Method for generating wind and photovoltaic typical weekly output scenes for medium-/long-term optimal dispatching

Abstract

The invention belongs to the technical field of complementary optimal dispatching of multi-energy power systems, and relates to a method for generating wind and photovoltaic typical weekly output scenes for medium- and long-term optimal dispatching. In order to solve the problem on multi-energy inter-seasonal complementary medium- and long-term optimal dispatching in a power system with wind powerand photovoltaic power, a polymerization method based on improved dual-scale AP clustering and the Markov chain is used to generate weekly scenes for time series simulation calculation. The output time series of a wind / photovoltaic uncertain power source are compressed to obtain compressed new output time series which can accurately reflect the probabilistic characteristics of the original outputtime series. Therefore, the problem that the solution cannot be obtained quickly due to multiple time sections in medium- and long-term optimal calculation is solved, and a guide is provided for themedium- and long-term power planning in system optimal dispatching.

Description

technical field [0001] The invention belongs to the technical field of multi-energy power system complementary optimal scheduling, and in particular relates to a method for generating typical weekly output scenarios of wind power photovoltaics for medium- and long-term optimal scheduling. Background technique [0002] With the depletion of non-renewable resources such as coal and oil and the increasingly serious energy dilemma, renewable energy such as wind energy, solar energy, tidal energy and biomass energy has attracted more and more attention worldwide. Among them, the use of wind / light natural resources is the most mature technology and the most development value of the two renewable energy sources in the renewable energy generation technology. The development of wind power and photovoltaic is of great significance to ensure energy security, adjust energy structure, reduce environmental pollution and achieve sustainable development. [0003] Natural wind and photovolt...

AI Technical Summary

Problems solved by technology

However, the results of the above aggregation methods (using a certain clustering algorithm) cannot well reflect the trend and volatility of the original output time series

Moreover, the traditional k-means clustering algorithm is highly sensitive to the selection of the initial cluster center, the stability of multiple clustering is poor, and the optimal classification number cannot be given at the same time.

The disadvantage of hierarchical clustering is: the amount of calculation is relatively large; in addition, because the hierarchical clustering uses a greedy algorithm, the result is obviously only the local optimum, not necessarily the global optimum

Therefore, the typical scenarios obtained by the above methods all have deficiencies.

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