Han, Ming-En
(2021)
The electrification of the built environment and transport through the utilisation of multi-vector community energy systems.
PhD thesis, University of Nottingham.
Abstract
‘Electrify everything’ is considered an important strategy to achieve the net-zero carbon emission goal by 2050, eliminating carbon emissions if renewable energy technologies produce the electricity supply. The phenomenon, however, places a considerable power demand increase on the distribution networks. To ensure the security of electricity supply, an efficient energy system and energy demand reduction play a critical role. This research, focusing on the residential sector, delivered an electrified community model through domestic heating and road transport electrification. The electricity demands of an electrified community were investigated and then addressed using a designed multi-vector community energy system performing smart management measures. This community energy system could flatten the demand peaks, decrease electricity demand, integrate an electrified heating network, electricity grid and decentralised generation, and was demonstrated in three models.
Firstly, an electrified heating network model comprising a central ground source heat pump (GSHP), low temperature district heating (LTDH) system, electric heaters and thermal storage, was established to measure the optimum distribution temperature. This heating network, when using a lower distribution temperature, reduced heat losses and increased the coefficient of performance (COP) of the GSHP. However, due to the hygiene requirement of domestic hot water (DHW) storage, the low-efficiency electric heaters were utilised to boost the storage temperature, which may result in greater overall electricity consumption. This research question was addressed using a scalable model that determined the optimum distribution temperature with the least electricity consumption. Secondly, an electrified community model illustrated hourly electricity demands and performances of a community energy system, which was then used to identify the required degree of housing thermal efficiency improvement (i.e., heating demand reduction). The demands included heating, electric vehicles (EVs) and Electricity (i.e., lighting and appliances). The third model assessed decentralised generation (DG) coupled with battery storage under various levels of housing thermal efficiency improvement. This model defined the installation criteria of DG that maintained the power demand below a targeted power.
The modelling result of the heating network indicated that the demand ratio of DHW to space heating (SH) determined the distribution temperature. In the context of buildings with higher thermal efficiency, a greater distribution temperature was enabled to reduce electricity demand. Furthermore, the electrification of a community increased the maximum electric power on the greatest demand day by over five times, converting heating demands into electricity directly. In contrast, a community energy system, applying an optimised heating network, EV smart charging and community-scale peak shaving, could possibly reduce the increased peak demand to only a 33% increase. Besides, the result indicated that when the thermal efficiency in buildings was improved by around 70%, the existing distribution network was able to handle an electrified community. A thermal efficiency improvement lower than 70% required support from PV/storage units that offset the demand exceeding the targeted maximum power. This model of PV/storage units was validated through a 12-week assessment, showing the reliability of a community energy system. Ultimately, a modelling tool was developed based on the mentioned models, providing four pathways to attain electrification. Users can adjust specific parameters and databases to align with the local conditions. The results indicated the electricity demands in the highest consumption period, requirements of building a community energy system and investment costs of an electrified community.
In conclusion, this research designed an efficient community energy system that reduced the electricity demand significantly. When accompanied by building performance improvement, this energy system enabled the existing distribution network to accommodate an electrified community. Moreover, the developed modelling tool, flexible with various climates, can guide the government or planner on developing electrified communities.
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