1. Microgrid system operation optimization model
A microgrid is a small power generation and distribution system composed of distributed power sources, energy storage devices, and energy conversion devices. It has the characteristics of low cost, low voltage, and low pollution. Due to environmental protection and energy pressure, the development potential of clean renewable energy and distributed energy industries is huge. The micro-grid controller can realize the centralized control of the power grid, the distributed power supply and load local controller can realize the transient control of the micro-grid, and the centralized energy management system of the micro-grid can realize the steady-state safety and economic operation analysis. Microgrids are autonomous systems that enable self-control, protection, and management.
Introduction to microgrid optimization model:
Introduction to Microgrid Multi-Objective Optimization Dispatch Model_IT Yuanshou's Blog-CSDN Blog
2. Growth optimization algorithm
The Growth Optimizer (GO) was proposed by Qingke Zhang et al. in 2023. The design of the algorithm is inspired by the learning and reflection mechanism of individuals in the growth process. Learning is a process in which individuals grow by acquiring knowledge from the outside world, and reflection is a process in which individuals check their own shortcomings, adjust individual learning strategies, and help individuals grow. Growth Optimizer (GO) provides MATLAB code_IT Yuanshou's Blog-CSDN Blog
references:
Qingke Zhang, Hao Gao, Zhi-Hui Zhan, Junqing Li, Huaxiang Zhang ,Growth Optimizer: A powerful metaheuristic algorithm for solving continuous and discrete global optimization problems ,Knowledge-Based Systems,261,2023
3. GO solves microgrid optimization
(1) Part of the code
close all;
clear ;
clc;
global P_load; % electrical load
global WT;% wind power
global PV;% photovoltaic
%%
TestProblem=1;
[lb,ub,dim,fobj] = GetFunInfo(TestProblem);
SearchAgents_no=100; % Number of search agents
Max_iteration=1000; % Maximum number of iterations
[Best_score,Xbest,Convergence_curve]=GO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
%% draw the result graph
figure(1)
semilogy(Convergence_curve,'g-','linewidth',2);
legend('GO');
xlabel('Number of iterations')
ylabel('Sum of operating costs and environmental protection costs')
(2) Partial results
