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New generation of Intelligent Efficient District Cooling systems

Periodic Reporting for period 1 - INDIGO (New generation of Intelligent Efficient District Cooling systems)

Reporting period: 2016-03-01 to 2017-08-31

In Europe, different prognosis show an increase in cooling demand of almost 60% in 2030 with respect to nowadays. District cooling (DC) can play a part in satisfying this demand in a sustainable way since it can offer 5 to 10 times higher efficiency solutions than on‐site stand‐alone distributed systems. Even if DC captures only minor portion of the prospective market, this will translate into a dramatic increase in the size of the global DC sector. INDIGO aims to develop a more efficient, intelligent and cheaper generation of DC systems by improving system planning, control and management, anticipating the aforementioned scenario.
This target will be achieved through the following specific objectives:
• Contribute to the wider use of DC systems and motivate the competitiveness of European DC market by the development of two open-source tools:
o A planning tool for DC systems with the aim of supporting their optimal design
o A library with thermo-fluid dynamic models of DC System components which will provide the designers detailed information about their physical behaviour
• Primary energy reduction over 45% addressed by a ground-breaking DC system management strategy. Its main characteristics is the predictive management, but it also will address other challenges such as:
o Integration of renewable energy sources
o Dealing with different types of cooling sources
o Suitable coupling between generation, storage and demand
All this, with the help of intelligent and innovative component controllers to be developed at all DC system levels, some of them including embedded self-learning algorithms. Developments carried out within INDIGO will be validated in a real District Heating and Cooling installation with appropriate conditions for testing the new functionalities.
To achieve these objectives, the following activities have been developed in the first period:
- Detailed models of typical components present in DC systems and building detailed models corresponding to the test sites for laboratory validations at WP6
- Reduced models of DC system components for controller’s development at WP3
- Simplified models of DC system components for management strategy development at WP4.
- Predictive controllers at consumer/building level architecture has been defined and first version of the cost function worked out. The required detailed models were integrated and tested in the simulation environment.
- A first version of an optimization algorithm that determine the operating point of the different pumps in the distribution system has been worked out.
- The types of existing ice storage systems were identified and analyzed. External ice-on-coil type was selected, and different configurations have been studied for the integration of this storage in the DC system. First analysis of suitable control strategies was performed.
- The development of MPCs for generation groups made up of chiller/s and cooling tower/s was started by defining its architecture and a first version of the cost function. A solar thermal system for feeding Basurto´s DC system was also designed and modelled.
In WP4 a preliminary version the two types of cost function was worked out. Regarding the High Management Level, the development of the manager MPC was performed for simple DC system configurations and first simulation results and conclusions were obtained. Related to the Low Management Level, the architecture of two supervision loops has been established. The hardware for the solution was specified along with the characteristics of the required signals.
WP5 has focused on the definition of the planning tool framework and specification. The overall process and the calculation methods for energy, economic and environmental analysis were identified. The investigated systems were defined setting the boundaries and scope for the analysis to be carried out. An environmental analysis component was worked out.
WP6 has focused on the development of laboratory and relevant environment validation sites scenarios and test plans. Test plans for detailed model validation were defined and some detailed models were customized with data from the sites. Missing hardware required for testing were defined along with limitations of existing hardware. Final definition of additional hardware to be installed was worked out.
Finally, WP7 is responsible for dissemination, WP8 for exploitation, and WP1 deals with the general project management.
A new management strategy will be developed to schedule the energy supply in the most optimized way and satisfy the consumer demand at every moment. The overall DC efficiency will be maximized, or running cost minimized, considering other factors like greenhouse gas emissions, system payback time, etc.
It will integrate:
- inputs from consumer predicted demand tools, and a self-learning algorithm for the correction of this prediction
- energy price forecast
- knowledge from fine-tuned models of DC system components.
The manager controller will include a set-point optimizer and a real-time feedback controller. Predictive Controllers will also be developed for Buildings, Storage and Generation systems, some of them including embedded self-learning algorithms.
An open-source planning tool for the evaluation/designing of existing/new DC systems will be developed. The tool will bind all levels of the DC system together, incorporating a simplified version of the management algorithm.
A specific open source library with parametric thermo-fluid dynamic models of DC System components, will be worked out. It will provide detailed information about physical behaviour for a better system design.

INDIGO will reduce DC primary energy consumption compared to current systems thanks to efficiency improvements at different levels:
- At building level by anticipating the building needs, the consumer will optimize its cooling needs, reaching energy savings that can be as high as 40%
- At distribution level INDIGO will increase the system ΔT by a factor of 2 leading to a raise of 50% on the cooling delivery capacity and an extra reduction of 2.5%-6.5% is expected in cooling losses.
- Optimal operation of the generation systems due to a suitable coupling between generation, storage and demand will lead to additional energy savings that can be up to 10%

INDIGO will enhance DC systems that help the environment by increasing efficiency and reducing air pollution, greenhouse gas carbon dioxide and ozone-destroying refrigerants. By making easier the intrusion of free-cooling systems and renewable as well as waste energy, CO2 emissions will be reduced even more that in conventional DC systems.
In the same way the most relevant social benefits will be:
- Reliability and cost-effectiveness in comparison with individual building cooling systems
- Aesthetics and comfort enhancement
- Improvement of electricity supply competitiveness and grid stability
- Skilled job opportunities and contribution to Europe’s Energy Security as a result from DC market development

Market penetration of intelligent DHC systems will be driven not only by the cost of energy but also by stakeholders’ capacities to make informed decisions. INDIGO will work on both factors:
- Payback time for DC systems will be greatly reduced.
- Free and open tools, addressed to managers and developers of DC systems and components and also to public administrators and end users, that will foster DC penetration.
INDIGO control levels (general management and local control)
INDIGO Expected impacts