High electric vehicles (EVs) penetration is expected to increase smart grid solicitation especially with various EV charging demands. As result, the EV charging process at the supply station has to be managed in the way to promote the EV satisfaction level while preserving smart grid stability. In this paper, the bidirectional power flow between EV and grid; Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G), is exploited. We make a profit from the unused electric power of EVs and we present an EV load management technique based on EV charging and EV discharging coordination. We propose a peak load management model (PLM) used to schedule EVs for charging or discharging service according to the power demand with the timing and location where each EV need to be served. Also, we propose an Electric Vehicle Supply Equipment (EVSE) selection model to guide EVs to the supply station. We develop a mathematical formalism for handling requests for EV charging/discharging at EVSE based on queuing theory. Those models are evaluated while considering the mobility of vehicles in an urban scenario and time-of-use-pricing (TOUP). Finally, extensive matlab simulations are conducted to validate the proposed approach and demonstrate its effectiveness.

The energy transition-revolution paradigm is coming with a new vision of interaction models to smartly manage the energy and data exchanged between all participants in the whole power system with solid regard to sustainability, resilience, cybersecurity and privacy. Added to the switching from fossil to renewable power sources the Demand-Side Management (DSM) which is a mix of software and hardware models with data analytics capabilities, is a crucial enabler of the energy transaction. More than the demand-Response (DR) functionalities, the DSM includes the overall management of the grid infrastructure while minimizing consumers’ discomfort and maximizing the grid stability with respect to environmental commitments. This paper reviews the application of several methodologies for DSM within the context of modern power grids. Existing DSM approaches are described and categorized while offering informative discussions to help reason through all considered studies. Future research avenues are pointed by suggesting DSM improvements for the context of the global trends in data analysis/cybersecurity for liberalizing electricity markets based on energy transactions and DSM-based communities, especially for Smart Cities. The standardization, the regulations, the data privacy, the cybersecurity issues, and their contributions to the energy system were reviewed as open issues in the context of a modern DSM.

Machine learning (ML) is efficiently disrupting and modernizing cities in terms of service quality for mobility, security, robotics, healthcare, electricity, finance, etc. Despite their undeniable success, ML algorithms need crucial computational efforts with high-speed computing hardware to deal with model complexity and commitments to obtain efficient, reliable, and resilient solutions. Quantum computing (QC) is presented as a strong candidate to help MLs reach their best performance especially for cybersecurity issues and digital defense. This paper presents quantum support vector machine (QSVM) model to detect distributed denial of service (DDoS) attacks on smart micro-grid (SMG). An evaluation of our approach against a real dataset of DDoS attack instances shows the effectiveness of our proposed model. Finally, conclusions and some open issues and challenges of the fitting of ML with QC are presented.

When cybersecurity is neglected, any network system loses its efficiency, reliability, and resilience. With the huge integration of the Information, Communication and Technology capabilities, the Connected Electric Vehicle (CEV) as a transportation form in cities is becoming more and more efficient and able to reply to citizen and environmental expectations which improve the quality of citizens’ life. However, this CEV technological improvement increases the CEV vulnerabilities to cyber-attacks resulting to serious risks for citizens. Thus, they can intensify their negative impact on societies and cause unexpected physical damage and economic losses. This paper targets the cybersecurity issues for CEVs in parking lots where a peer-to-peer(P2P) energy transaction system based on blockchain, and smart contract scheme is launched. A False Data Injection Attack (FDIA) on the electricity price and power signal is proposed and a Machine Learning/SVM classification protocol is used to detect and extract the right values. Simulation results are conducted to prove the effectiveness of this proposed model.

Connected electric vehicles (CEVs) can help cities to reduce road congestion and increase road safety. With the technical improvement made to the battery system in terms of capacity and flexibility, CEVs, as mobile power plants can be an important actor for the electricity markets. Especially, they can trade electricity between each other when supply stations are full or temporarily not available. In this article, we propose an advanced decentralized electricity trading framework between CEVs in parking lots based on consortium blockchain, machine learning, and Game theoretic model. We design a distributed smart contract solution based on a stochastic bidding process, which helps CEVs to sell and buy electricity with their maximum profitability. Finally, numerical simulations with MATLAB and Solidity are conducted to prove the effectiveness of our proposed solution. Also, a comparison with another method in terms of CEVs’ profitability improvement and energy trading management is provided.

As electric vehicles become more popular, public charging stations for such vehicles will become common. Since the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the waiting time for users to have their vehicles charged. In this paper, we present an approach for balancing the load among charging stations in an area while minimizing the charging time of electric vehicles. We propose a model where vehicles communicate beforehand with the grid to convey information about their charging status, and develop a mathematical model of handling requests for charging vehicles at public charging station based on queuing theory. Finally, we propose an algorithm for directing vehicles to charging stations in a way to minimize their waiting time to charge completion. The simulation results show the effectiveness of the proposed approach when considering both real electric vehicle and charging station characteristics and constraints.