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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Bibliographic Details
Published in:2024 6th International Conference on Electronic Engineering and Informatics (EEI) pp. 1261 - 1266
Main Authors: Tang, Yusi, Zhou, Bo
Format: Conference Proceeding
Language:English
Published: IEEE 28.06.2024
Subjects:
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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Summary: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