A Decentralised Informatics, Optimisation and Control Framework for Evolving Demand Response Services

Student thesis: Doctoral Thesis

Abstract

Centralised energy generation and distribution networks are becoming more vulnerable to energy security. Closure of fossil-fuelled power plants and an increase in more volatile decentralised renewable electricity generation is aggravating the situation further. Future storage technologies will inevitably play a more dominant role during the energy transition.
Paradoxically, as the number of renewables increase, there is a greater reliance on conventional power sources in providing back-up supply. Demand response is an important instrument offering a wide range of services how customers can modify their energy consumption when system reliability is jeopardised. This research focuses on integrated demand response in an energy system by evolving a decentralised informatics, optimisation and control framework. The contributions of this research are (1) the development of a low-cost, standalone frequency measurement instrument, (2) a short-term electricity demand forecasting methodology, and (3) an optimisation policy that guides the decision-making process by balancing the building occupant’s comfort, cost (tariff) and the current and predicted states of the system. Computer simulation and hardware-in-the-loop testing is used to evaluate an energy system operation. There are three significant findings in this research. First, a prototype frequency measurement instrument output is shown to be as effective as measured grid data. Second, a electricity demand forecaster is likely to have a positive influence on the operation and planning of supply and demand management. Third, the proposed optimisation and control framework reveals the effectiveness of the new methods in tackling the energy optimisation problem. This research recommends deployment of the optimisation and control framework, at scale, as part of a wider integrated demand response scheme or decentralised energy systems.
Date of Award12 Feb 2021
Original languageEnglish
Awarding Institution
  • Teesside University
SupervisorMichael Short (Supervisor), Tracey Crosbie (Supervisor) & Vladimir Vukovic (Supervisor)

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