Analysis on demand-side interactive response capability for power system dispatch in a smart grid framework

• Published in: Electric power systems research Vol. 90; pp. 11 - 17
• Main Authors: Jia, Wenzhao, Kang, Chongqing, Chen, Qixin
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.09.2012; Elsevier
• Subjects: Applied sciences; Correction method; Data clustering method; Demand side interactive response capability; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Miscellaneous; Multi-agent system; Power networks and lines; Smart grid; Data clustering method; Multi-agent system; Demand side interactive response capability; Smart grid; Correction method; Performance evaluation; Automatic classification; Implementation; Standards; Interactive system; Multiagent system; Electrical network; Power distribution; System simulation; Effectiveness factor
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2012.03.013

► A mechanism is proposed to reflect the process of demand-side interactive response. ► A standard data format is defined to formulate the submission of DIRC. ► A fuzzy-C-mean clustering method is implemented to analyse historical data. ► A correction method based on similarity identification is developed. In a smart grid framework, relations between the system operator (SO) and terminal consumers will become interactive and demand-side response capacities can be integrated as dispatch-able resources. This paper proposes a systematic analysis on demand-side response mechanism in smart grid. A multi-agent (MA) system is established to describe interactive relations between the SO and different kinds of consumers. On this basis, a novel mechanism is proposed to reflect the process of interactive response, which consists of three schemes: data clustering and release scheme, demand-side interactive response capability (DIRC) submission scheme, and submission correction scheme. Then, a standard data format is defined to formulate the submission of DIRC from basic consumers and a fuzzy-C-mean clustering method is implemented to generate and release typical interactive response modes (IRM) for different kinds of consumers. Moreover, a correction method based on similarity identification is developed to modify submission of DIRC by taking into account deviations between historical submissions and real performances. Finally, a simulation case verifies the effectiveness and rationality of the proposed mechanism, models and methods.