Modeling Grid Operations in China’s Partially-

Restructured Electricity Market

Michael R. Davidson, Valerie J. Karplus, Ignacio Perez-Arriaga

China’s wind integration challenges

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Curtailment = Available wind-generated electricity not accepted onto grid

2013-2014

Source: NEA

Dispatch priorities

Institutional arrangements important cause of large-scale wind integration challenges (Zhao et al 2012; Kahrl et al 2011)

Equitable dispatch (三公调度 )• Equal shares of benefits (& costs) for plants at

provincial level: generation quotas, equal curtailment allocation…

Minimum mode (最小方式 )• Sum of committed plants’ submitted minimum

outputs

Energy-efficient dispatch (节能调度 )• Post-MM: Prioritize RE high efficiency coal low

effic coal

Unit commitment and economic dispatch-like optimization is not regularly performed

3Sources: Interviews (2014, 2015) and Kahrl & Wang (2014)

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Goals of presentation

• Demonstrate approach to optimize generator scheduling under coupling regulatory constraints, with applications to partially-restructured electricity sectors

• Quantify economic and environmental cost of deviations from ideal operation, such as “administrative congestion”

Model

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Additional technical detail

Regulatory constraints

Optimality criteria ≤ 0.003

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• Generation quota / Full Load Hours (FLH):Minimum capacity factor of coal generators by type based on provincial averages

Lagrangian penalty for non-served FLH faster and more reliable than hard constraint

Regulatory constraints

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• Generation quota (FLH):Minimum capacity factor of coal generators by type based on provincial averages

• Provincial dispatch (PROV):Separate provincial reserve requirements

Similar treatment for downward reserves

Regulatory constraints

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• Generation quota (FLH):Minimum capacity factor of coal generators by type based on provincial averages

• Provincial dispatch (PROV):Separate provincial reserve requirements

• Transmission contracts (TRANS):Historical annual imports/exports limited transmission capacity and

directions between provinces

Regulatory constraints

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Clustering formulation

Commitment variables: binary integer

Palmintier & Webster (2014)

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Clustering formulation

Commitment variables: binary integer

Palmintier & Webster (2014)

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Experiments

• Week (168-hour) time horizon• Mean results for 6 winter wind profiles from 2011• Pre-solve for min of two wind scenarios: MIPSTART• System: Northeast China 2011, provincial nodes

(CEC 2010)

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Construction of generator dataset

ClusteringCluster same-type units into single unit, integer

commitments

Minimum modeCogeneration unit commitments (default: 80%)

12-type configurationAggregate units into: (25, 50, 135, 200, 350, 600) x (Heat, Thermal)

Units databaseInside-the-fence unit capacities and type (cross-check)

Power plant database (China Electricity Council)Plant capacities (2011) Names

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Aggregation results

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Aggregation results (2)

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Aggregation results (3)

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Aggregation results (4)

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Solution times (min)

• Implemented in GAMS using CPLEX 12.6.2• Each scenario was run using 8 parallel threads on a 64-bit

dual-socket quad core 2.7 GHz Intel nehalem machine with 12 GB RAM

Aggregation results (5)

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Aggregation summary

Acceptable aggregation errors of multi-node 12-type homogeneous and clustering methods• objective ~ 0.3% = tolerance threshold• wind ~ ± 2%

Node-specific incidences more varied• signal of Δwind, Δobj ~ 5% difficult to attribute

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Wind curtailment

Regulatory and transmission constraints increase objective

Interaction of PROV + TRANS increases curtailment Quota has zero or slightly negative impact on wind

curtailment

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Cogeneration sensitivity results

Black border: 90% cogen commitment

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Results summary

Absent regulatory design issues, there is curtailment, but still below observed levels of curtailment

Largest difference is usage of high-efficiency coal, changing among provinces based on transmission & other constraints

No congestion in reference scenarios marginals of transmission constraints show transmission is binding only in Limited TRANS case (“administrative congestion”)

Generator flexibility: Large gains from increased flexibility concentrated in small cogeneration units (~ 1% objective per MW decrease in minimum mode)

Curtailment is insensitive to minimum mode (of tested range)

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Validation80%, 90% cogen scenarios with distribution of actual Jan-Mar 2011

daily production

Authors’ calculation based on CEC monthly and SERC daily electricity production totals

Validation (2)

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Jan Feb Mar Apr May Jun AvgHeilongjiang

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

Jilin30.5

% 34.8% 42.5% 30.3% 19.5% 11.0% 28.1%

Liaoning23.6

% 20.4% 19.1% 13.8% 3.5% 1.4% 13.6%E. Inn. Mong.

24.1% 27.9% 23.6% 22.3% 12.7% 6.1% 19.4%

Wind curtailment (generation) by province (2012H1)

Source: China Association of Agricultural Machine Manufacturers

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Discussion

• Clustering method for multi-node systems have low aggregation errors for whole system, but potentially some issues with province heterogeneity

• Demonstrated methodology to measure “administrative congestion” and other regulatory impacts

• Potential for much broader suite of tests on coal unit flexibilities (need for better data on technical limits)

• Looking for further validation approaches

Thank you谢谢 !

Michael Davidson (michd@mit.edu) This work was supported by the founding sponsors of the China Energy and Climate Project, Eni S.p.A., ICF International, Shell International Limited, and the French Development Agency (AFD). Financial support was also provided by the MIT Joint Program on the Science and Policy of Global Change through a consortium of industrial sponsors and Federal grants, including the U.S. Department of Energy. This work is also supported by U.S. DOE Integrated Assessment Grant (DE-FG02-94ER61937).

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References

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