Multi-objective optimisation of a hydrogen supply chain

L'hydrogène produit à partir de sources renouvelables et utilisé dans les piles à combustible pour diverses applications, tant mobiles que stationnaires, constitue un vecteur énergétique très prometteur, dans un contexte de développement durable. Les « feuilles de route » stratégiques, élaborées au niveau européen, national ou régional, consacrées aux potentialités énergétiques de l’hydrogène, ainsi que l’analyse des publications scientifiques ont cependant identifié le manque d'infrastructures, comme l'un des principaux obstacles au développement de l'économie « hydrogène ». Cette étude s’inscrit dans le cadre du développement d’une méthodologie de conception d'une chaîne logistique « hydrogène » (production, stockage et transport). La formulation, basée sur une procédure de programmation mathématique linéaire en variables mixtes, implique une approche multicritère concernant la minimisation du prix de revient de l’hydrogène, l’impact sur le réchauffement climatique et un indice de risque, en prenant en compte une échelle tant régionale que nationale. L’optimisation multi-objectif repose sur une stratégie Ɛ-contrainte développée à partir d’une méthode lexicographique menant à la construction de fronts de Pareto offrant un grand nombre de solutions. La procédure d’aide à la décision M-TOPSIS est ensuite utilisée pour choisir le meilleur compromis. Le modèle est appliqué à une étude de cas en Grande-Bretagne, issue de la littérature spécialisée, qui sert de référence pour comparer les approches mono- et multi-objectif. Ensuite, la modélisation et l'optimisation de la chaîne d'approvisionnement d'hydrogène pour la région Midi-Pyrénées ont été étudiées dans le cadre du projet «H2 vert carburant». Un problème mono/multi-période est traité selon des scénarios d'optimisation basés sur la stratégie Ɛ-contrainte développée à partir d’une méthode lexicographique. Le système d’information ArcGIS® est ensuite utilisé pour valider les solutions obtenues par optimisation multi-objectif. Cette technologie permet d'associer une période de temps aux configurations de la chaîne logistique hydrogène et d’analyser plus finement les résultats de la conception du réseau H2. L’extension au cas de la France répond à un double objectif : d'une part, tester la robustesse de la méthode à une échelle géographique différente et, d’autre part, examiner si les résultats obtenus au niveau régional sont cohérents avec ceux de l'échelle nationale. Dans cette étude de cas, l'outil spatial ArcGIS® est utilisé avant optimisation pour identifier les contraintes géographiques. Un scénario prenant en compte un cycle économique est également traité. Les optimisations mono et multi-objectif présentent des différences relatives au mode de déploiement de filière, centralisé ou décentralisé, et au type de technologie des unités production, ainsi qu’à leur taille. Les résultats confirment l'importance d'étudier différentes échelles spatiales. ABSTRACT : Hydrogen produced from renewable sources and used in fuel cells both for mobile and stationary applications constitutes a very promising energy carrier in a context of sustainable development. Yet the strategic roadmaps that were currently published about the energy potentialities of hydrogen at European, national and regional level as well as the analysis of the scientific publications in this field have identified the lack of infrastructures as a major barrier to the development of a « hydrogen » economy. This study focuses on the development of a methodological framework for the design of a hydrogen supply chain (HSC) (production, storage and transportation). The formulation based on mixed integer linear programming involves a multi-criteria approach where three objectives have to be optimised simultaneously, i.e., cost, global warming potential and safety risk, either at national or regional scale. This problem is solved by implementing lexicographic and Ɛ-constraint methods. The solution consists of a Pareto front, corresponding to different design strategies in the associated variable space. Multiple choice decision making based on M-TOPSIS (Modified Technique for Order Preference by Similarity to Ideal Solution) analysis is then selected to find the best compromise. The mathematical model is applied to a case study reported in the literature survey and dedicated to Great Britain for validation purpose, comparing the results between mono- and multi-objective approaches. In the regional case, the modelling and optimisation of the HSC in the Midi-Pyrénées region was carried out in the framework of the project “H2 as a green fuel”. A mono/multi period problem is treated with different optimisation scenarios using Ɛ-constraint and lexicographic methods for the optimisation stage. The geographic information system (GIS) is introduced and allows organising, analysing and mapping spatial data. The optimisation of the HSC is then applied to the national case of France. The objective is twofold: on the one hand, to examine if the methodology is robust enough to tackle a different geographic scale and second to see if the regional approach is consistent with the national scale. In this case study, the ArcGIS® spatial tool is used before optimisation to identify the geographic items that are further used in the optimisation step. A scenario with an economic cycle is also considered. Mono- and multi-objective optimisations exhibit some differences concerning the degree of centralisation of the network and the selection of the production technology type and size. The obtained results confirm that different spatial and temporal scales are required to encompass the complexity of the problem

Rediseño de un embalaje de material automotriz que permite disminución de costos para el transporte e inventario

Los eslabones para una cadena de suministro pueden ser el origen, el transporte y el destino. El rediseño de embalaje de cajas de cartón que contiene autopartes es transportado vía marítima desde un país asiático (origen) a un país de América Latina (destino). En el origen se desean reducir costos de transporte e inventario, en la práctica se consideran cajas de cartón con idénticas dimensiones y pesos y sin posibilidad de rotación de ellas. El problema se modela mediante programación no lineal entero mixta para encontrar la máxima cantidad de piezas que pueden transportarse en un contenedor High Cube seco de 40”. Se consideraron en este caso de estudio los únicos seis escenarios factibles para realizar el cambio del embalaje que actualmente se utiliza para transportar el material. Para cada uno de ellos se consideraron especificaciones, dimensiones del contenedor, montacargas, resistencia del cartón para su peso con estiba a tres niveles, así como las necesidades del cliente. Con esos escenarios alimentamos al modelo mencionado, cuyo resultado proporciona el escenario que aumenta el número de piezas alojadas en el contenedor. El resultado logra un 10% en la reducción de costos sin verse afectados los gastos de inventario

Rediseño del embalaje y reducción de los costos de transporte de muestras automotrices: Un caso de estudio

El presente trabajo terminal de grado muestra un análisis de reducción de costos y mejora en la calidad del servicio del envío aéreo de muestras automotrices de la ciudad de Toluca, México a Farmington Hills, Michigan, EEUU. Para ese fin se proponen cuatro acciones: a) la reevaluación del proveedor de transporte, b) el rediseño del embalaje buscando la reducción de los volúmenes requeridos para enviar las muestras automotrices, c) evaluar la conveniencia de consolidar carga y d) la colocación óptima de productos en los embalajes. La selección de proveedores se realiza mediante el “proceso analítico jerárquico” (PAJ) y el elegido aunque es más caro, proporciona una mejora importante en la calidad del servicio. El rediseño del embalaje y la asignación óptima de los productos a las cajas reducen en un 22% los costos de transporte. La asignación se realiza mediante un modelo de optimización el cual se resuelve con GAMS que convierte el modelo algebraico en un archivo que es adecuado para ser leído por algún solucionador, para este caso es un solucionador CPLEX, una rutina numérica comercial para resolver modelos de programación lineal entera. Con el fin de simplificar y agilizar la actividad diaria de colocación de productos en los embalajes y para evitar ejecutar el modelo cada vez que se presente un nuevo pedido, se propone un método heurístico que proporciona una solución rápida e idéntica, cada vez que es ejecutado, a la proporcionada por el modelo de optimización. La evaluación del algoritmo se realizó utilizando una muestra de pedidos del año 2015. Los resultados obtenidos fueron presentados a la gerencia responsable del departamento de muestras automotrices y fueron aceptados e implementados a partir del año 2017

Assessment of mono and multi-objective optimization to design a hydrogen supply chain

This work considers the potential future use of hydrogen in fuel cell electrical vehicles to face problems such as global warming, air pollution, energy security and competitiveness. The lack of current infrastructure has been identified as one of the main barriers to develop the hydrogen economy. This work is focused on the design of a hydrogen supply chain through mixed integer linear programming used to find the best solutions for a multiobjective optimization problem in which three objectives are involved, i.e., cost, global warming potential and safety risk. This problem is solved by implementing an 3-constraint method. The solution consists of a Pareto front, corresponding to different design strategies in the associated variable space. Multiple choice decision making is then recommended to find the best solution through an M-TOPSIS analysis. The model is applied to the Great Britain case study previously treated in the dedicated literature. Mono and multicriteria optimizations exhibit some differences concerning the degree of centralization of the network and the selection of the production technology type

Design of Experiments for Sensitivity Analysis of a Hydrogen Supply Chain Design Model

Hydrogen is one of the most promising energy carriers in the quest for a more sustainable energy mix. In this paper, a model of thehydrogen supply chain (HSC) based on energy sources, production, storage, transportation, and market has been developedthrough a MILP formulation (Mixed Integer Linear Programming). Previous studies have shown that the start-up of the HSCdeployment may be strongly penalized from an economic point of view. The objective of this work is to perform a sensitivityanalysis to identify the major parameters (factors) and their interaction affecting an economic criterion, i.e., the total daily cost(TDC) (response), encompassing capital and operational expenditures. An adapted methodology for this SA is the design ofexperiments through the Factorial Design and Response Surface methods. Six key parameters are chosen (demand, capitalchange factor (CCF), storage and production capital costs (SCC, PCC), learning rate (LR), and unit production cost (UPC)).The demand is the factor that is by far the most significant parameter that strongly conditions the TDC optimization criterion, thesecond most significant parameter being the capital change factor. To a lesser extent, the other influencing factors are PCC andLR. The main interactions are found between demand, CCF, UPC, and SCC. The discussion has also shown that the calculationof UPC has to be improved taking into account the contribution of the fixed, electricity, and feedstock costs instead of beingconsidered as a fixed parameter only depending on the size of the production unit. As any change that could occur relative todemand or CCF could strongly affect the response variable, more effort is also needed to find the more consistent way to modeldemand uncertainty in HSC design, especially since a long horizon time is considered for hydrogen deployment

Multiobjective and social cost-benefit optimisation for a sustainable hydrogen supply chain

This article presents a comprehensive approach to design hydrogen supply chains (HSCs) targeting industrial and mobility markets. Even if the inclusion of sustainability criteria is paramount, only a few studies simultaneously consider economic, environmental, and social aspects - the most difficult to measure. In this paper, the safety risk and the social cost-benefit (SCB) have been identified as quantifiable social criteria that would affect society and the end-users. The objectives of this research are (1) to design a sustainable HSC by using four objective functions, i.e., levelized cost of hydrogen, global warming potential, safety risk and social cost-benefit through a mixed-integer linear programming model; (2) to compare results from SCB and multiobjective optimisation. The integration of the SCB criterion at the optimisation stage is not a trivial task and is one of the main contributions of this work. It implies the minimisation of the total cost of ownership (TCO) for buses and trucks. The evolution of the HSC from 2030 to 2050 is studied through a multiobjective and multiperiod optimisation framework using the ε-constraint method. The methodology has been applied to a case study for Hungary with several scenarios to test the sensitivity of demand type and volume as well as the production technology. The results analysis highlights that (1) it is beneficial to have mixed demand (industry and mobility) and a gradual introduction/migration to electrolysis technology and fuel cell vehicles (FCVs) for a smooth transition. Liquid hydrogen produced via water electrolysis powered by nuclear and wind energy can result in an average levelized cost of $4.78 and 3.14 kg CO2-eq per kg H2; (2) the frameworks for multiobjective optimisation and SCB maximisation are complementary because they prioritise different aspects to design the HSC. Taxes and surcharges for H2 fuel will impact its final price at the refuelling station resulting in a higher TCO for FCVs compared to diesel buses and trucks in 2030 but the TCO becomes almost competitive for hydrogen trucks from 2035 when SCB is maximised. The SCB function can be refined and easily adapted to include additional externalities

Deployment of a hydrogen supply chain by multi-objective/multi-period optimisation at regional and national scales

This study focuses on the development of a methodological framework for the design of a five-echelon hydrogen supply chain (HSC) (energy source, production, storage, transportation and fuelling station) considering the geographic level of implementation. The formulation based on mixed integer linear programming involves a multi-criteria approach where three objectives have to be optimised simultaneously, i.e., cost, global warming potential and safety risk. The objective is twofold: first, to test the robustness of the method proposed in De-Leon (2014) from a regional to a national geographic scale and, secondly, to examine the consistency of the results. A new phase of data collection and demand scenarios are performed to be adapted to the French case based on the analysis of roadmaps. In this case study, the ArcGIS® spatial tool is used to locate the supply chain elements before and after optimisation. The multi-objective optimisation approach by the ɛ-constraint method is applied, analysed and discussed. Finally, a comparison between the results of different geographic scale cases is carried out

Sustainable wastewater treatment plants design through multiobjective optimization

Nowadays, an adequate design of wastewater treatment plants taking into consideration ail sustainability dimensions- economic, environmental and social- is fundamental. This can be achieved by implementing systematic methodologies where conceptual and mathematical tools can be used together. This contribu­tion proposes a framework that uses total cost, consumed energy, and reclaimed wastewater as sustain­ability metrics. A mixed-integer nonlinear programming problem arises from a general superstructure for wastewater treatment plants. A case study from Mexico City is solved by a hybrid multiobjective opti­mization approach that combines lexicographie and e-constraint methods. Solutions are provided in the form of a Pareto front. A modified technique for order of preference by similarity to ideal solution (M­TOPSIS) analysis is used as a multiple criteria decision-making tool to find the best trade-off solution. The optimal sustainable configuration resulted consists of three levels of treatment and 100% of treated water reuse

Hydrogen supply chain optimization for deployment scenarios in the Midi-Pyrénées region, France

Several roadmaps and international projects are interested in the development of the hydrogen economy for the transportation system. Yet, the development of a hydrogen economy suffers from a lack of infrastructure to store and supply H2 fuel to the refuelling stations, while at the same time, hydrogen can be just seen as one alternative among others to compete with the current fossil fuels. To determine if hydrogen is a competitive option, many scenarios must be assessed considering not only the cost as the target to determine the feasibility but, also environmental and safety objectives. This work is focused on the design of a hydrogen supply chain for deployment scenarios in the Midi-Pyrénées region in France based on multi-objective optimization. Specific constraints related to the energy sources have been integrated and a multi-period long-term problem is examined (2020–2050). Two solution strategies will be implemented to solve this multi-period problem: a global optimization through ε-constraint method and a sequential optimization through lexicographic and ε-constraint methods. The consideration of different geographical scales and the impact of the initiation step in the development of a sustainable supply chain have been highlighted

Identifying social aspects related to the hydrogen economy: Review, synthesis, and research perspectives

Energy transition will reshape the power sector, and hydrogen is a key energy carrier that could contribute to energy security. The inclusion of sustainability criteria is crucial for the adequate design/deployment of resilient hydrogen networks. While cost and environmental metrics are commonly included in hydrogen models, social aspects are rarely considered. This paper aims to identify the social criteria related to the hydrogen economy by using a systematic hybrid literature review. The main contribution is the identification of twelve social aspects which are described, ranked, and discussed. “Accessibility”, “Information”, “H2 markets”, and “Acceptability” are now emerging as the main themes of hydrogen-related social research. Identified gaps are e.g., lack of the definition of the value of H2 for society, insufficient research for “socio-political” aspects (e.g., geopolitics, wellbeing), scarce application of social lifecycle assessment, and the low amount of works with a focus on social practices and cultural issues