Advances in Energy Systems Engineering

As a result of the discovery of substantial reserves of shale gas, it is important to develop effective strategies for the establishment of infrastructure supporting the growth of shale gas production and monetization. This chapter presents an optimization approach for the optimal planning of shale gas exploitation and infrastructure development in places that lack the infrastructure needed for production, treatment, and distribution. A multi-period optimization approach is presented to account for the variability in market. The different components of the infrastructure, the production schedules, and the time-value of money to maximize the net present value of the infrastructure are considered in the optimization model. The applicability of the proposed approach is shown through a case study from Mexico, where there are enormous reserves of shale gas that require the development of infrastructure. The results show attractive economic results for the exploitation and distribution of gas to satisfy a certain demand.

In recent decades, large-scale production of shale gas has been considered as a major issue in the U.S. energy industry. In accordance with its great economic potential and environmental concerns, shale gas process and supply chain optimization has become one of the most popular research areas. In this chapter, we provide a comprehensive overview of the supply chain management and process design problems in shale gas industry. We summarize four major research challenge areas, namely the design and planning of shale gas supply chain, water management in hydraulic fracturing, sustainability concerns in shale gas industry, and design and optimization in shale gas processing system. We further provide review and discussions of the major publications corresponding to each of the aforementioned topics. Potential opportunities in the shale gas system are presented as well to illuminate the future research.

This chapter is intended to discuss the application of mathematical programing methodologies to assist in the design and planning of energy supply chains. Accordingly, a comprehensive literature review of various different energy systems models as well as optimization approaches which have been developed to support capacity planning and investment decisions is presented. Two mathematical programming-based frameworks, one for shale gas supply chains and another for interconnected power systems are briefly described and employed for relevant case studies. These case studies are used not only to illustrate the methodology but more importantly to draw some insights that are relevant to policy development.

Hydrogen is a required key material for petroleum refineries that convert crude oil into a variety of products with higher economic value, e.g., gasoline. In chemical process plants and petroleum refineries, hydrogen is produced primarily by the steam methane reforming (SMR) process synthesizing hydrogen and carbon oxides from methane and superheated steam in the presence of a nickel-based catalyst network in a steam methane reformer. Traditionally, the optimized and profitable operating conditions of a steam methane reformer are analyzed and determined by on-site parametric study at industrial-scale plants or pilot-scale units, which is an experimental approach, and therefore, it must be conducted by changing process parameters in small increments over a long time period in order to prevent significant production and capital loss. Motivated by the above considerations, the present work focuses on developing a computational fluid dynamics (CFD) model of a pilot-scale steam methane reformer comprised of four industrial-scale reforming reactors, three industrial-scale burners and three flue gas tunnels. The pilot-scale reformer CFD model is developed by analyzing well-established physical phenomena, i.e., the transport of momentum, material and energy, and chemical reactions, i.e., combustion and the SMR process, that take place inside the steam methane reformer. Specifically, the \(P-1\) radiation model, standard \(k-\epsilon \) turbulence model, compressible ideal gas equation of state and finite rate/eddy dissipation (FR/ED) turbulence-chemistry interaction model are adopted to simulate the macroscopic and microscopic events in the reformer. The conditions for the tube-side feed, burner feed and combustion chamber refractory walls are consistent with typical reformer plant data Latham (2008) so that the simulation results generated by the pilot-scale reformer can be validated by the plant data. The simulation results are shown to be in agreement with publicly available plant data reported in the literature and also with the simulation data generated by a well-developed single reforming tube CFD model. Subsequently, the proposed pilot-scale reformer CFD model is employed for a parametric study of the mass flow rate of the burner feed, i.e., a \(20\,\%\) increase from its nominal value. The corresponding simulation results demonstrate the advantages offered by this CFD model for parametric study by showing that with the increased burner feed, the outer reforming tube wall temperature exceeds the maximum allowable temperature; these results were developed quickly with the aid of a CFD model, compared to the timescale on which parametric studies are performed on-site and without the potential for rupture of the reforming tubes during the study.

Production optimization and planning is a key part of the modern process industry operations. It plays a major role in sustaining the profitability of the operations and providing decision making guidelines to attain business objectives through achieving best margins and minimum operating costs. Numerous industries have benefited from planning models to achieve optimal operations subject to technologies involved, resources requirements, production constraints and available markets for the products sales.

Coal is the main primary energy in the world. However, most coal is combusted to generate electric and heat, which does not make effective of valuable hydrogen-rich volatile matter. A coal staged conversion process,which coupled circulating fluidized bed (CFB) combustion and coal pyrolysis,is a more efficient and clean technology for coal conversion and the principle is: part of the high temperature circulating material from CFB combustor as solid heat carrier is sent to the pyrolyzer to provide heat for coal pyrolysis and the coal incurs pyrolysis, producing tar and gas. The pyrolysis char is returned to CFB for further combustion to generate electricity and provide heat. Thus, poly-generation of gas, tar, heat and power can be realized in this system. According to the bed type of the pyrolyzer, the coal staged conversion technology is mainly divided into three types: the technology based on fluidized bed pyrolysis, that based on moving bed pyrolysis and that based on downer bed pyrolysis, and a lot of research has been done on this. Take the technology by Zhejiang University for example, industrial application and system analysis are discussed in detail. 12 MW and 40 t/h coal staged conversion process is established and operated. The test results showed that this system can realize poly-generation of heat, electricity, gas and tar. When the pyrolyzer does not run, the circulating fluidized bed can still be normally operated. This shows that the coal staged conversion technology can be applied to industrial scale poly-generation plant. Thermodynamic and economic of poly-generation system coupling 2 × 300 MW circulating fluidized bed and 2 fluidized bed pyrolyzer is analyzed. It shows that the coal staged conversion process system has higher energy and exergy efficiency and more profitable than single traditional 2 × 300 MW CFB plant. This justified the coal staged conversion process in a large scale is an efficient and economic technology.

Polygeneration, typically involving co-production of chemicals or liquid fuels and electricity, is an advanced and promising energy conversion technology which provides opportunities for high energy utilization efficiency, high economic benefits and low/zero emissions. This chapter firstly gives a brief introduction of characteristics, development and patterns on energy polygeneration systems. Basis and theories of the integration and optimization of energy polygeneration system are then introduced. Polygeneration is a highly flexible and integrated system which couples with different technologies and energy fuels feedstocks, and it is pointed out that the integration and optimization of an energy polygeneration system should comply with the principle of cascade utilization of energy and classify conversion of element to obtain the optimal comprehensive performance in terms of energy, economy and environment. Finally, the feasibility and development prospect of CO₂ conversion and use in polygeneration system are discussed. The everlasting pursuit in energy conversion and utilization is improving the utilization and efficiency of resources, whilst reducing CO₂ emissions. Polygeneration has inherent advantages of CO₂ recycle and reduction, and CO₂ recycle and use coupled with different energy polygeneration systems are analyzed. All of those present excellent sustainability in their energy use and environmental protection. Polygeneration with CO₂ recycle has a huge potential in CO₂ reduction, while shows low cost involved with CO₂ reduction.

Energy storage systems (ESSs) are one kind of advances in energy systems engineering and of great value to realize energy management and to support renewable generation. The combined operation of ESSs and renewables is one way to achieve output levelling and to improve the integration of sustainable energy. However, in a market-based environment, ESSs would make strategic decisions on self-schedules and arbitrage in energy and ancillary service markets, maximizing the overall profits. Will the strategic operation of ESSs promote renewable generation integration? To explicitly answer this question, this chapter proposes a multi-period Nash-Cournot equilibrium model for joint energy and ancillary service markets to evaluate the contribution of the ESSs for supporting renewable generation. Then, a reformulation approach based on the potential function is proposed, which can transform the bi-level equilibrium model into an integrated single-level optimization problem to enhance the computation efficiency. Numerical examples are implemented to validate the effectiveness of the reformulation technique. The results of the case study indicate that the ESSs indirectly but substantially provide improved flexibilities while pursuing individual profit maximization.

The contribution of power generation from renewable sources to the U.S. energy markets has witnessed an explosive growth, leading to increased variability and uncertainty in grid operations. Demand response (DR) operation of chemical processes, based on overproducing and storing product(s) during off-peak times, when grid demand is low, and reducing production rates and (partially) meeting product demand using the stored products during peak price times, can be used to create a virtual grid-scale “battery” and help alleviate power generation variability. In this chapter, we discuss the interplay between process-centric and utility-centric optimization of the operating schedule of DR chemical processes. Given the frequent production rate changes required in such circumstances, the scheduling calculations are carried out under a set of dynamic constraints that represent the scheduling-relevant closed-loop behavior of the processes. We propose a reconciliation mechanism, based on two-way communication of a limited set of information between the utility and process operators. Utility operators use a simplified, battery-like representation of the energy storage capabilities of each DR process to compute the utility-optimal process energy consumption profile. Then, process operators determine the energy price profile that would allow them to implement this consumption profile without incurring economic losses, and communicate it to the utility company. This scheme reduces the dimension of the optimization problem that must be solved at the utility level and provides an equitable and transparent means for chemical process operators to engage in DR schemes.

In this study, we present an analysis of future gasoline supply and consumption gap and a projection to future passenger car fuel consumption. In the first part, we study the gasoline and diesel supply from the refining sector in China. A virtual refinery model is established to analyze the productivity of gasoline and diesel. Based on the calculation of the model, we get minimum and maximum production of gasoline and diesel and the flexibility between them. In the second part, we study the ownership and sales of passenger car in China. A model for different types of passenger cars is established and many factors affecting the ownership are analyzed separately. Vehicle age distribution in passenger car is also taken into consideration. In the third part, alternative fuels and technologies for vehicles are studied. Fuel ethanol, natural gas, EV and PHEV are analyzed in detail. In the last part, we analyze the actual gasoline consumption rate and the average annual mileage of passenger car. Then we make an assumption as a basic scenario. Gasoline consumption in passenger car sector and other sectors in the future are calculated based on the assumption above. Gasoline consumption and supply are compared and a major finding is that the gasoline supply cannot meet the consumption since 2022 in basic scenario. To meet this gap, different measures in technology and policy which reducing gasoline consumption in passenger car sector are considered and analyzed.

Currently, most of studies on China’s energy demand and consumption took the huge country as a whole. As China is a huge country composed of more than 30 administrative regions with different scales, structures and intensities of energy consumption, this manuscript revealed the regional disparities in energy consumption of China’s 30 provinces. Based on a hybrid energy input-output model, the total energy consumption of different regions was decomposed and compared using three measurements of embodied energy in inter-regional trade: (1) only inter-regional energy trade was considered; (2) embodied energy in flow-out of final goods and services was considered; (3) embodied energy in flow-in of final goods and services was considered. According to the results of the second and third measurements, the 30 regions were categorized into four groups by their energy intensity and per capita GDP. This manuscript discussed the common characteristics of decomposed regional energy intensity, and provided policy implication for regional energy conservation. The results implicated that for developed regions with low energy intensities, such as Shanghai, energy conservation should focus on promoting low energy-consuming life style. For under-developed regions with low energy intensities, such as Guangxi, economic development is more urgent than energy conservation. For developing and energy absorbing regions, improving energy efficiency in industries is significant. For developing and energy exporting regions, transforming primary energy into high value-added products would be beneficial for economic development and energy conservation.

This Chapter is focused on the review of the development and application for alternative fuel pathways Life-cycle Analysis (LCA) models. A summary on the studies of multi-pathway comprehensive analysis and individualized studies of LCA models of the world is also made with emphasis on the methodologies currently being used in life-cycle analysis modeling for transportation fuels. Three major models are examined.

Power generation industry will become the key air emission (such as CO₂ and PM_2.5) control sector for China in the mid to long-term. To study the least-cost and maximizing co-benefits of emission reduction, a Reginal Power-generation System Optimization (RPSO) model is built using LEAP platform in this study. Taking Shanghai as a case study, multiple scenarios are designed to simulate the optimal technology development pathways and to evaluate mitigation co-benefits under different air emission targets. The results show that, fuel consumption is reduced while the share of clean generation unit is increased in all emission control scenarios, however, system cost is raised up as well. Ultra-supercritical units take the biggest share in all scenarios, natural gas combined cycle (NGCC) units and gas distributed units develop better in PM_2.5 constrained scenarios. Hydro and nuclear units reach the maximum capacity limits in all scenarios. Wind and solar units grows fast but account for very little share. Reduction co-benefits of PM_2.5 from carbon constrained scenario is larger than that of PM_2.5 constrained scenario which indicates carbon migration policy has greater influence on PM_2.5 reductions.

Fuel cell systems are a promising alternative to traditional power sources for a wide range of portable, automotive and stationary applications and have an increasing potential for wider use as the demand for clean energy is increasing and the focus is shifting towards renewable energy generation. This chapter has a multidisciplinary scope, the design of a computer-aided framework for monitoring and operation of integrated fuel cell systems and the development of advanced model-based control schemes. The behavior of the framework is experimentally verified through the online deployment to an automated small-scale fuel cell unit, demonstrating excellent response in terms of computational effort and accuracy with respect to the control objectives.

This chapter presents a generic mixed integer linear programming (MILP) model that integrates the unit commitment problem (UCP), i.e., daily energy planning with the long-term generation expansion planning (GEP) framework. Typical daily constraints at an hourly level such as start-up and shut-down related decisions (start-up type, minimum up and down time, synchronization, soak and desynchronization time constraints), ramping limits, system reserve requirements are combined with representative yearly constraints such as power capacity additions, power generation bounds of each unit, peak reserve requirements, and energy policy issues (renewables penetration limits, CO₂ emissions cap and pricing). For modelling purposes, a representative day (24 h) of each month over a number of years has been employed in order to determine the optimal capacity additions, electricity market clearing prices, and daily operational planning of the studied power system. The model has been tested on an illustrative case study of the Greek power system. Our approach aims to provide useful insight into strategic and challenging decisions to be determined by investors and/or policy makers at a national and/or regional level by providing the optimal energy roadmap under real operating and design constraints.

We present an analytical dynamic mathematical model and a simultaneous design and control optimization of a residential scale combined heat and power system (CHP). The mathematical model features a detailed description of the internal combustion engine based on a mean value approach, and simplified sub-models for the throttle valve, the intake and exhaust manifolds, and the external circuit. We treat the CHP unit as the interconnection of two distinct subsystems; the power production subsystem and the heat recovery subsystem. The validated zero-dimensional (0D) dynamic mathematical model of the system is implemented in gPROMS©, and used for optimization studies. A mixed-integer dynamic optimization problem is introduced that simultaneously determines the size of the internal combustion engine and the optimal control scheme of the CHP subsystems.

Over a third of the world’s primary energy is consumed by buildings, smart planning of energy supply to buildings is important to conserve energy and protect the environment. Most energy-consuming domestic tasks can be performed within a time period rather than at specific times. Energy cost or emissions could be reduced if these flexible tasks can be scheduled co-ordinately among multiple homes. This chapter addresses the problem of energy management of smart homes with microgrid, where the operation of distributed energy resources (DERs) and electricity-consumption household appliances are scheduled. A review of relevant literature works for smart homes with microgrid is presented. Then an optimisation-based framework is proposed to describe the related energy management problems of smart homes with microgrid. A mixed integer linear programming (MILP) model for three different objectives is developed: total cost minimisation, fair cost distribution, and cost versus CO₂ emissions. The application of this model is illustrated through an illustrative example of a smart building. The modelling approach developed in this work and the results obtained suggest that optimisation-based energy management of smart homes with microgrid results in cost saving and CO₂ emissions reduction. Moreover, the optimal operation schedules of the DERs, including thermal/electrical storage, are discussed.

The management of electricity demand, also referred to as demand side management (DSM), has been recognized as an effective approach to improving power grid performance. For electricity consumers, DSM constitutes the opportunity to benefit from financial incentives by adjusting their electricity consumption. The cost of electricity is the single largest variable operating cost incurred in cryogenic air separation plants; hence, there is a strong interest in reducing the electricity cost in such plants through DSM. However, to perform effective DSM, we need to develop systematic and innovative decision-making tools that can help us answer questions such as the following: How much potential for load adjustment exists in the plant? How can production and energy management decisions be optimized in an integrated fashion? How can we make long-term strategic decisions while considering hourly changing electricity prices? How do we deal with uncertainty in process data and future information? In this chapter, we draw insights from multiple projects in which we have used mathematical optimization approaches to perform industrial DSM and demonstrated the potential for air separation plants using real-world case studies. In particular, we emphasize the importance of accurate integrated scheduling models, the impact of considering uncertainty and risk in the decision-making, the implementation of robust models, and the modeling of multiple time scales.

In this chapter, a general optimizasion-based approach for the integrated operational and maintenance planning of compressor networks in air separation facilities is presented. The proposed mathematical programming model considers operating constraints for compressors, performance degradation for compressors, several types of maintenance policies and other managerial aspects. The operating status, the power consumption, the startup and the shutdown costs for compressors, the compressor-to-header assignments, the timing and the type of necessary maintenance tasks as well as the outlet mass flow rates for compressed air and distillation products are optimized. The power consumption in the compressors is expressed by regression functions that have been derived using technical and historical data. The proposed optimization model can be readily used within a rolling horizon scheme to deal with uncertainty. Several case studies of the air separation plant of BASF SE in Ludwigshafen are solved. The results clearly demonstrate the considerable energy and total cost savings due to the simultaneous planning of operational and maintenance tasks.

The share of volatile renewable energy generation is rapidly increasing in many countries around the world. As in electric power grids the supply always needs to equal the demand, the increasing volatility of energy supply imposes a major challenge to the stability of power grids. Demand response actions offer a very cost-efficient way to cope with this challenge. Especially energy-intensive industries such as metals, cement or pulp and paper offer a high potential to adjust their energy consumption towards the power grid in the form of large controllable loads. In this chapter we look into how this potential could be used without affecting the production volume. Advanced scheduling algorithms allow to efficiently plan the production at industrial sites. Enabling such scheduling algorithms for energy-aware production planning as well as the integration of scheduling and energy optimization solutions allows to leverage a high potential for shifting energy consumption from times with low to times with high renewable energy generation. In addition, this approach allows plant owners to significantly reduce their energy cost.

Heat integration across plants is an extension of conventional heat integration in a single plant for further improving energy efficiency. This chapter addresses the application of both Pinch Analysis and Mathematical Programming on solving heat integration problems across plants. For heat integration across plants, the required pipelines between plants is much longer than heat integration within a single plant, so more attentions must be paid on distance factor as it incurs more expense. A number of factors can affect the final design of pipelines between plants, for example, direct and indirect heat integration, the connection patterns between plants, the selection of intermediate fluid, etc. In this chapter, three connection patterns (series, split, parallel) for interconnectivity of individual plants in an area are presented. Each connection pattern has different performance on energy saving and pipeline length. To determine the energy target for the three connection patterns, a graphical methodology is presented. In addition, Mathematical Programming is used to determine the optimal design considering both direct and indirect heat integration. Parameters of intermediate fluid can be also optimized if indirect heat integration is applied. Some case studies are illustrated to demonstrate the capabilities of the presented models and graphic tool.

Energy demand and emissions are rising steadily, and are forecast to double by 2050 (IEA 2012). In fact, energy-related carbon dioxide \(\mathrm {CO_2}\) emissions have reached historic highs (IEA 2012). Moreover, the reliance on a narrow set of technologies and fossil fuels is a threat to energy security, which raises concerns (IEA 2012). It is therefore clear that current trends in energy supply are unsustainable—economically, environmentally and socially (IEA 2011).

Downstream processing of biofuels and bio-based chemicals often represents the bottleneck for the economic sustainable development of new processes. It is also a challenging problem for process synthesis and optimization, due to the intrinsic nonideal thermodynamics of the liquid mixtures derived from the (bio)chemical conversion of biomass. In this Chapter, a recent mathematical framework is outlined for the structural and parameter optimization of process flowsheets with rigorous and detailed models. The optimization problem is formulated within the Generalized Disjunctive Programming (GDP) framework and the solution of the reformulated MINLP problem is approached with a decomposition strategy based on the Outer-Approximation algorithm. At first, the mathematical formulation and the numerical implementation are outlined. In the second portion of the Chapter, several validation examples in the field of biorefineries are proposed spanning from the economic optimization of single distillation columns, the dewatering task of diluted bio-mixtures, up to the distillation sequencing with simultaneous mixed-integer design of each distillation column for a quaternary mixture in the presence of azeotropes.

This chapter describes the use of mathematical programming as the tool for the design of biomass-based supply chains. This tool is helpful to devise the most appropriate manner of integrating conversion and pretreatment technologies with the channels required to convert the raw biomass, available in the collection areas, into energy in the demand points. The project analysis should be carried out adopting a holistic view. The formulation described in this chapter does so by tackling the problem from a multiple objective approach which considers financial, environmental as well as social aspects. The problem is formulated as a mixed integer linear program (MILP). The insights gained by using this approach are demonstrated through three literature case studies. The first case study comprises an illustrative hydrogen supply chain, where hydrogen is synthesised from biomass and coal gasification. The second one considers regional electrification in rural areas by using gasification combined with gas engines. In this case, a social criterion is introduced. The third case study is a biomass-based supply chain designed to partially fulfil the demand of processing coal plants existing in Spain.

The production of transportation fuels from biomass is a promising renewable alternative to traditional fossil fuels. To achieve low carbon footprint of the overall biofuel supply chain however an efficient biomass procurement plan is essential. To this end, we discuss a novel approach to biomass procurement planning. In terms of transportation, we propose a region-to-point modeling approach based on mathematical integration over the sourcing region that has unique characteristics such as shape, location, and productivity. Both algebraic and numerical solution methods are discussed. In terms of system-level procurement planning, we develop a composite-curve-based approach that incorporates the regional transportation modeling method, and aims at identifying the biomass procurement plan that minimizes the total procurement cost (including biomass purchasing, harvesting and transportation). The specific steps for the generation of the composite curve, as well as insights into the procurement planning problem are discussed. We complete the chapter with a case study illustrating the applicability of the proposed methods.

The interconnection between a renewable power generation facility and a power grid poses challenges because of volatility and intermittent characteristics. Energy storage is one of the best solutions for this problem. The object of the present work is to evaluate the features and performances of energy storage system (ESS) with the aim to determine the best available ESS technology. For each one of the storage solutions presented, we have compared key parameters such as: efficiency, lifetime, energy density, capacity, and capital and response time. The paper presents an integrated ESS based on hydrogen storage, especially hydrogen energy technologies for hydrogen production, storage and utilization. Possibilities for integrated ESS coupled wind power to generate hydrogen using electrolyzer with hydrogen-oxygen combined cycle to generate power are discussed, wherein energy efficiency in the range of 49–55 % can be achieved. The results show that the proposed integrated system cannot be constrained by geological conditions and availability of materials, and appears to be an appropriate tool for the development of renewable power. Moreover, a case study is conducted for a special wind power plant. The integrated system is designed based on the daily wind load. Energy efficiency and preliminary economic comparison studies for the integrated system operated in two modes show that up to 50 % average net efficiency. Therefore, the integrated ESS can be useful to mitigate the bottleneck of renewable power development.

In this chapter, after the comprehensive exposition of the basic knowledge of state monitoring and fault diagnosis of wind turbine, a new targeted method for fault diagnosis is proposed. Starting from the non-stationary characteristics of fault signal of transmission system of wind turbine, a new improved method of combining wavelet packet and envelope spectrum for fault diagnosis is put forward. Finally, the author introduces the design idea and method of the overall structure and function of remote real-time state monitoring and fault diagnosis system of large-scale wind turbine in detail. Based on the field research of the author and his team into 3 MW wind turbine of a group company, the method is researched and developed for the state monitoring and fault diagnosis system of large-scale wind turbine, according to fault types and features of key equipment components of drive system of wind turbine as well as the actual needs of users. The system has been proved to have excellent performance in the field test. During introducing the design idea and method of the overall structure and function of remote real-time state monitoring and fault diagnosis system of large-scale wind turbine, the author focuses on the algorithm design of fault diagnosis software of the data acquisition instrument.

Identifying building designs with minimum cost and environmental impact is a fundamental topic in the transition towards a more sustainable residential sector. Energy efficiency strategies can reduce energy consumption in buildings, thereby decreasing their environmental impact without compromising comfort. Among these energy efficiency strategies, building insulation is particularly appealing due to its low cost and high potential energy savings. This study presents a systematic methodology for determining the optimal insulation thickness of the external surfaces of a building. Our approach, based on multi-objective optimization, minimizes simultaneously the cost and environmental impact associated with both the construction materials and the energy consumed over the operational phase of the building. The thermal loads are calculated by EnergyPlus, a building energy simulation program widely used by architects and engineers. The environmental impact is quantified following the life cycle assessment methodology and explicitly incorporated into the multi-objective model as an additional objective to be optimized along with the cost. We applied our approach to a case study of a house-like cubicle, for which four potential European locations with different climate conditions and electricity prices were considered. Solutions that reduce around 40 % both, the cost and environmental impact, with respect to a cubicle without insulation were identified. Our systematic method is intended to assist decision-makers in the design of more sustainable buildings.