SCALEX: SCALability EXploration of Multi-Agent Reinforcement Learning Agents in Grid-Interactive Efficient Buildings

被引:3
作者
Almilaify, Yara [1 ]
Nweye, Kingsley [1 ]
Nagy, Zoltan [1 ]
机构
[1] Univ Texas Austin, Austin, TX 78712 USA
来源
PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON SYSTEMS FOR ENERGY-EFFICIENT BUILDINGS, CITIES, AND TRANSPORTATION, BUILDSYS 2023 | 2023年
关键词
energy flexibility; demand response; multi agent system;
D O I
10.1145/3600100.3623749
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Renewable energy transition and decarbonization pose significant challenges for grid-interactive efficient building communities. The optimization of intermittent renewable energy can be achieved using advanced control architecture and energy storage, enhancing energy flexibility. Reinforcement learning (RL) offers potential solutions, but its scalability and computational demands in large-scale settings remain unclear. This paper examines the scalability of Soft-Actor Critic (SAC) in multi-agent systems, comparing decentralized-independent SACs and centralized SACs using CityLearn, an OpenAI Gym environment. We consider neighborhoods consisting of 2 to 64 single-family residential buildings, each equipped with cooling and heating storage devices, domestic hot water storage devices, electrical storage devices, and solar PV systems. Our findings suggest that independent controllers outperform the centralized controller with increasing number of buildings. We also show that the performance on the building level can differ from the aggregated performance.
引用
收藏
页码:261 / 264
页数:4
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