L’intégration de la recherche d’efficacité énergétique aux projets d’aménagement urbain est essentielle au vu du contexte actuel de transition énergétique et environnementale. Dans le but de réduire l’empreinte énergétique d’un quartier dès la phase de conception, un ensemble de contributions a été réalisé afin d’accompagner les aménageurs dans cette démarche. La plateforme de simulation DIMOSIM (DIstrict MOdeller and SIMulator) a été développée pour modéliser et simuler dynamiquement les flux énergétiques d’un quartier implanté au sein de son environnement urbain. La conception est optimisée à l’aide d’une procédure multiobjectif combinant les aspects énergétiques, économiques et environnementaux pour garantir la meilleure performance globale. Cette approche transversale est multi-étagée et intègre l’algorithme génétique NSGA-II afin de s’adapter aux spécificités du problème. La sélection de la solution préférentielle est ensuite facilitée par l’utilisation d’une méthode d’analyse multicritère de surclassement qui a été conçue dans le but de fournir une évaluation détaillée des différents concepts : la méthode ATLAS (Assistance TooL for decision support to Assess and Sort). Enfin, la procédure complète d’accompagnement a été appliquée à des projets de conception d’écoquartier pour en valider le fonctionnement mais également pour fournir l’aide à la décision nécessaire aux décideurs. / The integration of the research of energy efficiency into urban development projects is essential in the current context of energy and environmental transition. In order to reduce the energy footprint of a district already starting from the design phase, a set of contributions was elaborated to support the planners in this process. The DIMOSIM simulation platform (DIstrict MOdeller and SIMulator) has been developed to dynamically model and simulate the energy flows of a district located within its urban environment. The design of the district is optimized using a multi-objective procedure combining energy, economic and environmental aspects to ensure the best overall performance. A cross-cutting, multi-level approach integrating the NSGA-II genetic algorithm was implemented in order to adapt the procedure to the specificities of the problem. The selection of the preferred solution is then facilitated by the use of a multicriteria analysis method which was developed to provide a detailed evaluation of the different concepts : the outranking method ATLAS (Assistance TooL for decision support to assessment And Sort). Finally, the complete procedure dedicated to the district energy system design was applied to eco-district projects in order to validate its correct operation and also to provide the necessary support to decision-makers.

This study has been focusing on a school located in the municipality of Laholm, South of Sweden. Employing an energy balance of the last five (5) years, a proposal for measures is made in terms of performance for comparison with the baseline of the current consumption trend. This comparison allowed us to narrow down the alternatives for the renovation with the potential to have a great impact on the school's energy use and indoor environment, but also on the preserved characteristics without any violation of the laws and regulations. A complementary analysis was used to analyze important variables for decision-making and implementation of improvements. This analysis consists of an exergy analysis which was utilized as a pre-design tool for an optimized building renovation proposal. Exergy losses were calculated to assess the performance of the systems. The study revealed that in relation to the use of new technologies and materials, aerogel and vacuum insulation panels bring relevant savings as their insulation mechanisms are the most efficient for such a building in a climate like Laholm. As for the heating system, it was evident that the use of a geothermal heat pump associated to PV panels brings considerable energy benefits when compared to the current oil boiler system and given that the village does not yet have a local district heating system. When the proposed measures are applied during 2022- 2023, the results will show that also older buildings can be energy efficient which is demanded of the buildings stock throughout the European Union.

Passive solar design can be an effective means of reducing conditioning loads in residential buildings by utilizing free solar heat during the heating season, and blocking unwanted solar heat during the cooling season. The objective of this thesis was to use energy modeling software to simulate the effect that incorporating passive solar design strategies into typical New England style houses would have on their energy usage for heating and cooling. The designs that were studied were Capes, Colonials, and Saltboxes. Four versions of increasing energy efficiency were studied for each style. After measuring baseline energy usage for each model, four passive solar variables were incorporated: orientation, allocation of windows to southern façade, shading devices, and thermal mass. After determining the ideal orientation of each building, 300 combinations of window allocation, shading device depth, and amount of thermal mass were simulated for each model. From this pool of simulations, the model with the lowest conditioning costs was selected and compared to its respective baseline design. As a general trend for each style, as the level of energy efficiency decreased, the savings from incorporating passive solar design increased. For the colonial models, the savings ranged from $422-$150. For the Saltbox models, the annual savings ranged from$398-$116. For the Cape models, the savings ranged from $303-$75.

ABSTRACT Cities throughout Asia have experienced unprecedented economic development over the past decades. In many cases this has contributed to their rapid and uncontrolled growth, which has resulted in a multiplicity of problems, including rapid population increase, enhanced environmental pollution, collapsing traffic systems, dysfunctional waste management, and rapid increases in the consumption of energy, water and other resources. The significant energy use in cities is not very well perceived in Asian countries. Although a number of studies into energy consumption across various sectors have been conducted, most are from the national point of view. Energy demand analysis is not considered important at the level of the city. The thesis is focused on the dynamics of energy utilization in Asian mega-cities, and ultimately aims at providing strategies for maximizing the use of renewable energy in large urban systems. The study aims at providing an in-depth understanding of the complex dynamics of energy utilization in urban mega-centers. An initial general analysis is complemented by a detailed study of the current situation and future outlook for the city of Bangkok, Thailand. An integrated approach applied to the study includes identification of the parameters that affect the utilization of energy in mega-cities and a detailed analysis of energy flows and their various subsystems, including commercial, industrial, residential and that of transportation. The study investigates and evaluates the energy models most commonly used for analyzing and simulating energy utilization. Its purpose is to provide a user-friendly tool suitable for decision-makers in developing an energy model for large cities. In addition, a Multi-Criteria Decision-Making (MCDM) process has been developed to assess whether or not the energy systems meet the sustainability criteria. A metabolic approach has been employed to analyze the energy flow and utilization in selected Asian mega-cities, including Bangkok, Beijing, Shanghai, and Tokyo. The approach is applied to measure the majority of indirect energy flows or the energy embodied in the flows of goods and services involving the residents of those cities. Since the function of cities is to serve the lives of the residents, indirect energy consumption could be regarded as being of equal importance as that of direct energy use. The essence of embodied energy is that an indirect reflection upon behavior following direct energy consumption. It can illustrate how a city relies on the outside, for example other cities, countries, etc. and provides some interesting information that cannot be easily drawn from the direct energy demand. The study reveals that the indirect energy demand is more significant than the direct energy demand in Bangkok, Shanghai, and Tokyo, while direct energy demand is greater than the indirect energy demand in Beijing. This can be explained by the fact that Bangkok, Shanghai, and Tokyo have a greater reliance upon the outside in terms of energy demand. The Long-range Energy Alternative Planning (LEAP) system has been selected to perform Bangkok energy modeling. In a Bangkok case study a range of policy interventions are selected and how these would change the energy development in Bangkok by the year 2025 is examined. Different policies can be grouped by the sectors analyzed. The only supply-side policy considered meets an existing target of having 10% of electricity generated from renewable sources. The study period for the model started in 2005 and ends in 2025, with the year 2000 taken as the base year. The proposed scenarios were evaluated using the MCDM approach to rate their sustainability. Team members found that this method provided a methodology to help decision-makers to systematically identify management objectives and priorities. / QC 20101123

<p>1st essay abstract:   </p> <p>This study investigates the economic rents of the wind energy industry in the U.S. and their economic impacts on local economies, using Benton and White counties in Indiana as study regions. By calibrating a partial equilibrium model using 2007-2010 data of the industry, we find a resource rent of $9.72/MWh. We then use a general equilibrium model with Dutch Disease features to study the optimal tax levied on this rent, and the economic impacts of redistributing the tax revenues back to the county residents. An exhaustive rent tax increases real county personal income by as high as 9.1% and as low as 2%, depending on the county’s features. Applying an incentive compatible resource rent tax rate and redistributing the revenues to the county’s laborers leads to an increase of 3.5% and 16% in their income in White and Benton counties, respectively. We also perform robustness checks by allowing labor mobility between counties to examine the impacts of resource rents on the county economy under endogenous labor growth. </p> <p>1st essay data: All data acquired comes from the U.S. Census Bureau, county Quarterly Census of Employment and Wages, the National Renewable Energy Laboratory reports, the Lawrence Berkeley Laboratory, Indeed.com, news articles, and wind developers websites.</p> <p><br></p> <p>2nd essay abstract:   </p> <p>Using the Regional Energy Deployment System (ReEDS) model, we estimate the deadweight loss imposed by county-level wind power development restrictions in the form of increased electricity costs due to suboptimal siting. This is accomplished by optimizing the power system of the United States' Midcontinent Independent System Operator (MISO) from 2020 to 2050. We perform the optimization with and without land-use constraints arising from simulated potential local ordinances restricting wind power development, and under multiple scenarios reflecting different renewable portfolio standards (RPS). We find that local restrictions on wind power increase the total system cost by 0.15%-0.3% and the wholesale electricity price by 1.8%-2.7%, depending on the RPS scenario. Changes in the generation and installed capacity mixes are more substantial and depend on both the level of county restrictions on wind power, and RPS requirements, thus indicating an interaction between RPS requirements and local wind power restrictions. We also find that plausible restrictions on wind development do not pose major barriers to meeting renewable energy targets in a cost-effective manner.</p> <p>2nd essay data: All data is embedded inside the Regional Energy Deployment System (ReEDS) model of the National Renewable Energy Laboratory.</p> <p><br></p> <p>3rd essay abstract:   </p> <p>The USDA promotes adoption of conservation practices beneficial for soil health and environment through agricultural cost-share payment programs such as EQIP or CSP. Although the efficiency of these programs has been evaluated through additionality estimates, which represent the percentage of farmers who would adopt a practice only with payments, the potential complementarities between certain combinations of practices have often been overlooked. Unaccounted for, these complementarities may impact additionality estimates. This paper provides a thorough investigation of additionality estimates of common practices, including no-till, nutrient management and cover crops, accounting for potential complementarities between them. We find no significant differences between traditional additionality estimates and estimates accounted for potential complementarities between the three practices. The results thus indicate that despite agronomic evidence of synergies in co-adopting these three practices, we find no solid indication of adoption complementarity between them in reality. </p> <p>3rd essay data: Data is acquired from the U.S. Department of Agriculture and Esri maps.</p>

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Buildings play a central role for livability and carbon footprint of urban areas. Ambitious energy saving and emission reduction targets created a need for a new generation of decisionsupport methods and tools that allow for detailed analysis of urban energy on a large scale. Urban building energy modeling (UBEM) that has emerged recently is an efficient approach to assess energy performance of multiple buildings and system effects from urban energy interventions. However, the further upscale of UBEMs is significantly limited due to the lack of automation for building energy performance (BEP) simulations required for such models in large amounts. This thesis aimed to explore challenges for automation of BEP simulations, and to develop a prototype tool that would serve as a middleware between UBEM and BEP simulation engine, focusing on the IDA ICE simulation software. The result of this thesis is icepy — a tool for automation of BEP simulations in IDA ICE. It uses IDA ICE API and Lisp scripting to provide interaction between UBEM process and IDA ICE in order to generate initial simulation model (IDM), execute simulation and manage results in an automated way. Being implemented as a Python package, it allows to modify multiple IDMs or export simulation results with a few lines of code. The developed tool has been tested and validated for the case building in Minneberg, Stockholm. The automation capabilities provided by icepy has allowed to perform sensitivity analysis for building design parameters as was demonstrated for the window-to-wall ratio (WWR) and three various algorithms for window distribution. The resulting tool has limited functionality as it addressed building envelopes which is only one component of building simulation. However, it has proved to be an efficient approach to automate simulation process and has shown a good potential for further development of such tools. / Byggnader spelar en central roll för urbana områdens levbarhet och koldioxidavtryck. Ambitiösa mål för energibesparing och utsläppsminskning har skapat ett behov av en ny generation beslutsstödmetoder och verktyg som möjliggör detaljerad analys av städers energianvändning i stor skala. Urban byggnadsenergimodellering (UBEM) har nyligen utvecklats och är ett effektivt tillvägagångssätt för att bedöma energiprestanda för flera byggnader och systemeffekter för olika energiåtgärder inom den urban miljön. Den ytterligare uppskalningen av UBEM är dock begränsad på grund av bristen på automation av simulering som är inriktade på byggnadsenergiprestanda (BEP), vilket krävs för att hantera stora byggnadsbestånd. Det här examensarbetet syftar till att utforska utmaningar med automatisering av BEP-simuleringar och att utveckla en prototyp som ska fungera som en mellanprogramvara mellan UBEM och BEP-simuleringsmotorer, med fokus på IDA ICE(som är en simuleringsprogramvara). Resultatet av examensarbetet är icepy, som är ett verktyg för att automatisera BEP-simuleringar i IDA-ICE. Icepy använder IDA ICE API och Lispskript för att tillhandahålla interaktion mellan UBEM-processen och IDA ICE för att generera en initial simuleringsmodell (IDM), utför själva simuleringen och slutligen hanterar resultatet på ett automatiserat sätt. Genom att icepy implementeras som ett Pythonpaket kan den modifiera flera IDM:er och även exportera simuleringsresultat med några få kodrader. Området Minneberg i Stockholm har använts i en fallstudie för att validera och testa verktyget. Automatiseringsfunktionerna i icepy har möjliggjort känslighetsanalyser för olika byggnadsdesignparametrar, exempelvis studerades påverkan av olika värden på förhållandet mellan fönster och väggar genom användning av tre olika algoritmer för fönsterdistributioner. Det utvecklade verktyget har begränsningar i funktionalitet framförallt på grund av att enbart byggnadens ytterskal studerades i byggnadsenergisimuleringarna. Verktyget har dock visat sig vara ett effektivt tillvägagångssätt för att automatisera simuleringsprocesser, vilket visar på en god potential att också vidareutveckla dessa verktyg.

This dissertation explores the idea of inducing grid peak demand curtailment by turning commercial buildings into interactive assets for building owners during the demand control period. The work presented here is useful for both ab initio design of new sites and for existing or retrofitted sites. An analytical hierarchy process (AHP)-based framework is developed to curtail the thermal load effectively across a group of commercial buildings. It gives an insight into the amount of peak demand reduction possible for each building, subject to indoor thermal comfort constraints as per ASHRAE standards. Furthermore, the detailed operation of buildings in communion with the electric grid is illustrated through case studies. This analysis forms an outline for the assessment of transactive energy opportunities for commercial buildings in distribution system operations and lays the foundation for a seamless building-to-grid integration framework. The contribution of this dissertation is fourfold – (a) an efficient method of developing high-fidelity physics-based building energy models for understanding the realistic operation of commercial buildings, (b) identification of minimal dataset to achieve a target accuracy for the building energy models (c) quantification of building peak demand reduction potential and corresponding energy savings across a stipulated range of thermostat setpoint temperatures and (d) AHP-based demand curtailment scheme. By careful modeling, it is shown that commercial building models developed using this methodology are both accurate and robust. As a result, the proposed approach can be extended to other commercial buildings of diverse characteristics, independent of the location. The methodology presented here takes a holistic approach towards building energy modeling by accounting for several building parameters and interactions between them. In addition, parametric analysis is done to identify a useful minimal dataset required to achieve a specified accuracy for the building energy models. This thesis describes the concept of commercial buildings as interactive assets in a transactive grid environment and the idea behind its working. / Doctor of Philosophy / This dissertation titled "An Expert-based Approach for Grid Peak Demand Curtailment using HVAC Thermostat Setpoint Interventions in Commercial Buildings" tackles two important challenges in the energy management domain: –electric grid peak demand curtailment and energy savings in commercial buildings. The distinguishing feature of the proposed solution lies in addressing these challenges solely through demand-side management (DSM) strategies, which include HVAC thermostat setpoint interventions and lighting control. We present a methodology for developing highly accurate building energy models that serve as digital twins of actual buildings. These digital replicas can be used to quantify the impact of various interventions and reflect the realistic operation of commercial buildings across varied conditions. This enables building owners to control demand intelligently and transact energy effectively in the electricity market. The development of Internet of Things (IoT) market and advanced technologies such as smart meters and smart thermostats allows for the design of novel strategies that address traditional challenges faced by electric grid operators. This dissertation elaborates on how smart buildings can leverage IoT-based solutions to participate in the electricity market during demand control periods. We also developed an expert opinion-based demand curtailment allocation scheme resulting in grid peak demand reduction. The numerical results obtained reinforce the effectiveness of the proposed solution across varied climatic conditions.

Este trabalho foca o desenvolvimento de algoritmos de otimização para o problema de PSP puramente ab initio. Algoritmos que melhor exploram o espaço de potencial de soluções podem, em geral, encontrar melhores soluções. Esses algoritmos podem beneficiar ambas abordagens de PSP, tanto o modelo ab initio quanto os baseados em conhecimento a priori. Pesquisadores tem mostrado que Algoritmos Evolutivos Multiobjetivo podem contribuir significativamente no contexto do problema de PSP puramente ab initio. Neste contexto, esta pesquisa investiga o Algoritmo Evolutivo Multiobjetivo baseado em Tabelas aplicado ao PSP puramente ab initio, que apresenta interessantes resultados para proteínas relativamente simples. Por exemplo, um desafio para o PSP puramente ab initio é a predição de estruturas com folhas-. Para trabalhar com tais proteínas, foi desenvolvido procedimentos computacionalmente eficientes para estimar energias de ligação de hidrogênio e solvatação. Em geral, estas não são consideradas no PSP por abordagens que combinam métodos de otimização e conhecimento a priori. Considerando somente van der Waals e eletrostática, as duas energias de interação que mais contribuem para a definição da estrutura de uma proteína, com as energias de ligação de hidrogênio e solvatação, o problema de PSP tem quatro objetivos. Problemas combinatórios (tais como o PSP), com mais de três objetivos, geralmente requerem métodos específicos capazes de lidar com muitos critérios. Para resolver essa limitação, este trabalho propõe um novo método para a otimização dos muitos objetivos, chamado Algoritmo Evolutivo Multiobjetivo com Muitas Tabelas (AEMMT). Esse método executa uma amostragem mais adequada do espaço de funções objetivo e, portanto, pode mapear melhor as regiões promissoras deste espaço. A capacidade de lidar com muitos objetivos capacita o AEMMT a utilizar melhor a informação oriunda das energias de solvatação e de ligação de hidrogênio, e então predizer estruturas com folhas- e algumas proteínas relativamente mais complexas. Do ponto de vista computacional, o AEMMT é um novo método que lida com muitos objetivos (mais de dez) encontrando soluções relevantes / This work focuses on the development of optimization algorithms for the purely ab initio Protein Structure Prediction (PSP) problem. Algorithms that better explore the space of potential solutions can in general find better solutions. Such algorithms can benefit both ab initio and template-based PSP, that uses priori knowledge. Researches have shown that Multiobjective evolutionary algorithms can contribute significantly in the context of purely ab initio PSP. In this context, this research investigates the Multiobjective Evolutionary Algorithm based on Tables applied to purely ab initio PSP, which has shown interesting results for relatively simple proteins. For example, one challenge for purely ab initio PSP is the prediction of structures with -sheets. To work with such proteins, this research has developed computationally efficient procedures to estimate hydrogen bond and solvation energies. In general, they are not considered by PSP approaches combining optimization methods with priori knowledge. Only by considering van der Waals and electrostatic, the two interaction energies that mostly contribute to defining a protein structure, and the hydrogen bond and solvation energies, the PSP problem has four objectives. Combinatorial problems (such as the PSP) with more than three objective usually require specific methods capable of dealing with many goals. To address this limitation, we propose a new method for many objective optimization, called Multiobjective Evolutionary Algorithm with Many Tables (MEAMT). This method performs a more adequate sampling of the space of objective functions and, therefore, can better map the promising regions of this space. The ability of dealing with many objectives enables the MEANT to better use information generated by solvation and hydrogen bond energies, and then predict structures with -sheets and some relatively complex proteins. From the computational point of view, the MEAMT is a new method for dealing with many objectives (more than ten) finding relevant solutions

Este trabalho foca o desenvolvimento de algoritmos de otimização para o problema de PSP puramente ab initio. Algoritmos que melhor exploram o espaço de potencial de soluções podem, em geral, encontrar melhores soluções. Esses algoritmos podem beneficiar ambas abordagens de PSP, tanto o modelo ab initio quanto os baseados em conhecimento a priori. Pesquisadores tem mostrado que Algoritmos Evolutivos Multiobjetivo podem contribuir significativamente no contexto do problema de PSP puramente ab initio. Neste contexto, esta pesquisa investiga o Algoritmo Evolutivo Multiobjetivo baseado em Tabelas aplicado ao PSP puramente ab initio, que apresenta interessantes resultados para proteínas relativamente simples. Por exemplo, um desafio para o PSP puramente ab initio é a predição de estruturas com folhas-. Para trabalhar com tais proteínas, foi desenvolvido procedimentos computacionalmente eficientes para estimar energias de ligação de hidrogênio e solvatação. Em geral, estas não são consideradas no PSP por abordagens que combinam métodos de otimização e conhecimento a priori. Considerando somente van der Waals e eletrostática, as duas energias de interação que mais contribuem para a definição da estrutura de uma proteína, com as energias de ligação de hidrogênio e solvatação, o problema de PSP tem quatro objetivos. Problemas combinatórios (tais como o PSP), com mais de três objetivos, geralmente requerem métodos específicos capazes de lidar com muitos critérios. Para resolver essa limitação, este trabalho propõe um novo método para a otimização dos muitos objetivos, chamado Algoritmo Evolutivo Multiobjetivo com Muitas Tabelas (AEMMT). Esse método executa uma amostragem mais adequada do espaço de funções objetivo e, portanto, pode mapear melhor as regiões promissoras deste espaço. A capacidade de lidar com muitos objetivos capacita o AEMMT a utilizar melhor a informação oriunda das energias de solvatação e de ligação de hidrogênio, e então predizer estruturas com folhas- e algumas proteínas relativamente mais complexas. Do ponto de vista computacional, o AEMMT é um novo método que lida com muitos objetivos (mais de dez) encontrando soluções relevantes / This work focuses on the development of optimization algorithms for the purely ab initio Protein Structure Prediction (PSP) problem. Algorithms that better explore the space of potential solutions can in general find better solutions. Such algorithms can benefit both ab initio and template-based PSP, that uses priori knowledge. Researches have shown that Multiobjective evolutionary algorithms can contribute significantly in the context of purely ab initio PSP. In this context, this research investigates the Multiobjective Evolutionary Algorithm based on Tables applied to purely ab initio PSP, which has shown interesting results for relatively simple proteins. For example, one challenge for purely ab initio PSP is the prediction of structures with -sheets. To work with such proteins, this research has developed computationally efficient procedures to estimate hydrogen bond and solvation energies. In general, they are not considered by PSP approaches combining optimization methods with priori knowledge. Only by considering van der Waals and electrostatic, the two interaction energies that mostly contribute to defining a protein structure, and the hydrogen bond and solvation energies, the PSP problem has four objectives. Combinatorial problems (such as the PSP) with more than three objective usually require specific methods capable of dealing with many goals. To address this limitation, we propose a new method for many objective optimization, called Multiobjective Evolutionary Algorithm with Many Tables (MEAMT). This method performs a more adequate sampling of the space of objective functions and, therefore, can better map the promising regions of this space. The ability of dealing with many objectives enables the MEANT to better use information generated by solvation and hydrogen bond energies, and then predict structures with -sheets and some relatively complex proteins. From the computational point of view, the MEAMT is a new method for dealing with many objectives (more than ten) finding relevant solutions