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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.

This third version of the ZEN definition guideline report builds upon version 1.0 and 2.0 of the ZEN definition guideline reports and series of ZEN definition reports. This report gives further details on the ZEN KPI tool and on ZEN KPI reference, limit, and target values. A major change involves lifting the process KPIs out of spatial qualities and incorporating them into a process guideline for designing ZENs. Details have been added to each KPI to explain to what degree it contributes to the main goal of ZEN, and examples of best practice are given. Additional power KPIs have been added. The spatial qualities category has been renamed to urban form and land use. Denne tredje utgaven av ZEN definisjonsveilederen bygger på versjon 1.0 og 2.0 av ZEN definisjons-veilederne og ZEN definisjonsrapporter. Denne rapporten presenterer ZEN KPI verktøyet i mer detalj og gir mer informasjon om ZEN KPI referanse-, grense-, og målverdier. En stor endring inkluderer å løfte prosess KPI ut av stedskvaliteter kategorien og å tilpasse dem til en prosessveileder for design og planlegging av ZEN områder. Detaljer har blitt supplert på hver KPI for å forklare i hvilken grad de bidrar til målsetningen av ZEN samt gir eksempler på beste praksis. Noen nye KPI har blitt lagt til. Navnet på stedskvaliteter kategorien har blitt endret til byform og arealbruk.

This second version (V2.0) of the ZEN definition guideline report builds upon version V1.0 of the ZEN definition guideline and series of ZEN definition reports. The ZEN categories for GHG emissions, energy, and power have been updated after having been tested in selected pilots. In addition, a description of the ZEN KPI tool and framework has been added. Finally, the ZEN categories mobility, spatial qualities, economy, and innovation have been updated. Denne andre utgaven av ZEN definisjonsveilederen bygger på versjon V1.0 av ZEN definisjons-veilederen og ZEN definisjonsrapporter. ZEN kategoriene klimagassutslipp, energi og effekt er oppdatert etter å ha blitt testet ut i utvalgte piloter. I tillegg er det lagt til en beskrivelse av ZEN KPI-verktøyet og rammeverket. Videre er ZEN kategoriene mobilitet, stedskvaliteter, økonomi og innovasjon blitt oppdatert.

Dette er den andre årsrapporten fra Flexbuild-prosjektet og oppsummerer hovedfunnene fra forskningsarbeidet som ble gjort i prosjektets andre år. Potensial for småskala fjernvarme og storskala fjernvarme (kapittel 2) For det andre året i Flexbuild-prosjektet er modelleringen av den norske bygningsmassen delt inn i fem ulike regionale spotprisområder, NO1 til NO5. Videre har vi identifisert dagens og framtidig potensial for bygninger som betjenes av fjernvarme. For å konkludere måtte vi undersøke det geografiske potensialet for fjernvarmeforsyning, som oppsummert i figur 1. 1. På tilbudssiden: Ikke alle områder kan betjenes med fjernvarme i framtiden. Hvert markedsområde for strøm i Norge måtte dermed deles inn i tre delområder: • Delområdet med storskala fjernvarme er basert på Statistisk sentralbyrås definisjon av "byer". Dagens fjernvarmeanlegg som genererer > 100 GWh/år, er definert som storskala. • Delområdet med småskala fjernvarme er basert på Statistisk sentralbyrås definisjon av "tettbygde strøk". Dagens fjernvarmeanlegg som produserer < 100 GWh/år, er definert som småskala. • Delområdet med ingen termiske nettverk er basert på Statistisk sentralbyrås definisjon av "spredtbygde strøk", og for disse områdene antar vi at varmetetthet er for lav til å rettferdiggjøre utviklingen av fjernvarmenett. 2. På etterspørselssiden: Ikke alle bygg har eller kan ha et vannbåret varmesystem. Dette førte til følgende modelleringsantakelser og valg: • Andelen eksisterende bygninger med vannbåret varme er ca. 40 % for boligblokker og ca. 60 % for næringsbygg, basert på tilgjengelig statistikk om oppvarmingsteknologi og forbruk av fjernvarme. I nybygg antas andelen å bli ca. 90 % for både boligblokker og næringsbygg. • Det totale volumet av tilkobling til fjernvarmenettet til boligbygg er tildelt boligblokker. Dette er gjort for å være konsekvent med statistikken på bruk av energibærere og samtidig forenkle modelleringen. Det er et lite volum fjernvarme som blir levert til eneboliger. Samtidig er dette ikke en attraktiv kategori for utvidelse av fjernvarmenettet. 102020534

In Nordic climates, the energy use in buildings is dominated by space heating (SH) and domestic hot water (DHW). Heat load measurements with hourly resolution from smart meters are now becoming the standard. However, in most cases, only the total heat use in the building is metered, without separation into DHW and SH use. The analysis performed in this work is aimed at comparing and verifying different methods for estimating typical DHW load profiles by decomposition of heat load measurements into SH and DHW. Three methods have been used for the decomposition of the same set of measurements of the heat load from 78 buildings comprised of apartments and hotels: the seasonal method, the energy signature method and hybrid summer-signature method. All three methods have limitations, but in this article it is shown that the hybrid-summer signature method, which is a new method that is proposed in this article, has the closest similarity to measurements of DHW energy use from similar buildings. © The authors. Published by SINTEF Academic Press 2020. This is an open access publication under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The potential in cost and energy savings by replacing a feed forward weather compensated control (WCC) controlled radiator system with a linear MPC controller is investigated in a Modelica-Python setup. It is shown that if the MPC is optimized for minimum energy consumption it can reduce the energy consumption by up to 12 %. It is also demonstrated how variable price signal can influence the heat demand profile, and thereby shift energy consumption away from peak hours. By introducing a peak load tariff, it is also possible to reduce the rapid changes and large peaks often caused by optimization-based controllers

Identification of grey-box models to describe the heat dynamics of buildings is treated extensively in literature, However, to identify these models, data from controlled heating experiments is needed, which is not feasible in many cases. In this work, the aim is to overcome this threshold by using datasets from simulations of detailed white-box models to identify the grey-box models. Given that the white-box models are validated and calibrated against real measurement data, the need for real measurement data is bypassed, thus enabling the use of grey-box models identified from simulation data in real - world predictive controllers. The results show that a threestate grey-box model is able to capture the heat dynamics of the white-box model over a time period of ~1 week in the summer months.

Prosjektpartnerne har blitt enige om behovet for å definere langsiktige storylines for eksterne variabler som påvirker modelleringsaktivitetene. Et storyline-verksted for alle industrielle partnere og forskningspartnere ble avholdt i januar 2020. De fire identifiserte "storylines" er beskrevet i kapittel 2 og har fått navnene: • Energinasjonen Norge • Petroleumsnasjonen Norge • Naturnasjonen Norge • Klimapanikk Norge FlexBuild bruker et sett modeller for å gi innsikt i den fremtidige rollen og verdien av sluttbrukerfleksibilitet som er tilgjengelig i bygninger fra et norsk energisystemperspektiv. I FlexBuild bruker vi tre sektorspesifikke modeller for å gi detaljer om bestemte deler av energisystemet, mens energisystemmodellen brukes til å dekke koblingene mellom de forskjellige delene av energisystemet (se figur nedenfor). Modellene er: • EMPIRE, for kraftsystemforbindelser utenfor Norge, med resten av Europa • BUTLER, for byggesektoren i Norge • TIMES-Norge, for energisystemet i Norge • FanSi, for å zoome inn på vannkraftsektoren i Norge Definering av kvantitative data som er i samsvar med de kvalitative storylines, ble utført for hver modell og er beskrevet i de neste kapitlene. Modellene har forskjellige grensesnitt. I TIMES er for eksempel datasett og grensevilkår definert for tre av fire storylines; i EMPIRE er de fire storylines gruppert i to, og identifiserer introduksjonen av CCS-teknologi i Europa.For BUTLER og FanSi er ennå ikke storyline-forskjellene implementert. Dette arbeidet vil fortsette og foredles i de påfølgende årene for å sikre en sammenhengende implementering av storylines i alle modeller. Se påfølgende figur. Denne metoden gjør det mulig å utnytte styrken til hver modell, men utfordringen med å bruke en rekke modeller er at disse må harmoniseres og kobles sammen på en tilstrekkelig måte for å gi rimelig prosjektinnsikt. Metodikken for kobling av de forskjellige modellene er beskrevet i kapittel 3.

Grey-box models combine a relatively simple physical description of the building with a data-driven inference of key parameters and are often used for this purpose. A challenge with grey-box models is that the model identification process requires 'rich' datasets, meaning datasets containing enough statistical variability on both heating demand and indoor temperatures. Such datasets are scarcely available, usually only from dedicated experiments in living labs or similar research facilities. This study aims to present a series of datasets that can be used for the identification of grey-box models of apartment blocks. Special test periods are simulated in IDA ICE during which representative archetypes of apartment blocks in Norway are excited with trains of heating events, Pseudo-Random Binary Sequence (PRBS), aiming at exploring a wide and rapidly changing set of indoor temperatures within and outside the thermal comfort zone.

The increased use of electric vehicles (EVs) calls for new and innovative solutions for charging infrastructure. At the same time, it is desirable to improve the energy flexibility of neighbourhoods. This paper presents state-of-the-art for smart EV charging systems, with focus on Norway. Norway is a leading market for EVs, with more than 110,000 EVs and 2000 charging stations. The paper describes how charging stations can interact with the energy need in buildings and neighbourhoods, local energy production and local electric and thermal energy storage. Examples of commercial smart EV charging systems are described. Smart EV charging systems have the potential to improve energy flexibility in a Zero Emission Neighbourhood (ZEN). Such EV charging systems can also interact with heating loads in neighbourhoods. Piloting of new technologies and solutions can provide more knowledge about smart EV charging systems, and how they can participate in matching energy loads in buildings and infrastructure with local electricity generation and energy storage.

Domestic hot water (DHW) systems are significant consumers of energy in buildings. This article is dedicated to hourly and daily DHW energy use modeling, with the aim of achieving energy savings in buildings. The methods investigated in the article were tested using statistical data obtained from a hotel located in Oslo, Norway. For better modeling, the influence of various factors on DHW energy use in the hotel was studied. For this purpose, the wrapper approach was used. The analysis indicates that the most important variable that should be used in the model is number of guests. There are also other factors that can be taken in account, even though they do not have such strong influence. Traditionally, only daily data about number of guests are available in the hotels. These data do not allow us to develop accurate hourly model of DHW energy. The article therefore proposes a method which, based on introduction of artificial variables, improve accuracy of the hourly DHW model. Eight models are compered, based on criteria of their adequacy. The Support vector machine model shows the best results for daily modeling and the Partial least squares (PLS) regression for hourly modeling.

This report presents a set of guidelines to assist building designers in a methodological approach to analysis of energy systems in the early design phase of zero emission buildings. The guidelines are meant to accompany the use of a ZEB supporting tool, guiding through the necessary steps to evaluate performance and adapt dimensioning of different systems to the case at hand (Figure 1). The ZEB supporting tool is a post-processing tool based on outputs of hourly aggregated data produced by other building performance simulations (BPS) tools, such as thermal load, plant temperatures, mass flows and energy exchange between components. The interface is a text file written as output by a BPS tool and read as input by the ZEB supporting tool. In theory, any building performance simulation (BPS) tool may serve the purpose, as far as it is capable of producing hourly results for the required parameters and variables. In practice, the ZEB tool has been developed "in tandem" with the Early Stage Building Optimization plant model in IDA ICE, by means of a dedicated script that generates the required interface text file. This is a comma-separated csv file with hourly results of central parameters. The results of the support tool are presented mainly in graphical form, providing insights into several aspects of the energy system performance. E.g. load duration curves, split between base and top heating, temperature and COP diagrams for heat pumps, electricity and thermal carriers demand profiles, ZEB balance and mismatch factors, as well as global cost over its economic lifecycle following the principles of cost optimality (Cost-optimal methodology and accompanying technical guidelines, EU 244/2012). Chapter 6 discusses the feedback from a test carried out by Norwegian BPS practitioners and the possibility for further research and development of the tool. For the time being the ZEB tool is implemented as an Excel spreadsheet with embedded VBA (Visual Basic) code, thus favouring transparency and easiness of use over flexibility and computational performance. When standards and methods are in place, the tool can be further developed in other environments, e.g. Matlab, and/or directly incorporated into existing BPS tools.

This report is a part of Work Package 4 Energy Flexible Neighbourhoods. The goal for WP 4 is to develop knowledge, technologies and solutions for design and operation of energy flexible neighbourhoods. 4th generation district heating is evaluated as a sustainable solution for covering the heating demand in Zero Emission Neighbourhoods and reducing the strain on the electricity grid. There are, however, some technical challenges that must be solved before it is introduced. One of them is to determine how low the supply temperature could be in different building types, which again will determine the minimum district heating supply temperature. This report is evaluating the minimum supply temperature in Norwegian apartment blocks based on effects of improving the thermal envelope and reducing the temperature levels for the heating system. The analysis is based on building simulation and focuses on whether the reduced supply temperature guarantees the comfort in the building, considering the coldest room with a heating setpoint of 22 °C and a minimum acceptable indoor temperature of 19,0 °C. The simulated buildings are based on the data available from the IEE project Tabula. Generic models representative for Norwegian apartment blocks have been developed in IDA ICE. They consist of eight age classes and three levels of energy performance: • Prior to 1956, from 1956-1970, 1971-1980, 1981-1990, 1991-2000, 2001-2010, 2011-2020 and 2020 →. • Original, intermediate renovation and standard renovation For the intermediate renovation level, it is only the windows and infiltration rates that have been changed. Tabula also includes an ambitious renovation, but this has not been modelled as the results are expected to be similar to those for the newest age class. Simulations are performed with two different dimensioning temperature levels for the radiators typical for Norwegian buildings; 80/60 and 60/40 °C. The results showed that it is possible to reduce the supply temperature to the radiators from 80 to 60 °C for buildings from 1971-80 and all newer age classes, even for the non-renovated buildings. This is based on a minimum acceptable indoor temperature of 19.0 °C (according to the Norwegian building regulations, TEK). For the older age classes, an acceptable indoor temperature is not achieved for the non-renovated buildings when reducing the supply temperature. Although it is sufficient to perform the intermediate renovation to maintain temperatures above 19 °C, it is highly recommended to perform the standard renovation for these age classes to reduce the number of hours with a significantly reduced indoor temperature compared to the setpoint temperature. In addition to reduce the heating demand and thus lead to energy savings, this will also ensure that the occupants are satisfied with their thermal environment. It is important to note that the conclusions would be different if the minimum acceptable temperature was set higher, for instance at 20 or 21 °C. The results can be used by district heating companies, building owners, contractors and consulting companies in order to evaluate the introduction of 4th generation district heating in Norwegian apartment blocks. Both the models and excel sheets with hourly results are available for partners and researchers within FME ZEN

This report describes existing software tools for analysing operation and expansion of local energy systems and is written in WP5 in the Research Centre on Zero Emission Neighbourhoods in Smart Cities (ZEN). WP5 in FME ZEN aims to develop and apply methodologies that identify the socioeconomic optimal operation and expansion of energy systems within demarked areas. The eTransport model is planned to be further developed and used to analyse pilot cases in ZEN. This report provides a brief description of the eTransport model and other alternative models found in the literature or used by partners in FME ZEN. The models are assessed based on their suitability to address the planned research tasks in FEM ZEN WP5. The report also includes a description of models developed for global and international analysis

This report describes existing software tools for analysing operation and expansion of local energy systems and is written in WP5 in the Research Centre on Zero Emission Neighbourhoods in Smart Cities (ZEN). WP5 in FME ZEN aims to develop and apply methodologies that identify the socioeconomic optimal operation and expansion of energy systems within demarked areas. The eTransport model is planned to be further developed and used to analyse pilot cases in ZEN. This report provides a brief description of the eTransport model and other alternative models found in the literature or used by partners in FME ZEN. The models are assessed based on their suitability to address the planned research tasks in FEM ZEN WP5. The report also includes a description of models developed for global and international 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)...

Reduction of energy use and GHG emissions in the building sector is a high priority. The EU 2050 roadmap that was established in 2010, states that to achieve a global warming of less than 2°C in this century, Europe should reduce its GHG emissions by 80 % by 2050, using 1990 emissions as the reference. The roadmap shows that it is the power sector and the building sector that face the most severe reductions, with emission reductions of around 90%. The Norwegian research centre for Zero Emission Buildings (ZEB) was established in 2008, with the objective to "develop competitive products and solutions for existing and new buildings that will lead to market penetration of buildings that have zero GHG emissions of greenhouse gases related to their production, operation and demolition" (www.zeb.no). According to the ZEB Centre, a building may be defined as a Zero Emission Building when all the GHG emissions from the entire life cycle of the building are compensated by GHG emission credits from the generation of renewable energy. However, the calculation of the life time compensation of greenhouse gas emissions in ZEBs involves a number of difficult issues, and has been subject to extensive discussions among the ZEB researchers and among different professions. A central issue is the methodology for calculating the carbon emission credits for electricity use and generation, and how the generation of renewable energy in the operation phase should be valued with respect to offsetting the embodied carbon emissions from the production of the building. Since buildings have a life time of several decades, this involves the stipulation of the future carbon intensity of grid electricity. Another issue is how to balance the historic emissions from the production of materials against the future GHG emission offset of the renewable energy surplus from the operation phase of the building. These issues are elaborated on in the report. It includes a review of previous work and methods that have been applied in the ZEB Centre, with focus on the calculation of embodied emissions and the conversion factor for electricity in the operation phase. It also gives an overview of different methods and approaches for establishing emission credits from electricity use and generation, as well as related policy measures for GHG abatement. It discusses how to balance historic GHG emissions vs future emission credits, and challenges of the exchange of electricity between ZEBs and the grid. A key challenge of the center has been to produce innovations within the inherent space of uncertainty given by the related frontiers of the research. Thus, a research strategy for the ZEB centre has been to maximize the incentives for the development of different solutions that reduce the overall GHG emissions connected to a building. If the GHG emission credits favour only one solution to reach a zero emission balance, then the result may be easier to reach in the present. However, given the fundamental uncertainty of future developments, preparing only for one route to reach the goal is a risky navigational strategy. On the other hand, if the conversion factor is chosen in a way so that a zero emission balance is impossible to reach in the present no matter which solution is chosen, then interesting research may be the result, but the industry will be very unlikely to participate in the construction of an “impossible” building. Thus, on must find a balance between assumptions about the future that discourage innovation completely and assumptions that lead to only one innovation. The experiences from the pilot building projects within the ZEB center show that reaching the highest levels of ambition for ZEB is very challenging, given the boundary conditions and the applied CO2-factor for grid electricity. The analyses and discussion indicate that the CO2-factor that have been used in the ZEB pilot projects probably does not “favor” energy measures on ZEBs compared to other measures for CO2-mitigation. Nevertheless, such a challenging CO2-factor have promoted innovation in that it has spurred the teams to reach further than they otherwise would have done, resulting in new solutions being implemented and tested out. As such, the chosen CO2-factor may be said to have served its purpose.

Preface. This report is a deliverable of the project Nearly Zero Energy Neighbourhoods (ZenN). The project lasts from 2013 – 2017 and funded through EU's Seventh Framework Programme (FP7). In total, 12 partners from five countries are involved in the project: Tecnalia (Spain), CEA (France), IVL Swedish Environmental Research Institute (Sweden), SINTEF (Norway), ASM (Poland), NTNU (Norway), The municipality of Oslo (Norway), Debegesa (Spain), City of Eibar (Spain), Ville de Grenoble (France) EJGV (Spain) and the City of Malmö (Sweden). In the ZenN‐ project, residential areas in Sweden, Norway, Spain and France will function as nearly Zero energy building (nZEB) renovation demonstration projects where a number of measures have been done in connection with large‐scale renovations. The general objectives of the project are to demonstrate the feasibility (technical, financial and social) of innovative low energy renovation processes for buildings at the neighbourhood scale; identify and disseminate promising management and financial schemes to facilitate large scale replication and launch ambitious replication plans with the participation of local administrations. Deliverable 6.2 presents the replication plans at city level.

The main findings can be summarized as follows: •The aggregated annual thermal load is approximately the same as the electric load (3.3 GWh) for the entire Zero Village Bergen; •The aggregated annual PV generation (2.9 GWh) covers ca. 90% of the electric load; •Even so, PV peak generation (2.9 MW) is ca. 4 times higher than the electric peak load (0.7 MW) giving a GM2 of ca. 4; •This implies that the local electric grid dimensioning capacity might be determined by the PV peak generation rather than by the peak load (depending on the choice of the thermal system and the expected load from electric vehicles, not considered here). The heating system for the Zero Village Bergen is not yet decided, since this will be the task in step three. However, the two most probable options on the design table are either an all-electric solution (with heat pumps in the buildings or at a local district heating station) or a thermal-carrier solution with a local district system (whether or not connected to the city district heating). The analysis of the energy balance and mismatch between loads and PV generation offers useful insights for the next step in the design phase: •All-electric solution If the thermal load is met by heat pumps the total electric load will be ca. 1/3 higher, assuming a seasonal COP of ca. 3 for the heat pump system, meaning that the ZEB-O target is not reached unless further generation (or load reduction) measures are considered3. The peak load can roughly be estimated at around 2 MW, giving a GM of ca. 1.5, and meaning that the local electric grid does not need to be largely over dimensioned due to the PV system. This might normally be regarded as a positive feature; • Thermal-carrier solution If the thermal load is met by a biomass based cogeneration system, this would provide at the same time a small additional load – counted in carbon emissions – and extra electricity generation, so that the overall ZEB-O goal may actually be reached. This will depend on the specific conversion factors used for biomass and electricity. The electric peak load would remain unchanged and so the GM. Having a high GM might not be a problem and may even be an advantage. It simply means that the dimensioning of the grid capacity is based on the PV peak generation in summer, while that capacity is free overnight year-round to be used for charging e-vehicles.

Although several alternative definitions exist, a Net-Zero Energy Building (Net ZEB) can be succinctly described as a grid-connected building that generates as much energy as it uses over a year. The “net-zero” balance is attained by applying energy conservation and efficiency measures and by incorporating renewable energy systems. While based on annual balances, a complete description of a Net ZEB requires examining the system at smaller time-scales. This assessment should address: (a) the relationship between power generation and building loads and (b) the resulting interaction with the power grid. This paper presents and categorizes quantitative indicators suitable to describe both aspects of the building’s performance. These indicators, named LMGI - Load Matching and Grid Interaction indicators, are easily quantifiable and could complement the output variables of existing building simulation tools. The indicators and examples presented here deal only with electric generation and loads.