Rolling horizon wind-thermal unit commitment optimization based on deep reinforcement learning

Shi, Jinhao; Wang, Bo; Yuan, Ran; Wang, Zhi; Chen, Chunlin; Watada, Junzo

doi:10.1007/s10489-023-04489-5

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…In the 1980s and 1990s, the Lagrangian relaxation method became the main industry solution method and was widely applied [4] . More recently, heuristic optimization algorithms inspired by biological or natural phenomena, such as genetic algorithms, simulated annealing algorithms, and particle swarm optimization algorithms, have been developed and can handle complex nonlinear and nonanalytic constraints to provide high-quality solutions [5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Unit commitment with AC optimal power flow constraint

Liu,

Wang,

et al. 2023

J. Phys.: Conf. Ser.

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Because of the enormous economic gain it may offer, unit commitment (UC) has been a primary optimization job in the daily operation planning of contemporary power systems. The AC optimal power flow (ACOPF) constraint is a method for determining the ideal load arrangement in alternating current (AC) power systems. Using unit commitment and ACOPF limitations to determine optimal load distribution is an important approach to ensure the steady functioning of power systems. The unit commitment approach with ACOPF constraint is examined in this study. The aims and contents of the study are set by analyzing the current approaches, and a piecewise linearized unit commitment method is developed that takes into consideration the ACOPF limitations and has good accuracy and practicality. Finally, to validate the method’s effectiveness, it was tested using a six-bus test system.

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