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In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on load, power generation, and electricity pricing data from a typical northwest region in China in 2022. The findings indicate the following: (1) In producing the typical net load curves, the employed K-means++ technique outperformed the standard models in terms of the clustering effect by 4.27–26.70%. (2) Following optimization, there was a decrease of 1900 MW in the maximum monthly abandonment of renewable energy, a decrease of 0.31–53.94% in the peak–valley difference, and a decrease of 2.03–17.27% in the monthly net load cost. (3) By taking extra critical peak and deep valley time periods into account, the average net load cost decreased by 10.36% compared with conventional peak–flat–valley time period division criteria. [ABSTRACT FROM AUTHOR]

The electricity transmission and distribution tariff policy of the second supervision cycle in China has formulated a much better electricity transmission and distribution tariff supervision system. In this context, the research on the risk related to electricity transmission and distribution tariff regulation faced by power grid enterprises is helpful for power regulatory agencies and business operators to identify and avoid risks in time and promote the sustainable development of electric power industry. Firstly, the risk evaluation criteria system is established. Secondly, a risk evaluation model based on the best and worst method (BWM) and cloud model for electricity transmission and distribution tariff regulation is proposed. Finally, the risk level of power transmission and distribution tariff regulation faced by four provincial power grid enterprises is evaluated. The validity and practicability of the proposed model in this paper are proved by the empirical analysis.

The new round of power system reform has strengthened the supervision of transmission and distribution links, and changed the profit model of power grid enterprises by verifying the transmission and distribution price. In this context, the research on the regulatory risk faced by power grid enterprises under the electricity price regulatory policy can provide tools and decision support for power grid enterprises to accurately grasp the regulatory risk and reduce the risk loss. Firstly, combined with the transmission and distribution price supervision process faced by power grid enterprises, the possible risks faced by power grid enterprises in price supervision are analyzed. Secondly, the quantitative measurement model of electricity price regulation of power grid enterprises is constructed by using system dynamics, and the effectiveness of the model is verified. Finally, the future regulatory period is simulated and the results are analyzed, and policy recommendations are put forward.

A method of optimizing the design of the sightseeing island micro-gird is described and combined with the actual case, simulation analysis is performed using the HOMOER. The simulation results show that for the sightseeing offshore island with limited natural resources, diesel-renewable-storage mixed micro-grid is more suitable for practical application and is the best choice. In the planning of sightseeing island microgrid, environmental protection requirements and system full standby needs should be taken into account.

With the deepening of the reform on the selling side of electricity, the selling company must strengthen the analysis on the electricity consumption of users and arrange the purchase and sale of electricity scheme scientifically so as to occupy the target selling electricity market and obtain the profit of selling electricity. Based on the big data background, the paper uses the k-means clustering method to divide the load curve of 2,498 residential power users into 5 categories. On the premise of considering the system load, the above five categories are classified into three types: peak load, partial peak load, and stable power users.

Considering the goals of carbon peaking and carbon neutrality, along with their related policies, pumped storage power stations are set to develop quickly in China. The comprehensive performance of pumped storage power stations must urgently be evaluated, which can help investors in decision making and provide a reference for policymakers. In this paper, a hybrid novel fuzzy multicriteria decision-making (MCDM) method combining the fuzzy best worst method (BWM) and fuzzy TOPSIS was proposed for the comprehensive performance evaluation of pumped storage power stations in China. The fuzzy BWM was utilized to determine the criteria weights describing the comprehensive performance of pumped storage power stations, while the fuzzy TOPSIS was used to rank the comprehensive performance of pumped storage power stations. The index system for the comprehensive performance evaluation of pumped storage power stations in China incorporated economic, social, and environmental aspects. The comprehensive performance of four pumped storage power stations in China was empirically evaluated using the proposed hybrid novel fuzzy MCDM method, and the results indicate that pumped storage power station PSPS2 exhibited the best comprehensive performance, followed by pumped storage power stations PSPS1 and PSPS4, whereas pumped storage power station PSPS3 had the worst comprehensive performance. A sensitivity analysis and comparative analysis were also conducted. The results indicate that the proposed hybrid novel fuzzy MCDM method, combining the fuzzy BWM and fuzzy TOPSIS for comprehensive performance evaluation of pumped storage power stations, is robust and effective. [ABSTRACT FROM AUTHOR]