With a fast-growing electricity demand and a larger proportion of intermittent energy sources follows a greater need for flexible and balancing sources of electricity, such as hydropower. Planning of hydropower production is considered to be a difficult problem to solve due to several nonlinearities, combinatorial properties and the fact that it is a large scale system with spatial-temporal coupling. Optimization approaches are used for solving such problems and a common simplification is to disregard the effect of head variation on the power output. This thesis presents two methods for modeling the head dependency in optimization models for hydropower river systems, the Triangulation method and the Bilinear method. The Triangulation method implements a three-dimensional interpolation technique called triangulation, using a MILP formulation. This is a commonly used method found in the literature. The Bilinear method is a novel approach that applies a piecewise bilinear approximation of the power production function, resulting in a BLP problem. Also, a strategy for selecting which hydropower stations to include head dependence for is provided. The performance of the methods was evaluated on authentic test cases from Lule River and compared to results obtained by Vattenfall's current model without head dependency. The Triangulation method and the Bilinear method give higher accuracy, and are therefore considered more realistic, than the current model. Further, the results indicate that it is sufficient to include head dependence for a subset of stations since the error is significantly reduced. Mid- to long-term scenarios were solved with high accuracy when a subset of the stations was modeled as head dependent. Overall, the Bilinear method had a significantly shorter computational time than the Triangulation method.

Climate change drives policymakers to reduce emissions and enhance the integration of variable renewable energy sources (VRES) into the power system. Wind power is considered among the most beneficial VRES as it can generate cost-effectively carbon-free electricity but comes with inherent intermittency. Hydropower is a proposed solution among the research community to handle VRES output volatility and ensure balanced energy output to the electricity grid. This thesis addresses the problem by investigating the integration of intermittent wind power into a hydropower system cost-effectively. The research question "How does the integration of wind power affect the hydro operations and the cost of purchased electricity?" is answered within the Design Science Research framework by optimizing a subset of the Røldal-Suldal hydropower system in Norway's NO2 region. The cost-minimization model utilizes historical data from 2018 on water inflows, hourly electricity prices, hourly wind production, and hourly energy consumption for a smelter within the NO2 zone. To reduce the 8,760-time steps and computational concerns, the kmeans clustering algorithm is implemented to obtain four representative weeks. A multiperiod linear programming model is structured to assess the joint operation of wind and hydropower while ensuring a minimum energy production to satisfy the system's power demands. A benchmark scenario with no wind capacity is formed to serve as the basis for comparisons. Ten scenarios with 100-MW incremental steps of wind capacity are implemented. The minimized cost for the benchmark scenario is €104,981,312.34, with electricity purchases covering more than 75% of the energy demand and hydropower satisfying the remaining 25%. Adding 100 MW of wind capacity reduces costs by more than €2,000,000, restricting the purchased energy’s share by 1.49%, which is the equivalent increased share of wind power during each incremental step. A wind capacity of 1,000 MW leads to a 21.24% cost reduction. Hydropower production remains unaffected by the wind integration based on terminal values of reservoir level or turbined water volume. However, the distribution of hydropower production throughout the year changes after installing wind capacity enabling hydropower to utilize stored water optimally to minimize the costs of purchasing energy. A sensitivity analysis to assess the uncertainties tied with the model coefficients shows that increasing initial reservoir levels and adding 1,000 MW of wind capacity is the most influential factor in the optimization model.

No description available.

No description available.

[pt] No cenário atual da economia mundial, é essencial aumentar a integração entre os diferentes atores da cadeia de suprimentos das empresas, reduzindo custos operacionais e melhorando a eficiência. O roteamento de navios é parte imprescindível dessa integração no comércio marítimo global, sendo objeto de estudo de muitos autores. Neste trabalho, apresentamos diferentes metodologias para resolver variantes do Problema de Roteamento Marítimo com Estoques. Esse problema envolve um grande número de variáveis e é computacionalmente complexo de ser resolvido. Nossa principal motivação é resolver um caso real de roteamento de navios de uma grande empresa do setor de Óleo e Gás, obtendo soluções de alta qualidade em tempos computacionais plausíveis e melhorando os resultados atuais da empresa. Todas as metodologias desenvolvidas são baseadas em uma combinação de uma meta-heurística com um modelo matemático de programação linear. Uma das principais diferenças entre as metodologias está no modelo matemático para resolver o problema de estoque, onde testamos abordagens de tempo discreto e tempo contínuo. As outras diferenças dizem respeito ao número de produtos avaliados (único ou múltiplos produtos) e à meta-heurística usada (heurística de busca local com um fator de probabilidade de Simulated Annealing ou Hybrid Variable Neighborhood Search). Para a metodologia que utiliza um modelo de tempo discreto, os resultados são satisfatórios, com violações baixas e pontuais do estoque em um tempo computacional aceitável. Para a metodologia que utiliza um modelo de tempo contínuo, os resultados são ainda melhores, uma vez que, em reduzido tempo computacional, as violações de estoque permanecem baixas ou inexistentes, dependendo do cenário avaliado e da meta-heurística utilizada. Os resultados obtidos neste trabalho são notáveis e permitem sua aplicação prática em casos reais. / [en] In the current scenario of the world economy, it is essential to increase the integration between the different players in the companies supply chain, reducing operational costs, and improving efficiency. Ship routing is a substantial part of this integration regarding global maritime commerce, being the object of study by many authors. In this work, we present different methodologies to solve variants of the Maritime Inventory Routing Problem. This problem involves a large number of variables and is a computationally complex problem to solve. Our primary motivation is to solve a ship routing real case of a large company in the Oil and Gas sector, achieving high-quality solutions in plausible processing times and improving companies current results. All developed methodologies are based on a metaheuristic combination with a linear mathematical model. One of the main differences between the methodologies lies in the mathematical model to solve the inventory problem, where we tested discrete-time and continuous-time approaches. Other differences concern the number of evaluated products (single or multi-product) and the metaheuristic used (local search heuristics with a Simulated Annealing probability factor or Hybrid Variable Neighborhood Search). For the methodology using the discretetime model, the results are satisfactory, with low and punctual inventory violations in an acceptable computational time. For the methodology using the continuous-time model, the results are better once, in reduced computational time, inventory violations remain low or non-existent, depending on the scenario evaluated and the metaheuristic used. The results obtained in this work are remarkable and allow its practical application for real cases.

Harry Markowitz work in the 50’s spring-boarded modernportfolio theory. It gives investors quantitative tools to compose and assessasset portfolios in a systematic fashion. The main idea of the Mean-Varianceframework is that composing an optimal portfolio is equivalent to solving aquadratic optimization problem.In this project we employ the Maximally Predictable Portfolio (MPP) frameworkproposed by Lo and MacKinlay, as an alternative to Markowitz’s approach, inorder to construct investment portfolios. One of the benefits of using theformer method is that it accounts for forecasting estimation errors. Ourinvestment strategy is to buy and hold these portfolios during a time periodand assess their performance. We show that it is indeed possible to constructportfolios with high rate of return and coefficient of determination based onhistorical data. However, despite their many promising features, the success ofMPP portfolios is short lived. Based on our assessment we conclude thatinvesting in the stock market solely on the basis of the optimization resultsis not a lucrative strategy / Modern portföljteori har sitt ursprung i Harry Markowitz arbete på 50-talet. Teorin ger investerare kvantitativa verktyg för att sammansätta och utvärdera tillgångsportföljer på ett systematiskt sätt. Huvudsakligen går Markowitz idé ut på att komponera en investeringsportfölj genom att lösa ett kvadratiskt optimeringsproblem. Det här examensprojektet har utgångspunkt i Maximally Predictable Portfolio-ramverket, utvecklat av Lo och MacKinley som ett alternativ till Markowitz problemformulering, i syfte att välja ut investeringsportföljer. En av fördelarna med att använda den förra metoden är att den tar hänsyn till uppskattningsfelen från prognostisering av framtida avkastning. Vår investeringsstrategi är att köpa och behålla dessa portföljer under en tidsperiod och bedöma deras prestanda. Resultaten visar att det mha. MPP-optimering är möjligt att konstruera portföljer med hög avkastning och förklaringsvärde baserat på historisk data. Trots sina många lovande funktioner är framgången med MPP-portföljer kortlivad. Baserat på vår bedömning drar vi slutsatsen att investeringar på aktiemarknaden uteslutande på grundval av optimeringsresultatet inte är en lukrativ strategi.

PLAS is a cloud-based software used for planning and scheduling fleets of vehicles for material transport. PLAS consists of two components; the Logistic Flow Solver (LFS) and the Material Transport Scheduler (MTS). Based on transportation requests, the LFS generates a set of logistic flows. The MTS then transforms the logistic flows into tasks that are assigned to the vehicles. The LFS is implemented with Mixed Integer Linear Programming (MILP). Currently, the LFS and the MTS are decoupled from each other and there is information that is not considered in the LFS. Thus, the choice of logistic flows generated with the current formulation may negatively impact the final transport plan. The objective of this thesis is to investigate how the generation of logistic flows can be improved. Two alternative mathematical models for the LFS were developed using MILP formulation. Compared to the current model, more information is taken into account in the two new models. Three different objective functions were considered. Scheduling of the vehicles were modelled as pickup and delivery problems, where pickup and delivery pairs correspond to the generated logistic flows. The models were implemented using Google OR-Tools, an open-source software suite for optimization. The different mathematical formulations were evaluated based on their performance for test problems with different fleet compositions. The results show that problem characteristics influence the performance of the models and that there is no model that gives the best result for every type of problem. Therefore, it is necessary to analyse problem characteristics in order to choose a suitable model for generation of logistic flows. / PLAS är en molnbaserad mjukvara som används för planering och schemaläggning av fordonsflottor för materialtransport. PLAS består av två komponenter; Logistic Flow Solver (LFS) and Material Transport Scheduler (MTS). Baserat på transportbehov genererar LFS ett antal logistikflöden. MTS omvandlar sedan logistikflödena till uppdrag som är tilldelade till fordonen. LFS är implementerad med blandad heltalsprogrammering. För närvarande är LFS och MTS frikopplade från varandra och det finns information som inte tas hänsyn till i LFS. Därför kan valet av logistikflöden genererade med den nuvarande formuleringen negativt påverka den slutliga transportplanen. Målet med detta examensarbete är att undersöka hur genereringen av logistikflöden kan förbättras. Två alternativa matematiska modeller utvecklades med MILP-formulering. Jämfört med den nuvarande modellen, tar de två nya modellerna hänsyn till mer information. Tre olika målfunktioner beaktades. Modellerna implementerades med Google OR-Tools, en öppen programvara för optimering. De matematiska formuleringarna utvärderades baserat på deras prestation på testproblem med olika kompositioner av fordonsflottor. Resultaten visar att problemegenskaper påverkar modellernas prestationer och att det inte finns någon modell som ger bäst resultat för varje problemtyp. Därför är det nödvändigt att analysera problemegenskaper för att kunna välja en lämplig modell för generering av logistikflöden.

Aircraft design is an act of art requiring dedication and careful work to ensure good results. An essential tool in that work is a flight mechanics simulator. Such simulators are often built up of modules/models that are executed in a sequential order in each time iteration. This project aims to analyze potential improvements to the model execution order based on the dependency structure of one such simulator. The analysis method Design Structure Matrix (DSM), was used to define/map the dependencies and then Binary Linear Programming (BLP) was utilized to find five new potentially improved model orders to minimize the number of feedbacks from one iteration to the next one. Those five proposed execution orders were next compared and evaluated. The result is a model order that reduce the number of models receiving feedbacks from the previous iteration from 13 to 6, with insignificant changes in the precision of the simulator. / Vid flygplanskonstruktion krävs hårt och noggrant arbete för att säkerställa gott resultat. Ett oumbärligt verktyg är då en flygmekanisk simulator. Den typen av simulatorer är ofta uppbyggda av moduler/modeller som exekveras i en bestämd sekventiellt ordning i varje tidsteg. Syftet med detta projekt är att undersöka möjliga förbättringar av exekverings ordningen av de olika modellerna i en existerande simulator, baserat på beroendestrukturen. Analysmetoden Design Structure Matrix (DSM) användes för att bestämma beroendestrukturen och sedan utnyttjades Binär Linjär Programmering (BLP) för att hitta fem förbättrade modellordningar med avseende på att minimera antalet modeller som erhåller indata från föregående tidsiteration. De fem förbättringsförslagen jämfördes och utvärderades. Resultatet är en modellordning som kan minska antalet återkopplande modeller från 13 till 6, med insignifikanta skillnader i precisionen av simulatorn.

Throughout United States and world history, disasters have caused not only significant loss of life, property but also enormous financial loss. The tsunami that occurred on December 26, 2004 is a telling example of the devastation that can occur unexpectedly. This unexpected natural event never happened before in this area. In addition, there was a lack of an emergency response plan for events of that magnitude. Therefore, this event resulted not only in a natural catastrophe for the people of South and Southeast Asia, but it is also considered one of the greatest natural disasters in world history. After the giant wave dissipated, there were more than 230,000 people dead and more than US$10 billion in property damage and loss. Another significant event was the terrorist incident on September 11, 2001 (commonly referred to as 9/11) in United States. This event was unexpected and an unnatural, i.e., man-made event. It resulted in approximately 3,000 lives lost and about US$21 billion in property damage. These and other unexpected (or unanticipated) events give emergency management officials short- or no-notice to prevent or respond to the situation. These and other facts motivate the need for better emergency evacuation route planning (EERP) approaches in order to minimize the loss of human lives and property in short- or no-notice emergency situations. This research considers aspects of evacuation routing that have received little attention in research and, more importantly, in practice. Previous EERP models only either consider unidirectional evacuee flow from the source of a hazard to destinations of safety or unidirectional emergency first responder flow to the hazard source. However, in real-life emergency situations, these heterogeneous, incompatible flows occur simultaneously over a bi-directional capacitated lane-based travel network, especially in short- and no-notice emergencies. After presenting a review of the work related to the multiple flow EERP problem, mathematical formulations are presented for the EERP problem where the objective for each problem is to identify an evacuation routing plan (i.e., a traffic flow schedule) that maximizes evacuee and responder flow and minimizes network clearance time of both types of flow. In addition, we integrate the general human response behavior flow pattern, where the cumulative flow behavior follows different degrees of an S-shaped curve depending upon the level of the evacuation order. We extend the analysis to consider potential traffic flow conflicts between the two types of flow under these conditions. A conflict occurs when flow of different types occupy a roadway segment at the same time. Further, with different degrees of flow movement flow for both evacuee and responder flow, the identification of points of flow congestion on the roadway segments that occur within the transportation network is investigated.

Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendation about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV's route (set of links) from the ERV's origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge. / Doctor of Philosophy / Downstream vehicles detect an emergency response vehicle (ERV) through sirens and/or strobe lights. These traditional warning systems do not give any recommendations about how to react, leaving the drivers confused and often adopting unsafe behavior while trying to open a passage for the ERV. In this research, an advanced intra-link emergency assistance system, that leverages the emerging technologies of the connected vehicle environment, is proposed. The proposed system assumes the presence of a centralized system that gathers/disseminates information from/to connected vehicles via vehicle-to-infrastructure (V2I) communications. The major contribution of this dissertation is the intra-link level support provided to ERV as well as non-ERVs. The proposed system provides network-wide assistance as it also considers the routing of ERVs. The core of the system is a mathematical program - a set of equations and inequalities - that generates, based on location and speed data from connected vehicles that are downstream of the ERV, the fastest intra-link ERV movement. It specifies for each connected non-ERV a final assigned position that the vehicle can reach comfortably along the link. The system accommodates partial market penetration levels and is applicable on large transportation link segments with signalized intersections. The system consists of three modules (1) an ERV route generation module, (2) a criticality analysis module and (2) the sequential optimization module. The first module determines the ERV’s route (set of links) from the ERV’s origin to the desired destination in the network. Based on this selected route, the criticality analysis module scans/filters the connected vehicles of interest and determines whether any of them should be provided with a warning/instruction message. As the ERV is moving towards its destination, new non-ERVs should be notified. When a group of non-ERVs is identified by the criticality analysis module, a sequential optimization module is activated. The proposed system is evaluated using simulation under different combinations of market penetration and congestion levels. Benefits in terms of ERV travel time with an average reduction of 9.09% and in terms of vehicular interactions with an average reduction of 35.46% and 81.38% for ERV/non-ERV and non-ERV/non-ERV interactions respectively are observed at 100% market penetration, when compared to the current practice where vehicles moving to the nearest edge.