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 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.

scholarly journals Clustering Direct Load Control Appliances in the Context of Demand Response Programs in Energy Communities

2020 ◽  
Vol 53 (2) ◽  
pp. 12608-12613
Author(s):  
Rúben Barreto ◽  
Pedro Faria ◽  
Cátia Silva ◽  
Zita Vale
Keyword(s):  
Demand Response ◽  
Load Control ◽  
Direct Load Control ◽  
Demand Response Programs

Modeling of unforced demand response programs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hassan Jalili ◽  
Pierluigi Siano
Keyword(s):  
Demand Response ◽  
Load Shifting ◽  
Response Models ◽  
Temperature Changes ◽  
Proposed Model ◽  
Equipment Failures ◽  
Demand Response Programs ◽  
Simultaneous Implementation ◽  
Long Term Studies

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

Improving energy efficiency in colocation data centers for demand response

2021 ◽  
Vol 29 ◽  
pp. 100476
Author(s):  
Ce Chi ◽  
Fa Zhang ◽  
Kaixuan Ji ◽  
Avinab Marahatta ◽  
Zhiyong Liu
Keyword(s):  
Energy Efficiency ◽  
Demand Response ◽  
Data Centers
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