TransAlta Corp wanted to upgrade the turbines at its 355 MW Brazeau Power Station in Alberta, Canada to optimize revenue from energy sales and ancillary services. Three upgrade options were evaluated for each of the two units through modeling and optimization of plant operations over an 11-year historical period. The modeling allowed comparison of revenue potential from various upgrade alternatives and selection of the most valuable solution.

1. Autor:
Reinaldo Donié CI: 21.196.586
Ing. Mantenimiento Mecánico
INSTITUTO UNIVERSITARIO POLITÉCNICO
“SANTIAGO MARIÑO”
EXTENSIÓN PORLAMAR.
Textual Macroestructures
Porlamar 05/2015

2. Modeling Potential Turbine Upgrades for the Brazeau Plant
• One or two new runners for the units at the 355-MW Brazeau Power Station in Alberta,
Canada, would allow owner TransAlta Corp. to better optimize revenue and provide
valuable ancillary services.
• TransAlta Corp. (TAC) is evaluating options for replacement runners in the two Francis
units at its 355-MW Brazeau Power Station on the Brazeau River in Alberta, Canada.
• The Brazeau plant has been in operation since 1965
• TAC desires a replacement runner (or runners) with a spread of generating ranges and
efficiencies, with a goal of optimizing revenue potential for the station
• TAC determined that an hourly, optimization-based model should be developed to predict
operating strategies and expected revenue at Brazeau
• Three upgrade alternatives were evaluated for each unit
Textual Macrostructure
Macro-proposition

3. Macro-proposition
• For Brazeau, the objective function for each day is the plant's overall revenue potential from
sales of energy and ancillary services.
• Plant operations are represented adequately to compare the revenue of various runner
alternatives over a range of flow and pricing conditions during the 11-year period of record.
• Block optimization is more computationally efficient (about 10 times faster) but results in 2%
to 4% lower projected revenues.
• The results allowed TAC to create a business case for further execution and to add value
that TAC can achieve the expected benefits by reducing capital expenditures.

4. Micro-proposition
• Modeling of the various upgrade options allowed the utility to arrive at the most valuable
solution.
• Based on a 2006 index test, Unit 1 exhibits a relatively small rough operation in the middle
range, while a 2011 index test revealed Unit 2 exhibits a wider rough zone.
• with a goal of optimizing revenue potential for the station, given its water-limited nature and
opportunity for revenue in energy and ancillary services markets
• In most years, there are periods where sufficient water exists to run both units for energy
during the wetter season and one or both units for energy, regulating range and reserves
during the drier season.
• TAC has been exploring options for eliminating these rough operating zones in parallel to
the runner optimization study to increase operational flexibility.
• The model was calibrated with the existing unit performance data, historical hydrology and
market conditions to establish a reasonable baseline for comparison.

5. • The "best" or optimal value of the objective function determined by an optimization
algorithm depends on the initial conditions, constraints, numerical precisions and
convergence tolerances.
• HDR used a two-pass optimization approach for the Brazeau model that first generates an
optimum dispatch table for the two units and then optimizes the allocation of water within a
day to maximize revenue using the Solver add-in.
• he first-pass optimization provides a starting point that is relatively close to the hourly
optimum solution, which improves the efficiency and stability of the Solver's algorithm.
• Block optimization results in 95% to 98% of the revenue predicted by the two-pass
optimization over a multi-year period, with a small fraction of the run time necessary.
• The scenarios were run using daily flow and forebay elevation data provided by TAC for
Jan. 1, 2002, to Dec. 31, 2012.4 Hourly energy prices and ancillary services premiums were
used as inputs to the model.
Micro-proposition

6. • Plant operations are validated by examination of hourly results.
• When the value of ancillary services is low, the model can deviate significantly from historic
operations by using perfect foresight of the hourly pricing.
• All the upgrade options showed the potential for increased revenue.
• The most attractive upgrade options using the two approaches are consistent, with hourly
optimization predicting less benefit with the upgraded units.
• HDR recommended hourly optimization as the most refined approach for comparing runner
combinations.
Micro-proposition