xcel energy services big data challenges€¦ · xcel energy inc. is a major u.s. electric and...
TRANSCRIPT
Cary Oswald
Managing Director, Risk Strategy & Control
Xcel Energy Services Big Data Challenges
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Xcel Energy Inc. is a major U.S. electric and natural gas company, providing energy to customers in Colorado, Michigan, Minnesota, New Mexico, North Dakota, South Dakota, Texas and Wisconsin.
● Electricity Customers: 3.4M
● Gas Customers: 1.9M
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Prolonged Recession
Losing market share
Increased regulation
Budgetary pressures
Grow the business ABC Company (+14.14%)
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Government Response to Recession
Congress passes Dodd-Frank Wall Street Reform Act - 2010
Regulates OTC Energy Derivatives
Increased oversight from Commodity Futures Trading Commission (CFTC)
CFTC regulates OTC energy commodities (previously exempt)
CFTC more focused on market manipulation
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Dodd-Frank Act Impacts: All financial swaps must be reported and cleared with few exceptions
Market participants classified:
Swap dealers – Banks
Major swap participants
Non SP/MSP – “End Users”
Swap Data Repository – “SDR”
OTC Swaps between End Users must be reported within 48 hours but not cleared
Know Your Counterparty - requires additional transaction related elements to be created and captured
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Dodd-Frank Act Impacts (continued): SDRs created to report to CFTC. Market participants send/receive data
to SDRs, not CFTC
CFTC increased market monitoring activities due to transaction transparency
Federal Energy Regulatory Commission (FERC) increased market surveillance activity in response to Dodd-Frank Act
FERC Fines - Pending Regulatory Findings
● Barclays Bank $435M (fine pending)
● Deutsch Bank $1.5 M (fined)
● Gila River $2.5M (fined)
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Market Driven Response to Recession:
Grow market share
Leverage good credit ratings to transact more volume
Participate in new markets and products
Long term MISO Financial Transaction Rights (FTRs)
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Financial Transmission Rights (FTR): Created to hedge day-ahead congestion in organized electric market
A financial transaction between one physical delivery point (A) versus another delivery point (B)
Settles on day-ahead price between the two points
Purchase the difference between point “A” and “B” in auction
Prices can and do go negative
No secondary market, therefore likely to hold to maturity
Risk is greatly increased
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Long Term FTR Product: New risk metric must be developed
Up to one year in duration
No secondary market/liquidity
Traditional risk metrics such as Value-at-Risk assume short term liquidation – usually 1 to 3 days
Risk metric must be based on Monte Carlo simulation of all individual locations included in portfolio
Historical settled prices
Over 1,000 simulations
Create distributions of outcomes to assess spread risks
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Business Challenge: Data
Calculate product risks combined with overall portfolio risks
Large data sets
Example:
MISO FTR Market: 2,000 Unique pricing nodes
8,000 Potential monthly FTR paths
8,760 hours in a year with hourly prices for each node
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Business Challenge: Data Movement
Dodd-Frank Act requires movement of data from market participants to SDRs and then back to market participants
Additional attributes assigned at transaction execution and when data sent to/from SDR
Data storage requirement increased to five years past final settlement date
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Business Challenge: Analytics
Calculation of risk metric for long term FTRs
2 years hourly historical price data (8760 x 24 x 2)
1,000 simulations
Mean (expected return)
Risk distribution
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Business Challenge: Implementation
Dodd-Frank Act reporting for End Users is
Report “transitional swaps” July 2010 – August 2013
48 hour reporting requirement for post August 19, 2013 transactions
MISO Annual FTR Auction first round -
August 19, 2013
April 17, 2013
4/17/13 8/19/13
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Leverage SAS Relationship
What product and services exist to meet Dodd-Frank compliance requirements?
Partnered with SAS on their new Dodd-Frank module for their BookRunner Risk Management platform
New version for existing BookRunner product
Required joint design work, project scope and testing
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DODD FRANK COMPLIANCE
FUNCTIONAL OVERVIEW
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Leverage SAS Relationship (continued)
How to leverage existing SAS infrastructure to meet long term FTR risk metric?
Developed prototype using base SAS
Working with SAS consultants to transition prototype into existing SAS infrastructure
● SAS BookRunner v.11
● SAS BookRunner Dimensions
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SAS Solution: Dodd-Frank Act
Created business design document for Dodd-Frank module 4th Quarter 2012
Testing of solution February/March
Deploy Phase I Solution March 25, 2013
Ability to report historical transactions achieved April 8, 2013
CFTC postponed compliance 5pm EST on April 9, 2013
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SAS Solution: Long Term FTRs
Prototype developed and used February/March 2013
Evaluated bid package for April 17, 2013 auction
Approved 10.1MWh portfolio with net credit outlay of $2.0M
April 2013 began project to replace prototype for on-going risk metric calculations
May 2013 expected completion
Risk metric calculated within risk dimensions for existing and new portfolios
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Calculate the EAR based on distribution of portfolio revenue outcomes
EAR = 95th percentile negative change from original MTM
EAR limit will be a separate portfolio risk metric in addition
to VaR
Hours of simulation required for each portfolio
Example of PnL Distribution
EAR
MTM - Dayzer
(P5 – mean)
Earnings-At-Risk (EAR)
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Future Business Opportunities
CFTC Trade Surveillance Monitoring
Exponential increase in data:
“Real time” vs. “day-ahead”
Physical and financial markets
Virtual transactions
FTR transactions
Hourly positions over thousands of locations
Algorithms needed to monitor trader activity
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Future Business Opportunities (continued)
CFTC Trade Surveillance Monitoring
CFTC analyzing trades on next-day basis to act quickly against manipulative traders
Expectation that market participants police their trading floor and have necessary trade surveillance systems in place
Preliminary discussions with SAS
● Expect to issue RFP in late 2013
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DODD FRANK COMPLIANCE
PRODUCT ROADMAP - OVERVIEW