The paper entitled “Designing integrated and resilient multi-energy systems via multi-objective optimization and scenario analysis,” by Marco Tangi and Alessandro Amaranto of RSE’s Sustainable Development and Energy Sources Department, is online.

The published article presents a new decision-analytic framework for the optimal planning of sustainable and resilient multi-energy systems, integrating the single-objective simulation-optimization model calliope with multi-objective evolutionary algorithms (MOEAS).

Effective energy system planning requires accurate modeling tools, with a shift towards multi-energy systems that integrate diverse energy vectors and technologies to enhance flexibility and renewable energy hosting capacity. Traditional cost-focused approaches based on single metrics are evolving into multi-objective optimization strategies, balancing costs with sustainability goals such as global warming potential, environmental impacts, and reliability. The complexity of renewable energy sources, market volatility, and climate change necessitates robust solutions resilient to multiple sources of uncertainty.

The proposed modeling framework enables the optimal planning of multi-energy systems by coupling MOEAS with the single-objective simulation-optimization model calliope. Various MOEAS are tested, and the best-performing algorithm is used to extract optimal configurations under alternating scenarios of renewable energy generation potential and energy prices.

The framework is applied to a synthetic case study based on the Sulcis Iglesiente (SI) province in Sardinia, Italy. The study explores investment opportunities in renewable resources, the expansion of local renewable energy capacity, the installation of energy storage batteries, and the transition from gas and oil boilers to heat pumps and biomass generators. The analysis considers conflicting objectives, such as air quality, energy independence, economic factors, and emission targets.

The results demonstrate that the proposed methodology identifies multiple optimal configurations of the multi-energy system, incorporating different technology combinations based on the relative importance of objectives. Scenario analysis highlights that the attractiveness of certain technologies, particularly for heat generation, is highly sensitive to objectives and scenarios, while others, such as onshore wind farms, remain favorable across all conditions.