ANN based day-ahead spinning reserve forecast for electricity market simulation

Faria, Pedro; Vale, Zita; Soares, João; Khodr, H. M.

http://hdl.handle.net/10400.22/1475

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Resumo(s)

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Palavras-chave

Artificial neural networks (ANN) Ancillary services Multi-agent systems Spinning reserve Electricity markets Power systems Simulation

URI

http://hdl.handle.net/10400.22/1475

Editora

IEEE

DOI

10.1109/ISAP.2009.5352930

Coleções

ISEP – GECAD – Comunicações em eventos científicos

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