Two-stage robust optimization of thermal-ESS units scheduling under wind uncertainty

The uncertainties of wind turbine (WT) output have large impacts on the optimal operation of integrated energy systems. This paper investigates the two-stage robust optimization (RO) of hybrid energy integration regarding wind volatility. In RO method, the WT output is modeled with an uncertainty budget, which represents the degree of conservatism. Simulation results validate that when the uncertainty budget rises to a maximal value, ESS degradation cost increases 25.7%, and total cost decreases 2.0%. Additionally, demand response program (DRP) exhibits elastic and voluntary merits. With the implementation of DRP, 6139.9$ total savings are achieved compared with the ordinary RO method. Meanwhile, the merits of stochastic optimization (SO) have also been studied. Despite the ESS degradation cost and total cost in SO being lower than those in RO, SO requires accurate probability distribution of uncertain variables in real situations. RO can shortly provide worst-case schemes which are highly appreciated by power utility companies. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 2021 The 2nd International Conference on Power Engineering, ICPE, 2021.

Automated summary

This paper investigates the two-stage robust optimization of hybrid energy integration considering wind volatility. The simulation results show that increasing the uncertainty budget leads to higher degradation costs for the energy storage system (ESS) but decreases total costs. The introduction of a demand response program (DRP) can achieve cost savings compared to ordinary robust optimization. Stochastic optimization has lower costs than robust optimization but requires accurate probability distributions.