Optimizing the Operation of Bulk Energy Storage Devices to Find the Trade-offs Between Revenue and CO2 Emissions.

The purpose of this research is to encourage policy makers to craft policies that support environmentally sound design practices while integrating bulk energy storage into the electricity grid. Bulk energy storage technology can regulate electricity coming into the grid from different energy sources. Grid flexibility is a powerful tool to empower the clean energy movement because it enables the integration of renewable energy into the electrical grid. However, storage technology has the potential to become another one of the many “tragedy of commons”, considering that there are no regulations forcing storage companies to pursue environmental-friendly operation. Bulk energy storage devices which earn income through arbitrage, have the potential to increase grid emissions. Both energy losses and the variety of energy grid resources, largely damper the environmental advantages of bulk energy storage devices. By using a linear programming formulation that considers both revenue and emissions, this thesis proposes operational solutions where bulk energy storage technologies can retain a high revenue while simultaneously reducing their emissions from the current eGRID sub-regions. These results can be achieved by explicitly demanding small inexpensive changes in the operation of the system. Usually, only a few companies will follow sustainable practices by themselves. Therefore, a variety of policy implementations are suggested to support environmentally sound design principals for bulk energy storage technology

Identifying optimal technological portfolios for European power generation towards climate change mitigation: A robust portfolio analysis approach

Here, an integrative approach is proposed to link integrated assessment modelling results from the GCAM model with a novel portfolio analysis framework. This framework comprises a bi-objective optimisation model, Monte Carlo analysis and the Iterative Trichotomic Approach, aimed at carrying out stochastic uncertainty assessment and enhancing robustness. The approach is applied for identifying optimal technological portfolios for power generation in the EU towards climate change mitigation until 2050. The considered technologies include photovoltaics, concentrated solar power, wind, nuclear, biomass and carbon capture and storage, for which different subsidy curves for emissions reduction and energy security are considered. © 2019 Elsevier LtdThe most important part of this research is based on the H2020 European Commission Project “Transitions pathways and risk analysis for climate change mitigation and adaptation strategies—TRANSrisk” under grant agreement No. 642260

Multi-objective optimal power resources planning of microgrids with high penetration of intermittent nature generation and modern storage systems

Microgrids are self-controlled entities at the distribution voltage level that interconnect distributed energy resources (DERs) with loads and can be operated in either grid-connected or islanded mode. This type of active distribution network has evolved as a powerful concept to guarantee a reliable, efficient and sustainable electricity delivery as part of the power systems of the future. However, benefits of microgrids, such as the ancillary services (AS) provision, are not possible to be properly exploited before traditional planning methodologies are updated. Therefore, in this doctoral thesis, a named Probabilistic Multi-objective Microgrid Planning methodology with two versions, POMMP and POMMP2, is proposed for effective decision-making on the optimal allocation of DERs and topology definition under the paradigm of microgrids with capacity for providing AS to the main power grid. The methodologies are defined to consider a mixed generation matrix with dispatchable and non-dispatchable technologies, as well as, distributed energy storage systems and both conventional and power-electronic-based operation configurations. The planning methodologies are formulated based on a so-called true-multi-objective optimization problem with a configurable set of three objective functions. Accordingly, the capacity to supply AS is optimally enhanced with the maximization of the available active residual power in grid-connected operation mode; the capital, maintenance, and operation costs of microgrid are minimized, while the revenues from the services provision and participation on liberalized markets are maximized in a cost function; and the active power losses in microgrid´s operation are minimized. Furthermore, a probabilistic technique based on the simulation of parameters from their probabilistic density function and Monte Carlo Simulation is adopted to model the stochastic behavior of the non-dispatchable renewable generation resources and load demand as the main sources of uncertainties in the planning of microgrids. Additionally, POMMP2 methodology particularly enhances the proposal in POMMP by modifying the methodology and optimization model to consider the optimal planning of microgrid's topology with the allocation of DERs simultaneously. In this case, the concept of networked microgrid is contemplated, and a novel holistic approach is proposed to include a multilevel graph-partitioning technique and subsequent iterative heuristic optimization for the optimal formation of clusters in the topology planning and DERs allocation process. This microgrid planning problem leads to a complex non-convex mixed-integer nonlinear optimization problem with multiple contradictory objective functions, decision variables, and diverse constraint conditions. Accordingly, the optimization problem in the proposed POMMP/POMMP2 methodologies is conceived to be solved using multi-objective population-based metaheuristics, which gives rise to the adaptation and performance assessment of two existing optimization algorithms, the well-known Non-dominated Sorting Genetic Algorithm II (NSGAII) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). Furthermore, the analytic hierarchy process (AHP) is tested and proposed for the multi-criteria decision-making in the last step of the planning methodologies. The POMMP and POMMP2 methodologies are tested in a 69-bus and 37-bus medium voltage distribution network, respectively. Results show the benefits of an a posteriori decision making with the true-multi-objective approach as well as a time-dependent planning methodology. Furthermore, the results from a more comprehensive planning strategy in POMMP2 revealed the benefits of a holistic planning methodology, where different planning tasks are optimally and simultaneously addressed to offer better planning results.Las microrredes son entes autocontrolados que operan en media o baja tensión, interconectan REDs con las cargas y pueden ser operadas ya sea en modo conectado a la red o modo isla. Este tipo de red activa de distribución ha evolucionado como un concepto poderoso para garantizar un suministro de electricidad fiable, eficiente y sostenible como parte de los sistemas de energía del futuro. Sin embargo, para explotar los beneficios potenciales de las microrredes, tales como la prestación de servicios auxiliares (AS), primero es necesario formular apropiadas metodologías de planificación. En este sentido, en esta tesis doctoral, una metodología probabilística de planificación de microrredes con dos versiones, POMMP y POMMP2, es propuesta para la toma de decisiones efectiva en la asignación óptima de DERs y la definición de la topología de microrredes bajo el paradigma de una microrred con capacidad para proporcionar AS a la red principal. Las metodologías se definen para considerar una matriz de generación mixta con tecnologías despachables y no despachables, así como sistemas distribuidos para el almacenamiento de energía y la interconnección de recursos con o sin una interfaz basada en dispositivos de electrónica de potencia. Las metodologías de planificación se formulan sobre la base de un problema de optimización multiobjetivo verdadero con un conjunto configurable de tres funciones objetivo. Con estos se pretende optimizar la capacidad de suministro de AS con la maximización de la potencia activa residual disponible en modo conectado a la red; la minimización de los costos de capital, mantenimiento y funcionamiento de la microrred al tiempo que se maximizan los ingresos procedentes de la prestación de servicios y la participación en los mercados liberalizados; y la minimización de las pérdidas de energía activa en el funcionamiento de la microrred. Además, se adopta una técnica probabilística basada en la simulación de parámetros a partir de la función de densidad de probabilidad y el método de Monte Carlo para modelar el comportamiento estocástico de los recursos de generación renovable no despachables. Adicionalmente,la POMMP2 mejora la propuesta de POMMP modificando la metodología y el modelo de optimización para considerar simultáneamente la planificación óptima de la topología de la microrred con la asignación de DERs. Así pues, se considera el concepto de microrredes interconectadas en red y se propone un novedoso enfoque holístico que incluye una técnica de partición de gráficos multinivel y optimización iterativa heurística para la formación óptima de clusters para el planeamiento de la topología y asignación de DERs. Este problema de planificación de microrredes da lugar a un complejo problema de optimización mixto, no lineal, no convexos y con múltiples funciones objetivo contradictorias, variables de decisión y diversas condiciones de restricción. Por consiguiente, el problema de optimización en las metodologías POMMP/POMMP2 se concibe para ser resuelto utilizando técnicas multiobjetivo de optimización metaheurísticas basadas en población, lo cual da lugar a la adaptación y evaluación del rendimiento de dos algoritmos de optimización existentes, el conocido Non-dominated Sorting Genetic Algorithm II (NSGAII) y el Evolutionary Algorithm Based on Decomposition (MOEA/D). Además, se ha probado y propuesto el uso de la técnica de proceso analítico jerárquico (AHP) para la toma de decisiones multicriterio en el último paso de las metodologías de planificación. Las metodologías POMMP/POMMP2 son probadas en una red de distribución de media tensión de 69 y 37 buses, respectivamente. Los resultados muestran los beneficios de la toma de decisiones a posteriori con el enfoque de optimización multiobjetivo verdadero, así como una metodología de planificación dependiente del tiempo. Además, los resultados de la estrategia de planificación con POMMP2 revelan los beneficios de una metodología de planificación holística, en la que las diferentes tareas de planificación se abordan de manera óptima y simultánea para ofrecer mejores resultados de planificación.Línea de investigación: Planificación de redes inteligentes We thank to the Administrative Department of Science, Technology and Innovation - Colciencias, Colombia, for the granted National Doctoral funding program - 647Doctorad

Improving Sustainability and Circularity of European Food Waste Management with a Life Cycle Approach

In the past years, several research initiatives have been promoted in the area of food waste. Many of these were focused on the identification of key drivers of food wastage and on the quantification of food waste generation. While these initiatives provided fairly accurate information over European food waste generation and management routes, they did not always deliver comprehensive and comparable knowledge on the sustainability of food waste management and on ways to mitigate negative consequences at environmental, economic and social levels. Building on most recent methodological advancement and policy needs, the work presented in this report aims at providing policy/decision makers and waste managers with a life-cycle based framework methodology to quantify the environmental and economic sustainability performance of European food waste management. This methodology can help identify options for improvement of such performance, thus can offer relevant insight to the decision making process. A numerical case study is also developed. This is meant to give an example of simplified application of the proposed methodology to a fictitious European waste management context. The environmental dimension has been evaluated with the Life Cycle Assessment (LCA) software EASETECH, while the economic assessment is conducted based on a number of different indicators expressing the costs associated with food waste management. This methodology makes use of multi-objective optimization and Pareto optimality concepts in order to help identify most sustainable management options for food waste, intended as those that minimize environmental and economic impacts. In any case, the proposed methodology is meant to only provide relevant information that can support science-based decision making. The final choice will in fact depend on a number of additional aspects that are beyond the scope of this report and also depends on the preferences of the decision maker.JRC.H.8-Sustainability Assessmen

Sustainable supply chain network design integrating logistics outsourcing decisions in the context of uncertainties

Les fournisseurs de services logistiques (3PLs) possèdent des potentialités pour activer les pratiques de développement durables entre les différents partenaires d’une chaîne logistique (Supply Chain SC). Il existe un niveau optimal d'intégration des 3PLs en tant que fournisseurs, pour s’attendre à des performances opérationnelles élevées au sein de toute la SC. Ce niveau se traduit par la distinction des activités logistiques à externaliser de celles à effectuer en interne. Une fois que les activités logistiques externalisés sont stratégiquement identifiées, et tactiquement dimensionnées, elles doivent être effectuées par des 3PLs appropriés afin d’endurer les performances économiques ; sociales ; et environnementales de la SC. La présente thèse développe une approche holistique pour concevoir une SC durable intégrant les 3PLs, dans un contexte incertain d’affaires et politique de carbone. Premièrement, une approche de modélisation stochastique en deux étapes est suggérée pour optimiser à la fois le niveau d'intégration des 3PLs, et le niveau d'investissement en technologies sobres au carbone, et ce dans le contexte d’une SC résiliente aux changements climatiques. Notre SC est structurée de façon à capturer trois principales préoccupations du Supply Chain Management d’une entreprise focale FC (e. g. le fabricant) : Sécurité d’approvisionnement, Segmentation de distribution, et Responsabilité élargie des producteurs. La première étape de l'approche de modélisation suggère un plan stochastique basé sur des scenarios plus probables, afin de capturer les incertitudes inhérentes à tout environnement d’affaires (e. g. la fluctuation de la demande des différents produits ; la qualité et la quantité de retour des produits déjà utilisés ; et l’évolution des différents coûts logistiques en fonction du temps). Puis, elle propose un modèle de programmation stochastique bi-objectif, multi-période, et multi-produit. Le modèle de programmation quadratique, et non linéaire consiste à minimiser simultanément le coût logistique total espéré, et les émissions de Gaz à effet de Serre de la SC fermée. L'exécution du modèle au moyen d'un algorithme basé sur la méthode Epsilon-contraint conduit à un ensemble de configurations Pareto optimales d’une SC dé- carbonisée, avant tout investissement en technologie sobre au carbone. Chacune de ces configurations sépare les activités logistiques à externaliser de celles à effectuer en interne. La deuxième étape de l'approche de modélisation permet aux décideurs de choisir la meilleure configuration de la SC parmi les configurations Pareto optimales identifiées. Le concept de Prix du Carbone Interne est utilisé pour établir un plan stochastique du prix de carbone, dans le cadre d'un régime de déclaration volontaire du carbone. Nous proposons un ensemble des technologies sobres au carbone, dans le domaine de transport des marchandises, disposées à concourir pour contrer les politiques incertaines de carbone. Un modèle stochastique combinatoire, et linéaire est développé pour minimiser le coût total espéré, sous contraintes de l’abattement du carbone; limitation du budget, et la priorité attribuée pour chaque Technologie Réductrice de carbone (Low Carbone Reduction LCR). L'injection de chaque solution Pareto dans le modèle, et la résolution du modèle conduisent à sélectionner la configuration de la SC, la plus résiliente aux changements climatiques. Cette configuration définit non seulement le plan d'investissement optimal en LCR, mais aussi le niveau optimal d’externalisation de la logistique dans la SC. Deuxièmement, une fois que les activités logistiques à externaliser sont stratégiquement définies et tactiquement dimensionnées, elles ont besoin d’être effectuées par des 3PL appropriées, afin de soutenir la FC à construire une SC durable et résiliente. Nous suggérons DEA-QFD / Fuzzy AHP- Conception robuste de Taguchi : Une approche intégrée & robuste, pour sélectionner les 3PL candidats les plus efficients. Les critères durables et les risques liés à l’environnement d’affaires, sont identifiés, classés et ordonnés. Le Déploiement de la Fonction Qualité (QFD) est renforcé par le Processus Hiérarchique Analytique (AHP), et par la logique floue pour déterminer avec consistance l'importance relative de chaque facteur de décision, et ce, conformément aux besoins logistiques réels, et stratégies d'affaires de la FC. L’Analyse d’Enveloppement des Données (DEA) Data Envelopment Analysis conduit à limiter la liste des candidats, uniquement à ceux d’efficiences comparables, et donc excluant tout candidat moins efficient. La technique de conception robuste Taguchi permet de réaliser un plan d'expérience qui détermine un candidat idéal nommé 'optimum de Taguchi' ; un Benchmark pour comparer les 3PLs candidats. Par suite, le 3PL le plus efficient est celui le plus proche de cet optimum. Nous conduisons actuellement une étude de cas d’une entreprise qui fabrique et commercialise les fours à micro-ondes pour valider la modélisation stochastique en deux étapes. Certains aspects concernant l’application de l’approche sont reportés. Enfin, un exemple de sélection d’un 3PL durable pour s’occuper de la logistique inverse est fourni, pour démontrer l'applicabilité de l'approche intégrée & robuste, et montrer sa puissance par rapport aux approches populaires de sélection.The Third-Party Logistics service providers (3PLs) have the potentialities to activate sustainable practices between different partners of a Supply Chain (SC). There exists an optimal level of integrating 3PLs as suppliers of a Focal Company within the SC, to expect for high operational performances. This level leads to distinguish all the logistics activities to outsource from those to perform in-house. Once the outsourced logistics activities are strategically identified, and tactically dimensioned, they need to be performed by appropriate 3PLs to sustain economic, social and environmental performances of the SC. The present thesis develops a holistic approach to design a sustainable supply chain integrating 3PLs, in the context of business and carbon policy uncertainties. First, a two-stage stochastic modelling approach is suggested to optimize both the level of 3PL integration, and of Low Carbon Reduction LCR investment within a climate change resilient SC. Our SC is structured to capture three main SC management issues of the Focal Company FC (e.g. The manufacturer) : Security of Supplies; Distribution Segmentation; and Extended Producer Responsibility. The first-stage of the modelling approach suggests a stochastic plan based scenarios capturing business uncertainties, and proposes a two-objective, multi-period, and multi-product programming model, for minimizing simultaneously, the expected logistics total cost, and the Green House Gas GHG emissions of the whole SC. The run of the model by means of a suggested Epsilon-constraint algorithm leads to a set of Pareto optimal decarbonized SC configurations, before any LCR investment. Each one of these configurations distinguishes the logistics activities to be outsourced, from those to be performed in-house. The second-stage of the modelling approach helps the decision makers to select the best Pareto optimal SC configuration. The concept of internal carbon price is used to establish a stochastic plan of carbon price in the context of a voluntary carbon disclosure regime, and we propose a set of LCR technologies in the freight transportation domain ready to compete for counteracting the uncertain carbon policies. A combinatory model is developed to minimize the total expected cost, under the constraints of; carbon abatement, budget limitation, and LCR investment priorities. The injection of each Pareto optimal solution in the model, and the resolution lead to select the most efficient climate resilient SC configuration, which defines not only the optimal plan of LCR investment, but the optimal level of logistics outsourcing within the SC as well. Secondly, once the outsourced logistics are strategically defined they need to be performed by appropriate 3PLs for supporting the FC to build a Sustainable SC. We suggest the DEA-QFD/Fuzzy AHP-Taguchi Robust Design: a robust integrated selection approach to select the most efficient 3PL candidates. Sustainable criteria, and risks related to business environment are identified, categorized, and ordered. Quality Function Deployment (QFD) is reinforced by Analytic Hierarchic Process (AHP), and Fuzzy logic, to consistently determine the relative importance of each decision factor according to the real logistics needs, and business strategies of the FC. Data Envelopment Analysis leads to shorten the list of candidates to only those of comparative efficiencies. The Taguchi Robust Design technique allows to perform a plan of experiment, for determining an ideal candidate named ‘optimum of Taguchi’. This benchmark is used to compare the remainder 3Pls candidates, and the most efficient 3PL is the closest one to this optimum.We are currently conducting a case study of a company that manufactures and markets microwave ovens for validating the two-stage stochastic approach, and certain aspects of its implementation are provided. Finally, an example of selecting a sustainable 3PL, to handle reverse logistics is given for demonstrating the applicability of the integrated & robust approach, and showing its power compared to popular selection approaches. Keywords:Third Party Logistics; Green Supply Chain design; Stochastic Multi-Objective Optimization; Carbon Pricing; Taguchi Robust Design

Implementing a highly adaptable method for the multi-objective optimisation of energy systems

In order to mitigate climate change, the energy sector undergoes a transformation towards a climate-neutral future based on renewable energy sources. Energy system models generate insights and support decision making for this transformation. In the face of, e.g., growingly complex and important environmental assessments and stakeholder structures, considering multiple objectives in these models becomes essential to realistically reflect existing interests. However, there is a lack of highly adaptable energy system models incorporating multiple objectives. We present an implementation of the augmented epsilon-constraint method with the highly adaptable energy system optimisation framework Backbone. It enables the simultaneous optimisation of multiple objectives, such as the minimisation of costs, CO2 emissions or self-sufficiency for a broad range of energy systems including different sectors and scales. For this purpose, new objective functions and constraints are implemented in Backbone. They are used by an external algorithm in a sequence of parallelised optimisations to cope with the complexity of real-world applications. The method is adaptable to further objectives and scalable to large and complex systems. Applications to the Western and Southern European power sector in 2050 and a sector-coupled mixed- integer household-level model demonstrate its benefits and adaptability. Pareto fronts, technology use and trade-offs are analysed and quantified. In the European power sector, emission reductions of up to 90 % can be achieved at marginal CO2 abatement costs of below 100 EUR/(t CO2). For the household, energy imports from the public grids can be reduced by 70 % at 20 % higher cost and average cost of self-sufficiency of 2.6 ct/kWh. We expect that the presented methods and models reveal new valuable insights to modellers and decision makers

Modeling and Optimization of Renewable Energy Systems

This book includes solar energy, wind energy, hybrid systems, biofuels, energy management and efficiency, optimization of renewable energy systems and much more. Subsequently, the book presents the physical and technical principles of promising ways of utilizing renewable energies. The authors provide the important data and parameter sets for the major possibilities of renewable energies utilization which allow an economic and environmental assessment. Such an assessment enables us to judge the chances and limits of the multiple options utilizing renewable energy sources. It will provide useful insights in the modeling and optimization of different renewable systems. The primary target audience for the book includes students, researchers, and people working on renewable energy systems

On the design of a European bioeconomy that optimally contributes to sustainable development

The inevitability for a change in humankind's resource and fossil energy consumption is demonstrated by global crises such as the climate change, disturbances of natural cycles, and the loss of biodiversity. The sun provides sufficient energy to generate electricity and by photosynthesis, solar radiation is converted into energy chemically bound in biomolecules, which provide building blocks for the production of various materials, chemicals, or fuels. The bioeconomy puts biomass at the center of an economy that attempts to cover resource and energy demand by renewable materials to address the global challenges. However, the finiteness of the terrestrial surface limits renewables, requiring a prioritization of use. The Sustainable Development Goals (SDGs) provide a common ground for global peace, prosperity, improved health and education, reduced inequality, and spur economic growth while tackling climate change and biodiversity loss, making it the most comprehensive framework for defining objectives in the design of the bioeconomy. Against this background, this dissertation is particularly dedicated to the design of bioeconomic value chains based on agroforestry residues in the European Union, considering economic, environmental, and social objectives to optimally exploit the potential to contribute to a sustainable development. All objectives are matched to SDGs to unveil congruencies, conflicts and trade-offs between different goals, and to provide aggregated insights and courses of action in the agroforestry residue-based bioeconomy to politics, the scientific community, and corporate decision-makers. The availability of agroforestry residue volumes and their current uses is the first major concern of a bioeconomy aligned with the SDGs to be assessed in this work. Key findings are that the most promising agricultural residue in the EU is wheat straw, followed by maize stover, barley straw, and rapeseed straw, which together account for about 80% of EU’s cereals and oil crops residues. In forestry, waste bark from the two coniferous species, spruce and pine, are most promising with the highest supplies in Scandinavia and central EU. The time-series-based forecast model predicts a total increase of the bioeconomic potential of the prioritized agricultural feedstocks from 113 Mt in 2017 to 127 Mt in 2030. The forecast indicates the largest increase of all investigated crops for corn stover at up to 20% until 2030, while rapeseed straw production is forecasted to decrease in many regions. To take environmental and social aspects into account on a regional level, along with international competitiveness, this dissertation develops a multi-criteria strategic network design model for the planning of bioeconomic value chains. The environmental and social objectives are derived by means of Life Cycle Assessment and Social Life Cycle Assessment, respectively. The developed set of 35 economic, environmental, and social objective functions allows for the consideration of 16 of the 17 SDGs. The model is applied for the planning of a second-generation bioethanol production network based on agricultural residues in the EU. Single-criteria optimization shows that sustainably available agroforestry residues could substitute up to 22% of the petrol demand in the EU in 2018 under optimal production networks for certain objectives (i.a., global warming). For environmental objectives, the decision to substitute petrol or edible crops-based ethanol has the highest impact. The greenhouse gas benefits could amount to up to 59 Mt CO2 eq., conforming to about 1.35% of the EU’s 2018 total emissions. However, global warming optimization leads to opportunity costs for other objectives. While for ecosystem quality, for example, the achieved value reaches 50% of its optimum, other categories like land use and water consumption could even be net deteriorated by optimizing global warming. For objectives such as land use, only 19% of the total agroforestry residues is used to substitute 100% of the edible crops-based ethanol, which would free up 11.7 billion m2 crop land. Social objectives lead to large and labor-intensive production networks distributed all over the EU. Depending on the social objective, the value creation slightly shifts regionally. To optimize local employment, the network relocates to regions with high unemployment rates, such as Spain, Italy, and parts of France. Economically strong metropolitan regions are at a disadvantage in favor of weaker regions of Central and Eastern EU when optimizing economic development. At best, up to 140,000 new jobs could be created in the EU while 12,000 jobs could be lost due to substitution of reference products. In terms of network extend, most socially and environmentally optimal production networks are similar, although the substitution decision has little impact for social objectives. This means that interesting trade-offs between social and environmental objectives can be found with only minor sacrifices. Economically optimal networks are much smaller and more centralized than environmental ones, and lead to costs of about 0.75 €/l second-generation ethanol. Environmental optimization results in cost between 0.88 €/l to 2.00 €/l, which implies that large-scale bioethanol production is not economically feasible with today’s oil prices and taxes. While the single-criteria optimization reveals conflicts within and between the environment, social, and economic dimensions, Pareto optimization is conducted to unveil trade-offs between conflicting goals. Significant environmental and social benefits can often be realized with only small economic detriments, and vice versa, economic profitability can substantially be improved at low environmental opportunity cost. Furthermore, the applied Pareto optimization shows that the endpoints human health and ecosystem quality are suitable aggregators of environmental impact categories, wherefore they could serve as representative of the environmental dimension in decision-making. Nonetheless, a transparent consideration of a broad range of impacts and knowledge about the categories’ contributions remains indispensable to reveal possible negative consequences of a decision. In a final step, the objective functions are matched to SDGs, and opportunity cost between the objective functions are calculated to unveil congruencies and conflicts between different goals. The assessment of relationships between the different SDGs supports the perception that different aspects of sustainability are not equally directed. Sustainability, expressed by the SDGs, is rather case-specific and varies between a multitude of interdependent social, environmental, and economic criteria. Decision-makers, whether at the corporate level pursuing one or more business objectives or at the policy level, using the SDGs as a framework, should be aware of the reciprocities between the different criteria. The dissertation shows that the European bioeconomy has a great potential to contribute to sustainable development. Multi-criteria optimization models enable sound trade-off decisions that are aligned to the SDGs