Design of low-carbon multi-energy systems in the SecMOD framework by combining MILP optimization and life-cycle assessment

Reinert, Christiane; Nolzen, Niklas; Frohmann, Julia; Tillmanns, Dominik; Bardow, André

doi:10.1016/j.compchemeng.2023.108176

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Ultimately, Table 2 contains the overview of the case study parameters with average values and the respective data sources. We model the case study using the SecMOD MILP framework (Reinert et al, 2023).…”

Section: Case Study Descriptionmentioning

confidence: 99%

Flexibility-expansion planning of multi-energy systems by energy storage for participating in balancing-power markets

Nolzen,

Leenders,

Bardow

2023

Front. Energy Res.

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The growing need for balancing power combined with the shutdown of conventional power plants requires new balancing-power providers. In this context, industrial energy systems are particularly promising. However, the main task of industrial energy systems is to provide various energy forms. For this purpose, they operate interconnected units to maximize efficiency, but the interconnected operation also increases complexity, limiting flexibility due to the need to supply fixed demands. Energy storage can increase the flexibility of current and future industrial energy systems, thus enhancing the potential for sector coupling within the overall energy system at a low cost. To improve the flexibility of industrial energy systems, we propose a design optimization framework that accounts for investment in energy storage and for the provision of balancing power. Since the request of balancing power is uncertain, we present a stochastic program for the balancing-power market and propose two ways to model storage that both derive feasible storage operations while being computationally efficient. In a case study of a multi-energy system, cost savings between 6% and 17% can be achieved by increasing flexibility for participation in the balancing-power market with investment in heat storage. The sensitivity analysis identifies heat storage as particularly advantageous for heat-driven energy systems. Our method combines long-term investment decisions with short-term operational uncertainties to identify optimal investment decisions, which enhance the energy system’s flexibility for the provision of balancing power.

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Section: Case Study Descriptionmentioning

confidence: 99%