There has been a shift towards the use of Electric Vehicles (EV) in recent years. Though EVs offer many advantages, there are concerns on the cyber security of its components and the privacy of its users. When users charge their EVs at a charging station, they need to reveal their personal details. An attacker can compromise the users' privacy by identifying and tracking where users charge their EVs. Hence, there is a need to protect EVs from cyber-attacks and preserve its users' privacy. In this paper, we address the problem of privacy preservation of users while charging their EVs in a 5G-enabled vehicular charging system. We propose a user-centric authentication protocol for EV charging based on Decentralized Identifier (DID) and blockchain. We use Verifiable Credential (VC) together with DID which provides Zero-Knowledge Proof (ZKP) about the user. Users have complete control over their identities resulting in user-centric authentication. At the same time, a third party can verify the user's legitimacy before providing services. Hence, our protocol makes the charging service available in a secure way, preserving the privacy of the user.
Xinxin Ge, Di Yang, Yuntong Lv, Fei Wang · 9 authors
Nowadays, Direct Power Purchase (DPP) for industrial users has become an important part of the electricity market. This type of transaction usually needs to rely on regulatory agencies to assist in the transaction, and the transaction algorithm is relatively simple. Blockchain technology based on encryption algorithm has the function of distributed ledger, which can make DPP more transparent and reliable. This paper proposes a DPP transaction method, which incorporates the peak shaving characteristics of industrial users and the green certificate system into transactions, and completes related transactions on the blockchain platform. First, establish a DPP market structure based on blockchain technology. Second, establish a DPP transaction mechanism based on green certificates and peak shaving characteristics. Simulation results for four cases show that the proposed method can respond to peak shaving capacity and bring more profit to renewable energy companies.
Using microgrids to charge Electric Vehicles (EVs) is a significant step toward achieving Electric mobility. The microgrids generate electricity for self-use and sell surplus energy locally in Peer-to-Peer (P2P) manner, where seller and buyer meet to trade electricity directly on agreed terms without any intermediary. The energy trading decision for the microgrid is a challenging issue due to uncertainty of renewable energy yield, the electric demands of EVs and without knowing the offers of other competitive microgrids together makes it hard to decide the selling price of energy per unit. Further, there is a need to audit and verify the energy trading and store energy transactions securely in distributed manner to avoid collapse of the system in case of single point of failure. In this context, this paper presents a Blockchain and Quantum Reinforcement Learning based optimized Energy Trading (BQL-ET) model for E-mobility. Firstly, a double-auction mechanism is proposed to set optimal market-trading price by observing the selling price of each microgrid and the demand of EV's. Secondly, using smart contracts, consortium blockchain is deployed for the evaluation of overall utility, which includes energy supply, demand, and cost for both microgrids and EVs. Finally, Utility maximization problem is transformed into a Markov Decision Process (MDP), and in order to develop the learning policy and maximize overall utility, a QRL optimization for solving the MDP problem is proposed. Convergence analysis and performance results attest that BQL-ET convergences faster, maximizes the utility of both microgrids an EVs with lower transaction confirmation time and setting of the optimal market-trading price compared to state-of-the art models.
While blockchain and distributed ledger technology offer immense potential for applications in transparency, security, efficiency, censorship resistance, and more, they have been criticized due to the energy-intensive nature of the proof of work consensus algorithm, particularly in the context of Bitcoin mining. We systematically explore the state-of-the-art regarding the relationship between Bitcoin mining and grid decarbonization. We specifically focus on the role of flexible load response through proof of work mining as a potential contributor to renewable energy penetration and net decarbonization of the energy grid. The existing literature has not comprehensively examined this area, leading to conflicting views. We address the gap, analyzing the capabilities and limitations of Bitcoin mining in providing flexible load response services. Our findings show that renewable-based mining could potentially drive a net-decarbonizing effect on energy grids, although key adaptations in mining practices are needed to fully realize this potential. Overall, the paper suggests a re-evaluation of the environmental impact of Bitcoin mining, highlighting its potential role as a facilitator for renewable energy expansion, and decarbonization more broadly.
Hongxu Huang, Zhengmao Li, L. P. Mohasha Isuru Sampath, Jiawei Yang · 8 authors
In this paper, a blockchain-enabled distributed market framework is proposed for the bi-level carbon and energy trading between coal mine integrated energy systems (CMIESs) and a virtual power plant (VPP) with network constraints. To maximize the profits of these two entities and describe their complicated interactions in the market, the bi-level trading problem is formulated as a Stackelberg game considering integrating the energy market and the “cap-and-trade” carbon market mechanism. Meanwhile, in the CMIES, energy recovery units and belt conveyors can be flexibly scheduled and the pumped hydroelectric storage in the VPP is scheduled for energy management. To tackle uncertainties from PV outputs, the joint trading, and the energy management is solved by the distributionally robust optimization (DRO) method. In addition, for participants' privacy, the alternating direction method of multipliers (ADMM) - based DRO algorithm is applied to solve the trading problem in a distributed framework. Further, the Proof-of-Authority (PoA) blockchain is deployed to develop a safe and anonymous market platform. Finally, case studies along with numerous comparison cases are conducted to verify the effectiveness of the proposed method. Simulation results indicate that the proposed method can effectively reduce the system operation cost and regional carbon emission, reduce the conservativeness and protect the privacy of each participant.
Renewable energy sources (RES) and electric vehicles (EVs) are widely recognized as primary ways to reduce carbon emissions and essential components of low-carbon power systems. However, both of them have strong uncertainties which bring great challenges to power transactions and the operation of power grids. This paper defines the uncertainty cost of wind power producer(WPP) in day-ahead(DA) market pricing based on information entropy theory for the first time and proposes a EV charging management strategy with DA contract. The constructed low-carbon emission electricity market(LCEM) quantifies the uncertainty cost and contracts the disordered charging of EVs. It reduces the uncertainty and ensures the balance of power supply and demand. In addition, the transaction between WPP and EVs is described as the Stackelberg game, and its communication network is constructed through a blockchain network to ensure transaction efficiency and privacy security. Experiments show that LCEM can accurately measure the uncertainty of wind power generation, increase the net profit for WPP by more than 2% when the prediction error is greater than 10%, and adopt EV contract charging management in the DA market to minimize charging costs.
The organization and management of electricity markets worldwide are rapidly evolving, moving towards decentralized, distributed, and renewable energy-based generation with solutions based on real-time data exchange. A Vehicle-to-Vehicle (V2V) energy trading has emerged as one of the most promising alternatives for relieving the load imposed on the traditional grid enabling two individuals to buy and sell energy directly without intermediaries. However, the Internet of Electric Vehicles (IoEV) environment is trustless, and such P2P energy trading is prone to different kinds of cyber attacks. Blockchain technology has lately been proposed to implement V2V energy trading to securely and fairly share energy. The consensus mechanism is one of the most important modules of blockchain applied to the V2V network. It determines the efficiency and security among untrustworthy EVs of the energy trading blockchain (ETB). Nevertheless, most works on ETB have currently adopted traditional consensus mechanisms. Due to high computing power and communication overhead, these consensus algorithms are unsuitable for applications requiring real-time services such as energy trading. The efficient and secured Hashgraph is the revolutionary technology of consensus in blockchain and a promising technology suitable for V2V energy trading with frequent transactions. However, Hashgraph does not support the dynamic addition and deletion of nodes and is completely decentralized and vulnerable to Sybil Attack in a large-scale blockchain. Furthermore, this “complete decentralization” model may result in states losing the ability to macro-control the energy industry and even systemic energy security issues. Therefore, we propose a Block Alliance Consensus (BAC) mechanism to solve these problems. BAC can maintain the throughput of Hashgraph and resist Sybil Attack in a large-scale P2P energy trading network. We design a cryptography-based leader election mechanism and adopt a reputation incentive mechanism to motivate honest and cooperative electric vehicles (EVs). Finally, we implement ETB and the BAC consensus mechanism on the Hyperledger Fabric platform. The high efficiency and security of BAC and the blockchain-based V2V energy trading platform are verified through experiments.
Ümit Cali, Ugur Halden, Murat Kuzlu, Marco Pasetti · 8 authors
Power systems are evolving rapidly with the integration and active use of emerging digitalization technologies, including but not limited to Artificial Intelligence, Blockchain / Distributed Ledger Technology (B/DLT), and other Information and Communication Technologies. Besides the active integration of digitalization technologies into modern power systems, decarbonization efforts are also shaping the energy transition process to be further demystified under the Digital Green Shift convention in a joint manner. B/DLT appears to be one of the most promising enabling technologies to realize the full energy transition toward next-generation power markets and systems. Standardization of such technologies is playing a critical role in the rapid adoption of emerging technologies. This article aims to reflect the industrial and academic perspectives of practical approaches of B/DLT in the Energy Domain and the potential contributions of the proposed scope in terms of climate change and sustainability aspects.
Tao Zhang, Jianhua Yang, Kaiyuan Jin, Tianjun Jing
Abstract With the increasing penetration of distributed photovoltaic generation (DPVG) in the rural distribution network, some problems such as abandoning solar energy and increasing voltage fluctuation have become more and more prominent. In order to promote local or nearby power consumption of the photovoltaic (PV) generation in the distribution network, the profit strategy is given with an optimal electricity price based on blockchain technology (BCT) by considering the topology, the load fluctuation of the distribution network and the reputation value of agricultural park consumers. The electricity price in the blockchain (BC) transaction is used as an intermediate variable to establish the revenue model for the PV generation, the agricultural park consumers and the distribution network. The power trading mechanism of the PV generation is constructed in the decentralized management agency of the BC. The maximum overall comprehensive income of the PV generation and the park consumers is used as the objective function, and the Stackelberg game theory is applied to prove the existence of the optimal game strategy in the transaction. The local power consumption method of the PV generation is simulated with the optimal electricity price in the IEEE 33‐node distribution network. The problem of abandoning solar energy is effectively relieved within an appropriate voltage limitation, and the comprehensive benefit of the PV generation and the park consumers is raised in the distribution network.
This paper describes a blockchain-based approach for sharing data among all the actors involved in Vehicle-to-Grid programs. The shared information is used both for monitoring the health status of the vehicle’s battery and for remuneration in Vehicle-to-Grid programs. A blockchain platform and a set of appropriate smart contracts have been developed for supporting the interactions among the different actors and for creating battery usage profiles. To protect sensitive data, we limit the visibility scope of such data only to subsets of actors using blockchain channels. This approach preserves the privacy of the car owner on one hand and guarantees the compliance to correct charging and discharging modes, on the other hand, by using dedicated smart contracts to manage data for each of the channels. The approach here described proposes and develops a blockchain-based ecosystem for efficiently managing electric vehicles especially when these are called to provide electrical grid services, such as it happens for Vehicle-to-Grid programs. In this case, it is indeed possible that the battery undergoes an accelerated aging and thus certified recording of charging data for legal liability is one of the issues here dealt with. When possible, data are collected from multiple sources (e.g. the charging station and the Electric Vehicle) and from different parties with conflicting interests for validating data and breaking data silos that are within the car, the charging stations and the Battery Management System.
Liaqat Ali, M. Imran Azim, Jan Peters, Vivek Bhandari · 8 authors
This paper presents the application of a community battery energy storage system (CBESS)-integrated microgrid (MG) in a blockchain-enabled local energy market (LEM). The proposed LEM balances the community energy requirement while facilitating frequent peer-to-peer (P2P) energy transactions between several energy users in the presence of both energy supplier and energy operator. The architecture is formulated by taking a number of local market and network constraints, that include residential battery energy storage system (RBESS) constraints; CBESS constraints; P2P traded price constraints; P2P traded power constraints; margin constraints of the stakeholders; power grid export and import constraints; and network energy balance constraints, so as to not only incentivise energy users but also reduce import/export from/to power grid while keeping the margins of energy supplier and energy operator unaffected. Different types of transactions data including energy users’ P2P pricing bids and P2P traded energy volume are also stored in the blockchain database. Further, the developed LEM framework is also validated through a case study executed on an actual Australian power grid network, comprising 260 residential energy users; two energy suppliers; an energy operator; and a CBESS, and the performance of the proposed P2P trading-based LEM strategy is compared with the existing business-as-usual (BAU) that directs energy users to buy/sell energy at the time-of-use (ToU)/ feed-in-tariff (FiT) rate. The extensive and comparative simulation results confirm the superior performance of the proposed LEM mechanism in terms of minimising energy users’ electricity bill; lowering power grid import and export; and retaining margins of energy suppliers and the energy operator; and thus, emphasise its application suitability in the current electricity market.
Godwin C. Okwuibe, Thomas Brenner, Peter Tzscheutschler, Thomas Hamacher
Local energy markets (LEMs) provide opportunity to handle the challenges arising from the lower grid level while using the traditional top-down approach to manage distributed generated renewable energy resources. Blockchain based local energy markets (LEMs) have been introduced in recent years as a way to enable local consumers/prosumers to trade their energy locally in a distributed and highly secured manner in an LEM. However, there are still some challenges regarding the main factors that can drive local consumers/prosumers to participate in a blockchain based LEM, optimal community size and prosumer to consumer ratio for an efficient LEM. Also, there is still no information on how the quantifying factors for participation on a blockchain based LEM can affect the performance of an LEM. This paper presents a survey and simulation based analysis of quantifying factors for participation in a blockchain based LEM. The survey was distributed among local consumers/prosumers and a total of 261 responses were received from the responders. The results from the responders were analyzed using a Python code based statistical analysis model. The simulation based analysis was conducted using a community based LEM model and evaluated using data received from a combination of German household profiles and standard load profiles. The survey results showed that the major drive for local consumers/prosumers to participate on blockchain based LEM is their willingness to support renewable energy integration, transparency and trust offered by a blockchain network. On the other hand, the simulation based analysis showed that small and medium communities with prosumers to consumer ratios between 0.3 to 0.5 create more economic and technical benefits for local consumers/prosumers compared to large communities. The community based simulation results were modelled together with the survey results to determine how the individual quantifying factors for participating in a blockchain based LEM can affect the performance of an LEM.
Electric vehicles (EVs) and blockchain are the two fast-growing technologies in the current scenario. Both can solve several existing problems around us and can contribute significantly to the future. Features offered by blockchain are now taking Electric Vehicles to next level of security and safety. As EVs are a prominent option for solving global warming issues and reducing carbon footprinting to encourage sustainable development. The current challenges faced by EVs are the capacity to hold a charge, the availability of charging stations, and the time required for charging. The availability of charging stations and the time required for charging EVs are two major issues that are holding EVs back. In this paper, we are proposing a blockchain-based framework to address the issue of scheduling at charging stations. Blockchain ensures trust, privacy, secure transactions, and unbiased allocation of charging ports for charging. We also proposed a new charging scheduler algorithm inspired by the process scheduling algorithms used in scheduling CPUs. The quantitative analysis result shows that the proposed framework is efficient and can significantly reduce the waiting time of electric vehicles at charging stations.
Efficient aggregation of distributed charge-discharge loads is a key method to realize the value utilization of energy storage resources of electric vehicle power batteries. In order to adapt to the high-dimensional and complex trend brought by large-scale electric vehicles connected to the grid, the intelligent contract technology is introduced in this paper. Based on the whole-process modeling of charging and discharging behavior of electric vehicles, the boundary of electric vehicles convergent operation is established, and then the hierarchical scheduling optimization method of electric vehicles load is proposed. The effectiveness of the model was verified by analyzing three typical scenarios of disordered charging, ordered charging and V2G ordered charging and discharging.
Wenshuai Ma, Junjie Hu, Yao Li, Zhuoming Fu · 6 authors
Abstract With global concerns about carbon emissions, the proportion of renewable energy generation worldwide is increasing, and the demand for flexible resources in power systems is growing. In recent years, as a clean means of transportation, the number of electric vehicles has increased, and the optimal scheduling of electric vehicles has become a research hotspot. The rise of artificial intelligence, blockchain, and other innovative technologies has enriched research on optimal scheduling of electric vehicles. To reveal the latest developments in electric vehicle optimal scheduling studies, this paper summarises the application of state‐of‐the‐art technologies, including deep learning, deep reinforcement learning, and blockchain technology in the optimal scheduling of electric vehicles. Moreover, the advantages and disadvantages of various technical applications are highlighted. Finally, considering the shortcomings and developmental status of applications of the above three technologies, some suggestions for future research directions are proposed.
Abhinav Rakesh Chopra, Nirmal‐Kumar C. Nair, Rizki Dian Rahayani
VA3, is a novel web3.0 based individual peer-to-peer platform for electricity settlement between individual New Zealanders with the ability to trade at a sub-household level i.e., multiple accounts within a single home or a portable account per person. We were able create a web3 application based on the base Ethereum network to successfully automate at individual person’s power consumption and production which is fed into a smart contract using a software update to modify the home router to basically act as a home energy management system and then settlements are automatically managed by the smart contracts.