Energy flexibility in residential buildings clusters

Abstract

The building sector represents one of the most energy-consuming worldwide and a great part of its consumption is accounted for residential demand for space heating and cooling. Although it is necessary to promote the buildings energy efficiency, energy flexibility is also of paramount importance to optimize the balance between demand and supply. In fact, an energy flexible building is defined as able to change, in a planned manner, the shape of its energy demand curve, electrical and thermal, while the comfort of the end-users is still guaranteed. Objective of this work is to exploit the energy demand management ability of different buildings composing a cluster, when their aggregated demand derived from electric heating systems (i.e. heat pumps) is subject to demand response (DR) strategies. Users with different occupancy profile are considered. By supposing to be able to activate the energy flexibility of the single building with thermostatic load control, different scenarios of cluster composition are evaluated in order to provide guidelines to implement optimal strategies for energy flexibility exploitation without drawback effects connected to the event.

Demand response strategies in residential buildings clusters to limit HVAC peak demand

“…Following the cluster level assessments presented in [11], also in this paper a short-term peak shaving event is modelled in the DR scenario. However more detailed building models representing archetypal buildings have been implemented in Python [13].…”

“…As for the single user model described in [11] also in this analysis a lumped-parameter model based on the thermal-electricity analogy is selected. However, to allow the modelling of different cases in terms of level and position of thermal insulation and type of heating system, more detailed models have been implemented.…”

“…In this case 𝑄𝑄 ̇h is provided exactly to the innermost thermal node of the floor (Tfap in Figure 3). As showed in [11], with this architecture of the model, the thermal dynamic of the building can be represented with a linear state space formulation in which the temperatures of the nodes represent the state of the system while the external conditions (Tamb and Tground), the thermal gains (𝐺𝐺 ̇) and the heating power provided by the heating system (𝑄𝑄 ̇h) represent the inputs.…”

“…To model the DR event an optimization problem has to be defined to find 𝑄𝑄 ̇h [11]. Indeed, it is necessary that 𝑄𝑄 ̇h varies, within an acceptable range (0, Q ̇max ) allowed by the heating system, to activate the flexibility provided by TCLs.…”

“…In a previous work, the authors began to approach this issue with an analysis that aimed to investigate the role of the differentiation of the users involved in a DR event [11]: the users were differentiated according to the occupancy profile patters. In this work the goal is to extend the analysis by considering the role of the construction characteristics (level of thermal insulation) and the type of heating system (thermal inertia levels provided) of buildings when they are involved in a DR event at the aggregate level.…”