Title :
ANN Based Day-Ahead Spinning Reserve Forecast for Electricity Market Simulation
Author :
Faria, Pedro ; Vale, Zita A. ; Soares, João ; Khodr, Hussein
Author_Institution :
Center of the Electr. Eng., Polytech. Inst. of Porto (IPP), Porto, Portugal
Abstract :
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.
Keywords :
decision support systems; neural nets; power engineering computing; power markets; power system planning; power system simulation; software agents; artificial neural network; day ahead spinning reserve forecast; decision support tool; electricity market simulation; multi-agent based electricity market simulator; short term prediction; Economic forecasting; Electricity supply industry; IEEE members; ISO; Power generation economics; Power system economics; Power system simulation; Predictive models; Spinning; Strontium; Artificial neural networks (ANN); ancillary services; electricity markets; multi-agent systems; power systems; simulation; spinning reserve;
Conference_Titel :
Intelligent System Applications to Power Systems, 2009. ISAP '09. 15th International Conference on
Conference_Location :
Curitiba
Print_ISBN :
978-1-4244-5097-8
DOI :
10.1109/ISAP.2009.5352930
