Date of Award

23-9-2022

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Department

Mechanical, Biomedical and Manufacturing Engineering

First Advisor

Dr Ken Bruton

Second Advisor

Dr Paul O'Sullivan

Third Advisor

Dr Andrew Cashman

Abstract

Increasing the flexible capacity on national electricity grids will be key to maintain reliable control as the levels of renewables continue to increase annually. The industrial sector has been identified as being capable of contributing and potentially offering flexible capacities, following appropriate investigation. The aim of this thesis is to develop the knowledge, advance the engagement and encourage additional participation in national demand response programmes from the industrial sector to provide this flexibility. The potential within this sector, facilitated by the smart grid and demand response concepts is outlined, specifically detailing their benefits, driving factors and potential barriers as part of a detailed literature review. Based on these findings, a novel and appropriate six-step framework is presented, which assists the systematic identification, categorisation and risk-assessment of industrial assets, allowing suitably low-risk assets to be highlighted for demand response participation, providing flexible capacity to the national grid. Implementing the framework on a case study industrial site helps to ensure its suitability and provide a demonstration for prospective participants. Following the framework steps, the most suitable selected assets are subjected to further evaluation using the developed risk-assessment modelling tool encompassed in the framework. Presenting a modelling tool to assess the operational risk of selected air handling units participating in national demand response programmes. This modelling analysis illustrates the low-risk capabilities of the selected industrial air handling unit, also highlighting specific areas for further risk mitigation. This demonstrates that there is very low risk of these assets participating in the shorter demand response events, especially five-minute shutoffs, even in the most extreme scenarios. Also outlining that the case study air handling unit could have responded to previous actual grid frequency events, incurring no risk for at least twenty-minutes. Following the implementation, 35 kW to 75 kW of flexible capacity was found to be available on the case study industrial site. The impact of the identified low-risk assets participating in demand response is also outlined on a local, regional and national scale, demonstrating the impact achievable using this framework. Based on the scaling scenarios, flexible capacities between 7 MW and 18.5 MW and even up to 54 MW in the largest scenario considered may be achievable in Ireland. These capacities, comparable to some existing power plants, would help to maintain reliable control of the electricity grid. The site benefits including financial and environmental performance are also investigated, demonstrating the value and impact to encourage further engagement from this sector. The basic, lowest risk scenarios present potential earnings of €3,000 to €7,000 annually for participants, increasing from €11,000 to €26,500 once appropriate scalars are applied, which may account for 14% of a representative industrial site’s electricity expenditure. Participating sites may even earn between €21,500 and €167,000 annually if all stipulations are satisfied. Furthermore, the analysis demonstrates that a participating site could have reduced its annual CO2 emissions by up to 1.4 tonnes by engaging in this concept. Overall, the research presented in this thesis demonstrates the low-risk flexible capacity available from the industrial sector, which would benefit the participant and have a positive impact on the national electricity grid.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Access Level

info:eu-repo/semantics/openAccess

Project Identifier

info:eu-repo/grantAgreement/SFI/SFI Centre for Energy, Climate, and Marine Research/12/RC/2302/IE//MaREI/

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