In the context of achieving the “dual carbon” goal, the task of carbon emission reduction in the power industry urgently needs to be completed. Cross-regional electricity trading can facilitate the remote consumption of surplus renewable resources and contribute to the low-carbon transformation of the power system. Due to the inherent differences in power generation structures across interconnected regions, the impact of carbon costs on the clearing electricity prices within regions varies, leading to dynamic price differences between regions, which, in turn, affects the outcomes of cross-regional electricity trading, and further exacerbates the differences in power generation structures, thereby impacting interval electricity price differences. To address these complexities, this paper proposes an assessment model which considers both carbon costs and interval electricity price differences in evaluating cross-regional electricity trading volumes. The model aims to establish a coherent relationship between interval price differences and cross-regional electricity trading by incorporating carbon costs into the power system production process. It uses the dynamic interval price difference as a signal to determine the trading volume between regions in the evaluation of cross-regional electricity trading volumes. In assessing interval price differences, the model updates the intra-region power generation structure based on unit revenue rates, and contrasts the price differences before and after these structural iterations. Taking the cross-regional electricity trading between two interconnected areas as an example, the results of the computational simulations demonstrate that the proposed model effectively evaluates the dynamic price differences between regions and cross-regional electricity trading volumes. Additionally, it quantifies the impact of power generation structure evolution on interval price differences.

It is necessary to consider the carbon emission trading between prosumers when designing a distributed transaction mechanism in a distribution system. In this paper, a peer-to-peer (P2P) electricity-carbon transaction mechanism considering the carbon footprint of electricity trading is established. First, the carbon emission characteristic of energy storage system is analyzed, and a carbon emission flow model considering energy storage system is established. Next, a P2P electricity-carbon transaction model based on the carbon emission flow model is established. Based on the improved Benders decomposition method, the original problem is decomposed into the main problem considering network constraints and the subproblem of optimal scheduling for prosumers. Through distributed solutions, the optimal P2P electricity-carbon trading amount of prosumers is solved to protect the privacy of prosumers. Then, a P2P electricity-carbon trading settlement model based on the Nash bargaining model is established, and the cooperation benefit is distributed according to the contribution of prosumers in the electricity-carbon transaction. Finally, the effectiveness of the proposed model is validated by case studies on the improved IEEE 33-bus distribution system. The proposed model can not only ensure the secure operation of the distribution system, but also promote the reduction of demand-side carbon emission and fairly distribute the benefit among cooperative prosumers.

External factors such as bad weather and external failure seriously affect the reliability of a distribution network. To comprehensively and accurately assess the risk of faults in a distribution network, this paper proposes a fault risk assessment method that considers the difference in entropy value of rare factors. This method uses K-means clustering algorithm to classify failures based on their consequences and an improved association rule mining algorithm to analyze rare environmental factors and evaluate high-risk and low-probability factors, so as to realize the quantitative analysis of the association between rare factors and risk levels. By combining the Pearson correlation coefficient, the correlation of each environmental feature is analyzed and redundant features at different risk levels are eliminated. Then, the component criticality analysis method is used to adjust the risk weight of rare elements, which can quantitatively measure the degree of correlation between the occurrence of individual elements and the fluctuation of the overall risk of the system. According to the fluctuation difference of different factors, the risk weight optimization matrix considering the difference of rare factors is obtained, and the fault risk assessment model of the distribution network is established, in combination with the information theory. Finally, a distribution network fault risk assessment model is established, of which the accuracy and effectiveness is verified by a case analysis of an actual distribution network in a city.

Power side energy storage is a powerful tool for promoting the consumption of new energy and implementing the “dual carbon” strategy. In view of the imperfection of the bidding mechanism of the power side energy storage as an emerging entity participating in the competitive joint electricity market, and starting from the game relationship of the power side energy storage as a price maker participating in the spot electricity market, a bidding game two-level optimization model of the power side energy storage participating in the multi scenario application of the electric energy and reserve ancillary service market is proposed, considering the uncertainty of wind power output and the coupling of the electricity market with the introduction of carbon trading. The energy storage at the upper power supply side, as a leader, makes bidding decisions with the goal of maximizing its own comprehensive income, while the lower power trading center as a follower completes market clearing with the minimum cost of social power purchase and balance regulation. The MATLAB simulation verifies the rationality and effectiveness of the proposed model in optimizing the participation of power side energy storage in multi scenario application bidding in the electricity market and improving the comprehensive operating income of the energy storage.

To reduce the losses caused by major power outages in power systems, a two-stage optimization method for black start of power systems is proposed using a multi-energy microgrid (MEMG) as a black-start source. First, the load-side integrated demand response and the operation characteristics of the electric, thermal, and gas physical energy storage devices in the MEMG are modeled as generalized energy storage. Next, a two-stage optimization model for black start of power systems using the multi-energy microgrid as a black-start source is proposed, with the first stage being the optimization sub-model for non-black-start source restoration path and the second stage being the black-start strategy optimization sub-model considering the power system and the MEMG. Then, a Dijkstra’s algorithm-based solution method for the first stage sub-model and a mixed integer quadratic programming-based solution method for the second stage sub-model are respectively proposed. Finally, a case study of a real regional power system in China is conducted to verify the effectiveness and advantages of the proposed black start optimization method.

Massive air-conditioner resources on the consumer side have a huge potential to be involved in system frequency control, and massive scattered individual air-conditioners can be aggregated into air-conditioner groups to participate in system automatic generation control (AGC). As the operating status of individual air-conditioners is random, it is necessary to accurately evaluate the frequency control reserve capacity of air-conditioner groups. In this paper, a data-driven nonintrusive load disaggregation (NILD) method is proposed to incorporate massive air-conditioner groups into AGC frequency control reserve. First, the proposed method constructs a sequence-to-sequence model to nonintrusively forecast the frequency control reserve capacity of individual air-conditioners. Considering the fact that the participation of air-conditioners in AGC may affect the comfort of users, the feasibility of individual air-conditioners is evaluated to participate in AGC frequency control and a selection mechanism is constructed to determine whether they can be included in the frequency control reserve of air-conditioner groups. In the proposed method, individual air-conditioners within a sub-station that participate in AGC are aggregated to derive the air-conditioner group frequency control reserve of the sub-station, and the air-conditioner group frequency control reserve of all sub-stations are aggregated to derive the entire air-conditioner group frequency control reserve. The experiments simulate different degrees of system frequency drops in which air-conditioner groups work cooperatively with thermal units for AGC frequency control. The simulation results demonstrate the validity of the proposed method.

To address the issues of transient stability in heterogeneous wind farms with grid-forming energy storage, a transient analysis model of the heterogeneous system is developed, which incorporates both grid-following wind turbine units and grid-forming energy storage sources. The mechanism of frequency interaction among heterogeneous power sources during severe grid short-circuit faults is revealed by the model. It is discovered that grid-forming energy sources with slower rotor angle dynamics exhibits a frequency pinning effect on those with faster rotor angle dynamics, and the latter has a rotor angle co-driving effect on the former under the condition of frequency pinning. By solving the critical d-axis current, the impacts of wind power integration rate, the proportion of energy storage installation, and other parameters on frequency interaction are analyzed. The effects of wind turbine unit reactive power injection and the current limiting strategy of grid-forming energy storage sources are also discussed. Finally, the proposed mechanisms are comprehensively validated by simulations using MATLAB/Simulink platform.

In response to the new power system frequency safety issues caused by the high percentage of renewable energy sources connected to the grid, wind turbines mostly use integrated inertia control for the inertia and primary frequency regulation support provided by the power system. In order to better improve the inertia safety of the system and guarantee the grid frequency stability, dynamic modeling of wind turbines with integrated inertia control is conducted to derive the effective inertia of the wind turbine based on the kinetic energy contained in the wind turbine and the frequency support it provides to the grid. Then, a system frequency response model of the wind turbine in integrated inertia control is established, the analytical formula of the effective inertia time constant in the process of wind turbine frequency regulation is obtained, and the inertia support capability is analyzed. Based on the “equal area principle”, the equivalent inertia evaluation method of the wind turbine in integrated inertia control is derived, which can analyze the inertial support capacity provided by the wind turbine and give quantitative results. Finally, the validity and feasibility of the proposed method is verified in a case study, and the impact of different factors on the equivalent inertia of the wind turbine is analyzed.

Lithium-ion batteries experience capacity decline or even deterioration during the working process. Effective estimation of battery health status is a key challenge in the development of battery management systems. This paper proposes a method for estimating the state of health (SOH) of lithium-ion batteries based on the fusion of data-driven models and characteristic parameters. Using differential thermal voltammetry(DTV) to preprocess the experimental data of lithium-ion batteries, this method extracts six useful features, and establishes a SOH estimation model based on two-step Gaussian process regression (GPR) with different kernel functions. The results show that the established model can better approximate the experimental value and shorten the training and prediction time. The average absolute error of SOH estimation is 0.67%—0.97%, which is 20%—30% lower than that of single-step GPR. Therefore, the model has a high robustness and accuracy in estimating the state of health of lithium-ion batteries.

When using the extended Kalman filter (EKF) to estimate the state of charge (SOC) of an electric vehicle power battery, the change of system noise and model parameters may lead to a reduction in estimation accuracy, due to variable operating conditions, battery aging, and other factors. The NCR18650B ternary lithium-ion battery is selected, and the second-order RC model is established with identified parameters. Then, by using EKF as the main body with a fixed measurement noise covariance and adaptively adjusting process noise covariance based on the maximum likelihood estimation criterion, an adaptive extended Kalman filter is built to estimate the SOC of the battery. Simultaneously, a Kalman filter is used to estimate the ohmic resistance in real time. Thus, an adaptive dual extended Kalman filter (ADEKF) algorithm is formed. Finally, algorithm verifications are performed with testing data and public datasets. The ADEKF proposed is used to estimate the SOC of five groups of aged lithium batteries under three operating conditions, which are constant current, dynamic stress test, and Beijing dynamic stress test, and compared with that of EKF and other algorithms. The results show that compared with EKF, the average absolute error of the estimation results of ADEKF for different aged batteries under three operating conditions decreases by 1.868 percentage points, 2.296 percentage points, and 2.534 percentage points, respectively, which proves that ADEKF algorithm can effectively improve the SOC estimation accuracy under multiple operating conditions, battery aging and the combination of the two factors.

The inertial of the system and the maximum frequency deviation caused by the energy storage batteries when participating in grid frequency regulation need to be improved. Virtual synchronous generator control (VSG) for grid side converter is expected to improve the performance of storage battery energy participation in grid primary frequency regulation. Therefore, participation of energy storage batteries in primary frequency control for power grid is proposed considering dynamic frequency inertia characteristics. To utilize the inertial support and primary frequency regulation capability of the energy storage batteries for the grid, an additional active power module is constructed, and the active power generated by the virtual inertial control and droop control strategies of the energy storage batteries are used as additional power for variable rotor inertial control and output feedback model predictive control, respectively. For the mismatch between the inertia characteristics of the virtual synchronous machine and the grid demand, a variable rotor inertia control strategy considering system frequency deviation and system frequency change rate is designed to realize the real-time adjustment of rotor inertia to the system frequency. The output feedback model prediction control of VSG is further proposed to achieve dynamic prediction and compensation of system frequency deviation by establishing a feedback channel between rotor angular frequency increment and torque increment. A comparative analysis with three existing control strategies shows that the control strategy proposed can make full use of the frequency regulation capability of the energy storage battery and VSG to effectively reduce the frequency deviation and frequency variation rate of the grid.

Aimed at the accommodation problem of a high proportion of new energy connected to the power grid, a multi-time scale coordinated scheduling model for power generation systems with a high proportion of new energy including pumped storage power stations is proposed. Based on the characteristics that the prediction accuracy of wind, photovoltaic (PV) and load power improves step by step with the shortening of time scales, a three-phase coordinated scheduling model is constructed with the goal of minimizing the total operating cost of the system, which includes a 24-hour day-ahead plan, a 1-hour intraday plan and a 15-minute real-time plan. Through the coordination of multiple time scales, the power generation plan is revised step by step, which ensures that the pumped storage power, the wind power, the PV power, and the thermal power output track the load well. Based on the CPLEX commercial optimization software, a mixed integer linear programming method is used to simulate and analyze the wind-PV-thermal-pumped storage complementary power generation system with six pumped storage units. The results show that the pumped storage units can take advantage of short load response time and fast power regulation speed to reduce the regulatory burden of thermal power units and relieve their regulatory pressure effectively. The coordination and cooperation on multiple time scales reduces wind and PV power curtailment and improves new energy accommodation level.

The grid connection guidelines require wind turbines to have capability to quickly support reactive power in the events of grid faults. However, traditional proportional-integral controllers have the problems of slow transient response and poor adaptability dealing with changes in grid impedance and serious model disturbances. In order to solve this problem, this paper proposes an improved reactive power support control strategy which combines linear active disturbance rejection control(ADRC) with least error squares(LES) filters. The LES filter is used to quickly and accurately detect the amplitude of the fault grid voltage with background harmonics, which can effectively reduce the fault detection delay. The ADRC controller is used to effectively improve the transient reactive power response speed and the control adaptability and robustness of the wind turbines under grid impedance disturbance. The simulation model of direct drive wind turbine based on PSCAD/EMTDC is built considering two scenarios, weak power grid and power grid with background harmonics. The effectiveness of the proposed control strategy is verified by simulation.

Line-start permanent magnet synchronous motor (LSPMSM) has a great application potential in the field of water pump due to its self-starting ability, high efficiency, and high power factor. In order to improve the transient performance of the motor with pump load, a new type of asynchronous starting motor rotor topology with non-uniform distribution of rotor bar is proposed. By combining the non-uniform distribution of rotor bar with the built-in permanent magnet into a multi-layer magnetic flux barrier rotor structure, the rotor salient ratio is increased, and the pull-in synchronization ability of the motor is improved. Based on the finite element method, the no-load back EMF, starting ability, pull-in synchronization ability, steady-state electromagnetic torque, demagnetization, stress field, and temperature field distribution of the traditional structure motor and the new structure motor are comprehensively compared and analyzed. The results show that compared with the traditional rotor structure, the new rotor structure effectively improves the pull-in synchronization ability, the efficiency, and the power factor of the motor under rated operating conditions. Finally, a prototype is made and an experimental platform is built to verify the correctness of the simulation results and theoretical analysis.

Academic writing is a time-consuming process that requires authors to possess specific writing skills, as well as a thorough understanding of format standards and publishing ethics of scientific journals. This paper analyzes the submission process for special columns in Journal of Shanghai Jiao Tong University, aiming to address basic considerations that novice authors should keep in mind in the early stages of academic writing. The goal is to help these authors submit manuscripts that are logically structured and meet the academic standards of their target journals. This paper is unique in its focus on common issues encountered in submissions, providing practical advice through a point-by-point analysis. By incorporating easy-to-understand cases, it provides a helpful reference for novice researchers seeking to navigate the requirements and pitfalls of writing for scientific journals.