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.
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.
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.
Abdullah Umar, Prashant K. Jamwal, Deepak Kumar, Nitin Gupta · 6 authors
Renewable-driven microgrids require transparent and adaptive coordination mechanisms to manage variability in distributed generation and flexible demand. Conventional pricing schemes and centralized demand-side programs are often insufficient to regulate real-time imbalances, leading to inefficient renewable utilization and limited prosumer participation. This work proposes a blockchain-integrated Stackelberg pricing model that combines real-time price regulation, optimal demand-side management, and peer-to-peer energy exchange within a unified operational framework. The Microgrid Energy Management System (MEMS) acts as the Stackelberg leader, setting hourly prices and demand response incentives, while prosumers and consumers respond through optimal export and load-shifting decisions derived from quadratic cost models. A distributed supply–demand balancing algorithm iteratively updates prices to reach the Stackelberg equilibrium, ensuring system-level feasibility. To enable trust and tamper-proof execution, smart-contract architecture is deployed on the Polygon Proof-of-Stake network, supporting participant registration, day-ahead commitments, real-time measurement logging, demand-response validation, and automated settlement with negligible transaction fees. Experimental evaluation using real-world demand and PV profiles shows improved peak-load reduction, higher renewable utilization, and increased user participation. Results demonstrate that the proposed framework enhances operational reliability while enabling transparent and verifiable microgrid energy transactions.
Sustainable energy communities (ECs) are rapidly expanding in scale and heterogeneity, making fully centralized energy management increasingly impractical due to computational burden and privacy concerns. In this context, this review synthesizes distributed optimization (DO) as a practical management paradigm for ECs, identifies key application areas (demand response, distributed generation and storage management, and microgrid or smart-grid integration) and profiles scalability, privacy, and resilience characteristics. The survey follows a systematic protocol: records are sourced from Scopus, filtered with iteratively refined keyword sets, and screened following a PRISMA flow. Key technological enablers, such as blockchain/distributed ledgers, artificial intelligence, and game-theoretic constructs, are assessed and analyzed for how they support secure data exchange, real-time coordination, and incentive compatibility across multi-agent energy networks. The analysis highlights persistent challenges for DO at EC scale, including convergence under heterogeneity, time-varying conditions, communication delays, cybersecurity and privacy guarantees, while recent advances (e.g., ADMM) partially mitigate these issues without sacrificing local autonomy. Across representative studies, DO achieves near-centralized optimality with 0.0029% gap. Overall, we present an integrative framework that maps DO families to EC use cases and outlines research directions toward robust, privacy-preserving, and scalable EC optimization. • Recent advances in Distributed Optimization Methods. • Technological Enablers for Sustainable Energy Communities. • Technology innovations for distributed optimization in energy systems. • Distributed Optimization Challenges in energy systems
Nandhini S, Hrithik M, Kamalesh S, Aswin C · 6 authors
ABSTRACT: Centralized digital marketplaces dominate today’s online commerce but suffer from inherent limitations such as single points of failure, lack of transparency, data monopolization, and trust dependency on intermediaries. To address these challenges, this paper presents the design and implementation of a decentralized marketplace built on blockchain technology. The proposed system enables peer-to-peer trading without the involvement of centralized authorities, ensuring transparency, security, and fairness among participants. Smart contracts are employed to automate transactions, enforce business rules, and eliminate the need for trusted third parties. Distributed ledger technology ensures immutability of records, while cryptographic mechanisms provide secure identity management and transaction validation. The marketplace supports secure listings, decentralized payments, dispute resistance, and trustless execution, thereby reducing operational costs and increasing user autonomy. Experimental analysis demonstrates improved reliability, resistance to tampering, and enhanced trust compared to traditional centralized platforms. The proposed decentralized marketplace framework highlights the potential of blockchain-based systems in redefining digital commerce by promoting transparency, decentralization, and user empowerment. Keywords: Decentralized Marketplace, Blockchain Technology, Smart Contracts, Peer-to-Peer Trading, Distributed Ledger, Trustless Transactions, Cryptographic Security, Transparency, Digital Commerce, Disintermediation.
The rapid digitalization of the energy sector and the growth of distributed energy resources have exposed the limitations of traditional centralized energy management and trading models. This shift has created a need for more flexible, transparent, and user-focused solutions. Blockchain technology addresses these needs by enabling secure, traceable, and direct transactions through a decentralized and immutable record system. Peer-to-peer energy trading platforms on the public Ethereum network, for example, allow producers and consumers to exchange energy securely without intermediaries. This study presents a blockchain-based system architecture for peer-to-peer energy distribution and trading, known as the Decentralized Energy Management System (DEMS). The system is built on a permissioned Ethereum blockchain (PEDNET) using the Istanbul Byzantine Fault Tolerance (IBFT 2.0) consensus mechanism, and automates energy exchanges and payments using smart contracts, which enable secure, auditable, and traceable transactions through the use of energy tokens. An artificial intelligence-powered decision support module comprising three specialized neural network models has also been integrated to optimize users' energy purchasing preferences, achieving approximately 90% recommendation quality. The system has been validated through comprehensive testing with 500 simulated users over a 3-month period, demonstrating a 32% reduction in average transaction time and an 18% increase in user satisfaction compared to non-AI baselines. Performance benchmarking shows sub-2-second transaction finality and throughput exceeding 500 TPS on the PEDNET network. The study also addresses security considerations, regulatory compliance requirements, and provides a detailed cost analysis of smart contract operations. The study demonstrates the practical impact of combining blockchain and artificial intelligence technologies in P2P energy systems.
Blockchain technology is reshaping how electricity can be produced, traded, and governed, offering new possibilities for countries grappling with unreliable grids and persistent supply gaps. This paper investigates the emergence of blockchain‑enabled peer‑to‑peer (P2P) energy trading, using Nigeria as a lens to explore how decentralized digital infrastructure could redefine participation in electricity markets. Drawing on parallels with the rapid digitalization of financial services, the study examines how distributed ledger systems can support direct energy exchange between prosumers, shift utilities toward roles as market custodians, and improve system trust through transparent, tamper‑proof transaction records. The analysis evaluates regulatory readiness, technical prerequisites, and socioeconomic impacts within Nigeria's evolving energy ecosystem, where chronic shortages and grid instability create both urgency and opportunity for alternative market models. The findings highlight the potential for P2P trading to accelerate energy access, stimulate local investment, and catalyse a more resilient, consumer‑centric electricity sector.
The South African energy market is undergoing a fundamental shift toward renewable energy integration. In response to supply constraints and the global focus on sustainability, Eskom has proposed and is in the process of developing the Virtual Wheeling system, enabling independent power producers (IPPs) to sell energy via energy buyers– intermediaries matching off-taker energy requirements with IPP capacity– to off-takers through Eskom's grid infrastructure. While this system presents significant opportunities to open the energy market, foster competition, and accelerate renewable energy adoption, it also introduces risks for off-takers. These risks stem from the requirement for off-takers to continue paying their traditional electricity bills while simultaneously settling accounts with IPPs for alternative energy supply. The refunding process, which offsets the off-takers' double payment, follows a sequential payment process: first, distributors– typically municipalities– settle their Eskom bill. Eskom then calculates refunds and allocates funds to energy buyers. Finally, energy buyers allocate refunds proportionally to each off-taker in its portfolio, and ultimately off-takers are reimbursed. Any default in this process could jeopardise the entire system, while delays or estimations in refund calculations could impose temporary financial burdens on off-takers, discouraging participation and limiting the overall success of the system. This study explores the potential of blockchain-based smart contracts to address off-taker risks by automating the reconciliation and settlement of energy transactions within the Virtual Wheeling system. A prototype smart contract is developed to automatically calculate fees for each stakeholder and allocate funds in a single transaction upon off-taker payment, streamlining the multistep refunding process. The proposed system not only mitigates inherent process risks, but also enhances efficiency, transparency and trust in the Virtual Wheeling system. The research methodology includes a risk assessment of the current Virtual Wheeling system, the design and development of a smart contract prototype and the evaluation of its effectiveness in mitigating identified risks. The findings indicate that blockchain-enabled automation could significantly reduce default risks, enhance cash flow certainty for off-takers, and improve overall trust in the Virtual Wheeling system. However, regulatory challenges, interoperability with legacy infrastructure and scalability considerations remain critical factors for widespread adoption. This study contributes to the growing body of research on blockchain applications in energy markets and provides practical insights into how decentralised technologies can improve financial resilience in billing and settlement processes.
Integrating artificial intelligence (AI) like the large language model (LLM) for smart contract auto-generation standardises performance and security, reduces human error, and offers accessibility for non-developers.In decentralised autonomous systems (DASs) like decentralised finance (DeFi), the ability to AI-generate smart contracts strengthens the decentralisation and automation characteristics of the applications.In order to increase the effectiveness of a smart contract's fully decentralised and autonomous development, this study benchmarks gas-saving patterns in AI-generated DeFi smart contracts.Three DeFI smart contract development scenarios: token generation (ERC-20), tokenised vault (ERC-4626), and flash loan (ERC-3156), and the state-of-the-art LLMs (Code Llama and Code Llama -Python) are explored to study the gas-saving patterns of AI-generated smart contracts.These results help optimise DeFi smart contracts created by AI regarding gas fees for the same operations.
India’s carbon free power is on an exponential rise, and has recently surpassed 50 percent of installed capacity five years ahead of scheduled target. Growing penetration of renewables accounts to 184.62 GW which is 38 percent of the overall energy mix. By 2030, contribution of wind and solar energy is likely to cross the mark of 44 percent. Pradhan Mantri Suryoday Yojana (PMSY) gives major impetus to Residential based Roof Top Solar (RTS) scheme which alone is a significant component. Eventually, growing number of solar based Distributed Energy Resources (DERs) will result into availability of sufficient power in the households. Potential to trade excess power in the neighborhood will soon emerge and be a new norm. Conventionally, Power Purchase Agreements (PPAs) are executed between power producers and consumers forming a legal binding among the entities. Growing number of DERs will mandate resilient, secured, concurrent and faster contracting mechanisms. While, conventional PPAs are often associated with potential vulnerabilities of being tampered, thefts, inflicted destructions, foisted litigations, non-compliance issues, non-availability to all stakeholders etc. Seizing this problem, blockchain will serve as an effective solution. All requisites of contract being resilient, auto-executable, immutable and scalable will be well achieved using blockchain technology. Chaincode based PPA smart contract can ensure secured, transparent and accelerated contracting mechanism. The paper evolves client based solution in developing a decentralized application (dApp) for carrying out energy trading using Hyperledger fabric.
Ambati Satya Sai Vaishnavi, M. Veera kumari, K. Akash Sai, G. Pavan Kiran · 7 authors
Peer-to-peer (P2P) energy trading has emerged as an innovative solution to modern energy challenges by enabling decentralized electricity exchange among users. The Small-scale market allows prosumers to sell excess energy directly to consumers without relying on centralized authorities. Blockchain ensures transparency, security, and immutability of transactions, while smart contracts automate trading operations based on predefined conditions. A MATLAB-based simulation environment is developed to model energy generation, consumption, and transaction processes, along with a digital ledger for recording trades. The results of different case studies demonstrate efficient energy utilization, reduced transaction costs, and improved reliability. The system promotes renewable energy adoption and supports the transition toward decentralized smart grids. This work highlights the feasibility of integrating blockchain technology with energy systems for sustainable and scalable power trading solutions.
Electromobility requires transactive coordination that respects distribution-network limits while preserving auditability and privacy. This study presents a reproducible peer-to-peer energy trading system that integrates a network-constrained market with permissioned blockchain settlement. The market solves a convex welfare program with linearized power-flow limits and recovers nodal prices from dual variables to match bids and offers and determine clear quantities. Settlement uses Hyperledger Fabric via the Gateway API, including proposal endorsement, ordering, validation, and commit notifications. Meter evidence is hashed and, when necessary, stored with private data collections. A co-simulation harness links MATLAB/Simulink and MATPOWER for feeder dynamics and price formation with chaincode and client logic for settlement. Three case studies are evaluated: an urban microgrid, a suburban microgrid, and a mobile electric-vehicle swarm. An Ethereum testnet serves as a public-chain baseline. In the testbed, a tuned Fabric configuration sustained approximately 1.6 to 1.7 thousand transactions per second with 99th-percentile submit-to-commit latency near one second and full deadline compliance at a one-second clearing cadence. Energy delivery accuracy remained tight, Multi-Version Concurrency Control conflicts were low, and dynamic nodal prices reduced EV charging cost relative to a flat tariff while signaling congestion through predictable rent patterns. The contribution is a deployable blueprint that connects network economics to verifiable settlement, with an open repository, benchmarking artefacts, and practical targets for endorsement width, block size, and timeouts, and clear pathways to field trials, stochastic and robust clearing, zero-knowledge meter proofs, and city-scale deployment.
Lorenzo Fogli, Alberto Montaner, Apostolos Kapetanios, Ioannis Mandourarakis · 8 authors
This paper presents the ENPOWER Flexibility Marketplace Toolkit, an open-source platform for peer-to-peer trading of energy flexibility within energy communities. Using energy consumption and production time-series data, participants can publish flexibility needs, submit offers, and verify delivery against measured baselines. Transactions are settled automatically through blockchain smart contracts with collateral enforcement, while an Energy Data Space backbone governs data exchange, ensuring sovereignty and policy-controlled sharing. Non-fungible tokens provide immutable, auditable certificates of each fulfilled flexibility commitment. The toolkit covers the complete trading lifecycle from market creation and participant onboarding through offer matching, delivery verification, and financial settlement.
Flexible demand is increasingly important in energy systems with high renewable penetration. Bitcoin mining is often cited as a large, theoretically flexible load. Despite electricity consumption rivaling medium-sized industrial economies, the energy market behavior and impacts of Bitcoin miners remain largely unexplored. We exploit the large-scale relocation of Bitcoin mining to Texas, which became the world's largest mining hub following China's 2021 ban, to estimate its effects on local wholesale electricity prices. Combining a novel, hand-collected dataset on mining facility locations with high-frequency wholesale price data, we identify price impacts using a DiD design. We find that miners select into renewable-rich, high-GDP per capita counties with initially lower electricity prices on average. Mining entry has no significant effect on daytime prices but increases nighttime prices by 19.9%, indicating that Bitcoin miners fail to exploit their operational flexibility. Instead they increase baseload demand and reinforce fossil generation during low-renewable periods.
Patrick Woitschig, Ruting Wang, Wolfgang Karl Härdle
Blockchain networks have raised growing public concerns due to their substantial electricity consumption. The transition from Proof-of-Work (PoW) to Proof-of-Stake (PoS) on the Ethereum network is widely regarded as a landmark event in reducing blockchain energy use, with prior studies commonly reporting energy savings exceeding 99%. However, existing estimates vary substantially because of the strong assumptions embedded in the dominant top-down and bottom-up approaches. The top-down approach assumes that miners' electricity costs are closely tied to mining revenue under market equilibrium, whereas the bottom-up approach relies on the assumed average efficiency of the mining fleet, which is unobservable and highly sensitive to assumptions regarding hardware composition and utilization. "The Merge'' provides an observable profitability-based sorting mechanism that helps identify the efficiency distribution of mining hardware. By observing which miners could profitably migrate to Ethereum Classic after "The Merge'', we infer the efficiency threshold of economically viable machines and reconstruct the pre-Merge mining fleet more realistically. Using this framework, we estimate Ethereum's pre-Merge PoW electricity demand at 2.98 GW. The Ethereum Classic midpoint residual post-Merge PoW demand of 0.099 GW implies net electricity savings of 96.67%; including the broader Ethash-family residual yields savings of approximately 93.7-96.3%. To further investigate the determinants of estimation divergence, we estimate a VAR model and find that fluctuations in Ethereum prices significantly affect mining equilibrium and implied energy consumption. Overall, the paper provides a transparent, behaviorally grounded framework for estimating blockchain electricity use and offers refined evidence on the energy implications of consensus-mechanism design.
The increasing number of behind-the-meter distributed energy resources (DERs) is changing traditional distribution systems in a big way by adding new ways to control and monitor them. But the effectiveness and dependability of these systems depend heavily on the accuracy of the data (like measurements, control commands, etc.) that the prosumers, aggregators, and grid operators share with each other. In addition, traditional power systems rely entirely on trusted aggregators to gather data from these DERs. If these aggregators are hacked, the whole system could be at risk. In this paper, we respond to these concerns by suggesting a hierarchical blockchain-based framework that includes a distributed integrity auditing system for measuring DERs. By using hash functions and Merkle trees, a secure and lightweight blockchain-based hash aggregation protocol is made to make sure that behind-the-meter DERs' measurements are real. Also, an automated distributed sanity check of DERs' set points (control commands) is suggested to lower the risk of coordinated cyber attacks on a large number of DERs. The suggested framework is put into action and tested in a number of different situations to see how well it works and how safe it is. The results show that the framework can handle more work because it can cut its runtime and storage costs by about 47% and 44%, respectively.
Ileana Maria Muntean, Radu Tîrnovan, Horia G. Beleiu
As renewable generation becomes increasingly deployed at the local level, the reliability of microgrids depends not only on physical infrastructure but also on the credibility of the measurement data driving energy control decisions. In conventional Energy Management Systems (EMS) architectures, monitoring is implicitly assumed to be correct, even though no mechanism exists to verify the authenticity or integrity of the received data. This gap can lead to suboptimal or misleading control actions, especially in distributed environments involving multiple stakeholders. This paper introduces a trust-by-design approach in which monitoring and energy management processes are natively supported by a lightweight Distributed Ledger Technology (DLT) layer embedded within the EMS. Rather than relying on external trust assumptions, the proposed mechanism ensures built-in traceability and tamper-evidence, enabling independent validation of the microgrid’s operational history. A simple renewable microgrid with battery storage is used as a demonstrative case study to show how a DLT-based ledger can safeguard measurement integrity and control decisions without adding technical complexity to the EMS itself. The results demonstrate that verifiable data flows and tamper detection significantly enhance the transparency and robustness of EMS architectures, while enabling future extensions towards predictive or AI-assisted control strategies.
A infraestrutura das redes de registro distribuído (DLT) atravessa uma fase de escrutínio rigoroso quanto à sua viabilidade ambiental e eficiência operacional. Este relatório técnico analisa exaustivamente os três principais paradigmas de consenso contemporâneos: Proof of Work (PoW), Proof of Stake (PoS) e Proof of History (PoH), sob a ótica do custo-benefício energético e da segurança sistêmica. O Proof of Work, embora detentor de uma robustez histórica inigualável, apresenta um consumo elétrico de proporções nacionais, demandando cerca de 1.375 kWh por transação na rede Bitcoin. O Proof of Stake, consolidado pela transição do Ethereum, reduziu o dispêndio energético em 99,95%, operando com uma média de 0,0026 kWh por transação através da substituição da exaustão computacional pelo compromisso de capital. O Proof of History, atuando como um relógio criptográfico integrado ao PoS na rede Solana, otimiza a ordenação temporal e a escalabilidade, resultando em um consumo marginal de 0,00051 kWh por transação, o mais eficiente entre os protocolos de alta performance. O estudo conclui que a migração para modelos de baixo consumo e alta vazão (throughput) é impulsionada não apenas por avanços técnicos, mas por marcos regulatórios como o MiCA da União Europeia, que exige transparência absoluta sobre o impacto climático dos ativos digitais.<br>