Pumped storage power plants face many challenges in competing in the electricity market, and high pumping costs lead to high prices for their power generation, which is one of the important factors that has limited their development. To address this problem, this paper studies the pumped storage two-part tariff mechanism considering wind power accommodation and uses the peak-valley price difference of wind power to realize the rationality and economy of a pumped storage charging and discharging strategy. It can improve the competitiveness of pumped storage power plants participating in electricity market transactions. Then, by considering the economic advantages of “pumped storage + clean energy”, a pumped storage and wind power joint optimal dispatching model was established based on the original pumped storage pricing method. This model takes the total system cost reduction after the introduction of pumped storage as the objective function to derive a reasonable pumped storage strategy. After which, the two-part tariff for pumped storage power plants was formulated based on the principle of reasonable revenue. Finally, a sensitivity analysis of various relevant parameters of the power plant was conducted through case studies to verify the effectiveness of the two-part tariff mechanism of pumped storage. It was found that the electricity tariff is lowest when the ratio of plant capacity to upper reservoir capacity is 1:6.37 (MW/million m³).

Power big data-based artificial intelligence or data mining methods, which can be used to analyze electricity consumption behavior, have been widely applied to provide targeted marketing services for electricity consumers. However, the traditional clustering algorithm has difficulty in judging new electricity consumption patterns. Deep neural networks usually need large amounts of labeled data. However, there are few comparable electricity consumption features or basic data, and the labeled data cannot meet the actual needs. Therefore, an intelligent classification framework for electricity consumption behavior based on an improved k-means and long short-term memory (LSTM) is proposed, which not only extracts features effectively, but also establishes a mapping relationship between unlabeled electricity consumption behavior characteristics and user types. The features can be labeled to train the deep neural network to judge the electricity consumption behavior of new users. Firstly, nine typical characteristics were selected from aspects including electricity price sensitivity and load fluctuation rate. Secondly, the k value and initial clustering centers of the k-means algorithm were optimized. Thirdly, the users were labelled based on the clustering results, together with the features, and a dataset was formed, which was input into LSTM to train the classification model. Finally, the analysis of users in Shenyang, China, showed the results based on the proposed method were consistent with the actual situation. Moreover, compared to other methods, the efficiency and accuracy were higher.

In this study, we utilize the advantages of offshore wind power resources in coastal areas to make up for the shortage of freshwater. At the same time, freshwater can be used as raw material to supply hydrogen energy. An operation strategy considering power and water input for an electrolyzer is proposed and an electrolyzer variable efficiency model under optimal operation mode is also proposed. Considering both energy benefits and operation and maintenance costs, this paper sets up the wind–hydrogen–water power grid system optimal operation model. Simulation analysis is carried out from the aspects of economy, accommodation, uncertainty of impacts of the reservoir capacity, water satisfaction, and so on. The results show that the accommodation rate reaches 98.2% when considering the proposed operation strategy. The average daily operating cost of the system reaches USD 3.9 × 10⁴, and the strategy and model have good economic benefits and practical significance.

The manufacturing industry consumes electricity and natural gas to provide the power and heat required for manufacturing. Additionally, large amounts of electric energy and heat energy are used, and the electricity cost, amount of environmental pollution, and equipment maintenance cost are high. Thus, optimizing the management of equipment with new energy is important to satisfy the load demand from the system. This paper formulates the scheduling problem of these multiple energy systems as a multi-objective linear regression model (MLRM), and an energy management system is designed focusing on the economy and on greenhouse gas emissions. Furthermore, a variety of optimization objectives and constraints are proposed to make the energy management scheme more practical. Then, grey theory is combined with the common MLRM to accurately represent the uncertainty in the system and to make the model better reflect the actual situation. This paper takes load fluctuation, total grid operation cost, and environmental pollution value as reference standards to measure the effect of the gray optimization algorithm. Lastly, the model is applied to optimize the energy supply plan and its performance is demonstrated using numerical examples. The verification results meet the optimized operating conditions of the multi-energy microgrid system.

Increasing wind power generation has been introduced into power systems to meet the renewable energy targets in power generation. The output efficiency and output power stability are of great importance for wind turbines to be integrated into power systems. The wake effect influences the power generation efficiency and stability of wind turbines. However, few studies consider comprehensive corrections in an aerodynamic model and a turbulence model, which challenges the calculation accuracy of the velocity field and turbulence field in the wind turbine wake model, thus affecting wind power integration into power systems. To tackle this challenge, this paper proposes a modified Reynolds-averaged Navier–Stokes (MRANS)-based wind turbine wake model to simulate the wake effects. Our main aim is to add correction modules in a 3D aerodynamic model and a shear-stress transport (SST) k-ω turbulence model, which are converted into a volume source term and a Reynolds stress term for the MRANS-based wake model, respectively. A correction module including blade tip loss, hub loss, and attack angle deviation is considered in the 3D aerodynamic model, which is established by blade element momentum aerodynamic theory and an improved Cauchy fuzzy distribution. Meanwhile, another correction module, including a hold source term, regulating parameters and reducing the dissipation term, is added into the SST k-ω turbulence model. Furthermore, a structured hexahedron mesh with variable size is developed to significantly improve computational efficiency and make results smoother. Simulation results of the velocity field and turbulent field with the proposed approach are consistent with the data of real wind turbines, which verifies the effectiveness of the proposed approach. The variation law of the expansion effect and the double-hump effect are also given.

Coaxial coil topology is used as the transfer medium in traditional MCR-WPT (Magnetic Coupled Resonant Wireless Power Transfer) systems to improve the transfer characteristics. The intermediate coils are added to extend the transmission distance, whose positions are critical. This paper focuses on the optimal intermediate coil positions for an MCR-WPT system with four coaxial planar circular spiral coils. By modeling the MCR-WPT system, the mathematical expression of the self-inductance and the mutual inductance are used to calculate the load power of the MCR-WPT system, which is composed of four planar circular spiral coaxial coils, and using MATLAB. The optimal distance ratio between the adjacent coils for maximizing the power of load is proposed. Furthermore, the experiments are implemented from the network analyzer and the experimental platform. In the platform, the load power is measured at the different intermediate coil positions, and the optimal position at which the load power is maximized is found. Both experimental results obtained by the network analyzer and the experimental platform have validated the theoretical and simulation results and provided the correctness of the suggested ratios.