Blockchain Papers

Taner Çarkıt

No abstract is available for this record.

A.C. Vishnu Dharssini, S. Charles Raja, R. Thanga Meena, M. Praveen Kumar

No abstract is available for this record.

Sameen Fatima, Muhammad Junaid Arshad

The growing adoption of renewable energy and small-scale power producers has increased the need for reliable and transparent peer-to-peer (P2P) energy trading. Traditional centralized markets often struggle with high transaction fees, limited transparency, and a greater risk of manipulation, which restrict efficient energy distribution. To overcome these issues, this study presents a decentralized P2P trading framework that implements a fully functional blockchain-based trading system with smart grid simulation and demonstrates a prototype machine learning forecasting module (Random Forest, 84% accuracy) designed for future integration. The trading mechanism is developed using Ethereum smart contracts and a custom ERC-20 token, the TUM Energy Coin (TEC), enabling secure and traceable energy exchange. System security is strengthened through dual confirmation steps, role-based access control, and consensus-driven market clearing. A double-sided auction model is used to match buyers and sellers fairly. Real-time grid behavior such as fluctuating loads, prosumer generation, and consumer demand is modeled using MATLAB Simulink to reflect realistic operating conditions. To enhance decision-making, a Random Forest model is integrated for load forecasting and dynamic pricing, achieving an accuracy of 84%. The simulation results show improved transaction throughput, more stable pricing, and strong resilience against false-data injection attacks. The primary novelty of this work lies in (1) an entirely operational and validated blockchain-trading system simulation with synchronized time using Simulink, (2) a working Random Forest forecasting tool demonstrating feasibility for incorporation in the future, and (3) an analysis of the system’s robustness in the case of FDIA attacks. The authors point out that the ML component used is a prototype and not yet integrated into the functioning block chain.

Chathuri Gunarathna, Sajani Jayasuriya, Kaige Wang, Xun Yi · 7 authors

Adoption of peer-to-peer (P2P) trading is very challenging, mainly due to numerous issues and limitations such as lack of trust in the concept and awareness of the technical, economic and social benefits. This paper aims to understand how blockchain technology can address the current issues/limitations of P2P distributed solar energy (DSE) trading. A series of semi-structured interviews were conducted with 23 community energy stakeholders to confirm and expand the stakeholder issues identified in the literature review. A case representing community energy projects was selected to (1) develop and implement a blockchain system and (2) evaluate its ability to eliminate (or reduce) stakeholder issues and meet stakeholder expectations. A blockchain-enabled P2P trading platform was developed using an Ethereum backend. The system clearly demonstrated its ability to deliver full or partial solutions to 12 stakeholder issues. Two stakeholder issues are unable to be addressed via the blockchain platform since they uncovered the weaknesses of blockchain technology. The P2P trading platform has also demonstrated its ability to facilitate decentralized trading and data management. The outcome of this study indicates the areas of P2P trading projects that can be improved by the application of blockchain technology.

Pingyan Mo, Kai Li, Xin Hu, Y. P. Lu · 5 authors

A multi-agent transaction optimization decision-making method based on blockchain Berge-NS equilibrium is proposed, aiming to protect user interests and achieve carbon reduction objectives. Firstly, by improving the utility function of electricity users and quantifying the impact of blockchain technology on the electricity utility of market entities, a blockchain based P2P electricity trading architecture for microgrids is constructed, and a blockchain network and utility function for electricity users are designed; Secondly, the Evolutionary Game Theory based on bounded rationality decision-making is introduced to construct a Berge-NS game model on both sides of electricity supply and demand. The distributed iterative algorithm and step size control method are used to solve the Nash equilibrium, and the strategy evolution of demand side subjects in the game process is studied through dynamic processes; Finally, numerical simulations were conducted to analyze the trading strategies of bilateral contract markets, centralized trading markets, and dual layer decision-making models for electricity sellers, verifying the feasibility and effectiveness of the models and algorithms. The experimental results show that the proposed multi-agent trading Berge-NS decision-making method for microgrid electricity market exhibits certain performance advantages in reducing carbon emissions, lowering user electricity costs, and improving user satisfaction.

Uzair Aslam Bhatti, Gafur Namazov, Sabirov Sardor, Momogul Ismailova · 7 authors

Decentalized energy systems are a radical departure from the way we have traditionally produced, distributed and consumed electricity – relying upon centralized grids that depend on fossil fuels towards more sustainable, resilient and community-based models. These systems improve grid flexibility, save transmission costs and are fit for renewable power input like solar or wind, since prosumers can generate and distribute energy within the local area. Blockchain technology is going to be a key enabler of this transition, as it offers a reliable, transparent and decentralized platform for energy sharing and grid management. The blockchain technology utilizing the smart contracts and mutual system can provide reliable peer-to-peer power trading, accurate settlement, and less necessity of centralized agency. Its distributed ledger makes the equipment trustable for all participants, and meanwhile it realizes real-time transaction data sharing to optimize grid management like demand response of electricity and certificates tracing of green power production.

C. N. He, H. D. Chen, H. J. Tian, J. Zhang · 5 authors

This study investigates low-carbon investment strategies in power supply chains under the combined influence of carbon quota mechanisms (CQM) and blockchain technology (BCT). A two-echelon system consisting of a power generator and an electricity retailer is modeled, and four decision scenarios are constructed by considering blockchain adoption under both the grandfathering method (GFM) and benchmarking method (BMM). A Stackelberg game framework is employed to analyze the interactions among low-carbon technology investment, low-carbon electricity promotion, market demand, and enterprise profitability. Results show that the BMM consistently induces higher low-carbon investment levels, stronger market demand, and greater retailer profitability than the GFM, regardless of blockchain adoption. Furthermore, blockchain-enabled information traceability exhibits a significant threshold effect: when implementation costs remain below a critical level, trusted information transmission enhances consumer green trust, stimulates demand for low-carbon electricity, and improves the economic performance of supply-chain participants. Sensitivity analysis further demonstrates that consumer green trust, low-carbon preference, and responsiveness to low-carbon promotion positively influence both emissionreduction efforts and enterprise profitability, whereas excessive blockchain deployment costs weaken these benefits. The proposed framework provides a quantitative methodology for analyzing information-enabled lowcarbon decision making and coordinated investment strategies in modern power systems.

Ibrahim Aqeel

No abstract is available for this record.

Stephen Oko Gyan Torto, Rupendra Kumar Pachauri, Jai Govind Singh, Shubham Tiwari · 7 authors

Global projects are mobilizing technologies to fight power generation curtailment and smooth demand by exploiting excess energy via transactive energy management and control. Sharing and transferring energy between microgrids helps manufacturers and businesses create energy autonomously. The transition to Multi-Vector Multi-Agent Energy Systems (MMV-ES) demands a paradigm shift from traditional centralized control to decentralized, market-based coordination. Transactive Energy Management (TEM) has emerged as a key enabler in this context, supporting local flexibility, peer-to-peer (P2P) trading, and integrated energy vectors across distributed assets. This review systematically decomposes and classifies the existing state of TEM from several perspectives: the market topology, the interaction of the agent, game-theoretic models and the real deployment challenges. Moreover, two game-theory formulations (cooperative and non-cooperative) were given special attention and a detailed comparison between Shapley value and Nucleolus was provided as approaches for fair cost allocation. To enhance the adaptability of the market and the overall efficiency of the system, we introduce the Transactive Energy Reformulation Model (TE-RM), a hybrid model combining AI-powered congestion pricing with coalition formation and fairness-based incentives. The comparative tables in this paper summarize TEM and TE-RM's strengths and weaknesses and compare it to the centralized and conventional DSM methodologies. Lastly, key research gaps including scalability, regulatory fit, and AI model interpretability are reviewed, and future directions are proposed for the integration of future advanced technologies (e.g., reinforcement learning, blockchain, IoT) to enable stable, fair and interoperable energy markets.

Guorui Wang, Liang Zhong, Yixuan Zeng

The modern energy transition has outpaced the control and optimization frameworks built to govern it. As power and energy systems fragment into webs of renewable generators, storage operators, flexible loads, and carbon-constrained firms, the deterministic, single-optimizer models that once sufficed buckle against nonlinearity, bounded rationality, and strategic conflict among parties who learn and revise as they go. Evolutionary game theory (EGT), which traces how strategies propagate through populations by imitation and selection rather than instantaneous optimization, offers a route through this difficulty—one this review develops across three scales of low-carbon coordination central to cleaner production: enterprise-level industrial symbiosis, system-level smart energy operation, and market-level carbon governance. We synthesize three decades of theory alongside the recent fusion of EGT with artificial intelligence, where deep reinforcement learning approximates high-dimensional payoffs, federated learning lets rival firms co-train models without surrendering proprietary data, and blockchain underwrites decentralized mechanism execution. The synthesis is accompanied by two illustrative numerical case studies, constructed for this review rather than drawn from the surveyed literature, whose quantitative outputs are reported below as demonstrations of modeled behavior rather than as empirical measurements. In the first of these, cooperative emergence in industrial symbiosis hinges on critical thresholds that travel from 0.15 to 0.75 as subsidies and transaction costs vary, with anchor-enterprise targeting accelerating cooperation 2.4-fold while cutting outcome variance 3-fold. In smart energy coordination, AI-enhanced learning buys 32 to 41% faster convergence, yet pays 25 to 39% larger oscillations—a speed–stability tension whose resolution lives in a narrow learning-rate band near 0.08 to 0.12, outside which either sluggishness or instability takes hold. Carbon-market behavior turns on price thresholds: emitters switch abruptly from buying quotas toward investing in abatement once the clearing price clears firm-specific triggers, a discrete state switch that smooth equilibrium analysis misses entirely. Across all three domains, fragmented data, path dependence, and regime-switching dynamics recur as the binding constraints on modeling and on governance alike. Four mechanisms prove invariant to scale—the decisive weight of initial conditions, the catalytic leverage of well-positioned anchor agents, the equilibrium-shaping force of institutional design, and the computational reach added by AI integration—which suggests that insight earned in one domain transfers to the others. We close by mapping open problems in heterogeneity modeling, verification under deep uncertainty, and the still-unrealized coupling of digital twins with privacy-preserving learning. EGT emerges not as retrospective description but as prospective guidance for the cooperative transitions on which credible decarbonization depends.

Muhammaddiyor Isamiddinov

As of 2024, 730 million people worldwide lacked electricity access, roughly eight in ten of them in sub-Saharan Africa. Closing this gap requires engineering approaches suited to the technical, financial, and institutional constraints of low-resource settings, not conventional grid extension alone. This paper reviews four engineering pathways expanding renewable energy access in developing countries — decentralized mini-grids, IoT-enabled pay-as-you-go (PAYG) solar financing, frugal engineering, and AI-assisted smart-grid digitalization — using case evidence from Kenya, India, and East Africa's PAYG sector.

Michael Lane

ComputeGrid is a concept paper and feasibility framework for a utility-integrated network of provider-owned compute nodes hosted at commercial buildings, public facilities, and — through a heat-recovery variant — homes. Hosts receive full metered reimbursement of node electricity plus a separate credit; nodes are aggregated by an orchestration and settlement platform (ComputeGrid OS) into one secure, grid-aware, dispatchable compute layer beneath hyperscale data centers. Version 3.0 evaluates the concept against two structural facts: residential retail electricity is the most expensive power a compute operator can buy, and a decade of distributed-compute ventures (spot marketplaces, deploy-first token networks, and heat-recovery operators) shows which configurations survive. The paper therefore leads with commercial and public-building deployment, admits residential nodes only where waste heat displaces heating the host would otherwise purchase, and requires every site to pass at least one of four economic qualifiers — near-commercial power price, monetizable heat, a priced locality premium, or measurable grid-flexibility value — before hardware is committed. The paper includes a two-sided illustrative unit ledger, host consumer-protection and property-rights baselines, rules preventing host-purchased “income” hardware, a staged offtake-first pilot plan with published kill criteria, and a claim framework that treats all economics as illustrative pending Stage 0 diligence. It is an open concept paper, not peer reviewed; no field deployment or empirical dataset is reported. Version 1.0 drafting was assisted by OpenAI ChatGPT; Versions 2.0 and 3.0 critique and revision were assisted by Anthropic Claude under the author’s direction. The author is responsible for the final claims and release.

Tiago Ferreira Cavazin

O presente 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 o modelo híbrido baseado em Proof-of-History (PoH) combinado com PoS – examinando como as diferenças de consumo energético entre esses paradigmas se relacionam a propriedades de segurança, desempenho e sustentabilidade econômica. A partir de dados recentes sobre o consumo energético de redes públicas de referência – entre as quais o Bitcoin, o Ethereum antes e depois da transição para PoS (Merge) e a Solana – discute-se em que medida a evolução dos desenhos de consenso permite reduzir o uso de eletricidade por ordens de grandeza, sem necessariamente comprometer segurança e descentralização. A metodologia combina revisão bibliográfica de estudos acadêmicos e relatórios técnicos sobre consumo energético em blockchains, análise de estimativas consolidadas de uso anual de eletricidade e de energia por transação e discussão conceitual dos trade-offs entre eficiência energética, robustez criptográfica, requisitos de hardware e impactos regulatórios. As evidências empíricas revisadas indicam que o Bitcoin, ancorado em PoW, mantém consumo anual estimado em torno de 120 a 130 terawatt-hora (TWh), ao passo que o Ethereum, após a migração para PoS em setembro de 2022, reduziu seu consumo em mais de 99,9%, operando com menos de 0,01 TWh por ano. Relatórios de eficiência energética apontam que redes que combinam PoH e PoS, a exemplo da Solana, apresentam consumo de energia por transação da ordem de centenas de joules, valor inferior tanto ao de redes PoW quanto ao de diversas redes PoS de menor vazão, embora existam ressalvas metodológicas e debates acerca dos efeitos de centralização de infraestrutura associados a requisitos elevados de hardware e conectividade. Conclui-se que PoS e esquemas híbridos com PoH oferecem vantagens substanciais em termos de eficiência energética, mas que a avaliação de custo-benefício deve incorporar conjuntamente a segurança econômica, a distribuição de poder entre participantes, a maturidade do ecossistema e o alinhamento com agendas de sustentabilidade que tendem a moldar a evolução da infraestrutura Web3 nas próximas décadas.

Sumanta Bhattacharya

This chapter investigates the emerging integration of decentralized finance (DeFi) ecosystems and the Internet of Things (IoT) as a transformative pathway for developing inclusive and sustainable renewable energy markets. In an ideal digital energy economy, real-time data intelligence, transparent financial mechanisms, and decentralized governance structures function cohesively to ensure equitable access, operational efficiency, and long-term sustainability. Such a system envisions seamless peer-to-peer energy trading, automated financing, and adaptive grid management. In practice, however, existing energy infrastructures remain constrained by centralized financing, limited data interoperability, and fragmented technological adoption, which restrict scalability and social inclusion. Prior research on blockchain-based energy trading, smart grids, and IoT-enabled monitoring emphasizes efficiency gains and transparency in distributed energy systems. Parallel studies on DeFi highlight its potential to democratize capital access and automate financial processes 234through smart contracts. Yet, these streams largely operate independently, offering limited insight into their integrated sociotechnical and financial impacts. Moreover, ethical governance, data security, and cross-platform interoperability remain underexplored. Addressing these gaps, this chapter proposes an integrative conceptual framework grounded in digital ecosystem theory and decentralized governance principles. Through analytical synthesis, it demonstrates how IoT-driven energy data, blockchain-enabled smart contracts, and decentralized lending models can jointly enhance grid stability, financing inclusivity, and system trust. The findings provide strategic guidance for advancing resilient, ethical, and scalable smart energy economies.

Pranay Anchuri, Akaki Mamageishvili

We estimate the causal price elasticity of gas demand on Ethereum mainnet (L1) and Arbitrum One (L2), a quantity necessary for calibrating fee mechanism simulations, evaluating resource pricing reforms, and explaining observed usage patterns. A two-way fixed effects panel regression instrumented by each wallet's own lagged base fee removes the congestion-driven endogeneity that causes naive regressions to substantially underestimate demand sensitivity. On Ethereum mainnet (full year 2025), the pooled IV elasticity is -0.006***, near-inelastic: a 10% fee increase reduces total gas demand by approximately 0.06%. On Arbitrum One (October 2025--April 2026), the pooled IV elasticity is -0.036**. Both chains are inelastic in the aggregate, with L2 measurably more responsive than L1. A per-resource decomposition of L2 demand reveals elasticities ranging from modestly elastic computation (-0.027*) to -0.27*** for refunds, with storage growth (-0.15***) and calldata (-0.06*) in between. Behavioral clustering identifies always-on protocol wallets as near-inelastic and high-volume operators as substantially more responsive, with cluster-level elasticities up to roughly 6x the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.

Irene Aldridge, Gavhar Annaeva, Leyla Beriker, Zhiheng Cai · 23 authors

Abstract While blockchain technology is expected to reduce transaction costs, network congestion execution costs remain understudied in operations management. Analyzing 62,142 Ethereum transactions from seven firms (January–March 2026), we study "on-chain peak shaving"—scheduling transactions toward low-congestion windows to reduce gas fee exposure. Gas fees peak at 10 AM ET, driven by speculative-arbitrage rather than operational activity. Firm scheduling responses vary: only three firms transact off-peak, while four transact during peak windows due to deadlines or governance cycles. This heterogeneity is driven by transaction deferrability and gas intensity. We formalize these into an On-Chain Scheduling Matrix mapping firms to four regimes that predict fee savings and residual cost floors. Theoretically, we extend Transaction Cost Economics to account for time-varying execution costs from congestion externalities, classifying gas fees as execution costs in timing but maladaptation costs in origin. Ultimately, managing gas fees requires systematic operational planning akin to energy procurement.

Ushaa Eswaran, Vivek Eswaran, Keerthna Murali, Vishal Eswaran

This chapter examines the emerging role of decentralized finance (DeFi) as a transformative catalyst in the digitalization of electric vehicle (EV) charging infrastructure within the energy and utilities sector. While global sustainability agendas envision seamless, affordable, and interoperable charging networks to support large-scale EV adoption, existing systems remain fragmented, capital-intensive, and institutionally constrained. Ideally, charging ecosystems should enable transparent financing, efficient energy exchange, and user-centric governance. In practice, however, high deployment costs, limited grid flexibility, regulatory inconsistencies, and restricted access to investment continue to impede this vision. Building on prior research on smart grids, blockchain-enabled energy markets, and sustainable mobility frameworks, this work critically evaluates how 2 DeFi-driven models extend beyond conventional centralized approaches. Existing studies emphasize technical optimization and policy mechanisms, yet they often overlook decentralized financial governance, peer-to-peer energy trading, and tokenized infrastructure funding. Addressing this gap, the study develops an integrated conceptual model linking DeFi principles, smart grid technologies, and EV charging ecosystems. Through analytical synthesis and selected case evidence, the paper demonstrates how trustless transactions, decentralized autonomous organizations, and micropayment mechanisms can enhance financial inclusivity, operational transparency, and system scalability. By situating EV charging infrastructure within a broader digital-financial transformation paradigm, this study advances theoretical understanding and offers strategic insights for policymakers, utilities, and technology providers seeking to accelerate sustainable and resilient mobility transitions.

Sumanta Bhattacharya, Bhavneet Kaur Sachdev

This chapter examines the transformative role of artificial intelligence (AI) and the Internet of Things (IoT) in strengthening decentralized finance (DeFi) frameworks for renewable energy management. In an ideal sustainable energy ecosystem, intelligent monitoring systems, transparent financing mechanisms, and decentralized governance structures operate cohesively to optimize resource utilization and promote inclusive economic growth. Such a system is expected to support real-time energy forecasting, automated funding processes, and participatory decision-making. However, existing renewable energy infrastructures remain constrained by centralized financial control, limited predictive capability, and insufficient integration of digital intelligence, thereby restricting scalability and community participation. Building on prior research in AI-driven energy analytics, blockchainbased financing, and distributed governance models, this chapter critically 298 evaluates how their convergence reshapes green energy ecosystems. While earlier studies highlight the technical efficiency of smart grids and the transparency of blockchain platforms, they often overlook the systemic integration of predictive analytics, decentralized lending, and collaborative governance through decentralized autonomous organizations. Addressing this gap, the present work proposes an integrative conceptual framework grounded in digital ecosystem theory and financial decentralization principles. Through analytical synthesis, the chapter demonstrates how IoT-enabled data streams, AI-based forecasting, and smart contracts enhance risk assessment, financing accuracy, and operational resilience. The findings underscore the importance of ethical governance, data security, and algorithmic transparency in sustaining public trust. Ultimately, this research positions AI- and IoT-enabled DeFi as a critical pathway toward equitable, resilient, and sustainable renewable energy economies.

Neha Tandon, Sayali Kharpate

This chapter examines the growing convergence of peer-to-peer (P2P) energy trading and decentralized finance (DeFi) as a transformative force within contemporary energy markets. In an ideal decentralized energy ecosystem, producers and consumers engage directly through transparent, automated, and secure digital platforms that ensure fair pricing, efficient resource allocation, and inclusive financial participation. Such a system is expected to minimize intermediary dependence, enhance market responsiveness, and promote sustainable energy practices. However, existing energy infrastructures continue to rely heavily on centralized market mechanisms, regulatory rigidities, and limited financial accessibility, which constrain innovation and restrict equitable participation. Prior studies on blockchain-enabled energy trading and distributed energy resources emphasize operational efficiency and transaction transparency, while DeFi literature highlights liquidity enhancement and automated financial governance. Yet, these research streams frequently remain disconnected, offering limited insight into their integrated market 262 dynamics and socio-economic implications. Moreover, empirical evidence on regulatory adaptation, risk governance, and long-term scalability remains fragmented. Addressing these limitations, this chapter advances an integrative conceptual framework grounded in decentralized market theory and platform ecosystem models. Through analytical synthesis and selected case analyses, it demonstrates how smart contracts, decentralized exchanges, and liquidity mechanisms can enhance trust, resilience, and financial inclusion in P2P energy systems. The findings provide strategic guidance for policymakers, utilities, and innovators seeking to institutionalize decentralized, sustainable, and equitable energy marketplaces.

HyunJoong Kim

Peer-to-peer (P2P) energy trading requires network-aware coordination because transactions are physically realized through distribution networks. However, sensitivity-based coordination causes a confidentiality-verifiability tradeoff, as network sensitivities may reveal vulnerable components while undisclosed sensitivities prevent participants from verifying utility-provided transaction guides. This paper proposes a zero-knowledge-proof-based method for verifying the computational integrity of network-constrained transaction guides with respect to committed private network data, without exposing network-sensitivity information. The guide defines admissible injection and withdrawal volumes derived from sign-decomposed sensitivity matrices while satisfying balance, voltage, line-flow, and optimality conditions. These conditions are encoded in an arithmetic circuit, represented as R1CS constraints and a quadratic arithmetic program, and verified using a bilinear pairing. Blockchain commitments bind the approved circuit, public inputs, statement identifiers, proof, and verification result for tamper-evident auditability. The proposed proof certifies correct guide computation from committed network data; the authenticity of the committed network data is handled through an explicit registration and attestation assumption. Case studies on a modified IEEE 33-bus system show satisfaction of network constraints after clearing, rejection of public-input and witness-inconsistency attacks, and practical on-chain overhead, with an 806-byte proof.

Bhabani Sankar Samantray, K. Hemant Kumar Reddy

Energy demand in urban and metropolitan regions has been growing rapidly, often exceeding production capacity, leading to imbalances in energy distribution. Existing peer-to-peer (P2P) energy trading models, along with classical algorithms like FCFS and best-fit frameworks in smart cities, address some of these issues. However, they often face challenges such as limited transaction success percentage, inefficiencies in price matching, and privacy concerns during trades. To overcome these limitations, a framework is proposed that integrates game-theoretic pricing-based collaborative trading with Nash equilibrium and an additional pricing mechanism (CoGap) to enhance fairness and transaction success percentage in decentralized energy markets. The proposed framework is implemented on an Ethereum-based blockchain using Solidity smart contracts, incorporating cryptographic security through the Keccak-256 hash function and privacy-preserving zero-knowledge proofs (ZKPs). Moreover, it ensures security and price negotiations while maximizing transaction efficiency. Simulation results demonstrate that CoGap consistently achieves higher transaction success rates compared to four state-of-the-art collaborative energy trading schemes.

Subir Majumder

Large controllable loads, such as Bitcoin-mining facilities, are increasingly viewed as valuable sources of power-system flexibility, yet the conditions under which this flexibility is realized remain poorly understood. We examine this issue in the Texas power market, where large loads face both wholesale electricity prices and incentives created by coincident-peak-based transmission charges. We find that mining load declines as costs rise across both channels, and this response is moderated by hashprice, a measure of expected revenue for Bitcoin miners. When hashprice is higher, mining load is less responsive to electricity-sector costs. This pattern is consistent with aggregate mining load arising from heterogeneous devices operated around distinct breakeven points. The wholesale-price response illustrates this mechanism most clearly. Mining load remains largely online at low electricity prices but begins to decline once prices exceed an implied curtailment threshold, and higher hashprice shifts this threshold to higher wholesale prices. Bitcoin miners therefore respond to electricity-sector costs, but the available flexibility varies with revenue conditions in the crypto-financial sector. Treating such loads as stable demand-response resources may overstate their available flexibility.

Craig Wright

A tokenised energy market settles payment against metered dispatch, but the meter reading is the prosumer's private information: a self-interested prosumer can report more energy than it supplied and be paid for the difference. The companion papers in this programme assume meter integrity — truthful reporting — and build settlement, participation, and delivery contracts on top of it. This paper derives the verification contract that makes the assumption hold. A prosumer dispatches a quantity it observes privately and reports a possibly inflated figure to the settlement layer; the grid-telemetry layer can audit a report at a cost, detecting a discrepancy with a probability that reflects sensor accuracy, and a detected misreport forfeits a posted verification stake. We treat the audit probability, the stake, and the sensor accuracy as the designer's instruments and characterise the verification that makes truthful reporting weakly dominant at minimum cost. The baseline assumes a margin-independent detection probability and one-sided audit error (false negatives possible, false positives excluded); both are stated and the general margin-dependent condition is given. First, truthful reporting is weakly dominant if and only if the expected forfeiture covers the largest gain from admissible over-reporting, αφB ≥ Pm̄ (strict under strict inequality), where α is the audit probability, φ the per-audit detection probability, B the stake, and m̄ the largest admissible over-report; with a one-unit maximum this is αφB ≥ P (Proposition 1). Second, along this deterrence frontier the audit probability is α = Pm̄/(φB), and once the stake is itself chosen against its capital carry the least-cost interior contract is B* = √(κPm̄/(ρφ)), α* = √(ρPm̄/(κφ)), total cost 2√(ρκPm̄/φ), all decreasing in detection accuracy, so accurate telemetry drives the audit rate, the stake, and the cost down together (Theorem 1). Third, sensor accuracy is itself a procurable instrument with a convex capital cost, and the cost-minimising accuracy equates marginal sensor capital cost to the marginal audit-opex saving, a capex–opex frontier between better meters and more auditing (Proposition 2). Fourth, the per-report enforcement αφB is exactly the meter-integrity guarantee the companion papers assume; truthful reporting is weakly dominant on the binding frontier and strict under an arbitrarily small slack, so the reported quantity equals the dispatched quantity, discharging that assumption from primitives and closing the stack at its base (Proposition 3). Full proofs are in the online appendix.

Otilia Elena Dragomir, Florin Dragomir

Prosumer communities, aggregations of residential and commercial entities equipped with distributed energy resources (DER), including photovoltaic systems, battery storage, and flexible loads, are emerging as critical organizational units in decarbonising smart grid architectures. Managing these communities effectively requires balancing economic efficiency with equity, autonomy, and environmental sustainability, objectives that conventional centralized control methods and existing multi-agent reinforcement learning (MARL) implementations fail to address simultaneously. This article proposes a value-aligned hierarchical multi-agent reinforcement learning (VA-HMARL) framework as a formally unified architecture that embeds equity (Jain’s Fairness Index J ≥ 0.90), individual autonomy, and carbon sustainability as hard constraints within the MARL reward structure. The framework integrates: a multi-objective Value Alignment Module (VAM) combining economic, fairness, sustainability, and comfort objectives; attention-based implicit coordination for scalable agent interaction; and differentially private federated policy aggregation (ε = 1.0, δ = 10−5) for GDPR-compliant collaborative learning. Simulation on a 20-prosumer community modelled on the IEEE 33-bus feeder over 10 Monte Carlo runs (300 episodes each) demonstrates: a 6.2% energy cost reduction versus the Rule-Based baseline (p = 0.0004); a Jain’s Fairness Index of 0.912 ± 0.031 at policy convergence (final 50 episodes), satisfying the J ≥ 0.90 community equity floor; and an 18.0% reduction in CO2 emissions. The economic efficiency trade-off relative to performance-optimized MARL baselines is limited to 2.4%, within the 5% design target. These results establish VA-HMARL as a technically feasible and ethically grounded paradigm for autonomous decentralized energy governance.