Open Access

ARTICLE

Multi-Energy System Optimization of Costs Versus Carbon Dioxide Emissions for Flexibility. A Case Study in Italy

Marcelo Dario Rodas Britez^1,*, Francesco Ghionda², Vasileios Tatsis³, Dimosthenis Ioannidis³
1 Distributed AI for Dependable Cybersecurity Unit, Center for Cybersecurity, Fondazione Bruno Kessler, Trento, Italy
2 Center for Sustainable Energy, Fondazione Bruno Kessler, Trento, Italy
3 Centre for Research and Technology Hellas, Information Technologies Institute, Thessaloniki, Greece
* Corresponding Author: Marcelo Dario Rodas Britez. Email:
(This article belongs to the Special Issue: Selected Papers from the SDEWES 2025 Conference on Sustainable Development of Energy, Water and Environment Systems)

Energy Engineering https://doi.org/10.32604/ee.2026.078082

Received 23 December 2025; Accepted 12 February 2026; Published online 13 March 2026

Abstract

Current energy systems are increasingly complex, considering multi-energy systems, the integration of non-programmable renewable energy sources, and the simultaneous evaluation of multiple evaluation objectives (i.e., costs vs carbon dioxide emissions). This complexity opens the opportunity to explore optimization algorithms as assistance for systematic and automatic management of energy systems. The implementation of a multi-energy system poses multiple challenges, including managing multiple energy vectors with different technologies applied across energy production, energy storage, and renewable energy sources. Also, multi-objective evaluation should be considered to manage reductions in costs and carbon dioxide emissions. Therefore, this paper proposes a multi-objective, multi-energy optimization approach for managing the production of electricity, steam, and cold, integrated with photovoltaic (PV), a battery energy storage system (BESS), cogenerators (CCHP), and boilers. It implements a Linear Programming algorithm and evaluates costs vs carbon dioxide emissions on an hourly basis for three months. Historical data on electric energy and steam demand are provided from the evaluated industry. The data on prices (electric energy and natural gas) are taken from the historical hourly energy prices in Italy. The implementation has been tested in a pharmaceutical industry in Italy, enabling automatic optimization of the defined multi-energy system to evaluate energy production in terms of costs and carbon dioxide emissions. Also, the flexibility of the energy system, enabled by the storage system and the cogeneration system, is discussed in depth. The results show that the optimization system, using a Pareto front comparison, allows the selection between reducing costs and reducing carbon dioxide emissions, with values expressed as percentage differences from the actual operational baseline. The cost variations between the optimized solutions and the real cost at market prices range from −9% to 42%. Then, the corresponding variations in carbon dioxide emissions range from −61% to 16%. Finally, the flexibility of the multi-energy system is possible by managing the BESS and CCHP.

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Keywords

Multi-objective optimization algorithm; multi-energy system; energy flexibility

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