Figure 2 - uploaded by Joseph Chimento
Content may be subject to copyright.
Source publication
Urban energy systems represent a significant proportion of global energy consumption, with residential and commercial buildings accounting for around 40%. This could be significantly reduced through the use of smarter control strategies for space heating. In district heating networks, the supply limitations associated with the electrical grid can l...
Similar publications
Dispersed generation (DG) objects are usually connected to distribution grid. This-type of grid supposes to have power system automatics (PSA) that comprise emergency control system (ECS). ECS serves for normal power supply scheme recovery after fault elimination, fault development prevention and system parameters maintenance in acceptable limits w...
Citations
Decarbonisation of the building sector is driving the increased use of heat pumps. As increased electrification of the heating sector leads to stress on the electricity grid, the need for district level coordination of these heat pumps emerges. This paper proposes a novel hierarchical coordination methodology, in which a price coordinator reduces the total instantaneous power demand of a building network below a power supply limit using a price signal. Each building has a model predictive controller (MPC) which maximises thermal comfort and minimises electricity costs. An additional term in the MPC objective function penalises the heat pump power demand quadratically, which when multiplied by a pseudo electricity price allows the price coordinator to reduce the peak power demand of the building network. A 2 building network is studied to analyse the price coordinator algorithm’s behaviour and demonstrate how this approach yields a trade off between comfort, energy consumption and peak demand reduction. A 100 building network case study is then presented as a proof of concept, with the price coordinator approach yielding results similar to that of a centralised controller (less than 0.7% increase in energy consumption per building per year) and a roughly fourfold decrease in computation time.
