Robust metamodel-based simulation-optimization approaches for designing hybrid renewable energy systems

Pardis Pourmohammadi and Ahmed Saif

Applied Energy, 2023, vol. 341, issue C, No S0306261923004968

Abstract: We propose two robust metamodel-based approaches for designing stand-alone hybrid renewable energy systems that combine conventional and renewable energy generators with battery storage. Since the cost and reliability functions of the stochastic system do not have closed-form expressions, they can only be estimated via simulation. In the response surface methodology, the simulation input/output data is used first to locally fit first-degree surrogate functions for each supply scenario to perform a stochastic gradient descent, before a second-degree function is fitted and optimized when the gradient becomes small. In contrast, the global response surface technique globally fits a convex quadratic surrogate function on the simulation input/output data, then uses its minimizer to iteratively refine the search space. To account for estimation errors, a distributional ambiguity set based on a linear ϕ-divergence function is constructed around the nominal probability distribution of supply scenarios, and the expected reliability-adjusted cost of the system is computed using the worst-case distribution in this set. The validity of the proposed approaches and the value of considering distributional ambiguity are demonstrated through a realistic case study of a remote community in Northern Ontario.

Keywords: Hybrid renewable energy systems; Distributionally robust optimization; Simulation-optimization; Response surface methodology (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.apenergy.2023.121132

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