Institutional and Technical Analysis of Wind Integration Challenges in Northeast China

Michael DavidsonAdvisors: Ignacio Perez-Arriaga, Valerie J. Karplus

TMP – June 2014

2

Motivation

2013 Actual 2015 2020*

Thermal 862

Hydro 280 290 370

Wind 75 104 220

Nuclear 15 40 60

Solar 15 35 91

Biomass < 9 13 26

Source: China Electricity Council, NEA, ERI*Final 2020 targets are still under discussion

Wind is key focus of low-carbon electricity policy efforts in China.

Grid-connected wind capacity is expected to triple by 2020, and

may reach 400 / 1000 GW in 2030 / 2050.

Generation (2000-2013) Non-Fossil Capacity Targets

Wind integration challenges led to additional 9 Mt coal burned in 2012

Curtailment: Available wind turbines are instructed to not put power on the grid for economic, grid stability and other reasons

Spilled wind

3

Some technical causes

4

Some institutional causes

• Tariffs set administratively by NDRC for each province reflecting economic costs and affordability

• Annual “generation quotas” for each coal plant to recover costs

• Transmission quotas/limits between provinces most balancing is done within province

• “Energy efficient dispatch” piloted since 2007: implementation uneven, and inconsistent with power sector market reform

5Sources: Ma, 2011; Kahrl et al., 2013; Gao & Li, 2010

Research question

• What is the relative contribution of technical and institutional causes to wind curtailment in the Northeast?

• Evaluate the potential of the following solutions to reduce costs and wind curtailment:– More flexible operation of coal plants– Dynamic minimum outputs of CHP units based on heat load– Heat storage– Greater transmission interconnection

6

Model

Unit commitment optimization:

– Minimizes total operating cost = variable + startup costs– Week time period: T=168 hours– Fixed heating load constrains CHP plant operation– Hydropower dispatch with historic inter-season storage rates– Up and down spinning reserves– All prices in yuan ($1 = 6.2 Yuan)

7

Data

8

6 representative wind resource weeks Fixed weekly electricity load

(MW) %Thermal 71,459 80.2%Hydropower 7,005 7.9%Wind 10,606 11.9%

Total 89,069

Capacity (end of 2010)

Unit composition

• Database of all generators: CEC (2010)• Updated unit breakdowns, cogeneration status

9

Daily Heating Load Power-Heat Curve

Heat demand

10

11

Modeling institutional constraints

• “Generation quotas”: set minimum capacity factor of coal generators based on provincial average and reasonable summer/winter difference

• “Provincial dispatch”: (1) Set transmission limits and transmission directions between provinces. (2) Meet reserve requirement at provincial level.

Source: Kahrl et al., 2013; Gao & Li, 2010

Results

12

Reference Case (Technical Factors Only)

Objective (mil RMB)

Coal Use (Mtce)

Wind Generation (GWh)

Wind Share (% Generation)

Wind Curtailment (%)

Ja1 1,454.3 2.066 504.7 7.6% 5.0%Ja2 1,481.4 2.109 358.6 5.4% 5.9%Ja3 1,425.7 2.028 629.7 9.5% 5.9%

Ma1 1,432.4 2.035 606.2 9.1% 9.6%Ma2 1,443.9 2.050 555.5 8.4% 6.9%Ma3 1,390.3 1.972 815.2 12.3% 6.1%Avg 1,438.0 2.043 578.3 8.7% 6.6%

13

0 9 18 27 36 45 54 63 72 81 90 99 108 117 126 135 144 153 1620

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

Wind Profile: Ma1

wind

hydro

coal25

coal50

coal135

coal200

coal350

coal600

cogen25

cogen50

cogen135

cogen200

cogen350

cogen600

Hour

Gen

erat

ion

(M

W)

Ja – JanuaryMa - March

Flexible coal

• Lower minimum outputs (from 54% to 40%) improve cost and wind integration

• Startup/shutdown times, ramp limits have little effect • Startup costs have noticeable effect

14

    Objective (% Change from

Reference)

Wind Curtailment (% Change from

Reference)Pmin 40% -1.0% -47.9%Ramp Rates 50% 0.0% 4.2%Startup/ 24,24,6 0.0% 4.2% Shutdown 12,12,6 0.0% 0.0% Times 6,3,3 0.0% 0.0%Startup Costs 500 -0.1% -25.0%  400 -0.3% -42.7%

Heat

• Dynamic outputs in dispatch worsens curtailment Economic curtailment from not shutting down a

high must-run baseload unit• Storage has potentially huge impact

15

 Objective (% Change from

Reference)

Wind Curtailment (% Change

from Reference)

Dynamic, No Shift -0.9% 31.6%Dynamic, 4 Hour Shift -1.2% -4.2%Dynamic, 8 Hour Shift -1.8% -67.8%

Regulatory Features: Provincial Dispatch

• Reserve requirements at provincial level increase curtailment:

16Ja1 Ja2 Ja3 Ma1 Ma2 Ma3 Avg

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

RegionalProvincial

Generation Quota

• Highest curtailment for provincial dispatch with generation quota

• Difficulties w/model convergence

(Ma1 wind scenario)

17

Objective (mil RMB)

Wind Curtailment (%)

Regional Reference 1,432.40 9.6%Regional (Min CF) 1,442.90 11.3%Provincial Reference 1,444.30 12.0%Provincial (Min CF) 1,454.30 11.7%

18

Conclusions (1)

Technical

• Absent regulatory design issues, there is curtailment (6.6% average), but still below observed levels of curtailment (15-40% in winter months)

• Heat-electricity interactions can be measured: large impact of storage implies significant coupling and potential benefits from coordination

• Some flexibility changes in coal (e.g., lower mins and reduced startup costs) will reduce curtailment…but not all (e.g., shorter startup/shutdown times, higher ramp rates)

Conclusions (2)

Regulatory

• Provincial dispatch with minimum generation quotas increases curtailment on order of technical causes

• More broadly, this methodology helps identify province-level dynamics in an otherwise opaque system

• Future research: Due to economic curtailment, does cost-minimizing dispatch guarantee elimination of integration challenges?

19

20

Thank you谢谢

21

References

Gao, C., and Li, Y. (2010). Evolution of China’s power dispatch principle and the new energy saving power dispatch policy. Energy Policy, 38(11), 7346-7357.

Kahrl, F., Williams, J., Ding, J. H., & Hu, J. F. (2011). Challenges to China's transition to a low carbon electricity system. Energy Policy, 39(7), 4032-4041.

Kahrl, F., Williams, J. H., & Hu, J. (2013). The political economy of electricity dispatch reform in China. Energy Policy, 53(0), 361-369.

Kerr, T. (2008). CHP/DHC Scorecard: China. International Energy Agency. Liu, W., Lund, H., & Mathiesen, B. V. (2011). Large-scale integration of wind power into the existing

Chinese energy system. Energy, 36(8), 4753-4760.Ma, J. L. (2011). On-grid electricity tariffs in China: Development, reform and prospects. Energy Policy,

39(5), 2633-2645. Schuman, S. & Lin, A. (2012). China’s Renewable Energy Law and its impact on renewable power in

China; Progress challenges and recommendations for improving implementation. Energy Policy 51 (2012): 89-109.

Zhao, X., Zhang, S., Yang, R., & Wang, M. (2012). Constraints on the effective utilization of wind power in China: An illustration from the northeast China grid. Renewable and Sustainable Energy Reviews, 16(7), 4508-4514.

Zhang, D., Davidson, M., Gunturu, B., Zhang, X. & Karplus, V. J. An Integrated Assessment of China’s Wind Energy Potential (Report No. 261). (MIT JPSPGC, Cambridge, MA, 2014)

22

China power sector reform• 1949-1985: Vertically-integrated state-run utility (Ministry of

Water Resources and Electric Power, later Ministry of Electric Power)

• 1985-1997: Private & foreign investors allowed to invest in generation, “competed” with local utilities

• 1997-2002: Ministry broken up– Regulatory fns SETC, SDPC and later NDRC– State-owned generating assets Big Five SOEs– T&D assets, system operation State Grid, Southern Grid

• 2003-present: Reform slowed – markets, indep system operator were not created

• China does not fit either model – only partially unbundled

23

Why the Northeast

Kerr (2008)Zhang et al. (2014)

High proportion of combined heat and power (CHP) units

Regional electricity/heat institutions

24

State Grid Provincial Governments

Northeast Grid

Provincial Grids

Coal Generators

Wind Subsidiaries

Energy SOEs Wind

IPPs

GovernmentQuasi-gov’tSOEOther

Municipal Governments

Ele

ctri

city

Distric

t Hea

ting

Monthly Curtailment Figures

25

Jan Feb Mar Apr May Jun Avg

Jilin 30.5% 34.8% 42.5% 30.3% 19.5% 11.0% 28.1%

E. IM 24.1% 27.9% 23.6% 22.3% 12.7% 6.1% 19.4%

Gansu 25.3% 25.7% 20.9% 14.2% 14.2% 10.5% 18.4%

W. IM 25.8% 26.1% 24.5% 9.6% 5.1% 4.9% 16.0%

Liaoning 23.6% 20.4% 19.1% 13.8% 3.5% 1.4% 13.6%

Heilongjiang 19.2% 20.2% 22.3% 15.9% 2.7% 0.8% 13.5%

Wind curtailment (generation) by province (1st half 2012)

Source: China Association of Agricultural Machine Manufacturers

26

In a region with high overcapacity

Transmission

27

Transmission (Provincial Dispatch)

28