Open AccessStochastic network constrained payment minimisation in electricity markets
Author(s) -
Nouri Alireza,
Hosseini Seyed Hamid,
Keane Andrew
Publication year - 2019
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.2018.7068
Subject(s) - mathematical optimization , computer science , probabilistic logic , solver , minimisation (clinical trials) , payment , reliability engineering , schedule , electricity , electricity market , operations research , mathematics , engineering , statistics , electrical engineering , artificial intelligence , world wide web , operating system
This study presents a novel framework to incorporate the uncertainties associated with load fluctuations and components’ availability in a day‐ahead joint energy‐reserve payment cost minimisation (PCM). The payments are calculated based on locational marginal prices (LMPs). Considering these uncertainties, appropriate definitions are not available for energy and reserve LMPs (RLMPs) that suit the probabilistic PCM formulation and reflect the market characteristics. A tri‐level optimisation framework is proposed. The optimisation variables in the first‐level optimisation include commitment status. In the second‐level optimisation, the production schedule and allocated reserves are the optimisation variables. The third‐level problem includes different sub‐problems, each of which related to optimisation of units’ production in a single scenario. This tri‐level optimisation is managed to be converted to a linear single‐level optimisation and is solved using an off‐the‐shelf branch‐and‐cut solver. The 10‐unit system is first used to show the impacts of varying the degree of uncertainties. The IEEE Reliability Test System is next analysed to validate the proposed formulation for RLMPs.