Planning Strategies in the Energy Sector: Integrating Bayesian Neural Networks and Uncertainty Quantification in...

Area of Science:

• Energy Systems Analysis
• Optimization Under Uncertainty
• Renewable Energy Integration

Background:

• The global energy market faces challenges from rising demand, competition, and the transition to renewables.
• Effective decision-making requires advanced methods to manage uncertainty in investment and operations.
• Existing approaches may not fully capture complex future uncertainties in energy systems.

Purpose of the Study:

• To develop a flexible, scenario-based framework for capacity and investment planning in mixed energy systems (solar, wind, natural gas).
• To integrate Bayesian Neural Networks (BNNs) for probabilistic, data-driven scenario generation addressing forecast uncertainties.
• To enhance decision-making for cost-effective investments and operations in complex energy landscapes.

Main Methods:

• Integration of Bayesian Neural Networks (BNNs) to model uncertainties in energy generation and demand forecasts.
• Development of a two-stage stochastic multi-period mixed-integer linear optimization model.
• Generation of probabilistic, data-driven scenarios from BNN posterior distributions for robust planning.

Main Results:

• The framework provides detailed capacity expansion and investment strategies for natural gas, wind, and solar power plants.
• Demonstrated applicability through a case study in Texas, addressing increasing energy demand.
• The model effectively incorporates real-world constraints like construction lags and scenario-dependent demands.

Conclusions:

• The proposed framework enhances energy system flexibility and enables cost-effective, robust investment and operational decisions.
• It offers significant advantages over traditional methods by capturing nuanced uncertainty distributions.
• This approach supports strategic planning in the evolving modern energy landscape.