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.

h i g h l i g h t sThe CO 2 -abatement cost of residential heat pumps is determined in a future setting. The impact on electricity generation is considered via an integrated model. Multiple building and heating system cases are modeled and compared. Active demand response contributes significantly in lowering CO 2 -abatement cost. Great reductions are achieved in CO 2 emissions, curtailment and peak generation. a b s t r a c tHeat pumps are widely recognized as a key technology to reduce CO 2 emissions in the residential building sector, especially when the electricity-generation system is to decarbonize by means of large-scale introduction of renewable electric power generation sources. If heat pumps would be installed in large numbers in the future, the question arises whether all building types show equal benefits and thus should be given the same priority for deployment. This paper aims at answering this question by determining the CO 2 -abatement cost of installing a heat pump instead of a condensing gas boiler for residential space heating and domestic hot-water production. The electricity system, as well as the building types, are based on a possible future Belgian setting in 2030 with high RES penetration at the electricitygeneration side. The added value of this work compared to the current scientific literature lies in the integrated approach, taking both the electricity-generation system and a bottom up building stock model into account. Furthermore, this paper analyzes the possible benefits of active demand response in this framework. The results show that the main drivers for determining the CO 2 -abatement cost are the renovation level of the building and the type of heat pump installed. For thoroughly insulated buildings, an air-coupled heat pump combined with floor heating is the most economic heating system in terms of CO 2 -abatement cost. Finally, performing active demand response shows clear benefits in reducing costs. Substantial peak shaving can be achieved, making peak capacity at the electricity generation side superfluous, hence lowering the overall CO 2 -abatement cost.

Heating electrification and distributed renewable generation in the residential sector are among prominent solutions advocated for energy saving and carbon emission reduction. However, research shows these low-carbon technologies may create issues at the low-voltage (LV) distribution grid. High-level policy assessment currently lacks the support to take into account such local grid restrictions. To achieve this, we propose the use of a probabilistic simulation framework in combination with metamodeling, that allows to assess the potential LV grid impact for a wide range of cases. The probabilistic framework is first presented, which is developed for Belgian residential neighborhoods with air-source heat pumps and rooftop PV, based on previous work. Given the complexity and computational requirements of this approach, the paper furthermore proposes metamodeling as a technique to obtain inexpensive evaluation of low-voltage grid impact indicators, suitable for high-level assessments. Although metamodeling is extensively used in various engineering domains, no application in district-level grid-related indicators is available. Consequently, this paper's focus lies on discussing the various steps and options of the metamodeling procedure, while emphasizing problemspecific challenges. Lastly, the proposed metamodeling methodology is used for training simple metamodels for voltage indicators in neighborhood-level LV grids. Linear regression performed fairly well in predicting the minimum voltage levels, though less accurately close to the lower voltage limit, while logistic regression effectively detected feeders with violations.

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State-of-the-art Modelica tools for modeling and simulating multi-physical systems have reached certain maturity among the building physics community. Hence, simulation is widely used for control, sizing and performance assessment of energy systems. However, serious efficiency issues arise for large-scale models. This article proposes a practical application of co-simulation methods on detailed district energy systems. The aim of this study is to assess performance and scalability of cosimulation through Functional Mock-up Interfaces on a detailed and multi-physical district model. In particular, we propose a comparative analysis between classical simulation and co-simulation methods and a scalability analysis on a growing number of buildings. The models have been implemented using Modelica language and the OpenIDEAS library. A decomposition approach is taken for modeling the entire system, while stochasticity in the inputs is taken into account. Results are presented for various integration scenarios, including a classical integrated simulation for reference and co-simulations involving different master-algorithms within Dymola and DACCOSIM 2017. Scenarios are compared in terms of speed-up and accuracy of principal physical quantities representing key performance indicators such as indoor temperature, current and voltage at building's connection. The analysis shows that co-simulation can run up to 90 times faster than the integrated simulation for 24 buildings, while ensuring acceptable accuracy.

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