Metamodeling energy indicators in neighborhoods with growing deployment of heat pumps and rooftop photovoltaics

This paper evaluates simple metamodels to predict local electricity demand and grid restrictions, in residential neighborhoods with heat pumps and photovoltaics. The procedure and challenges of developing such models are described. Modeling is based on results obtained from detailed simulation of buildings and the grid. Linear and logistic regression models are developed for electricity demand and minimum voltage respectively, as a function of neighborhood characteristics, related to both building and electrical network properties. The paper shows that linear regression can be used for a first evaluation of electricity demand. For voltage violations, logistic regression gives acceptable results, however more complex models are needed to approximate voltage levels.status: publishe

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

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.publisher: Elsevier articletitle: Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties journaltitle: Applied Energy articlelink: http://dx.doi.org/10.1016/j.apenergy.2016.11.103 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved.status: publishe

Corrigendum to “Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties” [Appl. Energy 192 (2017) 268–281]

© 2017 The authors regret that an error occurred in the implementation of the simulation model, rendering its description inaccurate and altering few minor observations. Nevertheless, the main results and conclusions of the paper are not affected. More specifically, the domestic hot water (DHW) heating schedule has been mistakenly implemented the same in all buildings; therefore, the description in section 2.1.2 Modeling: Heating system, p. 272, would be more accurate as: “The water heating schedule is the same for all buildings, starting at 21:30 with a 5 h duration. Consumers are assumed to follow a certain advantageous tariff. Last, anti-legionella cycles are scheduled once a week during the evening, one hour after the daily heating starts [42]. The electrical immersion heater then boosts the water temperature from 55 to 65 °C.” Instead of: “The water heating schedule differs for the 100 simulated building cases, gradually starting between 21:30 and 00:30 with a 5 h duration. In this way diversity between consumers is taken into account, even though all are assumed to follow a certain advantageous tariff. Last, anti-legionella cycles are scheduled once a week during the evening, one hour after the daily heating starts [42]. The electrical immersion heater then boosts the water temperature from 55 to 65 °C. The day of the week varies from house to house.” The difference in results caused by this mistake has been investigated, with simulations performed as in the original description. The impact on the main results and conclusions was found to be insignificant, because the total demand remains the same, and the overall peak load and voltage were caused by high needs for space heating, rather than for DHW. Additionally, occupancy remains stochastic, leading to different space heating set points, lighting and plug loads. As an example, the 4th panel of Fig. 9 (p. 278) is reproduced for the new diversified DHW heating schedule, and compared to the one in the original paper (same schedule). The distribution of results for both indicators is largely the same, even when split by feeder size, heat pump (HP) penetration rate and cable type. Thus, the observations and conclusions made in the paper are still valid. [Figure presented] Comparison of Fig. 9 (4th panel) with same schedule (original) and with diversified DHW schedule: Imax and Vmin for rural feeders, based on number of buildings N, heat pump penetration rate HP and cable type. For each cable type the median, 5th and 95th percentiles of all feeders are plotted. Nevertheless, some differences exist for section 3.3 Load profile analysis, which are explained hereunder. [Figure presented] Comparison of Fig. 7 (panels b and c) with same schedule (original) and with diversified DHW schedule: Load duration curves for rural neighborhoods of N = 40 buildings and varying degrees of heat pump and PV penetration, as well as construction quality. Each curve is individually ordered after addition of a supplementary load. Starting from the base load, the heat pump load, DHW immersion back-up, HP instantaneous back-up heater and PV generation are successively included. Peak values are indicated by the dashed horizontal lines. In panels b and c of Fig. 7, the peak caused by addition of the DHW back-up element load is much lower, or non-existent, compared to previous results. This change can be easily justified by the difference in the DHW schedule implementation. The total peak load remains the same, however, because it occurs in times when space heating is needed rather than hot water. The comment on p. 276 should also be corrected to: [Figure presented] Comparison of Fig. 8 with same schedule (original) and with diversified DHW schedule: Simultaneity factors ks of different types of loads. Total is the combination of all other loads. Median (solid lines) and 5th and 95th percentiles are shown (filled areas). Significant decrease can be observed for the simultaneity factors for the DHW back-up loads and the heat pump loads, compared to the original results, as seen in the comparison of Fig. 8. This is explained by the correct implementation of the diversified schedule for DHW heating, which involves both the heat pump and back-up element. Nevertheless, for the total load, the factor remains largely the same, also indicating that the total load peaks are not related to DHW preparation. The relevant comment on p. 277 should be revised as: “On the contrary, heat pump loads have a much higher ks around 0.7, due to similar heating schedules for all houses, combined with the absence of buffer storage tanks. Even higher factors, but with wider spread, are found for the heat pump back-up, all operating in very cold weather conditions. For the DHW back-up, the simultaneity can be very high for some neighborhoods with 10 houses, but decreases fast as more houses are added, despite the fact that all consumers take advantage of the night tariff. When looking at the total electrical demand, the simultaneity varies between 0.25 and 0.6 for feeders with up to 40 consumers. It is important that these factors, used for network sizing, be updated to account for the use of heat pumps.” The authors would like to apologise for any inconvenience caused.status: publishe

Sensitivity of Low-Voltage Grid Impact Indicators to Modeling Assumptions and Boundary Conditions in Residential District Energy Modeling

Heat pump and photo-voltaic grid impact study at low-voltage level requires detailed simulation of multiple grids and scenarios, inevitably involving assumptions and scope restrictions. This paper investigates the influence of several assumptions on grid impact indicators, namely voltage levels and load, based on a probabilistic district simulation framework developed in previous work. Grid simplification, simulation and data temporal resolution and sampling repetitions are examined, along with variation in boundary conditions, namely transformer reference voltage and capacity, and heat pump power factor. The sensitivity study shows the accuracy loss resulting from low-resolution data and grid simplification, while also highlights the necessity to take into consideration uncertain parameters, such as the reference voltage and heat pump power factor.status: publishe

Exploring the impact of heat pump-based dwelling design on the low-voltage distribution grid

As a result of the requirements for energy efficiency and the related vast deployment of PV and heat pump systems, a rise in the electrical loads introduced on the low voltage distribution grid can be expected. The latter can lead to possible violations of grid technical constraints which will require adjustive measures to be taken. This study investigates the impact of the building design on several grid restriction criteria. A thorough sensitivity analysis is performed based on detailed simulation of two Belgian residential neighbourhoods, where the building parameters are varied in a Monte Carlo simulation. The analysis shows that building parameters aggregated for the neighbourhood show stronger correlations than average ones, and can be confidently linked to the grid performance indicators, with the potential to be used in meta-modelling of neighbourhoods. However, sampling efficiency and input correlation issues are found to require further attention. Such a meta-model, aiming at examining the influence of grid constraints on the efficiency of building energy policies, will be implemented in future work.status: publishe

Bottom-up modelling of the Belgian residential building stock: impact of building stock descriptions

Building stock modelling is a key element for the analysis of energy policy scenarios at an aggregate level, such as the integration of buildings in smart grids. To analyse the impact of new technologies and evaluate the dynamic behaviour at an aggregate level, bottom-up dynamic models are a prerequisite. Nevertheless, data on the building stock characteristics is scarce and assumptions need to be made. A comparison of two residential building stock typologies for Belgium is performed in this work with the aim of identifying their differences and investigating how variations in the representation of a building stock can influence the outcome of the model. For this purpose detailed models of the two typologies are implemented and simulated in Modelica using the IDEAS library. Qualitative and quantitative analysis of the heat demand and dynamic behaviour of the stock implementations showed that the inherent differences in the descriptions lead to strong differences in the results, especially when conclusions must be made for specific building cases. This study highlights the need for more reliable and comprehensive data for the building stock, which is a prerequisite for qualitative bottom-up modelling.status: publishe