Abstract: Disclosed is a coordinated peak shaving optimization method for a plurality of power supplies based on a fluctuation characteristic of a renewable energy source, including the following steps: s1: determining a weekly generated electricity quantity of hydropower based on an available capacity and a storage capacity of an electricity quantity; s2: predicting a renewable energy power generation curve and a load curve of a system weekly; s3: determining a start point of peak shaving of the hydropower based on an external transmission curve, the renewable energy generation curve, and the load curve of the system and a generating capacity of the hydropower; s4: determining a weekly peak shaving demand of the system; and s5: establishing an optimization model with a maximum peak shaving demand. The present disclosure proposes a reasonable arrangement for peak shaving, so as to resolve an accommodation problem caused by large-scale access of a renewable energy source.

Abstract: A peak shaving control method for emergent source-grid coordination in case of a faulty sending-end power grid. The method includes: S1: evaluating dispatchability of a cluster virtual wind power unit; S2: developing a method for calculating a dispatchability index of the cluster virtual wind power unit; S3: analyzing a source-load peak-shaving resource strategy; and S4: distributing a control strategy for tie-line peak shaving. The present disclosure has the following beneficial effects: In the present disclosure, real-time dispatchability of wind power participating in real-time power balance is first analyzed, specific evaluation indexes and calculation methods are provided, and calculation examples are given for verification. Then, an optimized real-time dispatch strategy is provided based on demand-side response resources, and DC and AC tie-lines are coordinated for operation.

Abstract: The present invention discloses a maintenance schedule optimization method for an electric power system including large-scale wind power to build objective function to make it get optimal results, then constrain the objective function, constraint conditions include: constrain according to maintenance natural environment; constrain according to maintenance time of wind power plant; constrain according to maintenance site of wind power plant; constrain according to system reliability at wind power plant; constrain according to maintenance continuity of generator unit. Under the above constraint conditions, the electric power system is modeled with heuristic algorithm according to the above objective function.

Abstract: The present invention discloses a testing method for dynamic model parameters of a wind power plant, wherein under testing conditions including stable running of wind turbine generators and reactive-load compensation equipment, stable wind speed between a cut-in wind speed and a rated wind speed, and the wind power plant output smaller than a rated output, finishes a wind speed transient disturbance test, a wind power plant unit tripping and inputting disturbance test, a wind power plant power change test, a disturbance test for reactive-load compensation equipment of the wind power plant, a tripping disturbance test for hydroelectric power plant and thermal power plant near the wind power plant and a manual single-phase ground short circuit test at the outgoing line part of the wind power plant, and records running curves of the wind power plant under each condition to finish the parameter test.

Abstract: The present invention discloses a short-term operation optimization method for a power system including large-scale wind power, comprising modeling the randomness of wind power output, modeling the randomness of the load of electric power system and modeling net load of electric power system. Net load refers that for probability distribution of net load that is too discretized, probability distribution curve of net load is divided into N intervals, the probabilities for each interval are obtained and probability distribution curve of net load is obtained through calculating and weighing each interval.

Abstract: The present invention discloses a testing method for dynamic model parameters of a wind power plant, wherein under testing conditions including stable running of wind turbine generators and reactive-load compensation equipment, stable wind speed between a cut-in wind speed and a rated wind speed, and the wind power plant output smaller than a rated output, finishes a wind speed transient disturbance test, a wind power plant unit tripping and inputting disturbance test, a wind power plant power change test, a disturbance test for reactive-load compensation equipment of the wind power plant, a tripping disturbance test for hydroelectric power plant and thermal power plant near the wind power plant and a manual single-phase ground short circuit test at the outgoing line part of the wind power plant, and records running curves of the wind power plant under each condition to finish the parameter test.

Abstract: Disclosed is a method for modeling a medium and long term wind power output model optimally operating in a medium and long term in a power system.

Abstract: The present invention discloses a maintenance schedule optimization method for an electric power system including large-scale wind power to build objective function to make it get optimal results, then constrain the objective function, constraint conditions include: constrain according to maintenance natural environment; constrain according to maintenance time of wind power plant; constrain according to maintenance site of wind power plant; constrain according to system reliability at wind power plant; constrain according to maintenance continuity of generator unit. Under the above constraint conditions, the electric power system is modeled with heuristic algorithm according to the above objective function.

Abstract: An ultra-short-term forecasting method including real-time monitoring of the effect of upper and lower courses, comprising: obtaining ultra-short-term model forecast results through the model lattices based on the T639 global spectral model course library data source, the CALMET wind field diagnostic model and static data; establishing statistical equations on the effect of upper and lower courses between the corresponding reference index station and each target wind tower for obtaining the effect of upper and lower courses of the target wind towers based on the wind tower database of the target wind power base and combined with the wind direction and speed real-time monitoring data of the reference index stations in the upper and lower courses, forecasting the ultra-short-term wind speed changes of each target wind tower and correct combined with the ultra-short-term model forecast results to form forecasting of the ultra-short-term wind speed changes of the wind towers in the target wind power base; after r