IEOR 180 Senior Project

1
Optimizing Electricity Procurement for theCity
of Palo Alto

• IEOR 180 Senior Project
• Toni Geralde
• Mona Gohil
• Nicolas Gomez
• Lily Surya
• Patrick Tam

2
Outline

• City of Palo Alto
• Energy deregulation
• Tradeoffs
• Palo Altos current decision making tools
• Our linear optimization model
• Results

3
Company Background

• Founded 1900
• Area 26 square miles
• Customers 58,100 including
• residential homes
• small businesses
• corporate offices
• manufacturing facilities
• excluding Stanford University Campus

4
California Energy Deregulation

• Began January 1, 1998
• Open buyer and seller market for electricity
• Purchase Energy X per Mega Watt Hour

5
California Energy Market

Flexible products variable amounts Spot
market WAPA
Inflexible products constant amount/ fixed
prices Forwards High Load Load Load All Week
6
Trade-Offs

• Futures contracts
• safeguard against price spikes versus cost of
  premium
• Spot Market
• flexibility of amount versus exposure to risk

7
Meeting Demand

Spot Market/WAPA
Product III
Sell to spot
pri
MWh
Product II
Demand Curve
Product I
12am
1159 pm
Time of Day
8
Palo Alto ModelChallenges

• How much WAPA should be utilized
• capacity charge based on maximum amount
• How much to purchase in advance via forwards

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City of Palo Alto Current Solution

• Optimize portfolio with two time periods
• Heavy load hours (HLH)
• Light load hours (LLH)
• Purchase options Forward contracts and WAPA

10
Problem Statement

• Optimize available energy sources with additional
  energy products and additional time periods to
  accommodate them
• WAPA
• HLH forwards
• LLH forwards
• E3 blocks
• All week forwards

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Approach Linear Program

• Based in Excel and Whats Best Solver

E3 II
E3 I
Load
WAPA
6am
10am
2pm
6pm
10pm
Time
12
Available Data

• Forecasted Load
• Hourly demand for one year
• Forecasted Market Prices
• Fixed Contract prices

13
Model features

• Flexible Let the user input values for all
  parameters.
• Accurate It follows the power demand closely by
  dividing the month into 150 periods.
• Handle risk Control exposure to spot market for
  different demand loads.
• Automated

14
Subscripts

• bBlock index (1,,5)
• dDay index (1,,31)
• KWeek index (1,,5)

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Decision variables

• Power from WAPAbd
• MAX
• Power from High Load Forward
• Power from Low Load Forward
• Power from All Week Forward
• Power from E3bk

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Parameters

• Upper and Lower limit for WAPA
• WAPA capacity cost
• Variable Cost of each product
• Demand Loadbd, during each period

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Objective function

• MIN
• SSSCost of Product bdk Product bdk
• (WAPA Capacity Cost MAX)
• - SSS(Load bdk - Product bdk)Cost Forward bdk

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Constraints

• WAPA Upper and Lower limit constraints
• MAX gt WAPAbd.
• Satisfy all demand
• All variables gt 0.

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Model Inputs
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Quantifying Risk

• Risk Defined
• exposure to spot market
• Risk Implementation
• exposure to spot market
• during high load periods
• during normal load periods

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Model Quantifying Risk

• Risk is the exposure to the spot market

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Model Outputsfor all product-decision variables
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Minimized
Model Outputsthe costs for different products
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The Option to Sell Back
Negative means unused capacity
Unused capacity multiplied by the corresponding
price
Revenue from selling back
25
Chart Output Percentage of different products
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Quantifying Results

• Model Comparison
• Run models under various scenarios
• Heavy load
• Light load
• Normal load
• Calculate cost reduction under new model

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Model Comparison

• Based on same inputs
• prices
• forecasted demand
• Compare models against an actual load
• Actual load average load during time intervals
  utilized in UCB model

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Model Comparison

• UCB Model is inherently better than Palo Altos
  current Model.

Load
6am
10am
2pm
6pm
10pm
Time
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Monthly Savings
30
Annual Savings
31
Reduction in Variance
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Summary of Results

• UCB Model Savings
• 1.121 million for 1998
• 4 cost reduction
• UCB with revenue Model
• additional 180,762 for 1998
• additional 1 cost reduction
• Reduction in Variance

33
Benefits of UCB Model

• Utilizes all available procurement options
• Low Run-time
• Partitions day into finer time intervals
• more closely follows demand curve
• reduction in variance from actual load
• Reduction in risk

34
Recommendations

• Replace existing model with UCB model
• Negotiate with WAPA to reduce lower capacity
  limit
• For June 1998, the max purchase quantity is 40
  mwh (no lower capacity limit)
• Incorporate spot market into decisions