A Virtual Power Plant (VPP) is a network of distributed power generating units, flexible power consumers, and storage systems. A VPP balances the load on the grid by allocating the power generated by different linked units during periods of peak load. Demand-side energy equipment, such as Electric Vehicles (EVs) and mobile robots, can also balance the energy supply-demand when effectively deployed. However, fluctuation of the power generated by the various power units makes the supply power balance a challenging goal. Moreover, the communication security between a VPP aggregator and end facilities is critical and has not been carefully investigated. This paper proposes an AI-enabled, blockchain-based electric vehicle integration system, named AEBIS This system is part of a project being developed at the Adaptive Systems Laboratory of the University of Aizu and Aizu Computer Science Laboratories, Inc., Aizu-Wakamatsu. for power management in a smart grid platform. The system is based on an artificial neural-network and federated learning approaches for EV charge prediction, in which the EV fleet is employed as a consumer and as a supplier of electrical energy within a VPP platform. The evaluation results show that the proposed approach achieved high power consumption forecast with R2 score of 0.938 in the conventional training scenario. When applying a federated learning approach, the accuracy decreased by only 1.7%. Therefore, with the accurate prediction of power consumption, the proposed system produces reliable and timely service to supply extra electricity from the vehicular network, decreasing the power fluctuation level. Also, the employment of AI-chip ensures a cost-efficient performance. Moreover, introducing blockchain technology in the system further achieves a secure and transparent service at the expense of an acceptable memory and latency cost.

A Virtual Power Plant (VPP) balances the load on a power grid by allocating power generated by various interconnected units during periods of peak demand. In addition, demand-side energy devices such as Electric Vehicles (EVs) and mobile robots can also balance energy supply and demand when effectively deployed. However, the fluctuation of energy generated by renewable resources makes balancing energy supply a challenging goal. This paper proposes a semi-decentralized robust network of electric vehicles (NoEV) integration system for power management in a smart grid platform. The proposed approach integrates an aggregator with EV fleets into a blockchain framework. The EVs execute a multi-stage algorithm to predict the power consumption based on a novel federated learning algorithm named Federated Learning for Qualified Local Model Selection (FL-QLMS). From the evaluation results, the proposed system requires 35% fewer transactions in short intervals and propagation delays than the previous approaches and achieves better network efficiency while maintaining a high level of security. Moreover, NoEV achieves a 5.7% lower root mean square error (RMSE) than the conventional approach for power consumption prediction, which is a significant improvement. In addition, the FL-QLMS approach outperforms state-of-the-art methods in terms of robustness to client-side attacks. The evaluation results also show that the performance of FL-QLMS is not affected when 10% to 40% percent of the models are manipulated.

In the present era, energy issues are a significant concern, and the energy trading market is the crucial sector to facilitate supply-demand balance and sustainable development. For better demand response and grid balancing, vehicle-to-grid (V2G) technology is rapidly gaining importance in energy markets. To narrow the gap between ideal V2G goals and actual applications needs, energy trading system has to overcome the challenges of over-centralized structure, inflexible timeline adaptation, limited market scale and energy efficiency, excessive feedback time costs, and low rate of economic return. To address these issues and ensure a secure energy market, we propose a decentralized intelligent V2G system called V2G Forecasting and Trading Network (V2GFTN) to achieve efficient and robust energy trading in campus EV networks. A multiple blockchain structure is proposed in V2GFTN to ensure trading security and data privacy between energy requests and offers. V2GFTN also integrates energy forecasting functions for EVs with a smart energy trading and EV allocation mechanism called SRET so that the EVs with driving tasks can supply their extra power back to the grid and achieve higher energy efficiency and economic profit. Through rigorous experimentation and compared with equivalent studies, V2GFTN system has demonstrated higher economic profit and energy demand fill rate by up to 1.6 times and 1.9 times than the state-of-the-art V2G approaches.

Nowadays, energy trading policies are revolutionizing the efforts and policies geared toward addressing global carbon emissions and protecting the environment. Smart grids and electric vehicles (EVs) are energy-saving tools for efficient power management. Although EVs can act as both energy consumers and suppliers, the effort required to balance the energy supply and demand in typical centralized trading systems inevitably reduces trading reliability. Another challenge is distributing EVs’ energy rationally to achieve better demand response and energy utilization. To manage the market securely and efficiently, we propose V2GNet ^a , a blockchain-based energy trading system using the vehicle-to-grid (V2G) network. The system combines a blockchain of energy exchanges (BoE) and a blockchain of EVs (BoEV), with the distinct transmission of energy requests and offers. Furthermore, to consider energy management from an economic viewpoint, we address the attack issue by proposing a robust energy trading ( RET ) algorithm. The proposed system demonstrates high robustness to malicious attacks. Our experimental results show that the RET reduces 30% energy loss when 20% of consumers are attacked. Moreover, malicious exchanges are excluded progressively from the trading market during each trading round. Also, the RET algorithm achieves better energy fulfillment and higher profit compared to state-of-the-art approaches.

Neuromorphic computing systems are biologically inspired approaches created from many highly connected neurons to model neuroscience theories and solve machine learning problems. They promise to drastically improve the efficiency of critical computational tasks such as decision-making and perception. Combining neuromorphic computing systems and 3D interconnect (3D-NoC) technology leads to an advanced architecture that inherits the benefits of both computing and interconnect paradigms. However, designing large-scale neuromorphic systems based on 3D-NoC faces several challenges, including thermal power, power distribution, increased power density at the heat sink interface, and fabrication requirements. This work tackles the thermal issues in designing large-scale neuromorphic systems by proposing a Balanced Thermal-State-Aware Mapping (BTSAM) for 3D-NoC-based neuromorphic systems. This includes a Periodic Activity Scoring (PAS), a Seesaw Neuron Clustering (SNC) method, and a thermal-aware genetic algorithm to eliminate hotspots, balance the thermal state, and lower the temperature while keeping the system’s accuracy acceptable. Evaluation results on various system configurations demonstrate a notable up to 12.4 K and 5.2 K temperature reduction compared to linear methods and HeterGenMap, respectively, and a 4× increase in Mean-Time-to-Failure (MTTF), with an acceptable power and area overheads and little degradation of the communication cost.

Collaborative edge learning has emerged as a fundamental paradigm shift in vehicular technology, improving the efficiency and privacy of machine learning in edge environments. Nevertheless, the prevailing blockchain-based methods for collaborative learning have faced challenges such as communication delays and network congestion, mainly due to the continuous exchange of local models. In response to these challenges, this paper proposed an innovative interlinked chain method (ICM). The ICM uses interconnected sub-networks in which selected vehicles serve as inner aggregators. This setup ensures that only global models are transmitted on the blockchain, optimizing communication infrastructures and minimizing overhead. Our experimental results show that the proposed ICM outperforms conventional approaches by exhibiting higher efficiency and requiring significantly less communication exchange compared to traditional clustered and unified architectures, and reducing the communication overhead by more than 70%. Remarkably, the interlinked architecture avoids the performance degradation of collaborative learning and maintains consistency even when different federated schemes are implemented.