Bhavya Bhasuran, G. Ashwin Prabhu, G. Subash, E. Raviendiran · 8 authors
Decentralized energy markets are transforming electricity generation, distribution, and consumption by enabling peer-to-peer trading, active prosumer participation, and localized energy governance. Blockchain technology underpins these markets by delivering transparent, tamper-resistant, and automated transaction infrastructures through smart contracts and distributed ledgers. The discussion analyzes key blockchain frameworks for decentralized energy trading, focusing on platform architectures, consensus mechanisms, and interoperability models suited to energy systems. It also examines evolving regulatory pathways across jurisdictions, emphasizing interactions between decentralized trading models and existing energy laws, market rules, and grid codes. Emphasis is placed on compliance models that reconcile innovation with legal certainty, consumer protection, data privacy, and grid stability, while outlining challenges, best practices, and policy directions for scalable and compliant blockchain-enabled energy trading ecosystems.
H. M. Imran Hassan, Syed Enam Reza, Md Rafiqul Islam, A N M Obaidullah
The study explores the development of the I-REC standardized REC Trading Framework for Bangladesh, which can facilitate both local & international REC trading through a Nodal Agency. To implement such REC trading system, it is essential to establish a reliable, robust, transparent and secured REC trading platform that can gain global recognition and acceptance. Blockchain technology can play a crucial role to ensure transparency, security and trust in REC trading and verification system, as it offers a tamper-proof distributed ledger where RECs can be tracked from the issuance to the retirement. The entire process can be made automated using the self-executing algorithm called Smart Contract. This approach accelerates transaction processing and improves efficiency of record-keeping, minimizing the administrative overhead. Transactions such as the issuance, transfer, and redemption of RECs are securely recorded on the blockchain using cryptographic hashes of each block, making the process tamperresistant and easily auditable by all parties, thereby reducing the risk of fraud. Since blockchain employs a distributed ledger that is accessible to all stakeholders, global validators can independently verify the REC claims. The system's viability for national-scale deployment was validated using Ethers.js and the Remix VM, while its resilience was confirmed through a STRIDE-based threat assessment.
Este artigo analisa o custo‑benefício energético de três mecanismos de consenso centrais no ecossistema de criptoativos: Proof‑of‑Work (PoW), Proof‑of‑Stake (PoS) e Proof‑of‑History (PoH) combinado a PoS, examinando como diferenças de consumo de energia se relacionam a segurança, desempenho e sustentabilidade econômica. A partir de dados recentes sobre consumo energético de redes públicas como Bitcoin, Ethereum antes e depois da transição para PoS e Solana, discute‑se em que medida a evolução dos mecanismos de consenso permite reduzir ordens de grandeza de uso de eletricidade por transação, sem necessariamente comprometer a segurança e a descentralização. A metodologia baseia‑se em revisão bibliográfica de estudos acadêmicos e relatórios técnicos sobre consumo de energia em blockchains, análise de estimativas consolidadas de uso anual de eletricidade e de energia por transação e discussão conceitual de trade‑offs entre eficiência energética, robustez criptográfica, requisitos de hardware e impactos regulatórios. Evidências indicam que o Bitcoin, ancorado em PoW, mantém consumo anual estimado em torno de 120–130 TWh, enquanto o Ethereum, após migrar para PoS em 2022, reduziu seu consumo em mais de 99%, passando a operar com menos de 0,01 TWh por ano. Relatórios de eficiência energética mostram que redes que combinam PoH e PoS, como a Solana, apresentam consumo de energia por transação da ordem de centenas de joules, inferior tanto a redes PoW quanto a muitas redes PoS, embora existam ressalvas metodológicas e discussões sobre centralização de infraestrutura. Conclui‑se que PoS e esquemas híbridos com PoH oferecem vantagens substanciais em termos de eficiência energética, mas a avaliação de custo‑benefício precisa incorporar conjuntamente segurança econômica, distribuição de poder, maturidade de ecossistema e alinhamento com agendas de sustentabilidade e descarbonização que tendem a moldar a evolução da infraestrutura Web3.<br>
Sadly Syamsuddin, Salama Manjang, Muhammad Bachtiar Nappu, Ady Wahyudi Paundu
The increasing demand for sustainable and decentralized energy systems has driven the adoption of blockchain technology in microgrid networks. However, conventional consensus mechanisms, such as proof of work (PoW) and proof of stake (PoS), suffer from high energy consumption, limited adaptability, and fairness issues, which hinder their suitability for dynamic microgrid environments. This paper proposes a novel consensus mechanism, proof of dynamic influence and participation activity (PoDIPA), which integrates prosumers’ real-time participation activity and historical influence into the validator selection process. The proposed mechanism is evaluated through deterministic simulations and compared with PoW and PoS in terms of energy efficiency, transaction processing time, and security resilience. Simulation results demonstrate that PoDIPA significantly reduces average energy consumption and adapts more rapidly to network dynamics while maintaining security performance comparable to existing consensus mechanisms under majority attack scenarios. Although PoDIPA exhibits higher short-term variability due to its adaptive nature, the overall efficiency–stability trade-off remains favorable. These results indicate that PoDIPA is a promising consensus solution for supporting fair, energy-efficient, and decentralized energy trading in future microgrid systems.
Blockchain users pay transaction fees to miners or block proposers who validate and add transactions to the distributed ledger. Ethereum introduces the concept of gas to decouple transaction costs from Ether's price volatility, calculating fees based on gas units. The current mechanism defined by Ethereum Improvement Proposal (EIP) 1559 dynamically adjusts the base fee according to block gas usage. However, its rule-based adjustment can lead to unstable gas consumption when demand fluctuates within a narrow range and struggles to respond efficiently to sudden demand spikes, such as during non-fungible token (NFT) drops. To address these limitations, we propose a deep reinforcement learning-based transaction fee mechanism that learns an adaptive base-fee update policy. Our approach maintains gas consumption close to the target level across various demand scenarios and stabilizes transaction fees and gas usage per block even under abrupt demand shifts. These results demonstrate that the proposed method provides a more adaptive and resilient fee adjustment mechanism compared to the current EIP-1559 model.
The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities.
Umar Majeed Sada, Abrar Ahmad, Najam Saqib, Md. Sarwar
Introduction: With the increasing adoption of solar photovoltaic, bioenergy, and wind energy in distributed networks, consumers are transforming into prosumers, utilizing renewable energy resources within distributed microgrid systems. In these environments, peer-to-peer (P2P) energy trading emerges as a highly promising energy management solution. Methods: This paper introduces a Polygon-based blockchain smart contract designed to execute trading and payment rules autonomously, significantly enhancing the security and fairness of energy trading compared to traditional database technologies. The smart contract consists of two core components: a bidding and settlement module and a payment module, both deployed on a Polygon-based test network, Cardona, to assess real-time interactions. Furthermore, to validate this model, simulations were conducted using realistic data on an Ethereum Virtual Machine (EVM) in a microgrid setting with six participants. Results: The results illustrate the model's potential to facilitate the integration of renewable energy within microgrids, thereby encouraging the adoption of distributed energy resources and supporting localized P2P energy trading. The proposed methodology shows that Polygon-based smart contracts with auction mechanisms can enable efficient, transparent, and automated P2P energy trading, enhancing grid stability in non-autonomous microgrids. However, the approach requires validation at larger scales to assess performance under real-world conditions . Conclusion: This approach offers a robust framework for designers aiming to build sustainable, decentralized energy markets.
Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning.
Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered.
Ashkan Safari, Amir Aminzadeh Ghavifekr, Amir Rikhtegar Ghiasi
• A private Ethereum-based discrete-event blockchain is developed for P2P energy trading. • Smart contracts using Solidity automate market matching, settlement, and tokenization. • Platform integrates ERC-20 token framework to support secure energy transactions. • Gas fee modeling and minimization are implemented for cost-efficient operations. • Validated on IEEE 14-Bus multi-community system with real dynamic market behavior. Due to the fast growth in renewable energy production, which enables households to sell excess power directly and better manage its intermittent nature, the Peer-to-Peer (P2P) energy market has become considerably more established, as it’s aligned with the decentralization and digitalization of power systems and local markets. It’s a system that lets energy consumers and producers trade energy directly with one another. Furthermore, the presence of blockchain technology increases these techno-economic advantages for energy systems, particularly when integrated with P2P energy trading. Consequently, a wide range of works have considered the integration of P2P and blockchain; however, few of them have investigated the full details of this system, including its performance, Transaction (TX) gas fee in a secure and private platform. Following this, the proposed work presents an Ethereum-based discrete event Private blockchain and its integration with P2P energy trading market in a Multi-Community Energy System (MCES). Considered on an IEEE 14-Bus MCES with 3 communities and 20 participating agents (11 consumers, 5 generators, and 4 not participating in the market), the platform uses Web3 and Ethereum Virtual Machine (EVM) for execution. Smart contracts, written in Solidity, handle tokenization by Ethereum Request for Comment 20 (ERC-20) standards and market matching/settlement discrete event processes. On the secure performance, the proposed platform is based on Keccak-256 for immutability, while TX gas fees are minimized. Results show synchronized peak demands up to 60 (MW), diurnal Renewable Energy Sources (RES) outputs peaking at 40 (MW), alongside the market prices, and agents’ revenues. Finally, the reliability of the platform is evaluated based on two main metrics of Transaction Success Rate (TSR) = 1 (100%) and Transaction Per Second (TPS) = 3.29, with a primary mode centered at 1.8–2.0 TPS, a secondary peak at 4.0–4.2 TPS.
The subject of research covers the theoretical, methodological, and applied aspects of implementing blockchain technology and smart contracts into microgrid management systems, as well as the automation processes of energy resource exchange between participants of a distributed energy system. The purpose of this work is to investigate the methodological foundations for the application of blockchain and smart contracts in microgrids through the analysis of contemporary scientific research, systematization of approaches to consensus algorithm implementation, classification of smart contracts by application areas, and experimental verification of the proposed solutions. To achieve this goal, the following tasks were addressed: analyzing existing microgrid architectures and management methods; conducting a comparative analysis of consensus algorithms (PoW, PoS, PoA, PBFT, RAFT, etc.) regarding their applicability in private and public energy grids; developing a classification of smart contracts based on their application areas; and investigating software tools for implementing decentralized applications. Research Methods. The study employs system analysis methods to investigate microgrid architecture, comparative analysis to evaluate the efficiency of consensus algorithms, and classification methods for grouping smart contracts. For the practical part, computer modeling and experimental verification methods were used: smart contract development in Solidity, testing in the Remix IDE environment, and simulation of a local blockchain network using the Hardhat toolkit. Research results. The research systematized the methodological foundations for integrating blockchain into microgrids. It was determined that hybrid or private consensus models are most effective for energy trading within local communities. A classification of smart contracts was developed and justified, covering four levels: energy trading, monitoring, distributed management, and cybersecurity. The practical result is the implementation of the EnergyTrading smart contract, which successfully automates the process of listing offers and purchasing electricity, as confirmed by experiments in a local environment. The implementation of smart contracts allows for the creation of a reliable P2P platform for electricity trading without intermediaries, increasing economic efficiency for households. The functionality of the automated settlement mechanism was experimentally confirmed. At the same time, key challenges were identified: the limited scalability of existing blockchain solutions and the need to improve cyber defense against vulnerabilities in contract code. Further development requires adaptation of the legislative framework and modernization of the hardware components of energy grids.
Smart grids require real-time ancillary services from large-scale distributed energy storage (DES), creating a conflict between second-scale physical response needs and the slow confirmation of trust mechanisms like blockchain. Traditional VPPs lack scalability and trust for massive participation, while decentralized approaches struggle with mismatched time scales. We propose a framework that decouples real-time dispatch from asynchronous settlement. An off-chain matcher uses a physics-aware model, including a novel “service holding time” (Tservice) constraint and power (kW) envelopes, for fast assignments. A separate on-chain proof-of-stake (PoS) layer handles incentives and penalties (slashing) asynchronously. We formulate the MILP dispatch problem and provide a fast online heuristic alongside a MINLP decomposition benchmark. Co-simulations (IEEE 33-node) show that our scheme significantly outperforms baselines in success rate and latency, is robust against non-compliant nodes due to the PoS mechanism, and thereby offers a scalable and trustworthy solution.
This paper introduces an energy-efficient, Blockchain-Based, Dual-Agent, Reinforcement Learning (BDARL) model of resilient EV-grid integrative effect. All EV energy transactions and agent decisions are validated by the blockchain layer that is integrated through a lightweight proof-of-stake consensus mechanism, and this ensures the tamper-proof functioning and decentralized trust. The proposed framework is applying to a MATLAB/Simulink-Python TensorFlow-Hyperledger Fabric co-simulation environment where the performance analysis shows a 98.8 per cent Resilience Coordination Index (RCI), a 36.7 per cent Energy Efficiency Gain (EEG), a 31.5 per cent Load Stabilization Score (LSS), and a transaction latency of 25 ms. Compared to baseline DRL and non-blockchain schedulers, BDARL offers 7.9% improvement in terms of resilience, 8.4% in terms of energy efficiency, and 14 ms better convergence, so it provides a safe, sustainable, and smart paradigm of managing next-generation EV-grid synergy.
Siri Sanjana Pasunoori, Swetha U, Ravi Kanth Kotha, Kumar Dorthi · 7 authors
The increasing integration of distributed energy resources (DERs) into modern smart grids has created new challenges related to load balancing, real-time coordination, and secure energy transactions. Traditional centralized grid architectures are no longer sufficient to handle bidirectional energy flow, dynamic pricing, and operational requirements. The current paper proposes a scalable smart grid load balancing framework by integrating lightweight Message Queuing Telemetry Transport (MQTT) communication with a hybrid blockchain-based consensus mechanism. Practical Byzantine Fault Tolerance (PBFT) and Proof of Stake (PoS) were used to achieve consensus. MQTT provides low-latency and efficient communication among prosumer devices. And the blockchain layer ensures secure, tamper-evident, and auditable power transactions. The proposed hybrid consensus model achieves quicker transaction finality and byzantine fault tolerance within local microgrids and supports a scalable and economically secure environment through PoS. Smart contracts were utilized to automate important functions such as settlement, marginal pricing, and bid matching. The simulation outcome shows communication latency within a second, around 85% prosumer participation in demand response programs, and also a 23% increase in renewable energy utilization. The proposed framework provides a secure, transparent, and interoperable solution for next-generation decentralized smart grid systems.
In March 2025, the United States established a Strategic Bitcoin Reserve by executive order. Bhutan had been quietly mining Bitcoin with hydroelectric power, accumulating over $1 billion without public announcement. By February 2026, more than 145 publicly traded companies held Bitcoin on their balance sheets, collectively exceeding one million BTC. No established academic framework predicted these developments. In his 2023 MIT thesis Softwar, Major Jason Lowery proposed that Bitcoin is best understood not as money or a hedge but as a power projection technology rooted in thermodynamic proof of work. This paper presents the first empirical evaluation of that framework. Nine falsifiable predictions are tested against observed data; five have been confirmed and one partially realized within three years of publication. The developments that monetary, financial, environmental, and security models fail to explain (strategic reserves, geopolitical competition for hash rate, sovereign mining operations) are precisely those the power projection framework predicts.
Distributed Energy Resources (DER) such as solar PV, wind micro-turbines, smart inverters, electric vehicles (EVs), and home energy storage systems are rapidly increasing in modern power systems. However, their decentralized nature introduces complexities in coordination, demand–supply balancing, and resilience. Existing blockchain-based DER frameworks primarily focus on peer-to-peer (P2P) trading, security, and certificate validation, but lack mechanisms for coordinated swarm-like behaviour among DER units. This paper introduces a novel concept—Blockchain-Based Swarm Coordination of DER Clusters, inspired by swarm intelligence principles such as self-organization, collaboration, local decision-making, and emergent global behaviour. The proposed system integrates blockchain, multi-agent coordination, and decentralized smart contracts to enable secure, autonomous, and scalable coordination of DER clusters. A layered architecture, cluster formation mechanism, consensus-driven decisioning, and energy-sharing algorithms are presented. The framework significantly enhances grid flexibility, improves energy distribution efficiency, reduces central-dependency, and enables real-time proactive response during grid fluctuations. Simulation-driven conceptual outcomes demonstrate improved DER responsiveness, fault tolerance, trust, and transparency. This work establishes a new research direction by merging blockchain with swarm intelligence for next-generation decentralized energy systems.
A.S. Kannan, E. Baraneetharan, R.Venkatasubramanian, S. Sasi · 6 authors
India's ambitious renewable energy targets of 500 GW by 2030 and net-zero emissions by 2070 necessitate transformative energy trading solutions capable of harnessing distributed renewable sources. This paper introduces a blockchain-enabled peer-to-peer (P2P) energy trading platform designed for India's diverse energy landscape, which includes rooftop solar, wind plants, and microgrids in both urban and rural areas. Built on the Ethereum foundation, the platform employs smart contracts to automate energy transactions between prosumers, reducing dependence on the conventional grid and advancing India's energy security goals. The system integrates machine learning algorithms trained on specific Indian usage patterns and weather conditions to forecast optimal trading times, accounting for seasonal changes, festivals, and industrial demand cycles. Key model assumptions include: (i) prosumers have bidirectional smart meters with IoT connectivity; (ii) weather data availability from Indian Meteorological Department stations; (iii) baseline electricity tariffs following state-level regulatory frameworks; and (iv) participants operate within Karnataka Electricity Regulatory Commission's P2P trading guidelines. Core parameters include LSTM networks with 50 hidden units, learning rate of 0.001, and 24-hour prediction horizons; Random Forest models with 100 estimators and maximum depth of 10; smart contract gas limits of$3,000,000$units; and dynamic pricing coefficients$\alpha=0.15$and$\beta=0.08$calibrated against Tamil Nadu industrial tariffs. Through automated transactions, the platform allows small-scale generators to sell surplus energy directly to local consumers, mitigating the$18-20 \%$distribution losses typical of the Indian grid. Pilot studies in Tamil Nadu and Maharashtra showcased significant results, including a 35-45% cost saving for participating industries and transparent carbon credit accounting, aligning with emerging ESG compliance needs. The platform contributes to the Digital India initiative by fostering a decentralized energy infrastructure that supports both economic development and environmental sustainability.
This paper introduces a novel decentralized autonomous organization (DAO) framework for microgrid governance, specifically targeting diverse stakeholder ownership. It integrates principles of decentralization, democratization, and digitization to streamline the just energy transition. The multifaceted model synthesizes key DAO mechanisms with microgrid elements by incorporating tokenomics, fund management, actor reputation, decision-making, tender selections and dispute resolutions. Preliminary conceptual validation in Python case studies demonstrates the feasibility of the approach. However, further research and validation are needed to pave the way from centralized structures to a more empowered, equitable and resilient future.
The accelerating adoption of electric vehicles (EVs) is intensifying pressure on urban power grids, particularly during evening peak hours. Existing smart-charging frameworks remain constrained by centralized control, static pricing, and limited integration of predictive intelligence. This study presents SMARGE, a hybrid AI–Blockchain smart charging platform that combines load forecasting, dynamic pricing, and cryptocurrency-based incentives to enhance decentralized EV energy management in Gaziantep Province. An ensemble of forecasting models (SARIMA, LightGBM, N-BEATS, and TFT) predicts 2026 hourly electricity demand, while an adaptive inverse-sigmoid pricing mechanism generates real-time incentives and disincentives for EV charging behavior. A fuzzy logic-based behavioral model simulates both unmanaged and managed charging across three scenarios. Results show that managed charging reduces peak load by 22.43%, shifts 67.45% of energy demand to off-peak periods, and achieves 94.86% charging fulfillment under constrained grid conditions. The blockchain layer—implemented through a custom ERC-20 token (SMARGE) on the Ethereum Sepolia testnet—enables secure, transparent, and low-cost microtransactions with an average confirmation time of 0.63 s. These findings demonstrate that tightly coupling AI forecasting with tokenized blockchain incentives can improve grid stability, lower operational costs, and enhance user autonomy in a scalable and decentralized manner. While promising, the study is limited by assumptions of synthetic user behavior and ideal communication conditions; future work will validate the platform in real-world pilot deployments and across different urban regions.
The transition from centralized power systems to decentralized infrastructures with a high share of renewable energy sources calls for reliable settlement in P2P electricity trading across “smart” regions. Blockchain platforms can enhance transparency and facilitate automated settlement; however, double-spend attacks still pose a threat to transaction finality and, consequently, undermine trust in the payment layer. This paper quantifies this risk through a probabilistic analysis of classical double-spend scenarios for Proof-of-Work (PoW) and Proof-of-Stake (PoS) blockchains augmented with periodic checkpoints, which render the chain history prior to the latest checkpoint effectively irreversible. We develop attack models for both consensus mechanisms and derive explicit formulas for the attacker’s success probability as a function of the adversarial share, the spacing between checkpoints, and the number of confirmation blocks. On this basis, we compute the minimum confirmation depth needed to satisfy a predefined risk threshold. Numerical evaluation using the derived expressions shows that checkpoints consistently reduce double-spend probability relative to checkpoint-free baselines; in the evaluated settings, the reduction reaches up to 44% and becomes more pronounced as the adversarial share increases. Finally, the analysis yields practical guidance for energy trading applications: accept a payment after the computed number of confirmations when it fits within a single checkpoint interval; otherwise, treat finality as reaching the next checkpoint.