Relaxed deep learning for real-time economic generation dispatch and control with unified time scale

Linfei Yin, Tao Yu, Xiaoshun Zhang and Bo Yang

Energy, 2018, vol. 149, issue C, 11-23

Abstract: To solve the collaboration problem of multi-time scale economic dispatch and generation control in power system, i.e., long term time scale optimization, short term time scale optimization, and real-time control, a real-time economic generation dispatch and control (REG) framework, which as a unified time scale framework, is designed in this paper. With a relaxed operator employed into deep neural network (DNN), relaxed deep learning (RDL) is proposed for the REG framework, which towards an alternative to conventional generation control framework, which combines unit commitment (UC), economic dispatch (ED), automatic generation control (AGC), and generation command dispatch (GCD). Compared with 1200 combined conventional generation control algorithms in two simulations, i.e., IEEE 10-generator 39-bus New-England power system and Hainan 8-generator power grid (China), RDL obtains the optimal control performance with smaller frequency deviation, smaller area control error, smaller total cost, and smaller number of reverse regulation. Although the RDL needs a relative long computation time in the pre-training, the simulation results verify the effectiveness and feasibility of the proposed RDL for REG framework.

Keywords: Relaxed deep learning; Real-time economic generation dispatch and control; Unified time scale; Deep neural network (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:149:y:2018:i:c:p:11-23

DOI: 10.1016/j.energy.2018.01.165

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