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The evolution of power system is a dynamic and complex process. Whether the evolution of power system can adapt to the macro environment and develop with all subsystems within the system depends on the matching and integration development among different evolutionary subjects. Based on the dissipation theory and Brusselator model, this paper constructs an evolutionary synergy model of the power system, and quantitatively analyzes the evolutionary synergy relationship between the evolving subjects of the power system and the overall evolutionary process of the power system. Then, from the socio-technical system transformation theory, we analyze the evolutionary elements of macro-environmental level, meso-institutional level and micro-beneficial level in the process of system transformation in China and the interaction among them. Finally, by comparing the entropy changes of the evolutionary participant subjects and the overall power system at each stage, the endogenous mechanism of the power system, the external elements and their interactions on the system development are studied in order to reveal the evolutionary synergistic law of China’s power system and make suggestions for the development of the new power system.

To solve the problem of inaccurate prediction of the stack life of proton exchange membrane fuel cells, this paper first proposed a fuel-cell aging prediction method based on method Savitzky–Golay Smoothing and Group Method of Data, which was based on the data drive. Savitzky–Golay Smoothing is an optimal piecewise fitting method based on polynomial in the time domain and using the least square method through moving window, which is widely used in data flow smoothing and denoising. Group Method of Data is a modeling method of the complex nonlinear dynamic system. The inner criterion and outer criterion are used in the training set and test set respectively, and the optimal solution is finally solved by iterative screening. The method presented in this paper was verified by 1020 h fuel cell aging experiment. The experimental results show that: the MSE, RMSE, R of the test data are respectively 6.3935e−05, 0.0079959, and 0.99616. The data-driven prediction method proposed in this paper can be used for fuel cell aging prediction and fault warning.

This paper introduces the development of clean energy in Sichuan province and the work done by the company in serving the development of clean energy. It mainly includes vigorously building power transmission channels to meet the needs of clean energy consumption, strengthening power grid planning and construction, serving the rapid development of new energy, tapping the potential of power grid operation, increasing the space of clean energy consumption, adopting multiple measures simultaneously, flexible trading, and increasing the intensity of clean energy consumption. And to solve the current situation and difficulties, put forward relevant policies and suggestions. It mainly includes suggestions to promote the construction of Sichuan electric power transmission channel, to call on the state to establish a policy mechanism to ensure Sichuan clean energy transmission and absorption, to arrange the development scale and progress of natural gas distributed energy, new energy, and other power sources in a reasonable and orderly manner. It is suggested that the development scale and progress of various power sources such as natural gas distributed energy and new energy should be reasonably and orderly arranged.

The fast development of renewable energy has resulted in great challenges to the power system, which urgently needs more flexible resources to maintain a system supply/demand balance. This paper established a multi-stage electricity market framework in the presence of a load aggregator (LA) including a day-ahead energy/reserve market and a real-time balanced market. To actively participate in the day-ahead energy market and reserve market, a load profile perception model for LA is proposed to evaluate in detail the response performance of consumers. Meanwhile, a market-bidding model of LA and a market-clearing model of the system operator for the day-ahead market are also established. To actively join the real-time balance market, a market-bidding model of LA for the real-time balance market based on surplus flexible resources is established. The system operator further clears the real-time balance market and dispatches the collected flexible resources according to the system supply-demand state. A modified IEEE 30 bus system is tested and shows that the proposed market framework can effectively promote consumers to respond to system regulation requirements and lowers the system supply-demand imbalance risk.

This paper proposes an optimal generation scheduling approach based on linear relaxation and mixed integer programming, which is used to solve the generation dispatch problem. The quadratic transmission loss constraint of each transmission line is converted into linear constraints by using the linear relaxation and mixed integer programming technique. Consequently, the original optimal generation scheduling problem is formulated as a quadratic programming or mixed integer quadratic programming problem that can be solved by commercial optimization solver. In order to improve the efficiency of algorithm, this paper further analyses the generation scheduling model and deletes the redundant variables and constraints. Three test systems, including IEEE 30-node system, IEEE 118-node system, and Polish 2746-node system, are employed to examine the effectiveness of the proposed method. The comparative results obtained by the proposed method, quadratically constrained quadratic programming method (QCQP), and solving constraint integer programs solver (SCIP) verify the effectiveness of the proposed method in solving the optimal generation scheduling problem.

Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are often not explainable. This paper handles both the automation and interpretability issues of ML-based TSA. An automated machine learning (AutoML) scheme is proposed which consists of auto-feature selection, CatBoost, Bayesian optimization, and performance evaluation. CatBoost, as a new ensemble ML method, is implemented to achieve fast, scalable, and high performance for online TSA. To enable faster deployment and reduce the heavy dependence on human expertise, auto-feature selection and Bayesian optimization, respectively, are introduced to automatically determine the best input features and optimal hyperparameters. Furthermore, to help operators understand the prediction of stable/unstable TSA, an interpretability analysis based on the Shapley additive explanation (SHAP), is embedded into both offline and online phases of the AutoML framework. Test results on IEEE 39-bus system, IEEE 118-bus system, and a practical large-scale power system, demonstrate that the proposed approach achieves more accurate and certain appropriate trust solutions while saving a substantial amount of time in comparison to other methods.

The gradual expansion of power transmission networks leads to an increase in short-circuit current (SCC), which has an impact on the secure operation of transmission networks when the SCC exceeds the interrupting capacity of the circuit breakers. In this regard, optimal transmission switching (OTS) is proposed to reduce the short-circuit current while maximizing the loadability with respect to voltage stability. However, the OTS model is a complex combinatorial optimization problem with binary decision variables. To address this problem, this paper employs the deep Q-network (DQN)-based RL algorithm to solve the OTS problem. Case studies on the IEEE 30-bus system and 118-bus system are presented to demonstrate the effectiveness of the proposed method. The numerical results show that the DQN-based agent can select the effective branches at each step and reduce the SCC after implementing the OTS strategies.

Addressing the shortcomings of existing low-frequency oscillation research on electrified railways, which has mainly focused on single-type trains and lacks the accurate modeling of traction inverter systems, in this paper we modeled and analyzed low-frequency oscillations in an electrified railway passenger and freight mixed-operation vehicle–grid system. First, an equivalent model of the DC side of the traction inverter was established, with the inverter system being equivalent to the parallel connection of the load resistance and the current source, and the specific mathematical expression was determined and verified by impedance measurement. Secondly, based on the equivalent model of the DC side of the traction inverter, a small signal model of the vehicle–grid system under the mixed operating conditions of CRH5 and HXD2B considering the inverter system was established. The generalized Nyqusit criterion was used to study the low-frequency oscillation characteristics under mixed transportation conditions. The accuracy of the established model and the correctness of the theoretical analysis were verified based on Matlab/Simulink. Finally, using the dominant pole theory to analyze the low-frequency stability conditions, the relationship between the number of mixed trains and the minimum short-circuit ratio was obtained, and the simulation verification was carried out.