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Investments to Reduce Power System Risk from Gas System Dependence

Industrial & Manufacturing Systems Engineering. Investments to Reduce Power System Risk from Gas System Dependence. Sarah M. Ryan and Dan Hu For presentation in “Electricity systems of the future: incentives, regulation and analysis for efficient investment”

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Investments to Reduce Power System Risk from Gas System Dependence

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  1. Industrial & Manufacturing Systems Engineering Investments to Reduce Power System Risk from Gas System Dependence Sarah M. Ryan and Dan Hu For presentation in “Electricity systems of the future: incentives, regulation and analysis for efficient investment” Isaac Newton Institute for Mathematical Sciences, Cambridge 19 March 2019

  2. Outline • Symptoms of vulnerability to risk • Risk quantification in economic terms • Optimization-based simulation • Input distributions estimated from real data • Not-so-real power grid combined with gas system • Use of risk metrics to evaluate alternative risk-mitigation strategies • Dual-fuel capability • Increased gas storage • Conclusions

  3. Growth of Variable Renewable Generation Gas units provide the needed flexibility for increasing renewables Source: US Energy Information Administration, Annual Energy Outlook 2019

  4. Asymmetric Electric-Gas Dependence 35% 35% Source: US Energy Information Administration, Annual Energy Outlook 2019

  5. Interruptible Contracts for Gas

  6. Winter, 2019, US Upper Midwest 17C

  7. Winter, 2018, Eastern US Gas Prices

  8. Winter, 2018, Eastern US Electricity Prices

  9. Winter, 2014, New England Prices natural gas prices ($/MMBtu) electric energy prices ($/MWh) natural gas price* DAM price RTM price *natural gas price is average of MA delivery points Source: ISO-NE

  10. Model and Methods • Optimization-based simulation to investigate dispatch cost under: • limits on availability of gas from interruptible contracts, combined with • high spot prices for gas, correlated with • demand for electricity • Hard to find good data: • available gas varied parametrically • electric demand and gas spot price modeled probabilistically

  11. Problem Setting Net load Power system economic dispatch (ED) gas spot price renewable sources output sources of uncertainty load Power system Dispatch cost conventional units availability gas availability

  12. Dispatch Linear Program Min Total daily dispatch cost • Gas costs from interruptible contracts and the spot market • Production cost of non-gas generators • Net cost of gas flows from storage • Penalties for non-served/excess electricity or gas s.t. Usual constraints given unit commitment, plus • Limit on availability of contracted gas • Gas balance • Limits on flows to/from storage • DC approximation of transmission constraints

  13. Impact of Gas Price Uncertainty and Constrained Gas Availability on Dispatch Cost Monte Carlo simulation schemes: • ED-PE: Economic dispatch (ED) with uncertain electric load and gas price Point Estimate • ED-PD: ED with joint electric load and gas price Probability Distribution Sources of uncertainty Load uncertainty only (Net) load • Economic Dispatch Model • Min daily dispatch cost, subject to • Usual dispatch constraints • Limit on gas available from interruptible contracts Load & gas price uncertainty Gas spot price

  14. Risk Quantification Procedure Estimate joint distribution of electricity load and gas spot price Risk measure (CVaR)

  15. Gas Spot Price and Electric Load Jointly Depend on Weather • Procedure for estimating joint distribution, illustrated for ISO-NE in winter • Cluster days based on average hourly temperature • Fit bivariate Normal distribution to transformed data • Estimate mean vector and covariance matrix in each cluster Algonquin Citygategas price & Electric load in Connecticut (CT) zone

  16. Clusters of Winter Days K-means clustering results -> We chose 4 segments Coldest Cold

  17. Daily Gas Price & Daily Load in CT Cold Days Coldest Days

  18. Joint Distributions of Log-Transformed Data Cold Days Coldest Days

  19. Histograms of 106 Bivariate Samples Coldest Days Cold Days

  20. Synthetic System for Simulation • Modified IEEE 24-bus system • Modified Belgian 20-node gas system • Nodes and buses linked by gas-fired generators • Load and weather data provided by ISO-NE • Load in CT scaled to match total and allocated to buses as in IEEE system • Gas spot price data from Algonquin citygate • Demand for gas by non-electric users same as in Belgian system • Units committed and gas transportation schedules optimized in pre-processing step

  21. Impact of Gas Price Uncertainty and Constrained Gas Availability on Dispatch Cost Coldest Days • Economic Dispatch Model • Min daily dispatch cost, subject to • Usual dispatch constraints • Limit on gas available from interruptible contracts Available gas (constraint rhs) varied systematically ED-PE: Economic dispatch (ED) with load marginal distribution and gas price Point Estimate ED-PD: ED with joint electric load and gas price Probability Distribution

  22. Impact of Gas Price Uncertainty and Constrained Gas Availability on Dispatch Cost Cold Days • Economic Dispatch Model • Min daily dispatch cost, subject to • Usual dispatch constraints • Limit on gas available from interruptible contracts Available gas (constraint rhs) varied systematically ED-PE: Economic dispatch (ED) with load marginal distribution and gas price Point Estimate ED-PD: ED with joint electric load and gas price Probability Distribution

  23. Distances Between Cost Distributions for Various Availability Levels of Contracted Gas Coldest Days Cold Days Wasserstein distance

  24. Generation Mixes Adjusted to “Bomb Cyclone,” January 2018 EIA Today in Energy, January 23, 2018 What if more gas storage capacity had been available?

  25. Alternative Risk-Mitigation Strategies:Simple Engineering Economic Estimates 1. Dual-Fuel Capability 2. Additional Gas Storage Same investment could be used to build and fill a gas storage facility with capacity 106Mcf Dispatch model modified to include this additional storage • Dual-fuel conversion for a unit in New England estimated to cost $3.15M • Dispatch model modified to include fuel-switching in the optimization

  26. Probability Metric Comparison Coldest Days Adding gas storage reduces risk (impact of uncertainty in gas price) more than same $ investment in dual-fuel conversion in cold weather Cold Days Moderate Days

  27. Conditional Value at Risk (CVaR) of the ED-PD Dispatch Cost Distributions Dispatch cost w/storage Dispatch cost w/dual-fuel CVaRDual-Fuel CVaRStorage

  28. CVaR of the ED-PD Cost Distribution Coldest Days Adding gas storage reduces risk of high dispatch cost more than same $ investment in dual-fuel conversion Cold Days Moderate Days

  29. Conclusions • Procedure to quantify the impact of gas spot price uncertainty on system operator’s electric energy purchase cost under restricted availability of contracted gas • Correlated electric load and gas spot price based on weather • Monte Carlo simulation of daily dispatch • Risk metrics to quantify difference in dispatch cost distribution with/without gas price uncertainty • Numerical study illustrates the procedure • Results indicate that gas storage mitigates risk more than dual-fuel conversion for the same dollar investment

  30. Future Work • Generate joint distributions of gas price and electric load on hourly rather than daily basis • Represent contracted gas availability probabilistically rather than in a sensitivity study • More realistic numerical test cases that represent the actual gas network supplying an actual power system • … all these extensions require more and better data!

  31. Acknowledgment This material is based upon work supported by the Power Systems Engineering Research Center (PSERC) in collaboration with George Gross, University of Illinois Urbana-Champaign. https://pserc.wisc.edu/publications/reports/2018_reports/M-36_Final_Report.pdf

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