The upcoming development in vehicle to grid network (V2G) allows for the flow of energy from battery powered Electric Vehicle (EV) to grid as well as the exchange of information between them. However, during the information exchange, the EV’s confidential information should be transferred from one charging station to another in a secure manner. Furthermore, the anonymity of the EV and charging station should be preserved. Despite the fact that many works on anonymous authentication and privacy preservation exist, there is an increase in computational cost in existing surveys. In this work, the new charging station authenticates the EV using blockchain technology without the involvement of a trusted entity, resulting in a reduction in computational time. Moreover, an efficient revoking mechanism is suggested to block the misbehaving charging station from the V2G network. In addition, security analysis section proves the resistant of our work against several possible well known attacks. Finally, to evaluate the performance of the work, the simulation is performed using CYGWIN platform and the results are proved to be noteworthy.
Electric Vehicles (EVs), as well as the number of applications for their management, are rapidly increasing due to the fact that Internet of Vehicles (IoV) becomes more informative and industrialized. According to the literature, IoV requires decentralization towards the energy Demand Response (DR) management, with secure energy trading, efficient charging scheduling, and incentives for making the drivers to participate. Blockchain and Distributed Ledger Technologies (DLTs) can be used to enable a foundational environment to support DR management. However, and to the best of our knowledge, none of the existing research works discussed in the literature adopts a holistic approach to address them. As such there is a demand for exploring a unified blockchain-based framework for distributed DR management. Therefore, the aim of this work is focused on the blockchain adoption in IoV-assisted smart cities. More specifically, we propose a blockchain based approach, to be used for building a secure and user-centric DR management framework. Our proposition aims to address the DRP in IoV through charging scheduling based on the generation of EV driving profiles, and optimal Vehicle-to-Vehicle/Grid (V2V | V2G) energy trading.
H. Martins, H. Farias, G. Fenner, Camilo Albeto Sepulveda Rangel · 6 authors
This paper presents a comprehensive model for electric vehicle chargers (EVC) focused on scheduling loads in electric vehicle charging stations (EVCS). It also applies practical information from data gathered by Open Charge Point Protocol (OCPP) for validation of results. The scheduling strategy applies an evolutionary particle swarm optimization (EPSO) metaheuristic to set the hours for charge of the EV. The battery charging model combines the Kinetic Battery Model (KiBaM) and a Voltage Model (VM). A practical validation for the battery model accuracy is made with real data gathered by OCPP. The results showed a good operation for the framework in the EVCS in terms of economic cost and grid impact.. The results also showed a good performance for the battery model. Finally, the OCPP confirmed the results of the model with low errors in terms of performance.
Electric vehicles (EVs), one of the most effective solutions to reduce gas emission and realize fossil fuels replacement, are enjoying growing popularity from governments to customers. The development of EVs leads to significant advances in vehicle automation and electrification, but meanwhile poses additional heavy charging and data processing burden on current smart grid. Considering the mutual demand and supply relationship between EVs and smart grid in both charging and computing tasks, we integrate vehicular fog computing (VFC) and smart EV charging for joint optimization and propose an integrated charging and computing (IC2) architecture for EV-included smart grid. In the proposed IC2 architecture, charging stations are profit-driven third-party power prosumers that also help compute tasks offloaded by smart grid while EVs act as both energy consumers and computation providers. We employ the contract theory to provide a multiattribute contract-based charging protocol for EVs and charging stations in an information asymmetry scenario. To obtain the optimal contract, we derive KKT conditions and design a convex–concave-procedure-based contract optimization algorithm. We also design a heuristic offloading algorithm to assign heterogeneous tasks toward different EVs. Numerical results indicate that the proposed multiattribute contract-based charging-computing scheme can effectively benefit both the charging stations and EVs, and meanwhile improves the task computation capability in EV-integrated smart grid.
Sudeep Tanwar, Riya Kakkar, Rajesh Gupta, Maria Simona Raboacă · 7 authors
The adaptation of intelligent transportation system has evolved the quality of life of people with the huge demand for electric vehicles. Moreover, it becomes essential to schedule an electric vehicle for charging optimally. Therefore, this paper proposes a blockchain-based electric vehicle charging reservation scheme for optimum pricing. It primarily aims to secure data transactions between electric vehicles and charging stations. It uses the communication channel as 5G to ensure ultra-low latency and extremely high reliability. Furthermore, the proposed scheme uses a double-auction mechanism to optimize the payoff for both electric vehicles and charging stations. The performance of the proposed scheme is evaluated by distinguishing it from the conventional networks such as 4G and LTE-A. The performance parameters are considered profit and loss for electric vehicles, scalability, cost overhead for data storage, communication overhead for data transactions, and data storage cost. Results show that the proposed scheme is secure and achieves the optimized payoff for electric vehicles and charging stations compared with traditional approaches.
As a representative of clean energy, photovoltaic is expected to become a major supplier of electricity in the future. The combination of electric vehicle (EV) battery and charging station provides a feasible way to promote the effective consumption of photovoltaic. However, the efficiency of mobile power supply is limited by information asymmetry and security problems, and it is urgent to optimize the distribution process. Firstly, the article introduces the energy blockchain to improve the security level of electricity transaction, and designs the photovoltaic-energy storage-charging supply chain. Secondly, based on the selected road network and the actual situation of EV mobile power emergency distribution, the distribution logistics network with 50 distribution points is built. Thirdly, taking the delivery time and comprehensive cost as objective functions, the mathematical model of emergency distribution route optimization for EV mobile power supply is established, and the adaptive NSGA-II algorithm is adopted for example analysis. Finally, the parameter variation of NSGA-II and comparison with two algorithms of GA and MOPSO are carried out to validate the feasibility and applicability of proposed method. The purpose of the research is to quickly and effectively select the optimal distribution route of mobile power supply from many roads by maximizing customer demands and reducing costs, so as to promote the photovoltaic consumption.
Riya Kakkar, Smita Agrawal, Rajesh Gupta, Sudeep Tanwar
This paper proposes a zero-sum game theory and blockchain-based secure and decentralized dynamic pricing scheme for electric vehicle charging. It aims to secure data sharing between electric vehicles and charging stations. We integrate the sixth-generation (6G) communication network to enable data transactions between electric vehicles and charging stations with low latency and high reliability. We employ a zerosum game theory approach to maximize the payoff of electric vehicles and charging stations. The performance of the proposed system with 6G is evaluated by comparing it with 5G and 4G traditional networks. The performance evaluation of the proposed system has been analyzed with various parameters latency, profit for electric vehicles, profit for charging station, and optimal payoff of the system. The results show that the proposed system is highly secure and reliable than traditional systems.
In recent years, advancements in electric vehicle (EV) technology and rising petrol prices have increased the demand for EVs and also made them important for the Smart Grid (SG) economy. During the high energy demand, Vehicle to Grid (V2G) comprises a notable feature that returns the stored energy back to the grid. However, due to dynamic nature of energy prices and EVs availability, determining the best charging and discharging strategy is quite difficult. The existing approaches need a model to predict the uncertainty and optimize the scheduling problem. Further, other issues like security, scalability, and real-time data accessibility of EVs energy trading (ET) data at low cost also exist. Though many solutions exist, they are not adequate to handle the aforementioned issues. This paper proposes a Secure V2G-Energy Trading (SV2G-ET) scheme using deep Reinforcement Learning (RL) and Ethereum Blockchain Technology (EBT). The proposed SV2G-ET scheme employs a deep Q-network for EVs scheduling for charging/discharging. SV2G-ET scheme uses InterPlanetary File System (IPFS) and smart contract (SC) for secure access of EV’s ET data in real time. The experimental results prove the efficacy of the proposed SV2G-ET scheme that leads to improved scalability, saving the EVs charging cost, low ET data storage cost, and increased EV owner’s profit.
Energy management in the Internet of Vehicles (IoV) is becoming more prevalent as the usage of distributed Electric Vehicles (EV) grows. As a result, Demand Response (DR) management has been introduced to achieve efficient energy management in IoV. Through DR management, EV drivers are allowed to adjust their energy consumption and generation based on a variety of parameters, such as cost, driving patterns and driving routes. Nonetheless, research in IoV DR management is still in its early stages, and the implementation of DR schemes faces a number of significant hurdles. Blockchain is used to solve some of them (e.g., incentivization, privacy and security issues, lack of interoperability and high mobility). For instance, blockchain enables the introduction of safe, reliable and decentralized Peer-to-Peer (P2P) energy trading. The combination of blockchain and IoV is a new promising approach to further improve/overcome the aforementioned limitations. However, there is limited literature in Demand Response Management (DRM) schemes designed for IoV. Therefore, there is a need for a systematic literature review (SLR) to collect and critically analyze the existing relevant literature, in an attempt to highlight open issues. Thus, in this article, we conduct a SLR, investigating how blockchain technology assists the area of DRM in IoV. We contribute to the body of knowledge by offering a set of observations and research challenges on blockchain-based DRM in IoV. In doing so, we allow other researchers to focus their work on them, and further contribute to this area.
The main roles of an advanced Battery Management System (BMS) are to dynamically monitor the battery packs and ensure the efficiency and reliability of the Battery Energy Storage System (BESS). Estimating the State of Charge (SoC), State of Health (SoH), State of Power (SoP), State of Energy (SoE), State of Temperature (SoT), and State of Safety (SoS) depends on collecting, aggregating, and analyzing real-time data of the BESS. Based on the applications of BESSs and their sizes, there are several restrictions in accessing and sharing data between battery manufacturers, power grid operators, and electricity consumers, while building necessary communication infrastructure is required. To resolve such issues, a conceptual and technological Blockchain-based system is developed in this paper to securely share the real-time data collected from BESSs for monitoring and control purposes, i.e., state estimation. The proposed system benefits from integrating the Internet of Things (IoT) devices in a decentralized structure and connectivity of such IoT nodes, data privacy, and transparency and auditability. The proposed Blockchain-based system is capable of accurately estimating SoC, SoH, SoP, SoE, SoT, and SoS of BESSs in small-and grid-scales.
Summary In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchain‐based secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing state‐of‐the‐art techniques.
Decarbonisation of energy sector is crucial to deliver the future net zero energy system with promoting and facilitating the large-scale electrification of end-user sectors. It is necessary to provide sustainable, cost-effective, resilient and scalable energy solutions to exploit the power of citizens to contribute to the clean energy transition, increasing the flexibility of the overall energy system. Energy community, as the new actor, create an integrated pan energy market by bringing together the local consumers and energy market players. However, diversity of energy community brings huge challenges in integration of decentralized renewables with regulated framework, interaction of decentralized marketplaces, as well as interoperability of the cross-border energy sectors with privacy, security and incentives. This paper intends to provide an in-depth investigation on the role of microgrid and blockchain, alone and together, in facilitating the energy community as the “enabling framework” to boost the potential solutions of electrification in the transportation, building, and industrial sectors, as well as rural/remote areas and islands towards a networking green ecosystem. This paper serves as a comprehensive reference to understand the modern microgrid on its control and communication technology with integration of blockchain services in promoting the techno-socio-economic innovations for the restructuring of the sustainable energy supply chain.
Nathan Patrizi, Sara Kathryn LaTouf, Eirini Eleni Tsiropoulou, Symeon Papavassiliou
Modern smart grid systems exploit a two-way interaction paradigm between the utility and the electricity user and promote the role of prosumer, as a new user type, able to generate and sell energy, or consume energy. Within such a setting, the prosumers and their interactions with the microgrid system become of high significance for its efficient operation. In this article, to model the corresponding interactions, we introduce a labor economics-based framework by exploiting the principles of contract theory, that jointly achieves the satisfaction of the various interacting system entities, i.e., the microgrid operator (MGO) and the prosumers. The MGO offers personalized rewards to the sellers and buyers, to incentivize them to sell and purchase energy, respectively. To provide a stable and efficient operation point, while aiming at jointly satisfying the profit and requirements of the involved competing parties, optimal personalized contracts, i.e., rewards and amount of sold/purchased energy, are determined, by formulating and solving contract-theoretic optimization problems between the MGO and the sellers or buyers. The analysis is provided for both cases of complete and incomplete information availability regarding the prosumers’ types. Detailed numerical results are presented to demonstrate the operation characteristics of the proposed framework under diverse scenarios.
Haseeb Javed, Muhammad Irfan, Moazzam Shehzad, Hafiz Abdul Muqeet · 7 authors
Peer-to-peer (P2P) energy trading platform is an upcoming energy generation and effective energy managing strategy that rewards proactive customers (acting as prosumers) in which individuals trade energy for products and services. On the other hand, P2P trading is expected to give multiple benefits to the grid in minimizing the peak load demand, energy consumption costs, and eliminating network losses. However, installing P2P energy trading on a broader level in electrical-based networks presents a number of modeling problems in physical and virtual network layers. As a result, this article presents a thorough examination of P2P studies of energy trade literature. An overview is given with the essential characteristics of P2P energy trading and comparatively analyzed with multiple advantages for the utility grid and individual prosumers. The study then addresses the physical and virtual levels that systematically categorize the available research. Furthermore, the technological techniques have been gone through multiple problems that need to overcome for P2P energy trading in electrical networks. Finally, the article concludes with suggestions for further research.
The synergy of clean energy and electric vehicles (EVs) is highly relevant in achieving low-carbon development. To promote the coordinated development of EVs and photovoltaics (PV) under the background of open power selling, a PV–EV bidding model that considers carbon emissions based on the distributed storage and smart contract technology of a blockchain is proposed in this study. This model aims to provide economic benefits to market players, ensure the safety and environmental protection of power grid operations, and integrate the ladder-type carbon emission trading mechanism. Then, it divides the electricity market into the electricity sales layer, i.e., active distribution networks (ADNs) and PV systems, and the electricity purchase layer, i.e., EVs. Smart contracts are signed with the agent layer, i.e., charging stations. The improved particle swarm optimization algorithm is used to solve the price and power of PV systems and EVs in the optimal contract, and the revenue of PV systems and EVs can be increased by over 35%. Moreover, the orderly charging and discharging of EVs is coordinated with the characteristics of the daily power generation and evening load peaks of PV systems. The proposed model can effectively provide an optimal scheduling scheme for peak load shifting and valley filling while reducing the carbon emissions of ADNs. It also promotes the local consumption of PV.
Sebastian Finke, Michele Velenderic, Semih Severengiz, Martin Fortkort · 6 authors
Mobility as a Service (MaaS) is a rapidly growing market with a large potential for the transition of the mobility sector. Business models based on sharing economy in the mobility sector open many opportunities for providers and users to benefit from new types of transportation modes such as light electric vehicles (LEVs). However, there are deficits in the sustainability of sharing systems with such vehicles, which result primarily from the origin of the energy for charging, as well as from the charging processes and the infrastructure used for this purpose. The electricity must be generated from renewable energies and vehicles should ideally be put on charge by the user on-site in the operation area to avoid service rides by mobility service providers. Distributed Ledger Technology (DLT) can provide solutions for these issues: on the one hand, it can provide proof of origin (PoO) for the electricity; in addition, tokens that can be used to pay for vehicle usage can be generated and assigned to users to incentivize behaviors like charging the vehicles. In this paper a crypto token-based scheme for a PoO for renewable energy, as well as for user incentives to charge the vehicles to make shared mobility services more sustainable is described. The proposed solution includes a concept for a network backend, implemented as a private blockchain network using the open source blockchain solution Hyperledger Fabric. The backend will store data about the usage of LEVs and batteries as well as relevant data of charging and swapping stations. The user incentive tokens and smart contracts for transaction automatization are stored in the backend. Furthermore, a front end consisting of two applications: one for users and stakeholders respectively, is part of the solution.
The Internet of Vehicles (IoV), where people, fleets of electric vehicles (EVs), utility, power grids, distributed renewable energy, and communications and computing infrastructures are connected, has emerged as the next big leap in smart grids and city sectors for a sustainable society. Meanwhile, decentralized and complex grid edge faces many challenges for planning, operation, and management of power systems. Therefore, providing a reliable communications infrastructure is vital. The fourth industrial revolution, that is, a cyber-physical system in conjunction with the Internet of Things (IoT) and coexistence of edge (fog) and cloud computing brings new ways of dealing with such challenges and helps maximize the benefits of power grids. From this perspective, as a use case of IoV, we present a cloud-based EV charging framework to tackle issues of high demand in charging stations during peak hours. A price incentive scheme and another scheme, electricity supply expansion, are presented and compared with the baseline. The results demonstrate that the proposed hierarchical models improve the system performance and the quality of service (QoS) for EV customers. The proposed methods can efficiently assist system operators in managing the system design and grid stability. Further, to shed light on emerging technologies for smart and connected EVs, we elaborate on seven major trends: decentralized energy trading based on blockchain and distributed ledger technology, behavioral science and behavioral economics, artificial and computational intelligence and its applications, digital twins of IoV, software-defined IoVs, and intelligent EV charging with information-centric networking, and parking lot microgrids and EV-based virtual storage. We have also discussed some of the potential research issues in IoV to further study IoV. The integration of communications, modern power system management, EV control management, and computing technologies for IoV are crucial for grid stability and large-scale EV charging networks.
Due to its highly energy-intensive process, Bitcoin has attracted the global attention of climate research and media. At the time of this submission, behind-the-meter Bitcoin mining has gained significant traction; however, not a single environmental impact assessment has been conducted on this type of operation. This study seeks to fill the gap, applying the established Life Cycle Assessment methodology to estimate the environmental footprint of a single case study. A life cycle assessment methodology of a natural gas power plant mining Bitcoin behind-the-meter in the state of New York following the ISO 14040 guidelines was applied. The functional unit (FU) is defined as the attributed generation capacity of 14 MW over the course of a regular full-calendar year in the attributional model. The FU is scaled to 22 MW and 104 MW in the predictive models to represent planned expansion. The TRACI 2.1 method was applied to characterize the environmental impact. The environmental impact categories considered in this study included global warming, acidification, smog formation, and particulate emissions. Located in New York State, Greenidge LLC, a natural gas power plant produces an estimated 88,440 metric tons of CO 2 -eq per year to mine Bitcoin behind-the-meter. Annual emissions would total 656,983 metric tons of CO 2 -eq if the plant devotes 100% of its generation to Bitcoin mining. The primary driver of greenhouse gas emissions is the generation of electricity itself, accounting for ~ 79% of the total emissions. At full capacity, annual emissions are comparable to the annual emissions of 140,000 passenger vehicles or the emissions resulting from the burning of 600 million lb of coal. Further, additional planned cases could produce an estimated 1.9 million tons tCO 2 -eq per annum. Behind-the-meter Bitcoin mining makes the power plant a significant contributor to global warming at a time when New York State is attempting to radically reduce its greenhouse gas emissions by 85% by 2050 and to have 100% carbon-free electricity by 2040. The environmental impact of this business model is not limited to individual sites but is spread out over upstream impacts as well. In combination, we see that behind-the-meter Bitcoin mining not only goes against local climate initiatives but also poses a significant danger to national initiatives due to feasible scalability, caused by an availability of existing infrastructure and favorable financials.
Governments’ net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced.
In recent years, in order to reduce the consumption of petroleum energy and environmental pollution, China has actively promoted the development of new energy vehicles and achieved good results. Car ownership has surged year after year. However, the development of supporting infrastructure (charging pile) for electric vehicles is seriously backward, and the layout of electric vehicles and charging piles is extremely uneven. The difficulty of charging has gradually become the main factor restricting the development of electric vehicles. Block-chain technology has the technical advantages of decentralization, traceability and non tampering, and is widely used. The proposed blockchain-based charging piles maintenance system is able to provide an end-to-end transparent and high reliability management mode for multi-agents including the charging pile, power distribution station, maintenance, calibration and supervision institutions. In order to propose this method, we first design realization of electric vehicle charging pile sharing system and decentralized scheduling model. Then we design functions of the blockchain based charging pile maintenance system. At last, we provide simulation results and analysis to verify the efficiency of our proposed method.
Rui Ma, Zhehan Yi, Yingmeng Xiang, Di Shi · 6 authors
The rapid development of Internet-of-Things in smart grid has enabled millions of grid-connected distributed controllable resources (DCR; e.g., electric vehicles, controllable loads) to provide service to the grid, such as frequency regulation and demand response. The integration of these DCRs may become a large virtual power plant network with various characteristics. This poses great challenges from both control and management perspectives, e.g., computation/communication burden, optimization complexity, scalability limitation, prosumer privacy, etc. In this article, we propose an effective autonomous incentive-based DCR control and management framework to integrate a large amount of DCRs to provide grid services, which simultaneously provides accurate active power adjustment to the grid, optimizes DCR allocations, and maximizes the profits for all prosumers and system operators. A model-free deep deterministic policy gradient-based method is designed to find the optimal incentives in a continuous action space to encourage prosumers to adjust their power consumptions. The method is implemented in a consortium open-source blockchain platform, Hyperledger Fabric, which facilitates controls and transaction management. To demonstrate the effectiveness of the framework, extensive experimental studies are conducted using real-world data.
Lei Zhang, Long Cheng, Fahad Alsokhiry, Mohamed A. Mohamed
This paper proposes a secure management framework for optimal scheduling and operation of energy systems in smart cities, which will bring balance in modern power systems. The expanded infiltration of energy systems along with stochastic performance and high data transfer rate can bring new challenges that may severely affect precise energy management. This research work investigates the optimal and secure operation of energy systems in the presence of smart transportation systems in a correlated energy system. To achieve the maximum efficiency from the transportation system, the ideas of vehicle-to-subway (V2S) and vehicle-to-grid (V2G) are utilized to develop a bilateral power flow strategy (charging/discharging or idle mode) to provide the chief charging requirements of energy systems. In this regard, the proposed model suggests a novel stochastic architecture based on unscented transformation (UT) to handle the operational uncertainties of the transportation systems when considering the correlated power generation of wind turbines (WTs). Moreover, blockchain technology is deployed to guarantee the security of data transfer within the smart city. Hence, there is a secure collaboration within all sub-systems in the smart cities, which has triggered the integrated management in this structure. The simulation results are discussed on a typical smart city test system to show the quality of the proposed model.