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The scale of electric vehicles (EVs) in microgrids is growing prominently. However, the stochasticity of EV charging behavior poses formidable obstacles to exploring their dispatch potential. To solve this issue, an optimization strategy for EV-integrated microgrids considering peer-to-peer (P2P) transactions has been proposed in this paper. This research strategy contributes to the sustainable development of microgrids under large-scale EV integration. Firstly, a novel cooperative operation framework considering P2P transactions is established, in which the impact factors of EV charging are regarded to simulate its stochasticity and the energy trading process of the EV-integrated microgrid participating in P2P transactions is defined. Secondly, cost models for the EV-integrated microgrid are established. Thirdly, a three-stage optimization strategy is proposed to simplify the solving process. It transforms the scheduling problem into three solvable subproblems and restructures them with Lagrangian relaxation. Finally, case studies demonstrate that the proposed strategy optimizes EV load distribution, reduces the overall operational cost of the EV-integrated microgrid, and enhances the economic efficiency of each microgrid participating in P2P transactions. [ABSTRACT FROM AUTHOR]

Summary: The application of modular multilevel converters (MMC) in medium voltage static synchronous compensators (STATCOMs) has been investigated in recent years. A huge challenge for MMC‐STATCOM is to compute the gain of the controllers. Therefore, the contribution of this paper is the proposal of a control tuning methodology for MMC‐STATCOM. For this purpose, the control and tuning strategies are presented. Since MMC‐STATCOM can employ different modulation strategies, two distinct modulation strategies are considered: nearest‐level control, with variable switching frequency; and phase‐shifted carrier pulse‐width modulation, with fixed switching frequency. Moreover, the proposal was validated through simulation and an experimental platform. The results indicate that the power response follows the reference, besides supplying the internal losses of the converter. The 10% ripple tolerance of the submodule voltages is respected in steady state for both modulation strategies. Moreover, the 5% total demand distortion (TDD) limit recommended by the Institute of Electrical and Electronics Engineers (IEEE) standard is attended. These results demonstrate the effectiveness of the controls employed. [ABSTRACT FROM AUTHOR]

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods. [ABSTRACT FROM AUTHOR]