Energy System Investment Models Paul Rowley & Simon Watson CREST Loughborough University

Renewable Energy Revolving Fund

Definitions Investment risk is the deviation of actual return from expected return. Uncertainty refers to the unpredictability of known possible future outcomes. A PESTEL framework is a useful tool to investigate disparate factors Due diligence is the process by which the risk involved in an investment is evaluated.

The System Lifecycle

A Whole System Perspective

Predicting the Future – Bayes Theorem

Case study – Tidal Stream Yell Sound, Shetland Fictional precommercial array Ten 250kW oscillating hydroplanes

Generating sub-system

Case study – Tidal Stream

Uncertainty and Building Energy Systems Paul Rowley & Simon Watson CREST Loughborough University

Problem:  Widespread & significant under-estimates of predicted building energy and carbon performance  In general, existing design & compliance modelling approaches are not ‘fit-for-purpose’  Impact of ‘human factors’ and technical risk poorly understood  Needs to be addressed – otherwise, forget our GHG and energy performance targets! The Building Energy Performance Gap

The Building Performance Gap The Building Energy Performance Gap

Source - Carbon Buzz The Building Performance Gap

Source - Carbon Buzz The Building Performance Gap

Source - Carbon Buzz The Building Performance Gap

Data-driven Modelling: Case study  UK government funded ‘sustainable exemplar’  7,500m 2 mixed use (offices, public spaces…)  Timber frame fabric  Gas/EAHP/mech vent  Comprehensive wireless monitoring

Case study – Sub-system analysis Comparison of modelled and monitored sub-system energy use

Data-driven Modelling Benchmark efficiency Condensing temp Boiler Return Water Temperature Distribution ?? Boiler Efficiency Distribution

Data-driven Modelling Gas Boiler – Sub-system analysis

Data-driven modelling & Bayesian Networks

Social Sustainability - Impact of PV on Fuel Poverty

Impact of PV on Fuel Poverty

Probabilistic Outcome

Solar Thermal Field Trial Data

Solar Thermal Performance Distribution

Causes of performance variation System size Orientation Inclination Shading Competency of installer Insulation DHW profile DHW volume Auxiliary timing Interplay between DHW profile, aux. timing and available solar energy Technical Factors Non-technical Factors

Uncertainty, Risk & Energy Systems London Array Case Study

Case Study – Offshore Wind The Potential Targets The Challenge Case Study: London Array The Future

UK Offshore Wind Speed Map (100m) Good onshore site ~7.5m/s mean annual wind speed at hub height For many of the offshore sites being developed: >10m/s

Targets EU: 20% of energy from renewable sources by 2020 UK: 15% of energy from renewable sources by 2020 Latest DECC roadmap estimates 13GW wind onshore and 18GW offshore by 2020 Today: 6GW onshore, 3.3GW offshore UK generating capacity: ~80GW

Crown Estates Development Sites 3 Development Rounds Water depths up to ~35m

The Challenge Installation – vessels, size of machines Sea bed – composition, depth Access - >100km from coast for some sites Reliability Hostile conditions – wind and wave Operations and maintenance Grid connection

Onshore Reliability and Downtime

The London Array © Siemens

Facts and Figures Offshore area of 100km 2 20km from shore Sea depth <25m 175 x 3.6MW Siemens wind turbines Two offshore & one onshore substation Nearly 450km of offshore cabling 630MW total installed capacity Capital cost ~£1.8billion ~£2.9million/MW Estimated LCOE~11p/kWh

The Developers and Timescales 50% share30% share20% share Onshore works started July 2009 Offshore works started March 2011 Final turbine installed December 2012 Fully operational April 2013

Turbines © London Array Ltd

Installation Vessels © London Array Ltd

Foundations © London Array Ltd

Substations © London Array Ltd

The Future Better understanding of the offshore environment Bigger more reliable turbines, health monitoring New materials, e.g. superconducting generators Different drive train configurations, e.g. direct drive, multiple drive trains More sophisticated control to reduce loads Holistic control – make more like a ‘power station’ HVDC vs HVAC, North Sea grid