Lithium-ion batteries on electric vehicles have been increasingly deployed for the enhancement of grid reliability and integration of renewable energy, while users are concerned about extra battery degradation caused by vehicle-to-grid (V2G) operations. This paper details a multi-year cycling study of commercial 24 Ah pouch batteries with Li(NiMnCo)O₂ (NCM) cathode, varying the average state of charge (SOC), depth of discharge (DOD), and charging rate by 33 groups of experiment matrix. Based on the reduced freedom voltage parameter reconstruction (RF-VPR), a more efficient non-intrusive diagnosis is combined with incremental capacity (IC) analysis to evaluate the aging mechanisms including loss of lithium-ion inventory and loss of active material on the cathode and anode. By analyzing the evolution of indicator parameters and the cumulative degradation function (CDF) of the battery capacity, a non-linear degradation model with calendar and cyclic aging is established to evaluate the battery aging cost under different unmanaged charging (V0G) and V2G scenarios. The result shows that, although the extra energy throughput would cause cyclic degradation, discharging from SOC 90 to 65% by V2G will surprisingly alleviate the battery decaying by 0.95% compared to the EV charged within 90–100% SOC, due to the improvement of calendar life. By optimal charging strategies, the connection to the smart grid can potentially extend the EV battery life beyond the scenarios without V2G.

Due to high penetration of renewable energy, the new electricity system oriented to carbon neutrality will face the bottleneck of insufficient flexibility resource. Electric vehicle (EV) shall become a core hub of energy consumption and storage. This paper proposes a coupled transportation-energy-electricity framework to evaluate the technical potential and whole-system value of smart orderly charging and vehicle-to-grid (V2G) in China. This approach simultaneously optimizes power supply installation, transmission network, storage capacity, as well as unit commitment, power dispatch and system reliability for real-time nationwide energy balancing, while considering EV ownership development, travel behavior and charging demand. Scenarios of unmanaged charging, smart charging and V2G are carried out for cases of 2025, 2030, 2040, 2050 and 2060. Results show that, in 2040, the total installed energy of EV batteries will be over 20 TWh and the flexible adjustment capacity exceeds 10 TWh. The V2G operation can reduce up to 730 Mt CO2 emission annually. The levelized whole-system cost for low-carbon transformation can be declined by up to 49.7% till 2060 under V2G scenario compared with unmanaged charging. Considering thermal power freely developing as business-as-usual case, the unit decarbonization cost could even be reduced by 100.2% to be minus through V2G integration in 2050.

The exploitation of distributed energy resources (DERs) is essential for the advancement of smart grids. However, the heterogeneity and scale of DERs pose major challenges to their aggregation, which is necessary to maximize their potential. This study adopts a linearized model that integrates a diverse array of DERs including power generation, energy storage, and flexible loads with varying dissipation rates. We used the model to analyze the compact form and mathematical implications of aggregation, for which we propose the subgradient vertex search method to solve. This method efficiently identifies vertices of the aggregate feasible region, which are used to construct its convex hull. Unlike traditional hyperplane-based methods, this method not only provides an inner approximation efficiently but also can converge to the exact solution. Numerical simulations validated its accuracy and efficiency. Additionally, we introduce an energy flowchart to visualize high-dimensional space of energy change processes and provide a case study involving two vehicle-to-grid (V2G) charging piles to explain the mechanism of feasible region scaling, demonstrating the practical significance of precise aggregation.