Open AccessTwo‐level decision‐making model for a distribution company in day‐ahead market
Author(s) -
Khazaei Hossein,
Vahidi Behrooz,
Hosseinian Seyed Hossein,
Rastegar Hasan
Publication year - 2015
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2014.0797
Subject(s) - computer science , purchasing , nash equilibrium , matrix (chemical analysis) , mathematical optimization , strategy , distribution (mathematics) , game theory , competition (biology) , reinforcement learning , power (physics) , operations research , artificial intelligence , mathematics , engineering , mathematical economics , operations management , mathematical analysis , ecology , materials science , physics , quantum mechanics , composite material , biology
This study presents a two‐level decision‐making (TLDM) model for a distribution company (Disco) in the day‐ahead market (DAM), where Disco has two additional resources, interruptible load (IL) and distribution generation (DG). At the upper level of the model, the competition among Discos for purchasing power from DAM is modelled using a matrix game with the assumption that the cost information of generators and Discos is common knowledge. In the lower level, each Disco's strategy on its ILs and DGs are derived through an optimisation problem. The TLDM model significantly reduces the size of the matrix game and thus lowers the computational barrier. Owing to implementation difficulties of mixed strategies, a reinforcement learning algorithm is used to derive Discos’ strategies from the matrix game. This algorithm always provides pure strategies for Discos, even if the matrix game has no pure Nash equilibrium. An 8‐bus system is used to illustrate the efficiency of the proposed model and solution method. The results are compared with those obtained using a bi‐level optimisation method reported in the literature.