N. V. Ravindhar, A. Manju, S. Murugesan, T. K. S. Rathish Babu
Electric Vehicle-to-Grid (V2G) arrangements stand at the center of bidirectional energy exchange in modern smart grids and are, however, challenged by real-time decision-making, load balancing, and the security of transaction validation. This paper has proposed an energy-efficient optimization framework based on a Bio-Inspired Deep Learning Controller using a Monarch Butterfly Optimization (MBO) algorithm with Gated Recurrent Unit (GRU) network for optimizing charging and discharging schedules across EV fleets. GRU networks forecast short-term grid demand and EV battery availability while MBO tunes the controller weights dynamically to adapt to scheduling under varying conditions. Furthermore, in order to maintain the trust over the transaction in a tamper-resistant fashion, a blockchain layer is embedded with the use of smart contracts to keep a track of authentication, pricing, and energy transfer log records for V2G. The proposed system shows charging cost reduction of 19.6%, peak load shaving efficiency of 23.2%, and forecast accuracy of 96.4%, in all mobility scenarios evaluated. The architecture also contributes to improving grid regulation response time by 28% and reducing EV queuing delay by 31%. Simulated by using MATLAB/Simulink, TensorFlow, and Ethereum-based blockchain, the architecture renders a scalable and secure framework for V2G coordination. It is noted that the findings are based on simulation, and co-simulation experiments, and the actual conditions of deployment like latency in communications, non-idealities of the hardware and regulatory factors are not factored into the analysis. Furthermore, the model facilitates real-time adaptation, strengthens grid resilience, and guides EV operation according to concurrent market conditions for energy.
This paper introduces an energy-efficient, Blockchain-Based, Dual-Agent, Reinforcement Learning (BDARL) model of resilient EV-grid integrative effect. All EV energy transactions and agent decisions are validated by the blockchain layer that is integrated through a lightweight proof-of-stake consensus mechanism, and this ensures the tamper-proof functioning and decentralized trust. The proposed framework is applying to a MATLAB/Simulink-Python TensorFlow-Hyperledger Fabric co-simulation environment where the performance analysis shows a 98.8 per cent Resilience Coordination Index (RCI), a 36.7 per cent Energy Efficiency Gain (EEG), a 31.5 per cent Load Stabilization Score (LSS), and a transaction latency of 25 ms. Compared to baseline DRL and non-blockchain schedulers, BDARL offers 7.9% improvement in terms of resilience, 8.4% in terms of energy efficiency, and 14 ms better convergence, so it provides a safe, sustainable, and smart paradigm of managing next-generation EV-grid synergy.
Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations.
Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much larger and smarter system with the help of emerging digitalization technologies, such as Artificial Intelligence (AI), Distributed Ledger Technology (DLT), and the Internet of Things (IoT). This study introduces a multi-layer Cyber–Physical Power Systems (CPPS) framework to explore the potential of V2X technologies allowing bidirectional charging. In addition, the impact of e-mobility is discussed from the V2X perspective. V2X has the potential to provide more practical use of electric vehicles and to bring advantages to the user in terms of both economy and comfort, thus accelerating the transformation of e-mobility and making it easier to accept.
The I-710 and CA-60 highways are key transportation corridors in the Southern California region that are heavily used on a daily basis by heavy duty drayage trucks that transport the cargo from the ports to the inland transportation terminals. These terminals, which include store/warehouses, inland-railways, are anywhere from 5 to 50 miles in distance from the ports. The concentrated operation of these drayage vehicles in these corridors has had and will continue to have a significant impact on the air quality in this region whereby significantly impacting the quality of life in the communities surrounding these corridors. To reduce these negative impacts it is critical that zero and near-zero emission technologies be developed and deployed in the region. A potential local market size of up to 46,000 trucks exists in the South Coast Air Basin, based on near- dock drayage trucks and trucks operating on the I-710 freeway. The South Coast Air Quality Management District (SCAQMD), California Air Resources Board (CARB) and Southern California Association of Governments (SCAG) — the agencies responsible for preparing the State Implementation Plan required under the federal Clean Air Act — have stated that to attain federal air quality standards the region will need to transition to broad use of zero and near zero emission energy sources in cars, trucks and other equipment (Southern California Association of Governments et al, 2011). SCAQMD partnered with Volvo Trucks to develop, build and demonstrate a prototype Class 8 heavy-duty plug-in hybrid drayage truck with significantly reduced emissions and fuel use. Volvo’s approach leveraged the group’s global knowledge and experience in designing and deploying electromobility products. The proprietary hybrid driveline selected for this proof of concept was integrated with multiple enhancements to the complete vehicle in order to maximize the emission and energy impact of electrification. A detailed review of all technologies included in the demonstrator is presented in this report. The project was completed in July 2015 with a final demonstration of the concept vehicle on a simulated drayage route around Volvo’s North American headquarters in Greensboro, NC. The route included all traffic conditions typical of drayage operation in Southern California as well as geofences defined to showcase the zero emission capabilities of the truck. The demonstrator successfully completed four consecutive trips with a gross combined vehicle weight of 44,000 lb., covering approximately 2 miles out of a total distance of 9 miles per trip in the Zero Emission (ZE) geofence. This vehicle is expected to use approximately 30% less fuel than a typical drayage truck in daily operation, and it is designed to allow full electric operation whenever operating in a marine terminal in the ports of Los Angeles / Long Beach. A paper study on the feasibility of expanding the capabilities of the plug-in hybrid concept developed as part of this project was also delivered as an addendum to the regular progress reports.
Albert Y. S. Lam, Ka-Cheong Leung, Victor O. K. Li
Due to various green initiatives, renewable energy will be massively incorporated into the future smart grid. However, the intermittency of the renewables may result in power imbalance, thus adversely affecting the stability of a power system. Frequency regulation may be used to maintain the power balance at all times. As electric vehicles (EVs) become popular, they may be connected to the grid to form a vehicle-to-grid (V2G) system. An aggregation of EVs can be coordinated to provide frequency regulation services. However, V2G is a dynamic system where the participating EVs come and go independently. Thus, it is not easy to estimate the regulation capacities for V2G. In a preliminary study, we modeled an aggregation of EVs with a queueing network, whose structure allows us to estimate the capacities for regulation-up and regulation-down separately. The estimated capacities from the V2G system can be used for establishing a regulation contract between an aggregator and the grid operator, and facilitating a new business model for V2G. In this paper, we extend our previous development by designing a smart charging mechanism that can adapt to given characteristics of the EVs and make the performance of the actual system follow the analytical model.