Planning 100% Renewable Energy Systems

1. Planning 100% Renewable Energy Systems Barbados Case Study  Guy Doyle – Chief Power Economist Christian Kaufmann – Energy Strategy and Innovation Andrea Gasparella – Energy Data Science

2. Agenda • Introducing Us • Context of Presentation • Technical Challenges • Modelling Approach • Electric Vehicle Modelling • Synchronous Generation • Conclusion: Lessons Learnt

3. Mott MacDonald: The Company One of the world’s leading engineering, management and development consultancies Multi-sector – covering energy, water, transport, built environment, health and education Independent and employee owned

4. Speakers 2100 employees Energy Unit Transm. & Distr. Nuclear Generation Guy Doyle Chief Power Economist Christian Kaufmann Energy Strategy and Innovation Andrea Gasparella Energy Data Scientist • Specialties: • Energy and Carbon Market Analysis • Market Regulation Advisory • Restructuring • Forecasting Studies • Techno-Economic of Renewable Sources and Flexible Resources • UK Panel of Technical Experts • Specialties: • Power system modelling (Plexos) • Markets and Technologies • Ancillary Services • Optimisation • Time-series Forecasting • Specialties: • Generation Master Plans • Optimization in Plexos • Time-series Forecasting: energy demand, renewable production • Energy Markets and Digital Technologies (Data Science) Technical Experts covering all aspects of energy systems

5. Barbados Policy of 100 % Renewable Energy Target in 2030 • Caribbean Island with 287’000 Inhabitants • Peak demand  150 MW • Yearly Energy  1000 GWh Electricity Generation Energy Mix (2015) • Observations • Thermal dominated but rapidly changing • Policy and Cost drivers • Excellent VRE Potential • Limited Geographical Dispersion • No hydro generation • Decarbonisation of Transport: EV 34 km 432 km2 Source: Barbados DOE 24 km Source: BLPC IRP 2014 5

6. Available Energy Resources Solar PV/ CSP Waste to Energy/ Biodiesel/ Biogas Biomass Wind On/Off Shore Hydro/ Pumped Hydro 6

7. Overview of Opportunities and Challenges 100 % Renewable Energy Target in 2030 Air pollution + CO2 emissions Benefits Challenges Energy price volatility Grid Stability + balancing RES Intermittency Electricity Price Financing Technology Suppliers Gov’ and public support Siting RES plants Energy Access Energy Security Economic growth 7

8. Modelling Challenge: Overview Modelling consumer behaviour Choosing system constraints Capturing temporal and spatial detail Dealing with uncertainty behavioural changes ensure system stability daily build constraints costs demand demand response annual seasonal resource availability spinning reserves EV demand demand resource availability

9. Modelling Approach • Critical Challenges of high VRE penetration: • System net load peak moves to night time with high ramp rates  • VRE intermittency requires Spinning Reserves (dependent on both load and VRE generation) • System Stability depends on Inertia and fault levels – cannot operate with low synchronous generation  • Solutions • Modelling to be done in Sampled Chronology to maintain daily cycling challenges but keep problem size manageable Ramp-up rate Ramp -down rate Net peak Min Inertia • Introduce Decision Variables that allow to reflect when non-optimal economic dispatch is required for technical reasons • Introduce Reserve Class Objects for Spinning Reserves, Inertia and Fault Level • Introduce “new” candidate technologies: Synchronous Condensers, Solar PV with different DC/AC ratios • Introduce Dynamic Definition of “Risk” and “Provision” of reserves linked to system conditions 9

10. Technical Challenge: Intermittency Mitigation Resources 10

11. Modelling Approach: EV load Testing different Scenarios of RES Production and EVs Penetration using PLEXOS Low PV production High PV production 11

12. Modelling Approach: EV load Testing different shares of Smart EV load in ST Scenarios • Observations: • Total EV share to system load 10% • EV total cost reduced of 40% when fully dispatchable • Decreasing Marginal benefits of increasing share of Smart EV • Total system generation cost is reduced by 7% (modelled in ST, i.e. with fixed system assets; LT sensitivity would be much higher) • Interruptible EV loads versus a non-interruptible case provides a small saving in cost of providing spinning reserve (~1%) 12

13. Modelling Approach: Synchronous Compensators (SCOs) Without SCOs With SCO • Significant planned build-out of Solar PV can lead to overbuild and curtailment due to fault-level and inertia constraints that override economic dispatch decisions • SCOs allow higher penetration of non-synchronous generation and can be optimised by Plexos ST & LT 13

14. Conclusion: Lesson Learnt • Correctly assessing and implementing technical requirements is critical when considering high VRE – so need to consider  • Generator and storage capabilities and performances for all types and designs, e.g. cycling, start & stop behaviour • System constraints from parallel system load-flow and stability analysis (using PSSE/Digsilent/ETAP) • Resource characteristics – requires good data and application of appropriate analytical tools • LT modelling needs to be done in a way that resembles ST chronology as closely as possible • Sampled Chronology key to doing so efficiently • High VRE share introduces new trade-offs in optimisation of system integration costs • Impact of diversity of technologies and geographic dispersion on spinning reserve requirements • Smart EV loads allow higher penetration of Solar PV and cheaper provision of spinning reserve • Optimal balance of non-synchronous generation with SCOs versus synchronous generation 14

15. Thank you Q&A Guy Doyle – Chief Power Economist Christian Kaufmann – Energy Strategy and Innovation Andrea Gasparella – Energy Data Science