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COHEAT @ 4DH 2016 - Actively Managed Heat Networks
- 1. Actively managed heat networks Marko Cosic Technical Director marko@coheat.co.uk +44 7774 524 114 2nd International Conference on Smart Energy Systems and 4th Generation District Heating 27-28 September 2016
- 2. The competition (€350 + €30/MWh)
- 4. A heat network Traditional instrumentation and control
- 5. How do we improve heat networks? • Know what customers are asking for • Know what the whole system is doing • Know what customers are receiving • Control how the whole system operates
- 6. A smart heat network
- 7. Ring Ethernet and Linux backbone (prototype hardware)
- 8. A user interface (prototype hardware)
- 9. Networked heat interface unit (HIU) (prototype hardware) (before insulation)
- 10. Networked energy centre (heat supply) (prototype hardware) (before insulation)
- 11. Networked radiators and room sensors (prototype hardware) (open source)
- 12. Network hardware (view later) (prototype hardware) (before insulation)
- 13. Low level hardware (view later) (prototype hardware) (before insulation)
- 15. What do customers ask for? (space heat) 15 (prototype hardware)
- 18. Empty home
- 19. What do customers ask for? (hot water) 19 (prototype hardware) • Blue columns – “What % of the time is spent at this power output? (zero power not shown) • Red curve – “What % of the time (when there is any hot water use) is the hot water power lower than X kW per home?
- 20. Details matter for small substations 20 (prototype hardware) 5 seconds or 5 minutes? 12.3 kW or 8.3 kW?
- 21. What are the real diversity factors? 21 (prototype hardware) (chart smoothed – do not use as design reference) 0.0 50.0 100.0 150.0 200.0 250.0 1 10 100 Power(kW) Number of homes DS439 EST 5-sec REDAN SDHA EST 5-Min
- 22. What is happening? (real time) 22
- 23. What happened before? (engineers)
- 24. What happened before? (accountants) 24
- 25. What did we deliver? (space heat) (prototype hardware)
- 26. In-home data
- 27. Heat meters see that there is a problem
- 28. Secondary sensors show the problem
- 29. Secondary sensors show other things 29 Towels/underwear on radiator House party
- 30. “I’m cold” 30
- 31. “We can’t rent out this home” 31
- 32. What did we deliver? (hot water: meter)
- 33. What did we deliver (hot water: actual)
- 34. Why might we care? (bypass)
- 35. Real time control • What is the system being asked for now? – Interrupt driven – Integrated with service level policy • Create a plan – Quickly and reliably – Handles constraints • Implement the plan – Delegates control authority
- 37. Real time service priority
- 38. Aggressive dP control Available pump capacity (%) (differential pressure = 0 kPa for 12 hrs/day)
- 39. Delegating control and linking equipment
- 40. Predictive control • Where am I now? – Historic data, physics model, state estimator • What am I being asked for next? – Heating schedule, keep-hot schedule • What else matters? – Weather forecast – Hot water demand forecast – Cost model for consumer comfort / tapping delays – Cost model for operating the network • What should I do next? – Calculate a plan for the real time planner to implement – Share the plan with other services on the platform
- 41. Model predictive control implemented
- 42. Model predictive control in action
- 43. Sharing information with the consumer
- 44. Implication for system sizing 12 homes, 120 metres 20 mm ID, 500 kPa peak dP Peak space heat @ 55/35 Peak DHW @ 55/25
- 45. Implication for system performance • Interim figures: • (usage: 3 MWh/home/yr) • (losses: 0.3 MWh/home/yr)
- 46. Learnings? • You can build a secure, resilient, real time control system all the way down to individual homes using an internet derived platform and low cost hardware. – Wireless solutions for individual rooms and radiators proved less suitable for commercial deployment (maintenance liability) • The monitoring information is useful for improving the design and operation of standard heat networks. – You can use less sensors and infer what is happening once you know what to look for (we are developing this next) • Basic real time (reactive) control reduces the impact of overload, and makes it more acceptable to overload a network regularly. • Advanced (predictive) control is promising but truly self- commissioning, self-optimising networks need more work – We have the platform (and want to work with others on algorithms)
Editor's Notes
- And a final reminder of why we want to control how the system operates. We use the sensing and control infrastructure to optimise how the network operates. By coordinating how heat is delivered across multiple homes we can schedule heat delivery to meet the consumers temperature demands whilst avoiding peaks in network load. This means less equipment to supply more homes and structurally reduces the cost of providing warm homes and hot showers. It also means we stick with smaller pipes, pumps, and boilers. These volume manufactured products arrive next day in a box or on a pallet and can be handled and installed without specialist access or lifting equipment. This really keeps the costs down and the speed of delivery up compared with traditional heat networks, and suits the 20-50 home market nicely. The heat mains exit the wall in 22 mm copper (20 mm ID) then transition to DN25 PEX twinpipe (20 mm ID) serving 12 homes and an office over a 150 metre run.