Publications

Authors: Negar Namazifard; Pieter Vingerhoets; Erik Delarue

Links: https://www.researchgate.net/publication/369659073_INVESTMENT_AND_OPERATIONAL_OPTIMIZATION_FOR_FUTURE_HYDROGEN_INFRASTRUCTURE_A_BELGIAN_CASE_STUDY

https://lirias.kuleuven.be/retrieve/719471 

Abstract: 

The European Union’s green deal and European Climate law have set a target for the EU to become climate neutral by 2050. The transition to climate neutrality must be balanced with other priorities such as energy security to strengthen the EU’s industry and energy system to create a level playing field in comparison with other countries outside the EU. Following the recent REpowerEU plan, hydrogen is a crucial candidate to reduce the emissions of the so-called hard-to-abate sectors that cannot be easily electrified. In particular, one can think of the industries where hydrogen can be used both as fuel and feedstock such as steel making, green fertilizer production, methanol synthesis and refineries. Belgium, the Netherlands, and North Rhine Westphalen (Germany) are currently the regions with the most concentrated hydrogen demand in Europe. However, the supply of cheap and green electricity for commercially viable production of green hydrogen is scarce in these countries. Therefore, options for large-scale hydrogen import from regions with higher renewable potential or low carbon “blue” hydrogen production are currently being considered. To obtain crucial insights on the required capacities and investments for potential deployment of the hydrogen-related technologies and pipeline networks, one should technically and economically investigate the different processes for renewable hydrogen production, and develop various roadmaps to deliver these renewable molecules to the demand clusters. In this study, an energy system investment and operational optimization model has been developed with high spatial resolution in the industrial clusters to analyze the Fluxys 2030 roadmap in Belgium for connecting the potential hydrogen production and import hubs to the demand nodes. Hypatia as an energy system modelling framework has been used following a linear programming technique with the objective function of minimizing the total discounted system cost to obtain the optimize future capacity of hydrogen network and hydrogen production technologies. In the first case study, the future non-energy hydrogen demand of industries located in the areas of Antwerp, Ghent and Mons has been considered based on the data taken from a project funded by European Commission called AidRES where the demand for hydrogen as potential future feedstock has been calculated in the steel, ammonia, methanol and refinery industries towards 2050. The case study starts from a greenfield in Belgium, i.e., without accounting for existing capacities and it is constrained by the network typology given by Fluxys. The green hydrogen production routes from renewables connected to PEM electrolyzers have been included in all the regions modelled in this case study, considering their resource availability and renewable technology’s (such as PV, onshore and offshore wind turbines) maximum technical potential. On the other hand, blue hydrogen production has only been included in the Antwerp area, where there have been already some steam-methane reforming (SMR) capacities for grey hydrogen production.

Journal: International Journal of Hydrogen Energy, 2024

Authors: Negar Namazifard, Pieter Vingerhoets, Erik Delarue

Link: https://www.sciencedirect.com/science/article/abs/pii/S0360319924007675

Abstract: 

The REpowerEU plan prioritizes combining electrification with low-carbon hydrogen for a green energy shift. However, uncertainties surrounding the future hydrogen market and its supply costs impede the establishment of its value chain. Without definite plans for hydrogen production and infrastructure, investing in low-carbon hydrogen industrial processes is risky. Simultaneously, the absence of solid market interest poses challenges in deploying a hydrogen backbone. In recent years, European gas Transmission System Operators (TSOs) in Belgium, Netherlands, and Germany, have introduced various roadmaps for their prospective national hydrogen network. Despite the infrastructure proposals, detailed quantitative scenario analysis for the future hydrogen supply chain is still missing. This paper presents a hydrogen infrastructure model that employs Mixed Integer Linear Programming (MILP) for investment and operational cost optimization at detailed spatiotemporal resolution. A regionalization method is proposed to allocate the potential hydrogen consumption in different industrial sectors across various clusters within a country, represented as the prospective hydrogen demand nodes. The model assesses extreme supply scenarios to examine the robustness of the resulting network infrastructure and compare the system-levelized cost of hydrogen. A real-life case study focusing on the potential Belgian hydrogen supply chain showcases the model's capabilities and outputs.

Authors: Muhammad Salman ^a, Daniel Flórez-Orrego ^b, Juan Correa-Laguna ^c, François Maréchal ^b,Grégoire Léonarda ^a

a - ULiege, b- EPFL, c-VITO

Journal: Computer Aided Chemical Engineering, 2024

Link: https://www.sciencedirect.com/science/article/abs/pii/B9780443288241503641

Abstract:

A systemic methodology was developed, employing key performance indicators (KPIs): specific total annual cost (TAC) (€/t_glass), specific emissions (tCO2/t_glass) and specific energy consumption (MWh/t_glass) to analyse various energy transition routes for flat glass production, such as NG oxy-combustion, H₂ and hybrid furnaces, and full electrification, along with glass recycle and carbon capture (CC). A Blueprint (BP) model, including steady-state values for mass and energy balance, as well as investment and operating costs, is developed. To determine the optimal route, the OSMOSE Lua optimization framework was employed, which solves the mixed integer linear programming (MILP) problem using the TAC as the objective function. Additionally, three scenarios, namely Central, Electrification and Clean Molecules were implemented, influencing costs of natural gas (NG), H₂, electricity, and CO₂ emission, for years 2030, 2040 and 2050. For 2030, the hybrid furnace becomes the most cost-effective route across all scenarios. However, considering a balance between emissions and cost, pathways such as the H₂ furnace, all-electric furnace, or NG furnace with CC suit moderate emissions target. For higher targets, hybrid with CC is the optimal choice, effectively combining cost efficiency with significant emissions reduction. In 2040, electrification with CC dominates in electrification scenario, achieving significant emissions and TAC reductions, while the hybrid with CC prevails in other scenarios, with 93% emission and 15-16% TAC reductions. By 2050, lower commodity costs and higher CO₂ favour CC-equipped routes of all-electric, H₂, and hybrid, reducing TAC by 34-39% and emissions by 93-95%. In conclusion, for the energy transition in glass sector, an excellent trade-off between all KPIs is required, based on future energy perspectives, to make the right investment decisions.

Authors: Muhammad Salman, Daniel Flórez-Orrego, Francois Marechal, Grégoire Léonard

Journal: Journal of Energy Resources Technology

Links: https://www.researchgate.net/publication/382275661_Superstructure-based_Approach_to_Assess_the_Heat_Pumping_and_Renewable_Energy_Integration_Potential_in_the_Sugar_Industry

Abstract: 

The study examines the feasibility of integrating heat pumps (HPs) and renewable energy in the sugar industry to advance decarbonization. It explores different routes for energy supply, contrasting them with a natural gas (NG)-fired base-case route. The alternatives include bio-digestion of beet and pulp waste to produce biomethane (bio-CH4) used in the process, hydrogen boiler, and electric boiler (full electrification using renewable electricity). Each route also incorporates HPs, utilizing waste heat primarily from evaporation and drying processes. Additionally, CO2 capture (CC) units can be optionally installed. Evaluation of the superstructure employs a systemic methodology with total specific cost (€/t sugar) and total specific emissions (tCO2/t sugar) as objective functions for each route. Detailed blueprint (BP) models of sugar production for each route cover mass and energy balances, CAPEX and OPEX, and material and energy resource costs. Optimization is conducted using the OSMOSE Lua framework with a mixed integer linear programming (MILP) approach. Three energy scenarios (2023, 2030, and 2050) are established, influencing prices of NG, hydrogen, electricity, and CO2 emissions. In the 2023 scenario, integrating bio-CH4 with HP emerges as the most cost-effective option, reducing costs by 15% compared to the base-case. However, the optimal solution adds CC and HP to the bio-CH4 route, reducing costs by 9% while achieving a 133% emissions reduction, resulting in net negative emissions. By 2030, routes with HP become more favorable with slightly lower electricity and hydrogen prices. bio-CH4 with HP and CC remains the best choice, cutting costs by 60% and maintaining 133% emissions reduction. In the 2050 scenario, decreased electricity and hydrogen prices, coupled with a higher CO2 emission price, make the base-case the most expensive. Nonetheless, bio-CH4 routes remain viable, with hydrogen and electric boiler-based routes also feasible due to cheaper energy prices.

Authors: Nienke D'Hondt, Francisco Mendez Alva, Greet van Eetvelde, UGent

Journal: Sustainability mdpi

Links: https://www.researchgate.net/publication/379167180_Introducing_Industrial_Clusters_in_Multi-Node_Energy_System_Modelling_by_the_Application_of_the_Industry-Infrastructure_Quadrant

https://biblio.ugent.be/publication/01HSV1C57SZC4KWERV7DA3Z24H 

Abstract: 

To reach climate neutrality and circularity targets, industry requires infrastructure guaranteeing available, accessible, affordable, and sustainable supply of renewable energy and resources. The layout and operation of the required grids are a key topic in energy system modelling, a research field under constant development to tackle energy transition challenges. Although industry is a core player, its transformation and related policy initiatives are not yet fully reflected, resulting in a research gap. The industrial cluster concept, stimulating local cross-sectoral co-operation, circularity, and optimisation, offers untapped potential to improve the spatial representation of industry in energy system models and paves the way for cluster transition research. This paper introduces the Industry–Infrastructure Quadrant to visualise the relationship between industry and infrastructure presence by means of five distinct area categories. A complementary methodology integrates industrial clusters for multi-node selection in energy system models, solely relying on open-source data and cluster algorithms (DBSCAN). A case study applied to Belgium results in ten nodes to represent the territory, accurately reflecting crucial infrastructure elements and future needs whilst improving industry representation in terms of space and composition. The work serves as a first step towards a deeper understanding of the prominence of industrial clusters in sustainable energy systems.

Authors: Enya Lenaerts, Negar Namazifard, Nienke d'Hondt, Pieter Valkering, Juan Correa Laguna

Journal: IEEE 20th International Conference on the European Energy Market (EEM), 2024

Link: https://ieeexplore.ieee.org/document/10608951

Abstract:

We discuss the development and functionalities of the Regionalization Tool (RT) which presents a user-friendly post-processing procedure of scenario results, provided by a long-term energy system investment planning model, as well as a national industrial database. The tool generates geographically explicit clusters of production or consumption volumes for different commodities, according to a selected pathway towards 2050. Doing so, the tool introduces a spatial component to the otherwise aggregated modeling results, thereby balancing the computational cost of the energy system model with increased geographical detail to be used as input for energy infrastructure investment decision models. We demonstrate this added value of the tool through a case study for a decarbonization scenario towards 2050 for Belgium, selected from the PATHS2050 study. The case study highlights the spatial component to the electrification trend that emerges in the scenario results, as well as a geographically varying volumes of hydrogen consumption and captured CO 2 , thereby providing input to infrastructure investment decision models.