This paper develops competitive bidding strategies for an online linear optimization problem with inventory management constraints in both cost minimization and profit maximization settings. In the minimization problem, a decision maker should satisfy its time-varying demand by either purchasing units of an asset from the market or producing them from a local inventory with limited capacity. In the maximization problem, a decision maker has a time-varying supply of an asset that may be sold to the market or stored in the inventory to be sold later. In both settings, the market price is unknown in each timeslot and the decision maker can submit a finite number of bids to buy/sell the asset. Once all bids have been submitted, the market price clears and the amount bought/sold is determined based on the clearing price and submitted bids. From this setup, the decision maker must minimize/maximize their cost/profit in the market, while also devising a bidding strategy in the face of an unknown clearing price. We propose DEMBID and SUPBID, two competitive bidding strategies for these online linear optimization problems with inventory management constraints for the minimization and maximization setting respectively. We then analyze the competitive ratios of the proposed algorithms and show that the performance of our algorithms approaches the best possible competitive ratio as the maximum number of bids increases. As a case study, we use energy data traces from Akamai data centers, renewable outputs from NREL, and energy prices from NYISO to show the effectiveness of our bidding strategies in the context of energy storage management for a large energy customer participating in a real-time electricity market.
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A data-driven analysis of supply bids in California ISO market: Price elasticity and impact of renewables
One month of supply bids in the California ISO day-ahead energy market are analyzed in this paper. A total of 1.5 million records of bid data are studied. The bids are studied based on their types and their distribution at different hours. The relationship between the market price and the offered supply capacity are investigated. A data-driven estimate is provided for the aggregated supply curve and accordingly the price elasticity of supply is identified for hours that price is highly inelastic. Importantly, this analysis shows the impact of the recent high capacity installations of renewable generation in the state of California on electricity price and price inelasticity. Finally, the undesirable consequences of price inelasticity, such as on creating price spikes and exercising market power, are discussed.
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- Award ID(s):
- 1711944
- NSF-PAR ID:
- 10073103
- Date Published:
- Journal Name:
- IEEE International Conference on Smart Grid Communications
- Page Range / eLocation ID:
- 1-6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SmartGridComm.2017.8340745
