Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system
Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system
We develop a stochastic optimization model for scheduling a hybrid solar-battery storage system. Solar power in excess of the promise can be used to charge the battery, while power short of the promise is met by discharging the battery. We ensure reliable operations by using a joint chance constraint. Models with a few hundred scenarios are relatively tractable; for larger models, we demonstrate how a Lagrangian relaxation scheme provides improved results. To further accelerate the Lagrangian scheme, we embed the progressive hedging algorithm within the subgradient iterations of the Lagrangian relaxation. We investigate several enhancements of the progressive hedging algorithm, and find bundling of scenarios results in the best bounds. Finally, we provide a generalization for how our analysis extends to a microgrid with multiple batteries and photovoltaic generators.
Battery storage, Chance constraints, Lagrangian decomposition, Microgrid, Out of sample validation, Photovoltaic power station, Progressive hedging, Solar power, Stochastic optimization, Virtual power plant
965-989
Singh, Bismark
9d3fc6cb-f55e-4562-9d5f-42f9a3ddd9a1
Knueven, Bernard
d59b0b4e-9ab1-4c08-8e3d-06f1ce50a4c5
August 2021
Singh, Bismark
9d3fc6cb-f55e-4562-9d5f-42f9a3ddd9a1
Knueven, Bernard
d59b0b4e-9ab1-4c08-8e3d-06f1ce50a4c5
Singh, Bismark and Knueven, Bernard
(2021)
Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system.
Journal of Global Optimization, 80 (4), .
(doi:10.1007/s10898-021-01041-y).
Abstract
We develop a stochastic optimization model for scheduling a hybrid solar-battery storage system. Solar power in excess of the promise can be used to charge the battery, while power short of the promise is met by discharging the battery. We ensure reliable operations by using a joint chance constraint. Models with a few hundred scenarios are relatively tractable; for larger models, we demonstrate how a Lagrangian relaxation scheme provides improved results. To further accelerate the Lagrangian scheme, we embed the progressive hedging algorithm within the subgradient iterations of the Lagrangian relaxation. We investigate several enhancements of the progressive hedging algorithm, and find bundling of scenarios results in the best bounds. Finally, we provide a generalization for how our analysis extends to a microgrid with multiple batteries and photovoltaic generators.
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More information
Accepted/In Press date: 18 May 2021
e-pub ahead of print date: 1 June 2021
Published date: August 2021
Additional Information:
Funding Information:
Bismark Singh thanks David Pozo for discussions on the early stages of this manuscript and Frederik Fiand for assistance with the GUSS implementations. Parts of this work were completed while the authors were affiliated with Sandia National Laboratories, and supported by Sandia’s Laboratory Directed Research and Development (LDRD) program. The authors thank Sandia for their time.
Publisher Copyright:
© 2021, The Author(s).
Keywords:
Battery storage, Chance constraints, Lagrangian decomposition, Microgrid, Out of sample validation, Photovoltaic power station, Progressive hedging, Solar power, Stochastic optimization, Virtual power plant
Identifiers
Local EPrints ID: 472281
URI: http://eprints.soton.ac.uk/id/eprint/472281
ISSN: 0925-5001
PURE UUID: 101e1795-cf01-4e0d-ba16-5d2cdf94371d
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Date deposited: 30 Nov 2022 17:45
Last modified: 18 Mar 2024 04:08
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Author:
Bismark Singh
Author:
Bernard Knueven
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