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If a trader could predict price changes in the stock market better than other traders, she would make a fortune. Similarly in the electricity market, a trader that could predict changes in the electricity load, and thus electricity prices, would be able to make large profits. Predicting price changes in the electricity market better than other market participants is hard, but in this paper, we show that attackers can manipulate the electricity prices in small but predictable ways, giving them a competitive advantage in the market. Our attack is possible when the adversary controls a botnet of high wattage devices such as air conditioning units, which are able to abruptly change the total demand of the power grid. Such attacks are called Manipulation of Demand via IoT (MaDIoT) attacks. In this paper, we present a new variant of MaDIoT and name it Manipulation of Market via IoT (MaMIoT). MaMIoT is the first energy market manipulation cyberattack that leverages high wattage IoT botnets to slightly change the total demand of the power grid with the aim of affecting the electricity prices in the favor of specific market players. Using real-world data obtained from two major energy markets, we show that MaMIoT can significantly increase the profit of particular market players or financially damage a group of players depending on the motivation of the attacker.
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Pricing Conditional Value at Risk-Sensitive Economic Dispatch
There are growing concerns over the ability of current electricity market designs to adequately model and optimize against the stochastic nature of renewable resources such as wind and solar. In this paper, we consider an economic dispatch problem that explicitly accounts for said uncertainty and enforces network and generation limits using conditional value at risk. Our key contribution is the definition and analysis of risk-sensitive locational marginal prices (risk-LMPs) derived from such a market clearing problem. Risk-LMPs extend conventional LMPs to the uncertain setting. Settlements defined via risk-LMPs compensate resources for both energy and reserve schedules. We study these prices via sample average approximation (SAA) on example power networks to demonstrate their viability for electricity pricing with large-scale integration of renewables.
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- Award ID(s):
- 2048065
- PAR ID:
- 10315056
- Date Published:
- Journal Name:
- 2021 IEEE Power & Energy Society General Meeting (PESGM)
- 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/PESGM46819.2021.9637845
