SOFTWARE TOOLS AND SOLUTIONS FOR HPP OPERATION OPTIMIZATION
Transcript of SOFTWARE TOOLS AND SOLUTIONS FOR HPP OPERATION OPTIMIZATION
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SOFTWARE TOOLS AND
SOLUTIONS FOR HPP
OPERATION OPTIMIZATION
TYPES OF TOOLS, APPROACH
AND EXAMPLES
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• Types of tools
• Maximum welfare models as SDDP
• Profit maximization tools
Table of contents
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There are two main approaches for hydropower
operation planning
Profit maximization
• Used under market environments
• The goal is to maximize the profits of
the company against the power market
• Only needs detailed characterization
of own plants but needs an electricity
price and water inflow forecast which
are typically stochastic
• Utilities use portfolio optimization
models either tailored or commercial
Maximum welfare
• Used in systems with centralized
planning as in the case of state-owned
producers
• The goal is to maximize social welfare
using the system cost as a proxy
• Requires detailed characterization of
the whole system considered
• SDDP is the most widely used model
(M. Pereira, PSR)
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Table of contents
• Types of tools
• Maximum welfare models as SDDP
• Profit maximization tools
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Table of contents
• Types of tools
• Maximum welfare models as SDDP
• Introduction
• Methodological approach
• Examples of application
• Overview of available software
solutions
• Profit maximization tools
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SDDP presents a sophisticated and powerful approach to
hydrothermal scheduling
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SDDP: provides optimal (least-cost) hydrothermal
dispatch (delivered quantities by plant and nodal
prices) for a given demand forecast and generation
expansion plan, with stochastic consideration of
water inflows (hydrologies) and medium/long term
optimization of reservoirs.
SDDP allows including in the optimization process
the constrains arising from other used of water like
irrigation, flood control, navigation and
environmental requirements
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The tool allows detailed representation of hydroelectric plants
considering hydrological uncertainty and the transmission grid
• Individual representation of hydroelectric plants:
– On cascade hydric balance.
– Spillage, filtration and evaporation.
– Variable production factor.
– Alert volume, minimum security storage and flood control storage
– Maximum and minimum total outflow constrains.
• Integrated analysis of hydrothermal systems together with the transmission
system.
• Hydrology uncertainty calculated through a stochastic model that takes into
account the temporal and spatial dependence.
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It requires parametrization of the power system including
demand, generation and transmission
• Reservoir operation planning requires to know:
– Demand side:
• Load Forecast
– Generation side:
• Expected new generation entries
• Hydro forecast (inflows to the reservoirs)
• Restrictions on water storage and release
• Expected maintenance outages
• Fuel prices forecast
– Power transmission:
• Available transfer capacities
• Operation and transmission constraints and reliability
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Table of contents
• Types of tools
• Maximum welfare models as SDDP
• Introduction
• Methodological approach
• Examples of application
• Overview of available software
solutions
• Profit maximization tools
# of plants # of states
1 202= 400
2 204=160 thousand
3 206= 64 million
4 208≈ 25 billion
5 2010≈ 10 trillion!!!!
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The traditional approach for hydropower optimization
becomes unsolvable with large numbers of reservoirs
• The computational effort increases
exponentially with the number of
reservoirs
• If both reservoir storage and inflows are
discretized into 20 levels:
• The traditional approach, the Stochastic
Dynamic Programming (SDP) recursion,
requires enumerating all combinations of
initial storage values and previous inflows
Initial
State
1 2
M
m
T-1 T
1
System states
(initial storage
level) for
stage T
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The introduction of the Future Cost Function avoids the need
for discretization and for assessment of all combinations
• With Dual Dynamic Programming
(DDP) the Future Cost Function (FCF)
is represented as a piecewise linear
function
• Hence there is no need to discretize
system states
• The slope of the FCF around a given
point can be obtained from the one-
stage dispatch problem
1 2 T-1 T Cost
Expected operation cost
Slope = derivation
of op. cost with
respect to storage
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Determining the optimal hydrothermal dispatch implies
considering both current and future costs
• Solving the hydrothermal
dispatch problem is complex
due to the time-coupling
characteristics
• Optimal use of stored water
should minimize total system
operation costs
• Operators need to evaluate the
tradeoff of using the water
today or saving it for the future
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1
2
3
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7
Operational Cost at
Current Stage
(Immediate Cost)
Future
Operational Cost
Total Cost
MINIMUM
Storage Level at the end
of current stage
Cost
[$]
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Assessing the trade-off between current and future costs is
challenging due to the variability of water inflows
The immediate cost function
• Is given by the thermal generation
costs required to complement hydro
production
• If more hydropower is generated in
one stage fewer fossil fuels are needed
and the immediate cost decreases but
the final storage level is lower
The future cost function
• Is associated with the expected future
thermal generation expenses
• This cost increases the lower the
storage level as less water becomes
available for future use
Due to the variability of inflows this simulation is carried out on a
probabilistic basis using several hydrological scenarios
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Cost
Stor. level
Cost
Stor. level
Cost
Stor. level
Cost
Stor. level
Cost
Stor. level
Cost
Stor. level
Month 2Month 1 Month N
Backward
recursion
Forward
iteration
SDDP leverages FCFs to enable the consideration of multiple
hydrological scenarios solvable with an iterative process
Recalculate
initial storage
levels
Iteration step Goal of the iteration
Check Repeat until target tolerance is reached
Refine FCF by
adding new
piecewise linear
functions
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The Water Value reflects the opportunity cost of the stored
water to produce electricity now or in the future
• The least cost of operation is reached when reservoirs are
at a level where the marginal immediate cost of power
generation equals the marginal future cost
• Both derivatives are known as Water Value and reflect the
opportunity cost of the stored water for considered initial
reservoir level
𝜕(𝑇𝑜𝑡𝑎𝑙𝐶𝑜𝑠𝑡)
𝜕𝑉=
𝜕(Im𝑚𝑒𝑑𝑖𝑎𝑡𝑒𝐶𝑜𝑠𝑡)
𝜕𝑉+𝜕(𝐹𝑢𝑡𝑢𝑟𝑒𝐶𝑜𝑠𝑡)
𝜕𝑉= 0
𝜕(𝐼𝑚𝑚𝑒𝑑𝑖𝑑𝑎𝑡𝑒𝐶𝑜𝑠𝑡)
𝜕𝑉= –
𝜕(𝐹𝑢𝑡𝑢𝑟𝑒𝐶𝑜𝑠𝑡)
𝜕𝑉=
$
𝑚3
ICF
FCF
Final Storage
ICF + FCF
Optimal
decision
Immediate
Cost
Function
Future
Cost
Function
Water
value
V = Stored Volume of Water in the reservoir
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Table of contents
• Types of tools
• Maximum welfare models as SDDP
• Introduction
• Methodological approach
• Examples of application
• Overview of available software
solutions
• Profit maximization tools
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SDDP produces a comprehensive set of outputs characterizing
the economics of the hydrothermal power system
• For each load block and hydrological scenario:
– Spot prices, including cost of losses and congestion
– Energy dispatch (production) of each generating unit modelled
– Load flow through each modelled element of the transmission grid
– Settlement surplus by each modelled element of the transmission grid
– All “marginal costs” associated to activated constraints (dual variables)
– Fuel consumption by generating unit and fuel type
– Unserved energy (curtailments) by node if transmission grid considered
– Carbon emissions
• For each stage and hydrological scenario:
– Water value (opportunity cost of stored water)
– Reservoirs’ level at the end of each stage
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SDDP can be used for medium term hydrothermal
dispatch optimization
• Installed capacity: 125 GW
• 160 hydro plants (85 with storage), 140 thermal plants
(gas, coal, oil and nuclear),
• 8 GW wind, 5 GW biomass, 1 GW solar
• Transmission network: 5 thousand buses
• 7 thousand circuits
• State variables: 85 (storage) + 160 x 2 = 405
• 120 monthly stages with 3 load blocks
• Number of SDDP iterations: 10
• Total execution time: 90 minutes
• 25 servers with 16 processors each
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It has been used in Central Asia on regional
integration studies
Tajikistan isolated Enhanced trade
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2,000
4,000
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2010 M
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2010 S
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2010 N
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2011 M
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2011 M
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2011 Jul
2011 S
ep
2011 N
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ep
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ep
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ar
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ay
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ep
2014 N
ov
[GWh]
Coal Gas Hydro RES
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2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
2010 Jan
2010 M
ar
2010 M
ay
2010 Jul
2010 S
ep
2010 N
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2011 M
ar
2011 M
ay
2011 Jul
2011 S
ep
2011 N
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2012 M
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2012 M
ay
2012 Jul
2012 S
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2012 N
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2013 M
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2013 S
ep
2013 N
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2014 Jan
2014 M
ar
2014 M
ay
2014 Jul
2014 S
ep
2014 N
ov
[GWh]
Coal Gas Hydro RES
• Power generation from coal runs flat being a base load
technology with low operation costs
• Gas-fired power plants adapt to changes in the load and in
the generation from hydropower
• In times of high power generation from hydropower gas-
fired generation is interrupted and coal fired becomes the
marginal technology
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And can be applied to develop power market outlooks where
other tools fail
• SDDP has been used since 2008
by Thomson Reuters to carry out
electricity market forecast in
Northern Europe
• Northern Europe presents a very
high share of power generation
from hydropower with large
storage capacities
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• Link to https://www.youtube.com/watch?v=K5s98kgQJ6Q
• Only until time 1:14
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Table of contents
• Types of tools
• Maximum welfare models as SDDP
• Introduction
• Methodological approach
• Examples of application
• Overview of available software
solutions
• Profit maximization tools
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PLEXOS does not have a strong hydropower focus but allows
co-optimizing power, water supply and gas systems
• Replace different tools with one integrated hub
– Co-optimization across electric power, water and gas
systems
– Four integrated tools: long-term, PASA, medium-term
and short-term scheduling
– Pass data across phases from long-term through short-
term
• Provide transparency for how the models are solved
– PLEXOS shows you its calculations
– Credible sources for audits and documentation
• Run your scenarios in minutes instead of hours
– Leverages innovations in distributed computing and
parallelization
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Plexos is used all around the world
• Worldwide installations of PLEXOS exceed 1060 at more than 175 sites worldwide in 37 countries.
• Users: Power Generation Companies, Transmission System Operators (TSOs), Independent System
Operators (ISOs), Electricity and Gas Market Operators, Energy Commission and Regulators, Price
Forecasting Agencies, Power Plant Manufacturers, Consultants, Analysts, Academics & Research Institutions
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• Link to https://www.youtube.com/watch?v=bfnAhrHHrIo
• Full video
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Table of contents
• Types of tools
• Maximum welfare models as SDDP
• Profit maximization tools
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Profit maximization models optimize the operation of power
generators for an expected market price
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we
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tio
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W]
Syst
em
Pri
ce [
EUR
/MW
h]
Pump Generation Price
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Portfolio optimization tools leverage profit maximization
models to automate power market participation
Forecast
Inflow Demand Price Wind Weather
Long/Medium Term Decision
Support
Cross Market Short Term Decision Support
Outage and limitation scheduling
Availability
planning
Restriction
handling
Long term
reservoir
management
Fuel contract
handling
Resource
value
Bilateral contracts
Long term earning forecast
Establish
optimal bids
Day ahead
market
Bilateral
trading
Sending and
receiving
bids
Optimize
production plan
Hydro
Thermal
Send ancillary
service data
to TSO
Operation –
Monitoring and
replanning,
including intraday
markets
Optimize bids
for balancing
market
Sending
bids
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• Link to: https://www.youtube.com/watch?v=lXPe5EStlHI
• Full video
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