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” Isaac Newton Institute for Mathematical Sciences, Cambridge 19 March 2019

Outline Conclusions 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

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

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

Interruptible Contracts for Gas

Winter, 2019, US Upper Midwest 17C

Winter, 2018, Eastern US Gas Prices

Winter, 2018, Eastern US Electricity Prices

Winter, 2014, New England Prices *natural gas price is average of MA delivery points electric energy prices ($/MWh) natural gas prices ($/MMBtu) DAM price RTM price natural gas price* Source: ISO-NE Data are measured from the original figure and thus may not be exact.

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 Some gas-fired generators procure gas in firm contracts but many do not. They have lower priority than other gas users and may have to buy gas on the spot market instead.

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

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

Impact of Gas Price Uncertainty and Constrained Gas Availability on Dispatch Cost Sources of uncertainty (Net) load Economic Dispatch Model Min daily dispatch cost, subject to Usual dispatch constraints Limit on gas available from interruptible contracts Load uncertainty only Load & gas price uncertainty Gas spot price 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

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

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 Citygate gas price & Electric load in Connecticut (CT) zone

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

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

Joint Distributions of Log-Transformed Data Coldest Days Cold Days

Histograms of 106 Bivariate Samples Coldest Days Cold Days

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 One persistent challenge was obtaining relevant data, especially for the gas system

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

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

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

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

Alternative Risk-Mitigation Strategies: Simple Engineering Economic Estimates 1. Dual-Fuel Capability 2. Additional Gas 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 Same investment could be used to build and fill a gas storage facility with capacity 106Mcf Dispatch model modified to include this additional storage

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

Conditional Value at Risk (CVaR) of the ED-PD Dispatch Cost Distributions Dispatch cost w/dual-fuel Dispatch cost w/storage CVaRDual-Fuel CVaRStorage Pause here: how to reduce risk?

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

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

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!

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