(371ak) Multistage Distributionally Robust Optimization for the Design of Hybrid Energy-Driven Multi-Network Processes Under Uncertainty

Recently, the demand for energy has surged in tandem with population growth and economic advancement. This surge has led to a significant increase in the consumption of fossil fuels and an unavoidable uptick in energy prices.^[1] In light of the imperative of sustainability and environmental consciousness, renewable energy resources emerge as indispensable and ecologically sound alternatives. They play a pivotal role in assuaging concerns surrounding the rapid depletion of conventional fossil fuels and the ominous specter of global warming.^[2]

Although renewable energy promises to diversify our electricity sources and enhances the flexibility of power systems, their inherent unpredictability and intermittency represent formidable challenges in their widespread adoption. Furthermore, the deployment of a singular renewable energy-based system risks incurring excessive costs, oversized infrastructures, and disruptions in energy supply. Therefore, in recognition of the uncertain characteristics of renewable energy, the adoption of hybrid renewable energy systems (HRESs) emerges as a pragmatic solution. This approach is deemed essential given the evolving emphasis on self-sufficiency in energy supply for economic and security considerations. The optimal sizing of hybrid energy systems ensures a cost-efficient power supply characterized by outstanding reliability and minimal atmospheric emissions.^[3]

This study predominantly delves into the systematic optimal design problem of hybrid energy-driven multi-network processes in the presence of uncertainty. By coupling hybrid energy supply systems with chemical processes, a multi-network framework that aims to integrate the xylitol production, water treatment, hybrid renewable energy supply and carbon capture, utilization, and storage (CCUS) is constructed. The hybrid energy systems designed here encompass wind, solar, biomass, natural gas, and energy storage.

A multi-stage distributionally robust optimization model is formulated to tackle the challenge of power generation planning under multiple uncertainties (e.g., demand variability, weather-dependent power generation fluctuations, or equipment failures).^[4] A tailored solution strategy based on column-and-constraint generation (C&CG) method is developed to solve this large-scale DRO model accurately and efficiently. This approach can identify the optimal topology and its corresponding optimal decision variables with the purpose of optimizing economic benefits as well as mitigating environmental impact. Comparative analysis reveals that the optimal solutions derived from the multi-stage distributionally robust optimization are more reliable than multi-stage stochastic programming and outperform the conventional robust optimization. The effectiveness of the model in hedging against the uncertainty of renewable energy sources is highlighted, affirming the substantial advantages of this comprehensive framework.

References

[1] Sharafi M, Elmekkawy T Y. Multi-objective optimal design of hybrid renewable energy systems using PSO-simulation based approach [J]. Renewable Energy, 2014, 68:67-79.

[2] Ghaffari A, Askarzadeh A. Design optimization of a hybrid system subject to reliability level and renewable energy penetration [J]. Energy, 2020, 193:116754.

[3] Gabrielli P, Fürer F, Mavromatidis G, et al. Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis [J]. Applied Energy, 2019, 238:1192-1210.

[4] Sun X A, Conejo A J. Robust Optimization in Electric Energy Systems [J]. International Series in Operations Research & Management Science, 2021.