Demand side self-scheduling under dynamic pricing uncertainty

  • Han Xu

Student thesis: Doctoral Thesis

Abstract

The ever increasing integration of renewable energy sources creates a challenge for electric network operation. Addressing the challenge called for changes in system operation, in particular at distribution level. Demand side flexibility is one of the key solutions proposed. Presently, customers start to actively manage their own energy consumption. To manage the growing demand side flexibility and utilise it to benefit grid operation, Demand Side Management (DSM) technologies are applied to manage the consumption, assist system balancing and ensure the security of supply. Direct Load Control (DLC) is a typical DSM technique, where demand corresponds to direct control signals and being directly controlled by an external entity with short notice. Under DLC, this may significantly discourage consumers to actively participate in DLC due to distrust and perceived intrusiveness. This thesis proposes a novel customer-centred self-scheduling concept that is capable to overcome the distrust and perceived intrusiveness issues caused by DLC. The self-scheduling approach encourages consumers to participate and make their own decisions regarding when and how much they are going to consume domestic appliances rather than remotely switched by operators /aggregators. Consumer-centred scheduling tools (a basic and a stochastic tool) have been developed in this research. The novelty of the developed scheduling tools is it minimizes the expense of end-users’ energy consumption by automatically schedule load devices, while satisfies consumer’s electricity usage preferences and their predetermined living patterns. Moreover, the novel stochastic scheduling tool also considered the rising uncertainty in the power system. It coordinates network/system operators’ request and dynamic end-users energy usage behaviour, by combining long term and short term planning into one procedure. The developed scheduling tools are able to aid consumers to monitor the electricity price signals intelligently, react to the network operators’ requirements, achieve energy bill savings automatically, and satisfy consumers’ energy consumption preferences at the same time.
Date of Award20 Mar 2020
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SponsorsUniversity of Strathclyde & EPSRC (Engineering and Physical Sciences Research Council)
SupervisorIvana Kockar (Supervisor) & Stephen McArthur (Supervisor)

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