Multi-Period Day-Ahead Storage Scheduling with Uncertain Inflow

With the widespread integration of renewable energy into the power grid, the inherent variability and intermittency of renewable sources can compromise grid reliability and security. Traditional deterministic approaches fall short in addressing this challenge, while uncertainty optimization methods...

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Veröffentlicht in:	2024 6th International Conference on Electronic Engineering and Informatics (EEI) S. 1261 - 1266
Hauptverfasser:	Tang, Yusi, Zhou, Bo
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 28.06.2024
Schlagworte:	Costs Dynamic programming Heuristic algorithms Multi-period day-ahead storage scheduling Programming Reliability Renewable energy sources Resource management Security Stochastic dual dynamic programming algorithm Stochastic processes Un-certain inflow Uncertainty
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Zusammenfassung:	With the widespread integration of renewable energy into the power grid, the inherent variability and intermittency of renewable sources can compromise grid reliability and security. Traditional deterministic approaches fall short in addressing this challenge, while uncertainty optimization methods offer a more effective solution. This paper addresses the water inflow uncertainty in the day-ahead storage scheduling. By leveraging the multiplicative autoregressive method, a multi-period stochastic programming model is established to minimize the expected daily operating cost. Then, a stochastic dual dynamic programming algorithm is devised to find the optimal generation dispatching strategy. Finally, case studies on the IEEE 30-bus systems are conducted to demonstrate the effectiveness of the proposed model and method.
DOI:	10.1109/EEI63073.2024.10696267