H2020 STORM - Self-organising Thermal Operational Resource Management

Transcription

1 H2020 STORM - Self-organising Thermal Operational Resource Management Johan Desmedt, EnergyVille Vleva event, 17/06/2016, Brussels, Belgium

2 Contents 1. The context of 4 th generation district heating networks 2. The challenges and solutions 3. The H2020 STORM project 4. First results of the project

3 4 th Generation Thermal Networks Source: Lund et al. 2014

4 The challenge for 4 th generation DHC The challenge is to make DHC networks ready to deal with uncontrollable or fluctuating production of heat/cold. However: DHC networks are demand driven, not production driven! The production always follows the demand. Demand Production How to make a network follow these fluctuating profiles then?

5 The solution to 4 th generation DHC Solution 1: Influence the demand. Then, by influencing the heat demand, 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 A controller can activate this flexibility the STORM project

6 STORM general info Project title: STORM = Self-organising Thermal Operational Resource Management Aim: Development & demonstration of a generic DHC network controller based on self-learning optimization techniques Funding Program: Horizon 2020 Secure, clean and efficient energy Work Program: EE Technology for district heating and cooling Starting Date: 1 st of March 2015 (42 months)

7 Objectives TRANSFERRING PROJECT OUTCOMES TO STAKEHOLDERS ACROSS THE EU CONTRIBUTING TO A WIDER DEPLOYMENT OF DHC NETWORKS AT EU LEVEL BOOSTING ENERGY EFFICIENCY AT DISTRICT LEVEL DEMONSTRATE THE CONTROLLER QUANTIFY THE BENEFITS DEVELOP THE CONTROLLER 7

8 Market for DHC controller Presence of DH and DC networks Source: Halmstad university Growth potential on DH grids: 40-70% of heat demand Source: IEE Stratego Project

9 Excellence through co-creation District heating and cooling controller Product Features Intelligent controller for DHC networks Learns and adapts to DHC network behaviour Balance supply and demand of other connected networks Interact with the electricity market Better use of network capacity Reduce peak loads in DHC networks (-30%) Cluster balancing to reduce transport losses Reduce primary energy by 20% Reduced energy cost for customers Less investment costs and operational costs Our USPs Generic and easy add-on to scada/hvac systems Integration of building behaviour Applicable to new/ existing networks and systems Scalable and upgradable to new energy sources Uses forecasting and optimising /self learning algorithms

10 The 2 demonstration sites Mijnwater (NL) and Rottne (SE) Demo site 1 Vaxjo in Rottne (SE) consumers - 2 wood chips boiler + bio fuel boiler - Design network operating temperatures C - Objective: eliminate operation of peak fuel boiler Demo site 2 Mijnwater in Heerlen (NL) - Low network temperatures - Heating + cooling demand - Coupled to underground mine water storage - Heat pumps in buildings - Exchange of energy between buildings - Objective: balancing of heat/cold producers and consumers These two sites cover a wide range of all European DH networks

11 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

12 First results of the project simulation environment Given the control objectives, which optimal consumption profile can be achieved, taking into account the forecast? Input: - The forecast - A cost function Output: - Optimal consumption profile Status: first algorithms developed and tested in software environment.

13 First results of the project - implementation Functionality: - Determine the control signals - Follow/track the proposed consumption profile Status: first prototype implemented in the Rottne network

14 First results of the project - forecasting

15 Measure and actions to enlarge the outcomes of the project Supporting a generic roll-out of the controller Developing innovative business models Investigating exploitation possibilities Distributing the value amongst the different market players by applying the control strategies in the controller Increasing awareness on the need to control DHC networks

16 Project website social media Project website : Social Media Channels Joint account with 3 other EU projects on energy efficiency at building, district and city levels 596 followers LinkedIn group «Sustainable Places Community» 174 members Contact: Johan Desmedt, project coordinator - Johan.desmedt@energyville.be

17 THANK YOU! Contact: Johan Desmedt, project coordinator storm-dhc.eu