scholarly journals Genetic Algorithm Based Temperature-Queuing Method for Aggregated IAC Load Control

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 535
Author(s):  
Zexu Chen ◽  
Jing Shi ◽  
Zhaofang Song ◽  
Wangwang Yang ◽  
Zitong Zhang
Keyword(s):  
Genetic Algorithm ◽  
Demand Response ◽  
Control Method ◽  
Frequency Fluctuation ◽  
Load Control ◽  
Air Conditioners ◽  
Ideal Object ◽  
Economic Operation ◽  
The Monte Carlo Method ◽  
Continuous Running

In recent years, demand response (DR) has played an increasingly important role in maintaining the safety, stability and economic operation of power grid. Due to the continuous running state and extremely fast speed of response, the aggregated inverter air conditioning (IAC) load is considered as the latest and most ideal object for DR. However, it is easy to cause load rebound when the aggregated IAC load participates in DR. Existing methods for controlling air conditioners to participate in DR cannot meet the following three requirements at the same time: basic DR target, load rebound suppression, and users’ comfort. Therefore, this paper has proposed a genetic algorithm based temperature-queuing control method for aggregated IAC load control, which could suppress load rebound under the premise of ensuring the DR target and take users’ comfort into account. Firstly, the model of the aggregated IAC load is established by the Monte Carlo method. Then the start and end time of DR are selected as the main solution variables. A genetic algorithm is used as the solving tool. The simulation results show that the proposed strategy shows better performance in suppressing load rebound. In the specific application scenario of adjusting the frequency fluctuation of the microgrid, the results of the case show that this strategy can effectively control the frequency fluctuation of the microgrid. The effectiveness of the strategy is verified.

A load control method for small data centers participating in demand response programs

2014 ◽  
Vol 32 ◽  
pp. 232-245 ◽  
Author(s):  
C.-J. Tang ◽  
M.-R. Dai ◽  
C.-C. Chuang ◽  
Y.-S. Chiu ◽  
W.S. Lin
Keyword(s):  
Demand Response ◽  
Data Centers ◽  
Control Method ◽  
Load Control ◽  
Small Data ◽  
Demand Response Programs

An Intelligent Household Electricity Load Control Method Based on Demand Response

2015 ◽  
Vol 740 ◽  
pp. 307-310 ◽  
Author(s):  
Zhao Yang Qu ◽  
Tian Hang Zhang ◽  
Jia Yan ◽  
Shao Qing Xu
Keyword(s):  
Demand Response ◽  
Control Method ◽  
Electricity Consumption ◽  
Load Control ◽  
On Demand ◽  
Complex Functions ◽  
Reduce Electricity Consumption ◽  
Electricity Load ◽  
Smart House ◽  
Design Experiments

This paper presents a method for smart house electricity load control. The method, combined with TOU price and Real-time pricing, arranges various appliances and meets daily household electricity demand at the same time, so that to reduce the daily electricity consumption and realize Demand Response. First, this paper attempts to summarize problem witch need to be solved for realizing load control in a smart house. Second, the smart house load control problem was described as high-dimensional complex functions unconstrained optimization model and solved with Particle Swarm Optimization. Finally, design experiments used the method for a smart house. Experimental results show that the method can arrange various appliances and reduce electricity consumption.

scholarly journals HVAC Optimization Genetic Algorithm for Industrial Near‐Zero Energy Building Demand Response

2021 ◽  
pp. 65-93
Author(s):  
Nikos Kampelis ◽  
Nikolaos Sifakis ◽  
Denia Kolokotsa ◽  
Konstantinos Gobakis ◽  
Konstantinos Kalaitzakis ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Demand Response ◽  
Zero Energy ◽  
Zero Energy Building

An efficient intelligent decision method for bionic motion unmanned system

2021 ◽  
pp. 095965182110655
Author(s):  
Haipeng Chen ◽  
Wenxing Fu ◽  
Yuze Feng ◽  
Jia Long ◽  
Kang Chen
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Motion Control ◽  
Control Method ◽  
Improved Genetic Algorithm ◽  
Decision Method ◽  
Multiple Traveling Salesman Problem ◽  
Intelligent Decision ◽  
Unmanned System

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

Sign in / Sign up

Export Citation Format

Share Document