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With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load...
Citations
... V2G coordination relies heavily on effective scheduling, which can be centralized or decentralized [13]. The centralized framework allows the grid operator to directly control and coordinate the charging activities, mitigating the risks associated with uncoordinated charging such as grid congestion and voltage fluctuations [14]. For instance, a centralized stochastic optimization model is employed in [15] to manage the spatiotemporal uncertainties of EVs and electricity markets, thus enhancing operational efficiency and economic benefits. ...
While blockchain offers inherent security, trust issues among stakeholders in vehicle-to-grid (V2G) applications remain unresolved due to a lack of regulatory frameworks and standardization. Additionally, a tailored decentralized privacy-preserved coordination scheme for blockchain in V2G networks is needed to ensure user privacy and efficient energy transactions. This paper proposes a V2G trading and coordination scheme tailored to the decentralized nature of blockchain as well as the interests of stakeholders utilizing smart charging points (SCPs) and Stackelberg game model. Case studies using real-world data from Southern University of Science and Technology demonstrate the efficacy of proposed scheme in reducing EV charging costs and the potential for supporting auxiliary grid services.
... For traditional algorithms, it is usually hard to find a feasible or optimal solution. In recent years, biomimetic-inspired intelligent optimization algorithms have become increasingly important in solving the optimal dispatch problem of the microgrid [9,10]. Popular algorithms such as PSO [11,12], genetic algorithm (GA) [13,14], and ant colony optimization (ACO) [15] have better global optimization ability and robustness. ...
Microgrid optimal dispatching has become one of the core issues of microgrid energy management and integrated control, which is of great significance to reduce energy consumption and environmental pollution. As a natural heuristic algorithm, the butterfly optimization algorithm (BOA) has the advantages of simple adjustment parameters and fast convergence speed. It is widely used to solve nonlinear programming problems. However, BOA is easy to fall into local optimization and poor convergence accuracy. Therefore, an improved butterfly optimization algorithm (IBOA) based on skew tent chaotic map, Cauchy mutation, and simplex method is proposed, and compared with particle swarm optimization (PSO), whale optimization algorithm (WOA), sparrow search algorithm (SSA), and BOA, the results show that the IBOA has high convergence speed and optimization accuracy. Finally, the IBOA is used to solve the optimization model. The simulation results show that the IBOA can effectively reduce the power consumption cost of the system, promote the effective utilization of renewable energy, and improve the operation stability of the microgrid cluster system.
An accurate electrical load forecast is essential for the effective implementation of vehicle-to-grid (V2G) technology to achieve optimal electric vehicle (EV) charging decisions, consequently, ensuring the security and stability of power grid. While prevailing evaluation metrics prioritize forecast quality, they often overlook the significant influence a forecast exerts when integrated into the V2G scheduling optimization. In this paper, a reliable metric is proposed for forecasts in the context of V2G scheduling from the perspective of forecast value. Firstly, we conducted meticulously designed experiments to expose the limitations of forecast quality metrics in the context of V2G scheduling, as well as reveal three key findings. Subsequently, to address computational challenges and enhance representativeness of scheduling results, statistical features of EV charging are used to construct the aggregate model of EV fleet. Then, a reliable metric called V2G scheduling value error (V2G-SVE) is proposed to evaluate the degradation rate of scheduling performance as the score for forecasting performance. Finally, extensive case studies provide compelling evidence for the effectiveness and broad applicability of V2G-SVE. Beyond proposing an evaluation metric, this paper also aims to provide valuable insights about potential direction of improvement for future load forecasting technology.
This work proposes an information security vehicleto- grid (V2G) scheduling solution, which combines Federated deep learning with distributed edge computing for V2G operation. In this framework, each charging point is equipped with an intelligent computing module to conduct distributed edge scheduling for the connected electric vehicle (EV), so that not only the computation of inference process is efficient, but also the privacy-preserving of EV users is guaranteed. Besides, the desensitized V2G data of charging points are used to train the deep neural network model in each charging station. Therefore, the accurate future data acquisition problem and the uncertainty handling challenges under traditional optimization methods is avoided. At the same time, the spatial-based and time-based clustering methods are applied to improve the accuracy of prediction. Finally, through federated learning, each charging station uploads the local model to the cloud server, and a stochastic client selection pattern is designed to improve the scalability of model aggregation in the cloud server. In this way, the digital assets of each charging station are protected, and computing and communication costs are reduced. Simulation results on real datasets show that the proposed framework has superior performance in terms of training accuracy, communication burden, and computing performance, while maintaining the privacy of EV users and the digital assets of charging stations.
Background
As many EVs (EVs) are connected to the grid, transactions between EVs and the grid will have problems such as poor privacy and affecting the stability of the grid. This paper uses consortium blockchain to design a safe and privacy-preserving scheme for the two-way power transaction between EVs and the grid.
Objective
: To reduce the adverse impact of disorderly charging of large-scale EVs on the power grid, the total load variance is minimized by optimizing EVs' charging/discharging period.
Methods
We propose to use a heuristic algorithm, an improved multi-objective gray wolf algorithm, to solve this problem.
Results
The simulation results show that the model can effectively smooth load fluctuations and improve user benefits.
Conclusion
Our method can effectively reduce the load fluctuation of the grid while ensuring the economic benefits of users. Qualitative security and privacy analysis show that the solution helps to improve the security and privacy of electricity transactions.
With the popularity of electric vehicles (EVs), vehicle-to-grid (V2G) technology is attracting increasing attention due to its crucial merit of enabling bidirectional power flows between EVs and grid, so as to enhance the grid security and stability by regulated dispatching. However, existing V2G approaches are confronted with several unrealizable challenges because of high computational complexity for large-scale EVs and impracticality for future power data acquisition. In this paper, an edge computing framework is proposed in a distributed manner to ensure the dispatching efficiently and provide the raw dataset flexibly. Meanwhile, the long short-term memory (LSTM) network is applied to prediction merely by the past and present power data. Moreover, attention mechanism and data clustering are utilized to improve the prediction accuracy and operation robustness. Experiments involving real dataset demonstrated that the proposed V2G scheme is able to achieve very satisfactory dispatching performance with the prediction accuracy up to 98.89%.
Vehicle-to-grid (V2G) technology plays an important part in achieving carbon neutrality. Hence, reducing the execution time under the real-time application becomes an urgent issue. In this paper, we develop a cyber-physical co-modeling to fulfill the fundamental insights into the internet of smart charging points (ISCP), wherein the local controllers are designed near the plug-in electric vehicles (PEVs), and are coordinated with each other. In perspective of energy dispatching, a hierarchical V2G scheduling is implemented in a distributed way to decompose the optimization problem into several sub-problems. Besides, the parallel computing is applied in the V2G problem to accelerate the speed of obtaining results. Moreover, the voltage regulation is applied near the energy coordinator with high-performance computer rather than by the local controller. In perspective of network communication, the small-world network is applied to ensure the communication efficiency and decrease the wiring costs. Besides, the privacy-preserving of both the energy coordinator and the PEV users is guaranteed by processing and storing the sensitive information of the two participants nearby. Finally, the cyber-physical co-modeling is performed in Matlab and Network Simulator 2. Results show load flatting, self-consumption of photovoltaic output, voltage regulation, and up/down regulation are achieved. Moreover, the delay of small-world network is 90.94 times faster than that of lattice network, and the cost of small-world network is nearly 500 times less than that of full mesh network. Particularly, the execution time for V2G operation at one-time interval is less than 1 s.
