Open AccessIntroduction to Laguerre‐based stochastic economic predictive functional control for optimal real‐time power dispatch under uncertainty
Author(s) -
Ramandi Mostafa Yousefi,
Bigdeli Nooshin,
Afshar Karim
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2019.0511
Subject(s) - economic dispatch , model predictive control , laguerre polynomials , computer science , wind power , control theory (sociology) , mathematical optimization , electric power system , power (physics) , power balance , control (management) , mathematics , engineering , artificial intelligence , mathematical analysis , physics , quantum mechanics , electrical engineering
This study presents a Laguerre‐based stochastic economic predictive functional control (L‐SEPFC) method for real‐time power dispatch of balance responsible parties (BRPs). In this regard, at the control layer, an L‐SEPFC method operates on the energy time scale and optimises the expected economic performance of the BRP. Calculated set points in this layer are average power injection of controllable generators, as well as known disturbance of prediction wind generation. The novel L‐SEPFC scheme is able to cope with the wind uncertainty occurring in the real‐time operation of BRPs. Furthermore, the proposed algorithm is able to take into account the technical constraints of generators and transmission line constraints of the power network. Due to the parameterisation of the future control signal based on the Laguerre function, the proposed method is faster than the nominal predictive algorithms. Simulations on an IEEE 118‐bus test system with proposed method shows improvements inability to track reference production, economic performance, imbalance energy and computational burden with respect to nominal predictive controllers.