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SPARK SUMMIT SESSION - …
SPARK SUMMIT SESSION -
A majority of the electricity in the U.S. is traded in independent system operator (ISO) based wholesale markets. ISO-based markets typically function in a two-step settlement process with day-ahead (DA) financial settlements followed by physical real-time (spot) market settlements for electricity. In this work, we focus on obtaining equilibrium bidding strategies for electricity generators in DA markets. Electricity prices in DA markets are determined by the ISO, which matches competing supply offers from power generators with demand bids from load serving entities. Since there are multiple generators competing with one another to supply power, this can be modeled as a competitive Markov decision problem, which we solve using a reinforcement learning approach. For power networks of realistic sizes, the state-action space could explode, making the RL procedure computationally intensive. This has motivated us to solve the above problem over Spark. The talk provides the following takeaways:
1. Modeling the day-ahead market as a Markov decision process
2. Code sketches to show the markov decision process solution over Spark and Mahout over Apache Tez
3. Performance results comparing Mahout over Apache Tez and Spark.
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I will begin talking about what electricity markets are and then discuss the need for them to be modeled. I will briefly highlight
Other markets that behave in a similar fashion. I will discuss why reinforcement learning (unsupervised machine learning)
Is a viable approach for this challenging problem. Then Vijay will discuss how we intend to exploit the inherent parallelisms within the algorithm and
Share a potential solution strategy over spark. WE will also briefly highlight the current state of work and discuss future work.
So we have multiple generators now competing to sell power and multiple large distribution companies or load serving entities competin to buy power.
This market is what we are looking to model and understand. And since this decision making happens each day, we have a very real need for it to be modeled and understood especially since there have been some market design flaws. We all remember about the California market crisis, where the prices went through the roof.