Embed Size (px)
Citation preview
Using Supply Chain Management Techniques to Make Wind Plant and Energy Storage Operation
More Profitable
By
Prashant Saran
&
Clay Siegert
Thesis Advisor: Dr. Jarrod Goentzel
Key Results
• Applying SCM techniques to Wind Plant and Storage operation works:– Increase gross profit up to 19% of base case revenues
– Increase operating profit up to 15% of base case revenues
– Make overall pre‐tax profit in certain scenarios
• Novel approach applying SCM to wind plant and storage operation
2Prashant Saran & Clayton Siegert
Agenda
Problems facing wind plants and energy storage
Our hypothesis: SCM can solve these problems
Relevant SCM techniques
Simulation model to test hypothesis
Simulation results and management insights
3Prashant Saran & Clayton Siegert
Problems Facing Wind Plants and Energy Storage
Wind plants earn sub‐optimal revenue– Issue #1: Remote location low prices
– Issue #2: Intermittency revenue unpredictability
– Issue #3: Intermittency low installed capacity payments
– Electrical energy absolutely perishable
Spatial and temporal variability of electricity prices– Spatial: At noon, $75 in Maine vs. $92 in Cambridge vs. $101 in Providence
– Temporal: At midnight in Maine = $55, while at noon in Maine = $75
– Premium prices paid for: predictability, availability in high‐demand areas at right time
Energy storage possible solution for wind plants– Rapidly‐evolving technology
– Existing research says not profitable
4Prashant Saran & Clayton Siegert
Our Hypothesis
SCM techniques can make wind plant and energy
storage operation more profitable
Network Design Decisions1
• Facility Location: Where should storage unit be located?
• Capacity Allocation: How much storage capacity should be built?
• Supply Allocation: How will storage unit get charged?
• Market Allocation: What markets should each facility participate in?
Daily Operating Policies (Inventory Management)2
• Input Policies for charging storage
• Output Policies for discharging storage
1 Chopra & Meindl, 2004; 2 Silver, Pyke, & Peterson, 1998 5Prashant Saran & Clayton Siegert
Relevant SCM Techniques:Network Design Decisions
Decisions:
‐Facility location?
‐Capacity allocation?
‐Supply allocation?
‐Market allocation?
“Co‐Located”
“Located” (Cambridge)
“Located” (Providence)
“Located” (Cos Cob)
6
Relevant SCM Techniques: Daily Operating Policies
• Electricity Characteristics and Model Assumptions– Product: Perishable – Planning Horizon: 1 Day– Demand: Infinite– Lead Time: Variable– Review Period: Continuous / Periodic
• Total Cost Function– Unit Variable Cost: Electricity Cost– Inventory Carrying Cost: Standby Loss– Ordering or Setup Cost: Round Trip Loss– Shortage Cost: Penalty in DA
• Single Period (Newsvendor) Problem– Cost of Overage: Sell after peak hours– Cost of Underage: Not enough for peak hours
Four Daily Operating Policies:
•Simple
•Cost Based
•Max Peak
•Rapid Arb
7Prashant Saran & Clayton Siegert
Daily Operating Policies
0
50
100
150
200
250
300
350
400
450
500
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Amou
nt of E
nergy Stored
in Storage Unit
(MWh)
Hour of Day
8
Max Energy
Min Energy
Charge
Discharge
Standby Loss
Standby Loss & Discharge
SimpleCost Based
Max Peak
Rapid Arb
Standby Loss, Charge & Discharge
Model
Wind PlantElectricity Output
Electricity Market Prices
Storage TechnologyCosts & Technical Specifications
Simulation Model
Supply Chain Management
NetworkDesign
Decisions
DailyOperatingPolicies
Supply Chain Management
Incremental Revenue & Profit
Simulation Model Runs
• 121K possible scenarios– Location, seasonality, market, operating policy, etc.
• 10K runs for each scenario:
• Intelligently reduced to 5,130 scenarios 51.3M simulations:‐ Rapid Arb ‐ 190 days (Summer & Winter)
‐ ‘Co‐Located’ and ‘Located’ ‐ ‘Hybrid’ vs. ‘Linked’
‐ Day‐Ahead and Real‐Time Markets ‐ Storage Capacity (10% to 100%)
10Prashant Saran & Clayton Siegert
Results: Daily Operating Policy
Design Scenario 1
LOCATION Co‐LocatedSTORAGE CAPACITY
as % of WindCapacity
50%
MARKET Real Time
SUPPLY LINKAGE Linked
Wind Day 95
Rapid Arb is the Best Policy
Scenario
11Prashant Saran & Clayton Siegert
Impact of Network Design Decisions (Summer)
Location:Cos Cob > Co‐Located
Supply Linkage:Hybrid > Linked
Market:Day Ahead > Real Time(if Installed Capacity
Payments) 12
Impact of Network Design Decisions (Winter)
Location:Cos Cob > Co‐Located
Supply Linkage:Hybrid > Linked
Market:Day Ahead > Real Time(if Installed Capacity
Payments)13
Storage CapacityLINKED HYBRID
14
Diminishing Returns: 20% optimal capacity if costs
increase linearly
Constant Increasing Returns: Limited by technology, space and
capital constraints
Prashant Saran & Clayton Siegert
P&L Snapshot
15
Alpha Battery: Capital Cost, Operating Profit & Installed Capacity Payments
16
Total: $14,922
Total: $8,623
Battery Life: 15 years
Prashant Saran & Clayton Siegert
Beta Battery: Capital Cost, Operating Profit & Installed Capacity Payments
17
Total: $11,470
Total: $5,171
Battery Life: 30 years
Prashant Saran & Clayton Siegert
Management InsightsOperators
Use SCM techniques to manage Wind Plant and Storage operationDaily Operating Policies
Use policies based on Inventory Management concepts to improve profitability
Iterate policies regularly
Network Design DecisionsLocate storage in densely‐populated areas
Operate storage with “Hybrid” policy
Size storage to maximize utilization
Use storage to commit in Day‐Ahead market
Battery Manufacturers
Lower capital and operating costs to the targets specified by our research
ISO Policymakers
Qualify batteries for Installed Capacity Payments drive penetration of more profitable wind energy
Prashant Saran & Clayton Siegert 18
Key Results
• Applying SCM techniques to Wind Plant and Storage operation works:– Increase gross profit up to 19% of base case revenues
– Increase operating profit up to 15% of base case revenues
– Make overall pre‐tax profit in certain scenarios
• Novel approach applying SCM to wind plant and storage operation
19Prashant Saran & Clayton Siegert
Thank You!
Questions?
20Prashant Saran & Clayton Siegert
