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- W1628053467 abstract "In electricity markets, credit collateral requirements for participants have traditionally been set based on historical price data that may not properly reflect future risks. A new predictive approach to determining credit risk is proposed in this paper. For any market that prices reserves in the real-time market, correlation exists between available reserve levels and real-time energy prices. This paper shows that it is possible to forecast hourly system-wide available reserves in a realistic system such as the Electric Reliability Council of Texas (ERCOT) market for up to a week ahead with high confidence. A credit collateral call can then be issued based on predicted system conditions due to the strong correlation between system-wide reserves and real-time energy prices. This in turn, would lower the risk of default for participants as it better reflects their actual risk. Case studies based on a representative ERCOT simulation show that potential scarcity conditions can be successfully identified as far as four days out." @default.
- W1628053467 created "2016-06-24" @default.
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- W1628053467 date "2015-07-01" @default.
- W1628053467 modified "2023-09-23" @default.
- W1628053467 title "A reserve forecast-based approach to determining credit collateral requirements in electricity markets" @default.
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- W1628053467 doi "https://doi.org/10.1109/pesgm.2015.7285727" @default.
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