Open AccessStochastic multi‐stage model for co‐planning of transmission system and merchant distributed energy resources
Author(s) -
Ranjbar Hossein,
Hosseini Seyed Hamid
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.2018.6324
Subject(s) - computer science , transmission (telecommunications) , distributed generation , energy resources , energy (signal processing) , stochastic modelling , stage (stratigraphy) , mathematical optimization , operations research , environmental science , business , engineering , geology , renewable energy , telecommunications , environmental protection , electrical engineering , mathematics , paleontology , statistics , finance
In recent years, while energy consumption is still increasing, system peak load is decreasing due to deregulation in power systems, application of demand response programmes, microgrids and smart grids implementation, and so on. Thus, transmission system operators (TSOs), which are usually a public entity, must reinforce the transmission systems to maintain power system reliability. They are not interested in investing transmission network because of high investment costs. In this regard, this study presents a new bi‐level model for co‐planning of transmission system and merchant distributed energy resources (DERs). The upper level takes the TSOs perspective and aims to minimise the line investment cost and cost of buying energy from conventional generators and DERs, while ensuring a given rate of return on DERs investments. The lower level simulates market clearing to find nodal prices for calculating DERs' profit and cost of buying energy. The proposed model is reformulated as a single‐level mixed integer linear programming problem by strong duality technique, bigM method, and the complementary slackness conditions. The IEEE RTS 24 bus test system has been used to show the usefulness of the proposed model.