Denna handlingsplan är framtagen inom projektet Regional samverkan kring vätgas som finansieras av Klimatledande Processindustri där Västsvenska Kemi- och Materialklustret ingår. Handlingsplanen utgår från det geografiska område i och i närheten av Göteborg, Stenungsund och Lysekil, det område där kemi- och raffinaderiindustrierna på västkusten är verksamma. Handlingsplanen innehåller prioriterade frågeställningar och aktiviteter att utföra i närtid och är framtagen av RISE i samarbete med Borealis, Chalmers, Göteborg Energi, Göteborgs Hamn, Inovyn, Linde Gas, Liquid Wind, Nordion Energi, Perstorp, Preem, St1, Uniper samt Vattenfall. Syftet med projektet är att skapa samverkan kring vätgasrelaterade frågor baserat på identifierade behov hos nyckelaktörer i regionen. Projektet ska även identifiera vilka former för samverkan som på bästa sätt kan underlätta och påskynda omställningen till ett klimatneutralt samhälle, givet regionens specifika utmaningar och möjligheter kopplat till vätgas.

Syftet med projektet är att utreda möjligheter och hinder för en vätgasproducent att leverera flexibilitetstjänster genom en framtida elektrolysöranläggning. Projektet avser även att samla kunskap för att öka förståelsen kring vad som krävs för att detta skall vara genomförbart där hänsyn tas till industrier som har ett kontinuerligt vätgasbehov. Fokus i detta projekt har varit de tekniska egenskaperna hos elektrolysörer och vätgaslager. Dessa egenskaper jämförs med efterfrågan av flexibilitet på olika tidsskalor inom elsystemet. Dessa sträcker sig från under sekunden för de snabbaste stödtjänsterna till upp mot timmen för lokala flexibilitetsmarknader. Projektet har genomfört en kortare litteraturstudie samt intervjuat deltagande parter och andra relevanta aktörer. För att det skall vara möjligt att köra elektrolysörerna flexibelt och samtidigt tillgodose det kontinuerliga flödet av vätgas som industrin efterfrågar krävs att det finns en annan källa till vätgas som kan kompensera för fluktuationerna, exempelvis ett vätgaslager. Det är tekniskt möjligt för elektrolysörer att bidra med flexibilitet till lokala marknader samt stödtjänster till Svenska kraftnät, dock behöver storleken på flexibilitetsbudet anpassas för att kunna matcha kraven. Hänsyn behöver även tas till flexibilitet i utformning och design av anläggningen, exempelvis avseende storlek och typ av elektrolysör, storlek på lager och prestanda för kompressorer. Nedan listas några ytterligare möjligheter och utmaningar som identifierats i projektet kopplade till flexibel körning av elektrolysörer: • Tillgång till ett vätgasnät ger förutsättningar för att köra elektrolysören flexibelt och om det finns ett vätgaslager kopplat till vätgasnätet blir förutsättningar ännu bättre • Utrymme inom industriområdet samt geologiska förutsättningar för underjordiskt lager begränsar möjligheterna till lagring av vätgas • Ett variabelt flöde av biprodukter kan hanteras med lager alternativt att inte nyttja biprodukterna fullt ut, vilket går i linje med att det inte är troligt att avsättning finns för all syrgas och värme • Flexibilitet kan möjliggöra tidigare anslutning till elnätet eftersom anläggningen tekniskt sett kan anpassa sitt eluttag efter nätkapaciteten • Elnätsavtal behöver kunna utformas så att flexibilitet kan komma nätet till nytta • Extra investeringar krävs för flexibilitet samtidigt som möjliga intäkter, besparingar och kostnader är svåra att uppskatta • Att välja bort en investering i flexibilitet medför också en risk då exponering mot framtida elpriser blir högre

Detta projekt syftar till att samla kunskap och kartlägga det framtida behovet av fossilfri vätgas på västkusten, inom västsvensk industri samt kraft- och värmesektorn. Därefter undersöka och utvärdera vilken infrastruktur som krävs och värdera centraliserade lösningar mot lokala. Projektet har intervjuat deltagande företag kring deras vätgasbehov, vätgasproduktion, önskade framtida roll, vätgasstrategier samt annat relaterat till den industriella omställningen till fossilfrihet. Alla 13 deltagande företag har intervjuats under mars/april 2021 och svaren har sammanställts för att få en aggregerad bild. Utöver intervjuer har det inom projektet också genomförts två workshops med deltagande företag. Bland de 13 deltagande företagen är det 7 industrier som har ett vätgasbehov idag och detta uppgår totalt till 6,4 TWh vätgas/år, vilket motsvarar 192 kton vätgas/år. Industrierna har gett uppskattningar på framtida behov av vätgas uppdelat på två scenarios, ett för minimum och ett för maximum. I minscenariot uppgår vätgasbehovet till 4,9 TWh vätgas/år vilket är en minskning om knappt en fjärdedel (-24 %) jämfört med dagens behov medan maxscenariot motsvarar mer än en fördubbling av dagens behov (+120 %) till totalt 14 TWh vätgas/år. Orsaken till det eventuellt minskade vätgasbehovet är att produktionsvolymer möjligen kan reduceras i framtiden samt en möjlig övergång till mer förädlade råvaror. I detta arbete har vi haft fokus på fossilfri vätgas som produceras genom elektrolys med fossilfri el för både centraliserad och decentraliserad produktion. För de decentraliserade lösningar har tre fiktiva industrier med olika stora behov av vätgas, satta för att täcka in behovsspannet för medverkande företag, jämförts med en centraliserad lösning för produktion av vätgas som sedan transporteras i rörledningar till fler användare. Den centraliserade lösningen som har utvärderats innefattar Göteborg, Stenungsund och Lysekil samt en sträckning för vätgasledningen på 120 km och har utgått från det uppskattade maxscenariot för behovet av vätgas. Rapporten visar att vätgasledningarna endast utgör en marginell del av kostnaden för centraliserad produktion och distribution av vätgas. Dessutom har den föreslagna vätgasledningen en stor överkapacitet och det finns därmed möjlighet att dela kostnaden för ledningarna över en ännu större vätgasvolym. Utöver den rent ekonomiska jämförelsen mellan centraliserad och decentraliserad vätgasproduktion och distribution så finns en rad andra aspekter att beakta. Den centraliserade lösningen kan medföra större flexibilitet för en konsument avseende mängden vätgas som köps in och över hur lång tid. En centraliserad lösning kan också behöva en lägre total kapacitet avseende produktion och lager då eventuell överkapacitet delas mellan alla som är anslutna till nätet. Etableringen av en storskalig vätgasinfrastruktur gör också att satsningarna blir mindre knutet till enskilda aktörer. Dessutom kan ett vätgasnät i regionen nyttjas av andra sektorer och locka nya aktörer att placera verksamhet här, vilket i sin tur kan stärka regionens konkurrenskraft. Den centraliserade lösningens uppbyggnad kräver dock en synkronisering av efterfrågan och produktion samt större investeringar vilket kan medföra längre ledtider än för en decentraliserad produktion hos en enskild aktör. En centraliserad lösning gör att placering av produktionskapacitet blir friare längst vätgasledningens sträckning vilket ger möjlighet att ta hänsyn till elnätskapacitet och avsättning för biprodukterna syrgas och restvärme. En viktig lärdom av projektet var att konstatera att uppbyggnad av en infrastruktur för produktion, transport och lagring av vätgas är en komplex fråga. Man måste samverka brett över flera sektorer för att identifiera många nyttor, för flertalet aktörer, om man vill räkna hem en sådan infrastruktursatsning. Dessutom måste många olika investeringar ske i rätt följd och vara väl samordnade. Därför ser de deltagande aktörerna positivt på ett fortsatt samarbete kring vätgasfrågan. Projektet har identifierat flera möjliga förslag till fortsatt arbete, bland annat: • En mer detaljerad förstudie som genomförs av ett ledande gas-/infrastrukturföretag. Studien bör inkludera produktion och distribution av både förnybar och blå vätgas och eventuellt ammoniak, samt en fördjupad analys av olika lagringsmöjligheter för vätgas. För att kunna genomföra förstudien behövs ett mer preciserat behovsscenario kring exempelvis när i tiden behovet uppstår och hur stort behovet är. • Utvärdering av systemperspektivet kring vätgas, el och andra närliggande sektorer. Exempelvis hur systemintegration kan främja resurseffektivitet och flexibilitet. • Initiering av ett regionalt samordningsprojekt, som bör ha fokus på det kortsiktiga perspektivet och konkreta aktiviteter. Förslag på relevanta aktiviteter i ett sådant projekt har identifierats och inkluderar följande: ‐ Kunskapsbevakning om teknikutveckling inom vätgasområdet, inklusive vätgasproduktion från biometan med CCS (möjlighet till negativa utsläpp) ‐ Kunskapsbevakning om utveckling av CAPEX för produktion, distribution och lagring av vätgas ‐ Bevaknings av sektorkopplingsfrågor i och med att tillgång till förnybar el, både produktion och elnätskapacitet, och elpriset anses vara avgörande för produktion av fossilfri vätgas. ‐ Harmonisering med omvärlden/EU, både i termer av regelverk och möjligheter till sammankoppling med dess vätgasledningar ‐ Påverkansarbete och policyfrågor

The scope of this document is to provide the procedure to be adopted by the project partners and subcontractors to produce, collect and process the data from the IRIS demonstration activities. The adopted procedure follows the guidelines provided by the European Commission in the document Guidelines on FAIR Data Management in Horizon 2020.

This document is based on the Horizon 2020 FAIR Data Management Plan (DMP) template (Version: 26 July 2016) [1], which provides a set of questions that the partners should answer. Furthermore, the Horizon 2020 template from DMP online [2] is utilized to expand the questions and provide more detailed explanations. This fourth report on DMP, submitted at M48 (Autumn 2021) of the project, describes a plan for data production, collection and processing, and the first input from the different lighthouse cities. It will be continuously updated until the end of the project, as part of work package 9, WP9 Monitoring and evaluation, activities. Specifically, DMP will be updated again in M60 (D9.11: Fourth and final update on the Data management plan).

The development of the DMP is part of the work undertaken in T9.2 Defining the data model and the data management plan for performance and impact measurement (M4-M60). Since the DMP development started in M4 (spring of 2018) of the project, this third report of the DMP provides templates for data reporting and emphasises on the interactions of task 9.2, T9.2 Defining the data model and the data management plan for performance and impact measurement, with other work packages.

An important part of this document is the data management template (DMP template). This template is supposed to be used by all partners who produce or handle datasets within the IRIS project. For example, the partners responsible for the implementation of the measures in the Lighthouse cities. By making use of this template, it is ensured that the project research data will be 'FAIR', that is findable, accessible, interoperable and re-usable. This is achieved by:

• Making data Findable, including provisions for metadata
• Making data openly Accessible
• Making data Interoperable
• Increase data Re-use (through clarifying licences)

The template is accompanied by a chapter which describes all topics that are required to be filled in. Further on, 3 DMP examples are added to illustrate what is expected, in order to facilitate the task of providing the data.

Besides the Ethical aspects as defined in the DMP template for all ‘sub’-projects, a separated chapter is written on these aspects on IRIS level.

The aggregation of data within the IRIS project has started after M30. Which means that data was generated within several measures. For this reason, the template as presented in D9.9 could be filled in as far as possible for 27 datasets. The resulting information about these datasets can be found in the DMP Excel sheet on EMDESK and as tables in Appendix 0.

The present document is the Deliverable D9.6 “Intermediate report after one year of measurement”. The document describes the work carried out within the task 9.5 entitled “Overall evaluation and impact analysis for impact enhancement”. The focus of this task is to provide intermediate results of the demonstration activities in the three Lighthouse (LH) cities and to present the data currently transferred to the IRIS Key Performance Indicators (KPI) tool.

The deliverable D9.6 is based on the work done in the Work Package (WP) 9, in particular the work in task 9.4 and task 9.5 (presented previously in D9.4 and D9.5). In this deliverable, the monitoring framework and established baselines developed in D9.5 are used to collect the data needed for the calculation of the KPIs. The KPIs are in turn used to evaluate the outcome and impact of the implemented measures. The collected data is transferred to the KPI tool, which was created and presented in D9.4. The tool processes and calculates the KPIs and visualizes the results. Data can be transferred to the KPI tool automatically, through a CIP, or manually through a template. A process which is described in this deliverable.

This deliverable was intended to be an intermediate report to provide an initial insight to the results for all measures in the IRIS project. However, due to the lack of data from measures, which in part is due to the Covid-19 pandemic, this report focuses more on providing information about the process of collecting data and transferring it into the KPI tool. This process is collaborative and has been carried out within the IRIS LH cites with support from the technical partners and the WP9 team. Complexity of APIs and the lack of standards have made data extraction and transfer into the KPI tool more difficult. Furthermore, not all measures in IRIS are connected to CIP which means that manual data collection was required and a systematic procedure for this collection needed to be developed and introduced to the partners.

There are several different reasons for lack of data and the resulting exclusion of some measures from this deliverable. A few measures are not yet in operation, while for other data collection have not started or the data transfer to the KPI tool has not been established yet. However, the work done in task 9.5 has provided new knowledge on issues and errors that can occur in the process of transferring data and establishing KPIs. Through dialogues with the project partners, the need to clarify some KPI cards with i.e. units, formulas or use cases has been highlighted. The close cooperation with the project partners has led to continued work on the definitions of the KPIs and what KPIs to include, taking steps in the direction of clearer interpretation and more consistent use. Further adaptation of several KPI-cards was done by the WP9 team. In the process of adjusting KPIs, the effect these adjustments would have on all measures that use them were considered. The process of developing KPIs involves a balance between finding indicators that can be used more generally and indicators that are more specific and thus better capture the purpose of a specific measure.

The improvements of KPIs and lessons learned in task 9.5 will be of great use in the continued work of WP9. Focus will be on transfer of data from all measures into the KPI tool. A continuous dialogue with responsible project partners to ensure this data transfer and discussions on deviation and errors in the initial results will be established.

Flexibility in the energy system has been studied previously but few results have been implemented in district energy (DE) pricing models. This means that pricing models are not accounting for existing information making them less efficient than they need to be. We have studied if and how business models of DE firms capture flexibility in the DE grid and suggest price model updates to harvest flexibility. A systematic literature search with content analysis of resulting scientific peer-reviewed publications and project reports has been performed. Thereby, the different business models which have been described in the literature have been categorized. Based on literature, efficient price models have been identified. Another source of information is six demonstrators aiming at generating knowledge about DE flexibility. They are part of the Flexi-sync Project (ERA-Net). Findings show that most DE grids are slow to recognize and capture flexibility that can be catalyzed through end-users, thermal inertia, heat pumps and other. Similarly, DE firms employ a marginal cost logic to determine whether flexibility should be operationalized, and often their business models and price models are not oriented towards expressing that value logic to their customers. We identify that there is a potential for DE companies to further capitalize on flexibility in the energy system. By inclusion of flexibility incentives in price models a win-win can be established by cutting operational costs for the DE provider and energy consumption of the end-user.

The apparent usefulness of prosumers in the district energy (DE) grid, with the transition to 4th generation district heating (4GDH) and smart energy system is beginning to be realized. Similarly, there is a burgeoning interest in the exploitation of flexibility in the DE grid. But, whether the business models for prosumers also capture the flexibility provided by the prosumers is doubtful. Our current study ponders on: what are the business models for prosumers in the heating sector? do they capture flexibility? And, if so, how do they capture flexibility? A directed Content Analysis of systematically selected scientific literature is analyzed to investigate the business models for prosumer integration in the DE sector, and how they capture flexibility. Fifteen scientific articles were chosen through a systematic selection process. The selected literature was analyzed under the following categories: research objectives or research questions, methodology used, key actors considered, key technologies, pricing logic of heat, control of the prosumer system, computation of benefits and flexibility consideration. The findings from the selected articles show that when looking at how prosumers can supply peak heat, most studies consider the marginal cost of heat supply as an important parameter in the price logic. Similarly, the benefits are computed in a system-wide manner as the difference between the marginal cost of heat either through production from DE system or through production from the prosumer. In calculating the marginal cost of heat investment costs are not considered, which is not conducive for positive decision-making by the potential prosumer looking to invest in heat pumps or excess-heat exploiting technologies. © 2021 The Authors

The IRIS project has defined goals and targets in the project proposal, and the monitoring and evaluation work package (WP) 9 will analyse to what extent the project reaches these goals and objectives. The monitoring and evaluation will also provide information concerning the performance of the different solutions demonstrated in the Lighthouse (LH) cities in IRIS which is important for the replication of the solutions both in the LH cities and in other cities. This is of importance for the replicability of the solutions, both in the LH cities (Utrecht, Nice and Gothenburg) and in other cities. The project consists of several demonstration projects which are divided by 5 transition tracks (TTs): TT1; Smart renewables and closed- loop energy positive districts, TT2; Smart Energy Management and Storage for Grid Flexibility, TT3; Smart e-Mobility Sector, TT4; City Innovation Platform (CIP) Use Cases, TT5; Citizen engagement and co-creation.

D9.5 is the result of 2 years of work with several iterative processes involving the LH cities and their partners with the ultimate goal to:

Define a set of Key Performance Indicators (KPIs) which evaluate the effectiveness and impact of the cities proposed measures.Setup monitoring plans for each IS to define how each parameter is being measured to ensure that the KPIs can be calculated.Define how the baseline and the targets are defined and measured.This work started as described in D9.2 (Report on monitoring and evaluation schemes for integrated solutions) [1] with:The definition of the initial list of KPIs and how to calculate them, based on Smart Cities Information System (SCIS) [2], the CITYKeys Project [3] and the IRIS project itself .The assignment of KPIs to relevant measures within the project.An evaluation plan to measure performance on project level, including aggregation of KPIs.

The process has continued with D9.3 (Report on data model and management plan for integrated solutions) [4] and D9.4 (Report on unified framework for harmonized data gathering, analysis and reporting) [5], which define the basis of the methodologies used to come to the results written in this report.

Feedback from several workshops on this topic has led to a guideline that supports the partners responsible for implementation of the demonstrators in setting up their projects such that:KPIs that are being measured are well understood.KPIs give a meaningful result.The right data is being measured to calculate the required KPIs during the implementation of the measures.

An important part of this process is to have a close look at the KPIs that are projected for each demonstrator, the calculation method of the KPIs, and the expected results. By means of KPI interpretation forms. By doing so:

• KPIs are defined and calculated such that only one way of interpretation is possible. This way results from different projects and cities are homogenized.

• It is well understood what result the measurement of a KPI leads to.The method and results of this process are described in this report, which is a revised KPI list where KPIs are added, removed or adapted.

In addition to this, the KPI interpretation forms created the basis for the formulation of detailed monitoring plans for all measures within the project. Together with template forms for reporting these plans and a common data structure, which were provided to the affiliated partners, these plans are obtained and described for all measures per Transition Track and per Lighthouse city in this report.

Another essential part of measuring the performance of the IRIS project is the establishment of the baseline measurements and review if targets are met. Tables with KPI data requirements, consisting of the associated parameters, data sources, baseline and (possible) targets for all measures are incorporated.

An important part of the monitoring strategy of the IRIS project is the KPI tool, which is described in detail in report D9.4 [5]. This tool is established to collect all relevant monitoring data from the IRIS project in order to calculate and visualize the performance of the project. The tool partly obtains it’s data by means of the City Information Platforms (CIP). The monitoring details combined with the updated KPIs, result in an inventory containing an overview of all data sources with as main objective:

• To make sure that all data sources are known and will be measured by the responsible partners.

• To know what kind of data needs to be collected by the KPI tool.

• To know when monitoring in each demonstrator starts and data can be expected.

• To have a clear overview for all responsible partners what to deliver.

Besides setting up the collection of the indicators data, D9.5 also continues the work on aggregation of KPIs. For each city a revised list is made that indicates which KPIs will be aggregated to Transition Track-, City- and IRIS-level.

In the conclusion the challenges that where met during the process of setting up the monitoring framework are described. Because of delays within the IRIS project, not all monitoring plans have been obtained yet. Therefore, a future update of this report will be submitted as soon as this information is available. Further on a perspective is described for future work to start gathering the data and visualize results of the IRIS project.

The target group for this report is mainly people who:

-  Are interested in how to apply a unified monitoring and evaluation scheme into a large Smart City project with many different partners and stakeholders. For example, people working on comparable (Smart City) projects, or the follower cities within the IRIS project.

-  Are interested in how the performance of several different Smart city projects can be evaluated.

-  Are interested in the implementation of KPIs from projects such as SCIS and CITYkeys.

-  Want to learn from project partners from within the IRIS project who work on similar projectsabout their monitoring. For example, partners from different cities affiliated with the same transition track or transition track leaders.

- Want to find out what kind of data can be expected from the IRIS project. For example, external researchers interested in the results of Smart City projects, but also partners working on WP4 (CIP) and WP9 (monitoring and evaluation).Want to learn what the current state is of the monitoring and evaluation of the IRIS project.

The scope of this document is to provide the procedure to be adopted by the project partners and subcontractors to produce, collect and process the data from the IRIS demonstration activities. The adopted procedure follows the guidelines provided by the European Commission in the document Guidelines on FAIR Data Management in Horizon 2020.

This document is based on the Horizon 2020 FAIR Data Management Plan (DMP) template (Version: 26 July 2016) [1], which provides a set of questions that the partners should answer. Furthermore, the Horizon 2020 template from DMP online [2] is utilized to expand the questions and provide more detailed explanations. This third report on DMP, submitted at M30 (spring 2020) of the project, describes a plan for data production, collection and processing, and will be continuously updated until the end of the project, as part of work package 9, WP9 Monitoring and evaluation, activities. Specifically, the DMP will be updated again in M42 (D9.10: Third update on the Data management plan), and in M60 (D9.11: Fourth and final update on the Data management plan).

The development of the DMP is part of the work undertaken in T9.2 Defining the data model and the data management plan for performance and impact measurement (M4-M60). Since the DMP development started in M4 (spring of 2018) of the project, this third report of the DMP provides templates for data reporting and emphasises on the interactions of task 9.2, T9.2 Defining the data model and the data management plan for performance and impact measurement, with other work packages.

An important part of this document is the data management template (DMP). This template is supposed to be used by all partners who produce or handle datasets within the IRIS project. For example the partners responsible for the implementation of the measures in the Lighthouse cities. By making use of this template, it is ensured that the project research data will be 'FAIR', that is findable, accessible, interoperable and re-usable. This is achieved by:

• Making data Findable, including provisions for metadata
• Making data openly Accessible
• Making data Interoperable
• Increase data Re-use (through clarifying licences)

The template is accompanied by a chapter which describes all topics that are required to be filled in. Further on, 3 DMP examples are added to illustrate what is expected, in order to facilitate the task of providing the data.

Besides the Ethical aspects as defined in the DMP template for al ‘sub’-projects, a separated chapter is written on these aspects on IRIS level.

After M30 the aggregation of data in the IRIS project will start to take place. Meaning that D9.10 (the third update of the data management plan) will be a version where the templates presented in this document will be largely filled in. Further on D9.10 will include the final template of data collection, which will be mainly defined by the experience built up during the collection of data during the project.

In Sweden, over 50% of building heating requirements are covered by district heating. Approximately 8% of the heat supply to district heating systems comes from excess heat from industrial processes. Many studies indicate that there is a potential to substantially increase this share, and policies promoting energy efficiency and greenhouse gas emissions reduction provide incentives to do this. Quantifying the medium and long-term economic and carbon footprint benefits of such investments is difficult because the background energy system against which new investments should be assessed is also expected to undergo significant change as a result of the aforementioned policies. Furthermore, in many cases, the district heating system has already invested or is planning to invest in non-fossil heat sources such as biomass-fueled boilers or CHP units. This paper proposes a holistic methodological framework based on energy market scenarios for assessing the long-term carbon footprint and economic benefits of recovering excess heat from industrial processes for use in district heating systems. In many studies of industrial excess heat, it is assumed that all emissions from the process plant are allocated to the main products, and none to the excess heat. The proposed methodology makes a distinction between unavoidable excess heat and excess heat that could be avoided by increased heat recovery at the plant site, in which case it is assumed that a fraction of the plant emissions should be allocated to the exported heat. The methodology is illustrated through a case study of a chemical complex located approximately 50 km from the city of Gothenburg on the West coast of Sweden, from which substantial amounts of excess heat could be recovered and delivered to heat to the city's district heating network which aims to be completely fossil-free by 2030.