Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 134, Issue -, Pages 9-18Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2015.12.020
Keywords
Artificial neural networks; Load forecasting; Photovoltaic generation; Scenario generation; Wind production
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Funding
- EU Seventh Framework Programme FP7 [309048]
- State Scholarships Foundation of Greece in the context of the IKY Fellowships of Excellence for Postgraduate studies in Greece-Siemens Program
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In this paper a novel scenario generation methodology based on artificial neural networks (ANNs) is proposed. The methodology is flexible and able to generate scenarios for various stochastic variables that are used as input parameters in the stochastic short-term scheduling models. Appropriate techniques for modeling the cross-correlation of the involved stochastic processes and scenario reduction techniques are also incorporated into the proposed approach. The applicability of the methodology is investigated through the creation of electric load, photovoltaic (PV) and wind production scenarios and the performance of the proposed ANN-based methodology is compared to time series-based scenario generation models. Test results on the real-world insular power system of Crete and mainland Greece present the effectiveness of the proposed ANN-based scenario generation methodology. (C) 2015 Elsevier B.V. All rights reserved.
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