Periodic Reporting for period 2 - iFLEX (Intelligent Assistants for Flexibility Management)
Reporting period: 2022-05-01 to 2024-04-30
The iFLEX project aimed at empowering the consumers by making it as easy as possible for them to participate in demand response. A core concept of the project is the iFLEX Assistant, a novel software agent that acts between consumer(s), and their energy systems, various stakeholders and external systems helping them to achieve mutual benefits through local energy management and DR. The iFLEX Assistants are designed to provide a common approach to enhance user experience, level of automation and personalization in a wide variety of DR and energy services. Because of different requirements of these services, the project provides a common software framework (i.e. iFLEX Framework) for developing application-specific iFLEX Assistants that are customized for the needs of particular service(s). The focus is especially on households and DR for supporting high penetration of renewables. In addition, there is a need for effective incentives and market structures that encourage consumers to invest in these innovative DR solutions. To this end, the iFLEX Assistants are customizable for different incentive and market mechanisms to allow exploitation of the solution in different countries and climatic regions, as well as, to enable A/B testing of different incentive and user engagement mechanisms with real-users. Although the focus is on electricity, the iFLEX project targeted to overcome the current silo-approaches and provided holistic energy management that optimizes across various energy vectors. Co-creation with end-users was inherent in different project phases and coordinated by an consumer organisation in the consortium. iFLEX validation was carried out with field pilots in three climatic regions.
The iFLEX project provided benefits for different users, both individual consumer/prosumers and professional users. The consumer/prosumer can benefit from a smart energy management system, reduction in energy cost, and contribution to a more sustainable environment. Professional users, such as electrical system operators will be benefited from the flexibility provided by the complete iFLEX solution to operate their system in a more cost-effective method. Furthermore, each individual tangible result developed in this project can be used by professional users as part of different solutions to model/manage/interface with flexibility assets, markets, and end-users. The final prototype of the iFLEX framework for energy & flexibility management has been implemented. The iFLEX Framework is a collection of libraries, tools and configuration scripts that provide means for the development and deployment of iFLEX Assistants into consumer/prosumer premises. The iFLEX Assistant is an innovative software agent solution that facilitates consumer participation in demand response. The iFLEX Assistant can learn consumer behaviour and the dynamics of their premises to provide optimal and personalized flexibility management for the consumer. Furthermore, the iFLEX Assistant provides consumers with natural and seamless ways for communicating their requirements and preference to tailor the flexibility management according to their needs. The iFLEX Framework consists of the following individual exploitable results: Resource Interface Module & Security Data Management, Hybrid Modelling and Flexibility Management, End-user Interface, and Aggregator/Market Interface. The iFLEX Assistant developed on top of the framework empowers consumers/prosumers by facilitating their participation in DR and energy markets. By automating the tedious tasks related to flexibility management while operating according to the individual end-user wishes (and providing them with a customised experience), the iFLEX Assistant tackles several barriers and promotes the increased use of DR and flexibility management in Europe and globally. While the iFLEX Framework is targeted at consumers and stakeholders that provide them with services (e.g. ESCOs, building automation companies, etc.) it also benefits other stakeholders in the power/energy system value chains. In addition to activating consumers, the main benefit provided to aggregators and utilities is the more accurate load and flexibility forecasting methods which improve the effectiveness of DR and holistic energy management services. Furthermore, the deterministic Demand-Side Flexibility Management (DSFM) methods make it feasible to utilize DSFM for continuous balancing in the power grid. The more deterministic DSFM can be utilized for different purposes and market settings, including TSO reserve markets, energy wholesale markets, imbalance settlements and reducing peaks in distribution networks, to name a few. The iFLEX Assistant was successfully deployed in all three pilots with 990 pilot users.
Short term load forecasting and flexibility modelling
Two novel hybrid modelling approaches have been investigated. 1) Physics based greybox models with few parameters learned from data are also applied for modelling the indoor temperature during the DR event and the total heating demand of the building. This approach has been published (Davud Topalović, Dušan Gabrijelčič, Estimating Household’s Physical Parameters Using Neural Ordinary Differential Equations. MIPRO Conference on Smart Industries and Digital Ecosystems 2024, Opatija, Croatia, MIPRO proceedings 2024, IEEE Xplore). 2) A physics-based simulator to train a common Artificial Neural Network (ANN) model with a large amount of data from different buildings. The idea is that this forces the ANN to learning the underlying physics and therefore provide more robust results when compared to typical ML approaches. This innovative approach is published as a scientific journal (Kannari, L., Kiljander, J., Piira, K., Piippo, J., & Koponen, P. (2021). Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator. Forecasting.).
Automated demand response
A key idea of the iFLEX project was to utilize digital twins to find optimal flexibility management actions with respect to dynamics prices, local production and explicit demand response. This work has been published as journal paper (Krasopoulos, C.T.; Papaioannou, T.G.; Stamoulis, G.D.; Ntavarinos, N.; Patouni, M.D.; Simoglou, C.K.; Papakonstantinou, A. Win–Win Coordination between RES and DR Aggregators for Mitigating Energy Imbalances under Flexibility Uncertainty. Energies 2024)
Artificial Intelligence for automated decision making in flexibility management
The hybrid Deep Reinforcement Learning approach developed in the project utilizes the consumer digital twins to plan and optimize control actions. A journal publication about the initial solution at TRL 5 is published in IEEE Access Access (Kiljander, J., Sarala, R., Rehu, J., Pakkala, D., Pääkkönen, P., Takalo-Mattila, J., & Känsälä, K. (2021). Intelligent consumer flexibility management with neural network based planning and control. IEEE Access, PP.)
User engagement strategies and incentive mechanisms for demand response campaigns
A public survey with more than 1000 participants in three EU pilot countries of iFLEX was employed for eliciting the preferences of residential users towards flexibility management and the results were published as scientific journal (Immonen, A., Kiljander, J. (2024). Flexibility services for household consumers in Finland: Requirements and provided properties. Renewable Energy Focus, Volume 49, 2024)
Two novel hybrid modelling approaches have been investigated. 1) Physics based greybox models with few parameters learned from data are also applied for modelling the indoor temperature during the DR event and the total heating demand of the building. This approach has been published (Davud Topalović, Dušan Gabrijelčič, Estimating Household’s Physical Parameters Using Neural Ordinary Differential Equations. MIPRO Conference on Smart Industries and Digital Ecosystems 2024, Opatija, Croatia, MIPRO proceedings 2024, IEEE Xplore). 2) A physics-based simulator to train a common Artificial Neural Network (ANN) model with a large amount of data from different buildings. The idea is that this forces the ANN to learning the underlying physics and therefore provide more robust results when compared to typical ML approaches. This innovative approach is published as a scientific journal (Kannari, L., Kiljander, J., Piira, K., Piippo, J., & Koponen, P. (2021). Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator. Forecasting.).
Automated demand response
A key idea of the iFLEX project was to utilize digital twins to find optimal flexibility management actions with respect to dynamics prices, local production and explicit demand response. This work has been published as journal paper (Krasopoulos, C.T.; Papaioannou, T.G.; Stamoulis, G.D.; Ntavarinos, N.; Patouni, M.D.; Simoglou, C.K.; Papakonstantinou, A. Win–Win Coordination between RES and DR Aggregators for Mitigating Energy Imbalances under Flexibility Uncertainty. Energies 2024)
Artificial Intelligence for automated decision making in flexibility management
The hybrid Deep Reinforcement Learning approach developed in the project utilizes the consumer digital twins to plan and optimize control actions. A journal publication about the initial solution at TRL 5 is published in IEEE Access Access (Kiljander, J., Sarala, R., Rehu, J., Pakkala, D., Pääkkönen, P., Takalo-Mattila, J., & Känsälä, K. (2021). Intelligent consumer flexibility management with neural network based planning and control. IEEE Access, PP.)
User engagement strategies and incentive mechanisms for demand response campaigns
A public survey with more than 1000 participants in three EU pilot countries of iFLEX was employed for eliciting the preferences of residential users towards flexibility management and the results were published as scientific journal (Immonen, A., Kiljander, J. (2024). Flexibility services for household consumers in Finland: Requirements and provided properties. Renewable Energy Focus, Volume 49, 2024)