THERMAL SCIENCE

International Scientific Journal

Menglin Hou

,

Zhumei Luo

,

Xiaoxu Zhang

,

Shan Qing

,

Xiaohui Zhang

OPTIMAL SCHEDULING MODEL OF A VIRTUAL POWER PLANT BASED ON ROBUST OPTIMIZATION WITH ADDITIONAL MOMENTUM TO IMPROVE THE PREDICTIVE OUTPUT OF BACK PROPAGATION NEURAL NETWORKS

ABSTRACT
In response to energy shortages, uneven distribution, and severe pollution, the global energy structure is rapidly changing. In the dispatching of power systems, the coordinated planning and flexible regulation of virtual power plants play a crucial role. This paper proposes a multi-objective model considering economic efficiency and carbon emissions to study the scheduling of virtual power plants and the proportion of new energy installed capacity. Firstly, the paper optimizes the power system load curve by implementing time-of-use pricing strategies, alleviating the additional pressure on installed capacity caused by demand differences during peak and off-peak periods. Secondly, an improved back propagation neural network method is employed to refine the robust interval, and by integrating feed-back historical data, the adaptive robust control theory is enhanced, thereby improving the system robustness and adaptability. Finally, through specific case analysis and scenario simulation, the paper finds that when the proportion of new energy in the system reaches 60%, it is possible to maximize economic efficiency and minimize carbon emissions while ensuring the stable operation of the virtual power plant.
KEYWORDS
virtual power plant, improving back propagation neural networks, robust optimization, risk tolerance
PAPER SUBMITTED: 2024-01-08
PAPER REVISED: 2024-03-28
PAPER ACCEPTED: 2024-04-19
PUBLISHED ONLINE: 2024-07-13
DOI REFERENCE: https://doi.org/10.2298/TSCI240108148H
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2024, VOLUME 28, ISSUE Issue 5, PAGES [4343 - 4355]
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Optimal scheduling model of a virtual power plant based on robust optimization with additional momentum to improve the predictive output of back propagation neural networks

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence