This research presents an innovative blockchain-based solution for the charging and energy trading of electric vehicles (EVs). By combining the strengths of two prominent consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), the proposed system balances security, decentralization, and energy efficiency. PoW secures the blockchain, while PoS enhances energy efficiency and scalability, key factors in meeting the growing demand for EV infrastructure. The system’s decentralized nature allows for EV owners, charging stations, and stakeholders to interact and transact transparently, without relying on centralized entities. The research conducts a comprehensive simulation to assess the performance of the proposed hybrid blockchain model, demonstrating significant improvements in cost-effectiveness, scalability, and energy management. Additionally, dynamic pricing mechanisms within the blockchain enable real-time energy trading, optimizing charging times and balancing grid demand efficiently. Through the use of smart contracts, automated pricing adjustments, and incentive-driven user behaviors, the proposed system paves the way for more sustainable, cost-effective, and efficient energy solutions in the future.
Seelammal Chinnaperumal, Sekar Kidambi Raju, Amal H. Alharbi, Subhash Kannan · 8 authors
The research is aimed at filling the gap regarding the development of long-lasting, secure technologies that help build decentralized systems. Other consensus models, such as the Proof of Work (PoW), prevailing in cryptocurrencies, are known to be expensive in terms of energy, hence the development of enlightened models like Proof of Lightweight Hash, whereby while developing the model, an emphasis is placed on energy efficiency without compromising on security. At the same time, new technologies such as battery storage and electric vehicles are disrupting consumer habits where renewable energy is favored, and a decentralized energy market is promoted. It hails the aspect of fine access control provided by blockchain in addition to decentralization; a permission system is vital for any entities that require strict access control due to the nature of the data they hold. Blockchain in IoT and AI makes strategies innovative, adaptable, large-scale, and inclusive to make unique changes that benefit different industries and need scalability. Due to this combining of energy innovations and digital technologies, both energy and data networks become nearer to consumers, advocating sustainable, efficient urbanism. Altogether, these improvements will lead toward the emergence of systems that, aside from being technologically innovative, are also environmentally sustainable and protected. So the interaction of technology, ecological stability, and viable security provides the basis for a cleaner, stronger, de-centralized future as applied to advanced technologies, thus inculcating an equilibrium and stronger society.
Pratyush Kumar Patro, Raja Jayaraman, Adolf Acquaye, Khaled Salah · 5 authors
The aviation industry's carbon emissions are forecast to rise to 22% by 2050, posing a significant challenge to the goal of achieving Net Zero Emissions by the same year. Regardless of the structural or agentic strategies implemented to reduce these emissions, ensuring effective traceability of emissions in airline operations is crucial, as it enables the development of effective mitigation measures. Existing systems fall short of effectively providing end-to-end traceability of emissions within an effective carbon accounting framework. Indirect emissions and the complexities associated with emissions tracking throughout the extended aviation sector also exacerbate carbon accounting and offsetting difficulties. In this paper, we present a blockchain-based framework to address these plausible challenges. The proposed work categorises both direct and indirect emissions under Scope 1, 2, and 3 classifications. A blockchain-based collaborative platform, also provides data transparency across all stakeholders, ensuring traceability and security in a decentralised and reliable manner. A prototype model of a blockchain-based system is therefore developed using Ethereum smart contracts. The paper presents a cost and security analysis of the system, while highlighting the challenges and opportunities for the development of sustainable aviation operations. The smart contract is made publicly available on Github for verification.
Vittorio Capocasale, Maria Elena Bruni, Guido Perboli
Purpose Blockchain and distributed ledger technologies are increasingly prominent, yet their adoption remains complex. This paper addresses the common misalignment between blockchain technology and actual needs, often leading to project failure. It introduces a decision-making framework focused on the technological aspects of blockchain adoption. Design/methodology/approach We designed the framework by analyzing key decision drivers from existing literature and applied it to a real-world use case in the electric vehicle supply chain. The blockchain solution was tested with live production data. Findings Blockchain is beneficial for use cases requiring decentralized governance, but it often needs to be supplemented with additional technologies in industrial applications. Originality/value The framework provides a set of managerial-level questions that simplify the decision-making process for those without deep technical expertise, helping determine when blockchain is appropriate, valuable and superior to other technologies.
Arvind Singh, R. Seshu Kumar, K. Reddy Madhavi, Faisal Alsaif · 6 authors
The integration of Electric Vehicles (EVs) into power grids introduces several critical challenges, such as limited scalability, inefficiencies in real-time demand management, and significant data privacy and security vulnerabilities within centralized architectures. Furthermore, the increasing demand for decentralized systems necessitates robust solutions to handle the growing volume of EVs while ensuring grid stability and optimizing energy utilization. To address these challenges, this paper presents the Demand Response and Load Balancing using Artificial intelligence (DR-LB-AI) framework. The proposed framework leverages Artificial intelligence (AI) for predictive demand forecasting and dynamic load distribution, enabling real-time optimization of EV charging infrastructure. Furthermore, Blockchain technology is employed to facilitate decentralized, secure communication, ensuring tamper-proof energy transactions while enhancing transparency and trust among stakeholders. The DR-LB-AI framework significantly enhances energy distribution efficiency, reducing grid overload during peak periods by 20%. Through advanced demand forecasting and autonomous load adjustments, the system improves grid stability and optimizes overall energy utilization. Blockchain integration further strengthens security and privacy, delivering a 97.71% improvement in data protection via its decentralized framework. Additionally, the system achieves a 98.43% scalability improvement, effectively managing the growing volume of EVs, and boosts transparency and trust by 96.24% through the use of immutable transaction records. Overall, the findings demonstrate that DR-LB-AI not only mitigates peak demand stress but also accelerates response times for Load Balancing, contributing to a more resilient, scalable, and sustainable EV charging infrastructure. These advancements are critical to the long-term viability of smart grids and the continued expansion of electric mobility.
Peer-to-peer (P2P) electricity trading has received a lot of attention in the last decade and recently there has been growing interest in the evaluation of market design, classical methods, and novel approaches for multi-energy trading. The existing literature only focused on electricity/electric-gas/electric-heat networks. Therefore, a comprehensive analysis of P2P multi-energy trading (P2P-MET) in a decentralized network is required for the multi-energy like electricity, hydrogen gas, and heat all in one framework for a sustainable future. This review study aims to provide an in-depth understanding of P2P-MET in a decentralized network including layers-defined multi-source network configurations, trading platforms, classical methods, novel approaches, and mechanisms. Considering MET, the rising number of prosumers take advantage of the opportunity and make this complicated decentralized network more complex. To handle the network complexities, smart contracts using distributed ledger-based blockchain, novel algorithm-based game theory, and some other approaches are reviewed. Furthermore, this review study covers the limitations, challenges, opportunities, and benefits of P2P-MET as well as current trends and future directions for a better understanding of the readers. It concludes that P2P-MET using blockchain/game theory in decentralized networks is alternatively better and more secure. • An overview of P2P multi-energy trading (MET) reviewed in a decentralized network. • This paper presents classical methods and core approaches for P2P-MET. • Numerous pilot and under-developed projects implemented globally are reviewed. • Simulating tools commonly used by researchers for P2P-MET are briefly discussed. • Current trends and future directions are reviewed for P2P-MET sustainable future.
Presently Rural Energy Communities (REC) are faced with challenges such as the inefficient distribution of energy from Renewable Energy Sources (RES), unfair pricing, and the inclusion of prosumers into the electricity market. Therefore, this article proposed an approach that employed enabling technologies such as Distributed Ledger Technologies (DLT), self-enforcing smart contracts-enabled Internet of Things (IoT), and Artificial Intelligence (AI) for sustainable energy sharing and tracking in REC. Additionally, a model is proposed based on key factors that influence the adoption of enabling technologies in REC. For the methodology qualitative data is collected from secondary sources and descriptive analysis is employed to present the key findings. Key findings from this study contributes to develop a decarbonized, decentralized, and digitized energy management approach to support the sustainability of REC. The deployment of AI can facilitate prediction short-term energy planning for RES production and consumption based on real-time data from IoT devices. More importantly, findings from this study presents use case scenarios of energy sharing and tracking, and green electric vehicle charging in REC suggesting that DLT based smart contracts, IoT, and AI offers an effective approach to accelerate the sharing and tracking of RES in REC. Besides, DLT and smart contracts enables real-time electricity consumption monitoring, energy trading management, and pricing.
With the increasing awareness for sustainable future and green energy, the demand for electric vehicles (EVs) is growing rapidly, thus placing immense pressure on the energy grid. To alleviate this, local trading between EVs should be encouraged. In this paper, we propose a blockchain and public key infrastructure (PKI)-based secure vehicle-to-vehicle (V2V) energy-trading protocol. A permissioned blockchain utilizing the proof of authority (PoA) consensus and smart contracts is used to securely store data. Encrypted communication is ensured through transport layer security (TLS), with PKI managing the necessary digital certificates and keys. A multi-leader, multi-follower Stackelberg game-based trade algorithm is formulated to determine the optimal energy demands, supplies, and prices. Finally, we propose a detailed communication protocol that ties all the components together, enabling smooth interaction between them. Key findings, such as system behavior and performance, scalability of the trade algorithm and the blockchain, smart contract execution costs, etc., are presented through numerical results by implementing and simulating the protocol in various scenarios. This work not only enhances local energy trading among EVs, encouraging efficient energy usage and reducing burden on the power grid, but also paves a way for future research in sustainable energy management.
Taher Al‐Shehari, Mohammed Kadrie, Taha Alfakih, Hussain AlSalman · 9 authors
The rise of Electric Vehicles (EVs) has introduced significant advancement and evolution in the electricity market. In smart transportation, the EVs have earned more popularity because of its numerous benefits including lower carbon footprints, higher performance, and sophisticated energy trading mechanisms. These potential benefits have resulted in widespread EV adoption across the world. Despite its benefits, energy management remains the biggest challenge in EVs and it is mainly because of the lack of Charging Stations (CSs) near EVs. This creates a demand for an effective, secure and reliable energy management framework for EVs. This study presents a secure data and energy trade paradigm based on Blockchain (BC) in the Internet of EVs (IoEV). BC technology prepares for the high volume of EV integration that serves as the foundation for the next generation, and to assist in developing unique privacy-protected BC-based D-Trading and storage Models. Entities evaluated for the proposed model include Trusted Authority (TA), Vehicles, Smart Meters, Roadside Units (RSU), BC, and Inter-Planetary File System (IPFS). In addition, E-trading involves several phases, including the acquiring E-trading demand requests, E-trading response requests, request matching and token assignment. Moreover, account mapping is performed using a Mayfly Pelican Optimization Algorithm (MPOA), which is created by merging the Mayfly Algorithm (MA) and Pelican Optimization Algorithm (POA). Various security features are used to protect data and energy trade in IoEV, including encryption, hashing, polynomials, and others. The testing results revealed that the MPOA outperformed the state-of-the-art results regarding memory consumption, trading rate, transaction cost, and trading energy volume with values of 4.605 MB, 91%, 0.654, and 90 kW, respectively.
In the networked enlarged electric vehicle (EV) charging infrastructures, the security and authenticity of the stakeholders involved in the EV energy market pool are prime important. This paper proposes an EV network hub (EVNH) comprising electric vehicles, electric vehicle aggregators (EVAs), and charging nodes in the networked EV energy market pool. The various EVAs implement different heterogeneous blockchains. The EV network hub facilitates blockchain-based secure and resilient energy trading under the grid to vehicle and vehicle to grid. The paper emphasizes interoperability challenges involving different blockchains to communicate and transfer assets or data between them. We suggest secure and trustworthy energy trading across various EVAs using multiple EV tokens for EV energy trading through cross-chain communications. The EVAs consider a Nash equilibrium-seeking strategy to find the Nash equilibrium in the non-cooperative game of EVAs. The effectiveness of the proposed EV network hub is tested using MATLAB, Solidity, and Python software.
The rapid growth of electric vehicles (EVs) and the deployment of vehicle-to-grid (V2G) technology pose significant challenges for distributed power grids, particularly in fostering trust and ensuring effective coordination among stakeholders. Establishing a trustworthy V2G operation environment is crucial for enabling large-scale EV user participation and realizing V2G potential in real-world applications. In this paper, an integrated scheduling and trading framework is developed to conduct transparent and efficacious coordination in V2G operations. In blockchain implementation, a cyber-physical blockchain architecture is proposed to enhance transaction efficiency and scalability by leveraging smart charging points (SCPs) for rapid transaction validation through a fast-path practical byzantine fault tolerance (fast-path PBFT) consensus mechanism. From the energy dispatching perspective, a game-theoretical pricing strategy is employed and smart contracts are utilized for autonomous decision-making between EVs and operators, aiming to optimize the trading process and maximize economic benefits. Numerical evaluation of blockchain consensus shows the effect of the fast-path PBFT consensus in improving systems scalability with a balanced trade-off in robustness. A case study, utilizing real-world data from the Southern University of Science and Technology (SUSTech), demonstrates significant reductions in EV charging costs and the framework potential to support auxiliary grid services.
As electric vehicles (EV) become more prevalent and advances in electric vehicle electronics continue, vehicle-to-grid (V2G) techniques and large-scale scheduling strategies are increasingly important to promote renewable energy utilization and enhance the stability of the power grid. This study proposes a hierarchical multistakeholder V2G coordination strategy based on safe multi-agent constrained deep reinforcement learning (MCDRL) and the Proof-of-Stake algorithm to optimize benefits for all stakeholders, including the distribution system operator (DSO), electric vehicle aggregators (EVAs) and EV users. For DSO, the strategy addresses load fluctuations and the integration of renewable energy. For EVAs, energy constraints and charging costs are considered. The three critical parameters of battery conditioning, state of charge (SOC), state of power (SOP), and state of health (SOH), are crucial to the participation of EVs in V2G. Hierarchical multi-stakeholder V2G coordination significantly enhances the integration of renewable energy, mitigates load fluctuations, meets the energy demands of the EVAs, and reduces charging costs and battery degradation simultaneously.
Around the world policymakers and regulators are struggling with the question of how to design retail electricity tariffs in the face of increasing penetration of local generation (e.g., solar PV), smart appliances, local storage, and electric vehicles. There is a widespread recognition that retail tariffs should vary dynamically across time and space, reflecting the changing conditions (congestion and losses) on the underlying networks. But, at the same time, there is recognition that such tariffs potentially expose retail customers to substantial risk. Risk averse retail customers desire protection against price spikes and volatile wholesale spot prices. This paper seeks to derive the optimal retail contract in the special case in which the uncertainty in the market is contractible (in the sense defined here). We show that the optimal retail contract exposes the prosumer to the wholesale spot price at the margin, but also perfectly insulates the customer from risk, achieving the first-best outcome. We show how the hedge component of this retail contract can be constructed from standard-form hedge contracts. We draw out several lessons for policymakers.
Abstract Electric vehicles (EVs) and their battery recycling have recently garnered heightened attention from both firms and consumers, primarily driven by concerns related to environmental sustainability. However, consumers often grapple with uncertainties regarding the green valuation of EVs. Integrating blockchain traceability technology presents a promising solution to mitigate these ambiguities by providing traceable, immutable, and precise information. Within this context, this research, grounded in a game-theoretical framework, delves into the strategies involving blockchain traceability in the pre-purchase and post-purchase stages of EVs. Specifically, the paper analytically studies the influence of three distinct strategies, namely, non-blockchain traceability, forward blockchain traceability, and Forward–reverse blockchain traceability, on the willingness of EV manufacturers to adopt blockchain technology. In addition, the study incorporates two prevalent government subsidies to scrutinize and contrast their implications on optimal outcomes. The findings of this study uncover the nuanced relationship between adopting blockchain traceability and its impact on EV sales. Notably, the research shows that the positive impact on consumers’ surplus from blockchain adoption depends on the cost coefficient of green low-carbon levels not exceeding a particular threshold. Moreover, regarding the use of government subsidies to enhance overall social welfare, it is shown that the forward blockchain traceability strategy should align with consumer-oriented subsidies and the Forward–reverse blockchain traceability strategy with EV maker-oriented subsidies.
In modern society, the proliferation of electric vehicles (EVs) is continuously increasing, presenting new challenges that necessitate integration with smart grids. The operational data from electric vehicles are voluminous, and the secure storage and management of these data are crucial for the efficient operation of the power grid. This paper proposes a novel system that utilizes blockchain technology to securely store and manage the black box data of electric vehicles. By leveraging the core characteristics of blockchain—immutability and transparency—the system records the operational data of electric vehicles and uses federated learning (FL) to predict their energy consumption based on these data. This approach allows the balanced management of the power grid’s load, optimization of energy supply, and maintenance of grid stability while reducing costs. Additionally, the paper implements a searchable black box data storage system using a public blockchain, which offers cost efficiency and robust anonymity, thereby enhancing convenience for electric vehicle users and strengthening the stability of the power grid. This research presents an innovative approach to the integration of electric vehicles and smart grids, exploring ways to enhance the stability and energy efficiency of the power grid. The proposed system has been validated through real data and simulations, demonstrating its effectiveness and performance in managing black box data and predicting energy consumption, thereby improving the efficiency and stability of the power grid. This system is expected to empower electric vehicle users with data ownership and provide power suppliers with more accurate energy demand predictions, promoting sustainable energy consumption and efficient power grid operations.
Dynamic energy contracts, offering hourly varying day-ahead prices for electricity, create opportunities for a residential Battery Energy Storage System (BESS) to not just optimize the self-consumption of solar energy but also capitalize on price differences. This work examines the financial potential and impact on the self-consumption of a residential BESS that is controlled based on these dynamic energy prices for PV-equipped households in the Netherlands, where this novel type of contract is available. Currently, due to the Dutch Net Metering arrangement (NM) for PV panels, there is no financial incentive to increase self-consumption, but policy shifts are debated, affecting the potential profitability of a BESS. In the current situation, the recently proposed NM phase-out and the general case without NM are studied using linear programming to derive optimal control strategies for these scenarios. These are used to assess BESS profitability in the latter cases combined with 15 min smart meter data of 225 Dutch households to study variations in profitability between households. It follows that these variations are linked to annual electricity demand and feed-in pre-BESS-installation. A residential BESS that is controlled based on day-ahead prices is currently not generally profitable under any of these circumstances: Under NM, the maximum possible annual yield for a 5 kWh/3.68 kW BESS with day-ahead prices as in 2023 is EUR 190, while in the absence of NM, the annual yield per household ranges from EUR 93 to EUR 300. The proposed NM phase-out limits the BESS’s profitability compared to the removal of NM.
The scientific community has recently focused on intelligent models for predicting and optimizing EV energy management. Despite numerous studies in energy management optimization, there’s a critical need to address the trade-off between energy consumption and occupant comfort. Existing IoT systems face challenges in data analytics security and authenticity, highlighting the need for contemporary models to overcome data privacy and cost-related issues. This study introduces a smart contract model based on optimization and control modules, aiming to manage energy consumption while satisfying user comfort requirements intelligently. Introducing a smart contract model with hierarchical layers—prediction, optimization, control, and Blockchain—the proposed approach intelligently manages energy consumption while meeting user comfort requirements. Utilizing a Kalman filter for prediction and the BAT algorithm for optimization, the model integrates modules to tailor user preferences and enhance comfort. The synergy between the optimization module and a convolutional FLC enhances system performance, ensuring minimized energy usage and elevated user comfort levels. The study also evaluates the model’s implementation of the Hyperledger Fabric network, assessing outcomes regarding caliper, latency, throughput, and resource utilization.
It is established that based on the industrial revolutions towards Industry 5.0, the population of the Earth and its well-being are increasing. To preserve and improve living conditions the development in all spheres of life is by the paradigm of sustainable development. Through the prism of the mining industry, one step could be the introduction of circular economy measures for digitized tracking of materials, repairs, consumed electricity, safety, and finances. Implementing of electric cars is an option to achieve this goal through automation, manageability, and data traceability via Industry 4.0 technologies. Such are blockchain technologies, IoT, V2V, and others. For this purpose, the appropriate types of power supplies for electric vehicles for the mining sector have been identified and information flows have been defined. They are quantity of material, consumed electricity, state of the fleet, charging of batteries and operation of charging stations, and finances. For their implementation, Hyperledger Fabric was chosen as a suitable DLT platform ([1], [2]). Based on Hyperledger Fabric, a conceptual model for tracking material quantities as a step of the circular economy is proposed. A method of communication is shown in the presence of two channels - material and repairs for an electric car.
Syed Muhammad Danish, Aroosa Hameed, Ali Ranjha, Gautam Srivastava · 5 authors
The increased charging demand resulting from the rapid development of electric vehicles (EVs) poses various challenges to the stable operation of the distribution network and smart grid. Due to the stochastic EV charging behaviour, the high charging demand at the charging stations (CSs) elevates the load curve which may lead to a spatially imbalanced load demand. As such, forecasting the highly stochastic EV charging load considering an individual EV's unique charging behaviour can result in maintaining the safe operation of the grid and distribution network. Therefore, in this work, we propose Block-FeDL, a blockchain-based Federated Learning (FL) approach for EV charging load forecasting considering the private and sensitive charging information of each EV user. Thereafter, we use a Bidirectional Long Short Term Memory (BiLSTM) model within the FeDL for predicting the EV charging load patterns at each client. Moreover, instead of using a centralized server for global model aggregation, we use blockchain technology, where the model aggregation is performed in a decentralized manner and the local model parameters shared by the FL clients can be validated and securely recorded. Lastly, the results show that the Block-FeDL outperform the second-best baseline method by 95%, 96% and 77% in terms of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for forecasting the EV charging load.
Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs’ energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies.
Carbon footprint reduction can be achieved through various methods, including the adoption of renewable energy sources. The installation of such sources, like photovoltaic panels, while environmentally beneficial, is cost-prohibitive for many. Those lacking photovoltaic solutions typically resort to purchasing energy from utility grids that often rely on fossil fuels. Moreover, when users produce their own energy, they may generate excess that goes unused, leading to inefficiencies. To address these challenges, this paper proposes innovative blockchain-enabled energy-sharing algorithms that allow consumers -- without financial means -- to access energy through the use of their own energy storage units. We explore two sharing models: a centralized method and a peer-to-peer (P2P) one. Our analysis reveals that the P2P model is more effective, enhancing the sharing process significantly compared to the centralized method. We also demonstrate that, when contrasted with traditional battery-supported trading algorithm, the P2P sharing algorithm substantially reduces wasted energy and energy purchases from the grid by 73.6%, and 12.3% respectively. The proposed system utilizes smart contracts to decentralize its structure, address the single point of failure concern, improve overall system transparency, and facilitate peer-to-peer payments.
The transformative potential of blockchain technology in the renewable energy sector is increasingly gaining recognition for its capacity to enhance energy efficiency, enable decentralized trading, and ensure transaction transparency. However, despite its growing importance, there exists a significant knowledge gap in the holistic understanding of its integration and impact within this sector. Addressing this gap, the current study employs a pioneering approach, marking it as the first comprehensive bibliometric analysis in this field. We have systematically examined 390 journal articles from the Web of Science database, covering the period from 2017 through the end of February 2024, to map the current landscape and thematic trajectories of blockchain technology in renewable energy. The findings highlight several critical thematic areas, including blockchain's integration with smart grids, its role in electric vehicle integration, and its application in sustainable urban energy systems. These themes not only illustrate the diverse applications of blockchain but also its substantial potential to revolutionize energy systems. This study not only fills a crucial gap in existing literature but also sets a precedent for future interdisciplinary research in this domain, bridging theoretical insights with practical applications to fully harness the potential of blockchain in the renewable energy sector.
This paper investigates a double auction-based peer-to-peer (P2P) energy trading market for a community of renewable prosumers with private information on reservation price and quantity of energy to be traded. A novel competition padding auction (CPA) mechanism for P2P energy trading is proposed to address the budget deficit problem while holding the advantages of the widely-used Vickrey-Clarke-Groves mechanism. To illustrate the theoretical properties of the CPA mechanism, the sufficient conditions are identified for a truth-telling equilibrium with a budget surplus to exist, while further proving its asymptotical economic efficiency. In addition, the CPA mechanism is implemented through consortium blockchain smart contracts to create safer, faster, and larger P2P energy trading markets. The proposed mechanism is embedded into blockchain consensus protocols for high consensus efficiency, and the budget surplus of the CPA mechanism motivates the prosumers to manage the blockchain. Case studies are carried out to show the effectiveness of the proposed method.