CHINA ENERGY TRANSITION THE ROLE OF ELECTRIC VEHICLES AND ENERGY STORAGE

CHINA ENERGY TRANSITION THE ROLE OF ELECTRIC VEHICLES AND ENERGY STORAGE Liu Jian Energy Research Institute of National Development and Reform Commission China National Renewable Energy Center

Renewable Energy Development Renewable Energy Use Non-fossil Energy Use Share of RE Share of Non-fossil Energy Non-fossil Energy Use 2005-2015 Non Fossil Share 11.5% (commercialized only) Non Fossil 560 mtce RE 509 mtce

enewable Power Capacity RE: 500GW, 33.2% 1.39PWh, 24.8% Geothermal and Oc Solar Power Biomass Power Wind Power Hydro Power Renewable Power Increase Renewable Power S

Annual Wind Generation Hours and Curtailment On-gird Wind Full load hours 2015 wind curtailment 32.8TWh, greatest in 12 th Five year period. 2012 wind curtailment rate 17%, highest in 12 th Five year period. 2015 Provincial curtailment: Inner Mongolia 18%), Gansu(39%), Xinjiang(32%),Jilin (32%). Wind Curtail Curtail Rate

Power Capacity Projection- 2030 tated Policies Scenario ind power: 210 GW by 2020 and 500 GW by 030 olar power: 160 GW by 2020 and 500 GW by 030 ereof 70 GW and 250 GW respectively as DG iomass, biogas and MSW: 15 GW by 2020, and 0 GW by 2030 igh RE Penetration Scenario (w+s=60%) ind power: 350 GW by 2020 and 1,000 GW by 030 olar power: 210 GW by 2020 and 1,100 GW by 030 ereof 92 GW and 611 GW respectively as DG iomass, biogas and MSW: 25 GW by 2020 and 7 GW by 2030 Win Sol Bio Hyd N.g Co Nu

Flexibility measures xibility is needed in the whole supply chain Power markets with namic pricing the most efficient way to give ntives wholesale and retail smission grids must be ntegrated in the power markets nsitional incentives for bility can be part of the long-term solution

Electric Vehicle Development EV sales: 60% of global total. US: 116099 EV stock: 40% of global total Source: CNREC,INSIDEEVS

EV providing flexibity Smart Charge Battery Second Use V2G EV EV - Variable load - Stationary - Variable load and Storage storage

mart charge potential behavior Car Official vehicle Charging Demand Taxi Bus Charging Demand

ttery 2 nd Use Costs Estimates Specific investment costs (RMB/kWh) PCS (RMB/kW) Annual O&M cost as % of investment costs (%) Conversion efficiency (%) Lifetime (cycles) Lithium-ion 1,950 650 3% 90% 2,000 VRB 4,225 1,300 3% 85% 13,000 Sodium sulphur 2,600 1,300 4% 80% 4,500 Lead carbon 1,300 650 3% 85% 1,000 Repurposed EV batteries 780 650 3% 90% 500 Pumped storage - 5,200 3% 78% 50 years CAES - 9,750 3% 60% 40 years NREL (RMB/kWh) 2015 2020 2030 Lithium Ion 2,110 1,810 1,330? Vanadium Flow 5,530 4,900 3,850 Sodium Sulphur 2,920 2,520 1,880 Lead carbon 1,460 1,340 1,120 Repurposed EV batteries 720 530 390 Pumped-storage 650 660 680 CAES 1,220 1,100 910 1 Euro=7.4 RMB

V2G high cost! Capital (RMB/kWh) Cycles Life Cycle Throughput (kwh) LCOS (RMB/kWh) Lithium-ion 2000 2000 2000 1 500 cycles * 300 km = 150,000 km

Battery Development US DoE NEDO 2030: 300-500Wh/kg State Council 2020 2030 300 wh/kg - EV 100 350 wh/kg 500 wh/kg State Grid 250wh/kg (2022)

V2G fast cost reduction? Capital (RMB/kWh) Cycles Life Cycle Throughput (kwh) LCOS (RMB/kWh) Lithium-ion 2000 6000 6000 0.3 Range Loss 450,000 km Substitution relationship with battery 2nd use! ERI 2016

EV smart charging, Energy Storage and DR Battery 2 nd Use EV smart charge Pumped Hydro

ystem values of EVs Better coordination between EV charging and RE generation facilitates power system transition. Peak shaving Spot market yields price signal which reflects the value of flexibility (system requirement at the moment). Now: peak-shaving; Future: load-shifting. Load shifting

Curtailment percentage VREintegration Curtailment is reduced initially by load growth, transmission expansion, thermal guaranteed FLH reduction and increased, adding flexible resourced Market prices motivate additional flexibility from demand response including EV smart charging (High RE scenario) and flexible interaction between power and district heating, through storages and power to heat. As VRE penetrations reach higher level, curtailment slowly climbs, especially solar which is more synchronized. Power Market and VRE Scheduling Progress Reduction in guaranteed generation hours Rigid transmission schedule Market based transmission Limited flexibility investment Market flexibility investment Increased confidence in VRE forecasts for unit commitment

MAIN FINDINGS AND RECOMMENDATIONS

Preparation for Energy Transition RE should be developed along with system flexibility. EV could be the biggest provider of flexibility, in particular through smart charging. Establish vehicle/battery standards, V2G communication protocol, TOU charging price. A better design of power market is the fundamental driver.

END THANKS FOR YOUR ATTENTION LIU JIAN liujianbox@hotmail.com

EXTRAS

Energy system modelling Primary energy demand Wind Solar Socioeconomic drivers Transformation Biomass processing Technologies Final energy demand Industry Policies Energy service demand Industrial Productio Service value added Scenarios are modelled in the CNREC modelling suite, Hydro Bioenergy Power generation and transmission Buildings Building heating Building cooling covering energy supply, energy transformation and end-use sectors. EDO electricity/heat LEAP energy demand CGE social economic Geothermal Ocean Nuclear Coal Oil Gas District heating production Heat production Fossil fuel processing Construction Agriculture Transport Food production New infrastructure Personal transport Transport for trade Manufacturing Citizen comfort Energy flows Investments and operating cost Emissions Socioeconomic impact

Transmission grid expansion Generally, it holds that when power systems increase the proportion of (variable) renewable energy capacity, the optimal relative capacity and capability of the power grid increases. Starting from existing transmission capacity, current plans for UHV grid expansions are assumed between 2016-2020, after which model based investments are determined Additional transmission capacity is deployed, yet the rate of transmission capacity expansion is reduced 2020-2030 despite the growth in renewables New transmission mainly: Connects large hydro areas to markets Increases the overall capacity within and between the central and north grid regions Less development very long distance transmission lines to connect with remote renewable (and coal) regions Load grows is rapid in the Western China, increasing local integration of remote RE Market opening promotes efficient use of transmission capacity for smoothening and balancing. Half solar capacity is distributed and deployed in load centers. Including wind resources down to 200 M/m2, increases wind development nearer to load centers. Offshore deployment is exclusively in the coastal provinces where load is high.

New subsidy schemes for RE 1. Establish the target guiding system for the development and utilization of renewable energy 2. Research on evaluation system of non-hydro renewable sources power generation quota and green certificate trading mechanism 3. Implementation of full-amount indemnificatory acquisition system for renewable sources power generation 4. Transformation of pricing mechanism of renewable energy power generation Match up with reformation of electric power system and unhook benchmark electricity price of renewable energy and that of coal power Adopt differentiated electric charge policy based on developmental stages of renewable energy technologies Implement tendering electricity price policy step by step

REAM-EDO: Main characteristics Least-cost unit commitment & economic dispatch with investments in power & district heating generation and storage and power transmission capacity adjusted by policies (e.g. targets) and procedures (e.g. dispatching) Provincial level geographical resolution wo step simulation: Annual optimization ( full foresight within the year) Flexible time representation : chronological or representative time-slices Can make investments [optional] myopic Generates boundary conditions Includes unit commitment [optional/relaxed/limited] Connection between time-slices and # of cycles (for UC & storage) Weekly optimization (as above no endogenous investments) Hourly time resolution Imports boundary conditions Investments in generation and transmission Weekly hydro use and water values Shadow values of: fuel availability constraints, emissions constraints, policies Circular constraints unit commitment, short-term storage,..

Coal reduction and coal taxation Coal use Coal can be rapidly be phased out of the end-use sector, substituted by energy efficiency measures, electricity and natural gas Coal has still a dominant role in the power sector towards 2030. Coal can, however, to some extent be substituted by wind power and solar power Coal pricing Use of coal has serious environmental impact, which currently is not reflected in the coal price Carbon pricing and coal taxation should be implemented to ensure a level playing fields between coal and renewable energy

END-USE model END-USE Output CNRECs END-USE model calculated the energy demand for industry, buildings, construction, agriculture and transport using either physical production and energy intensity, added value projections, or stock turnover Electricity demand District heating demand Fuel use in end-use sectors Energy use in transformation sectors (except power and district heating production) Environmental impact

EDO model EDO Output The EDO model calculates a least-cost solution for the power and district heating sector, optimising investments in and operation of the generators with the given constraints and targets for the whole power and DH sector The results are energy flows, costs (investments and operating costs) and environmental impact Electricity supply detailed dispatch Electricity flow in interconnectors District heating (DH) supply Fuel use in power and DH sectors Investments in new capacity (generation, transmission, storage) Costs (Fuel, O&M) for power and DH production Environmental impact Shadow prices for constraints

CREAM CGE CREAM CGE Output Macro economic impact of the energy transformation Changes in end-use sectors Job creation and destruction Environmental impact

Soft links between models The END-USE model calculated electricity demand and district heating demand, which are used as input to the EDO model The EDO model delivers investments figures for power generation to the CGE model The CGE model delivers activity levels for the end-use sectors to END-USE General framework conditions END-USE Output CREAM END-USE Electricity demand District heating demand Electricity Fuel use demand in end-use sectors District Energy heat use demand in transformation Fuel sectors use in the end-use sectors Environmental impact Sub-sector changes (one or two iterations) EDO CREAM Output EDO Electricity supply detailed dispatch Electricity Electricity supply flow in detailed dispatch interconnectors District Heat (DH) supply District heating (DH) supply Fuel use in power and DH sector Investments Fuel use in in power new capacity and DH (generation sectors and transmission) O&M Investments cost for the in power new capacity and DH sector Costs (Fuel, O&M) for power Environmental and DH production impact of the power supply Environmental impact Shadow prices for constraints Results Energy system development Energy environment - economy CGE Output CREAM CGE Macro economic impact of Macroeconomic the energy transformation impact of the energy Changes transition in end-use sectors Changes Job creation in end-use and sectors destruction Job Environmental creation/destruction impact Environmental impact

Distributed resources and RE heating Removing barriers for distributed generation Current challenges for DG The potential for DG Solutions for increased deployment Promoting RE heating Current situation for RE heating The potential for RE heating Barriers and challenges for RE heating Next step solutions

ELECTRIC VEHICLE/ENERGY STORAGE/DEMAND RESPONSE CNREC OCTOBER 2016

Key definitions Demand response: industrial/residential DR, EV smart charging. Storage: stationary storage, retired battery 2 nd use,v2g.

Charging Price Elasticity Survey in US Portland General Electric (PGE) San Diego Gas and Electric (SDG&E)

Outputs: EV smart charging (MW)

Battery Capacity Degradation Reinvestment Capital Invest (RMB/Wh) Fixed O/M (RMB/Wh/year) Variable O/M (RMB/kWh) Life (years) Lithium ion (2015) 2 0.06 0.05 10 Capital Invest (RMB/Wh) Fixed O/M (RMB/Wh/year) Variable O/M (RMB/kWh) Life (years) Lithium ion (2015) 2 0.06 0.8? 200

Outputs: 2 nd Use Battery Storage (MW) High collection and transport costs!

Lithium Resources 1. Global resource 41 million t, China 5.1 million t 2. > 1 billion Evs (40kWh/EV); 3. 1 kg Li = 5.23 kg Li2CO3, LiCoO2, 40kWh/EV, 0.9 billion EVs; Li-air 5.6 billion EVs LBNL, NREL

Demand Response Inputs Dispatchable Load Industrial Load EV smart charging 400 Rmb/kW 100 Rmb/kW Curtailable Load Residential Load 500 Rmb/kW Program Type Sector Scenarios Participation Rate 2030 Response Rate 2030 Dispatchable Load Curtailable Programs Residential Small C&I Medium C&I Small C&I Medium C&I Large Low 5% 90% Medium 10% 90% Low 1% 90% Medium 3% 90% Low 3% 90% Medium 5% 90% Low 2% 80% Medium 4% 60% Low 3% 80% Medium 5% 60% Low 8% 80% Medium 15% 60% Source: CNREC, NRDC

Outputs: Industrial DR (MW)

Questions/Next Step 1. Storage cost data - PCS/storage module cost, life cycles, OM cost, efficiency - 2020/2030/2050 (max cost reduction potential) 2. EV Smart charging cost/potential: - 100 RMB/kW (conventional DR higher, varying by time?) 3. Demand response: - potential by sectors: capacity to peak load, etc - participation rate, - response rate, - DR price data.

Flexibility measures Flexibility is needed in the whole supply chain Power markets with dynamic pricing the most efficient way to give incentives wholesale and retail Transmission grids must be integrated in the power markets System friendly RE plants Central storage Power/heat Power plant flexibility Non-market/transitional incentives for flexibility Technical measures Grid flexibility DG flexibility Storage strategies Demand response Distributed Production Transmission Distribution Demand Storage Transitional incentives for flexibility can be part of the long-term solution Wholesale power market Market dispatch of grids Power markets drivers Retail power market

Policy strategy analyses GRID DEVELOPMENT STRATEGIES

System issues represented in CREO scenarios 2015 2020 2025 2030 Generator output planning Min FLH based on 2015 Reduced Min FLH Market Based Dispatch Transmission Scheduling Day-Night Free flows Gate Closures for Unit Commitment DA UC More confidence in VRE Faster Unit Commitment Fast Markets Demand Side Management Negligible Smart charging of electric vehicles Peak shifting in industries and commercial Peak clipping in residential (air conditioner load)

Power Balance - North China High RE Power balancing task example for week 38 in 2030. Top: Demand side Local consumption Additional consumption from power to heating Exports (grey areas on top) Bottom: Supply side Imports (grey areas on top) Local generation (colored) Storage discharging, and DR reduction shows as generation. Storage charging and DR additional consumption shows as negative supply

West Deployment of RE West Net-export Coal and RE Northeast Coal balancing High RE Low hydro North Eas Central North Central Large RE volumes, hydro integrationen Import, export, transit North Energy and balance imports Coal balancing, storage, DR, DH

West West Net-export Coal and RE North Energy and balance imports Coal balancing, storage, DR, DH North Central Central Large RE volumes, hydro integrationen Import, export, transit East Import region Industrial CHP EB Lower solar share East

Central Central Large RE volumes, hydro integrationen Import, export, transit North Energy and balance imports Coal balancing, storage, DR, DH North South East South Limited external exchange Own balancing resources East Import region Industrial CHP EB Lower solar share