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Power System Portfolio Selection and CO 2 Emission Management Under Uncertainty Driven by a DNN-Based Stochastic Model

Author

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  • Carlo Mari

    (Department of Economics, Engineering, Society, Business Organization, University of Tuscia, 01100 Viterbo, Italy
    These authors contributed equally to this work.)

  • Carlo Lucheroni

    (School of Sciences and Technology, University of Camerino, 62032 Camerino, Italy
    These authors contributed equally to this work.)

  • Nabangshu Sinha

    (International School of Advanced Studies, University of Camerino, 62032 Camerino, Italy
    These authors contributed equally to this work.)

  • Emiliano Mari

    (SYDUS, 05018 Orvieto, Italy
    These authors contributed equally to this work.)

Abstract

A model is proposed to investigate the effects of power generation source diversification and CO 2 emission control in the presence of dispatchable fossil fuel sources and non-dispatchable carbon-free renewables. In a stochastic environment in which three random factors are considered, namely fossil fuels (gas and coal) and CO 2 prices, we discuss a planning methodology for power system portfolio selection that integrates the non-dispatchable renewables available in a given energy system and optimally combines cost, risk and CO 2 emissions. By combining the deep neural network probabilistic forecasting of fossil fuel path prices with a geometric Brownian motion model for describing the CO 2 price dynamics, we simulate a wide range of plausible market scenarios. Results show that under CO 2 price volatility, optimal portfolios shift toward cleaner energy sources, even in the absence of explicit emission targets, highlighting the implicit regulatory power of volatility. The results suggest that incorporating CO 2 price volatility through market mechanisms can serve as an effective policy tool for driving decarbonization. Our model offers a flexible and reproducible approach to support policy design in energy planning under uncertainty.

Suggested Citation

  • Carlo Mari & Carlo Lucheroni & Nabangshu Sinha & Emiliano Mari, 2025. "Power System Portfolio Selection and CO 2 Emission Management Under Uncertainty Driven by a DNN-Based Stochastic Model," Mathematics, MDPI, vol. 13(9), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1477-:d:1646609
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