ANN-Based LMP Forecasting in a Distribution Network with Large Penetration of DG

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Soares, T (Author) Other; Fernandes, F (Author) Other; Morais, H (Author) Other; Faria, P (Author) Other; Zita Vale (Author) Other

Conference proceedings International

Title: 2012 IEEE PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXPOSITION (T&D)

IEEE PES Transmission and Distribution Conference and Exposition

Orlando, FL, MAY 07-10, 2012

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 3 Citations

Other information

Authenticus ID: P-005-40J

DOI: 10.1109/tdc.2012.6281677

Abstract (EN): In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8