Optimising Heat Networks Gareth Jones

Transcription

1 Optimising Heat Networks Gareth Jones April 2017

2 Massive gap in performance vs potential Projected System Efficiency (%) 80% 70% 60% 50% 40% 30% 20% 10% 0% Current Gap Potential

3 Issues at all stages of the process Oversizing Poor design Poorly executed Testing regime not clear Sample based rather than exhaustive ERs & Spec Design Install Commissioning Operation Not comprehensive Too permissive Lack performance targets Skills gap Efficiency not monitored

4 Gap: Design and Implementation equally important Projected System Efficiency (%) 80% 70% 60% 20% 50% 40% 18% 30% 20% 10% 0% Current Design Operation Potential

5 Project initially focused on operation component Projected System Efficiency (%) 80% 70% 60% 20% 50% 40% 18% 30% 20% 10% 0% Current Design Operation Potential

6 SBRI focused on using data to improve performance

7 Last 12 months have focused on design component Projected System Efficiency (%) 80% 70% 60% 50% 40% 18% 30% 20% 10% 0% Current Design Operation Potential

8 Performance potential higher than initially modelled Projected System Efficiency (%) 90% 80% 70% 60% 50% 26% 40% 30% 20% 10% 0% Current Design Operation Potential

9 3 key issues in market 1. Over sizing 2. Over complexity 3. Poor design

10 3 drivers for oversizing 1. Assumed peak loads too large 2. Diversity curves too conservative 3. Conservative assumptions for pipe sizing

11 High DHW peaks very rare in practice 100% Probability of heat demand for all hot water usage events greater than 10 kw 90% 80% 70% Occurrance Rate, % 60% 50% 40% 30% 20% 10% 0% Heat Demand, kw

12 Selected peak loads not reflective of reality 100% Probability of heat demand for all hot water usage events greater than 10 kw 90% 80% 70% Occurrance Rate, % 60% 50% 40% 30% 20% Typically see range in DHW design peak load between 50kW to 100kW for similar dwelling types 10% 0% Heat Demand, kw

13 Open data: conservative approach on diversity

14 3 drivers for oversizing 1. Assumed peak loads too large 2. Diversity curves too conservative 3. Conservative assumptions for pipe sizing No LCA (despite CP1) Arbitrary kpa/m limits used Pipe sizes typically 2-3 times higher than should be (based on LCA based approach using data)

15 Tackling oversizing a massive opportunity Calculated costed impact for reducing oversizing (dropping from 300% to 150% of actual peak capacity) 1,650 reduction in CAPEX per dwelling

16 3 key issues in market 1. Over sizing 2. Over complexity Unnecessary equipment (e.g. PHX) = higher CAPEX, Losses, OPEX Often 1-2k in unnecessary spend per dwelling 3. Poor design

17 3 key issues in market 1. Over sizing 2. Over complexity 3. Poor design Designs that require high return temps = no condensing from boilers Use of bypasses, which will open most of the time (losses, no condensing from boilers) Boilers not able to handle actual (low) loads Poor (or absent) controls

18 With 20 C - 40 C return temps at a block level Block Level Performance: Flow and Return Temperature (13 April 17) ( C)

19 3 key issues in market 1. Over sizing 2. Over complexity 3. Poor design Designs that require high return temps = no condensing from boilers Use of bypasses, which will open most of the time (losses, no condensing from boilers) Boilers not able to handle actual (low) loads Poor (or absent) controls

20 Design brief fundamental ERs & Spec Design Install Commissioning Operation Get brief right from outset: Performance based specification Limit decision scope for designers: DHW peak load policy Specify diversity curve Standardized Design Guide Constrain equipment choice

21 Design Supplement developed to improve HNs

22 Design Supplement developed to improve HNs Guide to how to use the Code of Practice for client projects, rather than replacing it Sets out core design principles Provides specific objectives and performance criteria for projects Closely aligned with CP1 Client Checklist Already in use in contractual documents

23 Take performance based approach to design Data Based Design Approach: LCA based on actual data (e.g. pipe sizing) Model performance at design stage (against requirements) Challenge assumptions ERs & Spec Design Install Commissioning Operation Get brief right from outset: Performance based specification Limit decision scope for designers: DHW peak load policy Specify diversity curve Standardized Design Guide Constrain equipment choice

24 Summary: Use performance based approach 1. Get the design brief right: Performance based specification 2. Performance based design, backed by data 3. Data backed acceptance testing