Abstract
This chapter studies the optimal sizing of renewable, transmission, and energy storage capacities in a low-carbon electricity grid. Data-driven ambiguity sets based on Wasserstein distance are used for modeling renewable energy generation and load demand uncertainties. A distributionally robust bi-objective sizing model is proposed that minimizes the total investment cost and the worst-case expectation of carbon emissions in normal situations while considering the distributionally robust risk constraint of load shedding in extreme situations. Using a method based on the Lipschitz constant, worst-case carbon emission expectation and distributionally robust risk of load shedding are transformed into linear models. This makes the proposed sizing model a matter of solving mixed-integer linear programming problems. This chapter introduces the work in Xie et al. (Energy 263:125653, 2023), (2023).
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Xie, R., Wei, W. (2024). Sizing Renewable, Transmission, and Energy Storage in Low-Carbon Power Systems. In: Distributionally Robust Optimization and its Applications in Power System Energy Storage Sizing. Springer, Singapore. https://doi.org/10.1007/978-981-97-2566-3_10
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DOI: https://doi.org/10.1007/978-981-97-2566-3_10
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