Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning

Ali Ahmadian (), Kumaraswamy Ponnambalam, Ali Almansoori and Ali Elkamel
Additional contact information
Ali Ahmadian: Department of Electrical Engineering, University of Bonab, Bonab 55517-61167, Iran
Kumaraswamy Ponnambalam: Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Ali Almansoori: Department of Chemical Engineering, Khalifa University, Abu Dhabi P.O. Box. 59911, United Arab Emirates
Ali Elkamel: Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Energies, 2023, vol. 16, issue 2, 1-17

Abstract: Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make the most out of these resources by aggregating them as a single entity. On the other hand, VPP’s optimal management depends on its accuracy in modeling stochastic parameters in the VPP body. In this regard, an efficient approach for a VPP is a method that can overcome these intermittent resources. In this paper, a comprehensive study has been investigated for the optimal management of a VPP by modeling different resources—RESs, energy storages, EVs, and distributed generations. In addition, a method based on bi-directional long short-term memory networks is investigated for forecasting various stochastic parameters, wind speed, electricity price, load demand, and EVs’ behavior. The results of this study show the superiority of BLSTM methods for modeling these parameters with an error of 1.47% in comparison with real data. Furthermore, to show the performance of BLSTMs, its results are compared with other benchmark methods such as shallow neural networks, support vector machines, and long short-term memory networks.

Keywords: virtual power plant; deep learning; BLSTM networks; uncertainty modeling; electric vehicles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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