5 November 2015
power transmission planning, costing, power transmission economics, maintenance engineering, power transmission lines, evolutionary computation, Pareto analysis, overhead line conductors, multiobjective transmission reinforcement planning approach, Great Britain transmission network, nondominated set identification, thermal capacity constraints, multicriteria problem, investment cost, annual constraint cost saving, annual incremental operation, maintenance cost, outage cost, annual line loss saving, reconductoring, strength Pareto evolutionary algorithm 2, economic impact, Gone Green scenario, national grid
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.