The electricity consumption and economic growth are highly correlated. The financial crisis in 2008 brought a negative effect on China's economic growth, which also influenced the electricity consumption. The electricity demand of North China region was also greatly influenced by this financial crisis, the whole social electricity consumption growth rate of which decreased by 14.31% in 2008 compared to that in 2007. In order to analyze the random impulse effect of the financial crisis on the demand of electricity in North China, the monthly data is decomposed into deterministic trend, stochastic impact effect, and periodic trend using the Beveridge-Nelson decomposition method. After comparatively analyzing the impulse effect of the financial crisis on electricity consumption of six provinces in North China, we can draw the conclusions: (1) the electricity consumption of the whole society and the secondary industry are under larger negative impacts, and the random impulse effect of the secondary industry is more intense; and (2) the impact of the financial crisis on the tertiary industry, which is mainly influenced by seasonal changes, is smaller. Finally, some policy implications are proposed.

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Against the backdrop of achieving the “dual carbon” reduction goals, the proportion of renewable energy consumption in China is gradually increasing, and more and more thermal power units are gradually being operated at the deep peak shaving states, which puts forward higher requirements for stable operation of the power grid system. In view of the current situation, this paper designs a new type of Python-based parameter modeling software for turbine control systems, including the load selection module, data pre-processing module, parameter identification module, and result output module. To solve the problem that the primary frequency regulation parameters of the units operated at the deep peak shaving states change with the load, the software is designed to set key model parameters of different load segments of the units. The actual measurement results of a unit operated at the deep peak shaving state show that this developed software is convenient and friendly to operate, creates accurate parameter identification results, and has a wider range of applicability.

To reveal the performance of primary frequency regulation of the 600MW sub-critical thermal power units in a wide load range and accurately simulate the process, the primary frequency regulation performance of a 600MW sub-critical thermal power unit was studied with a wide load ranging from 20% to 90% under rated conditions. The model of steam turbine in BPA was modified in consideration of steam pressure and the valve throttling effect. The primary frequency regulation performance of the unit was simulated with the modified model. The research results show that the primary frequency regulation performance of the thermal power unit is poor at low load and the results of simulation with the modified model are highly consistent with the tested data due to consideration of steam pressure and valve throttling effect, meaning that the model can be used to simulate the primary frequency regulation of the thermal power unit with wide load operation accurately.

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