Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/25898
Title: A Model Predictive Control for the Dynamical Forecast of Operating Reserves in Frequency Regulation Services
Authors: Nikolaidis, Pavlos 
Partaourides, Harris 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Renewable energy sources;Load forecasting;Frequency regulation;Artificial neural network;Model predictive control
Issue Date: 2021
Source: Forecasting, 2021, vol. 3, no. 1, pp. 228-241
Volume: 3
Issue: 1
Start page: 228
End page: 241
Journal: Forecasting 
Abstract: The intermittent and uncontrollable power output from the ever-increasing renewable energy sources, require large amounts of operating reserves to retain the system frequency within its nominal range. Based on day-ahead load forecasts, many research works have proposed conventional and stochastic approaches to define their optimum margins for reliability enhancement at reasonable production cost. In this work, we aim at delivering real-time load forecasting to lower the operating reserve requirements based on intra-hour weather update predictors. Based on critical predictors and their historical data, we train an artificial model that is able to forecast the load ahead with great accuracy. This is a feed-forward neural network with two hidden layers, which performs real-time forecasts with the aid of a predictive model control developed to update the recommendations intra-hourly and, assessing their impact and its significance on the output target, it corrects the imposed deviations. Performing daily simulations for an annual time-horizon, we observe that significant improvements exist in terms of decreased operating reserve requirements to regulate the violated frequency. In fact, these improvements can exceed 80% during specific months of winter when compared with robust formulations in isolated power systems.
URI: https://hdl.handle.net/20.500.14279/25898
ISSN: 25719394
DOI: 10.3390/forecast3010014
Rights: © The Author(s).
Type: Article
Affiliation : Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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