Smart grid architecture model for control, optimization and data analytics of future power networks with more renewable energy

被引:104
作者
Panda, Deepak Kumar [1 ]
Das, Saptarshi [1 ,2 ]
机构
[1] Univ Exeter, Coll Engn Math & Phys Sci, Dept Math, Penryn Campus, Penryn TR10 9FE, Cornwall, England
[2] Univ Exeter, Inst Data Sci & Artificial Intelligence, Laver Bldg,North Pk Rd, Exeter EX4 4QE, Devon, England
关键词
Interoperability layers; Smart grid architecture model; SGAM mapping; Smart grid control; Distributed intelligent control; Smart grid cyber-security; DEMAND-SIDE MANAGEMENT; LOAD-FREQUENCY CONTROL; SMALL-SIGNAL STABILITY; JUST-IN-TIME; REACTIVE POWER; VOLTAGE CONTROL; STORAGE-SYSTEM; PREDICTIVE CONTROL; FLYWHEEL ENERGY; BIG DATA;
D O I
10.1016/j.jclepro.2021.126877
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper describes a generic methodology to develop mathematical and computational models of different components of the smart grid architecture model (SGAM). The SGAM inspired integrated mathematical modelling will help develop interoperable complex system simulations for integrating different smart grid components, associated communication models for data exchange and software modules, control, estimation, and data analytics functionalities with the business perspectives. This paper is based on the existing component models inspired by SGAM, which provides a holistic view for integrating the models under operational and security constraints. Achievable results and open research problems for the SGAM mapping have also been discussed in these models supporting the interoperability challenges. The models described in this paper can serve as a guideline to design efficient and robust control strategies for smart grids against uncertain loading, generation, and communication constraints, thus optimizing and improving the whole system's performance. Mathematical and computational models of cyber-physical systems have also been discussed along with their potential challenges. Based on the above concepts, unsolved and open challenges in the smart grid control, optimization and data analytics are highlighted. (c) 2021 Elsevier Ltd. All rights reserved.
引用
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页数:25
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