Non-Linear Programming-Based Energy Management for a Wind Farm Coupled with Pumped Hydro Storage System

Jannet Jamii, Mohamed Trabelsi, Majdi Mansouri (), Mohamed Fouazi Mimouni and Wasfi Shatanawi
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Jannet Jamii: Laboratory of Automatic, Electric System and Environment (LAS2E), ENIM, University of Monastir, Monastir 5000, Tunisia
Mohamed Trabelsi: Department of Electronics and Communications Engineering, Kuwait College of Science and Technology, Safat, Kuwait City 13133, Kuwait
Majdi Mansouri: Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha 23874, Qatar
Mohamed Fouazi Mimouni: Laboratory of Automatic, Electric System and Environment (LAS2E), ENIM, University of Monastir, Monastir 5000, Tunisia
Wasfi Shatanawi: Department of Mathematics and Sciences, College of Humanities and Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia

Sustainability, 2022, vol. 14, issue 18, 1-17

Abstract: The large-scale integration of renewable energy sources (RESs) has become one of the most challenging topics in smart grids. Indeed, such an integration has been causing significant grid stability issues (voltage and frequency control) due to the dependency of RESs on meteorological conditions. To this end, their integration must be accompanied by alternative sources of energy to attenuate the power fluctuations. Energy storage systems (ESSs) can provide such flexibility by mitigating local peaks/drops in load demands/renewable power generation. Therefore, the development of energy management strategies (EMSs) has been attracting considerable attention in the management of the power generated from the RESs associated with that which is stored/provided by the ESSs. Then, the optimization of the EMS leads to substantial savings in operation and maintenance and to correct decisions for the future. This study presents an optimized EMS for a wind farm, coupled with a pumped hydro energy system (PHES). The proposed day-ahead EMS consists of two stages, namely the forecasting and the optimization stages. The forecasting module is responsible for predicting the wind power generation and load demand. A random forest (RF) method is used to perform the power forecasting after the extraction of the weather data features using a kernel principal component analysis (KPCA) technique. Then, a nonlinear programming (NLP)-based optimization technique is proposed to define the day-ahead optimal energy of the PHES. The purpose of the optimization is to maximize the profit cost in a day-ahead horizon, taking into consideration the system constraints.

Keywords: renewable energies; wind energy; power forecasting; random forest; pumped hydro storage system; energy management system; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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