Impact of demand charges on cost optimal energy system management: A case study of the Brf Viva energy system

Abstract

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.