Robust methodologies for Transmission Network Planning for systems with Significant Renewable Generation

Abstract

Electricity generation from fossil fuel-based technology is considered to be one of the major sources of greenhouse gases in the world. For this reason, some environmental policies have been enacted to encourage electricity generators to seek alternatives in generating clean, affordable and reliable electricity. This has led to the emergence of low carbon technologies such as wind and solar in the power system. This development poses two major challenges in the system; 1) how to accommodate the increasing level of uncertainty in the network. 2) The high cost of upgrading the transmission network to accommodate the capacity of renewable energy in the system. Building new transmission networks is capital-intensive as shown, by the 2014 Ten-year Network Development plan of the European Network of Transmission System Operators for Electricity (ENTSO-E), where about 150 billion euros will be needed to integrate up to 60% of renewables by 2030. The traditional transmission expansion planning (TEP) approach is no longer suitable in today’s system as it lacks flexibility and is considerably expensive. Therefore, transmission planners must develop cost effective and flexible strategies to utilise network assets while minimising the spillage of renewable generation. In this thesis, three different flexible control options namely, demand response, electrical energy storage and generation re-dispatch are integrated into the transmission expansion process for evaluation. The evaluation is implemented by comparing the TEP models with and without the flexible control options. In the first study a new comparison tool known as transmission expansion cost-reliability (TECR) plane is used to measure the goodness of the proposed transmission expansion plans with and without electrical energy storage. In another study, stochastic TEP models with energy storage and generation re-dispatch are evaluated against the traditional planning approach considering different levels of solar energy penetration at each stage of the transmission expansion planning. Furthermore, a TEP model with demand response aggregation is also proposed. In this model, uncertainty in the amount of demand response provided by aggregators is modelled using a new approach that is based on expected utility theory and certainty equivalent. The results obtained from these studies show that significant economic benefits can be derived by planning the network with theses flexible control options.

Date of Award	1 Aug 2020
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Joseph Mutale (Supervisor) & Pierluigi Mancarella (Supervisor)