Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project DURABROADS (No. 605404).The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 605404

Sustainable Design of Urban Rooftop Food-Energy-Land Nexus

Funding Information: Authors in particular M.G. would like to acknowledge the UK Engineering and Physical Sciences Research Council ( EPSRC ) for providing financial support for research under project “Resilient and Sustainable Bio-renewable Systems Engineering Model” [ EP/N034740/1 ]. A.H. would like to acknowledge financial support from Natural Environment Research Council (NERC) ADVENT project [ 1806209 ].Peer reviewedPublisher PD

Integrating Pro-Environmental Behavior with Transportation Network Modeling: User and System Level Strategies, Implementation, and Evaluation

Personal transport is a leading contributor to fossil fuel consumption and greenhouse (GHG) emissions in the U.S. The U.S. Energy Information Administration (EIA) reports that light-duty vehicles (LDV) are responsible for 61\% of all transportation related energy consumption in 2012, which is equivalent to 8.4 million barrels of oil (fossil fuel) per day. The carbon content in fossil fuels is the primary source of GHG emissions that links to the challenge associated with climate change. Evidently, it is high time to develop actionable and innovative strategies to reduce fuel consumption and GHG emissions from the road transportation networks. This dissertation integrates the broader goal of minimizing energy and emissions into the transportation planning process using novel systems modeling approaches. This research aims to find, investigate, and evaluate strategies that minimize carbon-based fuel consumption and emissions for a transportation network. We propose user and system level strategies that can influence travel decisions and can reinforce pro-environmental attitudes of road users. Further, we develop strategies that system operators can implement to optimize traffic operations with emissions minimization goal. To complete the framework we develop an integrated traffic-emissions (EPA-MOVES) simulation framework that can assess the effectiveness of the strategies with computational efficiency and reasonable accuracy. ^ The dissertation begins with exploring the trade-off between emissions and travel time in context of daily travel decisions and its heterogeneous nature. Data are collected from a web-based survey and the trade-off values indicating the average additional travel minutes a person is willing to consider for reducing a lb. of GHG emissions are estimated from random parameter models. Results indicate that different trade-off values for male and female groups. Further, participants from high-income households are found to have higher trade-off values compared with other groups. Next, we propose personal mobility carbon allowance (PMCA) scheme to reduce emissions from personal travel. PMCA is a market-based scheme that allocates carbon credits to users at no cost based on the emissions reduction goal of the system. Users can spend carbon credits for travel and a market place exists where users can buy or sell credits. This dissertation addresses two primary dimensions: the change in travel behavior of the users and the impact at network level in terms of travel time and emissions when PMCA is implemented. To understand this process, a real-time experimental game tool is developed where players are asked to make travel decisions within the carbon budget set by PMCA and they are allowed to trade carbon credits in a market modeled as a double auction game. Random parameter models are estimated to examine the impact of PMCA on short-term travel decisions. Further, to assess the impact at system level, a multi-class dynamic user equilibrium model is formulated that captures the travel behavior under PMCA scheme. The equivalent variational inequality problem is solved using projection method. Results indicate that PMCA scheme is able to reduce GHG emissions from transportation networks. Individuals with high value of travel time (VOTT) are less sensitive to PMCA scheme in context of work trips. High and medium income users are more likely to have non-work trips with lower carbon cost (higher travel time) to save carbon credits for work trips. ^ Next, we focus on the strategies from the perspectives of system operators in transportation networks. Learning based signal control schemes are developed that can reduce emissions from signalized urban networks. The algorithms are implemented and tested in VISSIM micro simulator. Finally, an integrated emissions-traffic simulator framework is outlined that can be used to evaluate the effectiveness of the strategies. The integrated framework uses MOVES2010b as the emissions simulator. To estimate the emissions efficiently we propose a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the link driving schedules for MOVES2010b. Test results using the data from a five-intersection corridor show that HC-DTW technique can significantly reduce emissions estimation time without compromising the accuracy. The benefits are found to be most significant when the level of congestion variation is high. ^ In addition to finding novel strategies for reducing emissions from transportation networks, this dissertation has broader impacts on behavior based energy policy design and transportation network modeling research. The trade-off values can be a useful indicator to identify which policies are most effective to reinforce pro-environmental travel choices. For instance, the model can estimate the distribution of trade-off between emissions and travel time, and provide insights on the effectiveness of policies for New York City if we are able to collect data to construct a representative sample. The probability of route choice decisions vary across population groups and trip contexts. The probability as a function of travel and demographic attributes can be used as behavior rules for agents in an agent-based traffic simulation. Finally, the dynamic user equilibrium based network model provides a general framework for energy policies such carbon tax, tradable permit, and emissions credits system

Development Of A Methodology For Fast Optimization Of Building Retrofit And Decision Making Support

The condition of current building stock in the United States raises the question of whether the energy performance of existing buildings can ever be environmentally sustainable. In the United States, buildings accounted for 39% of total energy consumption and 72% of total electricity consumption (USEPA 2009). In addition, current building energy use is projected to increase by 1.7% annually until 2025 (J.D. Ryan 2004). The great potential for energy reduction in existing buildings has created opportunities in building energy retrofit projects (Noris et al. 2013). A building renovation project must not only be affordable, taking into account factors such as investor budgets, payback period, economic risks and uncertainties, but also create a thermally comfortable indoor environment and is sustainable through its lifetime. The research objective of this dissertation is to develop a novel method to optimize the performance of buildings during their post-retrofit period in the future climate. The dissertation is organized in three sections: a) Develop a data-driven method for the hourly projection of energy use in the coming years, taking into account global climate change (GCC). Using machine learning algorithms, a validated data-driven model is used to predict the building’s future hourly energy use based on simulation results generated by future extreme year weather data and it is demonstrated that GCC will change the optimal solution of future energy conservation measure (ECM) combination. b) Develop a simplified building performance simulation tool based on a dynamic hourly simulation algorithm taking into account the thermal flux among zones. The tool named SimBldPy is tested on EnergyPlus models with DOE reference buildings. Its performance and fidelity in simulating hourly energy use with different heating and cooling set points in each zone, under various climate conditions, and with multiple ECMs being applied to the building, has been validated. This tool and modeling method could be used for rapid modeling and assessment of building energy for a variety of ECM options. c) Use a non-dominated sorting technique to complete the multi-objective optimization task and design a schema to visualize optimization results and support the decision-making process after obtaining the multi-objective optimization results. By introducing the simplified hourly simulation model and the random forest (RF) models as a substitute for traditional energy simulation tools in objective function assessment, certain deep retrofit problem can be quickly optimized. Generated non-dominated solutions are rendered and displayed by a layered schema using agglomerative hierarchical clustering technique. The optimization method is then implemented on a Penn campus building for case study, and twenty out of a thousand retrofit plans can be recommended using the proposed decision-making method. The proposed decision making support framework is demonstrated by its robustness to the problem of deep retrofit optimization and is able to provide support for brainstorming and enumerate various possibilities during the process of making the decision

Passive mosaic energy optimization: toward free-running school buildings

Reconeixement: els articles de l'editorial Elsevier es poden consultar en les seves URL: https://doi.org/10.1016/j.buildenv.2021.108058, https://doi.org/10.1016/j.jclepro.2020.122993, https://doi.org/10.1016/j.buildenv.2021.108407,Technic, economic, environmental, and architectural constraints in existing building stocks set an energy renovation framework not always compatible with passive strategies. In deep energy renovations, the first line of action ought to be the passive improvement of the building envelope. Nonetheless, building renovation strategies use energy metrics suited for conditioned mode rather than thermal performance in passive mode. The predominance of energy metrics is due to the belief that mechanical systems guarantee indoor comfort conditions. However, thermal comfort is a pressing challenge in spaces without local controls. Such is the case of classrooms in primary schools where children cannot regulate the indoor environment and have limited adaptation possibilities. It is best to foster a passive free-running operation in classrooms due to the occupants' higher sensitivity and preference toward environmental conditions. This thesis addressed the passive optimization in existing educational building stock in free-running operations, i.e., not relying on active systems to maintain indoor comfort conditions. Reliably predicting the performance of passive strategies is troublesome since it is a function of the building emplacement, materiality, occupancy, the range of renovation measures, and the sequence of interventions. Building-integrated agriculture systems improve the thermal performance of the building envelope while increasing food security, social cohesion and provide learning opportunities. The energy modelling of such strategies is not implemented fully in energy simulation engines and, thus, requires the inclusion of the plant's heat exchange in the building's thermal balance. Identifying an optimal renovation strategy requires holistic assessment models encompassing all sustainability dimensions and an iterative simulation process based on stochastic optimization algorithms. However, exiting optimization tools do not handle complex simulation routines like those required for building-integrated agriculture systems. This thesis is presented as an article compendium where each article develops a specific aspect of passive optimization for building stocks. The first article develops a reductive urban energy model tailored for free-running building stocks. This model was applied to primary educational building stocks in Barcelona and Quito with accurate results at the building and urban levels. The second and third articles address the assessment of building-integrated agriculture systems from a holistic sustainability perspective and an indoor comfort perspective. For the latter, energy simulation includes the plant's heat and mass exchange as a function of the crop's growth. A future article will present an integer-constraint optimization algorithm that finds the most suitable passive strategies for each surface in the building envelope based on their environmental exposure. This method and a proof-of-concept are presented in this thesis. This surface-by-surface optimization results in a passive renovation strategy mosaic tailored to each building stock.Las limitaciones técnicas, económicas, medioambientales y arquitectónicas del parque edilicio imponen estrategias de rehabilitación energética no siempre compatibles con actuaciones pasivas. En rehabilitaciones energéticas la primera línea de actuación debe ser la mejora pasiva de la envolvente del edificio. No obstante, los proyectos de renovación evalúan su eficacia en base a consumo energético en vez de desempeño térmico en modo pasivo. El predominio de índices energéticos se debe a la creencia de que los sistemas mecánicos garantizan las condiciones de confort interior. Sin embargo, en espacios sin controles activos locales, el confort es un desafío. Esto sucede en las aulas de las escuelas primarias donde los niños no pueden regular el ambiente interior y tienen limitadas posibilidades de adaptación. En las aulas se debe fomentar el funcionamiento pasivo debido a que los niños tienen mayor sensibilidad y preferencia hacia las condiciones ambientales. Esta tesis abordó la optimización pasiva del parque educativo existente desde una perspectiva de confort y sin depender en sistemas activos. Es desafiante predecir de manera confiable el desempeño de las estrategias pasivas ya que intervienen simultáneamente el emplazamiento del edificio, la materialidad, la ocupación, las estrategias de renovación y la secuencia de intervenciones. Los sistemas agrícolas integrados en edificios mejoran el rendimiento térmico de la envolvente del edificio al tiempo que aumentan la seguridad alimentaria, la cohesión social y brindan oportunidades de aprendizaje. El modelado energético de tales estrategias no está implementado en los motores de simulación energética y, por lo tanto, requiere la inclusión del intercambio de calor de las plantas en el balance térmico del edificio. Identificar una estrategia de renovación óptima requiere modelos de evaluación holísticos que abarquen todas las dimensiones de la sostenibilidad y un proceso de simulación iterativo basado en algoritmos de optimización estocástica. Sin embargo, las herramientas de optimización existentes no manejan rutinas de simulación complejas como las requeridas para modelar sistemas agrícolas integrados. Esta tesis se presenta como un compendio de artículos donde cada artículo desarrolla un aspecto específico de la optimización pasiva para el parque edilicio. El primer artículo desarrolla un modelo energético urbano reductivo especializado en parques edilicios no acondicionados. Este modelo se aplicó al parque de edificios escolares de Barcelona y Quito dando buenos resultados tanto a nivel de edificio como a nivel urbano. El segundo y tercer artículo abordan la evaluación de los sistemas agrícolas integrados en edificios desde una perspectiva de sostenibilidad holística y una perspectiva de confort interior. Para este último, la simulación energética incluyó el intercambio de calor y masa de la planta en función del crecimiento del cultivo. Un artículo futuro presentará un algoritmo de optimización con restricción de números enteros para encontrar las estrategias pasivas más adecuadas para cada superficie en la envolvente del edificio en función de su exposición ambiental. Este método y una prueba de concepto son presentados como parte de esta tesis. Esta optimización superficie por superficie da como resultado una estrategia tipo mosaico de renovación pasiva adaptada a cada edificio.Les limitacions tècniques, econòmiques, mediambientals i arquitectòniques del parc edificatori existent imposen estratègies de rehabilitació energètica no sempre compatible amb actuacions passives. En rehabilitacions energètiques, la primera línia d'actuació ha de ser la millora passiva de l'envolupant de l'edifici. No obstant això, els projectes de renovació avaluen la seva eficàcia sobre la base del consum energètic en comptes del rendiment tèrmic en mode passiu. El predomini d'índexs energètics és degut a la creença que els sistemes mecànics garanteixen les condicions de confort interior. Tot i això, en espais sense controls actius locals, el confort és un desafiament. Això passa a les aules de les escoles de primària on els nens no poden regular l'ambient interior i tenen limitades possibilitats d'adaptació. A les aules s'ha de fomentar el funcionament passiu pel fet que els nens tenen més sensibilitat i preferència cap a les condicions ambientals. Aquesta tesi va abordar l’optimització passiva del parc educatiu existent des d’una perspectiva de confort i sense dependre de sistemes actius. És problemàtic predir de manera fiable el rendiment de les estratègies passives ja que intervenen simultàniament l'emplaçament de l'edifici, la materialitat, l'ocupació, les estratègies de renovació i la seqüència d'intervencions. Els sistemes agrícoles integrats en edificis milloren el rendiment tèrmic de l'envolupant de l'edifici alhora que augmenten la seguretat alimentària, la cohesió social i ofereixen oportunitats d'aprenentatge. El modelatge energètic d'aquestes estratègies no està implementat als motors de simulació energètica i, per tant, requereix la inclusió de l'intercanvi de calor de les plantes al balanç tèrmic de l'edifici. Identificar una estratègia de renovació òptima requereix models d'avaluació holístic que abastin totes les dimensions de la sostenibilitat i un procés de simulació iteratiu basat en algorismes d'optimització estocàstica. Tot i això, les eines d'optimització existents no manegen rutines de simulació complexes com les requerides per modelar sistemes agrícoles integrats. Aquesta tesi es presenta com un compendi d’articles on cada article desenvolupa un aspecte específic de l’optimització passiva per al parc edilici. El primer article desenvolupa un model energètic urbà reductiu especialitzat en parcs edilicis no condicionats. Aquest model es va aplicar al parc d´edificis escolars de Barcelona i Quito donant bons resultats tant a nivell d´edifici com a nivell urbà. El segon i el tercer article aborden l'avaluació dels sistemes agrícoles integrats en edificis des d'una perspectiva de sostenibilitat holística i una perspectiva de confort interior. Per a aquest darrer, la simulació energètica va incloure l’intercanvi de calor i massa de la planta en funció del creixement del cultiu. Un article futur presentarà un algorisme d'optimització de restriccions enteres per trobar les estratègies passives més adequades per a cada superfície a l'envolupant de l'edifici en funció de la seva exposició ambiental. Aquest mètode i una prova de concepte es presenten en aquesta tesi. Aquesta optimització superfície per superfície dóna com a resultat una estratègia mosaic de renovació passiva adaptada a cada edificiPostprint (published version

Carbon-Dioxide Pipeline Infrastructure Route Optimization And Network Modeling For Carbon Capture Storage And Utilization

Carbon capture, utilization, and storage (CCUS) is a technology value-chain which can help reduce CO2 emissions while ensuring sustainable development of the energy and industrial sectors. However, CCUS requires large-scale deployment of infrastructure for capturing feasible amounts of CO2 that can be capital intensive for stakeholders. In addition, CCUS deployment leads to the development of extensive pipeline corridors, which can be inconsistent with the requirements for future CCUS infrastructure expansion. With the implementation and growth of CCUS technology in the states of North Dakota, Montana, Wyoming, Colorado and Utah in mind, this dissertation has two major goals: (a) to identify feasible corridors for CO2 pipelines; and (b) to develop a CCUS infrastructure network which minimizes project cost. To address these goals, the dissertation introduces the CCSHawk methodology that develops pipeline routes and CCUS infrastructure networks using a variety of techniques such as multi-criteria decision analysis (MCDA), graph network algorithms, natural language processing and linear network optimization. The pipeline route and CCUS network model are designed using open-source data, specifically: geo-information, emission quantities and reservoir properties. The MCDA of the study area reveals that North Dakota, central Wyoming and Eastern Colorado have the highest amount of land suitable for CO2 pipeline corridors. The optimized graph network routing algorithm reduces the overall length of pipeline routes by an average of 4.23% as compared to traditional routing algorithms while maintaining low environmental impact. The linear optimization of the CCUS infrastructure shows that the cost for implementing the technology in the study area can vary between $24.05/tCO2 to$42/tCO2 for capturing 20 to 90MtCO2. The analysis also reveals that there would be a declining economic impact of existing pipeline infrastructure on the future growth of CCUS networks ranging between 0.01 to 1.62$/tCO2 with increasing CO2 capture targets. This research is significant, as it establishes a technique for pipeline route modeling and CCUS economic analysis highly adaptable to various geographic regions. To the best of the author\u27s knowledge, it is also the first economic analysis that considers the effect of pre-existing infrastructure on the growth of CCUS technology for the region. Furthermore, the pipeline route model establishes a schema for considering not only environmental factors but also ecological factors for the study area

Proceedings of the XIII Global Optimization Workshop: GOW'16

[Excerpt] Preface: Past Global Optimization Workshop shave been held in Sopron (1985 and 1990), Szeged (WGO, 1995), Florence (GO’99, 1999), Hanmer Springs (Let’s GO, 2001), Santorini (Frontiers in GO, 2003), San José (Go’05, 2005), Mykonos (AGO’07, 2007), Skukuza (SAGO’08, 2008), Toulouse (TOGO’10, 2010), Natal (NAGO’12, 2012) and Málaga (MAGO’14, 2014) with the aim of stimulating discussion between senior and junior researchers on the topic of Global Optimization. In 2016, the XIII Global Optimization Workshop (GOW’16) takes place in Braga and is organized by three researchers from the University of Minho. Two of them belong to the Systems Engineering and Operational Research Group from the Algoritmi Research Centre and the other to the Statistics, Applied Probability and Operational Research Group from the Centre of Mathematics. The event received more than 50 submissions from 15 countries from Europe, South America and North America. We want to express our gratitude to the invited speaker Panos Pardalos for accepting the invitation and sharing his expertise, helping us to meet the workshop objectives. GOW’16 would not have been possible without the valuable contribution from the authors and the International Scientific Committee members. We thank you all. This proceedings book intends to present an overview of the topics that will be addressed in the workshop with the goal of contributing to interesting and fruitful discussions between the authors and participants. After the event, high quality papers can be submitted to a special issue of the Journal of Global Optimization dedicated to the workshop. [...

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California