Periodic Reporting for period 2 - InDeal (Innovative Technology for District Heating and Cooling)
Berichtszeitraum: 2017-12-01 bis 2019-02-28
-Artificial intelligent meters
-Identifying and evaluating the network’s energy demand
-Predicting short-term weather conditions
-Monitoring and model the level of energy stored
-24/7 monitoring of the DHC system by a central control platform and
-Minimizing heat losses via novel pipe design solutions and innovative insulation materials.
The target of InDeal is to turn the current DHCS into a new next-level automated DHCS that will guarantee the increase of the overall system energy efficiency. In light of this, InDeal makes a significant step forward contributing to wider use of intelligent DHC systems.
-Smart metering devices were manufactured and installed
-An energy storage monitoring and modelling framework was developed using a variety of advanced machine learning algorithms, achieving mean square errors of less than 0.01 in modelling the temperature dynamics of the energy storage tank and the connected components
-An AI-powered short term weather forecasting module was also developed using using 30 years of observations for both use cases (Slovenia and Montpellier)
-A new sustainable insulation material was developed, based on nanocellulose, whose final thermal conductivity value is lower by 11% compared to the traditional PUR insulation material for pipes.
-A new quick-fit joint was also developed after many design loops. The quick-fit joint parts were quoted, fabricated and assembled, in order to validate the prototype physically.
-An internal pipe coating was developed to reduce pressure head losses caused by scale deposition and corrosion during the system performance.
-Two different energy harvesting modules were developed in the INDEAL project by proposing innovative designs, fabrications and experimental measurements in laboratory test-benches as well as in real conditions (real case studies).
The design of the necessary ICT architecture was finally realised during the second reporting period. A Machine Learning (ML) library was implemented for energy demand prediction locally at the substations level achieving MSE performance < 0.1. The DSS was implemented as a Model Predictive Control algorithm (MPC) based on the MINLP optimization model.The consortium implemented the optimal control strategy that constitute the DSS investigating issues such as different solvers, optimal prediction and control horizons, modifications and testing in simulation scenarios. The development of the web-based monitoring control was completed. The CMCP was further improved and finalised by adding more visualisation functionalities and the final integration of the different modules. The performance of the integrated InDeal system was validated under operational conditions in relevant environment (TRL6). The outputs of the InDeal DSS were calculated (optimal suggested production scheduling) and compared with the (currently adopted) plant operation. The proposed system managed to improve significantly the efficiency of the system (~50% reduction of the overproduction rate: main KPI of the integrated system achieved).
LCA studies were conducted for the assessment of the economic and environmental impact. For the exploitation and the dissemination activities a holistic innovation management approach was implemented. A combined strategy for IP and exploitation was followed during implementation of InDeal. The project quality and indicators were also documented. The final PUDK was developed that included business plan analysis covering all the relevant aspects such as the value proposition of the InDeal solutions, canvas-made analysis and a list of extracted business plan findings. Extensive dissemination activities took place with our partners’ participation in events, workshops and the organisation and attendance of the final InDeal conference.
- Advanced modelling framework based on a hierarchical architecture and a combination of physical models, predictive stochastic models and AI tools.
- Novel weather and energy demand prediction based on the latest SoA deep learning algorithms.
- Intelligent DSS whose novelty is attributted on its capability to use, analyse and take advantage of heterogeneous data sources such as actual energy values from smart meters, predicted values as well as energy storage modeling outputs .
- Novel solutions to reduce heat losses (new sustainable insulation materials, new pipe designs)
- 2 energy harvesting solutions with innovative designs, fabrications and experimental measurements in laboratory test-benches as well as in real conditions.
- Accurate energy predictions for DHC plants (MSE<0.1)
- Improved DHC efficiency by reducing the overproduction rate by 50%.
- Reduced heat losses after the application of the new insulation materials and the new piping system
- Energy production using energy harvesting to support the future development of self powered smart meters
The InDeal project is expected to contribute to the pan-European effort of making the DHC systems more efficient, intelligent and cheaper, leading to a multi dimensional impact on the environment, science, technology and economy. The project is expected to boost industrial competitiveness, support employment and at the same time significantly contribute towards a more green and sustainable economy by improving import-export balance and benefitting local spending.
The project’s successful completion leads to the generation of important key exploitable results and the novel knowledge produced will pave the way for the enhancement or modification of existing quality control and reliability testing protocols in DHC applications. The benefits generated by the implementation of the novel method guarantee the establishment of more holistic approaches at the European DHC sector. Moreover, the data generated during the project will be studied and elaborated and will lead to useful conclusions and directives for the industry, as a pool of knowledge of huge importance will be created. Future renewable energy related research and innovation projects will be able to use this knowledge leading to more improved and affordable solutions. The InDeal project is expected to contribute to the pan-European effort of satisfying the EU energy needs, while in parallel will act in favor of the environment.