Demand response control strategy of groups of central air-conditionings for power grid energy saving

2016 ◽  
Author(s):  
Yu Zhou ◽  
Yongxian Yi ◽  
Gaoying Cui ◽  
Ping Jin ◽  
Xingxi Guo ◽  
...  
Keyword(s):  
Energy Saving ◽  
Demand Response ◽  
Control Strategy ◽  
Power Grid ◽  
Response Control

scholarly journals Hierarchical and distributed demand response control strategy for thermostatically controlled appliances in smart grid

2016 ◽  
Vol 5 (1) ◽  
pp. 30-42 ◽  
Author(s):  
Wenting WEI ◽  
Dan WANG ◽  
Hongjie JIA ◽  
Chengshan WANG ◽  
Yongmin ZHANG ◽  
...  
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Control Strategy ◽  
Response Control

A fast chiller power demand response control strategy for buildings connected to smart grid

2015 ◽  
Vol 137 ◽  
pp. 77-87 ◽  
Author(s):  
Xue Xue ◽  
Shengwei Wang ◽  
Chengchu Yan ◽  
Borui Cui
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Control Strategy ◽  
Power Demand ◽  
Response Control

scholarly journals Control Strategy for Inverter Air Conditioners under Demand Response

Processes ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 407 ◽  
Author(s):  
Yanbo Che ◽  
Jianxiong Yang ◽  
Yuancheng Zhao ◽  
Siyuan Xue
Keyword(s):  
Energy Storage ◽  
Demand Response ◽  
Control Strategy ◽  
Air Conditioning ◽  
Power Grid ◽  
Thermal Parameter ◽  
State Of Charge ◽  
Operating Characteristics ◽  
Air Conditioners ◽  
Storage Model

Air conditioning loads are important resources for demand response. With the help of thermal energy storage capacity, they can reduce peak load, improve the reliability of power grid operations, and enhance the emergency capacity of a power grid, without affecting the comfort of the users. In this paper, a virtual energy storage model for inverter air conditioning loads, which reflects their operating characteristics and is more conducive to practical application, is established. Two parts are involved in the virtual energy storage model: An electrical parameter part, based on the operating characteristics, and a thermal parameter part, based on the equivalent thermal parameter model. The control function and restrictive conditions of the virtual energy storage are analyzed and a control strategy, based on virtual state-of-charge ranking, is proposed. The strategy controls the inverter air conditioners through re-assigning indoor temperature set-points within the pre-agreed protocol interval and gives priority those with a higher virtual state of charge. As a result, electric power consumption is reduced while the temperature remains unchanged, so that a shortage in the power system can be compensated for as much as possible, while the comfort of users is guaranteed. Simulation and example analyses show that the strategy is effective in controlling air conditioning loads. Additionally, the influences of load reduction target magnitude and communication time-step on the performance of the control strategy are analyzed.

A multi-objective operation strategy optimization for ice storage systems based on decentralized control structure

2020 ◽  
Vol 42 (1) ◽  
pp. 62-81
Author(s):  
Yanhuan Ren ◽  
Junqi Yu ◽  
Anjun Zhao ◽  
Wenqiang Jing ◽  
Tong Ran ◽  
...  
Keyword(s):  
Energy Saving ◽  
Decentralized Control ◽  
Optimization Algorithm ◽  
Control Strategy ◽  
Load Distribution ◽  
Air Conditioning ◽  
Control Structure ◽  
Ice Storage ◽  
Swarm Optimization ◽  
Multi Objective

Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy. Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.

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