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CO2 emission reduction of sectors such as aviation, maritime shipping, road haulage, and chemical production is challenging but necessary. Although these sectors will most likely continue to rely on carbonaceous energy carriers, they are expected to gradually shift away from fossil fuels. In order to do so, the prominent option is to utilize alternative carbon sources—like biomass and CO2 originating from carbon capture—for the production of non-fossil carbonaceous vectors (biofuels and e-fuels). However, the limited availability of biomass and the varying nature of other carbon sources necessitate a comprehensive evaluation of trade-offs between potential carbon uses and existing sources. Then, it is primordial to understand the origin of carbon used in sustainable aviation fuel (SAF) to understand the implications of defossilizing aviation for the energy system. Moreover, the production of SAF implies deep changes to the energy system that are quantified in this work. This study utilizes the linear programming cost optimization tool EnergyScope TD to analyze the holistic French energy system, encompassing transport, industry, electricity, and heat sectors while ensuring net greenhouse gas neutrality. A novel method to model and quantify carbon flows within the system is introduced, enabling a comprehensive assessment of greenhouse gas neutrality. This study highlights the significance of fulfilling clean energy requirements and implementing carbon dioxide removal measures as crucial steps toward achieving climate neutrality. Indeed, to reach climate neutrality, a production of 1,046 TWh of electricity by non-fossil sources is needed. Furthermore, the findings underscore the critical role of efficient carbon and energy valorization from biomass, providing evidence that producing fuels by combining biomass and hydrogen is optimal. The study also offers valuable insights into the future cost and impact of SAF production for air travel originating from France. That is, the European law ReFuelEU would increase the price of plane tickets by +33% and would require 126 TWh of hydrogen and 50 TWh of biomass to produce the necessary 91 TWh of jet fuel. Finally, the implications of the assumption behind the production of SAF are discussed.

The global transition to decentralized energy systems signifies a fundamental transformation toward sustainable energy paradigms. This study specifically focuses on the Swiss energy system, analyzing how dynamic pricing influences the strategic decisions of different actors. The main contributions include 1) a detailed examination of pricing models tailored to the Swiss context, 2) an exploration of strategic financial burden shifts among end-users, TSOs, and DSOs, and 3) a comparison of decentralized versus centralized energy models, highlighting their respective efficiencies and resilience. This research differentiates from existing literature by providing an in-depth actor-based analysis within a Swiss context, offering valuable insights into decentralized energy system optimization. This study tackles the problem of how pricing influences strategic decisions across different actors in Switzerland’s evolving decentralized energy landscape. Here we show that a carefully tailored pricing model, designed for the Swiss context, enables optimized strategies that balance local efficiencies with systemic equity and resilience. The analysis reveals that decentralized approaches, in contrast to centralized models, not only accommodate diverse stakeholder preferences but also enhance system robustness against market and operational disruptions. Moreover, the study illustrates the strategic financial burden shifting where end-users compensate for cost shifts, with observed additional costs up to 5200 CHF/year cap when service providers are prioritized as objective actors. Notably, the most frequently selected system configuration in the primal problem, which optimizes the total system costs, aligns with the preferences of TSO and DSO for a 47.1 GW PV deployment. However, end-users demonstrate a preference for increased PV installations, constrained by urban grid capacities. Additionally, the study highlights significant regional disparities across Switzerland, necessitating tailored pricing approaches that reflect varied urban forms. The emergence of prosumers catalyzes new business models, redistributing investments across TSOs (256–261 CHF/cap/year), DSOs (244–413 CHF/cap/year), and prosumers (556–764 CHF/cap/year), showcasing the evolving dynamics of energy system economics.

The transition towards renewable and decentralized energy systems is propelled by the urgent need to address climate concerns and advance sustainable development globally. This transformation requires innovative methods to integrate stochastic renewable sources such as solar and wind power and challenging traditional energy paradigms rooted in centralized and continuous energy production. The present study focuses on the Swiss energy system to explore the optimization of energy planning strategies that incorporate decentralized energy production within a centralized framework. Here, we show that a strategic approach to decentralization can significantly reduce annual system costs by 10% to CHF 1230 per capita and increase self-consumption to 68% of the decentralized PV production, emphasizing the need for a hybrid energy-planning model that balances centralized and decentralized models for enhanced system resilience, efficiency, and cost-effectiveness. This research underscores the strategic importance of diversifying energy sources, enhancing energy storage, improving grid flexibility, and laying a foundational framework for policy making and strategic planning. It encourages further investigation into climate impacts, technology synergy, and the integration of district heating, aiming to establish a resilient, sustainable, and autonomous energy future.

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low-voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom–up approach. District energy systems with a building scale resolution are modeled in a mixed-integer linear programming problem. The Dantzig–Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of a renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale reveals a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Placed in perspective with the optimal PV capacity forecast at 15.4 GW peak at the national level, coordinated investment between local and national actors is needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems from 12% to 83% and curtails 48% of local renewable electricity.

The transition towards renewable energy is leading to an important strain on the energy grids. The question of designing and deploying renewable energy technologies in symbiosis with existing grids and infrastructure is arising. While current energy system models mainly focus on the energy transformation system or only investigate the effect on one energy vector grid, we present a methodology to characterize different energy vector grids and storage, integrated into the multi-energy and multi-sector modeling framework EnergyScope. The characterization of energy grids is achieved through a traditional energy technology and grid modeling approach, integrating economic and technical parameters. The methodology has been applied to the case study of a country with a high existing transmission infrastructure density, e.g., Switzerland, switching from a fossil fuel-based system to a high share of renewable energy deployment. The results show that the economic optimum with high shares of renewable energy requires the electric distribution grid reinforcement with 2.439 GW (+61%) Low Voltage (LV) and 4.626 GW (+82%) Medium Voltage (MV), with no reinforcement required at transmission level [High Voltage (HV) and Extra High Voltage (EHV)]. The reinforcement is due to high shares of LV-Photovoltaic (PV) (15.4 GW) and MV-wind (20 GW) deployment. Without reinforcement, additional biomass is required for methane production, which is stored in 4.8–5.95 TWh methane storage tanks to compensate for seasonal intermittency using the existing gas infrastructure. In contrast, hydro storage capacity is used at a maximum of 8.9 TWh. Furthermore, the choice of less efficient technologies to avoid reinforcement results in a 8.5%–9.3% cost penalty compared to the cost of the reinforced system. This study considers a geographically averaged and aggregated model, assuming all production and consumption are made in one single spot, not considering the role of future decentralization of the energy system, leading to a possible overestimation of grid reinforcement needs.

Annealing furnaces are critical for achieving the desired material properties in the production of high-quality aluminum products. In addition, energy efficiency has become more and more important in industrial processes due to increasing decarbonization regulations and the price of natural gas. Thus, the current study aims to determine the opportunities to reduce energy consumption in an annealing continuous furnace and the associated emissions. To this end, the heat transfer phenomenon is modeled and solutions for the decreasing fuel consumption are evaluated so that the overall performance of the process is enhanced. A heat transfer model is developed using the finite difference method, and the heat transfer coefficient is calculated using machine learning regression models. The heat transfer model is able to predict the heat transfer coefficient and calculate the aluminum temperature profile along the furnace and the fuel consumption for any given operating condition. Two solutions for boosting the furnace exergy efficiency are evaluated, including the modulation of the furnace temperature profiles and the energy integration by the recycling of exhaust flue gases. The results show that the advanced energy integration approach significantly reduces fuel consumption by up to 20.7%. Sensitivity analysis demonstrates that the proposed strategy can effectively reduce fuel consumption compared with the business-as-usual scenario for a range of sheet thicknesses and sheet velocities.

Endogenous and exogenous uncertainties exert significant influences on energy planning. In this study, we propose a systematic methodology to excavate the uncertainty space, by combining mix-integer linear programming (MILP), Monte Carlo simulation, and machine learning for quantification of the uncertainty impacts on a national-level energy system from global and local perspectives. This approach allows in-depth correlation analysis highlighting potential synergies and risks in the energy transition, and can be easily applied for assisting policy making. The case study for Switzerland shows that both carbon neutrality (even negative emissions) and energy autonomy can be achieved by 2050, but the energy system’s configuration varies significantly under uncertainty. Through conditional distribution analyses, carbon capture and storage (CCS), Photovoltaic (PV), and wood gasification show the most strong correlation for decarbonization. This study is based on the whole uncertainty space taking into account heterogeneous uncertainties, leading to increased reliability compared to sensitivity analysis from single scenarios’ comparisons. The synergy between energy models and artificial intelligence (AI) is promising to be widely applied in energy planning area, particularly for emerging technologies with large uncertainty in development.

A shift from fossil fuels to renewable energy sources is essential to reduce global greenhouse gas emissions and climate change effects. Biofuels represent a promising low-carbon alternative for sectors that are hard to electrify, such as freight transport or aviation. This work investigates possible pathways for increasing the value of biomass at a Kraft pulp mill, focusing on black liquor and bark streams. Mathematical programming is coupled with superstructure optimization and systematic solution exploration to identify meaningful process configurations. The analysis of solutions under market variations allows for the identification of robust and competitive configurations for the co-production of pulp and fossil fuel alternatives. The results show that the integration of biorefineries in pulp mills results in better resource use and higher energy efficiency - diversifying the product portfolio and providing bio-based fuel products to the market while being economically viable. By incorporating fuel production in the conventional Kraft process, the carbon conversion efficiency of the mill can be increased from 48% to up to 67%. Extending the analysis, up to 2% of the European road freight transportation fuel could be provided with combined pulp and fuel production, and 5% of the worldwide fuel demand for passenger aviation.

Electricity markets are currently experiencing a period of rapid change. The intermittent nature of renewable energy is disrupting the conventional methods used in operational planning of the electrical grid, causing a shift from a day-ahead forecast policy to a real-time pricing of delivered electric power. A path towards a more renewable, robust and intelligent energy system is inevitable but poses many challenges to researchers and industry. In the field of process industry, strategies based on demand side response are receiving attention and could represent a partial solution for this challenge. Coordination between production scheduling and procurement of electric power is of high importance and can contribute to reducing cost and emissions associated with production. A methodology to quantify such benefits is presented here with a case study, which reveals the potential benefits of flexible operation. In this case, the minimum compensation for flexibility services ranges between 5 and 20 € per unit (MWh) of restricted power. However, such a compensation depends on geographic location (electricity prices) and the frequency of restrictions. The method follows a rolling scheduling approach that provides optimization of the short-term schedule. This work introduces the concept of representing flexible processes as ‘equivalent batteries’ which store electricity from low-cost periods as intermediate products and consume the embedded energy during high-cost periods. Cost related to providing flexibility combined with the profits from optimized process scheduling contribute toward monetization of flexibility as an ancillary service for the grid. Balancing this service with the cost of implementing DSR solutions provides a means for calculating a pricing strategy for grid flexibility.

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation models integrated in multi-level optimization of a process and energy system superstructure with surrogate models, applying an active learning strategy to continuously enrich the database on which the surrogate models are trained and evaluated. Surrogate models are generated and trained on an initial data set, each featuring the ability to quantify the uncertainty with which a prediction is made. Until a defined prediction quality is met, new data points are continuously labeled and added to the training set. They are selected from a pool of unlabeled data points based on the predicted uncertainty, ensuring a rapid improvement of surrogate quality. When applied in the optimization superstructure, the surrogates can only be used when the prediction quality for the given data point reaches a specified threshold, otherwise the original simulation model is called for evaluating the process performance and the newly obtained data points are used to improve the surrogates. The method is tested on three simulation models, ranging in size and complexity. The proposed approach yields mean squared errors of the test prediction below 2% for all cases. Applying the active learning approach leads to better predictions compared to random sampling for the same size of database. When integrated in the optimization framework, simpler surrogates are favored in over 60% of cases, while the more complex ones are enabled by using simulation results generated during optimization for improving the surrogates after the initial generation. Significant time savings are recorded when using complex process simulations, though the advantage gained for simpler processes is marginal. Overall, we show that the proposed method saves time and adds flexibility to complex superstructure optimization problems that involve optimizing process operating conditions. Computational time can be greatly reduced without penalizing result quality, while the continuous improvement of surrogates when simulation is used in the optimization leads to a natural refinement of the model.

The expected increase of the penetration of distributed renewable energy technologies into the electricity grid is expected to lead to major challenges. As a main stakeholder, authorities often lack the appropriate tools to frame and encourage the transition and monitor the impact of energy transition policies. This paper aims at combining relatively detailed modeling of the PV generation potential on the building’s envelope while retaining the energy system optimization approach. The problem is addressed as a multiobjective, mixed-integer linear programming problem. Compared to the existing literature in the field, the proposed approach combines advanced modeling of the energy generation potential from PV panels with detailed representation of the district energy systems, thus allowing an accurate representation of the interaction between the energy generation from PV and the rest of the system. The proposed approach was applied to a typical residential district in Switzerland. The results of the application of the proposed method show that the district can achieve carbon neutrality based on PV energy alone, but this requires covering all the available district’s rooftops and part of the district’s facades. Whereas facades are generally disregarded due to their lower generation potential, the results also allow concluding that facade PV can be economically convenient for a wide range of electricity prices, including those currently used by the Swiss grid operators. Achieving self-sufficiency at district scale is challenging: it can be achieved by covering approximately 42–100% of the available surface when the round-trip efficiency decreases from 100 to 50%. The results underline the importance of storage for achieving self-sufficiency: even with 100%, round-trip efficiency for the storage, very large capacities are required. However, energy demand reduction through renovation would allow reaching self-sufficiency with half of the PV and storage capacity required.

Biomass, bioenergy and negative emission technologies are inherent to the future design of energy systems. Urban clusters have a growing demand for fuel, heat and electricity, which is both a challenge and an opportunity for biomass-based technologies. Their deployment should meet demand, while minimizing environmental impact and staying cost-competitive. We develop a systematic approach for the design, evaluation and ranking of biomass-to-X production strategies under uncertain market conditions. We assemble state-of-the-art and innovative conversion technologies, based on feedstock, by-products and waste characteristics. Technical specifications, as well as economic and environmental aspects are estimated based on literature values and industry experts input. Embedded into a bi-level mixed-integer linear programming formulation, the framework identifies and assesses current and promising strategies, while establishing the most robust and resilient designs. The added value of this approach is the inclusion of sub-optimal routes which might outperform competing strategies under different market assumptions. The methodology is illustrated in the anaerobic digestion of food and green waste biomass used as a case study in the current Swiss market. By promoting a fair comparison between alternatives it highlights the benefits of energy integration and poly-generation in the energy transition, showing how biomass-based technologies can be deployed to achieve a more sustainable future.

With a growing energy demand in a carbon-constrained society, fuels cells powered by renewable fuels, and specifically solid waste, are seen as interesting contributors to the energy portfolio. The alternative energy industry needs to reduce costs, enhance efficiency, and demonstrate durability and reliability to be economically feasible and attractive. This paper addresses biomass waste gasification in distributed energy systems, using a solid oxide fuel cell (SOFC) to produce electricity and heat. The potential and optimal plant efficiency and layout (i.e., anode off-gas (AOG) recirculation point via small-scale turbomachinery and heat exchanger network) are analyzed through a multi-stage approach that includes scenario evaluation and multi-objective optimization via a hybrid optimization strategy with heuristics and mathematical programming. The results in this paper summarize the most convenient operating conditions and provide an optimized heat exchanger network (HEN). The AOG recirculation toward the gasifier combustor is the preferred option; the electrical and thermal efficiencies can separately go up to 49 and 47%, respectively. The combined total efficiency ranges between 76 and 82%, and the area of heat exchange, which corresponds to an amount of heat exchanged between 91 and 117 kW, is within 6–14 m2.

Ancient Romans called urbs the set of buildings and infrastructures, and civitas the Roman citizens. Today instead, while the society is surfing the digital tsunami, urbs and civitas tend to become much closer, almost merging, that we might attempt to condensate these into a single concept: smart grid. Internet of things, artificial intelligence, blockchain, and quantum cryptography are only a few of the technologies that are likely to contribute to determining the final portrait of the future smart grid. However, to understand the effective sustainability of complex grids, specific tools are required. To this end, in this article, a new taxonomic framework has been developed starting from a general analysis of the emerging solutions, identifying intersectoral synergies and limitations with respect to the ‘smart grid’ concept. Finally, from the scenario portrayed, a set of issues involving engineering, regulation, security, and social frameworks have been derived in a theoretical fashion. The findings are likely to suggest the urgent need for multidisciplinary cooperation to address engineering and ontological challenges gravitating around the smart grid concept.

Electricity production and consumption must be balanced for the electrical grid. However, the rapidly growing intermittent power sources are now challenging the supply-demand balance, leading to large flexibility needs for grid management. The plant integrating biomass gasification and reversible solid-oxide cell stacks can be potential means of flexibility, which could flexibly switch among power generation, power storage, and power neutral modes. This paper investigates the economic feasibility of such grid-balancing plants, i.e., plant capital expenditure (CAPEX) target, via a systematic overall decomposition-based methodology for real geographical zones and flexibility-need scenarios. The plant CAPEX target (€/ref-stack) is defined as the maximum affordable investment cost for each reference stack (active cell area 5,120 cm2). The results show that, for a 5-year payback time, 5-year stack lifetime, and 40 €/MWh grid balancing price, the plant concept with 10–100 MWth gasifier has high economic potential with target reaching 17,000 €/ref-stack; however, the plant concept with 100–1,000 MWth gasifier has a limited commercialization potential with the target reaching below 1,000 €/ref-stack due to high biomass supply costs. Considering the sale of chemical product, plant CAPEX target can reach up to 22,000 and 3,000–12,000 €/ref-stack for the plants with 10–100 and 100–1,000 MWth, respectively. The plant CAPEX target is decreased by increasing the total capacities of all plants deployed since more and more capacities will be put into power neutral mode (isolated from the electrical grid) via the coordination of multiple plants. The plant CAPEX target can be further increased by higher grid up/down-regulating price and longer payback years.

In the context of increasing concern for anthropogenic CO2 emissions, the residential building sector still represents a major contributor to energy demand. The integration of renewable energy sources, and particularly of photovoltaic (PV) panels, is becoming an increasingly widespread solution for reducing the carbon footprint of building energy systems (BES). However, the volatility of the energy generation and its mismatch with the typical demand patterns are cause for concern, particularly from the viewpoint of the management of the power grid. This paper aims to show the influence of the orientation of photovoltaic panels in designing new BES and to provide support to the decision making process of optimal PV placing. The subject is addressed with a mixed integer linear optimization problem, with costs as objectives and the installation, tilt, and azimuth of PV panels as the main decision variables. Compared with existing BES optimization approaches reported in literature, the contribution of PV panels is modeled in more detail, including a more accurate solar irradiation model and the shading effect among panels. Compared with existing studies in PV modeling, the interaction between the PV panels and the remaining units of the BES, including the effects of optimal, scheduling is considered. The study is based on data from a residential district with 40 buildings in western Switzerland. The results confirm the relevant influence of PV panels’ azimuth and tilt on the performance of BES. Whereas south-orientation remains the most preferred choice, west-orientationed panels better match the demand when compared with east-orientationed panels. Apart from the benefits for individual buildings, an appropriate choice of orientation was shown to benefit the grid: rotating the panels 20° westwards can, together with an appropriate scheduling of the BES, reduce the peak power of the exchange with the power grid by 50% while increasing total cost by only 8.3%. Including the more detailed modeling of the PV energy generation demonstrated that assuming horizontal surfaces can lead to inaccuracies of up to 20% when calculating operating expenses and electricity generated, particularly for high levels of PV penetration.

Industrial process integration based on mixed integer linear programming has been used for decades to design and improve industrial processes. The technique has later been extended to solve multi-period and multi-scale problems for the design of urban energy systems. Assistance is indeed required for the elaboration of coordinated investment scheduling strategies to promote renewable and efficient urban energy infrastructure shaping the future energy context for the next decades. Major energy consumers, such as hospital complexes, airports, or educational campuses can act as a driving force for the development of renewable energy cities by attracting profitable large-scale energy networks and infrastructure. The proposed methodology generates optimal alternatives for the replacement, in a long-term perspective, of the various energy supply units and systems considering the evolution of the energy demand and the availability of the energy resources. Energy integration techniques are coupled to a parametric multi-objective optimization routine to select and size the energy equipment with both financial profitability and CO2 emission reduction as objectives. The originality of the developed method lies in the integration of a multi-period mixed integer linear programming formulation to generate long-term investment planning scenarios. The method has been demonstrated on a complex of eight hospitals totaling 466,000 m2 and an operating budget of 1.85 billion USD per year. The energy integration of new centralized and decentralized equipment has been evaluated on a monthly basis over four periods until the year 2035. The results show that among the four scenarios identified, the most optimistic alternative allows to decrease the final energy consumption of about 36%, cut the CO2 emissions by a half, multiply the renewable energy share by a factor 3.5 while reducing the annual total cost by 24%. This scenario considers mainly the integration of a very low temperature district heating with decentralized heat pumps to satisfy the heat requirements below 75°C, as well as heat recovery systems and the refurbishment of about 33% of the building stock.

Decarbonization gained prominence with the witnessed rise of temperature over recent years, particularly in the aftermath of the adoption of the Paris agreement for limiting the temperature increase within 2°C until 2050. Biogenic resources are explicitly indicated as carbon-neutral from Life Cycle Assessment perspective by the IPCC, shedding light on the carbon-neutral society by applying Biogenic Energy Carbon Capture for creating negative emissions. This article proposes a novel modeling approach by introducing carbon layers with specification on the principal carbon sources and sinks based upon an optimization algorithm, in order to solve the carbon loop issue in a highly interconnected energy system due to increasing penetration of biomass and carbon capture, use, and storage. This study contributes to quantifying biogenic and nonbiogenic carbon footprints, and optimizing the circular economy associated with a net-zero-emission society, in favor of policy-making for sustainable development in long terms.

Wastewater treatment and sludge disposal are responsible for considerable costs and emissions in a global scale. With population and urbanization growing, tackling the rational and efficient use of energy while fulfilling the desired effluent standards are imperative. In this work, a superstructure-based approach is designed to incorporate alternative treatments for wastewater. In particular, technologies like hydrothermal liquefaction and gasification, coupled with technologies for CO2 conversion to value-added products are studied. Multi-objective optimization is applied as a way to generate multiple solutions that correspond to different system configurations. From a reference treatment cost of almost 0.16 $/mWW3, an environmental impact of 0.5kgCO2/mWW3 and an energy efficiency of 5%, different configurations are able to transform a waste water treatment plant to a net profit unit, with a net environmental benefit and energy efficiency close to 65%. The investment in hydrothermal liquefaction producing biocrude coupled with catalytic hydrothermal gasification demonstrated to yield consistently better total costs and environmental impacts. Parametric analysis is performed in the inlet flow of wastewater to account for different sizes of waste water treatment plant, with smaller inlets achieving values closer to those of the state-of-the-art configuration.

This research presents a mathematical formulation for optimizing integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The framework is constructed using mixed-integer linear programming (MILP) which exhibits rapid conversion and a global optimum with well-defined solution methods. The framework builds upon previous efforts in process integration and considers materials and energy with thermodynamic constraints imposed by formulating the heat cascade within the MILP. The model and method which form the fundamentals of process integration problems are presented, considering exchange restrictions and problem formulation across multiple time-scales to provide flexibility in solving complex design, planning, and operational problems. The work provides the fundamental problem formulation, which has not been previously presented in a comprehensive way, to provide the basis for future work, where many process integration elements can be appended to the formulation. A case study is included to demonstrate the capabilities and results for a simple, fictional, example though the framework and method are broadly applicable across scale, time, and plant complexity.

In order to reduce the CO2 emissions in the transportation sector, one can electrify the vehicle, switch to biofuel, or capture and store CO2 on board. In this study, integration of an on board CO2 capture and storage unit with an internal combustion engine has been proposed. The technology can be applied for various internal combustion or Stirling engines with targeted applications in the transportation sector. Truck transport for goods delivery is used as an example for on board CO2 capture and storage system design. The investigated system integrates a temperature swing adsorption system for CO2 capture with a turbo-compressor system to compress and liquefy the captured CO2 using the waste heat of the exhaust gases of the engine. Energy and exergy analyses of the proposed CO2 captured system are studied in details. The CO2 capture system for engine exhaust stream (car, truck, bus, ship, or train) can capture 90% of the emitted CO2, without any energy penalty. This system can be integrated into overall mobility system (fuel-engine-CO2-fuel), where captured CO2 can be recycled as conventional liquid or gaseous fuels produced from renewable energy sources.

This study investigates the technical and economic feasibility of replacing throttling valves with smale-scale, oil-free turbomachinery in industrial steam networks. This is done from the perspective of the turbomachine, which has to be integrated into a new or existing process. The considered machines have a power range of P=[0.5,…,250 kW] and have been designed using real industrial data from existing processes. Design guidelines are developed, which take into account the thermodynamic process as well as engineering aspects of such a turbomachine. The results suggest that steam conditioning prior to heat exchange could be completed by small expanders to produce mechanical work, reducing exergy destruction and improving site-wide energy efficiency compared to throttling valves. Cost estimates for such machines are presented, which serve as a basis for case-specific investment calculations. The resulting payback times of less than 18 months highlight the economic potential such solutions.

The large share of energy consumption in the residential sector has necessitated better understanding and evaluation of its energy needs, with the objective of identifying possible pathways for improvement. This work uses heat signature models and climate data to build a parameterized residential sector profile for different climatic zones in Europe. The sector profile is validated using Rotterdam, NL as a case study and the results show variations from the real energy demand profile of less than 10%, primarily caused by cultural and climatic differences between Rotterdam and the rest of Western Europe. The energy and service profile constructed herein is well-suited for exploring the best technologies for supplying residential requirements, drawing from the domain of process integration. This work demonstrates the usefulness of the residential profile by applying process integration techniques within a mixed integer linear programming formulation to evaluate optimal energy conversion technologies for different district energy networks and potential waste heat recovery from industrial plants located in the vicinity of the residential area. The results show that switching to a fully electric energy providing system can lead to operating cost savings of 48% and CO2 emission savings up to 100%, depending on the mix of electricity generation. The utilization of the sector profile is also exemplified using the city of Rotterdam, where it is shown that industrial waste heat recovery can lead to operating cost and environmental impact savings of 9% and 20%, respectively.

The priority of the automotive industry is to reduce the energy consumption and the emissions of the future passenger cars and to deliver an efficient mobility service for the customers. The improvement of the efficiency of vehicle energy systems promotes an active search to find innovative solutions during the design process. Engineers can use computer-aided processes to find automatically the best design solutions. This kind of approach named “multi-objective optimization” is based on genetic algorithms. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions will be optimal from technical, economic and environmental point of view. The “genetic intelligence” is tested for the holistic design of the environomic vehicle powertrain solutions. The environomic methodology for design is applied on D-class hybrid electric vehicles, in order to explore the techno-economic and environmental trade-off for different hybridization level of the vehicles powertrains. For powertrain efficiencies between 0.25 and 0.35 the electrification of the powertrain reduces the global CO2 emissions. Hybrid electric and plug-in hybrid electric vehicles are reaching these levels. The break point of the electrification effect on the GWP occurs on 0.35 % of powertrain efficiency.For battery capacity value higher than 13 kWh the global reduction of the CO2 emissions is not obvious. The method gives also an overview of the evolution of environmental categories indicators as a function of the cost of the vehicles. A direct relation links the economic and the environmental performances of the solutions.

Solid oxide fuel cells (SOFC) have developed to a mature technology, able to achieve electrical efficiencies beyond 60%. This makes them particularly suitable for off-grid applications, where SOFCs can supply both electricity and heat at high efficiency. Concerns related to lifetime, particularly when operated dynamically, and the high investment cost are however still the main obstacles toward a widespread adoption of this technology. In this paper, we propose a hybrid cogeneration system that attempts to overcome these limitations, in which the SOFC mainly provides the baseload of the system. Introducing a purification unit allows the production and storage of pure hydrogen from the SOFC anode off-gas. The hydrogen can be stored, and used in a proton exchange membrane fuel cell (PEMFC) during peak demands. The SOFC system is completed with a battery, used during periods of high electricity production. We propose the use of a mixed integer-linear optimization framework for the sizing of the different components of the system, and particularly for identifying the optimal trade-off between round-trip efficiency and investment cost of the battery-based and hydrogen-based storage systems. The proposed system is applied and optimized to two case studies: an off-grid dwelling, and a cruise ship. The results show that, if the SOFC is used as the main energy conversion technology of the system, the use of hydrogen storage in combination with a PEMFC and a battery is more economically convenient compared to the use of the SOFC in stand-alone mode, or of pure battery storage. The results show that the proposed hybrid storage solution makes it possible to reduce the investment cost of the system, while maintaining the use of the SOFC as the main energy source of the system.

Data are essential to urban building energy models and yet, obtaining sufficient and accurate building data at a large-scale is challenging. Previous studies have highlighted that the data impact on urban case studies has not been sufficiently discussed. This paper addresses this gap by providing an analysis of the impact of input data on building energy modelling at an urban scale. The paper proposes a joint review of data impact and data accessibility to identify areas where future survey efforts should be concentrated. Moreover, a Morris sensitivity analysis is carried out on a large-scale residential case study, to rank input parameters by impact on space heating demand. This paper shows that accessible data impact the whole modelling process, from approach selection to model replicability. The sensitivity analysis shows that the setpoint and thermal characteristics were the most impactful for the case study considered. Solutions proposed to overcome availability and accessibility issues include organising annual workshops between data users and data owners, or developing online databases that could be populated on a volunteer-basis by data owners. Overall, overcoming data challenges is essential for the transition towards smarter cities, and will require an improved communication between all city stakeholders.

Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads toward production periods. Therefore, this article introduces a systematic approach to provide a preliminary evaluation of the thermoeconomic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an ϵ-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed-integer linear programming technique. To illustrate the contribution of MPC, different applications on the single- and multi-dwelling level are presented and analyzed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single-building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock.

This article presents the development and application of a methodology that employs optimization not to seek the single or few optimal plan(s) but to provide planners with a systematic overview of their decision space. As urban development projects are not only subject to decisions of planners but also to those of many actors, the insight about how different actors would decide based on the decisions of the planners should enable planners to already adapt their decisions to ensure that final project targets are reached. The existence of different decision makers is considered via a multiparametric mixed-integer linear programming (mpMILP) approach. Along with the methodology a model was developed, which incorporates multiple domains and scales. Those domains include social, environmental, form, energy, and economic aspects. The considered scales range from single floors up to a neighborhood. Model and methodology were applied to a greenfield development project. Two practical questions are answered, which address the impact of planning decisions about (a) the building density and the sustainability of the energy supply on costs of different actors and (b) the building density and the share of parks on the view on a landmark. The capturing of the decision space revealed trade-offs in terms of chosen energy supply system and urban form, respectively. The presented computational method forms part of the decision support tool URBio, which shall assist urban planning.

Woody biomass, a renewable energy resource, accumulates solar energy in form of carbon hydrates produced from atmospheric CO2 and H2O. It is, therefore, a means of CO2 mitigation for society as long as the biogenic carbon released to the atmosphere when delivering its energy content by oxidation can be accumulated again during growth of new woody biomass. Even when considering the complete life cycle, usually, only a small amount of fossil CO2 is emitted. However, woody biomass availability is limited by land requirement and, therefore, it is important to maximize its CO2 mitigation potential in the energy system. In this study, we consider woody biomass not only as a source of renewable energy but also as a source of carbon for seasonal storage of solar electricity. A first analysis is carried out based on the mitigation effect of woody biomass usage pathways, which is the avoided fossil CO2 emissions obtained by using one unit of woody biomass to provide energy services, as alternative to fossil fuels. Results show that woody biomass usage pathways can achieve up to 9.55 times the mitigation effect obtained through combustion of woody biomass, which is taken as a reference. Applying energy system modeling and multi-objective optimization techniques, the role of woody biomass technological choices in the energy transition is then analyzed at a country scale. The analysis is applied to Switzerland, demonstrating that the use of woody biomass in gasification–methanation systems, coupled with electrolysers and combined with an intensive deployment of PV panels and efficient technologies, could reduce the natural gas imports to zero. Electrolysers are used to boost synthetic natural gas production by hydrogen injection into the methanation reaction. The hydrogen used is produced when there is excess of solar electricity. The efficient technologies, such as heat pumps and battery electric vehicles, allow increasing the overall efficiency of the energy system while generating demand for the solar electricity.

Urban energy system planning (UESP) is a topic of growing concern for cities in deregulated energy markets, which plan to decrease energy demand, reduce their dependency on fossil fuels, and increase the share of renewable energy sources. UESP being a highly multisectoral and multi-actor task, multicriteria decision analysis (MCDA) methods are frequently used in the decision processes. These methods may provide support in organizing and identifying solutions to problems with conflicting objectives. However, knowing which method to use is generally not straightforward, as the appropriateness of a method or combination of methods depends on the decision problem’s context. Therefore, this article reviewed scientific papers to characterize and analyze MCDA problems and methods in the context of UESP. The review systematically explores issues such as the scope of the problems, the alternatives and criteria considered, the expected decision outcomes, the decision analysis methods and the rationales for selecting and combining them, and the role of values in driving the decision problems. The final outcome is a synthesis of the data and insights obtained, which may help potential users identify appropriate decision analysis methods based on given problem characteristics.

The industrial sector has a large presence in world energy consumption and CO2 emissions, which has made it one of the focal points for energy and resource efficiency studies. However, large investments are required to retrofit existing industrial plants, which remains the largest barrier to implementing energy saving solutions. Process integration methods can be used to identify the best investments to improve the efficiency of plants, yet their timing remains to be answered using an optimisation approach. Even more critically, such decisions must also account for future investments to avoid stranded or regretted investments. This paper presents a method incorporating investment planning over long time horizons in the framework of process integration. The time horizon is included by formulating the problem using multiple investment periods. Investment planning is conducted using a superstructure approach, which permits both commissioning and decommissioning of units in the beginning of each period. The method is applied to a large case study, with an industrial cluster neighbouring an urban centre to also explore options of heat integration between industries and cities. Compared to the business-as-usual operation, optimal investment planning improves the operating cost of the system by 27% without budget constraints and 16−26% with constraints on budget and investment periods, which is reflected as an increase in net present value and a decrease in CO2 emissions. In all cases, the operating cost benefits pay off the investment in less than two years. The present work is efficient in finding energy saving solutions based on the interest of industries. This method adds additional perspectives in the decision-making process and is adaptable to various time horizons, budgets and economic constraints.

Carbon capture and utilization are promising to tackle fossil-fuel depletion and climate change. CO2 hydrogenation can synthesize various chemicals and fuels, such as methanol, formic acid, urea, and methane. CO2-to-methanol integrated with solid-oxide electrolysis (SOE) process can store renewable power in methanol while recycling recovered CO2, thus achieving the dual purposes of storing excess renewable power and reducing lifetime CO2 emissions. This paper focuses on the techno-economic optimization of CO2 hydrogenation to synthesize green methanol integrated with solid-oxide electrolysis process. Process integration, techno-economic evaluation, and multi-objective optimization are carried out for a case study. Results show that there is a trade-off between energy efficiency and methanol production cost. The annual yield of methanol of the studied case is 100 kton with a purity of 99.7%wt with annual CO2 utilization of 150 kton, representing the annual storage capacity of 800 GWh renewable energy. Although the system efficiency is rather high at around at 70% and varies within a narrow range, methanol production cost reaches 560 $/ton for an electricity price of 73.16 $/MWh, being economically infeasible with a payback time over 13 years. When the electricity price is reduced to 47 $/MWh and further to 24 $/MWh, the methanol production cost becomes 365 and 172 $/ton with an attractive payback time of 4.6 and 2.8 years, respectively. The electricity price has significant impact on project implementation. The electricity price is different in each country, leading to a difference of the payback time in different locations.

The large potential for waste resource and heat recovery in industry has been motivating research toward increasing efficiency. Process integration methods have proven to be effective tools in improving industrial sites while decreasing their resource and energy consumption; however, location aspects and their impact are generally overlooked. This paper presents a method based on process integration, which considers the location of plants. The impact of the locations is included within the mixed integer linear programming framework in the form of heat losses, temperature and pressure drop, and piping cost. The objective function is selected as minimisation of the total cost of the system excluding piping cost and ϵ -constraints are applied on the piping cost to systematically generate multiple solutions. The method is applied to a case study with industrial plants from different sectors. First, the interaction between two plants and their utility integration are illustrated, depending on the piping cost limit which results in the heat pump and boiler on one site being gradually replaced by excess heat recovered from the other plant. Then, the optimisation of the whole system is carried out, as a large-scale application. At low piping cost allowances, heat is shared through high pressure steam in above-ground pipes, while at higher piping cost limits the system switches toward lower pressure steam sharing in underground pipes. Compared to the business-as-usual operation of the sites, the optimal solution obtained with the proposed method leads to 20% reduction in the overall cost of the system, including the piping cost. Further reduction in the cost is possible using a state of the art method but the technical and economic feasibility is not guaranteed. Thus, the present work provides a tool to find optimal industrial symbiosis solutions under different investment limits on the infrastructure between plants.

The residential sector accounts for a large share of worldwide energy consumption, yet is difficult to characterise, since consumption profiles depend on several factors from geographical location to individual building occupant behaviour. Given this difficulty, the fact that energy used in this sector is primarily derived from fossil fuels and the latest energy policies around the world (e.g., Europe 20-20-20), a method able to systematically integrate multi-energy networks and low carbon resources in urban systems is clearly required. This work proposes such a method, which uses process integration techniques and mixed integer linear programming to optimise energy systems at both the individual building and district levels. Parametric optimisation is applied as a systematic way to generate interesting solutions for all budgets (i.e., investment cost limits) and two approaches to temporal data treatment are evaluated: monthly average and hourly typical day resolution. The city center of Geneva is used as a first case study to compare the time resolutions and results highlight that implicit peak shaving occurs when data are reduced to monthly averages. Consequently, solutions reveal lower operating costs and higher self-sufficiency scenarios compared to using a finer resolution but with similar relative cost contributions. Therefore, monthly resolution is used for the second case study, the whole canton of Geneva, in the interest of reducing the data processing and computation time as a primary objective of the study is to discover the main cost contributors. The canton is used as a case study to analyse the penetration of low temperature, CO2-based, advanced fourth generation district energy networks with population density. The results reveal that only areas with a piping cost lower than 21.5 k€/100 m2ERA connect to the low-temperature network in the intermediate scenarios, while all areas must connect to achieve the minimum operating cost result. Parallel coordinates are employed to better visualise the key performance indicators at canton and commune level together with the breakdown of energy (electricity and natural gas) imports/exports and investment cost to highlight the main contributors.

Industrial operations consume energy and water in large quantities without accounting for potential economic and environmental burdens on future generations. Consumption of energy (mainly in the form of high pressure steam) and water (in the form of process water and cooling water) are essential to all processes and are strongly correlated, which requires development of systematic methodologies to address their interconnectivity. To this end, the subject of heat-integrated water allocation network design has received considerable attention within the research community in recent decades while further growth is expected due to imposed national and global regulations within the context of sustainable development. The overall mathematical model of these networks has a mixed-integer nonlinear programming formulation. As discussed in this work, proposed models in the literature have two main difficulties dealing with heat−water specificities, which result in complex formulations. These difficulties are addressed in this work through proposition of a novel nonlinear hyperstructure and a sequential solution strategy. The solution strategy is to solve three sub-problems sequentially and iteratively generate a set of potential solutions through the implementation of integer cut constraints. The novel mathematical approach also lends itself to an additional innovation for proposing multiple solutions balancing various performance indicators. This is exemplified with both a literature test case and an industrial-scale problem. The proposed solutions address a variety of performance indicators which guides decision-makers toward selecting the most appropriate configuration(s) among a large number of potential possibilities. Results exhibit that despite having a sequential solution strategy, better performance can be reached compared to previous approaches with the additional benefit of providing many potential solutions for further consideration by decision-makers to select the best case-specific solution.

Efficient consumption of energy and material resources, including water, is the primary focus for process industries to reduce their environmental impact. The Conference of Parties in Paris (COP21) highlighted the prominent role of industrial energy efficiency in combating climate change by reducing greenhouse gas emissions. Consumption of energy and material resources, especially water, are strongly interconnected and, therefore, must be treated simultaneously using a holistic approach to identify optimal solutions for efficient processing. Such approaches must consider energy and water recovery within a comprehensive process integration framework which includes options such as organic Rankine cycles for electricity generation from low–medium-temperature heat. This work addresses the importance of holistic approaches by proposing a methodology for simultaneous consideration of heat, mass, and power in industrial processes. The methodology is applied to a kraft pulp mill. In doing so, freshwater consumption is reduced by more than 60%, while net power output is increased by a factor of up to six (from 3.2 MW to between 10–26 MW). The results show that interactions among these elements are complex and therefore underline the necessity of such comprehensive methods to explore their optimal integration with industrial processes. The potential applications of this work are vast, extending from total site resource integration to addressing synergies in the context of industrial symbiosis.

To reach optimal/better conceptual designs of energy systems, key design variables should be optimized/adapted with system layouts, which may contribute significantly to system improvement. Layout improvement can be proposed by combining system analysis with engineers’ judgments; however, optimal flowsheet synthesis is not trivial and can be best addressed by mathematical programming. In addition, multiple objectives are always involved for decision makers. Therefore, this paper reviews progressively the methodologies of system evaluation, optimization, and synthesis for the conceptual design of energy systems, and highlights the applications to thermal power plants, which are still supposed to play a significant role in the near future. For system evaluation, both conventional and advanced exergy-based analysis methods, including (advanced) exergoeconomics are deeply discussed and compared methodologically with recent developments. The advanced analysis is highlighted for further revealing the source, avoidability, and interactions among exergy destruction or cost of different components. For optimization and layout synthesis, after a general description of typical optimization problems and the solving methods, the superstructure-based and -free concepts are introduced and intensively compared by emphasizing the automatic generation and identification of structural alternatives. The theoretical basis of the most commonly-used multi-objective techniques and recent developments are given to offer high-quality Pareto front for decision makers, with an emphasis on evolutionary algorithms. Finally, the selected analysis and synthesis methods for layout improvement are compared and future perspectives are concluded with the emphasis on considering additional constraints for real-world designs and retrofits, possible methodology development for evaluation and synthesis, and the importance of good modeling practice.

This paper studies the optimal contract offered by a risk-neutral principal to a risk-averse agent when the agent’s hidden ability and action both improve the probability of the project being successful. We show that if the agent is sufficiently prudent and able, the principal induces a higher probability of success than under moral hazard, despite the costly informational rent given up. Moreover, there is distortion at the top. Finally, the conditions to avoid pooling are difficult to satisfy because of the different kinds of incentives to be managed and the overall trade-off between rent extraction, insurance, and efficiency involved.

Fundamental and phenomenological models for cells, stacks, and complete systems of PEFC and SOFC are reviewed and their predictive power is assessed by comparing model simulations against experiments. Computationally efficient models suited for engineering design include the (1+1) dimensionality approach, which decouples the membrane in-plane and through-plane processes, and the volume-averaged-method (VAM) that considers only the lumped effect of pre-selected system components. The former model was shown to capture the measured lateral current density inhomogeneities in a PEFC and the latter was used for the optimization of commercial SOFC systems. State Space Modeling (SSM) was used to identify the main reaction pathways in SOFC and, in conjunction with the implementation of geometrically well-defined electrodes, has opened a new direction for the understanding of electrochemical reactions. Furthermore, SSM has advanced the understanding of the COpoisoning-induced anode impedance in PEFC. Detailed numerical models such as the Lattice Boltzmann (LB) method for transport in porous media and the full 3-D Computational Fluid Dynamics (CFD) Navier-Stokes simulations are addressed. These models contain all components of the relevant physics and they can improve the understanding of the related phenomena, a necessary condition for the development of both appropriate simplified models as well as reliable technologies. Within the LB framework, a technique for the characterization and computer-reconstruction of the porous electrode structure was developed using advanced pattern recognition algorithms. In CFD modeling, 3-D simulations were used to investigate SOFC with internal methane steam reforming and have exemplified the significance of porous and novel fractal channel distributors for the fuel and oxidant delivery, as well as for the cooling of PEFC. As importantly, the novel concept has been put forth of functionally designed, fractal-shaped fuel cells, showing promise of significant performance improvements over the conventional rectangular shaped units. Thermo-economic modeling for the optimization of PEFC is finally addressed.