Net zero energy buildings: A consistent definition framework

The term Net ZEB, Net Zero Energy Building, indicates a building connected to the energy grids. It is recognized that the sole satisfaction of an annual balance is not sufficient to fully characterize Net ZEBs and the interaction between buildings and energy grids need to be addressed. It is also recognized that different definitions are possible, in accordance with a country's political targets and specific conditions. This paper presents a consistent framework for setting Net ZEB definitions. Evaluation of the criteria in the definition framework and selection of the related options becomes a methodology to set Net ZEB definitions in a systematic way. The balance concept is central in the definition framework and two major types of balance are identified, namely the import/export balance and the load/generation balance. As compromise between the two a simplified monthly net balance is also described. Concerning the temporal energy match, two major characteristics are described to reflect a Net ZEB's ability to match its own load by on-site generation and to work beneficially with respect to the needs of the local grids. Possible indicators are presented and the concept of grid interaction flexibility is introduced as a desirable target in the building energy design.Acknowledgements The work presented in this paper has been largely developed in the context of the joint IEA SHC Task40/ECBCS Annex52: Towards Net Zero Energy Solar Buildings.acceptedVersio

Zero Village Bergen. Energy system analysis

Based on discussions with the project partners, three possible solutions have been investigated for the energy system of Zero Village Bergen: 1. District Heating (DH) 2. Biomass fired Combined Heat and Power (Bio CHP) 3. Ground Source Heat Pump (GSHP) When comparing the three systems against one another, two sets of key performance indicators are considered: ZEB target and system cost. Furthermore, technical system performance indicators are also calculated, but are not used to compare the systems because either they do not have an implicit goodor-bad value (e.g. thermal capacity) or such value is already embedded in the other indicators. The results show that, with the conversion factors used in this study, only the Bio CHP system meets the ZEB balance target, actually achieving a slightly negative balance; see Figure below left). Furthermore, the results show that while the DH system has the highest operational cost (and minimum investment cost) and the Bio CHP system has the highest investment cost (and intermediate operational cost), the two end up having approximately the same global cost. The GSHP system has the lowest operational cost (and intermediate investment cost) and ends up with the lowest global cost; significantly lower than the two other systems; see Figure below right)...publishedVersio

A method for generating complete EV charging datasets and analysis of residential charging behaviour in a large Norwegian case study

Electric vehicles (EVs) are part of the solution to achieve global carbon emissions reduction targets, and the number of EVs is increasing worldwide. Increased demand for EV charging can challenge the grid capacity of power distribution systems. Smart charging is therefore becoming an increasingly important topic, and availability of high-grade EV charging data is needed for analysing and modelling of EV charging and related energy flexibility. This study provides a set of methodologies for transforming real-world and commonly available EV charging data into easy-to-use EV charging datasets necessary for conducting a range of different EV studies. More than 35,000 residential charging sessions are analysed. The datasets include realistic predictions of battery capacities, charging power, and plug-in State-of-Charge (SoC) for each of the EVs, along with plug-in/plug-out times, and energy charged. Finally, we analyse how residential charging behaviour is affected by EV battery capacity and charging power. The results show a considerable potential for shifting residential EV charging in time, especially from afternoon/evenings to night-time. Such shifting of charging loads can reduce the grid burden resulting from residential EV charging. The potential for a single EV user to shift EV charging in time increases with higher EV charging power, more frequent connections, and longer connection times. The proposed methods provide the basis for assessing current and future EV charging behaviour, data-driven energy flexibility characterization, analysis, and modelling of EV charging loads and EV integration into power grids.publishedVersio

Energy efficiency and district heating to reduce future power shortage. Potential scenarios for Norwegian building mass towards 2050

Energy efficiency, district heating and heat pumps for reduced power consumption The Norwegian power system is facing enormous challenges in the transition to a fossil-free society. The on-going electrification of transport and industry, together with establishment of new power-intensive industries, calls for rapid and extensive increase in both production of renewable electricity and the transmission grid capacity. This increase could partly be avoided through energy efficiency measures, heat pump adoption, and the use of alternative energy carriers for heating. Reduced energy delivered to buildings together with increased use of district heating have a great potential to reduce Norwegian electricity demand, and at the same time contribute to increased energy system flexibility when the grid is under the highest load. The aim of this study was to quantify the potential for increased use of district heating and heat pumps on reducing buildings’ future electricity demand in Norway. The future energy demand of the Norwegian building stock, divided into three different groups with regards to population density, was first modelled in different scenarios with respect to energy efficiency and potential access to district heating network. The outcome was then applied in an energy system model to account for different energy sources and the flexibility available in the production of district heating. The study shows that increased use of district heating reduces buildings’ electricity consumption, and in particular the buildings’ peak power demand. Comparing to 2020 level, continuing with business-as-usual will lead to 3% increase in buildings’ electricity demand by 2030, and +7% by 2050. The corresponding increase in buildings’ peak power demand is +2% by 2030 and +5% by 2050. Maximizing the use of district heating without ambitious energy efficiency standards will allow the buildings’ electricity demand to remain at the 2020 level, while buildings’ peak power demand could be reduced with -1% by 2030 and -5% by 2050. A net reduction in both total electricity and peak power demand in buildings is achieved only when maximal use of district heating is combined with ambitious energy efficiency standards and maximising the use of heat pumps in rural areas where district heating is not feasible. This scenario allowed a reduction of -12% in buildings’ electricity demand by 2030 and -26% by 2050, compared to 2020 levels. The buildings’ peak power demand could be reduced with -17% by 2030 and -35% by 2050. The results are of utmost importance for all stakeholders involved in the development of the energy system in Norway, at local and national level. Cold periods in the winter, and the inefficient use of electricity for heating, are the driving force for investments in the power system. Massive extension in the power production and transmission capacity can be partially avoided with strong emphasis on buildings’ energy efficiency, together with the use of district heating in urban areas and heat pumps in rural areas. This can reduce the total system costs for energy production and spare the natural environment for unnecessary further intervention.publishedVersio

Integrating Thermal-Electric Flexibility in Smart Buildings using Grey-Box modelling in a MILP tool

In a smart grid setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. However, most literature assume stationary temperature set points for heating and cooling. In this work, we propose a grey-box model to investigate how the energy flexibility of the thermal mass of the building may impact its energy flexibility potential as well as the investment decisions of the energy system within a building, by using an already developed investment decision tool, BUILDing’s OPTimal operation and energy design model (BUILDopt) (Lindberg et al. (2016)). As BUILDopt is a Mixed Integer Programming (MIP/MILP) tool, the flexibility models must be linear as well. We evaluate the energy flexibility potential, here called comfort flexibility, for use cases reflecting different heating systems (electric panel ovens vs. ground source heat pump) and operation (flexible vs. non-flexible). The case study of an Office building is performed, which considers electric specific demand, domestic hot water demand and space heating demand. Real historical data for weather and energy prices from Oslo are used, including grid tariffs related energy and monthly peak power. Most of the savings are obtained through peak load reduction, which can reach up to 13-16%. These and the savings from shifting demand away from peak prices lead to total savings of around 2%. Yet, these actions do not require additional investment in heat supply or storage components, nor in building renovations: only system measurement and control components are needed.publishedVersio

Development and testing of load flexibility KPIs in the ZEN definition

This paper discusses the flexibility KPIs proposed in the context of the Zero Emission Neighborhood (ZEN) definition for characterizing how a building or neighborhood exchanges energy with the surrounding energy system and presents preliminary results of testing them on single, archetype buildings. The KPIs are calculated as the deviation of a flexible load from a baseline, typical load. The results depend on the flexibility sources activated, as well as the flexibility drivers and flexibility goals deployed for the activation. It is shown how the mechanism of flexibility works and how the KPIs can be graphically represented, with emphasis on space heating. Numeric values of the KPIs are given in ranges, given their intrinsic case to case variability and the limited experience so far accumulated with testing them. This stated, it is shown that activating flexibility can bring reductions in ΔCost (in the range of 0% to 20%), in ΔEnergy Stress and ΔPeak power (in the range 20% to 50%) even if this is accompanied by a modest increase in ΔEnergy (in the range 0% to +5%) due to some energy losses.publishedVersio