Abstract
With the increasing installed capacity of renewable energy in the energy system, the uncertainty of renewable energy has an increasingly prominent impact on power system planning and operation. Renewable energy such as wind and solar energy is greatly affected by the external weather. How to use a reasonable method to describe the relationship between weather and renewable energy output, so as to measure the uncertainty of renewable energy more accurately, is an important problem. To solve this problem, this paper proposes a renewable energy scenario generation method based on a conditional generation countermeasure network and combination weighting method (CWM-CGAN). In this method, the combination of AHP and the entropy weight method is used to analyze the meteorological factors, the weather classification is defined as the condition label in the conditional generation countermeasure network, and the energy scenario is generated by the conditional generation confrontation network. In this paper, the proposed method is tested with actual PV data, and the results show that the proposed model can describe the uncertainty of PV more accurately.
Highlights
Academic Editors: Péter Balogh, Accompanied by the energy system’s pursuit of green energy and clean energy, and the urgent need for a reduction in energy and emissions, an increasing amount of renewable energy sources are connected to the system, and the penetration rate of renewable energy continues to increase
The final label value was obtained from the combined weight of Equation (7), and different data category labels were given to the historical data samples of renewable energy and the data samples generated by condition generative adversarial network (CGAN) generator
The second region is the range of historical data: the inner side is the scene range generated by the CW-CGAN method proposed in this paper, and the innermost side is the generation range of autoregressive moving average model (ARMA) method
Summary
Academic Editors: Péter Balogh, Accompanied by the energy system’s pursuit of green energy and clean energy, and the urgent need for a reduction in energy and emissions, an increasing amount of renewable energy sources are connected to the system, and the penetration rate of renewable energy continues to increase. Scenario generation technology is one of the common methods for analyzing the output characteristics of renewable energy. The artificial intelligence scenario method is not constrained by the establishment of a renewable energy scenario model It can learn the situation of renewable energy resources in the region by analyzing the information contained in historical data and replicate the law of scenario changes. As a common deep learning method in the field of data generation, GAN can be used in the scene construction of an integrated energy system. It can build more scene sets with certain characteristics, based on limited original samples. The combination weighting method is applied to the weather label classification of the original wind and solar data, and the weather labels of the data are determined by determining the weights of different meteorological factors to provide support for the CGAN to generate wind and solar output scenarios
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