# Presentation: 763463

## Production Cost Modeling

***PacifiCorp***

**Notes:**
This section will go over the load data needed

## Load Forecast Data & Load Modeling

***Kurt Granat***

**Notes:**
This section will go over the load data needed

## Powerflow Areas

**Notes:**
The load data needs to be developed for each area that you want to model. Typically production cost modeling is done for one company, that may not have any internal transmission constraints. For regional transmission planning, we need to model loads in each load area that we split out. Remember, the bubbles are only to help us visualize the system – the full transmission grid is loaded in later!

## Load Forecast Data

- For each powerflow area
- Three types of load data:
- Monthly (or annual) peak and energy forecast
- Hourly shape of load (e.g. hourly forecast or FERC 714)
- A spatial distribution of the load in the powerflow area
- The data is needed for each powerflow area modeled

## Peak & Energy Forecast

- This sets the maximum MW peak and the total MWhs that the model needs to cover in a time period (typically a year).
- Depending on the model’s requirements may be
- annual peak & energy
- an annual growth rate from base
- monthly peak and energy.

## Hourly Load Shape

- Transmission loadings vary with hourly loads. To get a reasonable hourly shape either
- Develop an hourly load forecast
- Use historic hourly loads (e.g. FERC 714 report)
- Adjust to forecast by an Affine Transformation

**Notes:**
We have the peak load and the total energy for the time period, but we need to get hourly loads. You can either develop an hourly load forecast, or load a load shape and transform the shape to hit the projected peak and energy values. Again, many models perform this automatically.

## Spatial Distribution of Loads

- Powerflows include a distribution of loads for the load condition modeled (e.g. Light Spring or Heavy Summer) based on the operators experience.
- Fixed percentages can be developed based on the powerflow data.
- If the total area load is 1000 MW in the powerflow, and 100 MW is at a bus, that bus gets 10% of the hourly load for the area for _all_ modeled hours.
- Would like to bring in several load distributions –
- E.g. Heavy Summer, Light Summer, Heavy Winter, etc

**Notes:**
An area might be “Northern California”. How do you decide the amount of load in Sacramento vs San Francisco vs San Jose? The Powerflow reflects the operators experience for the type of condition being modeled (e.g. Light Spring). Several models develop fixed load precentages within each area based on the loads in the powerflow. So if San Francisco has 15% of Northern california load in the Powerflow, while San Jose has 25% and Sacramento 10%, these percents are maintained in every hour. 

## Sources of Forecast Data

- WECC Load & Resource Report
- Regional Peak and Energy forecasts
- FERC 714 Reports
- Historic hourly shapes by control areas or companies
- Sometimes has forecast data included
- WECC Powerflow (FERC 715)
- Distribution of loads across areas & grid

## Problems with Data

- Some errors cannot be helped, if only because collecting data and setting up the model takes time.  By the time you’re ready to start, things have changed.
- WECC data is for Summer peak, Winter peak, and annual energy for six regions not powerflow areas
- Need peak/energy numbers for each powerflow area
- Need to develop data & review
- Need for More Review of data
- Some SSG-WI 2013 monthly forecasts were below the 2008 estimates on both peak and energy!
- Some regions might need more powerflow Areas
- RMATS expanded the model to 33 areas.

## Transmission Network and Constraint Modeling

***Jamie Austin***

## Overview

- Types of models
- Model Inputs
- Background and Terminology
- What is the objective function of a typical model?
- How paths/interfaces constraints are created?
- What is a nomogram?
- What is “security constrained”?

***Seams Steering Group of the Western Interconnection***

## Types of Models

**Ignores transmission**
**OK for small control areas**
**No congestion, no tariffs**
**‘****Bubble’ view**
**Major interconnects only**
**Transportation model**
**Better, but...**
**Power doesn’t flow like that! **
**Detailed view**
**Very complex**
**Only way to track line flows**

**Ease**

**Simple**

**Difficult**

**Good**

**Poor**

**Accuracy**

**Types of Models**

**Source: ABB**

***Seams Steering Group of the Western Interconnection***

**Notes:**
There is a huge variety of models for analyzing the economics of power markets and transmission networks, ranging from the novel and esoteric applications of game-theoretic techniques to develop trading strategies and predict outcomes, through those using purely econometric/stochastic techniques to identify trends and estimate values of the fundamental market drivers, ending with and including those based on well-known deterministic optimization algorithms to minimize production cost with the network constraints/transmission rights modeled in greater or lesser detail.
And not forgetting the more familiar analysis tools of network planning - which while not usually considered in the context of economic analysis, nonetheless are essential tools in this area.
I’m not going to waste your time talking about the first two groups - you probably know more about them than I do.  But I do want to spend some time looking at the features and capabilities - and the limitations - of the third group.
Finally a word on using and interpreting model results: be careful!  I know that’s two, but who’s counting?  Seriously, we tend sometimes to forget what we are simulating and imagine what we’re not.  Not only are the results only as good as the quality of the data we feed in, but the best we can say about our simulations is that they show us the effect (on prices, congestion, etc) of major changes in the model -such as generation fuel price scenarios or transmission expansion options
Crystal balls they’re not - if they were, would I be telling you about market prices in 2008?  

Econometric models - Stochastic models that correlate trades and prices to time of year, weather or hydro conditions, fuel price & supply, etc. Used for predicting forward price curves, spark spreads, price spikes, etc. For M&A analysis, project assessment, for assessing credit risk, risk management, trade analytics and support, etc.
Game theory models - Models that simulate traders’ behavior (always within the rules!).  Used for developing/testing trading strategies; for stress testing of market designs, detecting/monitoring unusual trades, etc.
Power market models - That optimize centrally-dispatched markets; or minimize traders’ production costs in meeting their load service obligations within transmission rights; or price transmission rights/congestion rents, etc. 
System planning tools - For generator interconnection design and impact studies; for network expansion planning, for computing flowpath ratings, etc.

## Trade-off in Market vs Network modeling

**Trading**
**models**

**System**
**planning**
**models**

**Production**
**simulation models**

**Power**
**market**
**models**

**Source: ABB**

***Seams Steering Group of the Western Interconnection***

**Notes:**
Another way to look at this is as a trade-off between increasing sophistication of the market model as against reduced complexity of the network model.
The more sophisticated the market model, the more it might be able to capture the locational element of market prices or the variance in cost of transmission rights, and the less need there might be for an explicit network representation.
At the other extreme, system planning models traditionally give very little weight to the economic or market aspect of their analysis, concentrating on a deterministic design of network reinforcement to meet typically quite extreme system conditions.  Their need is for a very detailed and faithful network representation, with no modeling of the market at all - though that might change.
And as always, there is a range of useful compromises: a less sophisticated market model and a bit more complexity of the network.  By minimizing (or eliminating) that feature of the model that is of least interest in the context of the problem being studied, we are able to concentrate our computing power and complexity in the area that matters most for the application.
At least so far - the perfect combination of sophisticated market model and complex network is always just round the corner. Meantime, let’s apply ourselves to finding an appropriate compromise.

## DC OPF: Transmission network and constraint modeling

**DC Optimal Power Flow: What is it?**
- Transmission constrained economic dispatch w/ linearized power flow model
**Why Linearize?**
***Speed:*** Enables fast Linear Program (LP) formulation
***Convergence:*** LP always a convex problem
***Accuracy:*** Flows on HV circuits modeled well

**Source: ABB**

***Seams Steering Group of the Western Interconnection***

**AC OPF is ideal, but may not solve**

## Data Inputs

## Transmission Data Required (1)

- Transmission facility (buses, lines, transformers, etc) thermal limits using normal ratings
- Transmission facility limits considering (N-1) contingencies using emergency ratings
- Paths and nomograms limits due to voltage and stability problems
- Paths and nomograms limits considering (N-2) contingencies
- Data is obtained from solved power flow cases

***Seams Steering Group of the Western Interconnection***

## AC Transmission Lines
Location: Bus connections
Reactance: for Flow Model
Resistance: for Losses			Powerflow Data (14K bus)
Thermal Capacity
Same for transformers

Data format: PSS/E, PowerWorld, GE, Others
Data source: User model or FERC 715

- Location: Bus connections
- Reactance: for Flow Model
- Resistance: for Losses			*Powerflow Data **(14K bus)*
- Thermal Capacity
- Same for transformers
*Data format: PSS/E, PowerWorld, GE, Others*
*Data source: User model or FERC 715*

**Transmission Data Required (2)**

**Source: ABB**

***Seams Steering Group of the Western Interconnection***

## Transmission Data Required (3)

**HVDC**
- DC lines are modeled as matched pair of withdrawal (or load) MWs and injection (or generation) MWs taking place at fixed points on the grid. The model doesn't see any lines or control devices, only MWs in and matched MWs out. The model will simply move the MWs to minimize costs (moving the power to higher LMPs) and assumes that what we enter will work.
- Act like valved flow
- Capacity
- Losses
- Operating Schedule

***Seams Steering Group of the Western Interconnection***

## Transmission Data Required (4)

**Phase Shifting Transformers (Phase Shifters)**
- Reactance
- Thermal Capacity
- Angular Limits
- Schedule
- ‘Cost for moving’

***Seams Steering Group of the Western Interconnection***

## Transmission Data Required (5)

**Path (Interface, TOT)** **Definition**
- Which Lines (AC, DC, and/or PS)
- What Direction
*Example: WECC Path Rating Catalog**     *

- A

- B

***Seams Steering Group of the Western Interconnection***

## Background and Terminology

## What is the Optimization Objective of a Typical Model?

**Minimize production costs **
**Subject to**
- DC load flow
- Generation constraints
- Line limits
- Transmission path limits
- Limits for Phase Shifters, DC lines
**Solution yields**
- Lowest production costs, and
- Spot prices, shadow prices

***Seams Steering Group of the Western Interconnection***

## How are Transmission Constraints Developed?

- Generation dispatch
- Magnitude and distribution of load
- Reliability measures
- Level of system security
- Angular Stability
- Voltage Stability
- Area interchange limits
- Treaties
- Imposed TTC limits

**WECC Security Constraints**

## What is a Nomogram?

- Simultaneous transfer limits across two interfaces (i.e., interfaces A & B)
- Thermal limits
- Voltage Security
- Voltage Stability
- Inertial limits
- N-1 and relevant N-2 contingencies
- Transmission access impacts each limit and is addressed using appropriate analysis (i.e., Power Flow analysis, Transient Stability, Voltage Stability.)

- Transient
- Stability Limit

- Thermal Limit

- Thermal Limit

- Feasible Region

- Interface (A)
- Flow MW

- Interface (B)
- Flow MW

**Nomograms define the operating limit of transmission system:**

- N-1

- N-2

***Seams Steering Group of the Western Interconnection***

## What is Security Constrained?

- Optimal Power Flows (OPF) have been used in planning real time operation for active and reactive power dispatch (1)  to minimize generation costs and system loses (2) to improve voltage profile.  Traditionally, these two problems have been assumed decoupled and thus treated independently.
- As systems operate closer to its stability limits (i.e., closer to the voltage collapse point) due to market pressures, the decoupling assumption become obsolete
- OPF formulations that “optimize” a system considering both costs and voltage stability criteria to minimize operating costs and losses while maximizing the “distance” to voltage collapse conditions

***Seams Steering Group of the Western Interconnection***

## Security Tools

- Power system model
- Network reduction
- SCADA
- Power Flow
- State Estimation
- Static contingency analysis
- Security constrained dispatch

- Optimal Power flow (OPF)
- Security constrained OPF
- Voltage/VAR dispatch
- Transient stability analysis
- Mid-term stability analysis
- Long-term stability analysis
- Eigenvalue analysis

## Security Margin Estimation

- State Estimator

- Major Equipment Outages

- Security Margin Estimators

- Minimum

**Margin Estimate**

***Seams Steering Group of the Western Interconnection***

**Notes:**
Transmission Reliability Research Review – Integrated Security Analysis
Per Sauer, PSERC – Illinois, Kevin Tomsovic, PSERC –WSU, Steve Widergren, PNNL
January 27-29, 2004; Washington, D.C.

## Voltage Security Margin Results (WECC)

***Seams Steering Group of the Western Interconnection***

## Dynamic Security Margin Results (WECC)

***Seams Steering Group of the Western Interconnection***

## Thermal Generation Modeling

***James Gall***

**Notes:**
The thermal generation modeling is one of the most sophisticated part of a typical production cost model, this is because the thermal generation meets the remaining load requirements after Hydro, Wind or even contracts have been queued to dispatch on a least cost methodology given the transmission and plant characteristics.  Wind and Hydro methodologies will be discussed following this discussion, but contract modeling is usually ignored for system wide planning, with some exceptions that I will mention later. Thermal units are quequed after wind and hydro because they have higher marginal costs.

I will start off by informing you about the two types of thermal inputs (1) Plant Characteristics and (2) Cost/Pricing Characteristics.  I will discuss some of the differences in types of inputs and will walk you through a simple example of how a model will choose to dispatch a plant.

## Key Data Inputs

- Plant Characteristics
- Location (bus/area)
- Start/end dates
- Nameplate/maximum dependable operating capacity (and minimum dispatch)
- Fuel/plant type
- Heat rate
- Commitment logic
- Planned outage schedule
- Forced outages
- Reserve capability (spinning and/or non-spinning)

- Cost/Pricing
- Fuel cost ($/MMBtu)
- Variable O&M ($/MWh)
- Startup cost or MMBtu for cycling plants
- Environmental adders ($/MWh)
- Fixed costs

**Notes:**
There are two types of inputs for thermal plants (1) plant characteristics these are operating traits the plant most operate around. The second type of inputs are cost or pricing these inform the model when the plant is economic. Before I get into the Cost/Pricing I will discuss some of the plant characteristics.

 Location- this is the bus the plant is connected to on the grid, or if you use a transport model which does not have the transmission detail that Jamie previously eluded to a plant would be placed in an area/zone.
 Start/End Dates are when the plant comes on line or retires
 Nameplate or Maximum dependable operating capacity is the most power that could be dispatch from a particular unit under normal operating conditions.  The minimum dispatch is just how it says the minimum about of dispatch the plant could operate at.
 Fuel type is whether it is coal, gas, oil, Simple Cycle (Peaker) Combined Cycle
 Heat rate is a efficiency rating of how well a plant burns fuel, there are several ways you can input this into a model, which I will discuss if a few moments
 Commitment logic is limitations of how a plant can operate.  These inputs are minimum up time, how quickly it can ramp up/down, its intitial state, if it is must run.  I will discuss each of these in detail in a few minutes
 Planned outage schedule is forecasted maintance schedule for a plant
 Forced outages or derates are inputs to limit production for unplanned outages- I will discuss outage schedules in a few mintutes.
 The last plant characteristic is reserve logic- depending on the model this area can be substative or not. But the main point I want to get a across is that inputs are required to indicated in plants can qualify for spinning  or non-spinning reserves

Cost/Pricing characteristics are used to indicate if a plant should dispatch, since the model is solving for the least cost this is an important input and is a major driver in the results you get at the end of a run.
The most important input that all models have is the Fuel Cost this is inputted as Dollars per MMBtu.  A plants fuel cost with conjunction of the heat rate is the methodology production models use to dispatch a plant give all other constraints equal.
 Variable O&M this is the incremental cost to produce an additional MW that is not fuel related.  These cost can be chemicals for scrubbers or additional maintance on the unit- these amounts are usually very small
 Startup cost or MMBtu.  This is the amount a fuel/cost a plant requires to start up.  These are usually only input for plants that can cycle which means they have frequent start ups
 Environmental adders.  This is a dollar adder to each MW produced for items such as SO2, NOx, CO2 or Mercury emissions if required
 Fixed cost are inputs that some models have the ability to input these are items such as operating costs, these costs are normally handled outside the production cost modeling

## Heat Rates

- Three different types of ways to input heat rates- this will depend on the model and its level of accuracy
- Marginal Heat Rate Curve
- Heat Rate for each MW of dispatch
- Average Heat Rate Curve
- Piece-wise curve: usually 2 – 4 estimates for dispatch levels.
- Average Heat Rate
- Single estimate for all dispatch. Average of normal plant dispatch levels

- Less                              More

**Accuracy & Data**** **

- MW

- Heat Rate

**Average Heat Rate Curve**

**Marginal Heat Rate Curve**

**Average Heat Rate **

**Notes:**
Heat Rates.

There are three different types of ways to input heat rates which I mentioned earlier.  They range in accuracy and data intensity.

The Marginal Heat Rate Curve is the requires the most data and is the most accurate.  It requires a heat rate for each level of dispatch
An average heat rate curve creates a piece-wise curve using point estimates for 2-4 MW dispatch ranges
The simplest input, but is not that accurate is an average heat rate. Which is an average of the heat rates for the operational levels of output. This may not be as accurate as others but is rather simple to input and requires far less data then the next two methodologies

## Commitment Logic

- Depending on the model’s area of focus, the level of commitment logic varies
- Required Inputs for Commitment Logic
*Initial state*: plants status for the hour before the first hour of the model simulation
*Minimum down/up time*: controls how much the plant can cycle; coal plants have longer down times than a gas fired plant
*Must run:* plants that must run not matter if economic or not; such as a QF or plant under contract
*Ramp Rate*: controls how fast a unit can move from one dispatch level to another; coal plant:  4-5MW/min, CCCT: 2.5MW/min, SCCT: 2.5 – 5MW/min

**Notes:**
Commitment logic is used to indicate how a plant can dispatch.  It is the constraint the prevents it from operating in a manner that is beyond its capability, such as a coal plant from operating like a simple cycle gas unit.
Each model has a different level of commitment logic- listed below are a few of the key inputs.
Initial state: this the plants status for the hour before the fist hour of model simulation-  This is so that model knows what plants are committable for the first hour of the simulation
Minimum down/up time indicates how long a plant most remain down for or for how long a plant must remain in operation.  These control prevent a plant from going on and off each hour if it is not able to, such as a coal plant.
Must run: These are plants that you can indicate that no matter if the plant is economic or not, it must dispatch.  This can happen given some contracts or QF plants
Ramp Rate: These are controls the regulate how fast a plant can move from one level of output to another. 

## Plant Outages

- Planned maintenance
- Manually entered schedule a plant will be out for maintenance
- Forced outages, the outages range from 1-10% depending on plant type and age
- Two ways to model forced outages
*Derate*: percent of maximum capacity withheld from production; The problem with this methodology is that transmission implications are not shown when all plants are operating at full capacity.
*Monte Carlo*: Random outages based upon percent of time per year with an unplanned outage

**Notes:**
Plant Outages:

There is two types of plant outages. Planned and unplanned.
Planned outages is a manually entered outage for maintance- usually follows it forecasted/historical maintance schedule and is input by the modeler
The other type of outage is a forced outage, these are outages that are unexpected or unplanned. Models usually handle these outages in two ways a derate which deducts an amount from the maximum dispatch, which provides an average of the cost of the outage. The other type is a Monte Carlo (random) these are outages based a random element of outages- since outages occur at one time rather then a small percent over each year.  

The level of forced outages range from 1-10% depending on the plant type and age

## Simple Example: Need 50MW to serve load

- Two plants with available generation:
- Cold start gas single cycle unit with a fuel price of $6.00 MMBtu and the heat rate of 11,000; start up cost of $120
- Ramp up a gas combined cycle with a fuel price of $6.25 MMBtu, heat rate is 7,500
- Program will choose lowest cost plant that has available transmission to dispatch
- Cost to serve load
- simple cycle: $3,420
- combined cycle: $2,344

- _Single Cycle Calculation_

- _Combine Cycle Calculation_

**Notes:**
This is a simple example of how a model would chose to dispatch a plant.  In this example we will assume that 50 MW is needed to serve the load and that transmission is not constrained.  In this example we will assume there are 2 available plants- a single cycle that must start cold and a combined cycle plant that must ramp up.  Since the model chooses the lowest cost plant it must go through a couple calculations to see which plant is the lowest cost to serve.  

Walk through calculation

The Combined Cycle plant is the lowest cost. 

## Modeling Hydro and Wind Generation

***Kurt Granat***

**Notes:**
This section will go over the load data needed

## Hydro Modeling

- Some hydro is run of river, some has pondage, some has considerable storage (years of river flow)
- Limited energy plant (optimize on revenue)
- Hard to do with a large number of plants
- Peak shaving algorithm
- Max MW for time period
- Total MWh for time period
- Min MW for period (run-of-river portion)
- Fixed dispatch
- Need to create a dispatch or load history

**Notes:**
How to model, depends on Model Capability

## Run of River Hydro

## Peak Shaving Hydro

## Peak Shaving with Minimum Flow Requirement

## Peak Shaving w/ Min Flow and Ramp Rate Limit

## Wind Modeling

- Depending on model capabilities & data availability
- Average energy
- Often used, but can miss transmission limitations
- Ignores need for shaping wind output
- Thermal plant with a high forced outage rate
- Can overstate transmission limits
- Overstates need for shaping
- Input pattern
- Used in SSGWI and RMATS effort, but hard to get data
- SSGWI data from grnNRG, while RMATS was by NREL
- State matrix
- Also hard to get this data

**Notes:**
Need to Reflect the range of output for the wind plants

## 25 MW Windfarm as Average Output

## 25 MW at 62% Forced Outage 

## 25 MW with a Pattern

## Market Simulation Outputs

***James Gall***

**Notes:**
Once you have all the inputs in and you run the model... what do you get out of it?  Over next few minutes I’ll walk you through typical model outputs.

## Typical Model Outputs

- Production costs
- Generation dispatch
- Locational Marginal Prices (LMP) and average area prices
- Generator Revenue and Load Cost
- Transmission shadow prices (opportunity costs)
- Transmission utilization

**Notes:**
Most models if not all will give you these range on outputs.  Each of these I’ll discuss in detail.

## Production Costs & Generation Dispatch

**Production Costs**
*Fuel Costs*: The total dollars of fuel burned for each hour for each unit/plant, including start up costs
*Other VOM*: incremental costs associated with plant operation that are not fixed.  May include environmental costs
*Available Reports*: Hourly – Annual by plant/unit, area/zone, fuel type

**Generation Dispatch**
*Dispatch*: hourly- annual by plant/unit, area/zone, fuel type
*Capacity Factors*: dispatch as a percent of maximum dispatch
*Outage Rates*: percent time on maintenance/forced outages
*Fuel Burn*: quantity of fuel burned
- Environmental Emissions: quantity of CO2, SO2, NOx, and Hg

**Notes:**
Read Slide

## Locational Marginal Prices (and Area Averages)

- LMP is the price to access one additional MW at a bus; shadow price of load at a bus
- Model outputs a marginal price at each bus you specify for each hour.
- Models also allow you to average the LMP based on the area the bus resides, so that you can get a general price for each area
- Some models segregate the LMP into three pieces; energy, congestion, and losses

**Notes:**
Read Slide- Mention that LMPs will be discussed later in the day
Tie in to next slide: LMPs can be used for calculating Generation Revenues and Load Costs

## Generator Revenue and Load Cost

- Usually compiled by area:
***Generator Revenue*** is the LMP at each generator bus multiplied by the total MW dispatched- summed for a period of time
- To arrive at ***Generator Gross Margin***; production costs must be subtracted from revenues
***Load Cost*** is the LMP at each load bus multiplied by the total MW served- summed for a period of time
- Assumes a perfectly competitive market, with all costs and revenues priced at the marginal cost

**Notes:**
Read Slide

## Transmission Shadow Prices(Opportunity Costs)

- For each line or interface that is monitored, the model indicates the savings if you increase a path by 1MW for an entire year
- In other words, Transmission Shadow Prices are the opportunity cost of not increasing a path by 1 MW for the entire year
- These values help indicate which paths to further study for expansion projects

**Notes:**
Read Slide

## Transmission Utilization

- Models indicate for each line or interface monitored the amount of line loading/utilization
- Available outputs are:
- Percent of time at path capacity
- Min, max and average line utilization
- Annual duration curves

**Notes:**
Explain duration curve

## Model Limitations

***Kurt Granat***

## Model Assumptions & Simplification

- The whole point of a model is to help people gain insight about a system that is too complex to comprehend at a glance
- Model assumption of Loads and Load behavior
- Hard to capture load uncertainties (weather, economy)
- Model assumptions on generator plant behavior
- How to price peaking & cycling units
- Hydro dispatch & bidding behavior
- Degree of foresight in model
- Many optimization routines have perfect foresight
- Single Market Dispatch
- How to model ownership or contract rights
- Simplification for Mathematical Solutions
- Modelers set up problems that can be solved

## Linear Programming Issues

- System only approximately linear
- Shadow prices fall out of the solution for constraints
- Reflects the marginal benefit of relaxing the constraint by one unit
- Good for rough screening, but poor approximation for lumpy capital additions

## Lumpy Additions

- Shadow cost (opportunity cost) of constraint is for next unit. Transmission lines often come in big increments.
- Need to consider how the incremental capacity compares to the existing total capacity. Is it a moderate capacity addition or a huge addition?
- 100 MW addition is 1% of West-of-River (Path 46), but over 500% of Silver Gate to Control (Path 52)!

## Low Voltage Transmission & Distribution

- Powerflow has simplified grid by equivalencing selected low voltage transmission & distribution elements
- There are additional parts of the system that are manually switched to different configurations in certain conditions (e.g. low load or high load seasons)
- Operators sometimes make changes to low voltage elements rather than re-dispatch generation (may have no choice on their system). Local impact on flows
- Many models run all year with one transmission configuration and modifications are difficult
- Not monitoring low voltage elements can approximate these local or seasonal changes.

## Odd Results

- These are the most informative outputs!
- Either you have a modeling error (data or code)
- Or the model is working and you have gained insight into how the system works – or how the system might be improved!

## It is a Model Result

- The best of these are but shadows and the worse, no worse, if imagination is applied.  (BS)
- A model is supposed to be simple!
- Need to remember what was simplified & why
- Need to consider if more details & data would be available, would be solvable, or would help with decisions!
- The best modeling still leaves considerable uncertainty.
- Both action & inaction can be expensive & risky
- Inaction implies gas generation near load – at some price

## Fixed Cost Modeling

***Jamie Austin***

## Data Required to Model Capital Costs 

- Initial capital investment requirements
- Transmission & customized equipment
- Generation
- Grid integration costs
- Annualized capital investment costs and other incremental costs associated with the investment, such as;
- Capital charge,
- Fixed O&M,
- Plant wear & tear,
- Opportunity costs/savings
- Change in production costs from simulation model

## Initial Investment Example from RMATS

- RMATS

- Initial Generation
- Capex

- Initial Transmission
- Capex

**Notes:**
Total initial capital investments requirements of each alternative are shown in the column labeled “Initial Investment” and are grouped into generating resource and transmission investments.  

## Annualized Costs Example from RMATS

- Change in Production
- Costs

- Incremental Costs

- Incremental Costs

**Notes:**
Annualized costs associated with each alternative are shown in the column labeled “Representative Year”. In this column, the production costs developed by ABB Market Simulator are combined with annual fixed costs associated with the total investment requirements of each alternative.