The virtual power plant (VPP) is a cloud-based aggregator that combines heterogeneous distributed generations (DG) to give a reliable and friendly power supply. In this paper, a VPP which consists of natural gas network, P2G equipment, flexible load and other resources is introduced in electricity market and natural gas market considering the uncertainties of renewable energy outputs and demand responses. Moreover, the optimal scheduling strategy of VPP includes power and gas dispatch considering bidirectional flows of power and gas, and the optimization model considering dual energy markets which include electricity market and natural gas market is investigated by developing different scenarios to maximize the VPPs profit. Case studies have been carried out to analyze the economy and scheduling strategy of the VPP. Simulation results show that the VPP studied in this paper can effectively reduce the proportion of renewable curtailment by selling electricity or P2G equipment. Besides, it is beneficial to maintain pressure stability and security operation of natural gas network, and the VPP could decrease unbalanced penalty cost and reduce the impact from uncertainties in dual energy markets.

Non-dominated sorting genetic algorithm II is a classical multi-objective optimization algorithm but it suffers from poor diversity and the tendency to fall into a local optimum. In this paper, we propose an improved non-dominated sorting genetic algorithm, which aims to address the issues of poor global optimization ability and poor convergence ability. The improved NSGA-II algorithm not only uses Levy distribution for global search, which enables the algorithm to search a wider range, but also improves the local search capability by using the relatively concentrated search property of random walk. Moreover, an adaptive balance parameter is designed to adjust the respective contributions of the exploration and exploitation abilities, which lead to a faster search of the algorithm. It helps to expand the search area, which increases the diversity of the population and avoids getting trapped in a local optimum. The superiority of the improved NSGA-II algorithm is demonstrated through benchmark test functions and a practical application. It is shown that the improved strategy provides an effective improvement in the convergence and diversity of the traditional algorithm.

With the continuous expansion of the power system scale, the requirements of the power system intelligence are getting higher and higher and the power grid fault diagnosis has been made to become the focus of the power system research. In this paper, the research field of power grid fault diagnosis is reviewed, and the data sources and characteristics and data preprocessing are analyzed. Moreover, the diagnosis mechanism, characteristics and deficiencies of various fault diagnosis methods, the improvement results and future research direction are reviewed in recent years, and the design of online diagnosis system architecture mode are established. A new type of power system fault monitoring and networking method is proposed which is more concise and reliable than the traditional scheme. The power system fault diagnosis strategy based on knowledge representation and the fault diagnosis model with distributed architecture is structured which showed the efficiency is effectively improved. Finally, the problems faced by the power system fault diagnosis under the background of regulatory big data are discussed, and the development direction of the power grid fault diagnosis under this new situation is deeply analyzed.

The emission reduction of global greenhouse gases is one of the key steps towards sustainable development. Demand response utilizes the resources of the demand side as an alternative of power supply which is very important for the power network balance, and the virtual power plant (VPP) could overcome barriers to participate in the electricity market. In this paper, the optimal scheduling of a VPP with a flexibility margin considering demand response and uncertainties is proposed. Compared with a conventional power plant, the cost models of VPPs considering the impact of uncertainty and the operation constraints considering demand response and flexibility margin characteristics are constructed. The orderly charging and discharging strategy for electric vehicles considering user demands and interests is introduced in the demand response. The research results show that the method can reduce the charging cost for users participating in reverse power supply using a VPP. The optimizing strategy could prevent overload, complete load transfer, and realize peak shifting and valley filling, solving the problems of the new peak caused by disorderly power utilization.

The critical components of gas turbines suffer from prolonged exposure to factors such as thermal oxidation, mechanical wear, and airflow disturbances during prolonged operation. These conditions can lead to a series of issues, including mechanical faults, air path malfunctions, and combustion irregularities. Traditional model-based approaches face inherent limitations due to their inability to handle nonlinear problems, natural factors, measurement uncertainties, fault coupling, and implementation challenges. The development of artificial intelligence algorithms has provided an effective solution to these issues, sparking extensive research into data-driven fault diagnosis methodologies. The review mechanism involved searching IEEE Xplore, ScienceDirect, and Web of Science for peer-reviewed articles published between 2019 and 2025, focusing on multi-fault diagnosis techniques. A total of 220 papers were identified, with 123 meeting the inclusion criteria. This paper provides a comprehensive review of diagnostic methodologies, detailing their operational principles and distinctive features. It analyzes current research hotspots and challenges while forecasting future trends. The study systematically evaluates the strengths and limitations of various fault diagnosis techniques, revealing their practical applicability and constraints through comparative analysis. Furthermore, this paper looks forward to the future development direction of this field and provides a valuable reference for the optimization and development of gas turbine fault diagnosis technology in the future.