Description
or renewable energy generators, seamless integration with the electric grid is very important. The key to ensuring high
profitability of such integration lies in placing intelligent bids in time-ahead markets. Most bidding strategies traditionally use
predictions of wind farm power yield and the price. But such predictions are susceptible to error. On the other hand, energy storage
can be used to handle the variability and can mitigate the impact of prediction inaccuracies; however, it also entails considerable
expense.
This paper outlines a few recommendations for a bidding strategy that reduces dependence on energy storage and minimizes the
impact of prediction errors, resulting in higher profits for wind energy producers.
A Robust Bidding Strategy for
Wind Energy Trading
By Dr Venkata Ramakrishna P, Dr Arunchandar Vasan, Dr Venkatesh Sarangan
Abstract
For renewable energy generators, seamless integration with the electric grid is very important. The key to ensuring high
profitability of such integration lies in placing intelligent bids in time-ahead markets. Most bidding strategies traditionally use
predictions of wind farm power yield and the price. But such predictions are susceptible to error. On the other hand, energy storage
can be used to handle the variability and can mitigate the impact of prediction inaccuracies; however, it also entails considerable
expense.
This paper outlines a few recommendations for a bidding strategy that reduces dependence on energy storage and minimizes the
impact of prediction errors, resulting in higher profits for wind energy producers.
About the Authors
Dr Venkata Ramakrishna P is a Scientist in the Innovation Labs at Tata Consultancy Services (TCS), where he works in the areas of
wind engineering and energy conservation. He has a PhD degree from the Indian Institute of Technology (IIT) Madras, India.
Dr Arunchandar Vasan is a Senior Scientist at TCS' Innovation Labs, where he focuses on the role of IT in sustainability. He is
currently working on the use of IT in water distribution and building energy management. He holds a PhD degree in Computer
Science from the University of Maryland, USA, and has conducted extensive research on the design, analysis, and implementation
of wireless networks, as well as scalable network simulation.
Dr Venkatesh Sarangan is a Senior Scientist at TCS Innovation Labs. His research focus is on using Information and Communication
Technologies (ICT) to make water distribution networks and buildings more sustainable. He holds a PhD degree in Computer
Science and Engineering from The Pennsylvania State University, USA.
Introduction
Trading in the energy market is tricky when it comes to wind power. Energy is typically traded in two types of markets: spot and
futures markets. Pricing in the spot market is volatile as it driven by the real-time demand and supply situation, which requires the
trader to focus on shortfalls and perform balancing to close the gaps in the earlier trades. Here, the small wind power generator has
little say in the selling price. However, in a futures (typically a 'day-ahead') market, the producer can set a selling price for the
power. Therefore, trading in the futures market is more attractive for the producer. Wind energy producers would prefer to bid on
these markets in order to maximize profits. Missed forecasts (if the bid quantity is not delivered), on the other hand, entail penalties.
Most producers therefore decide on the quantity of a bid in a futures markets based on farm yield predictions and the price in the
time-ahead market. Renewable power portfolio managers, power brokers, and producers have to consider constraints such as the
varying accuracy in yield prediction, the limitations of buffers available for energy storage, and the fluctuations in price (involving
penalties). In this challenging environment, data-driven approaches can be employed to develop better trading strategies.
Wind energy installations are expected to grow, with several governments offering incentives for generating wind power. The
cumulative growth rate for the wind energy market was 12.5 percent in 2013.¹ While wind energy has reached grid parity even
without subsidies in certain geographies, many producers and traders in wind energy are still reaping sub-optimal profits. This
paper examines the challenges involved in the wind energy sector, and suggests an approach to reduce the risk created by the
uncertainties in wind power production.
A Point of View
Global Wind Energy Council, Global Wind Report: Annual Market Update 2013, April 2014, accessed in December 2014,http://www.gwec.net/wp-content/uploads/2014/04/GWEC-Global-Wind-Report_9-April-2014.pdf
2
The Wind Power Production Landscape
n
Difficulties in wind power prediction: There are several methods for predicting the yield of a wind farm, but they are still evolving.
Weather observation data, numeric weather prediction models, and statistical models are typically used for prediction. The
unpredictability of the weather, sudden turbulences, and the impact of topography, as well as mechanical issues in the generating
equipment, contribute to fluctuations in output. Since no single model fits every scenario, errors in prediction are inevitable.
n
Technical and financial challenges in energy storage: As wind energy is intermittent and accurate prediction is not possible, this energy
needs to be stored or 'buffered'. Many of the existing energy storage technologies are poorly suited to accommodate the type of
variability that wind energy adds to the electric grid. Existing storage technologies can handle variability in the minute-to-minute
timeframe, but fail to accommodate the variability in wind energy output that occurs over time periods of 30 minutes or more. The high
cost of energy storage is another major constraint. Consequently, renewable energy producers need to lower their reliance on buffers.
n
Uncertainties in the market price: A typical trading day is divided into blocks of several hours each. There are non-peak blocks where the
'exchange spot price' is low and peak blocks when it is high. The price also fluctuates from time to time. Apart from hourly and daily
variations, there are seasonal variations as well. At times, wind power is inexpensive enough to create a 'merit order effect' where its low
price reduces the power price as a whole. However, as an individual supplier, the wind energy producer cannot exert significant influence
on the market prices.
The Bidding Process and Trading Strategy
The integration of wind power with the electricity grid involves a producer making a bid to supply an agreed quantity of electricity at a future
time. This involves placing a bid in a time-ahead market. For instance, in a day-ahead bidding process, the producer makes a bid for supplying
wind power one day ahead of the actual generation. When a producer makes such a bid, it is based on the estimated value of parameters
such as:
n
The actual quantum of energy produced
n
The price of energy in the near future
n
The predicted yield of the farm
Given the challenges involved in prediction and storage, a wind producer's trading strategy should not only be robust as far as prediction
errors are concerned, but should also minimize the dependence on storage.
In trading wind energy, making low risk bids and then selling excess energy in spot trading is not really advantageous, as spot trading prices
are highly variable. On the other hand, bidding aggressively involves high risk. In this context, striking the right balance is of critical
importance. A mathematically sound approach to identifying the right trading strategy should enable traders to determine the right bids.
Most existing bidding tools and strategies consider bidding to be a 'one-period ahead' problem. This implies that the available solutions aim
to optimize the revenues only for the next window of bidding, and therefore, have a limited scope of operation. There are a few approaches
that optimize bids over an 'n-period ahead.' However, these are based on unrealistic assumptions, such as that market prices are samples
obtained from uniform or normal distribution, or that wind speeds are normally distributed (while Weibull distribution is more realistic). Such
assumptions restrict the usefulness of these approaches in real-world situations.
Recommendations for a Robust Bidding Strategy
Optimizing bids for 'one period ahead' ignores valuable information in the projected prices (troughs and peaks) and yield estimations
obtained through appropriate statistics. The supplier should account for the interplay between the projected price, a specific buffer capacity,
and the predicted yield across a longer time horizon.
A tool that best aids the supplier must take into account:
n
Prediction algorithms, error margins, and the associated statistics
n
Multiple bidding timeframes, including longer horizons
n
The nature of a specific market, including factors like local market conditions and wind penetration
n
Pricing variations over a time period
3
n
Penalties and regulation costs for under-delivery, as well as storage capacity and operating costs
n
Discount factors to translate cash flows between slots for an accurate net present value (NPV)
Such a tool can be created by first modeling the bidding process specific to a particular market. Next, under a set of simplifying assumptions,
an optimal bid can be determined that maximizes the cash flows over a future horizon. The solution can be evaluated on realistic data sets,
and can be improved to also handle situations where the assumptions are invalid.
Conclusion
Wind power producers can implement a strong bidding strategy aided by a reliable prediction tool. A good bidding strategy not only
reduces the impact of prediction errors in the profits, but also reduces reliance on high-priced energy storage facilities. A mathematically
sound approach that works in real-world situations can facilitate this and can help wind energy producers reap optimal profits.
Further Reading
For a more detailed read on this approach, refer to the paper ‘Windy with a chance of profit: bid strategy and analysis for wind integration’ at
the ACM Digital Library.
Contact
For more information about TCS’ Innovation Labs visit:http://www.tcs.com/about/tcs_difference/innovation/Pages/default.aspx
Email: [email protected]
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About Tata Consultancy Services Ltd (TCS)
Tata Consultancy Services is an IT services, consulting and business solutions organization that
delivers real results to global business, ensuring a level of certainty no other firm can match.
TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering
TM
and assurance services. This is delivered through its unique Global Network Delivery Model ,
recognized as the benchmark of excellence in software development. A part of the Tata Group,
India’s largest industrial conglomerate, TCS has a global footprint and is listed on the National
Stock Exchange and Bombay Stock Exchange in India.
For more information, visit us at www.tcs.com
IT Services
Business Solutions
Consulting
All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is
correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in
any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and
other applicable laws, and could result in criminal or civil penalties. Copyright © 2015 Tata Consultancy Services Limited
About TCS' Innovation Labs
Established in 1981, TCS' Innovation Labs address real-world business problems, bringing scientific rigor
to the study of computational concepts, and delivering solutions that make computation more reliable,
efficient, and agile for our clients.
With a focus on three key aspects—Software, Systems, and Applications Sciences—our Innovation Labs
build on new ideas to create solution frameworks that deliver high business impact across domains. Our
Co-Innovation Network (COIN™) gives our clients access to potentially disruptive technologies through
our partnerships with emerging technology companies, venture funds, academic research units, multi-
lateral bodies, and Tata Group companies.
The global network of Innovation Labs is equipped with sophisticated infrastructure to support leading-
edge research. Our researchers file over a hundred patents each year, and our research-based papers are a
recognized voice on global thought leadership platforms. Our breakthroughs have won several industry
awards, including the MIT Technology Grand Challenge Award and the Stockholm Challenge Award.
doc_298213989.pdf
or renewable energy generators, seamless integration with the electric grid is very important. The key to ensuring high
profitability of such integration lies in placing intelligent bids in time-ahead markets. Most bidding strategies traditionally use
predictions of wind farm power yield and the price. But such predictions are susceptible to error. On the other hand, energy storage
can be used to handle the variability and can mitigate the impact of prediction inaccuracies; however, it also entails considerable
expense.
This paper outlines a few recommendations for a bidding strategy that reduces dependence on energy storage and minimizes the
impact of prediction errors, resulting in higher profits for wind energy producers.
A Robust Bidding Strategy for
Wind Energy Trading
By Dr Venkata Ramakrishna P, Dr Arunchandar Vasan, Dr Venkatesh Sarangan
Abstract
For renewable energy generators, seamless integration with the electric grid is very important. The key to ensuring high
profitability of such integration lies in placing intelligent bids in time-ahead markets. Most bidding strategies traditionally use
predictions of wind farm power yield and the price. But such predictions are susceptible to error. On the other hand, energy storage
can be used to handle the variability and can mitigate the impact of prediction inaccuracies; however, it also entails considerable
expense.
This paper outlines a few recommendations for a bidding strategy that reduces dependence on energy storage and minimizes the
impact of prediction errors, resulting in higher profits for wind energy producers.
About the Authors
Dr Venkata Ramakrishna P is a Scientist in the Innovation Labs at Tata Consultancy Services (TCS), where he works in the areas of
wind engineering and energy conservation. He has a PhD degree from the Indian Institute of Technology (IIT) Madras, India.
Dr Arunchandar Vasan is a Senior Scientist at TCS' Innovation Labs, where he focuses on the role of IT in sustainability. He is
currently working on the use of IT in water distribution and building energy management. He holds a PhD degree in Computer
Science from the University of Maryland, USA, and has conducted extensive research on the design, analysis, and implementation
of wireless networks, as well as scalable network simulation.
Dr Venkatesh Sarangan is a Senior Scientist at TCS Innovation Labs. His research focus is on using Information and Communication
Technologies (ICT) to make water distribution networks and buildings more sustainable. He holds a PhD degree in Computer
Science and Engineering from The Pennsylvania State University, USA.
Introduction
Trading in the energy market is tricky when it comes to wind power. Energy is typically traded in two types of markets: spot and
futures markets. Pricing in the spot market is volatile as it driven by the real-time demand and supply situation, which requires the
trader to focus on shortfalls and perform balancing to close the gaps in the earlier trades. Here, the small wind power generator has
little say in the selling price. However, in a futures (typically a 'day-ahead') market, the producer can set a selling price for the
power. Therefore, trading in the futures market is more attractive for the producer. Wind energy producers would prefer to bid on
these markets in order to maximize profits. Missed forecasts (if the bid quantity is not delivered), on the other hand, entail penalties.
Most producers therefore decide on the quantity of a bid in a futures markets based on farm yield predictions and the price in the
time-ahead market. Renewable power portfolio managers, power brokers, and producers have to consider constraints such as the
varying accuracy in yield prediction, the limitations of buffers available for energy storage, and the fluctuations in price (involving
penalties). In this challenging environment, data-driven approaches can be employed to develop better trading strategies.
Wind energy installations are expected to grow, with several governments offering incentives for generating wind power. The
cumulative growth rate for the wind energy market was 12.5 percent in 2013.¹ While wind energy has reached grid parity even
without subsidies in certain geographies, many producers and traders in wind energy are still reaping sub-optimal profits. This
paper examines the challenges involved in the wind energy sector, and suggests an approach to reduce the risk created by the
uncertainties in wind power production.
A Point of View
Global Wind Energy Council, Global Wind Report: Annual Market Update 2013, April 2014, accessed in December 2014,http://www.gwec.net/wp-content/uploads/2014/04/GWEC-Global-Wind-Report_9-April-2014.pdf
2
The Wind Power Production Landscape
n
Difficulties in wind power prediction: There are several methods for predicting the yield of a wind farm, but they are still evolving.
Weather observation data, numeric weather prediction models, and statistical models are typically used for prediction. The
unpredictability of the weather, sudden turbulences, and the impact of topography, as well as mechanical issues in the generating
equipment, contribute to fluctuations in output. Since no single model fits every scenario, errors in prediction are inevitable.
n
Technical and financial challenges in energy storage: As wind energy is intermittent and accurate prediction is not possible, this energy
needs to be stored or 'buffered'. Many of the existing energy storage technologies are poorly suited to accommodate the type of
variability that wind energy adds to the electric grid. Existing storage technologies can handle variability in the minute-to-minute
timeframe, but fail to accommodate the variability in wind energy output that occurs over time periods of 30 minutes or more. The high
cost of energy storage is another major constraint. Consequently, renewable energy producers need to lower their reliance on buffers.
n
Uncertainties in the market price: A typical trading day is divided into blocks of several hours each. There are non-peak blocks where the
'exchange spot price' is low and peak blocks when it is high. The price also fluctuates from time to time. Apart from hourly and daily
variations, there are seasonal variations as well. At times, wind power is inexpensive enough to create a 'merit order effect' where its low
price reduces the power price as a whole. However, as an individual supplier, the wind energy producer cannot exert significant influence
on the market prices.
The Bidding Process and Trading Strategy
The integration of wind power with the electricity grid involves a producer making a bid to supply an agreed quantity of electricity at a future
time. This involves placing a bid in a time-ahead market. For instance, in a day-ahead bidding process, the producer makes a bid for supplying
wind power one day ahead of the actual generation. When a producer makes such a bid, it is based on the estimated value of parameters
such as:
n
The actual quantum of energy produced
n
The price of energy in the near future
n
The predicted yield of the farm
Given the challenges involved in prediction and storage, a wind producer's trading strategy should not only be robust as far as prediction
errors are concerned, but should also minimize the dependence on storage.
In trading wind energy, making low risk bids and then selling excess energy in spot trading is not really advantageous, as spot trading prices
are highly variable. On the other hand, bidding aggressively involves high risk. In this context, striking the right balance is of critical
importance. A mathematically sound approach to identifying the right trading strategy should enable traders to determine the right bids.
Most existing bidding tools and strategies consider bidding to be a 'one-period ahead' problem. This implies that the available solutions aim
to optimize the revenues only for the next window of bidding, and therefore, have a limited scope of operation. There are a few approaches
that optimize bids over an 'n-period ahead.' However, these are based on unrealistic assumptions, such as that market prices are samples
obtained from uniform or normal distribution, or that wind speeds are normally distributed (while Weibull distribution is more realistic). Such
assumptions restrict the usefulness of these approaches in real-world situations.
Recommendations for a Robust Bidding Strategy
Optimizing bids for 'one period ahead' ignores valuable information in the projected prices (troughs and peaks) and yield estimations
obtained through appropriate statistics. The supplier should account for the interplay between the projected price, a specific buffer capacity,
and the predicted yield across a longer time horizon.
A tool that best aids the supplier must take into account:
n
Prediction algorithms, error margins, and the associated statistics
n
Multiple bidding timeframes, including longer horizons
n
The nature of a specific market, including factors like local market conditions and wind penetration
n
Pricing variations over a time period
3
n
Penalties and regulation costs for under-delivery, as well as storage capacity and operating costs
n
Discount factors to translate cash flows between slots for an accurate net present value (NPV)
Such a tool can be created by first modeling the bidding process specific to a particular market. Next, under a set of simplifying assumptions,
an optimal bid can be determined that maximizes the cash flows over a future horizon. The solution can be evaluated on realistic data sets,
and can be improved to also handle situations where the assumptions are invalid.
Conclusion
Wind power producers can implement a strong bidding strategy aided by a reliable prediction tool. A good bidding strategy not only
reduces the impact of prediction errors in the profits, but also reduces reliance on high-priced energy storage facilities. A mathematically
sound approach that works in real-world situations can facilitate this and can help wind energy producers reap optimal profits.
Further Reading
For a more detailed read on this approach, refer to the paper ‘Windy with a chance of profit: bid strategy and analysis for wind integration’ at
the ACM Digital Library.
Contact
For more information about TCS’ Innovation Labs visit:http://www.tcs.com/about/tcs_difference/innovation/Pages/default.aspx
Email: [email protected]
T
C
S
D
e
s
i
g
n
S
e
r
v
i
c
e
s
M
0
1
1
5
I
I
I
About Tata Consultancy Services Ltd (TCS)
Tata Consultancy Services is an IT services, consulting and business solutions organization that
delivers real results to global business, ensuring a level of certainty no other firm can match.
TCS offers a consulting-led, integrated portfolio of IT and IT-enabled infrastructure, engineering
TM
and assurance services. This is delivered through its unique Global Network Delivery Model ,
recognized as the benchmark of excellence in software development. A part of the Tata Group,
India’s largest industrial conglomerate, TCS has a global footprint and is listed on the National
Stock Exchange and Bombay Stock Exchange in India.
For more information, visit us at www.tcs.com
IT Services
Business Solutions
Consulting
All content / information present here is the exclusive property of Tata Consultancy Services Limited (TCS). The content / information contained here is
correct at the time of publishing. No material from here may be copied, modified, reproduced, republished, uploaded, transmitted, posted or distributed in
any form without prior written permission from TCS. Unauthorized use of the content / information appearing here may violate copyright, trademark and
other applicable laws, and could result in criminal or civil penalties. Copyright © 2015 Tata Consultancy Services Limited
About TCS' Innovation Labs
Established in 1981, TCS' Innovation Labs address real-world business problems, bringing scientific rigor
to the study of computational concepts, and delivering solutions that make computation more reliable,
efficient, and agile for our clients.
With a focus on three key aspects—Software, Systems, and Applications Sciences—our Innovation Labs
build on new ideas to create solution frameworks that deliver high business impact across domains. Our
Co-Innovation Network (COIN™) gives our clients access to potentially disruptive technologies through
our partnerships with emerging technology companies, venture funds, academic research units, multi-
lateral bodies, and Tata Group companies.
The global network of Innovation Labs is equipped with sophisticated infrastructure to support leading-
edge research. Our researchers file over a hundred patents each year, and our research-based papers are a
recognized voice on global thought leadership platforms. Our breakthroughs have won several industry
awards, including the MIT Technology Grand Challenge Award and the Stockholm Challenge Award.
doc_298213989.pdf