Strategies for Solar and Wind Integration by Leveraging

Strategies for Solar and Wind Integration by Leveraging Flexibility from Electric Vehicles Emanuele Taibi Power Sector Transformation Strategies PLEXOS User Group Meeting, Valencia 12 June 2019

Agenda 1. Introduction to IRENA and Power Sector Transformation 2. Modelling Electric Vehicles in PLEXOS ◦ EV charging as a precalculated demand profile ◦ EV optimized unidirectional charging – V 1 G ◦ EV optimized bidirectional charging – V 2 G 3. Main results from application to Barbados ◦ EV analysis ◦ 100% renewable electricity scenario 4. Conclusions

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

Power Sector Transformation at IRENA Market design, regulation, business models § Adapting electricity market design to high shares of VRE (2017) § Country regulatory advice § Innovation Landscape for a renewablepowered future (Jan 2019) Long term, least cost capacity expansion plan § Best practices in longterm scenario-based modelling* report, Planning for the renewable future § Recommendations werediscussed at a different regional workshops (LATAM, MENA, Kazakhstan) Find the optimal pathway for power sector transformation Unit commitment and economic dispatch § Production cost modeling with PLEXOS Grid studies § Power system flexibility for the energy transition and IRENA Flex. Tool § Technical network studies (Fiji, Vanuatu) § Developing a electricity storage valuation framework (Q 3 2019) § Analytical briefs on integration costs and demand side flexibility § Transforming smallisland 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

Flexibility needs to be harnessed in all sectors of the energy system 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” » Main flexibility sources » Generation » Hydro, gas » Grid » Variable rating lines, T&D enhancement » Smart Grids » Storage » Pumped Hydro » Batteries » V 2 G » Demand » Conventional: DSM, aggregation » Sector coupling: Heat pumps, boilers, H 2 » Market/Institutional » Unlock flexibility/remove barriers » Regulation needs to support flexibility Source: Power System Flexibility for the Energy Transition, IRENA, 2018

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

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

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 » Implemented in the EU PLEXOS model initially developed by UCC » 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 » See Box 4 and Annex E for modelling assumptions

Modelling Vehicle-to-Grid (I) Two options considered PLEXOS Market participant operating the fleet of EVs Discharge Charge Ancillary Services 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 Option 2: Battery and Purchaser » Battery class using Max Power, Min So. C and Max So. C 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

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 (Do. D, T) and calendric degradation (So. C) » 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

Modelling Vehicle-to-Grid (III) Adding a degradation constraint Can be improved N is variable Energy Arbitrage Generation (GWh) Degradation No Degradation 2. 07 5. 85

Real application to Barbados EVs scenarios in the Roadmap 3. 6 k. W 24 k. Wh 0. 18 k. Wh/km

Real application to Barbados Impact of EVs in production costs » Production Costs » EVs increase productions costs » V 2 G 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

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 1. Calculate the amount of non-supplied energy in each scenario, using reference as base 2. Identify the scenarios that reduce Non-Served Energy (NSE) compared to reference 3. Start reducing MWh of grid-connected energy storage progressively 4. Stop when the amount of NSE equals the one from reference scenario 5. Calculate amount of avoided grid-connected energy storage » Results » All V 2 G scenarios reduce the amount of grid connected storage » EV Static day also reduces storage even more than night V 2 G » V 2 G can reduce it up to a 20% Source: Taibi et al. (2018), Strategies for solar and wind integration by leveraging flexibility from electric vehicles: The Barbados case study

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

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 » V 1 G or V 2 G using the Pumped Hydro Modelling from PLEXOS » V 1 G or V 2 G 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

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