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智能优化算法 神经网络预测 雷达通信 无线传感器 电力系统
信号处理 图像处理 路径规划 元胞自动机 无人机
🔥 内容介绍
在当今全球能源转型的背景下，电力系统的可持续发展成为了各国政府和能源公司的重要议题。为了实现可再生能源的大规模应用，储能技术被广泛认为是解决电力系统不稳定性和间歇性的有效手段之一。然而，储能系统的运行优化对于实现电力系统的经济性至关重要。
储能系统的运行优化涉及到多个方面，包括储能设备的调度、能量管理策略的制定以及市场机制的设计等。在电力系统中，储能设备可以通过储能和释放能量来平衡电力供需之间的差异，从而提高系统的稳定性和灵活性。然而，储能设备的运行成本较高，因此如何在满足电力需求的同时降低储能系统的运行成本成为了一个重要的问题。
为了实现储能系统的经济性，需要制定合理的能量管理策略。能量管理策略可以通过优化储能设备的充放电策略来实现对电力系统的经济调度。例如，在电力需求高峰期，储能设备可以通过释放储存的能量来满足电力需求，从而避免了高成本的燃煤发电机组的运行。而在电力需求低谷期，储能设备可以通过充电来储存廉价的电力，以备不时之需。通过合理的能量管理策略，可以实现储能系统的最优运行，从而降低系统的运行成本。
除了能量管理策略，市场机制的设计也对储能系统的经济性起到了关键作用。在传统的电力市场中，储能设备的参与度较低，因此其经济效益无法得到充分发挥。为了解决这个问题，一些国家和地区开始探索新的市场机制，例如引入储能容量市场和灵活性市场。储能容量市场可以通过向储能设备提供资金奖励来鼓励其提供备用容量，从而提高储能设备的经济效益。而灵活性市场可以通过向储能设备提供灵活性服务的奖励来鼓励其参与市场交易，从而提高储能系统的经济性。通过合理的市场机制设计，可以激励储能设备的参与度，从而提高系统的经济性。
总之，储能系统的运行优化对于实现电力系统的经济性至关重要。通过制定合理的能量管理策略和设计合理的市场机制，可以实现储能系统的经济调度和参与度，从而降低系统的运行成本。在未来的能源转型中，储能系统将扮演着越来越重要的角色，为电力系统的可持续发展做出贡献。
📣 部分代码
%%%%%%%%% the signal components are crossing %%%%%%%%%%%%%%
% this example is adopted from paper：Chen S, Peng Z, Yang Y, et al, Intrinsic chirp component decomposition by using Fourier Series representation, Signal Processing, 2017.
clc
clear
close all
SampFreq = 100;
t = 0:1/SampFreq:15;
Sig1 = cos(2*pi*(0.23+15*t + 0.2*t.^2));
IF1 = 15 + 0.4*t;
amp2 = 0.5*cos(2*pi*0.3*t)+1;
Sig2 = amp2.*cos(2*pi*(5*sin(pi/4*t) +5*t + 1.2*t.^2 ));
IF2 = 5*pi/4*cos(pi*t/4) + 2.4*t + 5;
Sig3 = cos(2*pi*(0.35+35*t - 0.8*t.^2));
IF3 = 35 - 1.6*t;
Sig = Sig1 + Sig2 + Sig3;
figure
set(gcf,'Position',[20 100 320 250]);
set(gcf,'Color','w');
plot(t,Sig);
xlabel('Time / Sec','FontSize',12,'FontName','Times New Roman');
ylabel('Amplitude','FontSize',12,'FontName','Times New Roman');
set(gca,'FontSize',12)
set(gca,'linewidth',1);
%% STFT
window = 128;
Nfrebin = 1024;
figure
[Spec,f] = STFT(Sig',SampFreq,Nfrebin,window);
imagesc(t,f,abs(Spec));
axis([0 15 0 50]);
set(gcf,'Position',[20 100 320 250]);
xlabel('Time / Sec','FontSize',12,'FontName','Times New Roman');
ylabel('Frequency / Hz','FontSize',12,'FontName','Times New Roman');
set(gca,'YDir','normal')
set(gca,'FontSize',12);
set(gcf,'Color','w');
%% ridge extraction and smoothing
bw = SampFreq/80;% the bandwidth of the TF filter for ridge extraction
beta1 = 1e-4; % beta1 should be larger than the following beta
num = 3; % the number of the components
delta = 20;
[fidexmult, tfdv] = extridge_mult(Sig, SampFreq, num, delta, beta1,bw,Nfrebin,window);
figure
set(gcf,'Position',[20 100 640 500]);
set(gcf,'Color','w');
plot(t,f(fidexmult),'linewidth',3); % detected ridge curves
xlabel('Time / Sec','FontSize',24,'FontName','Times New Roman');
ylabel('Frequency / Hz','FontSize',24,'FontName','Times New Roman');
set(gca,'FontSize',24)
set(gca,'linewidth',2);
axis([0 15 0 50]);
%% ridge path regrouping (RPRG)
% the RPRG algorithm is developed to extract ridge curves of crossed signal modes
% more details about the RPRG can be found in paper: Chen S, Dong X, Xing G, et al, Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition, IEEE Sensors Journal, 2017.
thrf = length(f)/30;
[findex,interset] = RPRG(fidexmult,thrf);
figure
set(gcf,'Position',[20 100 640 500]);
set(gcf,'Color','w');
plot(t,f(findex),'linewidth',3);
xlabel('Time / Sec','FontSize',24,'FontName','Times New Roman');
ylabel('Frequency / Hz','FontSize',24,'FontName','Times New Roman');
set(gca,'FontSize',24)
set(gca,'linewidth',2);
axis([0 15 0 50]);
%% parameter setting
alpha = 1e-5;
beta = 1e-5; % this parameter can be smaller which will be helpful for the convergence, but it may cannot properly track fast varying IFs
iniIF = curvesmooth(f(findex),beta); % the initial guess for the IFs by ridge detection; the initial IFs should be smooth and thus we smooth the detected ridges
var = 0;% noise variance
tol = 1e-8;%
tic
[IFmset IA smset] = VNCMD(Sig,SampFreq,iniIF,alpha,beta,var,tol);
toc
figure
set(gcf,'Position',[20 100 640 500]);
set(gcf,'Color','w');
plot(t,iniIF(:,:),'linewidth',3); % initial IFs
xlabel('Time / Sec','FontSize',24,'FontName','Times New Roman');
ylabel('Frequency / Hz','FontSize',24,'FontName','Times New Roman');
set(gca,'FontSize',24)
set(gca,'linewidth',2);
axis([0 15 0 50]);
%% Relative errors of the initial IFs
iniRE1 = norm(iniIF(1,:)-IF1)/norm(IF1)
iniRE2 = norm(iniIF(2,:)-IF2)/norm(IF2)
iniRE3 = norm(iniIF(3,:)-IF3)/norm(IF3)
%% estimated IF
figure
plot(t,[IF1;IF2;IF3],'b','linewidth',3) % true IFs
hold on
plot(t,IFmset(:,:,end),'r','linewidth',3) % finally estimated IFs
set(gcf,'Position',[20 100 640 500]);
xlabel('Time / Sec','FontSize',24,'FontName','Times New Roman');
ylabel('Frequency / Hz','FontSize',24,'FontName','Times New Roman');
set(gca,'YDir','normal')
set(gca,'FontSize',24);
set(gca,'linewidth',2);
set(gcf,'Color','w');
axis([0 15 0 50])
%% Relative errors of the finally estimated IFs
RE1 = norm(IFmset(1,:,end)-IF1)/norm(IF1) % one can find that the accuracy of the finally estimated IFs is significantly improved compared with the initial IFs
RE2 = norm(IFmset(2,:,end)-IF2)/norm(IF2)
RE3 = norm(IFmset(3,:,end)-IF3)/norm(IF3)
%% Reconstructed modes
figure
set(gcf,'Position',[20 100 640 200]);
set(gcf,'Color','w');
plot(t,smset(1,:,end),'linewidth',2) % estimated mode
hold on
plot(t,Sig1 - smset(1,:,end),'k','linewidth',2) % estimation errors
hold on
plot(t,IA(1,:),'r','linewidth',3) % estimated IAs
xlabel('Time / Sec','FontSize',24,'FontName','Times New Roman');
ylabel('m1','FontSize',24,'FontName','Times New Roman');set(gca,'YDir','normal')
set(gca,'FontSize',24);
set(gca,'linewidth',2);
axis([0 15 -1.5 1.5])
figure
set(gcf,'Position',[20 100 640 200]);
set(gcf,'Color','w');
plot(t,smset(2,:,end),'linewidth',2) % estimated mode
hold on
plot(t,Sig2 - smset(2,:,end),'k','linewidth',2) % estimation errors
hold on
plot(t,IA(2,:),'r','linewidth',3) % estimated IAs
xlabel('Time / Sec','FontSize',24,'FontName','Times New Roman');
ylabel('m2','FontSize',24,'FontName','Times New Roman');set(gca,'YDir','normal')
set(gca,'FontSize',24);
set(gca,'linewidth',2);
axis([0 15 -2 2])
figure
set(gcf,'Position',[20 100 640 200]);
set(gcf,'Color','w');
plot(t,smset(3,:,end),'linewidth',2) % estimated mode
hold on
plot(t,Sig3 - smset(3,:,end),'k','linewidth',2) % estimation errors
hold on
plot(t,IA(3,:),'r','linewidth',3) % estimated IAs
xlabel('Time / Sec','FontSize',24,'FontName','Times New Roman');
ylabel('m3','FontSize',24,'FontName','Times New Roman');set(gca,'YDir','normal')
set(gca,'FontSize',24);
set(gca,'linewidth',2);
axis([0 15 -1.5 1.5])⛳️ 运行结果
🔗 参考文献
[1] 王颖.面向复杂不确定性的电力系统运行优化研究[D].东南大学,2018.
[2] 邱再森.储能规划及不同运行模式下经济性研究[D].郑州大学[2023-10-12].DOI:CNKI:CDMD:2.1018.109662.