy transition, offering decentralized generation, reduced transmission losses, and potential resilience benefits. Yet, despite ambitious national targets and substantial technical potential, RTS adoption has lagged behind expectations. This narrative review synthesizes peer-reviewed literature (2019–2025), government program documents, and policy reports to examine the trends, barriers, enablers, economics, regional signals, stakeholder outcomes, and policy implications shaping rooftop solar adoption in India. Findings indicate that commercial and industrial consumers have historically dominated the sector due to favorable tariffs and access to credit, while residential uptake accelerated only after the launch of flagship initiatives such as PM Surya Ghar. Persistent barriers include high upfront costs, affordability gaps, regulatory uncertainty, procedural delays, information asymmetries, and built environment constraints. At the same time, innovations such as simplified subsidy pipelines, digitalized approval portals, DISCOM performance incentives, vendor certification, and emerging business models including RESCO/OPEX and group or virtual net metering demonstrate viable pathways to expand adoption. The review identifies future research needs in program evaluation, financial innovation, grid integration, apartment governance, and quality assurance, and emphasizes the importance of stable regulatory frameworks, inclusive finance, and community-oriented models.
J. Balamurugan, Devineni Poojitha, R Bindu, Archana Pallakonda · 8 authors
Decentralized energy trading has been designed as a scalable substitute for traditional electricity markets. While blockchain technology facilitates efficient transparency and automation for peer-to-peer energy trading, the majority of current proposals lack real-time intelligence and adaptability concerning pricing strategies. This paper presents an innovative machine learning-driven solar energy trading platform on the Ethereum blockchain that uniquely integrates Bayesian-optimized XGBoost models with dynamic pricing mechanisms inherently incorporated within smart contracts. The principal innovation resides in the real-time amalgamation of meteorological data via Chainlink oracles with machine learning-enhanced price optimization, thereby establishing an adaptive system that autonomously responds to fluctuations in supply and demand. In contrast to existing static pricing methodologies, our framework introduces a multi-faceted dynamic pricing model that encompasses peak-hour adjustments, prediction confidence weighting, and weather-influenced corrections. The system dynamically establishes energy prices predicated on real-time supply–demand forecasts through the implementation of role-based access control, cryptographic hash functions, and ongoing integration of meteorological and machine learning data. Utilizing real-world meteorological data from La Trobe University’s UNISOLAR dataset, the Bayesian-optimized XGBoost model attains a remarkable prediction accuracy of 97.45% while facilitating low-latency price updates at 30 min intervals. The proposed system delivers robust transaction validation, secure offer creation, and scalable dynamic pricing through the seamless amalgamation of off-chain machine learning inference with on-chain smart contract execution, thereby providing a validated platform for trustless, real-time, and intelligent decentralized energy markets that effectively address the disparity between theoretical blockchain energy trading and practical implementation needs.
The smart grid (SG) plays a seminal role in the modern energy landscape by integrating digital technologies, the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) to enable bidirectional energy flow, real-time monitoring, and enhanced operational efficiency. However, these advancements also introduce critical challenges related to data privacy, cybersecurity, and operational balance. This review critically evaluates SG systems, beginning with an analysis of data privacy vulnerabilities, including Man-in-the-Middle (MITM), Denial-of-Service (DoS), and replay attacks, as well as insider threats, exemplified by incidents such as the 2023 Hydro-Québec cyberattack and the 2024 blackout in Spain. The review further details the SG architecture and its key components, including smart meters (SMs), control centers (CCs), aggregators, smart appliances, and renewable energy sources (RESs), while emphasizing essential security requirements such as confidentiality, integrity, availability, secure storage, and scalability. Various privacy preservation techniques are discussed, including cryptographic tools like Homomorphic Encryption, Zero-Knowledge Proofs, and Secure Multiparty Computation, anonymization and aggregation methods such as differential privacy and k-Anonymity, as well as blockchain-based approaches and machine learning solutions. Additionally, the review examines pricing models and their resolution strategies, Demand–Supply Balance Programs (DSBPs) utilizing optimization, game-theoretic, and AI-based approaches, and energy storage systems (ESSs) encompassing lead–acid, lithium-ion, sodium-sulfur, and sodium-ion batteries, highlighting their respective advantages and limitations. By synthesizing these findings, the review identifies existing research gaps and provides guidance for future studies aimed at advancing secure, efficient, and sustainable smart grid implementations.
Waqas Amin, Qi Huang, Jianping Li, Abdullah Aman Khan · 6 authors
An increase in the popularity of peer-to-peer energy trading in smart grids due to the massive integration of renewable energy sources demands effective and competitive pricing and energy allocation policies to ensure fairness within the market framework. Considering the scalability issues, technical complexity, and operational costs of distributed ledger technology such as blockchain, the reputation of the participants becomes a prominent factor to ensure trustworthiness, reduce risk, and increase market efficiency. This paper proposes a novel method to determine the reputation of participants within the energy market. Based on the evaluated reputation of the participants, an effective pricing method along with an energy distribution technique is devised by considering several market dynamics that significantly affect the pricing and energy allocation method. Extensive experiments have been conducted to validate the effectiveness of the proposed model. The results demonstrate that through the proposed model, the energy bills of the buyers can be reduced by 44%. This highlights the tangible benefits and practical applicability of the proposed approach in optimizing energy costs for consumers in the P2P energy trading ecosystem.
This study investigates the relationship between gas consumption and value transferred in Ethereum smart contracts, offering insights into resource utilization and efficiency within the blockchain ecosystem. Analyzing a dataset of 1,000 smart contracts, a moderate positive correlation r=0.45,p<0.05 was observed, indicating that higher gas consumption generally corresponds to larger financial transactions. The average gas consumption per contract was found to be 58,451,329.47 units, with a standard deviation of 20,123,456.89, highlighting significant variability in computational resource usage. Similarly, the average value transferred was 7,851.47 ETH, ranging from 0.001 ETH to over 100,000 ETH, showcasing the diverse financial applications of smart contracts. Efficiency analysis, measured as the ratio of value transferred to gas consumed, revealed an average efficiency of 0.00013 ETH per unit of gas, with some contracts achieving up to 0.01 ETH per unit of gas and others as low as 0.000007 ETH per unit of gas, reflecting varying levels of optimization. Outliers with disproportionately high gas consumption relative to value transferred were identified, suggesting inefficiencies or unique use cases. These findings underscore the importance of optimizing smart contract design to minimize gas costs and improve performance. Future research directions include functionality-specific analyses, anomaly detection, comparative studies across blockchain platforms, and exploring the economic implications of gas consumption. This work provides actionable insights for developers, researchers, and policymakers aiming to enhance the efficiency and sustainability of decentralized systems.
This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.
Urban areas in the Global South are at the forefront of the climate crisis, contributing over 70% of global CO2 emissions while lacking access to intelligent, transparent, and equitable carbon governance systems. Existing carbon markets, plagued by opacity, centralization, and static MRV (Monitoring, Reporting, and Verification) practices, are inadequate for dynamically managing decentralized, sectoral emissions in rapidly evolving megacities. This research proposes a novel, AI-powered carbon market intelligence framework that integrates cutting-edge technologies: Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), blockchain-enabled smart contracts, federated learning (FL), digital twins, and explainable AI (SHAP, LIME). The system is modular, privacy-preserving, and designed for real-time urban-scale decarbonization, adaptive policymaking, and citizen-level participation. Using Dhaka, Bangladesh, a climate-vulnerable megacity, as the primary use case, and Nairobi as a secondary scalability testbed, this study simulates a comprehensive pipeline: IoT sensors stream data to digital twins; AI models forecast emissions and carbon prices; smart contracts trigger transparent offset issuance; and federated models ensure localized learning without compromising data sovereignty. The system achieves high predictive accuracy (R2 > 0.92), 27.6% emission reductions in waste-energy sectors, and 12.3% gains in offset ROI over static baselines. Smart contract execution remains under 4.5 seconds, with negligible energy use under Proof-of-Stake blockchain. The explainability layer enhances stakeholder trust and policy interpretability, while gamified P2P carbon trading and participatory digital twins democratize climate action. The framework aligns with global instruments, including the UNFCCC Enhanced Transparency Framework, Article 6 mechanisms, Verra and Gold Standard protocols, and ICAO’s CORSIA, positioning it for integration into national and voluntary carbon markets. Ethical safeguards address algorithmic bias, data privacy, system resilience, and governance decentralization via DAOs. A full AI sustainability audit quantifies environmental trade-offs, demonstrating that avoided emissions exceed compute footprints by orders of magnitude. This paper delivers the first end-to-end, federated-AI and blockchain-driven carbon governance system for urban infrastructures in the Global South. It enables a paradigm shift toward real-time, transparent, and just carbon markets, offering a scalable blueprint for Net Zero-aligned smart cities worldwide. The proposed architecture not only advances scientific frontiers but also lays the groundwork for high-impact funding, policy integration, and global replication.
M. Zulfiqar, Muhammad Babar Rasheed, Daniel Rodríguez, María D. R‐Moreno
Contemporary power grid systems increasingly rely on sophisticated energy trading mechanisms to optimize resource allocation and operational performance. While prior studies have examined the coordination roles of energy intermediaries and utility operators, particularly through distributed ledger technologies that ensure data provenance and transaction verifiability in decentralized energy marketplaces, significant security vulnerabilities persist. Notably, fraudulent practices by energy suppliers characterized by payment collection without corresponding energy delivery pose substantial risks to market integrity and participant confidence. This research presents the Blockchain-based Energy Trading with Multi-Factor Trust Framework (BC-ET-MF), a novel architecture that addresses critical security deficiencies through advanced cryptographic protocols and consensus mechanisms. The framework utilizes anonymous credential systems to safeguard participant privacy while implementing time-locked commitment schemes that ensure transaction fairness and verifiability. The architecture incorporates granular access control mechanisms for secure service orchestration and establishes a consortium blockchain infrastructure among energy intermediaries to facilitate distributed transaction validation and immutable record-keeping. To mitigate computational overhead associated with conventional consensus algorithms, we introduce a Proof-of-Verifiability protocol that dynamically calibrates to real-time energy production and consumption patterns. This adaptive mechanism reduces system resource requirements while maintaining security guarantees. Experimental evaluation demonstrates that BC-ET-MF achieves substantial performance improvements: energy consumption reduction of 43.0 %, peak-to-average ratio optimization from 8.27 to 3.21 and 5.88 under 25 % and 50 % demand reduction scenarios respectively, and establishment of 92.5 % participant trust levels. The framework additionally yields 37.6 % transaction latency reduction while preserving user anonymity and enabling comprehensive audit capabilities, thus establishing a secure, efficient, and trustworthy energy trading ecosystem.
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.
Ala’a Shamaseen, Mohammad Qatawneh, Basima Elshqeirat
Traditional systems in real life lack transparency and ease of use due to their reliance on centralization and large infrastructure. Furthermore, many sectors that rely on information technology face major challenges related to data integrity, trust, and counterfeiting, limiting scalability and acceptance in the community. With the decentralization and digitization of energy transactions in smart grids, security, integrity, and fraud prevention concerns have increased. The main problem addressed in this study is the lack of a secure, tamper-resistant, and decentralized mechanism to facilitate direct consumer-to-prosumer energy transactions. Thus, this is a major challenge in the smart grid. In the blockchain, current consensus algorithms may limit the scalability of smart grids, especially when depending on popular algorithms such as Proof of Work, due to their high energy consumption, which is incompatible with the characteristics of the smart grid. Meanwhile, Proof of Stake algorithms rely on energy or cryptocurrency stake ownership, which may make the smart grid environment in blockchain technology vulnerable to control by the many owning nodes, which is incompatible with the purpose and objective of this study. This study addresses these issues by proposing and implementing a hybrid framework that combines the features of private and public blockchains across three integrated layers: user interface, application, and blockchain. A key contribution of the system is the design of a novel consensus algorithm, Proof of Energy, which selects validators based on node roles and randomized assignment, rather than computational power or stake ownership. This makes it more suitable for smart grid environments. The entire framework was developed without relying on existing decentralized platforms such as Ethereum. The system was evaluated through comprehensive experiments on performance and security. Performance results show a throughput of up to 60.86 transactions per second and an average latency of 3.40 s under a load of 10,000 transactions. Security validation confirmed resistance against digital signature forgery, invalid smart contracts, race conditions, and double-spending attacks. Despite the promising performance, several limitations remain. The current system was developed and tested on a single machine as a simulation-based study using transaction logs without integration of real smart meters or actual energy tokenization in real-time scenarios. In future work, we will focus on integrating real-time smart meters and implementing full energy tokenization to achieve a complete and autonomous smart grid platform. Overall, the proposed system significantly enhances data integrity, trust, and resistance to counterfeiting in smart grids.
Aiming at the critical challenges of fragmented environmental-economic value tracking and inefficient multi-stakeholder coordination in green electricity trading, this study proposes a blockchain-based collaborative management method integrating environmental attributes (e.g., carbon offsets) with economic transactions. Leveraging blockchain’s decentralized, tamper-proof distributed ledger, the method ensures transaction transparency, automates settlement via smart contracts, and establishes a verifiable audit trail for environmental benefits. Experimental comparisons demonstrate that the blockchain platform reduces transaction costs by 30%, shortens settlement time by 75%, and significantly enhances market liquidity and transparency versus traditional modes. This approach optimizes resource allocation, minimizes intermediary dependencies, and provides a robust technical pathway for scaling green power adoption. Key implementation barriers include blockchain’s energy consumption, smart contract vulnerabilities, and regulatory fragmentation across jurisdictions. Future work will focus on enhancing blockchain energy efficiency and developing cross-regional regulatory frameworks for green power markets.
Ümit Cali, Annabelle Lee, Barry Hayes, Cláudio Lima · 23 authors
The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization , leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems . This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services , and peer-to-peer energy trading . The model’s rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector.