Optimal Design of Hybrid Multigeneration Systems Through CVaR-Based Risk-Averse Programming

This paper presents a risk-averse stochastic programming framework for the optimal design of hybrid multigeneration energy systems, integrating renewable-based technologies, advanced storage solutions, and green fuel-powered devices. The approach incorporates Conditional-Value-at-Risk (CVaR) to address uncertainties in energy demand, renewable generation, and market price fluctuations, supporting robust decision-making under uncertain and potentially adverse conditions. By considering CVaR, the framework aims to enhance system resilience by accounting for both expected performance and the potential impact of rare, high-cost events. The model unifies strategic planning for system configuration and capacity with operational management, formulating a comprehensive optimization problem that reflects the complexities of modern energy systems. Detailed modeling of energy conversion processes, storage dynamics, and system interactions is included to ensure realistic and practical solutions. The proposed framework is adaptable to a wide range of scenarios and risk preferences, providing a balanced approach to efficiency, sustainability, and risk management. This research contributes to the advancement of sustainable energy system design by offering a practical methodology for integrating diverse energy resources and managing uncertainty, ultimately supporting the development of resilient and economically viable hybrid multigeneration energy infrastructures.