Year: 2022
Type: article
Abstract: As more and more loads start to join the electricity market and the role of loads in power system balancing and regulation becomes more and more important, the study of the response characteristics of polymorphic loads becomes more and more important. This paper firstly reviews the aggregation methods for loads, introduces its parameter aggregation model using temperature-controlled loads as an example, and introduces the use of machine learning methods to first cluster and then aggregate. It then reviews methods for the study of load response characteristics, with an increasing number of neural networks and hybrid neural networks being introduced into the field. Finally, a review of load uncertainty is presented, introducing sources of load uncertainty and methods for coping with load uncertainty.
Source: Energy Reports
Authors: Mingmei Zhang, Quan Qing, Yu Lei, Qiang Lan, Jie Xu, Dezhi Li, Xiaotian Li, Qingguang Yu
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,240
OpenAlex ID: W4308219235