H2020 STORM Project. Sofia Lettenbichler. DHC+ Technology Platform c/o Euroheat & Power. Project Officer, Dissemination leader for STORM project

Transcription

1 H2020 STORM Project Sofia Lettenbichler Project Officer, Dissemination leader for STORM project DHC+ Technology Platform c/o Euroheat & Power

2 Digitalisation in District Heating Watch the video! 2

3 The STORM Project 3

4 The context: 4 th generation DHC Decarbonising our energy system by making it more efficient & integrating more renewables 4th generation DHC vs. 3 rd generation Uncontrollable production sources with fluctuating input Need for flexible solutions

5 The challenge for 4 th generation DHC DHC networks are demand driven, not production driven Demand Production How to make a network follow these fluctuating production profiles then?

6 The solution to 4th generation DHC Solution 1: Influence the demand. Then, production will follow. Solution 2: Decouple demand and production. Demand Production Both solutions can be achieved by a smart controller that makes optimal use of the flexibility in the network.

7 The solution to 4th generation DHC Natural flexibility in DHC networks 1.Thermal mass of buildings 2.Water in network pipes Artificial flexibility in DHC networks 3. Physical thermal storage buffers The flexibility is there! A intelligent network controller can activate this flexibility the STORM project

8 The STORM project STORM = Self-organising Thermal Operational Resource Management Aim: Develop & demonstrate a generic intelligent DHC network controller based on self-learning optimization techniques Start date: 1 st of March 2015, 42 months

9 What are the objectives of the project? To develop a generic controller for district heating and cooling (DHC) networks To demonstrate the developed generic controller in two existing DHC networks. and to quantify the benefits, develop innovative business models, increase the awareness and ensure market-uptake 9

10 Multiple control strategies For typical networks with a smaller sustainable energy source (biomass boiler, heat pump) and a larger fossil backup Elimination of fossil fuel. For networks coupled to the electric grid by heat pumps/chps Switching the devices at interesting power price. For more sophisticated networks: balance supply and demand of heat/cold in a cluster increased efficiency.

11 Implementation of the controller algorithm 3 modules Forecaster What will be the energy consumption of the network for the next 24h? i.e. reference consumption Planner Given the control objectives (peak shaving/elec. market interaction/cell balancing), which optimal cluster consumption profile can be achieved, taking into account this forecast? Dispatcher-Tracker Which individual control signals are necessary to follow/track the optimal consumption profile?

12 The demonstration sites Rottne, Växjö, Sweden A very typical 3 rd generation network 175 consumers 2 wood chips boiler (1.5 MW MW) + bio fuel boiler (3MW) (backup) Design temperature C Objective: eliminate the operation of the expensive peak fuel boiler

13 The demonstration sites Heerlen, the Netherlands A highly innovative 4th generation network Very low temperatures ( hot pipe 28 C cold pipe 16 C) Heating & cooling Coupled to underground mine water storage Objective: balancing of heat/cold producers and consumers

14 Status of Demo Sites Heating season 2017/2018: STORM Controller v2 (forecaster, planner and tracker) running in the Rottne demo site Testing on peak shaving, market interaction features carried out in last months Buildings connected in Mijnwater site Testing on cell balancing under way

15 TRAININGS FOR PROFESSIONALS AND UNIVERSITIES Syllabus on Digitalisation in District Energy Part of study programme at Zuyd Hogeschool as of September 2018 Trainings workshops Professionals Universities of Applied Science & other teaching institutions to increase awareness of the need for smart control of energy networks and the improvements created by the innovative control strategies to encourage uptake of syllabus at other universities across Europe 15

16 AWARD- WINNING RESEARCH Operational Demand Forecasting in District Heating Systems Using Ensembles of Online Machine Learning Algorithms

17 THANK YOU! Contact: Sofia Lettenbichler, Dissemination Leader Funded by the European Union's H2020 Programme under grant agreement n

18 THE STORM CONTROLLER Watch the video! 18

19 STORM Functionality and evaluation Dr Christian Johansson NODA 29/05/2018 Brussels, Belgium

20 Intelligent energy systems? Intelligence is the aggregate or global capacity of the individual to act purposefully, to think rationally and to deal effectively with his environment - David Wechsler 20

21 Intelligent energy systems? Intelligence is the aggregate or global capacity of the individual to act purposefully, to think rationally and to deal effectively with his environment - David Wechsler Access The ability to access and collect relevant data from our environment Analyse The ability to analyse the data and come up with plans based on that analysis Act The ability to act and implement these plans within your environment 21

22 STORM Controller Access, Analyse, Act The STORM controller is based on the NODA Smart Heat Grid system A range of integration possibilities to facilitate generic compatibility with external systems The system has three basic conceptual layers 22

23 Access layer The communication protocols and system compatibility of the STORM controller is managed through the Access layer. There are three basic ways to interact with the STORM Controller; the Energy Controller, the Energy Gateway and the Demat system integration solution One to one One controller is connected to one grid node or building Proof of concept or small scale NODA IEC (sensor override) Abelko Ultraheat or other interface units One to many One gateway is connected to many grid nodes or buildings Traditional automation systems In STORM an Abelko controller was used, but most controllers can be used One to many One DeMat is connected to many grid nodes or buildings Software only Several systems are already compatible Easy to add more 23

24 Platform layer 24

25 Function layer 25

26 Function layer 26

27 STORM Controller evaluation 27

28 STORM Controller evaluation 28

29 STORM Controller evaluation 29

30 STORM Controller evaluation 30

31 STORM Controller evaluation The flexibility matrix Provides an estimation of the thermal flexibility Both static and dynamic flexibility matrix (static shown) Percentage is in relation to controllable load Values in kw Short 1-3h, Medium 4-6h, Long 6+ hours (in most cases) 31

32 STORM Controller evaluation 32

33 STORM Controller evaluation 33

34 STORM Controller evaluation STORM Second version activated during February 2018 Total discharge during period is 33 MWh, including calibration Full functionality during March, with 23 MWh in discharge All discharge during peak load activity 34

35 THANK YOU! Contact: Christian Johansson storm-dhc.eu 35

36 The generic features of controller Generic = able to deal with a wide range of networks Guaranteed by a number of features: 1. Add-on to existing network controllers and SCADA-systems 2. Open-source communication protocols 3. Self-learning algorithms to prevent model tuning 4. Multiple thermal storage concepts 5. Multiple control strategies 6. 3 rd and 4 th generation demonstration sites

37 guarantee high replication potential! All District Heating networks benefit that: Have at least two sources of heat generation: renewable/ fossil fuels cheaper/ more expensive Have at least one CHP as heat generation source Have two or more levels of hierarchy in the network The cells (lower level) The cluster of cells (higher level) Let s roll out STORM and its control algorithms in EU & beyond 37

38 WHAT S NEXT? STORM project partners look into extension to further test & evaluate the performance of the controller TEMPO project as follow-up: low-temperature DHC Will add to functionalities of STORM controller Controller balances the demand of heat to fluctuating renewable and residual heat sources further reduce the return temperature by influencing the demand behaviour at the consumer side & coordinate at a network level Supervision ICT platform for detection and diagnosis of faults in district heating substations Visualisation tools for expert and non-expert users More info: 38

39 THANK YOU! Contact: Sofia Lettenbichler, Dissemination Leader Funded by the European Union's H2020 Programme under grant agreement n