Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Extreme weather events that are increasing in frequency and impact can cause extended disruptions to power grids. To increase the resilience of power grids, focus is shifting from infrastructure hardening to quickening the restoration of distribution systems after a disaster. One method to achieve this is through the use of microgrids leveraging distributed generation in islanded mode. While much work has been done in using microgrids for system restoration, the stochastic nature of distributed energy resources (DERs) have so far been neglected. Here, a data-driven approach to leverage stochastic DERs to form ad hoc microgrids to supply local critical loads prior to wide-scale system restoration is presented. The proposed method is tested on a modified IEEE European LV Test Feeder. Results show that considering the stochastic nature of the resources during microgrid formation greatly affects their supply adequacy confidence levels.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
The stability and economic level of the power system operation during the penetration of Wind Power Plants (WPPs) are much determined by the variability and uncertainty of the wind power output. The characteristics of seasonal wind power output can be used to define the optimal operating reserves of a stable and cost-effective power system operation. This paper proposes a comprehensive algorithm of hybrid Artificial Intelligence (AI) approach that combines the Seasonal Autoregressive Integrated Moving Average (SARIMA) and selected Neural Network Variants (NNVs) in Seasonal Daily Variability and Uncertainty (SDVU) scheme. Among all NNVs, Long Short-Term Memory (LSTM) shows the most consistent and accurate results. With the hybrid AI approach, this algorithm calculates the Dynamic Confidence Level (DCL) to determine hourly operating reserves on a daily basis. The proposed algorithm has been successfully tested using historical data of real-world WPPs that operated in Indonesia. Furthermore, the comparison toward non-seasonal with a Static Confidence Level (SCL) in several percentile scenarios is made to prove the cost-effectiveness advantages of this new algorithm that may save up to 4.2% of total daily energy consumption. An interface application is added so that the results of this research can be directly utilized by users both on the observed power system and generally in Indonesia.INDEX TERMS dynamic confidence level, neural network, operating reserve, wind power, SARIMA.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
BlogTerms and ConditionsAPI TermsPrivacy PolicyContactCookie PreferencesDo Not Sell or Share My Personal Information
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.