Fuel Switching Strategies for Decarbonising the Glass Industry Using Renewable Energy and Hydrogen-Based Solutions

1. Introduction

Heavy industry accounts for around 70% of industrial emissions [1] and is classified as hard-to-abate due to structural and economic constraints [2]. Energy-related emissions, both direct and indirect, constitute nearly 80% of total emissions [3], highlighting the central role of energy-focused decarbonisation strategies across industrial subsectors. While process-emission-intensive sectors such as steel and cement focus on chemical and material innovations to address intrinsic chemical reactions that release CO₂, energy-emission-intensive sectors such as chemicals, aluminium, glass, and paper prioritise energy efficiency and fuel switching to reduce fossil fuel consumption.

Focusing on energy-related emissions, energy efficiency has contributed transversally to substantial gains in industrial decarbonisation driven by the large impact of energy on production costs. For instance, the energy intensity of steel production decreased from 50 GJ/t in 1960 to 20 GJ/t in 2020 [4], while that of glass manufacturing from about 20 GJ/t in 1930 to around 6 GJ/t in 1990 [5]. Nonetheless, as thermodynamic limits of material production are approached [6], complementary strategies to reduce the carbon intensity of energy use are increasingly needed.

Renewable energy integration is essential to the sustainability of fuel switching options such as hydrogen combustion and direct electrification, requiring the alignment of intermittent renewable electricity with the high and continuous thermal demands of heavy industry. Regardless of whether direct or indirect electrification pathways are considered [7], the magnitude and temporal mismatch between supply and demand requires careful strategic planning of renewable generation plants, energy infrastructure, and industrial systems. Industrial decarbonisation studies increasingly shift from treating renewable energy as exogenous to optimising its integration. While renewable energy discussions largely centre on electricity generation, the integration of renewables into hard-to-abate sectors is constrained by the limited feasibility of direct process electrification. In this context, green hydrogen offers a promising pathway to bridge this gap by converting renewable electricity into a flexible energy carrier capable of supplying high-temperature industrial thermal demand [8,9,10].

Table 1 summarises the most relevant contributions on renewable energy integration in hard-to-abate industrial sectors, reporting the industrial sector, plant size and site location considered, together with the modelling approaches adopted, the inclusion of sensitivity analyses, and the renewable energy sources, fuel switching options, and load-matching technologies examined. While these studies provide valuable insights into renewable energy integration in hard-to-abate industries, they are characterised by a high degree of heterogeneity in terms of the industrial sectors addressed, the technological solutions considered, and the scale of the analysed plants.

Reflecting its dominant contribution to industrial emissions, the metallurgical industry emerges as the most extensively analysed sector, whereas other energy-intensive industries, including glass manufacturing, remain comparatively underrepresented and often investigated under less homogeneous assumptions. Among the studies, plant capacities reach about 1 Mt/year for steel, cement, and aluminium, while only around 100 kt/year for glass. Regional coverage spans different continents, including Europe, Asia and North America, with solar and wind energy analysed depending on local availability and often studied in combination to identify optimal generation mixes. Hydrogen is frequently analysed as a comprehensive strategy to cover plant thermal energy demands, while batteries are included when direct electrification is possible or when electric loads are considered. Technical evaluations are conducted either with simulation or optimisation approaches [11], sometimes complemented by sensitivity analyses on economic and environmental parameters.

Among hard-to-abate industrial sectors, the glass industry is particularly relevant to investigate alternative fuel switching pathways, due to its limited plant size and energy intensity as well as the potential to combine direct electrification and hydrogen combustion in hybrid glass furnaces. Indeed, while electrification of glass production appears the most straightforward pathway, it is constrained by small nominal pull rates, restrictions in cullet use, lower flexibility, and shorter furnace lifetime [12]. On the other hand, hydrogen can offer more operational flexibility for renewable energy integration while exerting fewer constraints on existing melting furnaces.

Several studies have addressed decarbonisation pathways in the glass industry. At a sectoral level, technological roadmaps were defined for the UK under scenarios of reasonable action and radical transition [29], for Germany, highlighting possible compliance with carbon dioxide budgets targets of 2°C [30], and for Italy, contrasting green fuels with carbon capture [31]. However, none of these roadmaps is supported by quantification of renewable capacity addition requirements. Plant-specific analysis rely on the assessment of representative furnaces, such as a small 100 t/d container glass furnace [16], a large 300 t/d container glass furnace [12], or a large 800 t/d flat glass furnace [32]. Renewable energy integration has been based on existing nearby renewable plants [26], optimised based on economic and environmental performance [26] or on the achievement of specific decarbonisation targets [27].

This study pursues a twofold objective: first, to provide a general perspective on the decarbonisation opportunities and challenges of the glass sector; and second, to explore and assess potential renewable energy integration strategies through a representative case study. The analysis combines a detailed techno-economic assessment of electrification and hydrogen pathways with a comprehensive sensitivity analysis on key economic parameters. The case study illustrates the application of the methodology to a typical oxyfuel container glass furnace, integrated with renewable energy sources, electrochemical storage, and hydrogen infrastructure. The results aim to guide the sector’s energy transition by identifying broadly applicable, cost-optimal decarbonisation pathways. The work is organised as follows. Section 2 presents the issue of decarbonising the glass industry, outlining energy and carbon profiles. Section 3 presents technical constraints to decarbonisation of glass melting, focusing on electrification and hydrogen combustion as the most promising decarbonisation pathways. Section 4 describes a general methodological framework, including the system layout, energy balances and optimisation approach, key performance indicators, and detailed component modelling. Section 5 analyses the impact of design choices on decarbonisation costs and conducts a comprehensive sensitivity analysis under varying economic parameters. Lastly, Section 6 pinpoints the main findings and discusses their implications for deep decarbonisation of the glass industry through renewable energy.

2. Decarbonising the Glass Industry: Status and Prospects

Glass manufacturing is among the most energy-intensive industrial processes and has traditionally relied on fossil fuels, primarily natural gas. As a hard-to-abate sector, it faces persistent decarbonisation challenges, particularly in production contexts where high-temperature thermal demand remains dominant.

The glass industry comprises several product categories, including container glass, flat glass, glass fibres, and specialty glasses, each characterised by different production volumes and energy requirements. Among these, container and flat glass represent the largest shares of production, accounting respectively for 44% and 29% at the global level, 50% and 25% in the US, and 58% and 23% in the EU [33].

Figure 1 schematically illustrates the glass production process, showing the main steps together with their characteristic temperature ranges and typical SEC and highlighting the magnitude of the thermal and energy demands. The glass production process can be divided into four main steps: batching, melting, forming, and finishing. Batching and melting are common to all glass types, while forming and finishing differ depending on the product segment. Melting is the step reaching the highest temperatures, typically in the range of 1500–1600 °C and is the most energy-intensive step of the process. In container glass manufacturing, it accounts for 80% of the total energy consumption, of which about 90% is supplied by fossil fuels, primarily natural gas. The extreme peak temperatures required determine the critical challenges associated with modifying or decarbonising this process. This schematic represents a general overview, with temperatures subject to variation based on production type and recycled material content.

From a sectoral perspective, the glass industry is characterised by significant environmental impact and overall energy consumption, with a strong reliance on natural gas due to its high thermal requirements. Several studies in the literature have quantified these impacts and energy uses at regional and global scales. While global energy consumption amounts to approximately 650 EJ (over 180,000 TWh), highlighting the relatively modest contribution of the glass sector, the data summarised in Table 2, arranged by the authors and constructed using data from the literature, provide a consistent picture of the industry’s current energy and carbon profiles.

The discrepancies observed between the regional data for the US and Europe and the global figures likely reflect the use of alternative, more carbon-intensive fuels in certain production contexts, in addition to natural gas. This highlights that energy mixes can vary significantly across regions, influencing the environmental profile of glass production.

To place the energy and emission impacts of the sector in context with glass products, Table 3 reports the specific energy consumption (SEC, in kWh/kg) for different glass product categories [38]. The melting and refining stage dominates the energy demand across all products, accounting for roughly 50–60% of the total energy required for unit mass of product, in general referred as Specific Energy Consumption (SEC). Product-specific differences are evident: flat and container glass require moderate energy (around 2–3 kWh/kg), while specialty products such as “other pressed and blown glass” and glass wool show higher intensities, up to 5 kWh/kg. Forming and finishing stages contribute smaller shares, but still represent meaningful portions of total energy, highlighting opportunities for efficiency improvements across multiple process steps.

Beyond general sector-level figures, which can only provide a broad indication, it is useful to examine energy use and CO₂ emissions relative to actual production. To this end, more detailed data on specific glass types help illustrate how energy and carbon intensity vary across products and regions. From a product perspective, median specific emissions in the EU range from 0.36 tCO₂/t for coloured container glass to 0.50 tCO₂/t for flat glass, while in the US, values are slightly higher, at 0.40 tCO₂/t for container glass and 0.54 tCO₂/t for flat glass [42,43,44]. Energy-related emissions dominate across all glass types, accounting for 75–95% of total emissions, with process emissions contributing the remainder [45]. Fuel combustion represents the largest share (around 60–80%), while electricity uses accounts for 10–30%, varying with geographic location and production context [45].

As a result, several glass companies have committed to reducing their carbon emissions, defining short- and long-term decarbonisation targets for Scope 1, 2, and 3 emissions. Regarding EU container glass companies, Scope 1 and 2 emission reduction targets ranging from 25% to 50% have been established for 2030, with more ambitious values of up to 64% set for 2035 and carbon neutrality by 2050 [46]. For Scope 3 emissions, the most ambitious target identified corresponds to a 37.5% reduction by 2034 [46]. The targets set correspond to average annual reduction rates of around 4% for Scope 1 and 2 emissions and around 2% for Scope 3, testifying the relevance of the commitment made.

3. Decarbonising the Glass Industry: Technical Framework

Several decarbonisation strategies have been proposed for the glass industry [45,47,48,49,50,51,52,53,54], most of which focus on addressing the key challenge of supplying the very high thermal power required for melting in glass furnaces with low-carbon energy. Broadly, these strategies can be grouped into three categories:

(i)

general implementation of energy efficiency and savings measures;

(ii)

replacement of conventional fuels through electrification of the melting process supported by renewable electricity;

(iii)

process-oriented combustion modifications, such as oxyfuel combustion and the use of green hydrogen in blended combustion. Together, these approaches aim to maintain process performance while significantly reducing carbon emissions.

Examining the entire process provides insight into where targeted interventions can maximize energy use and carbon savings. As illustrated in Figure 2(a), glass melting requires a very specific temperature profile to ensure proper elimination of gas bubbles and thermal homogenisation of glass properties. As a result, glass melting furnaces share common structural features, including a melting tank, refining zones, and delivery systems, but differ in energy supply, firing direction, and heat recovery mode, as shown in Figure 2(b). The most common furnace technologies—recuperative, regenerative, oxyfuel, and all-electric—differ in efficiency, scale, and operating costs, which make them more or less suitable for specific glass types.

Recuperative furnaces, used widely for smaller or cost-sensitive production, exchange heat continuously through metal or ceramic recuperators that preheat combustion air up to about 750 °C, allowing stable firing, fine tuning of temperature profiles, low investment cost, and relatively low NO_x emissions, but at the expense of higher fuel consumption and lower overall thermal efficiency. In contrast, regenerative furnaces, either cross-fired or end-port, use paired checker-brick chambers that alternately store and release heat, preheating the combustion air to 1100–1300 °C for significantly higher efficiency. Cross-fired regenerative furnaces, predominant in float glass production, place burner ports on opposite sides to tailor temperature profiles for high-quality fining, though they are less energy-efficient and more expensive to build. End-port regenerative furnaces, the most common for container glass, use two ports on one side to form a long U-shaped flame, giving excellent energy utilization but limited longitudinal energy control, high investment cost, and high NO_x emissions. Oxy-fuel furnaces fire eliminates nitrogen-heating losses, achieving very high specific loads, reduced fuel use, and drastically lower NO_x emissions, with the drawback of costly oxygen generation and condensation issues in flue gases. All-electric furnaces melt batch exclusively by high-current molybdenum electrodes, enabling compact, low-emission operation ideal for high-purity glasses, though they require expensive uninterrupted power supply and are limited for melts with strong infrared absorption or strongly oxidizing conditions.

Figure 3 provides a schematic overview of typical input power and pull rates for different furnace types. While the figure is illustrative rather than exhaustive, it allows identification of plausible furnace sizes for various technological solutions. All-electric furnaces are generally limited to smaller scales (below 10 MW) and lower productivity (below 250 tonnes/day of production), whereas hybrid designs combine the high efficiency of electric and oxyfuel melting with the size advantages of regenerative furnaces.

Figure 4 illustrates representative changes in heat transfer modes associated with hybrid furnaces operating at substantially increased levels of electric boosting, as well as with hydrogen-fired configurations. While a limited electric contribution is already present in many conventional glass furnaces, further electrification alters the balance between radiative and convective heat transfer, modifying convection patterns within the glass melt. Similarly, hydrogen combustion introduces distinct flame characteristics. These changes can influence product quality, helping to explain the cautious uptake of technological innovations in the glass industry. High electric boosting reduces radiative heat transfer above the melt while enhancing convective heat transfer within the melt, effects that can be mitigated through appropriate furnace design [55]. Hydrogen combustion, by contrast, features longer, hotter flames with lower emissivity; however, numerical [56] and experimental studies [57] confirm the possible interchangeability with natural gas. From an infrastructure perspective, all major decarbonisation options entail a substantial increase in system power levels and in the input of renewable energy.

Electrification entails a substantial increase in the use of electricity within the plant, not necessarily through dedicated on-site generation, but by leveraging industrial facilities as flexible and large-scale consumers capable of valorising electricity produced by renewable power plants. This shift raises challenges related to power quality and grid integration. Hydrogen-based pathways follow a similar strategy, enabling the conversion of renewable electricity into a storable energy carrier that can be used to supply thermal demand when direct electrification is constrained. This approach, however, introduces additional energy losses due to electrolysis, currently operating at 65–70% efficiency, and requires compatible burners, adapted pipelines, and enhanced safety systems.

4. Methodological Framework for Assessing Renewable-Based Electrification and Hydrogen Pathways in the Glass Industry

As discussed in the previous section, the decarbonisation of hard-to-abate industrial sectors as glass sector ultimately relies on the use of renewable electricity to supply high-temperature thermal energy. This can be achieved either through direct electrification of industrial processes or indirectly through energy storage vectors such as hydrogen, which provides a clear link between renewable power generation and continuous thermal energy requirements.

In the glass industry, this challenge is compounded by the large scale of industrial furnaces, with thermal power requirements of several tens of megawatts, and by the fact that process modifications and additional plant components inevitably increase system complexity and costs. Under these conditions, assessing decarbonisation options requires not only a technical evaluation, but also a consistent economic comparison of alternative strategies. Taking into account these considerations, this section proposes a methodological framework to systematically assess renewable-based electrification and hydrogen pathways in the glass industry. Beyond identifying cost-optimal solutions for a single configuration, the proposed methodological approach is designed to explore the conditions under which different decarbonisation strategies become viable. By systematically varying key design, dimensional, and operating parameters, the framework enables the identification of the regimes in which direct electrification, hydrogen-based solutions, or hybrid configurations represent the most suitable option for glass furnace decarbonisation.

The framework is then applied to a representative case study to translate general decarbonisation concepts into a coherent quantitative analysis. Several plant configurations are examined in order to assess the respective margins and trade-offs associated with direct electrification, hydrogen use, and their combined application in hybrid solutions. The analysis explicitly acknowledges the complexity of the problem, given the large scale of industrial glass furnaces and the need to preserve strict product quality requirements. Accordingly, the modelling framework integrates system boundaries and energy balances with optimisation methods and performance indicators, supported by a detailed representation of the relevant process components.

4.1. System Layout and Energy Balances

Figure 5 illustrates three alternative integrated system layouts, selected as representative of the main renewable energy integration options for the glass industry: direct, battery-based, and hydrogen-based integration. In the direct integration configuration, Fig. 5(a) electricity generated by solar and/or wind plants is supplied directly to the furnace. In the battery-based configuration, Fig. 5(b), surplus electricity is stored in batteries and later used on-site to offset electricity demand. In the hydrogen-based configuration, Fig. 5(c) excess electricity is routed to an electrolyser, for example a Proton Exchange Membrane (PEM), where it is converted into hydrogen that is either supplied to the furnace burners or compressed and stored for later use, thereby reducing natural gas consumption.

The electric balance of the integrated system under different configurations is described by Eq. (1), where $W_{PV}^t$ and $W_{wind}^t$ are the hourly solar and wind power generation, $W_{gr,in}^t$ and $W_{gr,out}^t$ are the hourly electricity import from and export to the electricity grid, $W_{fur}^t$ is the furnace electrical input (including oxygen generation requirements), $W_{BSS,ch}^t$ and $W_{BSS,dis}^t$ are the hourly charge and discharge of the battery storage, $W_{PEM}^t$ is the power consumption of the electrolyzer, and $W_{H_{2}C}^t$ is the power consumption of the hydrogen compressor.

$$\left\{\begin{array}{c}{W}_{PV}^t+W_{wind}^t+W_{gr,in}^t-W_{gr,out}^t-W_{fur}^t=0Directintegration\\ W_{PV}^t+W_{wind}^t-W_{BSS,ch}^t+W_{BSS,dis}^t+W_{gr,in}^t-W_{gr,out}^t-W_{fur}^t=0Batteryintegration\\ W_{PV}^t+W_{wind}^t-W_{PEM}^t-W_{H2C}^t+W_{gr,in}^t-W_{gr,out}^t-W_{fur}^t=0Hydrogenintegration\end{array}\right.$$

(1)

The thermal balance of the integrated system under different configurations is described by Eq. (2), where $Q_{gr,in}^t$ is the thermal energy input from the natural gas grid, and $Q_{fur}^t$ represents the furnace thermal input, $Q_{PEM}^t$ is the energy content of hourly electrolyzer hydrogen generation, and $Q_{H_{2}S,ch}^t$ and $Q_{H_{2}S,dis}^t$ are the hourly charge and discharge of the hydrogen storage tank.

$$\left\{\begin{array}{c}{Q}_{gr,in}^t-Q_{fur}^t=0Directintegration\\ Q_{gr,in}^t-Q_{fur}^t=0Batteryintegration\\ Q_{PEM}^t-Q_{H2S,ch}^t+Q_{H2S,dis}^t+Q_{gr,in}^t-Q_{fur}^t=0Hydrogenintegration\end{array}\right.$$

(2)

4.2. Optimisation Formulation and Key Performance Indicators

Among the possible optimisation criteria, cost minimisation is selected in this study to reflect the central role of economic feasibility in guiding investment and decisions in energy-intensive industrial processes. Optimal hourly energy supply for the hybrid glass furnace is determined using a Mixed Integer Linear Programming (MILP) optimisation, solved with an hourly time resolution over a 7-days horizon. The objective function, $f_{obj}$, is defined as the melting cost, calculated by accounting for the costs of natural gas and electricity, obtained by multiplying purchase volumes $Q_{gr,in}^t$ and $W_{gr,in}^t$ by their specific prices $c_{ng}$ and $c_{el}$, the costs of direct carbon emissions obtained by multiplying the natural gas input by the CO2 emission factor $E{F}_{ng}$ and the price of CO2 $c_{C{O}_2}$, and the cost of water, obtained by multiplying water purchase volumes $H_2{O}_{in}^t$ by its price $c_{H_{2}O}$, [58].

$$f_{obj}=\sum _t^T\left[Q_{gr,in}^t\cdot c_{ng}+W_{gr,in}^t\cdot c_{el}+Q_{gr,in}^t\cdot E{F}_{ng}\cdot c_{C{O}_2}+{H_{2}O}_{in}^t\cdot c_{H_{2}O}\right]\cdot ∆t$$

(3)

The economic performance is evaluated by calculating specific melting costs, ${SC}_{melt}$, as the sum of natural gas, electricity, carbon, and water costs, $C_{ng}^t$, $C_{el}^t$, $C_{C{O}_2}^t$, and $C_{H_{2}O}^t$, divided by the amount of glass produced, obtained by multiplying furnace size ${sz}_{fur}$ by the number of operative days $N_d$

$${SC}_{melt}={\displaystyle \frac{C_{melt}}{{sz}_{fur}\cdot N_d}}={\displaystyle \frac{\sum _t^{8760}{C}_{ng}^t+C_{el}^t+C_{C{O}_2}^t+C_{H_{2}O}^t}{{sz}_{fur}\cdot N_d}}$$

(4)

The environmental performance is assessed by calculating specific melting emissions, $S{E}_{melt}$, as the sum of direct emissions from natural gas combustion, obtained by multiplying natural gas imports $Q_{gr,in}^t$ by an emission factor $E{F}_{ng}$ and indirect emissions from electricity consumption, obtained by multiplying electricity imports $W_{gr,in}^t$ by a variable grid emission factor $E{F}_{el}^t$ [59], divided by the amount of glass produced.

$$S{E}_{melt}={\displaystyle \frac{E_{melt}}{{sz}_{fur}\cdot N_d}}={\displaystyle \frac{\sum _t^{8760}{Q}_{gr,in}^t\cdot {EF}_{ng}+W_{gr,in}^t\cdot {EF}_{el}^t}{{sz}_{fur}\cdot N_d}}$$

(5)

Beyond operational considerations, the costs of CO₂ avoided, or levelised decarbonisation costs, $LCOD$, are defined as the sum of capital (CAPEX), annualised operational (OPEX), and replacement expenditures (REPEX) for the various components involved, $CAPE{X}_j$, $OPE{X}_j$, and $REPE{X}_j$, minus the annualised operational energy cost savings compared to the conventional configuration, $C_{melt}-C_{melt}^0$, divided by the overall carbon reductions compared to the conventional configuration, $E_{melt}^0-E_{melt}$.

$$LCOD={\displaystyle \frac{\sum _j^{N_c}\left(CAPE{X}_j+{\sum }_n^{N_y}\frac{OPE{X}_j}{{\left(1+d\right)}^n}+REPE{X}_j\right)+\sum _n^{N_y}\frac{C_{melt}-C_{melt}^0}{{\left(1+d\right)}^n}}{(E_{melt}^0-E_{melt})\cdot N_y}}$$

(6)

Obviously, the economic model proposed does not explicitly capture all scale- and implementation-related effects, but it remains useful for a first methodological analysis. The comparison between the decarbonisation costs of different design configurations can inform on the optimal decarbonisation pathways to be pursued.

5. Case Study and Results

To translate the general decarbonisation strategies discussed into a quantitative framework, this section focuses on a representative case. The analysis considers an oxyfuel furnace, with a size $s{z}_{fur}$ of 300 t/d, operating at constant pull rates throughout the entire year and a nominal SEC of 3.8 GJ/t (about 1 kWh/kg).

To investigate performance under varying system designs and constraints, a comprehensive sensitivity analysis is performed on furnace electric boosting levels, ${fur}_{bst}$, renewable energy coverage, $co{v}_{ren}$, renewable energy mix, $mi{x}_{ren}$, battery storage and hydrogen electrolyzer coverage, $co{v}_{BSS}$ and ${cov}_{PEM}$, as well as electricity, natural gas, and CO2 price, $c_{el}$, $c_{ng}$, and $c_{CO2}$. The details are provided in Appendix A. The thermal and electric SEC of the glass furnace are assumed to be linear with boosting levels, as in [27]. Beyond the parameters detailed in Appendix A, additional assumptions include a water cost $c_{H_{2}O}$= 3.58 EUR/m³ and a natural gas emission factor $E{F}_{ng}$ equal to 0.2 tCO₂/MWh.

The case study is parameterised using energy-system data representative of the European context, with a specific focus on Italy. For renewable plants, nation-wide solar and wind hourly load profiles, $l{p}_{PV}$ and $l{p}_{wind}$, are obtained from ENTSO-E [60], while capacity factors $c{p}_{PV}$ and $c{p}_{wind}$ are derived from TERNA [59]. The electrolyzer load factor $l{f}_{PEM}$ is assumed to range between 15–100% of nominal power, with efficiency varying linearly with the load factor, as in [61], and a standby power consumption of 1.5%, as in [58]. For battery and hydrogen storage components, constant charge and discharge efficiencies ${\eta }_{BSS,ch|dis}$ and ${\eta }_{H2S,ch|dis}$, have been considered, with constraints from [62]. A discount rate $d$ of 4% is assumed, and the evaluation is conducted over a 20-year period.

5.1. Operational Behaviour

To capture the system behaviour under a range of design and operating conditions, multiple integration scenarios were simulated. As an illustrative example, Figure 6 shows the electric and thermal energy balances for a representative week of the year under direct, battery, and hydrogen integration scenarios, assuming 50% furnace electric boosting, 75% renewable energy coverage, a balanced renewable mix (50% solar–50% wind), and battery and electrolyzer coverage both set at 40%.

To ensure the practical relevance of the analysis, the operational simulations are complemented by the adoption of representative electricity, natural gas, and carbon price assumptions, enabling a consistent economic assessment of the different integration strategies. Reference prices are set equal to 150 EUR/MWh for electricity, 50 EUR/MWh for natural gas, and 75 EUR/t for CO2, reflecting current price levels in Italy [63,64].

In the direct integration scenario, excess renewable generation is entirely exported to the grid, necessitating electricity purchases during periods of low renewable output and natural gas purchases to meet thermal demand. Battery integration allows surplus electricity to be stored, minimising external electricity purchases. The concept of hydrogen integration is to convert excess electricity into hydrogen, which is used to reduce natural gas consumption.

In the battery integration scenario, only low levels of battery coverage appear beneficial across. At low boosting levels, the large electricity excess cannot be directed to the furnace given the minimal electric input required, whereas at high boosting levels, the direct use of electricity minimises the need for storage in batteries. In the hydrogen integration scenario, lower levels of electricity excess ratio are obtained across all furnace boosting levels. Low furnace boosting levels minimise electricity excess provided that minimum electrolyzer coverage levels are implemented.

5.2. Techno-Economic Assessment

This section presents the results of the techno-economic assessment of renewable energy integration in glass furnaces, focusing on the combined effects of furnace electrification, renewable energy coverage, and solar–wind generation mix. The analysis focuses on decarbonisation costs and cost-optimal decarbonisation pathways, supporting industrial decision-making with quantitative design criteria regarding renewable energy plants, furnace electrification levels, battery storage, and hydrogen infrastructure.

5.2.1. Direct Integration

The operational cost and emission reductions that can be achieved through the direct integration of solar and wind plants with the glass furnace. Figure 7 reports levelised decarbonisation cost maps for low, medium and high boosting levels. Each curve represents combinations of renewable energy coverage and furnace electrification that yield the same levelised cost of CO₂ avoided, explicitly illustrating the trade-off between renewable availability and electric boosting in minimising decarbonisation costs. The achievement of negative values means that the required progress in terms of emissions reduction is profitable when contrasting the increased capital expenditures and the resulting operational cost savings. For electrification rates of 20%, the lowest cost of CO₂ avoided is equal to −130 EUR/tCO₂. At a level of 50%, the optimal value is −65 EUR/tCO₂, whereas at boosting levels of 80%, the optimal value is −40 EUR/tCO₂ for a renewable coverage of 75%. Higher electrification levels progressively shift the cost-optimal region towards higher renewable penetration, reflecting the increasing reliance on low-carbon electricity.

Figure 8 provides a graphical interpretation of the trade-off between emission reductions and decarbonisation costs for the different system configurations analysed. Each point in the figure represents a feasible system configuration, defined by a specific combination of furnace electric boosting, renewable energy coverage, and renewable mix. The horizontal axis quantifies the achieved CO₂ emission reduction relative to the reference case (natural gas), while the vertical axis reports the levelised cost of CO₂ avoided.

The distribution of points highlights how technological choices shift the balance between environmental and economic performance. Configurations characterised by higher furnace boosting and greater renewable coverage tend to cluster towards higher emission reductions, whereas cost-effective solutions are favoured by higher shares of wind energy in the renewable mix. This reflects the ability of wind generation to provide a more continuous low-carbon electricity supply, better aligned with the steady thermal demand of glass furnaces. Overall, the figure shows that deep decarbonisation of glass melting can be achieved without disproportionate cost penalties when electrification and renewable integration are coherently designed, and it visually identifies the regions where favourable compromises between cost and emission reduction are attained.

5.2.2. Advanced Integration

A realistic representation of the economic and environmental suitability of advanced integration scenarios for the case study can be obtained by referring to the current capacity addition mix of Italy. In 2023, this was correspondent to 10% wind and 90% solar [65].

More detailed maps of cost and emission reductions achieved by battery and hydrogen integration compared to the conventional furnace are discussed in Appendix B.

Battery integration achieves higher cost and emission reductions for large levels of renewable coverage and medium and large levels of furnace boosting. Conversely, hydrogen integration yields maximum cost and emission reductions for low boosting levels. The analysis reveals the importance of proper sizing since optimal values of battery coverage are around 30–40% while electrolyzer coverage around only 20–30%. Figure 9 shows maps of levelised decarbonisation costs for battery and hydrogen integration under varying furnace boosting, renewable coverage, and battery and electrolyzer coverage.

No negative values are obtained in these cases, highlighting cost increases compared to direct integration and conventional fired furnaces. In the battery integration scenario, minimum costs of CO₂ avoided are in the range of 100–200 EUR/t. The higher the boosting levels, the higher the renewable coverage for which these optimal values are obtained. At 50% boosting, optimal renewable coverage is around 50% while it reaches 75% at 80% boosting. In the hydrogen integration scenario, optimal values of decarbonization costs are in the range of 200–300 EUR/t. Except for the region of low furnace boosting levels and renewable coverage in the range of 75–100%, the lower the electrolyzer coverage, the lower the decarbonization costs, highlighting how the costs of the technology significantly impact on its cost-effectiveness. The relationship between decarbonisation costs and emission reduction under realistic renewable capacity addition mix are shown in Figure 10(a) for direct, battery, and hydrogen integration. Both batteries and hydrogen extend maximum emission reductions from around 50% up to 80%. For battery integration, solutions that allow for emissions reduction higher than 55% correspond to boosting levels above 60% and renewable coverage above 75%, and yield minimum decarbonization costs of 140 EUR/t, with average values of 215 EUR/t. For hydrogen integration, these correspond to renewable coverage above 125% and boosting levels below 70%, yielding minimum decarbonization costs of 315 EUR/t, with average values of 385 EUR/t. This reveals that, at current energy and carbon prices, batteries represent a more cost-effective solution for the deep decarbonization of the glass industry.

5.3. Sensitivity Analysis

The sensitivity of deep decarbonisation cost options (>55% emission reduction) for battery and hydrogen integration was analysed under varying electricity, natural gas, and carbon prices, as shown in Figure 11. The comparison is performed on a consistent basis, with average emission reductions of about 60% for both configurations. Decarbonisation costs exhibit different sensitivities to energy prices depending on the integration strategy.

Battery integration costs are only weakly affected by electricity prices, as batteries primarily shift the timing of renewable electricity use rather than increasing overall electricity demand. Consequently, their economic performance is mainly driven by natural gas and carbon prices, which determine the value of avoided fuel consumption and emissions. Hydrogen integration, by contrast, shows a pronounced sensitivity to electricity prices, reflecting the additional electricity demand and conversion losses associated with electrolysis. In this case, electricity price variations directly affect the cost of hydrogen production and, therefore, the cost of CO₂ avoided.

In both configurations, lower electricity prices combined with higher natural gas and carbon prices improve economic performance. Minimum decarbonisation costs range from −425 to 240 EUR/t for battery integration and from −445 to 420 EUR/t for hydrogen integration. Despite these differences, optimal design parameters remain largely stable across price regimes and consistently require high levels of electrification (≈80% boosting). Battery-based solutions are favoured at moderate renewable coverage (≈75%), whereas hydrogen integration requires higher renewable energy availability (≈125%), confirming large-scale electrification as a necessary condition for deep decarbonisation. Fig. 12 compares the cost-effectiveness of battery and hydrogen integration across combined fuel and electricity price regimes. At current electrolyser costs (1200 EUR/kW), battery integration is generally more cost-effective across a wide range of price conditions.

Figure 12. Maps of battery and hydrogen decarbonisation cost differences: (a) electrolyzer price of 1200 EUR/kW, (b) electrolyzer price of 700 EUR/kW, (c) electrolyzer price of 300 EUR/kW.

Figure 12. Maps of battery and hydrogen decarbonisation cost differences: (a) electrolyzer price of 1200 EUR/kW, (b) electrolyzer price of 700 EUR/kW, (c) electrolyzer price of 300 EUR/kW.

Hydrogen integration emerges as a competitive option under price regimes characterised by high fuel and carbon prices combined with low electricity prices, reflecting its ability to decouple renewable electricity availability from continuous thermal demand. As electrolyser costs decline, the set of price regimes in which hydrogen becomes economically attractive expands substantially. This trend highlights hydrogen not merely as an alternative to batteries, but as a complementary flexibility option that becomes increasingly relevant in systems with high renewable penetration, limited electrical storage, and stringent decarbonisation targets. In this context, hydrogen enables the temporal extension of renewable electricity use beyond direct electrification, supporting continuous furnace operation. It should be noted that the optimisation framework relies on linear cost assumptions, implicitly if energy prices and technology costs scale proportionally with system size and utilisation. In practice, price formation mechanisms, grid constraints, contractual arrangements, and economies of scale may introduce non-linear effects that are not captured by the model. Accordingly, the identified price thresholds should not be interpreted as strict competitiveness boundaries, but rather as indicative markers of the conditions under which hydrogen-based solutions begin to deliver system-level value. Nevertheless, the analysis remains valuable in consistently identifying relative trends, trade-offs, and boundary conditions under which electrification-, battery-, and hydrogen-based strategies contribute to cost-effective deep decarbonisation.

6. Conclusions

Deep decarbonisation of hard-to-abate industrial sectors ultimately hinges on the ability to supply large and continuous thermal energy demands with low-carbon energy sources. In energy-intensive industries such as glass manufacturing, this challenge is exacerbated by the scale of industrial furnaces, the stringent requirements on process stability and product quality, and the intermittency of renewable electricity generation.

Under these conditions, electrification, either direct or mediated through energy storage vectors such as hydrogen, emerges as an interesting opportunity, but its practical feasibility depends on the coordinated design of process technologies, energy systems, and economic boundary conditions.

In this context, the paper proposes a methodological approach to assess alternative decarbonisation strategies for hard-to-abate industrial sectors, and demonstrates its application through a case study in glass manufacturing.

A representative 300 t/d industrial glass furnace is used as a case study to exemplify how variable renewable electricity can be integrated into high-temperature processes through three alternative configurations: direct electrification, battery-supported electrification, and hydrogen-supported electrification via on-site electrolysis and storage.

The study quantifies both operational behaviour and system performance, focusing on energy use, emissions, and economic costs. Under current electricity, natural gas, and carbon prices (150 EUR/MWh, 50 EUR/MWh, and 75 EUR/t, respectively), results for the direct integration scenario show that high levels of furnace electrification combined with wind-dominated renewable supply achieve levelised decarbonisation costs of –40 EUR/t, demonstrating the practical viability of this approach.

Under renewable mix of wind and solar energy that are consistent with current typical capacity additions, battery- and hydrogen-based integration strategies enable a substantial increase in achievable decarbonisation levels minimising the burden on the energy system. When combined with high furnace electrification and sufficient renewable availability, both configurations extend achievable emission reduction targets from around 50% of direct renewable integration to values approaching 80%.

For solutions achieving deep decarbonisation targets (>55%), battery integration is associated with lower levelised decarbonisation costs under current cost and price assumptions, whereas hydrogen-based solutions require higher renewable overcapacity to compensate for electrolysis losses and therefore exhibit higher specific costs. However, these results should not be interpreted as prescriptive with respect to technology deployment or storage sizing. The comparison highlights structural differences between the two pathways rather than identifying a universally optimal solution. Battery storage favours short-term load balancing and high utilisation of directly electrified furnaces, while hydrogen provides a complementary flexibility option that decouples renewable electricity generation from continuous thermal demand, albeit at the cost of additional conversion losses. Sensitivity analyses across a wide range of electricity, fuel, and carbon price regimes confirm that large-scale furnace electrification is a necessary condition for deep decarbonisation in all configurations. Within this framework, hydrogen pathways become increasingly relevant in scenarios characterised by high renewable penetration, limited electrical flexibility, and rising carbon costs, especially when electrolyser costs decline.

An analysis such as the one proposed inevitably involves simplifications and does not explicitly capture all the dimensional, technical, and organisational aspects that may complicate the real-world deployment of the investigated solutions. Some technical constraints related to plant integration of battery and hydrogen, operational flexibility, and scale effects are treated implicitly or assumed to be manageable, whereas in practice they may play a decisive role. Nevertheless, the proposed framework represents a valuable first step, as it allows the systematic comparison of alternative decarbonisation pathways under consistent assumptions, highlighting relative trends, trade-offs, and boundary conditions. In this sense, the analysis should be interpreted as an exploratory tool rather than a prescriptive design exercise, providing guidance on where deeper, technology-specific investigations are most warranted.