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Title: Exploring future opportunities and challenges of Demand Side Management with Agent Based Modelling
Author: Subkhankulova, Dina
ISNI:       0000 0004 7660 2497
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Electricity systems worldwide are transforming in-line with the global decarbonisation goals. On the supply side, renewable energy resources are replacing fossil fuels which introduces uncertainty in electricity generation. On the demand side, heating and transport electrification coupled with continuous integration of small scale renewables and energy storage are transforming the interactions between consumers and generators. These changes are raising new challenges for system operators in terms of balancing electricity in the grid. Demand-side management (DSM), whereby electricity consumption is coordinated with variable supply from renewables, has been shown to offer a promising solution to the above problem. However, the extent to which the future impact of DSM has been holistically assessed is arguable. Current model-based assessment of DSM primarily focuses on its benefits, ignoring the potential challenges since the testing tends to be carried out in an isolated and idealistic setting. This work proposes a model for Electricity System Management using an Agent based approach (or ESMA), which includes heterogeneous consumers, aggregators, the system operator, and market. The main feature of the model is its capability to simulate different regimes of DSM: decentralised (performed by consumers), semi-centralised (performed by aggregators), and centralised (performed by the system operator). The impact of each DSM regime is assessed in terms system costs, greenhouse gas emissions and consumer bills in the context of the British electricity system for 2015-2050. It is found that a trade-off exists between consumer autonomy and system optimality with regards to DSM. It is argued that the level of information sharing between consumers and the system can be minimised, as better learning and predicting algorithms are developed. The thesis is concluded with a discussion on the potential consumer tariff structure which would reward consumer flexibility.
Supervisor: Barrett, M. ; Manley, E. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available