Optimal Sizing and Management of Distributed Energy Resources in Smart Buildings

被引:0
|
作者
Ahmad, Ashfaq [1 ,2 ]
Khan, Jamil Yusuf [1 ]
机构
[1] Univ Newcastle, Sch Elect Engn & Comp, Callaghan, NSW 2308, Australia
[2] Air Univ, Dept Elect & Comp Engn, Islamabad 44000, Pakistan
关键词
Energy management; Smart building; Renewable energy; Electric vehicles; Realtime optimization; Optimal sizing;
D O I
10.1016/j.energy.2022.123110
中图分类号
O414.1 [热力学];
学科分类号
摘要
This article investigates optimal sizing and realtime control of electrical and thermal distributed energy resources (DERs) in smart buildings. Initially, a comprehensive system architecture is presented considering both electrical and thermal DERs. Then, the DERs are optimally sized through a planning optimization problem with respect to minimization of the total initial investment cost, replacement cost, operations-and-management cost and environmental cost normalized by energy delivered from renewables and controllable non-renewables. Finally, the DERs are optimally controlled in a realtime through a one-slot-look-ahead (OSLA) optimization technique with respect to minimization of load scheduling delay cost, energy procurement cost, energy storage degradation cost, DERs operations-and management cost, and environmental deterioration cost. The proposed OSLA based technique has the following distinct features and benefits: (i) it introduces a customer-oriented specific duration based modelling that makes it useful for practical customer energy needs and available budget limitation; (ii) it relies on the current states of the system inputs only making it applicable in general scenarios; (iii) it employs special techniques of problem modification, transformation, approximation and separation with a significantly lower computational complexity making it useful for practical implementation. Performance of the proposed approach is validated through simulations. Results show that the proposed algorithm can reduce the monthly energy consumption bill of BEMS customers by 20.53%. Also, it can execute 88.15% of customer load tasks accurately accounting for realtime changes in system inputs.(c) 2022 Elsevier Ltd. All rights reserved.
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页数:19
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