Impact of demand charges on cost optimal energy system management: A case study of the Brf Viva energy system
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Examensarbete för masterexamen
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An increased digitalisation and electricity usage in our society, in combination with ageing
electricity grids with larger shares of variable renewable energy in the energy mix, has
created new challenges for the current electricity system. To reduce congestion issues and
free up flexibility for the grid, new types of electricity tariff structures could be utilised.
To achieve the desired effects from such tariff structures the active control and the energy
use in buildings becomes of great importance. One such building is the housing complex
Brf Viva in Gothenburg. Its energy system consists of solar PVs, batteries, heat pumps
and thermal energy storage and is connected to the local electricity grid and district heating
network.
The aim of this project is to investigate how optimal system management for the minimised
total energy cost of Brf Viva is affected by changes in electricity tariff. An additional
aim is to establish recommendations for a real-time energy management system over
Brf Viva, currently under development by RISE, when demand charges are implemented
to the tariff structures. The project is sprung from a collaboration between the companies
Göteborg Energi and RISE. The aim is fulfilled by the construction of an optimisation
model over the energy system, written in Python and utilising the optimisation software
CpleX. Four different types of demand charge structures are imposed on the modelled
energy system.
The results show that savings in energy cost can be made if the building management
system is optimised in accordance with the aim of the imposed electricity tariff. The
real-time control system should optimise the system management for the tariff type in
place, with a few exceptions. An estimated default value of the peak is used in the model,
of which the value should be close to the largest peak obtained for the same month the
previous year. When the model is optimised for short time periods in a step-wise manner
within a month, e.g. 24 hours, the length of the resolved part of the 24 hours should be
longer compared to the saved solution values. The importance of a trustworthy forecast
increases when demand charges are implemented to the system. Depending on future
district heating prices, it might become more important to focus on optimisation of the
thermal energy storage, rather than the heat pumps. In conclusion, the results show that
there is a lot of flexibility available to aid the large scale electricity grid from energy
systems like Brf Viva if smart tariff structures on electricity are imposed to the system.
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building energy management system, demand charge, electricity tariff, power demand, flexibility