# Presentation: 139137

## CAISO Transmission Evaluation Assessment Methodology(TEAM)SSG-WI Planning Work Group June 15, 2004Mingxia Zhang, Ph.D.Principal EconomistCAISO Market Analysis

## Presentation Outline

**Briefly explain the five key components of the CAISO methodology**
**Focus on one key component: market price modeling**
**Present the results of the Path 26 application  **

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## The CAISO Methodology

- Objective: Develop a comprehensive methodology to evaluate the economic benefits of transmission expansion in a wholesale market regime
- Our June 2 CPUC filing of the methodology with Path 26 Study represents the CAISO’s research and development over two years.
- The general methodology developed jointly with London Economics was filed with CPUC in February 2003
- The refined general methodology with Path 26 Study was filed with CPUC on June 2, 2004
- The methodology and the Path 26 application went through a steering committee and stakeholder process during this two-year period

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## The Five Key Components of the CAISO Methodology

**Benefits Framework** - Standard framework to measure benefits  regionally and separately for consumers, producers, and transmission owners
**Market prices** – Utilize market prices to evaluate  transmission expansion
**Uncertainty** -  Consider a wide range of future system conditions to compute expected benefit, most likely range of benefit, and insurance value of a proposed upgrade
**Network representation** – Demonstrate flow is physically feasible
**Generation/Demand side substitution** – Evaluate alternatives to transmission expansion

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## Flow Chart of the CAISO Methodology

**Determine Input Data**
- Existing transmission topology	- Existing generation
- Demand forecast		- Hydro availability
- Natural gas price forecast		- Long-term energy contracts
- Transmission limits		- Near-term new generation/transmission

**Sensitivity Case Selection**
-  Demand
-  Natural gas price
-  Hydrology
-  New generation entry
-  Price-cost markup
-  Contingency events

**PLEXOS Model**
-  WECC detailed transmission network and DC power flow
-  Security constrained economic dispatch
-  Generation marginal cost bidding or strategic bidding
-  Benefit components calculation

**Sensitivity Results**

**Sensitivity Weighting**

**Expected Benefit**
**Benefit Range**
**Insurance Value**

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## What is Market Based Simulation?

- Resource planning studies typically rely on production cost simulations to evaluate the economic benefits of new infrastructure.
- In a market environment, prices are determined by market bids, which may or may not reflect a unit’s marginal cost of production.
- Assuming marginal cost bidding can lead to inaccurate benefit estimates and may fail to capture the benefits that infrastructure investments can have in improving market competition.
- Assessing the benefits of transmission and generation additions in a market environment should be based on a predictive model of “strategic bidding” rather than an assumption of marginal cost bidding.
- “Strategic bidding” – Suppliers bid differently depending on system conditions and/or expectations of how others are bidding.

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## Today’s Topic: Market Based Simulation

- Issue: How generators’ bidding behavior should be modeled in a wholesale market regime?
- Traditionally, cost-based bidding
- Historical evidences indicate that generators might bid above their marginal costs
- More importantly, transmission expansion can enhance market competitiveness and our methodology should capture this benefit

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## Today’s Topic: Market Based Simulation

- Goal is to perform transmission evaluation based on market prices rather than traditional cost-based analysis.
- More specifically, to model suppliers’ strategic bidding behavior and how their bidding behavior changes with the transmission upgrade.

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## Today’s Topic: Market Based Simulation

- Modeling strategic bidding is difficult
- Game theoretical approach
- Cournot-Nash game (physical withholding)
- Supply function equilibrium (economic withholding)
- These methods are difficult to implement in a complex network model
- Empirical approach
- Regression relates price-cost mark-up with Residual Supply Index
- Regression parameters estimated for California
- Parameters for outside control areas could be based on backcast simulation and calibration (or regression analysis)
- Can be applied to zonal configuration of network models
- Can be applied with calibration to nodal network models

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## An Empirical Approach to Model Strategic Bidding

- Develop historical relationship (regression) between _price-cost markups_ and certain system conditions.
- Use the regression results to predict _bid-cost markups_ under future system conditions.
- Apply the bid-cost markups to the supply bids and run the model to determine dispatch and market clearing prices.
- Note:
- Historical Price-Cost Markups are based on the difference between actual zonal market prices and estimated competitive prices.
- Bid-Cost Markups are estimated and used prospectively in the transmission study. Bid-Cost Markups reflect the difference between the variable cost of a generating unit and a market-based bid.

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## Price-Cost Markup Regression Results

- Estimate relationship between price-cost markups (PMU) and system conditions
- Using hourly data covering Nov-99 to Oct-00 and 2003.
- The price-cost markup (PMU) is expressed as the Lerner Index.
-  Lerner Index at region ***i*** and hour ***t*** is:
- PMUit=(Pit-Cit)/Pit
- where 	Pit   =  Actual price in region *i* and hour *t*
- 		Cit   =  Estimated competitive price in region *i* and hour *t*
- System conditions are represented by several key variables (e.g., RSI, % of Un-hedged load, etc.)

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## Residual Supply Index (RSI)

- A Residual Supply Index provides a good measure on the extent to which the largest supplier in the market is “pivotal” to meeting demand.
- RSI = _(Total Supply – Largest Supplier’s Supply)_
- 				Total Demand
- An RSI value less than 1 indicates the largest supplier is pivotal in meeting demand.
- In the CAISO markets, RSI values less than 1.2 have generally been associated with market prices in excess of estimated competitive levels.
- RSI can capture the impact of transmission upgrade on supply/demand balance.

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## Regression Results

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## 	Application of Regression Results to Predict Bid-Cost Markups

- Apply regression results prospectively to predict hourly price-cost markups in years 2008 and 2013.
- Use predicted price-cost markups as bid-cost markups.
- Markups are estimated separately for each hour and each demand region (i.e. PG&E, SCE, SDGE).
- 3 Levels of Bid-Cost Markups: Base, High, and Low.
- Base Markup Case: directly derived using regression coefficient estimates with some calibration.
- High and Low Markup Cases: derived based on 90% confidence intervals of predicted markups with some calibration.

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**Future System Conditions**

**Predicted Markup**

**Base Markup Case **

**High Markup Case**

**Low Markup Case**

**	****High, Low, and Base Markup Cases**

***90% Confidence Interval***

## Implementing Bid-Cost Markup Approach  in PLEXOS

- Bid-Cost Markup functionality is incorporated directly into PLEXOS
- RSI and other determinants of predicted bid-cost markups can be computed internally in PLEXOS
- The projected bid-cost markups can be automatically incorporated into the market-price run
- The benefit computation can be computed directly in PLEXOS

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## Potential Future Enhancements to Market Price Modeling

- Further refinements to econometric approach
- Regression based on “bid-cost” markups rather than “price-cost” markups
- Refine the methodology to compute the competitive market clearing price
- Explore game theoretical approaches
- Conjectural model (developed by London Economics)
- Cournot model applied in the full network model
- Supply Function Equilibrium approaches

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## Application of the CAISO Methodology to the Path 26 Upgrade

- A complete walk through of the CAISO methodology using this case study
- The intension is to demonstrate almost all aspects of the methodology.
- It is not a definitive Path 26 upgrade benefit-cost analysis.

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## Path 26

**Path 26**

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## Path 26 consists of three 500 kV lines between Midway and Vincent
On 7/17/2003, WECC-PCC approved path rating increase from bidirectional 3000 MW  to 3400 MW (N-S) and 3000 MW (S-N)
New rating required implementation of SPS to trip generation north of Midway to mitigate for N-2 overload

- On 7/17/2003, WECC-PCC approved path rating increase from bidirectional 3000 MW  to 3400 MW (N-S) and 3000 MW (S-N)
- New rating required implementation of SPS to trip generation north of Midway to mitigate for N-2 overload

**Path 26 Latest Rating**

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## Possible short term upgrade to 3700 MW (N-S) by expanding the SPS.
Based on high-level screening analysis, new proposed rating is 4400 MW (N-S) and 4000 MW (S-N)
Model of Midway – Vincent #3 will be the same as Midway – Vincent #1 and #2

- Based on high-level screening analysis, new proposed rating is 4400 MW (N-S) and 4000 MW (S-N)
- Model of Midway – Vincent #3 will be the same as Midway – Vincent #1 and #2

**Proposed Path 26 Upgrade**

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## New rating will require, 
Reconductoring of Midway – Vincent #3 Line 
Replacing Midway – Vincent #3 series capacitors
Replace wave traps, breakers and current transformers
Reconductoring Vincent – Antelope #1 230 kV Line

- Reconductoring of Midway – Vincent #3 Line
- Replacing Midway – Vincent #3 series capacitors
- Replace wave traps, breakers and current transformers
- Reconductoring Vincent – Antelope #1 230 kV Line

**Proposed Path 26 Upgrade**

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## Path 26 Case Study Results2008, Base System Condition

|  | Cost-Based | Market-Based |
| --- | --- | --- |
| WECC Total Societal Benefit (Production Cost Saving) | $ 1.00 M | $ 4.28 M |
| WECC Total Modified Societal Benefit | $ 1.00 M | $ 7.04 M |
| CAISO Participants Modified Benefit | $ 0.50 M | $ 11.99 M |
| CAISO Ratepayers Modified Benefit | $ 2.10 M | $ 19.00 M |

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## Path 26 Case Study Results2013, Base System Condition

|  | Cost-Based | Market-Based |
| --- | --- | --- |
| WECC Total Societal Benefit (Production Cost Saving) | $ 0.55 M | $ 2.21 M |
| WECC Total Modified Societal Benefit | $ 0.55 M | $ 12.93 M |
| CAISO Participants Modified Benefit | $ 0.67 M | $ 18.04 M |
| CAISO Ratepayers Modified Benefit | $ (0.04) M | $ 8.07 M |

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## Path 26 Case Study Results2008, Probability-Weighted Expected Benefits

|  | Cost-Based | Market-Based |
| --- | --- | --- |
| WECC Total Societal Benefit (Production Cost Saving) | $ 0.89 M | $ 3.05 M |
| WECC Total Modified Societal Benefit | $ 0.89 M | $ 4.46 M |
| CAISO Participants Modified Benefit | $ 1.46 M | $ 10.29 M |
| CAISO Ratepayers Modified Benefit | $ 0.24 M | $ 8.65 M |

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## Path 26 Case Study Results2013, Probability-Weighted Expected Benefits

|  | Cost-Based | Market-Based |
| --- | --- | --- |
| WECC Total Societal Benefit (Production Cost Saving) | $ 0.67 M | $ 2.11 M |
| WECC Total Modified Societal Benefit | $ 0.67 M | $ 8.64 M |
| CAISO Participants Modified Benefit | $ 0.63 M | $ 12.17 M |
| CAISO Ratepayers Modified Benefit | $ 0.49 M | $ 6.43 M |

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## Path 26 Case Study ResultsInsurance Value Under Contingency Situation (PDCI Outage)

| Year 2008 | Cost-Based | Market-Based |
| --- | --- | --- |
| WECC Total Societal Benefit (Production Cost Saving) | $ 2.28 M | $ 7.60 M |
| WECC Total Modified Societal Benefit | $ 2.28 M | $ 18.76 M |
| CAISO Participants Modified Benefit | $ 1.44 M | $ 13.26 M |
| CAISO Ratepayers Modified Benefit | $ (0.09) M | $ 14.96 M |

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