Project Image Pool

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Terms of use: The pictures on this site originate from the projects in the frame of the programmes City of Tomorrow, Building of Tomorrow and the IEA Research Cooperation. They may be used credited for non-commercial purposes under the Creative Commons License Attribution-NonCommercial (CC BY-NC).

Diversity-specific flexibility framework for demand-side management

The framework illustrates the connections between the diversity dimensions of gender, age, and income, as well as the subcategories of parenthood and ownership, with willingness to be flexible, acceptance of external control, and flexibility driven by technology or social factors. Women exhibit an increased socially-driven flexibility, as they are more often responsible for the relevant activities, while men tend to have greater technologically-driven flexibility and are more interested in automation and financial benefits. Younger and older individuals are generally more willing to be flexible, with younger people being more open to accepting external control, whereas older individuals are more likely to reject it. Technologically and socially-driven flexibility is significantly influenced by parenthood, which tends to limit social flexibility but can enhance technological flexibility and foster willingness. Both higher and lower incomes can promote willingness, with financial motives being a central driver for those with lower incomes. Higher incomes also positively impact the ability to be flexible (both technologically and socially). Ownership of electric vehicles (EVs) or single-family homes plays a key role, enhancing willingness, acceptance of external control (in the case of homeowners with prosumer technologies), and technologically-driven flexibility.

Central Characteristics of Energy Communities and Their Interactions

Representation of the central characteristics of energy communities (ECs) and their interrelationships. A strong mutual influence is found between the actors of the EC and the management and decision-making processes, as well as between the actors and value propositions. Influences during the founding process occur between value propositions and founding actors, value propositions and the initiation mode, and founding actors on the initiation mode, funding options, and management and decision-making. Operational influences include the impact of value propositions on the management of energy resources and general management and decision-making, as well as the influence of the initiation mode on management and decision-making, and of funding options on the management of energy resources.

Analysis of Gender and Diversity Factors in Relation to Energy Consumption Flexibility

The graphic provides an overview of the approach to the quantitative analysis of household flexibility and the conclusions derived from it. Electricity consumption, recorded via smart meters, was combined with diversity dimensions and associated aspects such as gender, household structure, income, and available technologies. Treatments implemented during field tests, such as tariff reductions for peak load reductions, were documented in the dataset. Quantitative methods, including F-tests or Levene's test for variance, regression analysis, and plots of average consumption, were used to gain insights into consumption patterns and identify diversity-specific differences. Building on these findings, data quality criteria for future research were formulated, and a stronger inclusion of diverse user groups in demand-side management (DSM) programs was recommended.

Bottlenecks and challenges in transformation processes

Bottlenecks and challenges in transformation processes

Overview transformation plan

Overview transformation plan

Overview demand response types

Combining the two action and control types there can be four different demand response types: 1) Direct Automated (e.g. action and control types are characterised by high reliability; 2) Indirect Automated (e.g. model predictive control in the building reacting to the DHC broadcasted signal), action and control types are characterised by low & high reliability, respectively; 3) Direct Manual (e.g. DHC operator vising the house or sitting in the control room and pressing the button), action and control types are characterised by high & low reliability, respectively; 4) Indirect Manual (e.g. end users changing the settings physically of via using the remote technology (walking in the house, sitting on the sofa and using app) as the reaction to the broadcasted signal), action and control types are characterised by low reliability.

Joint workshop IEA EBC Annex 84 & IEA ES Task 43 („Standardized Use of Building Mass as Storage for Renewables and Grid Flexibility“)

Joint workshop IEA EBC Annex 84 & IEA ES Task 43 („Standardized Use of Building Mass as Storage for Renewables and Grid Flexibility“)

Terminology used for demand response within the framework of IEA EBC Annex 84

EBC Annex 84 distinguished between different "action types" and "control types".

Value creation and business model framework

Development of business cases based on a 12-step approach

Benefits, revenues, and cost analysis

Benefit, revenue, and cost analysis to evaluate the economic performance

Economic evaluation tool

Economic assessment tool for determining acceptable storage costs

National Task 41 team

Kick-off meeting for Task 41 at the Energieinstitut an der JKU Linz

National workshop on the economic feasibility of energy storage

Stakeholder workshop on the economic feasibility of energy storage. Presentations and discussion on thermal, electrical, and chemical storage technologies and their potential applications.

Cross-actor collaboration on forward-looking, long-term grid planning

A new structured methodology to facilitate cross-actor collaboration on forward-looking, long-term grid planning, enabling key actors to explore their respective roles and interdependencies, thereby facilitating development of efficient grid planning strategies.

Policy Brief 2024

Policy Brief: presentation at CEM15/MI-9

Combined building and plant simulation in real time

In a dynamic building simulation, the zones (rooms) are in contact with their surroundings and with the adjacent building components, the people, equipment and objects located in them. In the combined building and plant simulation, the dynamic interaction between building, plant and control is also taken into account. A building and plant simulation, adapted in real time to the actual weather conditions and current measurement data from the building, can help to optimize the control of the building services and thus reduce energy costs and increase user comfort.

Two Austrian demonstration building digital twin

In recent years, AEE INTEC has completed two projects for the development and initial implementation of a digital twin on real buildings in Austria. This digital twin is a detailed simulation model using IDA ICE software, which is compared in real time with measurement data from a real building. This creates a model that represents the real condition of the building and its building services at any given time. This model can then be used for automated fault detection or to optimize control systems. The aim is to reduce energy consumption and improve user comfort.

Annex 81 Policy Package

Graphic representation of the policy package of measures developed in Annex 81 to promote Data-Driven Smart Buildings.

Group picture of IETS Task 21 at the IETS Conference 2023

At the IETS conference from May 9-11, 2023 in Gothenburg, the subtasks and activities of IETS Task 21 were presented, and two key note presentations and two elevator pitches on the task were given.

Gruppenfoto IETS Task 21 Consortium Meeting in Graz

On April 9, 2024, a full-day meeting of IETS Task 21 took place in Graz. The meeting provided a valuable platform for exchanging insights and planning further steps for the successful implementation of the tasks within Task 21.