manual
Code-related resources: Matlab implementation of particle swarm integrated energy system optimization
MATLAB code: A day-ahead optimal scheduling model for integrated energy systems considering flexible loads
Key words: flexible load demand response integrated demand response day-ahead optimal dispatch integrated energy system
Reference documents: "A day-ahead optimization scheduling of community integrated energy systems considering user-side flexible loads" refers to the flexible load and basic model section; "Low-carbon economic optimal scheduling of integrated energy systems considering flexible loads" refers to its carbon trading section
Simulation platform: MATLAB yalmip+cplex
Main content: On the basis of the integrated energy system of cooling, heating and power, innovatively and meticulously divide and research the resources on the user side. In terms of load, it is further divided into three types: load that can be reduced, load that can be transferred, and load that can be shifted. The flexible load is added to the integrated energy dispatching system as a demand response resource, so that various loads can be flexibly adjusted according to market electricity prices, and peak load can be cut and filled. In addition, in order to enrich the content, a step-by-step carbon trading is also considered, and a comprehensive energy low-carbon economic dispatch model considering step-by-step carbon trading and comprehensive demand response is constructed, and multiple comparison scenarios are set up to verify The effectiveness of the proposed model, thus reflecting the workload, is a rare code!
% Using CPIEX to solve the operation optimization of a certain microgrid, the microgrid obtained from the optimization of the lower layer to purchase or sell electricity from the distribution network, as well as the output of each unit% is based on the concept of energy hubs, combined with the translation and transfer of flexible loads on the demand
side , can reduce the characteristics, and build
the IES model including wind and storage, gas turbine, flexible load and so on. Taking the system operation cost and carbon transaction cost into consideration, an IES low-carbon economy scheduling model with the optimization goal of minimizing the total cost is established
, and the cplex solver is used to solve the calculation example.
%Scenario 3 does not consider the flexible load participating in the optimal scheduling of the system
clc;clear;close all;
%read data
%electric load, thermal load, photovoltaic, wind turbine, power purchase price, power sale price
e_load=[160 150 140 140 130 135 150 180 215 250 275 320 335 290 260 275 270 280 320 345 310 220 160];%Power Load
H_LOAD = [135 140 150 135 140 115 115 115 160 180 140 145 200 220 220 160 140 130] ;% thermal load
ppv=[0 0 0 0 0 10 15 25 45 75 90 100 80 100 50 40 30 15 10 0 0 0 0 0 ];% photovoltaic forecast data
pwt=[60 65 70 75 80 85 90 100 125 150 130 110 100 120 125 130 140 160 180 200 175 160 155 150];% fan forecast data