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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.

Denne tredje utgaven av ZEN-definisjonsveilederen bygger på versjon 1.0 og 2.0 av ZEN-definisjonsveilederne 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-nøkkelindikatorer ut av stedskvaliteterkategorien og å tilpasse dem til en prosessveileder for design og planlegging av ZEN-områder. Detaljer har blitt supplert for hver nøkkelindikator for å forklare i hvilken grad de bidrar til målsetningen av ZEN samt gir eksempler på beste praksis. Noen nye nøkkelindikatorer har blitt lagt til. Navnet på kategorien stedskvaliteter har blitt endret til byform og arealbruk. 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.

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

The development of the definition, assessment criteria and key performance indicators of Zero Emission Neighbourhoods (ZEN), is an ongoing process that will last throughout the program period of FME ZEN. This work will enable an assessment of the performance of the ZEN pilot areas. Based on the draft for the ZEN definition, the KPIs for ZENs can be divided into the following categories: GHG Emissions, Energy, Power, Mobility, Spatial qualities, economy and innovation. The scope of this report is to test the suggested KPIs for Energy and Power on six different pilot areas. The purpose is to evaluate the KPIs when used on different pilot areas in different development stages. The selected pilot areas (cases) are Ydalir, Oksenøya (Fornebu), Zero Village Bergen, Dolvik, Campus Evenstad and Mære landbruksskole. Two of these pilots (Evenstad and Mære) have local heating systems, while the others are connected to a district heating network. In the report, the KPIs are calculated for each of the pilots as they are planned (the ZEN scenario) and in 1-2 representative reference scenarios which represent the pilots in a business as usual case. The testing of the KPIs for Energy and Power shows that the pilots get different results in the ZEN scenarios compared to the reference scenarios. The energy savings in net energy demand is only estimated to be between 8-32 %. This KPI has only been estimated for new areas, and the reduction in net energy demand is small in the ZEN scenario, due to the reference buildings already being rather efficient. The electricity savings is estimated to be significantly higher; the net delivered electricity is expected to be reduced between 28 – 58 % in the ZEN scenario (compared to the reference case with district heating) due to efficiency measures and local electricity production. When compared to a reference case with electric heating, the reduction becomes even larger, at 66 % – 78 %, due to efficiency, local electricity production and the transition from electric heating to non-electric heating (district heating and bio based local heating). The testing also show some potential for large reduction in the peak load (peak import), as a consequence of both more energy efficiency and reduced net delivered electricity, up to 63 – 83 % compared to the the reference case with electric heating. On the other hand, the peak export of electricity may become larger, up to 3 times (300%) of the peak import in the pilots where large areas of PV panels are planned (ZVB and Dolviken). The case studies suggest that when the KPIs are estimated through the means of simulations (for pilots in the planning phase), the methodology and simulation programs used may have an effect on the results. When a pilot is in the operational phase, measurements should be used for KPI calculations as far as possible. Using measurements for the KPI calculations are linked to several challenges as obtaining measurement data is often time consuming, and there is usually missing data points and few, disorganized meters available. The KPIs seem to perform the main role of providing a way to quantify and grasp the main features of a complex reality where different solutions/technologies might have conflicting effects. The process of working with the KPI calculations show that a professional with competence in energy use measurements and calculations is needed to calculate the KPIs for the pilots, and that there is a need for a standardized tool with a simple interface and standardized methods to simplify this process. There is still a need for further work on system boundaries, definition of the reference scenario, and finding standard methodologies. The results of the study will be used in further work to establish threshold values for evaluating the pilots against the ZEN definition. Involved ZEN-partners in this study have been SINTEF, Elverum vekst, Elverum kommune, Bærum kommune, ByBo, Bergen kommune, Steinkjær kommune and Statsbygg.

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.