12 June 2019

1. Strategies for Solar and Wind Integration by Leveraging Flexibility from Electric VehiclesEmanuele TaibiPower Sector Transformation StrategiesPLEXOS User Group Meeting, Valencia 12 June 2019

2. Agenda • Introduction to IRENA and Power Sector Transformation • Modelling Electric Vehicles in PLEXOS • EV charging as a precalculated demand profile • EV optimized unidirectional charging – V1G • EV optimized bidirectional charging – V2G • Main results from application to Barbados • EV analysis • 100% renewable electricity scenario • Conclusions

3. About IRENA International organization supported by governments of member countries • HQ: Abu Dhabi, UAE • Members:160 members (+23 in accession) • Mandate: Sustainable deployment of six RE resources • Biomass, Geothermal, Hydro, Ocean, Solar, Wind Programme of Work: 1. Centre of Excellence for Energy Transformation • Transformation pathways Including power sector transformation 2. Global Voice of Renewables 3. Network Hub 4. Source of Advice and Support

4. Market design, regulation, business models Power Sector Transformation at IRENA Find the optimal pathway for power sector transformation Long term, least cost capacity expansion plan • Adapting electricity market design to high shares of VRE (2017) • Country regulatory advice • Innovation Landscape for a renewable-powered future (Jan 2019) Unit commitment and economic dispatch • Best practices in long-term scenario-based modelling* report, Planning for the renewable future • Recommendations werediscussed at a different regional workshops (LATAM, MENA, Kazakhstan) Grid studies • Production cost modeling with PLEXOS • Power system flexibility for the energy transition and IRENA FlexTool • Developing a electricity storage valuation framework (Q3 2019) • Analytical briefs on integration costs and demand side flexibility • Technical network studies (Fiji, Vanuatu) • Transforming small-island power systems – SIDS Guide • Technical assessments for larger systems – Dominican Republic PST Knowledge Framework • Help Members identify next steps on their pathway for power system decarbonization • Development of a tool to measure progress on integration of renewables based on experience from front runner countries

5. Flexibility according to IRENA (2018): “Flexibility is the capability of a power system to cope with the variability and uncertainty that VRE generation introduces into the system at different time scales, from very short to the long term, avoiding curtailment of VRE and reliably supplying all the demanded energy to customers” Flexibility needs to be harnessedin all sectors of the energy system • Main flexibility sources • Generation • Hydro, gas • Grid • Variable rating lines, T&D enhancement • Smart Grids • Storage • Pumped Hydro • Batteries • V2G • Demand • Conventional: DSM, aggregation • Sector coupling: Heat pumps, boilers, H2 • Market/Institutional • Unlock flexibility/remove barriers • Regulation needs to support flexibility Source: Power System Flexibility for the Energy Transition, IRENA, 2018

6. Smart charging for Electric Vehicles EV decarbonise power sector Batteries in EVs can provide flexibility to the power system Flexible power system can integrate more VRE and charge EVs with RE VRE decarbonise transport sector Smart Charging is key to unleash synergies between clean transport and low-carbon electricity Source: Innovation outlook: Smart charging for electric vehicles, IRENA, 2019

7. Modelling EVs unidirectional charging (I) Precalculated demand profiles • EVs as a precalculated demand profiles added to the system demand • Three charging scenarios: • Uncontrolled evening charging • As soon as EVs arrive home in the evening they charge at maximum power • Controlled night charging • EVs charging distributed during the night • Controlled day charging • EVs charging coinciding with solar PV profile • PLEXOS property: RegionLoad 1 1 2 2 3 3

8. Modelling EVs unidirectional charging (II) Optimizing smart charging • EVs charging strategy is optimised by PLEXOS • Two charging scenarios: • Night smart charging • Day smart charging • We used Pumped Hydro in PLEXOS • Constraints: • Minimum charge at certain hour with Min Volume of Storage, using Timeslices • Contemporaneity factor by scaling Pump Load of Generator and using Timeslices • EVs cannot discharge power by using a Constraint • Implemented in the EU PLEXOS model initially developed by UCC • See Box 4 and Annex E for modelling assumptions

9. Modelling Vehicle-to-Grid (I) Two options considered PLEXOS Market participant operating the fleet of EVs Discharge Charge Ancillary Services Option 2: Battery and Purchaser • Battery class using Max Power, Min SoC and Max SoC for the minimum charge at certain hour • Purchaser used to discharge the battery without affecting the power system (mobility) • Constraint: EV Battery generation = Purchaser Load Option 1: Pumped Hydro Storage • Same approach as with Smart Charging • Instead of only using Pump Load, we use Max Power as well (option to discharge energy) • EVs (Generator) are allowed to participate in reserve provision

10. Modelling Vehicle-to-Grid (II) Why didn’t we use the Battery Class? • The analysis was developed with PLEXOS 7.4, a version in which the battery module was not fully developed. There were two main reasons why we didn’t use a battery: Reason 1: Could not capture the gradual connection/disconnection of EVs • Batteries ‘Max Power’ property is not dynamic and there is no ‘Rating Factor’ property • If this is not considered, we are assuming that all EVs connect to the grid at the same time Reason 2: Could not capture the degradation of the battery due to operation • Batteries suffer from cyclic degradation (DoD, T) and calendric degradation (SoC) • Capacity and Power Degradation properties are defined as a % of degradation per cycle, however ‘total cycles’ is something that varies depending on operation. • The approach doesn’t account for penalization on operation in the objective function • We added a user-defined constraint using the Generator class – next slide

11. Modelling Vehicle-to-Grid (III) Adding a degradation constraint • Add to objective function: • Input in PLEXOS as a user-defined constraint • As of today, it is not possible to account for degradation due to operation in the objective function using the Battery Module, although degradation property exists • Most relevant results found: • Reduces energy arbitrage • Increases reserves provision • Increases Short Run Marginal Cost (SRMC) • Decreases Reserves Price Can be improved N is variable

12. Real application to Barbados EVs scenarios in the Roadmap 3.6 kW 24 kWh 0.18 kWh/km

13. Real application to Barbados Impact of EVs in production costs • Production Costs • EVs increase productions costs • V2G during the day could save up to an 85% w.r.t the uncontrolled evening charging • Charging during the day is more advantageous • Most of the reduction in production cost is due to the reduction in VRE curtailment – mostly solar PV • Reserve cost savings are mostly related to wind Source: Taibi et al. (2018), Strategies for solar and wind integration by leveraging flexibility from electric vehicles: The Barbados case study

14. Real application to Barbados Reduced need for grid-connected storage capacity • Methodology • How much storage can I avoid while maintaining the same level of reliability? • Similar to ELCC calculation • Results • All V2G scenarios reduce the amount of grid connected storage • EV Static day also reduces storage even more than night V2G • V2G can reduce it up to a 20% • Calculate the amount of non-supplied energy in each scenario, using reference as base • Identify the scenarios that reduce Non-Served Energy (NSE) compared to reference • Start reducing MWh of grid-connected energy storage progressively • Stop when the amount of NSE equals the one from reference scenario • Calculate amount of avoided grid-connected energy storage Source: Taibi et al. (2018), Strategies for solar and wind integration by leveraging flexibility from electric vehicles: The Barbados case study

15. Real application to Barbados 100% Renewable Electricity Scenario • The country requested the simulation of a 100% renewable electricity scenario as per the new national policy • IRENA proposes two possible solutions: 1. High Biomass Scenario • Increase of biomass from 18 MW to 54 MW, by building two additional power plants • Need to replace inflexible steam turbines with flexible generators working on liquid biomass or biogas • 100% RE scenario proposed in the current Roadmap • Only PLEXOS ST was run, replacing remaining fossil fuel generators with biomass 2. High VRE Scenario • 18 MW of biomass decommissioned • PLEXOS LT was run • Results: 292 MW solar PV, 165 MW wind, 33 MW biogas, 240 MW/960 MWh storage system

16. Conclusions • The increasing penetration of VRE calls for higher flexibility needs in the system • Flexibility must be harnessed in all energy sectors, including transport (Electric Vehicles) • IRENA uses PLEXOS to model Electric Vehicles using three approaches • Precalculated EVs demand profile on top of power system load • V1G or V2G using the Pumped Hydro Modelling from PLEXOS • V1G or V2G using the Battery and Purchaser Classes from PLEXOS • Real application for Barbados completed in 2016, update in 2019 • Further need to explore Battery class to model electric vehicles • Future idea: Use the multi-stage stochastic hydro of PLEXOS to model the stochastic behavior of EVs drivers

17. Emanuele Taibi Power Sector Transformation Strategies ETaibi@irena.org www.irena.org