A risk-sharing-based resilient renewable energy supply network model under the COVID-19 pandemic

Area of Science:

• Energy Systems Engineering
• Operations Research
• Environmental Economics

Background:

• The COVID-19 pandemic disrupted renewable energy projects, impacting technology and finance supply chains.
• Lockdowns led to decreased demand, reducing the efficiency of existing power plants.
• These disruptions pose significant challenges to the resilience of renewable energy supply networks.

Purpose of the Study:

• To design a resilient renewable energy supply network considering supply, demand, and payment risks posed by the COVID-19 pandemic.
• To optimize electric power generation and storage, alongside a risk-sharing index, to maximize plant profit.
• To determine optimal price adjustments to minimize consumer costs, incorporating a government subsidy-based risk-sharing model.

Main Methods:

• Development of a robust fuzzy-stochastic programming model to handle uncertainties from random and risk events.
• Design of a government subsidy-based risk-sharing model to bolster network resilience during the pandemic.
• Computational experiments conducted on a renewable energy supply network in Vietnam.

Main Results:

• The resilient energy supply network with the risk-sharing model demonstrated stabilized total profit across various COVID-19 impact levels.
• The proposed model effectively managed uncertainties from both random and hazardous events.
• Compared to robust optimization, the model achieved higher profits and reduced computational time.

Conclusions:

• A resilient renewable energy supply network design is crucial for mitigating pandemic-related risks.
• The robust fuzzy-stochastic programming model with a risk-sharing mechanism enhances network stability and profitability.
• Government subsidies can play a key role in enhancing the resilience of renewable energy infrastructure.