Dynamic Multiobjective Optimal Dispatch of Distribution Network Considering Time of Use–Based Demand Response
Publication: Journal of Energy Engineering
Volume 148, Issue 2
Abstract
A dynamic dispatch program schedules the controllable generation units during the entire dispatch period to minimize the economic cost and increase other benefits. On the other hand, demand response (DR) improves energy utilization and increases power quality. The coordination of dynamic dispatch with DR is potentially important to increase the benefits to distribution network areas. In this paper, a dynamic multiobjective optimal dispatch (DMOD) problem is developed to effectively integrate with the time of use (TOU)-based DR for optimal distribution network dispatch. DR is concerned with the optimal pricing during peak, off-peak, and valley periods such that the load curve of the dispatch is optimally shaped. In a combined dispatch procedure, dynamic multistage generators, energy storage operations, and power exchanged between upstream grid dispatches are simultaneously optimized to decrease the system operational cost and the active power loss along with the responsive load based on a DR scheme. In addition, a hybrid teaching-learning multiobjective particle swarm optimization algorithm (HTL-MOPSO) is proposed for solving the DMOD incorporation with the DR model. The proposed method is validated in a simulation study using an IEEE 33 bus system. The comprehensive numerical results reveal not only the efficiency of the proposed combined DMOD with the DR model but also the practical DR benefits to customers and distribution network operators.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author on reasonable request.
Acknowledgments
This work was financially supported by the Natural Science Foundation of Anhui Province (No. 2008085QE239); Visiting Scholarship of State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University) (No. 2007DA10512718405), the Doctoral Special Research Fund of Hefei University of Technology (No. JZ2019HGBZ0146), and China Postdoctoral Science Foundation (2020M682645).
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Published In
Journal of Energy Engineering
Volume 148 • Issue 2 • April 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Sep 24, 2020
Accepted: Aug 30, 2021
Published online: Dec 24, 2021
Published in print: Apr 1, 2022
Discussion open until: May 24, 2022
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