An Intelligent System for Decentralized Load Management
ABSTRACT This work proposes a model of an intelligent short term demand side management system based on a MAS. The system is designed to avoid peaks of power request greater than a given threshold and to give maximum comfort to user. The proposed system is composed of a distributed network of processing nodes (PN). Each PN hosts one agent and it is able to manage a single socket tap allowing or disallowing it to supply power. Each agent reacts to a new critical condition entering in competition with the others to gain the access at a shared limited resource. As the results shown the proposed agency can be the consumer's key to take advantage of a DSM program automatically
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ABSTRACT: Load modelling is an essential task in the economic analysis, operation and planning of distribution systems. Particularly, when a demand side management system is taken into account on a deregulated energy market, the knowledge of load profiles is of the greatest importance. Forecasting of daily demand, based upon load models, uses comparable load research data for a different customer mix. For the given season and day of the week, the shape of a daily load curve depends mainly on the customer composition. Difficulties in defining objective customer classes significantly complicate the forecasting process. Usage of statistical clustering and neural network approaches makes possible to improve the load modelling accuracy. This paper presents load modelling methods useful for the long-term planning of power distribution systems. The theoretical statement is illustrated by examples which correspond to Polish and German distribution systemsTransmission and Distribution Conference, 1999 IEEE; 05/1999
- 11/2003; 17:507-535.