Open AccessMulti‐objective transmission reinforcement planning approach for analysing future energy scenarios in the Great Britain network
Author(s) -
Barnacle Malcolm,
Galloway Stuart,
Elders Ian,
Ault Graham
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.2014.0398
Subject(s) - reinforcement learning , computer science , reinforcement , pareto principle , constraint (computer aided design) , transmission (telecommunications) , electric power transmission , set (abstract data type) , network planning and design , transmission line , grid , operations research , reliability engineering , mathematical optimization , engineering , operations management , telecommunications , artificial intelligence , mechanical engineering , geometry , electrical engineering , mathematics , structural engineering , programming language
A multi‐objective transmission reinforcement planning framework has been designed to evaluate the effect of applying a future energy scenario to the Great Britain transmission network. This is achieved by examining the identified non‐dominated set of transmission reinforcement plans, which alleviate thermal capacity constraints, for the multi‐criteria problem of five objectives: investment cost, annual constraint cost saving, annual incremental operation and maintenance cost, outage cost and annual line loss saving. The framework is flexible and utilises a systematic algorithm to generate reinforcement plans and alter the associated reinforcements should they exacerbate thermal constraints; hence a pre‐determined set of reinforcements is not required to evaluate a scenario. The reinforcements considered are line addition (single circuit and double circuit) and line upgrading through reconductoring. The Strength Pareto Evolutionary Algorithm 2 is utilised to explore varying locations, configurations and capacities of network reinforcement. The solutions produced achieve similar cost savings to solutions created by the transmission network owners, showing the suitability of the approach to provide a useful trade‐off analysis of the objectives and to assess the network‐related thermal and economic impact of future energy scenarios. Here, the framework is applied to the 2020 generation mix of the Gone Green scenario developed by National Grid.