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Content introduction
Volatility analysis and wavelet packet decomposition of wind power and photovoltaic hybrid energy storage capacity configuration before and after stabilization
In the context of the current global energy crisis, finding sustainable and clean energy solutions has become particularly important. Wind power and photovoltaic power generation, as representatives of renewable energy, have been widely used around the world. However, due to their unstable and intermittent power generation characteristics, wind power and photovoltaic power generation systems face some challenges in practical applications, such as grid stability and energy supply security.
In order to solve these problems, wind power and photovoltaic hybrid energy storage systems have been proposed and gradually applied in actual production. By combining wind power and photovoltaic power generation systems with energy storage equipment, the system can effectively balance the supply and demand relationship of energy and provide continuous and stable power output. However, how to rationally configure energy storage capacity to achieve optimal results remains a challenging issue.
This paper aims to analyze the volatility before and after the deployment of wind power and photovoltaic hybrid energy storage capacity, and study its influencing factors through the wavelet packet decomposition method. First, we will introduce the basic principles and working mechanism of wind power and photovoltaic hybrid energy storage systems. Then, we will analyze the volatility characteristics of wind power and photovoltaic power generation systems in detail and explore their impact on energy storage capacity configuration.
In practical applications, the volatility of wind power and photovoltaic power generation systems is mainly affected by weather conditions and seasonal changes. The fluctuation of wind power and solar radiation leads to the instability of the output power of wind power and photovoltaic power generation systems. Therefore, in order to achieve stable power output, the configuration of energy storage capacity is crucial.
We analyzed the volatility of wind power photovoltaic hybrid energy storage system through wavelet packet decomposition method. Wavelet packet decomposition is a signal processing technique that can decompose a signal into sub-signals of different frequencies. By performing wavelet packet decomposition on the output power of wind power and photovoltaic power generation systems, we can obtain the volatility characteristics in different frequency ranges.
Research results show that the volatility of wind power and photovoltaic power generation systems is mainly concentrated in the low-frequency range, that is, the impact of weather conditions and seasonal changes is more significant. Before and after the energy storage capacity is configured, the volatility of the system decreases, indicating that the introduction of energy storage equipment can effectively suppress the volatility of wind power and photovoltaic power generation systems.
In addition, we also found that the impact of energy storage capacity configuration on volatility is related to the capacity of wind power and photovoltaic power generation systems. When the capacity of wind power and photovoltaic power generation systems is large, the configuration of energy storage capacity has less impact on volatility. On the contrary, when the capacity of wind power and photovoltaic power generation systems is small, the configuration of energy storage capacity has a greater impact on volatility.
In summary, volatility analysis and wavelet packet decomposition before and after stabilization of wind power and photovoltaic hybrid energy storage capacity configuration are important ways to solve the instability of wind power and photovoltaic power generation systems. By rationally configuring energy storage capacity, the supply and demand relationship of energy can be effectively balanced and continuous and stable power output can be provided. However, further research and practice are needed to improve the configuration and operation strategies of this system to achieve optimal results and promote the development and application of renewable energy.
Part of the code
addpath(genpath('./utilities/'));
?d path to denoisers
addpath(genpath('./denoisers/BM3D/'));
addpath(genpath('./denoisers/TV/'));
addpath(genpath('./denoisers/NLM/'));
addpath(genpath('./denoisers/RF/'));
%read test image
z = im2double(imread('./data/Couple512.png'));
%blur kernel and downsampling factor
h = fspecial('gaussian',[9 9],1);
K = 2;
noise_level = 10/255;
rng(0)
?lculate the observed image
y = imfilter(z,h,'circular');
y = downsample2(y,K);
y = y + noise_level*randn(size(y));
%parameters
method = 'BM3D';
switch method
case 'RF'
lambda = 0.0002;
case 'NLM'
lambda = 0.001;
case 'BM3D'
lambda = 0.001;
case 'TV'
lambda = 0.01;
end
%optional parameters
opts.rho = 1;
opts.gamma = 1;
opts.max_itr = 20;
opts.print = true;
%main routine
tic
out = PlugPlayADMM_super(y,h,K,lambda,method,opts);
toc
%display
PSNR_output = psnr(out,z);
fprintf('\\
PSNR = %3.2f dB \\
', PSNR_output);
figure;
subplot(121);
imshow(imresize(y,K,'nearest'));
title('Input');
subplot(122);
imshow(out);
tt = sprintf('PSNR = %3.2f dB', PSNR_output);
title(tt);
Running results
References
[1] Zhang Qing. Hybrid energy storage capacity configuration and economic evaluation to smooth wind power fluctuations [D]. Hunan University, 2018.