Time Series Model of Wind Power Forecasting Error by using Beta Distribution for Optimal Sizing of Battery Storage

Dui, Xiaowei; Ito, Masakazu; Fujimoto, Yu; Hayashi, Yasuhiro; Zhu, Guchuan; Liangzhong, Yao

doi:10.1541/ieejpes.139.212

Cited by 3 publications

(1 citation statement)

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“…Furthermore, the uncertainty in the forecast results of WF output is not only heterogeneous but also generally timedependent; therefore, the forecast results for each timeslot of interest do not ideally behave independently [33]. Such time-dependent uncertainties in the prediction results make it even more difficult to appropriately estimate the energy capacity [34] needed to control energy storage (e.g., a reservoir in the PSHG) so that the BG output is consistent with the plan already submitted. Therefore, to achieve sustainable economic operation of the BG consisting of PSHG and WF based on bidding to the day-ahead market, it is necessary to advance BG operational management frameworks further to understand better the heterogeneity and time-dependence of wind power forecast results and to make the best use of them in operations.…”

Section: A Literature Reviewmentioning

confidence: 99%

Day-Ahead Scheduling of Wind-Hydro Balancing Group Operation to Maximize Expected Revenue Considering Wind Power Output Uncertainty

Fujimoto,

Inagaki,

Kaneko

et al. 2023

IEEE Access

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To use energy generated in wind farms (WFs), which contain uncertainty in their output, as a primary power source in a power system, a sophisticated balancing operation scheme is required. This study focuses on a balancing group (BG) scheme combining WFs and a variable-speed pumped-storage hydro generator (PSHG), which has a large capacity to compensate for the WF output. The proposed BG operational scheduling approach aims to maximize the expected revenue obtained in the day-ahead power market under the uncertainty in WF output and market price by considering the operational constraints of the PSHG, i.e., the water storage capacity and frequency of operation for switching pump-up/-down. In such a BG scheme, it is crucial to consider time-varying and time-dependent uncertainties in WF output to manage PSHG capacity constraints, as well as to derive a reasonable plan in practical computation time. The proposed BG operational scheduling scheme derives a set of WF output scenarios that represents the heterogeneous and time-dependent characteristics of real-world WF output behavior from probability density distributions derived by a cutting-edge prediction approach and implements the expected revenue maximization problem with scenario-based chance constraints of water storage transition by introducing computationally effective iterative optimization algorithm based on surrogate functions. Simulation results suggest that the proposed scheme provides an effective BG operation by considering the uncertainty in the WF output.

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Section: A Literature Reviewmentioning

confidence: 99%