OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulation

被引:1
|
作者
El Helou, Rayan [1 ]
Lee, Kiyeob [1 ]
Wu, Dongqi [1 ]
Xie, Le [1 ]
Shakkottai, Srinivas [1 ]
Subramanian, Vijay [2 ]
机构
[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
[2] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48103 USA
关键词
Biological system modeling; Electricity supply industry; !text type='Python']Python[!/text; Pricing; Load modeling; Games; Ecosystems; Distribution electricity market; open-source platform; artificial intelligence; demand response; OPTIMAL POWER-FLOW;
D O I
10.1109/TSG.2022.3213240
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents OpenGridGym, an open-source Python-based package that allows for seamless integration of distribution market simulation with state-of-the-art artificial intelligence (AI) decision-making algorithms. We present the architecture and design choice for the proposed framework, elaborate on how users interact with OpenGridGym, and highlight its value by providing multiple cases to demonstrate its use. Four modules are used in any simulation: (1) the physical grid, (2) market mechanisms, (3) a set of trainable agents which interact with the former two modules, and (4) environment module that connects and coordinates the above three. We provide templates for each of those four, but they are easily interchangeable with custom alternatives. Several case studies are presented to illustrate the capability and potential of this toolkit in helping researchers address key design and operational questions in distribution electricity markets.
引用
收藏
页码:1555 / 1565
页数:11
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