Incorporating deep learning of load predictions to enhance the optimal active energy management of combined cooling, heating and power system

Energy ◽  
2021 ◽  
pp. 121134
Author(s):  
Yuan Zhou ◽  
Jiangjiang Wang ◽  
Yi Liu ◽  
Rujing Yan ◽  
Yanpeng Ma
Thermo ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 63-76
Author(s):  
Mengxuan Yan ◽  
Dongxiao Wang ◽  
Chun Sing Lai ◽  
Loi Lei Lai

Microgrids have become increasingly popular in recent years due to technological improvements, growing recognition of their benefits, and diminishing costs. By clustering distributed energy resources, microgrids can effectively integrate renewable energy resources in distribution networks and satisfy end-user demands, thus playing a critical role in transforming the existing power grid to a future smart grid. There are many existing research and review works on microgrids. However, the thermal energy modelling in optimal microgrid management is seldom discussed in the current literature. To address this research gap, this paper presents a detailed review on the thermal energy modelling application on the optimal energy management for microgrids. This review firstly presents microgrid characteristics. Afterwards, the existing thermal energy modeling utilized in microgrids will be discussed, including the application of a combined cooling, heating and power (CCHP) and thermal comfort model to form virtual energy storage systems. Current trial programs of thermal energy modelling for microgrid energy management are analyzed and some challenges and future research directions are discussed at the end. This paper serves as a comprehensive review to the most up-to-date thermal energy modelling applications on microgrid energy management.


Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1660
Author(s):  
Seydali Ferahtia ◽  
Ali Djeroui ◽  
Tedjani Mesbahi ◽  
Azeddine Houari ◽  
Samir Zeghlache ◽  
...  

This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery–supercapacitor hybrid power system. The hybrid power system consists of a lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using the salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC/DC converter model is derived utilizing the first-principles method and computes the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power elements’ life expectancies and supplies the power system with the required power.


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