(13e) Power System Planning and Scheduling Integrating Hydrogen and Ammonia Long-Term Storage Via Time-Adaptive Modelling Towards Net-Zero Heat & Power Sectors

High penetration of renewable energy sources in the energy systems and novel pathways for their exploitation become imperative towards Net Zero (Davis et al., 2018). However, increased renewable energy can impose volatility in the grid as solely battery energy storage systems could not exploit excessive renewable energy loads. Thus, dense energy carriers (DEC), such as hydrogen and ammonia, which can be produced by exclusively consuming renewable power, constitutes promising alternative options for efficient energy management. While hydrogen is a challenging carrier for long-term storage, ammonia has emerged as a promising alternative to store the hydrogen-based energy (Lan et al., 2012). Therefore, green ammonia synthesis constitutes a topical subject of research (Reese et al., 2016; Verleysen et al., 2020).

Ammonia as energy carrier displays additional advantages for the energy systems, as it constitutes a competitive option for long-term energy storage and transportation (Valera-Medina & Bañares-Alcantara, 2021). Energy systems studies have recently included both renewable-based ammonia production and ammonia-to-power systems in their considerations (Armijo & Philibert, 2020; Allman et al., 2017; Cesaro et al., 2021). Moreover, Bounitsis & Charitopoulos (2023) considered hydrogen and ammonia pathways in a spatio-temporal strategic planning and operational optimisation Linear Programming (LP) problem. They investigated UK’s power and heat systems decarbonisation and highlighted the crucial role of ammonia for long-term storage of excessive renewable energy.

Nonetheless, integrated planning and scheduling of power systems with dense energy carriers for long-term storage constitutes a quite challenging problem. Firstly, modelling the long-term storage of dense energy carriers requires an adequately large and fine-grained temporal resolution (Gonzato et al., 2021). Moreover, unit commitment considerations in the optimisation model are necessary to obey to the technological limitations of both Haber-Bosch process and thermal electricity generation units, but generally increase the computational complexity (Armijo & Philibert, 2020; Schwele et al., 2020). This work aims to extend the spatially explicit snapshot model by Bounitsis & Charitopoulos (2023) to a Mixed-Integer Linear Programming problem which further includes scheduling decisions for the wide power system. Regarding the reduction of the temporal resolution, a multi-chronological clustering approach is adopted which initially generates linked representative days and employs a novel priority-based chronological time-period clustering to reduce their intra-day resolution (Domínguez & Vitali, 2021; Nahmmacher et al., 2016; Pineda & Morales, 2018). Then, a time-adaptive unit commitment formulation is inspired from the work by Pineda et al. (2019) and it is implemented to model the scheduling decisions.

The proposed framework is examined on a case study concerning integrated planning and scheduling of UK’s power and heat systems towards Net Zero. Especially, various scenarios of the heat system configuration are considered. Ultimately, the approximation errors induced from the time aggregation methods are quantified, and the impact of the unit commitment constraints is evaluated based on the planning decisions and key performance indicators of the wide system.

Acknowledgments

The authors gratefully acknowledge funding under the EPSRC grants HUMAN (EP/T022930/1) & AIOLOS (EP/V051008/1)

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