Muhammad Umar Javed, Nadeem Javaid, Abdulaziz Aldegheishem, Nabil Alrajeh · 6 authors
In this work, Electric Vehicles (EVs) are charged using a new and improved charging mechanism called the Mobile-Vehicle-to-Vehicle (M2V) charging strategy. It is further compared with conventional Vehicle-to-Vehicle (V2V) and Grid-to-Vehicle (G2V) charging strategies. In the proposed work, the charging of vehicles is done in a Peer-to-Peer (P2P) manner; the vehicles are charged using Charging Stations (CSs) or Mobile Vehicles (MVs) in the absence of a central entity. CSs are fixed entities situated at certain locations and act as charge suppliers, whereas MVs act as prosumers, which have the capability of charging themselves and also other vehicles. In the proposed system, blockchain technology is used to tackle the issues related with existing systems, such as privacy, security, lack of trust, etc., and also to promote transparency, data immutability, and a tamper-proof nature. Moreover, to store the data related to traffic, roads, and weather conditions, a centralized entity, i.e., Transport System Information Unit (TSIU), is used. It helps in reducing the road congestion and avoids roadside accidents. In the TSIU, an Inter-Planetary File System (IPFS) is used to store the data in a secured manner after removing the data’s redundancy through data filtration. Furthermore, four different types of costs are calculated mathematically, which ultimately contribute towards calculating the total charging cost. The shortest distance between a vehicle and the charging entities is calculated using the Great-Circle Distance formula. Moving on, both the time taken to traverse this shortest distance and the time to charge the vehicles are calculated using real-time data of four EVs. Location privacy is also proposed in this work to provide privacy to vehicle users. The power flow and the related energy losses for the above-mentioned charging strategies are also discussed in this work. An incentive provisioning mechanism is also proposed on the basis of timely delivery of credible messages, which further promotes users’ participation. In the end, simulations are performed and results are obtained that prove the efficiency of the proposed work, as compared to conventional techniques, in minimizing the EVs’ charging cost, time, and distance.
Indrasena R. Aenugu, Gomanth Bere, Justin J. Ochoa, Taesic Kim · 6 authors
This paper proposes a blockchain-powered battery data management and analytics platform which fully utilizes blockchain technology for battery health monitoring in battery energy storage systems (e.g., electric vehicles) and accelerating battery cell development. The proposed platform consists of five distinct components: 1) blockchain clients using application SDK such as battery energy storage systems and a battery tester; 2) a multichannel blockchain network for enhanced data security, privacy, and management; 3) data preprocessing; 4) data analytics engine executing analytics tools and health monitoring algorithms; and 5) user-friendly service visualization. The proposed platform is implemented in an AWS cloud and tested using real-time battery data from a battery energy storage system (BESS) client and batch data from a battery cell tester client. The results show that each blockchain channels can independently manage and analyze data sources from two clients in a blockchain network. The proposed platform will enable a new level of data security and privacy preserved battery intelligence system used for both battery product development and lifetime battery health monitoring.
George Cristian Lăzăroiu, Mariacristina Roscia, Soheil Saadatmandi
Electric vehicles (EVs) are spreading more and more in Europe, thanks to CO2 standards, which require car manufacturers to reach an average sales share of 5% EV in 2020 and up to 10% in 2021 and close to 20% in 2025. To allow these new diffusion scenarios of electric vehicles, adjustments to the electricity grid are needed, including an increase in charging points and financing mechanisms. The spread of electric vehicles will contribute to urban sustainability, thanks to the delocalization of air pollution, the reduction of noise pollution, the implementation of the use of renewable sources in widespread generation. However, uncontrolled recharging could increase the peak load in the smart grid, which therefore requires distribution-level controls and correct planning for the recharging stations is needed in order to power the EVs avoiding network congestion. In fact, if electric vehicles are charged at the same time in an uncontrolled way, this would lead to an increase in energy demand, with the possible peak increase on the network, contributing to the overload and the need for updates at the distribution level, if not the need to adapt the generation capacity, with modified cost profiles. This opens new models for charging, business and regulatory systems, for managing the fleet of electric vehicles. This paper aims to analyze the new scenarios for Smart Cities that will be outlined from the point of view of tariff and regulatory systems.
Jun 1, 2020·2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Mostafa Kermani, Giuseppe Parise, Erfan Shirdare, Luigi Martirano
In the last decade, the importance of modern grids is more sensible than before due to provided higher efficiency, reduced peak demand, improved security resulting in the alteration of grid shape from conventional grids to smart grids. The case study is the port of Long Beach (POLB), placed in California, which consists of 11 independent piers operating as a single microgrid that has an independent energy management system. This paper proposes an integrated energy management strategy based on blockchain technology for the POLB including all piers that significantly reduce the amount of peak power imposing extra cost from the port manager's point of view. In addition, the benefits of smart grids that are operating based on blockchain technology, such as high-level security, and efficient maintenance cost, will be discussed.
The increasing role of renewables, together with the escalation of digital technologies and the pressure for a more active role of consumers and prosumers, are the natural basis for the development of Local Electricity Markets (LEM). The goal of this paper is to contribute to the current debate on LEM, drafting several proposals about key issues to be considered in outlining the LEM business model and market design. We take advantage of the ongoing project "NEMoGrid", which aims at defining and validating a prototype of LEM by integrating local PVs generation into the grid and with a peer-to-peer trading scheme. Transactions are settled on the Ethereum blockchain and the LEM is validated through onsite tests in Switzerland. Such tests are still running, therefore we use preliminary findings to make our suggestions, also highlighting several caveats and policy complexities. Keywords: local energy markets, peer-to-peer, renewable energy sources, electricity market design, electricity business models.
In small distribution systems, the "smart microgrid (μG)" concept is materialized for growing power savings and the allocation of energy distributed sources, and helping distribution system operators (DSOs) to choose the best investment strategies, to achieve a better grid operation, to increase the system efficiency, and to reduce adverse environmental impacts. In this context, the new specialized platforms for an advanced analysis and management of the energy market must be extended to the μG level, by reconsider of the actual grid infrastructures with "smart" μG clusters (μGC). The paper proposes a prosumers fair load sharing and surplus trading approach based on transactive energy concept in μG using an anonymous blockchain trading ledger-based clustering algorithm. In this way the trading process consider a new vision based on μGC for selection the trading peers' priority solved with Ward hierarchical algorithm. The developed method is tested on a real μG model to check its accuracy. Finally, an analysis regarding traded quantities and pecuniary peers' benefits is performed.
The proliferation of Internet of Things (IoT) has brought an array of different services, from smart health-care, to smart transportation, all the way to smart cities. For a truly connected environment, different sectors need to collaborate. One use case of such overlap is between smart grids and Intelligent Transportation System (ITS) giving rise to Electric Vehicles and their charging infrastructure. Being such a lucrative opportunity for investors and the research community, many efforts have been made toward providing the end-user with an extraordinary Quality of Service (QoS). However, given the current protocols and deployment of the Electric Vehicle (EV) charging infrastructure, some key challenges still need to be addressed. In particular, we identify two main EV challenges: (1) vulnerable charging stations and EVs, and (2) non-optimal charging schedules. With these issues in mind, we evaluate the integration of Blockchain and AI with the EV charging infrastructure. Specifically, we discuss the current AI and Blockchain charging solutions available in the market. In addition, we propose a couple of use cases where both technologies complement each other for a secure, efficient and decentralized charging ecosystem. This article serves as starting point for stakeholders and policymakers to help identify potential directions and implementations of better charging systems for EVs.
Jun 1, 2020·2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)
Our research aims to study, design and build the electronic payment system for electric vehicle (EV) charging using blockchain and smart contract technologies. Both technologies are used to control and manage payments and to decentralize the payment system, so the devices can automatically pay each other. In addition, our system lessens the inadequate of charging station for EV when travel in the long distance because our proposed let EV owners who also have charging facilities at their homes share their facilities and automatically get paid. Blockchain node is set up in charging station and is controlled by the application for EV owners. The application can show the charging information and control the payment and charging process automatically. For EV part, we simulated the data transmission between charging station and EV.
Xi Chen, Tianyang Zhang, Wenxing Ye, Zhiwei Wang · 5 authors
The rising proportion of renewable energy (RE) penetration with high variability introduces immense pressure on the stability of power grids. At the same time, a rapid increase in electric vehicle (EV) penetration level leads to uncoordinated charging loads, which poses significant challenges to operators. By properly guiding and scheduling the charging behaviors, EV may no longer be a burden, but a valuable asset to mitigate the RE integration problem. In this brief, we first propose a prioritization ranking algorithm of EV drivers based on their driving and charging behaviors, and then we propose a blockchain-based EV incentive system to maximize the utilization of RE. The proposed system is secure, anonymous, and decentralized. By incorporating the utilities, EV drivers, EV charging service providers, and RE providers into the proposed incentive system, this brief provides a plan to guide the EV users to charge at the desired time frames with higher RE generation. The market mechanism of the incentive system is discussed. The effectiveness of the system is verified by simulation.
Electric Vehicles (EVs) have generated a lot of interest in recent years, due to the advances in battery life and low pollution. Similarly, the expansion of the Internet of Things (IoT) allowed more and more devices to be interconnected. One major problem EVs face today is the limited range of the battery and the limited number of charging or battery swapping stations. A solution is to not only build the necessary infrastructure, but also to be able to correctly estimate the remaining power using an efficient battery management system (BMS). For some EVs, battery swapping can also be an option, either at registered stations, or even directly from other EV drivers. Thus, a network of EV information is required, so that a successful battery charge or swap can be made available for drivers. In this paper two blockchain implementations for an EV BMS are presented, using blockchain as the network and data layer of the application. The first implementation uses Ethereum as the blockchain framework for developing smart contracts, while the second uses a directed acyclic graph (DAG), on top of the IOTA tangle. The two approaches are implemented and compared, demonstrating that both platforms can provide a viable solution for an efficient, semi-decentralized, data-driven BMS.
Ifiok Anthony Umoren, Syeda Sanober Ali Jaffary, Muhammad Zeeshan Shakir, Konstantinos Katzis · 5 authors
This article presents a blockchain-based scheme for energy trading between electric vehicles (prosumers) and critical load (consumer) in a logical network. Unlike traditional wholesale energy markets where retailers sell energy to consumers, our proposed model directly connects prosumers with consumers to meet temporary energy demands. We exploit blockchain technology to establish a trusted energy trading ecosystem and develop an application to remotely monitor energy trading activities between trading entities. Experimental results illustrate that the energy trading system is effective in finding, associating, and routing prosumers to consumers, while protecting privacy of entities. Numerical results show a favorable performance of our optimization model in comparison to traditional frameworks.
To introduce the opportunities brought by plug-in hybrid electric vehicles (PHEVs) to the energy Internet, we propose a local vehicle-to-vehicle (V2V) energy trading architecture based on fog computing in social hotspots and model the social welfare maximization (SWM) problem to balance the interests of both charging and discharging PHEVs. Considering transaction security and privacy protection issues, we employ a consortium blockchain in our designed energy trading architecture, which is different from the traditional centralized power systems, to reduce the reliance on trusted third parties. Moreover, we improve the practical Byzantine fault tolerance (PBFT) algorithm and introduce it into a consensus algorithm, called the delegated proof of stake (DPOS) algorithm, to design a more efficient and promising consensus algorithm, called DPOSP, which greatly reduces resource consumption and enhances consensus efficiency. To encourage PHEVs to participate in V2V energy transactions, we design an energy iterative bidirectional auction (EIDA) mechanism to resolve the SWM problem and obtain optimal charging and discharging decisions and energy pricing. Finally, we conduct extensive simulations to verify the proposed DPOSP algorithm and provide numerical results for a comparison with the performance of the genetic algorithm and the Lagrange algorithm in achieving EIDA.
Electric Vehicles (EVs) have generated a lot of interest in recent years, due to the advances in battery life and low pollution. Similarly, the expansion of Internet of Things (IoT) allowed more devices to be interconnected. One major problem electric vehicles face today is the limited range of the battery and the limited number of charging or battery swapping stations. A solution is to not only build the necessary infrastructure, but also to be able to correctly estimate the remaining power, using an efficient battery management system (BMS). For some EVs, battery swapping can also be an option, either at registered stations, or even directly from other EV drivers. Thus, a network of EV information is required, so that a successful battery charge or swap can be made available for drivers. In this paper, a blockchain implementation for an EV BMS is presented, using the IOTA tangle as the network and data layer of the application.
Syed Muhammad Danish, Kaiwen Zhang, Hans‐Arno Jacobsen
The untrusted centralized nature of energy markets and electric vehicle (EV) charging infrastructures result in several privacy and security threats to the private information of EV users. These security and privacy threats include targeted advertisements, privacy leakage, selling data to third party, etc. In this work, we propose a blockchain-based privacy-preserving intelligent charging station (CS) selection for EVs to ensure the security and privacy of the EV users and availability of the CSs. We introduce a blockchain-based framework to implement secure charging services and trusted reservation for EVs through the execution of smart contracts. We also formulate the problem of privacy-preserving intelligent CS selection and propose a mechanism for EVs to select the optimal CS locally based on dynamic requirements. Finally, we present an example scenario of our proposed framework.
With ever increasing people's awareness of low carbon and environmental protection, electric vehicles are gradually gaining wide popularity. However, the driving endurance of the electric vehicle is the biggest shortage that hinders the fully acceptance of this new vehicle technology. To deal with this shortage, this paper proposed a vehicle-to-vehicle (V2V) electricity trading scheme based on Bayesian game pricing in blockchain-enabled Internet of vehicles (BIoV). Specifically, the Bayesian game is adopted for pricing in the distributed BIoV with incomplete information sharing. The optimal pricing under the linear strategic equilibrium has been obtained which maximizes the utilities of both sides of electricity transaction. The transaction volume is determined from the formulated convex problem that maximizes the social welfare. Then, the pricing game is implemented by the dedicated smart contract. Blockchain guarantees its trustworthiness, security, and reliability. Finally, the experimental results show that referring to the benchmark of static game with complete information, the proposed Bayesian game with incomplete information can achieve approximate satisfaction of users. The degree of approximation can reach to 98% when the pricing ranges of buyers and sellers are close. Moreover, the proposed scheme has great advantages over the static game with complete information in terms of communication overhead and timeliness in the decentralized IoVs.
This article utilizes consortium blockchain to design a decentralized, secure, and privacy-preserving scheme for bidirectional power trading between electric vehicles (EVs) and the power grid. To reduce adverse effects induced by disordered charging of massive EVs to the power grid, we minimize the total load deviation through optimizing the charging and discharging time period of EVs. Our optimization scheduling is a large-scale mixed-integer programming problem of which the number of variables and constraints are enormous. Hence, we propose to adopt the heuristic algorithm, an improved krill herd (KH) algorithm to solve it. Simulation results indicate that our model can effectively smooth the load fluctuations, and improved KH can improve the rate and accuracy of solving this model effectively. Implementation of Hyperledger Fabric evaluates the performance and scalability of our scheme. Qualitative security and privacy analysis demonstrate that our scheme helps to improve the security and privacy of power trading.
With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.
Yuris Mulya Saputra, Diep N. Nguyen, Dinh Thai Hoang, Thang X. Vu · 6 authors
In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively.
Zahra Foroozandeh, Sérgio Ramos, João Soares, Fernando Lezama · 7 authors
Efficient alternatives in energy production and consumption are constantly being investigated and conducted by increasingly strict policies. Buildings have a significant influence on electricity consumption, and their management may contribute to the sustainability of the electricity sector. Additionally, with growing incentives in the distributed generation (DG) and electric vehicle (EV) industries, it is believed that smart buildings (SBs) can play a key role in sustainability goals. In this work, an energy management system is developed to reduce the power demands of a residential building, considering the flexibility of the contracted power of each apartment. In order to balance the demand and supply, the electrical power provided by the external grid is supplemented by microgrids such as battery energy storage systems (BESS), EVs, and photovoltaic (PV) generation panels. Here, a mixed binary linear programming formulation (MBLP) is proposed to optimize the scheduling of the EVs charge and discharge processes and also those of BESS, in which the binary decision variables represent the charging and discharging of EVs/BESS in each period. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis are considered. The results point to a 65% reduction in peak load consumption supplied by an external power grid and a 28.4% reduction in electricity consumption costs.
The combined cooling, heating and power (CCHP) system is a typical distributed, electricity-gas integrated energy scheme in a community. First, it generates electricity by use of gas, and then exploits the waste heat to supply community with heat and cooling. In this paper, we consider a smart city consisting of a number of communities (CCHPs) and an agent of power grid (APG), where CCHPs can sell energy to the APG according to its bid. To study all utilities of entities in such a city from energy trading, a noncooperative Stackelberg game between APG and CCHPs is formulated. Here, the APG gives a bid for buying the energy from CCHPs, then CCHPs respond to the APG with their optimal energy supply that maximizing their utilities according to this bid. We show that the maximum profit to the APG and utilities to the CCHPs can be obtained at the Stackelberg equilibrium, which is guaranteed to exist and unique. Because the complete information about energy supply of each CCHP is unknown to the APG in advance, we propose a distributed algorithm that is able to find the point of equilibrium through a limited number of iterations. Taking privacy protection and transaction security into consideration, we design a blockchain-enabled energy management system. This system is composed of Internet of Energy (IoE) sub-system and blockchain sub-system, where the information interactions as well as energy transactions between APG and CCHPs can be carried out effectively and safely. Finally, security analysis and numerical simulations show the effectiveness and accuracy of our proposed mechanism.
Gonzalo Munilla Garrido, Daniel Miehle, André Luckow, Florian Matthes
The increase of renewable energy generated in certain countries has outpaced the expansion of their power grid, causing grid congestion. Currently, grid operators use flexibility measures to counter this challenge. However, these measures struggle to cope with the growth in renewables. There are numerous proposals to improve flexibility measures using distributed energy resources such as electric vehicles (EVs). However, there is a need for a platform whereby EVs can be leveraged directly by grid operators. In answer to the decentralized quality of EVs and the requirements defined by our automotive industry partner, we propose a platform based on a distributed ledger technology (DLT). To achieve this goal, we first designed a concept for a decentralized flexibility market for the stakeholders of the ecosystem. The concept serves as the blueprint for the implementation of the platform. With the design and its implementation and simulation, we validated the use case and technical feasibility of the chosen DLT. We conclude that our prototype has the potential to allow grid operators to leverage idle EVs in aggregation to mitigate congestion.
Gijs van Leeuwen, Tarek AlSkaif, Madeleine Gibescu, Wilfried van Sark
In this paper, an integrated blockchain-based energy management platform is proposed that optimizes energy flows in a microgrid whilst implementing a bilateral trading mechanism. Physical constraints in the microgrid are respected by formulating an Optimal Power Flow (OPF) problem, which is combined with a bilateral trading mechanism in a single optimization problem. The Alternating Direction Method of Multipliers (ADMM) is used to decompose the problem to enable distributed optimization and a smart contract is used as a virtual aggregator. This eliminates the need for a third-party coordinating entity. The smart contract fulfills several functions, including distribution of data to all participants and executing part of the ADMM algorithm. The model is run using actual data from a prosumer community in Amsterdam and several scenarios of the model are tested to evaluate the impact of combining physical constraints and trading on social welfare of the community and scheduling of energy flows. The scenario variants are trade-only, where only a trading mechanism is implemented, grid-only where only OPF optimization is implemented and a combined scenario where both are implemented. Results are compared with a baseline scenario. Simulation results show that import costs of the whole community are reduced by 34.9% as compared to a baseline scenario, and total energy import quantities are reduced by 15%. Total social welfare is found to be highest without a trading mechanism, however this platform is only viable when all costs are equally shared between all households. Furthermore, peak imports are reduced by over 50% in scenarios including grid constraints.