Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties

Abstract

Heating electrification powered by distributed renewable energy generation is considered among potential solutions towards mitigation of greenhouse gas emissions. Roadmaps propose a wide deployment of heat pumps and photovoltaics in the residential sector. Since current distribution grids are not designed to accommodate these loads, potential benefits of such policies might be compromised. However, in large-scale analyses, often grid constraints are neglected. On the other hand, grid impact of heat pumps and photovoltaics has been investigated without considering the influence of building characteristics. This paper aims to assess and quantify in a probabilistic way the impact of these technologies on the low-voltage distribution grid, as a function of building and district properties. The Monte Carlo approach is used to simulate an assortment of Belgian residential feeders, with varying size, cable type, heat pump and PV penetration rates, and buildings of different geometry and insulation quality. Modelica-based models simulate the dynamic behavior of both buildings and heating systems, as well as three-phase unbalanced loading of the network. Additionally, stochastic occupant behavior is taken into account. Analysis of neighborhood load profiles puts into perspective the importance of demand diversity in terms of building characteristics and load simultaneity, highlighting the crucial role of back-up electrical loads. It is shown that air-source heat pumps have a greater impact on the studied feeders than PV, in terms of loading and voltage magnitude. Furthermore, rural feeders are more prone to overloading and under-voltage problems than urban ones. For large rural feeders, cable overloading can be expected already from 30% heat pump penetration, depending on the cable, while voltage problems start usually at slightly higher percentages. Additionally, building characteristics show high correlations with the examined grid performance indicators, revealing promising potential for statistical modeling of the studied indicators. Further work will be directed to the assessment of meta-modeling techniques for this purpose. The presented models and methodology can easily incorporate other technologies or scenarios and could be used in support of policy making or network design.

Integrated distribution system and urban district planning with high renewable penetrations

“…Nonetheless, it is still common to use hourly time steps for distribution system modeling with renewable DERs, partially because of a lack of fine‐resolution weather input data. Recently, however, district design studies have been emphasizing the use of weather data with increments of 1–15 min (Babacan et al, ; Molitor, Groß, Zeitz, & Monti, ; Protopapadaki & Saelens, ). Unfortunately, high computation time is a second impediment to use fine‐resolution time‐series data, but methods are also being developed to analyze an entire year's data without prohibitive computation time or unacceptably large errors (Deboever, Grijalva, Reno, Zhang, & Broderick, ; Mather, ).…”

Interactions between electric mobility and photovoltaic generation: A review

“…Heating, ventilation and air-conditioning (HVAC) represent more than half of buildings energy consumption [65]. As a distributed energy resource, heat pumps are meant to play a role in smart grids [66]. Among other, their interaction with PV energy has been noticed because of the heat pump peak shaving potential [67].…”

Assessing scalability of a low-voltage distribution grid co-simulation through functional mock-up interface

“…Simulation #1 is the scenario that serves as reference for accuracy, and it represents the integrated simulation with the smallest integrator error tolerance of 10 −8 . Simulation #2 consists in a state-of-the-art setup, using a tolerance of 10 −6 , that is usually encountered in the literature (Protopapadaki and Saelens 2017;Protopapadaki, Baetens, and Saelens 2015). Simulation #3 uses a lower tolerance (of 10 −3 ) in order to quantify speed-up and accuracy, and highlight the limits of the trade-off between tolerance and accuracy.…”

“…Then, the network behavior is simulated by applying the building loads to assess energy quality, such as voltage drops and thermal constraints. This last method proved to be useful for statistical analysis and energy policy assessment (Protopapadaki and Saelens 2017;Protopapadaki, Baetens, and Saelens 2015). Nevertheless, in this case, interaction between building and grid can only be one-sided, prohibiting any kind of feedback control or data communication during run-time.…”