A photovoltaic output time sequence simulation method based on a multi-scene state transition matrix and conditional probability sampling

Abstract

The invention discloses a photovoltaic output time sequence simulation method based on a multi-scene state transition matrix and conditional probability sampling. The photovoltaic output time sequencesimulation method is used for simulating and generating photovoltaic time sequence output considering seasonal characteristics, daily characteristics, weather characteristics and fluctuation characteristics. The method comprises the following steps: firstly, aiming at a monthly photovoltaic output sequence, taking FCM clustering as internal optimization, and taking DB (-)clustering effectivenessindex as external optimization to form an original photovoltaic output sequence scene with clearer data characteristics. Secondly, establishing photovoltaic output state transfer matrixes of differentscenes, generating a photovoltaic output time sequence through a Markov chain Monte Carlo method, in the process, carrying conditional probability sampling through the Copula theory, generating a photovoltaic output state value at the next moment, and superposing the fluctuation amount conforming to the original probability distribution characteristic. Compared with an existing model, the probability statistics characteristic and the time sequence characteristic of the data are more accurate, and the implementation process is simple and easy to implement.

Description

technical field [0001] The invention belongs to the field of new energy power generation modeling, and relates to a photovoltaic output time series simulation method based on multi-scenario state transition matrix and conditional probability sampling. The output characteristics under the date and date provide a large amount of similar but different basic data for power grid operation and planning. Background technique [0002] In recent years, my country's renewable energy represented by solar energy has been connected to the grid on a large scale and distributed with a high penetration rate, which has effectively alleviated the energy crisis and environmental pollution. At the same time, since photovoltaic power generation is affected by natural factors such as climate and environment, it has obvious intermittency, randomness and volatility, which brings more challenges to the dispatching operation and planning of the power system. Therefore, starting from the "source", it...

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Problems solved by technology

The state transition matrix in the original Markov chain model cannot fully reflect the seasonal characteristics, weather characteristics and daily characteristics of photovoltaic output. Therefore, some studies have improved the above problems by dividing the photovoltaic sequence according to weather types and taking into account the fluctuation characteristics of photovoltaics. , but the above division method is a method that is biased towards human experience, and there is still strong randomness in the sampling process

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