Low-carbon Optimal Dispatch of Smart Building Based on Interval Multi-objective Optimization with Deep Reinforcement Learning

被引：0
|
作者：
Hou H. [1 ,4 ]
He Z. [1 ]
Chen Y. [1 ,2 ]
Hou T. [3 ]
Tang J. [1 ,4 ]
Wu X. [1 ,4 ]
机构：
[1] School of Automation, Wuhan University of Technology, Wuhan
[2] Shiyan Power Supply Company, State Grid Hubei Electric Power Company, Shiyan
[3] Economics and Technology Research Institute, State Grid Hubei Electric Power Company, Wuhan
[4] Shenzhen Research Institute, Wuhan University of Technology, Shenzhen
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2023年 / 47卷 / 21期
基金：
中国国家自然科学基金;
关键词：
carbon trading; deep reinforcement learning; interval multi-objective optimization; smart building; uncertainty;
D O I：
10.7500/AEPS20230428001
中图分类号：
学科分类号：
摘要：
In order to fully tap and effectively utilize the energy saving and emission reduction potential of buildings, a low-carbon optimal dispatch method of smart buildings based on interval multi-objective optimization with deep reinforcement learning is proposed. Firstly, the unit parameters, building temperature and multiple uncertainties of source and load in the system are modeled by using interval number and other methods. Secondly, taking into account the system carbon emission and carbon trading mechanism, the system operation is optimized with the goal of the lowest comprehensive operation cost and the best user comfort. In order to solve the interval multi-objective optimization problem, a new multi-objective optimization algorithm is proposed, which combines deep Q network of deep reinforcement learning and interval multi-objective particle swarm optimization algorithm, and performs“offline training”and“online guidance”to efficiently solve the low-carbon optimization dispatch problem of smart buildings under multiple uncertainties. The case results show that the proposed interval multi-objective optimization algorithm with deep reinforcement learning and dispatch model can take into account the low carbon, economy and user comfort of the system, and effectively improve the ability of the system to deal with multiple uncertainties. © 2023 Automation of Electric Power Systems Press. All rights reserved.
引用
收藏
页码：47 / 57
页数：10
相关论文
共 41 条
  • [1] China building energy efficiency annual development research report 2022 （public building special topic）［M］
  • [2] TANG H, WANG S W., A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services［J］, Applied Energy, 305, (2022)
  • [3] An assessment of power flexibility from commercial building cooling systems in the United States［J］, Energy, 221, (2021)
  • [4] Hong LIU, WANG Yiran, LI Jixun, Et al., Coordinated heat and power dispatch of micro-energy network of countryside considering heat balance model of building and flexible indoor comfort constraint［J］, Automation of Electric Power Systems, 43, 9, pp. 50-58, (2019)
  • [5] ZHAO P，, HENZE G P，, BRANDEMUEHL M J，, Et al., Dynamic frequency regulation resources of commercial buildings through combined building system resources using a supervisory control methodology［J］, Energy and Buildings, 86, pp. 137-150, (2015)
  • [6] Potentials and economics of residential thermal loads providing regulation reserve［J］, Energy Policy, 79, pp. 115-126, (2015)
  • [7] GE Shaoyun, LIU Jingyi, LIU Hong, Et al., Economic dispatch of energy station with building virtual energy storage in demand response mechanism［J］, Automation of Electric Power Systems, 44, 4, pp. 35-43, (2020)
  • [8] QUAN Z H，, JING H R，, Et al., Performance and operation strategy optimization of a new dual-source building energy supply system with heat pumps and energy storage［J］, Energy Conversion and Management, 239, (2021)
  • [9] LIAN J M，, SUN Y N，, Et al., Hierarchical control framework for integrated coordination between distributed energy resources and demand response［J］, Electric Power Systems Research, 150, pp. 45-54, (2017)
  • [10] MEHRJERDI H., Simultaneous load leveling and voltage profile improvement in distribution networks by optimal battery storage planning［J］, Energy, 181, pp. 916-926, (2019)
12345