Taking a closer look at tomorrow’s power systems with high resolution modelling

1. Taking a closer look at tomorrow’s power systems with high resolution modelling Fiac Gaffney & Dr. Paul Deane MaREI, University College Cork, Ireland fiac.gaffney@ucc.ie | jp.deane@ucc.ie 1st May 2019 | Energy Exemplar Webinar

2. Topics New Challenges. Increasing the Robustness of Results. High Temporal Resolution Modelling.

3. New Challenges Simulating tomorrow’s challenges today. Decarbonisation can add many complexities to power system operation.

4. A new era in power system modelling Driven by decarbonisation ambitions, focus is turning to the variable generation/system flexibility nexus. New and exciting modelling challenges lie ahead. • Modelling must now expand beyond the power sector into the wider expanse. • Storage needs to account for electric vehicles integration, seasonal storage, etc. • Demand flexibility via power-to-gas (or x), demand response, etc., are essential. • Generation must account for new technologies, e.g. carbon capture and storage. • System operation must measure frequency stability (inertia, ancillary services capabilities) and ensure portfolio adequacy.

5. Is this the typical generation portfolio of the future? Significant change from 2017 system conditions • Generation capacities of an extremely ambitious 100% decarbonised energy system scenario for the European region in 2050. • Scenario accounts for all energy sector requirements, i.e. transport, industry, agriculture, etc. • Wind, water and sunlight provide the majority of energy. • Total generation capacity is 7891 GW • 82% variable renewable energy (VRE)

6. Or is this the storage required for a 100% RE system? Thermal storage is the primary medium Limited ability to discharge electricity from stored energy

7. How flexible can demand feasibly become? Flexible Load: 75% Inflexible Load: 100% Inflexible Load: 25%

8. Simulating the power system (plus the wider expanse!) • Simulating Thermal Sector • Region: Split in thermal and power • Node: Power • Node: Thermal • Inter-sector unilateral energy flow • Transfer of excess electricity into thermal energy for seasonal storage via dielectric heaters, heat pumps, et cetera. Electricity converted to thermal energy for seasonal storage Thermal energy cannot be converted back to power

9. Modelling Flexible Demand: Power-to-X • Flexible demand is modelled as two individual parts: Demand and Flexibility. • Demand via the purchaser class • Constant load. • Flexibility via storage & turbine combination. • Storage sized to hold X hours/days of hydrogen. • Turbine includes hard constraint ensuring the correct amount of energy is generated constantly – aligning with constant purchaser load. • Decarbonisation Scenario Assumptions: • Hydrogen from power-to-gas is used for transportation purposes only. • It is a constant demand and not used for demand response. • This demand must be met using previously stored hydrogen or hydrogen converted at the time of demand. Initial volume is empty in upper storage to mimic an empty hydrogen storage at beginning of simulation, i.e. energy must be stored previous or converted at time of demand.

10. Analysing Operational Concerns: System inertia • Comparing the system inertia of different decarbonisation pathways proposed for Europe. • Continental European synchronous area inertia is heading into unknown territory – operationally. • The effect of introducing small levels of new dispatchable capacity. Not in original portfolios. * Based on preliminary results from analysis carried out by F. Gaffney & P. Deane

11. Evaluating Risk Exposure: Ancillary Services • Comparing reserve provision by technology types for continental Europe in 2050. • ‘Combo Reserve’ combines secondary, tertiary and replacement reserves. (Focusing on upward regulation). • Static (contingency) reserve is 3 GW. • Dynamic reserve is set as a percentage of generating VRE1. • Traditional reserve provision capacity is greatly reduced. • Including new dispatchable capacity reduces the exposure to unknown strategies regarding de-loading variable generation. • 1 Methodology aligned with ‘Holttinen H, Milligan M, Kirby B, Acker T, Neimane V, Molinski T. Using standard deviation as a measure of increased operational reserve requirement for wind power. Wind Engineering. 2008;32:355-77.’ 15% De-loading VRE generation 66% 85% Traditional reserve providers 34% * Based on preliminary results from analysis carried out by F. Gaffney & P. Deane

12. VRE Setting Reserve Risk • Setting up VRE as a reserve contingency • Dummy Generator • Dummy Region • Hard Constraint • Reserve (X% of VRE generating) 6.8% of generating VRE sets dynamic reserve

13. Increasing the Robustness of Results Wind & solar generation varies on scales of minutes to years. Understanding these impacts is important.

14. Overview • Multi-sample approach to power generation representation. • Analysing the impacts of inter-annual wind and solar variations on power systems using normalised weather variability for +30 years. • Examples of publicly available data sources: • Renewable Ninja • EC EMHIRES Database

15. Analyzing the impacts of variable generation • Impacts of inter-annual wind and solar variations on the European power system1. • Simulating the effects of weather variability on power system operation. • 30 years of wind & solar generation profiles used. • Scenarios represent several generation portfolios for the 2030 European power system from the European Network Transmission System Operator for Electricity. • Graph: Comparing wholesale prices between visions • 1 Collins S, Deane P, Ó Gallachóir B, Pfenninger S, Staffell I. Impacts of Inter-annual Wind and Solar Variations on the European Power System. Joule. 2018.

16. European CO2 system-wide intensity

17. Visualization of individual samples * Based on preliminary results from analysis carried out by F. Gaffney & P. Deane

18. How to set up your model • Generator • Variable • Datafile • Stochastic • ST • Report • How to reduce solution size? • Associate scenario with specific simulation and execute… Deselect

19. High Temporal Resolution Modelling Resolution matters. Increased temporal resolution captures more costs.

20. Improved technical insights • Investigate the impact of temporal resolution on power system modelling. A case study Ireland’s power system. • Higher resolution modelling allows a more in depth representation of real time conditions. Deane, J.P., Drayton, G., Ó Gallachóir B., 2014. The impact of sub-hourly modelling in power systems with significant levels of renewable generation. Applied Energy113 pp 152-158

21. Capturing more system costs • Investigate the impact of temporal resolution on power system modelling. • Increasing temporal resolution from 60 minutes to 5 minutes increases annual system costs by €4.5 billion, equating to an approximate 3% increase. €4.5 Billion Difference Gaffney F, Deane J, Drayton G, Glynn J, Gallachóir BÓ. Comparing negative emissions and high renewable scenarios for the European power system. Applied Energy. 2019; (In Review).

22. (BUT!!) How easy it is.. • Limitations are never too far away.. • Machine-based restrictions. • Solution: Interleave the 60 minute & 5 minute simulations! • 60 minute model unit commitment conditions to the 5 minute simulation. • Shortened horizon also made the problem smaller, thereby easier to solve. Horizon shortened to 6 hours in 5 minute simulation

23. Interleaving Power Markets Day-ahead Steps 2 1 3 Model “Day-ahead” Day 1 Model “Day-ahead” Day 2 Model “Day-ahead” Day 3 “Day-ahead” Day 1 Solution “Real Time” Day 1 End State “Day-ahead” Day 2 Solution “Real Time” Day 2 End State “Day-ahead” Day 3 Solution Model “Real Time” Day 1 Model “Real Time” Day 2 Model “Real Time” Day 3 Real-time Steps 1 … 24 48 72 2 Schematic showing a day ahead market interleaved with a real time market.

24. Thank you! Questions? All feedback welcome!