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Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system

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  • Bismark Singh

    (Friedrich-Alexander-Universität Erlangen-Nürnberg
    Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Bernard Knueven

    (National Renewable Energy Laboratory)

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.

Suggested Citation

  • Bismark Singh & Bernard Knueven, 2021. "Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system," Journal of Global Optimization, Springer, vol. 80(4), pages 965-989, August.
    • Handle: RePEc:spr:jglopt:v:80:y:2021:i:4:d:10.1007_s10898-021-01041-y
    DOI: 10.1007/s10898-021-01041-y
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