Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value. However, the randomness of wind power generation puts forward higher requirements for electricity market transactions. Therefore, the carbon trading market is introduced into the wind power market, and a new form of low‐carbon economic dispatch model is developed. First, the economic dispatch goal of wind power is be considered. It is projected to save money and reduce the cost of power generation for the system. The model includes risk operating costs to account for the impact of wind power output variability on the system, as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment. The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions, and analyze the impact of different carbon trading prices on the system. A low‐carbon economic dispatch model for the wind power market is implemented based on the following two goals. Finally, the solution is optimised using the Ant‐lion optimisation method, which combines Levi's flight mechanism and golden sine. The proposed model and algorithm's rationality is proven through the use of cases.

In the smart grid and big data environment, accurate and large amount of power load data for users can be obtained with the wide application of non-intrusive load monitoring technology. In the research process of customers’ information, information security protection of users’ electricity data has become a research hotspot urgently. This article proposes a new type of load decomposition method for electric vehicle load information and compares it with hidden Markov model algorithm to verify its accuracy. On this basis, the elliptic curve encryption algorithm is used to encrypt the users’ electricity data, and the function and effectiveness of the encryption algorithm are verified by comparing the load decomposition of the electric vehicle with the unencrypted data.

The vibration signal associated with the operating process of circuit breakers(CBs) includes a detailed operating status in the formation of the operating mechanism. To effectively extract the characteristic information of vibration effectively for diagnosis and analysis, a new feature extraction method for the CBs operating mechanism is proposed. First, a new denoising method, the improved complete ensemble empirical mode decomposition with adaptive noise-multi-resolution singular value decomposition (ICEEMDAN-MRSVD), is introduced, which can effectively remove the influence of noise on faults. Then, a quantitative method is proposed to extract the characteristic information of the CB, i.e. the variational mode decomposition (VMD)-power spectrum entropy (PSE) is proposed. By using this method, the difference of CB vibration signals in different fault states can be quantified. Through comparative analysis of different recognition models, experiments show that the support vector machine model based on ICEEMDAN-MRSVD noise reduction and VMD-PSE features has a high recognition accuracy of 98.61%, which has high application value.

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy.

Contraposing to the issues of strong randomness, large volatility, and high prediction error of wind power, this paper proposes a short-term wind power prediction method based on feature engineering and support vector machine optimized by the seagull optimization algorithm (ISOA-SVM). First, in feature engineering, the abnormal values are preprocessed and utilizing the Pearson correlation coefficient (PCC) method to analyze the correlation between wind power and every feature. On this basis, a set of new features are constructed. Then, in the construction of the combined model, the seagull optimization algorithm is utilized to optimize the parameters of the support vector machine, so as to realize the complementary advantages of the optimization characteristics of the seagull algorithm and the support vector machine in simplifying the high-dimensional space complexity problems. Finally, the short-term wind power prediction model is constructed based on the ISOA-SVM model. The emulation experiment is carried out with the relevant measurement data of a wind power field. The results show that, in relation to other models, the proposed ISOA-SVM model has more excellent precision and dependability of short-term wind power prediction, which provides a certain reference for power dispatching.