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ANN Based Day-Ahead Spinning Reserve Forecast for Electricity Market Simulation
handle: 10400.22/1475
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.
- Polytechnic Institute of Porto Portugal
- Polytechnic Institute of Porto Portugal
Electricity markets, Multi-agent systems, Artificial neural networks (ANN), Ancillary services, Power systems, Spinning reserve, Simulation
Electricity markets, Multi-agent systems, Artificial neural networks (ANN), Ancillary services, Power systems, Spinning reserve, Simulation
