Against the background of carbon neutrality, the power dispatching operation mode has undergone great changes. It not only gradually realizes the coordinated control of source–grid–load–storage, but also strives to realize the multi-level coordination of the transmission network, distribution network and microgrid. Disorderly charging and discharging of large-scale electric vehicles (EVs) will have a great negative impact on the distribution network, but aggregating EVs and guiding them to charge and discharge in an orderly manner will play a positive role in delaying investment in the distribution network. Therefore, it is urgent to adopt an effective scheduling control strategy for electric vehicle charging and discharging. First, a variety of indexes were set to analyze the influence of EVs access on distribution network and the correlation between the indexes. Then, by defining the EVs penetration rate and the load simultaneous rate, the charging load planning of EVs was calculated. Based on the simultaneous load rate, the regional electricity load plan was calculated, and a configuration model of distribution capacity suitable for charging loads in different regions was constructed. Finally, an optimal dispatch model for electric vehicles considering the safety of distribution network was proposed and the distribution transformer capacity allocation model was used as the optimization target constraint. Compared with most optimized dispatch models used to maximize aggregator revenues and reduce peak-to-valley differences and load fluctuations in distribution networks, this model could effectively reduce unnecessary investment while meeting regional distribution transformer needs and maintaining distribution network security. Taking the improved IEEE 34-bus systems as an example, the simulation analysis was carried out and the investment demand of distribution network under the condition of disordered and orderly charge and discharge was compared. The results show that the proposed optimal scheduling method can effectively reduce the load fluctuation of distribution network, keep the voltage offset within the allowable voltage deviation range, and can effectively delay the investment of distribution network.

In recent years, China has promoted many new energy projects in order to meet the growing demand for electricity. Therefore, China's offshore wind power installed capacity has grown rapidly. China has a long coastline and abundant offshore wind energy resources. Offshore wind power is an important area for the development of renewable energy, which can promote wind power technology advancement and energy structure adjustment. Therefore, conducting effective research and forecast on the cumulative installed capacity of China's offshore wind power will help the government to rationally deploy and reduce the risk of investment in offshore wind power. In order to accurately predict the future prospects of offshore wind power in China, this paper firstly constructed a set of influencing factors and used gray correlation analysis to screen the main influencing factors. Then, this paper proposed a novel forecasting model named e‐VMD‐PCA‐RELM. The algorithm is based on the traditional RELM (robust extreme learning machine) algorithm, which effectively processes the noise information through the PCA (principal component analysis) algorithm, and extracted the feature elements of the RELM hidden layer to reduce the information redundancy. At the same time, the e‐VMD (variational mode decomposition optimized by entropy) algorithm is used to decompose the original time series to obtain multiple components. By comparing with the other forecasting algorithms, it is proved that the proposed forecasting model has strong generalization ability and has achieved good prediction result. Finally, the e‐VMD‐PCA‐RELM model is used to predict the scale of offshore wind farms in China from 2019 to 2035. We find that the cumulative installed capacity of China's offshore wind power will exceed 60 GW in 2035, and the installed capacity will increase year by year. In 2030, there will be a large increase, with a relative growth rate of 20%.

In photovoltaic (PV) power generation, the intermittency and uncertainty caused by meteorological factors pose challenges to grid operations. Accurate PV power prediction is crucial for optimizing power dispatching and balancing supply and demand. This paper proposes a PV power prediction model based on Density Peak Clustering Algorithm (DPCA)–Crested Porcupine Optimizer (CPO)–Random Forest (RF)–Gated Recurrent Unit (GRU)–Kolmogorov–Arnold Network (KAN). First, the DPCA is used to accurately classify weather conditions according to meteorological data such as solar radiation, temperature, and humidity. Then, the CPO algorithm is established to optimize the factor screening characteristic variables of the RF. Subsequently, a hybrid GRU model with a KAN layer is introduced for short-term PV power prediction. The Shapley Additive Explanation (SHAP) method values evaluating feature importance and the impact of causal features. Compared with other contrast models, the DPCA-CPO-RF-KAN-GRU model demonstrates better error reduction capabilities under three weather types, with an average fitting accuracy R² reaching 97%. SHAP analysis indicates that the combined average SHAP value of total solar radiation and direct solar radiation contributes more than 70%. Finally, the Kernel Density Estimation (KDE) is utilized to verify that the KAN-GRU model has high robustness in interval prediction, providing strong technical support for ensuring the stability of the power grid and precise decision-making in the electricity market.