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The use of photovoltaic (PV) arrays as a source of renewable energy has become increasingly popular in the USA. Despite their wide usage, rooftop PV arrays are vulnerable to damage under strong winds. This can be attributed to the underestimation of peak wind loads on these systems where dynamic effects are unaccounted for. This study consists of investigating the wind-induced dynamic effects on rooftop PV arrays based on an experimental-numerical program and field calibration. Field measurements were conducted on a rooftop PV array at Central Washington University. Finite Element Modeling was performed to design the PV array model for experimental testing such that its dynamic properties are comparable to the in-situ array. Impact hammer and wind loading tests were carried out at the NHERI Wall of Wind Experimental Facility at Florida International University. The experimental tests were calibrated and validated based on the field measurements. Significant wind-induced vibrations were observed and their effect on the structure’s response was shown to increase with increasing wind speed.
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Optimal residential battery storage operations using robust data-driven dynamic programming
In this paper, we consider the problem of operating a battery storage unit in a home with a rooftop solar photovoltaic (PV) system so as to minimize expected long-run electricity costs under uncertain electricity usage, PV generation, and electricity prices. Solving this dynamic program using standard techniques is computationally burdensome, and is often complicated by the difficulty of estimating conditional distributions from sparse data. To overcome these challenges, we implement a data-driven dynamic programming (DDP) algorithm that uses historical data observations to generate empirical conditional distributions and approximate the cost-to-go function. Then, we formulate two robust data-driven dynamic programming (RDDP) algorithms that consider the worst-case expected cost over a set of conditional distributions centered at the empirical distribution, and within a given Chi-square or Wasserstein distance, respectively. We test our algorithms using data from homes with rooftop PV in Austin, Texas. Numerical results reveal that DDP and RDDP outperform common existing methods with acceptable computational effort. Finally, we show that implementation of these superior operational algorithms significantly raises the break-even battery cost under which a homeowner is incentivized to invest in a residential battery rather than participate in a feed-in tariff or net energy metering program.
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- Award ID(s):
- 1752125
- PAR ID:
- 10129316
- Date Published:
- Journal Name:
- IEEE Transactions on Smart Grid
- ISSN:
- 1949-3053
- Page Range / eLocation ID:
- 1 to 1
- 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
- Journal Article:
- https://doi.org/10.1109/TSG.2019.2942932
