Open AccessOptimizing smart grid operations from the demand side
Author(s) -
Zheng Yongqing,
Yu Han,
Shi Yuliang,
Zhang Kun,
Zhen Shuai,
Cui Lizhen,
Leung Cyril,
Miao Chunyan
Publication year - 2021
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v42i2.15096
Subject(s) - smart grid , electricity , demand response , power demand , process (computing) , grid , computer science , demand side , power (physics) , electric power system , power consumption , shutdown , dynamic demand , operations research , consumption (sociology) , power grid , business , environmental economics , engineering , economics , electrical engineering , social science , physics , mathematics , quantum mechanics , sociology , nuclear engineering , operating system , geometry
As demand for electricity grows in China, the existing power grid is coming under increasing pressure. Expansion of power generation and delivery capacities across the country requires years of planning and construction. In the meantime, to ensure safe operation of the power grid, it is important to coordinate and optimize the demand side usage. In this paper, we report on our experience deploying an artificial intelligence (AI)–empowered demand‐side management platform – the Power Intelligent Decision Support (PIDS) platform – in Shandong Province, China. It consists of three main components: 1) short‐term power consumption gap prediction, 2) fine‐grained Demand Response (DR) with optimal power adjustment planning, and 3) Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. PIDS has been deployed since August 2018. It is helping over 400 companies optimize their power usage through DR, while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS due to forced shutdown has been reduced from 16% to 0.56%.