Techno-economic Parameters Research Articles

Economic viability assessment of bioenergy production from agroindustrial wastes through fast pyrolysis

Spanish mainland crops, including olives, almonds, and pistachios, play a pivotal role in capital circulation and employment generation. This comprehensive study explores the economic feasibility on the utilization of diverse agro-industrial wastes from Spanish harvests as a promising feedstock for bioenergy production through fast pyrolysis. The fast pyrolysis process is simulated using Aspen Plus® software, employing techno-economic parameters such as Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), break-even, and minimum product sales price to analyze economic viability of the project. Two different scenarios, based on experimental data from previous studies, are compared. The profitability of this proposal is closely linked to the harvest, which serves as a limiting factor. In all scenarios, the project requires two years to recover the initial investment, demonstrating significant profits once production begins. When considering the impact of scaling up, the NPV of Scenario 2 for PS reached €369.55 M€. Sensitivity analysis reveals that the profitability of pyrolysis depends on the bio-oil selling price and the total production volume. Overall, this study confirms the economic viability of producing bioenergy through fast pyrolysis using various biomass residues. It highlights the potential of harnessing bioenergy from Spanish agro-industrial waste, paving the way for sustainable and economically viable solutions, as indicated by the NPV and IRR metrics.

Co-Design of a Wind–Hydrogen System: The Effect of Varying Wind Turbine Types on Techno-Economic Parameters

Green hydrogen is crucial for achieving climate neutrality and replacing fossil fuels in processes that are hard to electrify. Wind farms producing electricity and hydrogen can help mitigate stress on electricity grids and enable new markets for operators. While optimizing wind farms for electricity production is well-established, optimizing combined wind–hydrogen systems is a relatively new research field. This study examines the potential profit of wind–hydrogen systems by conducting a case study of an onshore wind farm near the North Sea. Varying turbine types from high wind-speed turbines (with high annual energy production) to low wind-speed turbines (with high full-load hours) are examined. Findings indicate that in a combined hydrogen system, the low wind-speed turbines, which are sub-optimal for mere electricity production, yield lower levelized costs of hydrogen at a higher hydrogen production. Although high wind-speed turbines generate higher profits under current market conditions, at high hydrogen prices and low electricity prices, low wind-speed turbines can yield higher total profit at this site. Therefore, an integrated optimization approach of wind–hydrogen systems can, in certain cases, lead to better results compared to an isolated, sequential optimization of each individual system.

A probabilistic approach for the levelized cost of energy of floating offshore wind farms

This paper aims to analyze the levelized cost of energy (LCOE) of floating offshore wind farms from a probabilistic point of view. Understanding and addressing the uncertainty associated with the main parameters that influence the wind energy generated by a wind farm during its lifetime is crucial for the economic evaluation of offshore wind energy in the broader energy landscape. The methodology for probabilistic assessment of LCOE is introduced, and the uncertainty in input parameters are discussed. In a base case study, an assumed Floating Offshore Wind Farm (FOWT) consisting of 250 5-MW wind turbines is considered. The use of bias and randomness in key random variables is discussed and studied in detail. Results indicate that LCOE estimates of 15 EURc/kWh for offshore wind turbines are achievable with reasonable confidence, while estimates of 5 EURc/kWh require careful consideration of uncertainty in the wind farm’s parameters. The feasibility analysis showed that techno-economic parameters are more influenced by wind characteristics and efficient use of wind turbines than by the cost of the wind farm. This paper provides general guidance on how to carry out early-stage techno-economic analysis of FOWFs.

Spatial and temporal evolution of cost-competitive offshore hydrogen in China: A techno-economic analysis

China's ascendancy as the premier offshore wind power developer and principal hydrogen consumer underscores the expanding demand for green hydrogen, which is vital to its decarbonization trajectory. Offshore wind power is recognized as a crucial pathway for the future supply of green hydrogen, and further exploration of its development potential and economic viability can provide important insights for optimizing resource allocation, reducing greenhouse gas emissions, and ensuring energy security. This study analyzed the potential and regional economic differences in offshore wind energy for hydrogen production in China's coastal regions by considering the temporal evolution of techno-economic parameters and the spatial and temporal characteristics of geospatial resources. The findings suggest a potential hydrogen production capacity of 434–493 Mt/year in Chinese waters, with the average levelized cost of hydrogen projected to decline from 7.13 $/kgH2 in 2025 to 3.77 $/kgH2 by 2050, which is attributed to advancements in wind turbine technology and cost reductions in components. By 2030, hydrogen production of 40 Mt/year is expected to be cost-competitive, especially in Liaoning and Hebei, with projections indicating an increase to 435 Mt/year by 2050. Notably, floating wind power, contributing to 54 % of China's total offshore hydrogen capacity, holds significant promise for provinces such as Guangdong, Hainan, and Zhejiang. The study conclude that China's offshore hydrogen production has significant potential, with cost-competitive production achievable by 2030 and broad economic viability anticipated by 2050.

Towards Sustainable Energy Communities: Integrating Time-of-Use Pricing and Techno-Economic Analysis for Optimal Design—A Case Study of Valongo, Portugal

This paper presents a comprehensive analysis of optimal energy community design, leveraging time-of-use pricing mechanisms and techno-economic parameters. Focusing on a case study of Valongo, Portugal, this study explores the intricate interplay between energy infrastructure planning, economic considerations, and pricing dynamics. Through a systematic approach, various factors, such as renewable energy integration, demand–response strategies, and investment costs, are evaluated to formulate an efficient and sustainable energy community model. Time-of-use pricing schemes are incorporated to reflect the dynamic nature of energy markets and consumer behavior. By integrating techno-economic analyses, this study aims to optimize energy resource allocation while ensuring cost-effectiveness and environmental sustainability. The influence of optimized sizes of photovoltaics (PV), battery storage, and electrical vehicles (EVs) on self-sufficiency rates, self-consumption rates and CO2 savings is analyzed. The findings offer valuable insights into the design and implementation of energy communities in urban settings, highlighting the importance of adaptive strategies in the transition towards a resilient and low-carbon energy future. The novelty of this paper lies in its comprehensive approach to energy community design, which integrates time-of-use pricing mechanisms with techno-economic parameters. By focusing on the specific case of Valongo, Portugal, it addresses the unique challenges and opportunities present in urban settings. Additionally, the analysis considers the interaction between renewable energy production, demand profiles and investment costs, providing valuable insights for optimizing resource allocation and achieving both cost-effectiveness and environmental sustainability.

Rational capacity investment for renewable hydrogen-based steelmaking systems: A multi-stage expansion planning strategy

To improve energy efficiency and mitigate environmental impact, the iron and steel industry (ISI) is responsible for implementing a low-carbon transition. The renewable hydrogen-based steelmaking system (RHSS) has emerged as an innovative energy system in the pathway of reducing the carbon footprint of ISI. Nonetheless, the sustainable development requirements of RHSS emphasize the critical need for well-planned capacity investments to boost its economic viability. This study introduces a novel planning strategy for RHSS referred to as a multi-stage expansion planning (MSEP) model, which is aimed to minimize the levelized cost of crude steel over the planning horizon from 2025 to 2050 (EU climate neutrality year). The MSEP model incorporates the construction sequence and dynamic techno-economic parameters to formulate investment strategies that adapt to changing system requirements and external conditions over the planning horizon. To address the complexity of the MSEP model, a rolling-horizon approach (RHA) is proposed to solve the model sequentially while integrating updated information. The proposed approach is benchmarked against both the single-stage approach and the perfect-sight approach through case studies. The numerical results show that the proposed RHA (i) guarantees a rational capacity investment strategy for RHSS considering the construction sequence and dynamic techno-economic parameters over multiple planning stages; (ii) achieves a reduction in the levelized cost of crude steel by approximately 50% relative to the single-stage approach; (iii) resolves the convergence issues encountered with the perfect-sight approach while achieving an optimal solution in a reasonable time frame. Additionally, sensitivity analysis evaluates the uncertainties of multiple factors on the planning results, while the robustness analysis confirms the applicability and adaptability of RHA across various scenarios.

Techno-Economic Modelling of Bioclarified Water Recovery from Petroleum Wastewater Using ASPEN Software and Box-Behnken Design

Application and efficiency of bio-coagulants for petroleum wastewater biocoagulation, flocculation, and sedimentation (bioclarification) are still confined to laboratory proof-of-concept practice. The scale-up process design of bio-clarified water recovery from petroleum wastewater has been a major research lacuna. This paper presents ASPEN Base Case Scale-Up Simulation (BCSUS) using optimum laboratory experimental data and techno-economic predictive models for bioclarified water production from petroleum wastewater. The BCSUS, process design, and economics were performed in the ASPEN Batch Process Developer V10 environment. Predictive models for predicting and optimizing techno-economic parameters such as return on investment (ROI), payback time (PBT), and production rate (PR) were achieved in RSM via the Box-Behnken Design (BBD) technique of Design Expert V13. The base case techno-economic model gave batch size, annual production cost, Total Capital Investment (TCI), PBT, and ROI of 406kg, $9195, $631484, 5.18 years, and 17%, respectively, at the selling price of 2 $0.225. The regression models gave R values of 0.9984 for ROI and 0.9920 for PBT, while the production rate was 0.8867. This study shows that a petroleum wastewater bioclarification scale-up design is feasible.

To what extent can a phase-out of pesticides in viticulture be achieved? Learning from the efforts of a large farm network after 10 years

In accordance with European Directive 2009/128/CE, France has set up national policies to curb the reliance of agriculture on phytosanitary inputs. The DEPHY network is one such initiative;, which has been designed to drive, support and document the fieldwork of 3,000 farms voluntarily taking part in a collective input reduction programme. This study aimed to describe the achievements of the network’s viticultural sector, as its perennial crop is heavily reliant on pesticide applications. We chose a sample of 343 wine-growing systems across mainland France, analysed records of their vineyard operations from before they joined the network, and compared them to those from the 2017-2020 period. We used the Treatment Frequency Index (TFI) to assess pesticide reliance together with other techno-economical parameters: production costs, workloads, greenhouse gas emissions and disease control. A significant decrease in pesticide use was found over the whole study period, with, for instance, an average TFI reduction of 24 %, which is consistent with national objectives but unmatched outside of the network. We observed an overall shift towards pesticide inputs with lower repercussions on health or the environment, as well as different TFI reduction trajectories based on initial pesticide dependency. We described the alternative practices introduced to the systems, and our results suggest that reductions in pesticide use stem from quite small and minor changes made to the cropping systems. These new practices only slightly lowered the vineyards’ technical and economical performances in specific situations involving an indepth revamping of the systems; e.g., conversion to organic viticulture. To increase pesticide phase-out, further research is needed on redesigning cropping systems, and on how to scale up the network’s results to the national level.

Decisional tool development and application for techno-economic analysis of fungal laccase production

Textile and medical effluents causing bioaccumulation and biomagnification have been successfully biodegraded by fungal laccases. Here, a decision-making tool was developed and applied to evaluate 45 different laccase production strategies which determined the best potential source from a techno-economical perspective. Laccase production cost was calculated with a fixed output of 109 enzymatic units per batch (USD$per109U) and a sensitivity analysis was performed. Results indicate that optimization of enzymatic kinetics for each organism is essential to avoid exceeding the fermentation time point at which production titer reaches its peak and, therefore, higher production costs. Overall, the most cost-effective laccase-producing strategy was obtained when using Pseudolagarobasidium acaciicola with base production cost of USD $42.46 per 109 U. This works serves as platform for decision-making to find the optimal laccase production strategy based on techno-economic parameters.

Estimation of gross calorific value of coal: A literature review

ABSTRACT The gross calorific value (GCV), also known as the higher heating value (HHV) of coal is the amount of heat emitted upon complete combustion of coal. The GCV of coal is used to estimate various technoeconomic parameters such as boiler efficiency, combustion values, and production costs. This study seeks to examine the methods that are used to estimate the GCV of coal. These methods can be classified as either mathematical, experimental, or online methods.” It was found that linear and non-linear regression, differential scanning calorimetry (DSC), artificial intelligence (AI) methods have all been frequently used to estimate the GCV. The challenge with these approaches is that they include sophisticated machinery that demand expert operation, and the task is time consuming. Accurate and timely analysis of the GCV coal is a crucial stage in power plants, which can be achieved with online monitoring of the GCV of coal. There is not much literature on the topic of online monitoring of the coal GCV because it hasn’t been studied extensively. In power plant operation, online monitoring of the GCV of coal is a promising technology that hasn’t been studied extensively.

Techno-economic assessment of hybrid renewable energy system with multi energy storage system using HOMER

Energy storage systems (ESS) address the uncertainty of renewable energy sources (RES) in renewable energy based isolated microgrids. One type of ESS is unable to provide 100 % supply reliability. Moreover, there are either excess energy or reliability issues due to a lack of energy management techniques. Therefore, this paper proposes hybrid RES (HRES) with multiple-ESS (MESS), including battery and hydrogen storage system (HSS). The techno-economic analysis and optimal design of HRES-based microgrids, which include wind turbine (WT), solar photovoltaic (PV), fuel cell (FC), electrolyzer, HSS, and battery energy storage (BES) are performed using HOMER. The design objectives are to minimize the net present cost (NPC) and cost of energy (COE) in the proposed HRES with MESS. The analysis is performed for a multi-energy storage system. The results show that WT/PV/FC/Electrolyzer/HSS/BES/converter is the optimal microgrid with the lowest NPC of $838832, $679605, and COE of 0.232 $/kWh, 0.189 $/kWh at capacity shortages of 0.0 % and 1.0 %, respectively. The excess energy and unmet loads are also the lowest for the system at the same capacity shortage. The sensitivity analysis shows the impact of uncertain techno-economical parameters on NPC and COE.

Techno-economic simulation and sensitivity analysis of modular cogeneration with organic rankine cycle and battery energy storage system for enhanced energy performance

This paper presents a comprehensive evaluation of the efficiency and cost implications of a modular cogeneration plant integrated with a Battery Energy Storage System (BESS) and an Organic Rankine Cycle (ORC) plant. The objective is to assess the performance of the system when waste heat recovery through the ORC cycle is added to the exhaust line and compare it with the original system. Techno-economic parameters were analyzed for two different configurations: one with the maximum BESS size and another with the maximum Primary Energy Savings (PES).The results demonstrate that the incorporation of the ORC plant yields several notable outcomes. While there is a slight increase in the Simple Payback Period (SPB) and Net Present Value (NPV) in both configurations, the magnitudes of these increases are relatively low. In contrast, the introduction of the ORC cycle leads to substantial reductions in carbon dioxide (CO2) emissions and significant improvements in Primary Energy Savings (PES). For the Max PES configuration, a remarkable 25% reduction in CO2 emissions and a substantial 22% improvement in PES were observed. To further investigate the system's response to varying economic conditions, a sensitivity analysis was performed. The analysis considered the effects of changes in the cost of battery, Combined Heat and Power (CHP) unit, ORC components, electricity, and fuel on the system's Simple Payback Period (SPB) and Net Present Value (NPV). The results revealed that the sensitivity of the Max PES configuration to economic parameters was higher compared to the Max BESS system.These findings highlight the considerable advantages of integrating an ORC plant into a cogeneration system with a BESS. The significant reduction in CO2 emissions and the noteworthy improvement in Primary Energy Savings (PES) outweigh the relatively minor negative impact on the Simple Payback Period (SPB) and Net Present Value (NPV). This integrated configuration demonstrates both environmental benefits and energy efficiency improvements. The results provide valuable insights for the design, optimization, and decision-making processes involved in the implementation of cogeneration systems with energy storage, promoting sustainable and cost-effective solutions.

Optimizing energy management of hybrid wind generation-battery energy storage units with long-term memory artificial hummingbird algorithm under daily load-source uncertainties in electrical networks

Due to the unpredictable nature of renewable energy sources, it is necessary to have a storage system to store energy during off-peak hours and supply it during peak hours. However, if the storage units in a network are not properly connected, the benefits of the storage system cannot be realized. To improve the performance of radial distribution networks, this research proposes an optimal locating and sizing problem of battery energy storage (BES) and a renewable source of wind turbine distributed generation (WTDG). To solve this allocation problem, a multi-objective function (MOF) based on techno-economic parameters of total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices is optimized using a modified version of the Artificial Hummingbird Algorithm (AHA) that incorporates a long-term memory component. With this enhancement, the algorithm can make decisions based on multiple past experiences, allowing it to consider a wider range of promising locations during the optimization process. This integration of long-term memory reduces the risk of premature convergence or stagnation and allows the algorithm to gain a broader perspective, potentially leading to improved performance and exploration of the solution space. The results demonstrate the effectiveness of the Long-term Memory Artificial Hummingbird Algorithm (LMAHA) in optimizing the MOF and enhancing the performance of the electrical distribution networks of IEEE 33 and 69 bus.

Adaptive Decentralized Payback Normalization for Socio-Economic Rebalancing of RE subsidies - A Case Study

Clean energy subsidies are designed to stimulate consumer participation and deliver greater benefits to society. India has a fixed centralised federal subsidy model for residential rooftop solar (RTS). Different states have different tariff schemes for electricity consumption and RTS feed-in. Thus, grid parity, payback period (PBP), and willingness to pay (WTP) differ for consumers of different states and income classes, resulting in social and geographical disparity in RTS implementation. A Socio-Economic Rebalancing of RE subsidies thus becomes necessary. This paper proposes a novel adaptive decentralised heterogeneous PBP equalisation-based subsidy policy to enhance the WTP for all prosumers and ensure uniformity of RTS implementation. A two-stage subsidy optimisation is conducted using a case study in diverse states to achieve Grid Parity initially and subsequently desired PBP for all states and income classes. The proposed subsidy ensures grid parity for all, sufficient to transit all the prosumers to 100% net electricity from RTS. The subsidy is further normalised by equalising the PBP to a target PBP thereby ensuring uniformity of RTS implementation in the country bridging the rural-urban WTP disparity, leading to a better social scenario. The strategy is adaptive to the future, as the subsidy can be revised with changes in techno-economic parameters.

Techno-economic analysis for design and management of international green hydrogen supply chain under uncertainty: An integrated temporal planning approach

The international green hydrogen supply chain (HSC) is anticipated to play a pivotal role in the ongoing energy transition. Our research endeavors to assess the implications of considering fast timescale volatility in renewable energy sources and uncertainties in demand on the design and evaluation of the international green hydrogen supply chain. To achieve this objective, we introduce a comprehensive bi-level optimization method for evaluating the economic viability of the HSC between nations. In the upper level of the optimization, capacity investments are determined using Bayesian optimization and Hyperband (BOHB) for multiple components, including turbines, PV panels, batteries, water electrolyzers, hydrogen liquefaction plants, hydrogen tanks, and liquid hydrogen (LH2) ships. The lower level focuses on the optimal scheduling of LH2 shipments and the operation of each facility, utilizing a mixed-integer linear programming (MILP) model that considers lead time and uncertainties in both supply and demand. We conduct case studies involving nine projects, encompassing three potential import and export countries, to demonstrate and analyze the effectiveness of the proposed framework. Furthermore, we delve into the impacts of various factors, such as economies of scale, lead time, and the minimum operating constraint of the electrolyzer, on the economic performance of the green HSC project. The study also explores the optimal balance between facility installation scales and their relationships with techno-economic parameters, providing valuable insights for future research, technological advancement and supply chain design.

Improvement of Strategic Investment Decision Making Process in Petrochemicals Industry

Joint ventures, commercial conglomerates, and public-private partnerships must go through various stages of planning prior constructing mega-petrochemical projects. One of the most important steps is the Final Investment Decision (FID). Before approving and ordering large-scale petrochemical project construction, corporations and project partners must follow these steps. FID comes after careful preparation and before project implementation. Market attractiveness, proven production technology, and high investment return affect FID. The project's investment return—determined by IRR, NPV, and PP—is the main factor. An approved FID shows the company's willingness to invest and make a profit, while an unapproved FID prevents further losses if the project goes ahead. NPV, IRR, and PP not meeting criteria prevents a final investment decision. Economic return failure is caused by high taxes, total investment costs, product spread, capital costs, and operating costs. This study aims to improve the current techno-economic model in the petrochemicals industry's final investment decision process by investigating the main parameters of existing FID techno-economic models, developing a new model for the industry, and validating its accuracy. This study began by populating relevant final investment decision criteria, assembling production technology techno-economic data, and determining techno-economic parameters. This study is expected to improve the current techno-economic model and allow more projects to achieve successful FID for long-term profit, while preventing further losses if the economic return is still unfavourable by incorporating sensitivity and real option analysis. An improved techno-economic model should meet the needs of the petrochemical industry and provide prominent outputs for business risk management by allowing precise input consideration.

Optimal Allocation of Distribution Static Synchronous Compensators in Distribution Networks Considering Various Load Models Using the Black Widow Optimization Algorithm

Incorporating Distribution Static Synchronous Compensator (DSTATCOM) units into the radial distribution network (RDN) represents a practical approach to providing reactive compensation, minimizing power loss, and enhancing voltage profile and stability. This research introduces a unique optimization technique called the Black Widow Optimization (BWO) algorithm for strategically placing DSTATCOM units within the RDN. The primary objective is to minimize power loss while simultaneously evaluating various techno-economic parameters such as the voltage profile index (VPI), voltage stability index (VSI), and annual cost savings. The analysis of optimal DSTATCOM allocation, employing the proposed BWO algorithm, encompasses different load models, including constant impedance (CZ), constant current (CI), constant power (CP), and composite (ZIP) models. These analyses consider three distinct scenarios: single and multiple DSTATCOM integration. To gauge the effectiveness of the proposed BWO technique, it is applied to the IEEE 33-bus and 69-bus RDNs as test cases. Simulation results confirm the efficiency of the proposed approach across all four load models. Notably, in the case of the constant power model, the percentage reduction in power loss is substantial, with a reduction of 34.79% for the IEEE 33-bus RDN and 36.09% for the IEEE 69-bus RDN compared to their respective base cases.

Feasibility of green ammonia trading via pipelines and shipping: Cases of Europe, North Africa, and South America

Ammonia is a key chemical for the agriculture and chemical industries, and a potential future marine fuel. Potential growth in ammonia demand further highlights the importance of ammonia production defossilisation to comply with climate goals. Widely available renewable resources and declining costs of solar photovoltaics and wind power enable the production of green e-ammonia at many locations and potential exports to countries with less optimal resources and other restrictions. This paper aims to compare the cost of imported e-ammonia to Germany, Finland, and Spain from regions with excellent renewable resources, Morocco and Chile, to the cost of local production, and to quantify potential economic savings due to trading. Transportation costs are calculated in this study with techno-economic parameters reported in detail to assess their impact on final import costs, and to allow comparison of shipping costs to pipeline transmission costs over various distances. Pipeline imports from Morocco were found to be significantly higher in cost compared to imports by sea from Chile and Morocco or local production. Imports from Chile were found to be economically feasible for Germany and Finland, with import costs 10–33% lower than domestic e-ammonia production in 2030–2050. Imports to Spain may be attractive in 2030s, but, after 2040, local solar resources enable local e-ammonia production costs at the same or lower cost as imported e-ammonia. Ammonia trading in the future may depend on access to the sea. Other factors such as creation of jobs, additional revenues, and energy security concerns may also impact e-ammonia trade.

ROOT CAUSES OF MISROLL IN REROLLING MILLS

Steel has been known to humanity for 4,000 years. The level of per capita consumption of steel is treated as one of the most important indicators of socioeconomic development and living standards of the people in a country. Steel rerolling mills, also called reduction mills, are widely distributed across the world. The major concerns in the steel rerolling mill are misroll and cobble, which adversely affect most of the technoeconomic parameters, including the mill utilization, material yield, mill productivity, overall equipment effectiveness, overall line efficiency, and specific heat consumption of the reheating furnace. This paper discusses the major issues that lead to misroll and cobble in the rerolling mills, and these issues were identified via a shop floor analysis and breakdown records at an iron and steel rerolling mill. After the identification of the root causes of misroll, solutions are suggested.

Techno economic analysis tool for the sizing and optimization of an off-grid hydrogen hub

This study focuses on analyzing the characteristics of a techno-economic optimization tool to evaluate off-grid green hydrogen production costs. The tool determines the renewable energy power plants, electrolyzer and storage capacities and hydrogen transport form that minimizes the levelized cost of hydrogen, for a fixed hydrogen demand. The tool estimates the energy cost based on the renewable energy resource of the specific geographic coordinates and techno-economic parameters. To this end, the tool considers a wide set of inputs, which include possible economic incentives, taxes and financing structure, scale economies and technology cost projections. Additionally, the tool can consider hydrogen contractual characteristics such as compromised hydrogen volume, time frame, penalties for not supplied hydrogen and transport distance. Technical restrictions such as generation, electrolyzer and/or storage capacity limits can also be considered. Finally, a location in Argentina was selected as study case in order to describe the processing logic and review the model obtained results. The minimum achievable hydrogen production cost varies between 3.2 and 4.0 USD/MWh, while total cost including conversion/processing transportation and storage varies between 3.6 and 5.6 USD/MWh.