Abstract
Electric system operators rely on regulation services to match the total system supply to the total system load in quasi real-time. The regulation contractual framework requires that a regulation unit declares its regulation parameters at the beginning of the contract, the operator guarantees that the regulation signals will be within the range of these parameters, and the regulation unit is rewarded proportionally to what it declares and what it supplies. We study how this service can be provided by a unit with a non-ideal storage. We consider two broad classes of storage technologies characterized by different state of charge evolution equations, namely batteries and flywheels. We first focus on a single contract, and obtain formulas for the upward and downward regulation parameters that a unit with either a battery or a flywheel should declare to the operator to maximize its reward. We then focus on a multiple contract setting and show how to analytically quantify the reward that such a unit could obtain in successive contracts. We quantify this reward using bounds and expectation, and compare our analytical results with those obtained from a dataset of real-world regulation signals. Finally, we provide engineering insights by comparing different storage technologies in terms of potential rewards for different contract durations and parameters.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1813-1823 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Smart Grid |
| Volume | 7 |
| DOIs | |
| State | Accepted/In press - Nov 4 2015 |
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ASJC Scopus subject areas
- Computer Science(all)
Cite this
Energy Storage and Regulation : An Analysis. / Fooladivanda, Dariush; Rosenberg, Catherine; Garg, Siddharth.
In: IEEE Transactions on Smart Grid, Vol. 7, 04.11.2015, p. 1813-1823.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Energy Storage and Regulation
T2 - An Analysis
AU - Fooladivanda, Dariush
AU - Rosenberg, Catherine
AU - Garg, Siddharth
PY - 2015/11/4
Y1 - 2015/11/4
N2 - Electric system operators rely on regulation services to match the total system supply to the total system load in quasi real-time. The regulation contractual framework requires that a regulation unit declares its regulation parameters at the beginning of the contract, the operator guarantees that the regulation signals will be within the range of these parameters, and the regulation unit is rewarded proportionally to what it declares and what it supplies. We study how this service can be provided by a unit with a non-ideal storage. We consider two broad classes of storage technologies characterized by different state of charge evolution equations, namely batteries and flywheels. We first focus on a single contract, and obtain formulas for the upward and downward regulation parameters that a unit with either a battery or a flywheel should declare to the operator to maximize its reward. We then focus on a multiple contract setting and show how to analytically quantify the reward that such a unit could obtain in successive contracts. We quantify this reward using bounds and expectation, and compare our analytical results with those obtained from a dataset of real-world regulation signals. Finally, we provide engineering insights by comparing different storage technologies in terms of potential rewards for different contract durations and parameters.
AB - Electric system operators rely on regulation services to match the total system supply to the total system load in quasi real-time. The regulation contractual framework requires that a regulation unit declares its regulation parameters at the beginning of the contract, the operator guarantees that the regulation signals will be within the range of these parameters, and the regulation unit is rewarded proportionally to what it declares and what it supplies. We study how this service can be provided by a unit with a non-ideal storage. We consider two broad classes of storage technologies characterized by different state of charge evolution equations, namely batteries and flywheels. We first focus on a single contract, and obtain formulas for the upward and downward regulation parameters that a unit with either a battery or a flywheel should declare to the operator to maximize its reward. We then focus on a multiple contract setting and show how to analytically quantify the reward that such a unit could obtain in successive contracts. We quantify this reward using bounds and expectation, and compare our analytical results with those obtained from a dataset of real-world regulation signals. Finally, we provide engineering insights by comparing different storage technologies in terms of potential rewards for different contract durations and parameters.
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UR - http://www.scopus.com/inward/citedby.url?scp=84946763035&partnerID=8YFLogxK
U2 - 10.1109/TSG.2015.2494841
DO - 10.1109/TSG.2015.2494841
M3 - Article
AN - SCOPUS:84946763035
VL - 7
SP - 1813
EP - 1823
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
SN - 1949-3053
ER -