A Multi-Objective Stochastic Approach to Hydroelectric Power Generation Scheduling
2016 Power Systems Computation Conference (PSCC 2016) 2016
Antans Sauļus Sauhats, Romāns Petričenko, Kārlis Baltputnis, Zane Broka, Renāta Varfolomejeva

In this paper, we propose a novel stochastic approach to multi-objective optimization of hydroelectric power generation short-term scheduling. Maximization of profit is chosen as the main objective with additional sub-objective—to reduce the number of startups and shutdowns of generating units. The random nature of future electricity prices and river water inflow is taken into account. We use an artificial neural network-based algorithm to forecast market prices and water inflow. Uncertainty modeling is introduced to represent the stochastic nature of parameters and to solve the short-term optimization problem of profit-based unit commitment. A case study is conducted on a real-world hydro-power plant to demonstrate the feasibility of the proposed algo-rithm by providing the power generation company with the day-ahead bidding strategy under market conditions and a Pareto optimal hourly dispatch schedule of the generating units.

Atslēgas vārdi
dynamic programming; hydropower scheduling; multi-objective optimization; stochastic optimization; unit commitment

Sauhats, A., Petričenko, R., Baltputnis, K., Broka, Z., Varfolomejeva, R. A Multi-Objective Stochastic Approach to Hydroelectric Power Generation Scheduling. No: 2016 Power Systems Computation Conference (PSCC 2016), Itālija, Genoa, 20.-24. jūnijs, 2016. Piscataway, NJ: IEEE, 2016, 56.-62.lpp. ISBN 978-1-4673-8151-2. e-ISBN 978-88-941051-2-4. Pieejams: doi:10.1109/PSCC.2016.7540821

Publikācijas valoda
English (en)
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