Yes / This article proposes a novel technique for operation of distribution networks with considering active network management (ANM) schemes and demand response (DR) within a joint active and reactive distribution market environment. The objective of the proposed model is to maximize social welfare using market-based joint active and reactive optimal power flow. First, the intermittent behavior of renewable sources (solar irradiance, wind speed) and load demands is modeled through scenario-tree technique. Then, a network frame is recast using mixed-integer linear programming, which is solvable using efficient off-the-shelf branch-and cut solvers. Additionaly, this article explores the impact of wind and solar power penetration on the active and reactive distribution locational prices within the distribution market environment with integration of ANM schemes and DR. A realistic case study (16-bus UK generic medium voltage distribution system) is used to demonstrate the effectiveness of the proposed method. / This work was supported in part by the Ministry of Higher Education Scientific Research in Iraq and in part by British Academy under Grant GCRFNGR3\1541.

With increasing shares of variable renewable energy sources in the power mix, the need for energy storage solutions is projected to increase as well. Storage can in such combined systems help mitigate the issues with relying on intermittent sources by time-shifting the supply and smoothing out frequency fluctuations, to name some examples. This thesis has focused on Azelio ABs flagship product, the TES.POD, which is a long-duration thermal energy storage technology. When integrated with, for example, solar PV power, the TES.POD can store excess energy and dispatch it during times of low supply or when during the evening/night. The aim of the thesis has been the development of a day-ahead dispatch optimization tool for systems that include multiple TES.PODs, combined into a Cluster, and solar PV. The model was to be built using the Python programming language and based on Mixed-Integer-Linear-Programming (MILP) methods. The PV+storage system was then allowed to be connected to supplementary power sources such as a larger electric grid, or diesel generators in off-grid locations. The purpose of the optimization model is to find the most economic way to operate the individual TES.PODs while also keeping track of other system components, using a cost-based objective function (minimize costs). A focus has been on using high time resolution (small time step) in order to investigate the impact that the TES.PODs dynamic constraints has on operation. Another strength compared to pre-existing models was the ability to operate individual units indifferent to each other, as opposed to having them all operated in unison. Final results from benchmarking tests and two case studies indicated that using the optimization tool with smaller time steps had an effect on key indicators, and could lead to improved economy in the system. It was observed in both cases that the cost of electricity was reduced by running the optimization tool with time steps of either two or three minutes when compared with using an hourly resolution. Furthermore, several usage parameters for the TES.PODs, notably the total amount of operated hours and energy output per cycle, saw improvements which could lead to reduced cost of operation and maintenance. While not the main intent, testing different Cluster sizes and amount of installed PV capacity with the model, it could also be used in strategic decisions for system sizing. However, due to rapidly growing computational times in systems with large TES.POD clusters and using smaller time steps, the possibility of adding more complexity to the model in future work must be done with caution. To combat this issue, either improvements to the model formulation could be attempted, or by using more powerful hardware or optimizer (imported software algorithm that handles solving the model).

The concept of positive energy districts (PED) encompasses a range of policies and strategies in response to climate protection targets in urban areas. Due to the limited potential of renewable energy in urban neighborhoods, broader definitions of PED are proposed that allow for energy exchange through the grid infrastructure. This study evaluates demand side management in combination with a virtual power plant (VPP) to assess the impact on the design of PED. In particular, the optimal customer behavior in response to flexible electricity tariffs is analyzed. A techno-economic energy system model is proposed for an urban area in Germany that optimizes the customer cost and the VPP’s margin. This includes electrical energy generation, storage, demand, and access to the short-term electricity market. Based on economic analysis, a dynamic market-based tariff allows the VPP to maximize profit margins. Consumers benefit when the local balances of renewable energy supply and demand are integrated into the dynamic tariff.

There is an ever-increasing usage of mobile data with global traffic having reached 115 exabytes per month at the end of 2022 for mobile data traffic including fixed wireless access. This is projected to grow up to 453 exabytes at the end of 2028, according to Ericssons 2022 mobile data traffic outlook report. To meet the increasing demand radio access networks (RAN) used for mobile communication are continuously being improved with the current generation enabling larger virtualization of the network through the Cloud RAN (C-RAN) architecture. This facilitates the usage of commercial off-the-shelf servers (COTS) in the network replacing specialized hardware servers and making it easier to scale up or down the network capacity after traffic demand. This thesis looks at how we can efficiently identify servers needed to meet traffic demand in a network consisting of both COTS servers and specialized hardware servers while trying to reduce the energy consumption of the network. We model the problem as a network where the antennas and radio heads are connectedto the core network through a C-RAN and a specialized hardware layer. The network is then represented using a graph where the nodes represent servers in the network. Using this problem model as a base we then generate problem instances with varying topologies, server profiles, and traffic demands. To find out how the traffic should be passed through the network we test two different methods: A mixed integer linear programming (MILP) method focused on energy minimization and a graph attention network (GAT) predictor combined with the energy minimization MILP. To help evaluate the results we also create three other methods: a MILP model that tries to spread the traffic as evenly as possible, a random predictor combined with the energy minimization MILP and a greedy method. Our results show that the energy optimization MILP method can be used to create optimal solutions, but it suffer from a slow computation time compared to the other methods. The GAT model shows promising results in making predictions regarding what servers should be included in a network making it possible to reduce the problem size and solve it faster with MILP. The mean energy cost of the solutions created using the combined GAT/MILP method was 4% more than just using MILP but the time gain was substantial for problems of similar size as the GAT was trained on. With regards to computation time the combined GAT/MILP method used was 85% faster than using only MILP. For networks of almost double the size than the ones that the GAT model was trained on the solutions of the combined GAT and MILP methods had a mean energy cost increase of 7% while still showing a strong speedup, being 93% faster than when only using MILP.

There is growing interest in commercial aircraft formation flight as a means of reducing both airspace congestion and the carbon footprint of air transportation. Wake vortex surfing has been researched extensively and proven to have significant fuel-saving benefits, however, commercial air transportation has yet to take advantage of these formation benefits due to understandable safety concerns. The realization of these formations requires serious consideration of formation contingencies and safety during closer-in maneuvering of large commercial aircraft. Formation contingency scenarios are much more complex than those of individual aircraft and have not yet been studied in depth. This thesis investigates the utility of optimization modeling in providing insight into generation of aircraft escape paths for formation contingency planning. Three high-altitude commercial aircraft formation scenarios are presented; formation join, formation emergency exit, and formation escape. The model-generated paths are compared with pilot-generated escape plans using the author’s pilot expertise. The model results compare well with pilot intuition and are useful in presenting solutions not previously considered, in evaluating separation requirements for improvement of escape path planning and in confirming the viability of the pilot-generated plans. The novel optimization model formulation presented in this thesis is the first model shown to be capable of generating escape paths comparable to pilot- generated escape plans and is also the first to incorporate avoidance of persistent and drifting wake turbulence within the formation.

High electricity prices have become the title of several news articles recently in Sweden and the prices have experienced large sudden fluctuations during certain periods. In this thesis work, a smart control model for the electricity usage in three different households has been developed with the main purpose to minimize the electricity cost. This has been implemented by using mixed-integer linear programming (MILP) to optimize the cost 24 hours ahead, and by forecasting two of the main inputs; the load and the electricity spot prices for bidding zone three (SE3) in Sweden. The units included in the model are the photovoltaic system, the batteries, the electricity consumption in the house and the electric vehicles. However, the main task of the smart control was to determine when and in which amount the energy should flow from one unit to another, or to/from the grid. In other words, it decides the charging/discharging of the batteries, the selling/buying of electricity and the charging of the electric vehicle (EV). Different amounts of cost savings/profits have been obtained when applying the smart control on the three houses, which have different annual consumption, capacities of the components, heating systems and more. The results showed that it is most optimal to run the model between the time interval 13.00-00.00, when the spot prices for the next day are known, in order to avoid the remarkable impact accompanied with the use of forecasted electricity prices as input to the model. The forecasting of the load is, on the other hand, required to run the model, but this thesis showed that the effect of the uncertainties in this forecast is relatively small. Three types of machine learning methods were implemented to perform the forecasts, namely linear regression (LR), decision tree regression and random forest regression. After measuring especially the mean absolute error (MAE) to validate the results, the random forest regression showed the least error and the other methods showed close results when looking at the electric load prognosis.

For unmanned vehicles, autonomy means that the vehicle’s route can be planned and executed according to some pre-defined rules in the absence of human intervention. Autonomous vehicles (AVs) have become a common type of vehicle for various kinds of transport, for example autonomous forklifts within a warehouse environment. Volvo Autonomous Solution (VAS) works with autonomous vehicles in different areas. To better understand how different methods can be used for planning of autonomous vehicles, VAS initiated this project. To increase the efficiency of AVs, several problems can be examined. One such problem is the allocation problem, also called Multi-Robot Task Allocation, which aims to find out which vehicle should execute which task to achieve a global goal cooperatively. The AVs used by VAS handle Planning Missions (PMs). A PM is, for example, to move goods from a loading point to an unloading point. So, the problem examined in this study is how to assign PMs to vehicles in the most efficient way. The thesis also includes a collection of publications on the area. The problem is solved by using three methods: Mixed Integer Linear Programming (MILP), a Genetic Algorithm that was originally proposed for task assignment in a warehouse environment (GA – Warehouse), and a Genetic Algorithm that was initially proposed for train scheduling (GA – Train). With the MILP method, the problem has been formulated mathematically and the method guarantees an optimal solution. However, the major drawback of this approach is the large computational time required to retrieve a solution. The GA – Warehouse method has a quite simple allocation process but a more complicated path planning part and is, in its entirety, not as flexible as the other methods. The GA – Train method has a lower computational time and can consider many different aspects. All three methods generate similar solutions for the limited set of simple scenarios in this study, but an optimal solution can only be guaranteed by the MILP method. Regardless of which method is used, there is always a trade-off: a guarantee of the optimal solution at the expense of high computational time or a result where no optimal solution can be guaranteed but can be generated quickly. Which method to use depends on the context, what resources are available, and what requirements are placed on the solution. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

A crucial challenge introduced by the decentralized installations of photovoltaic (PV) systems in the residential sector, is the mismatch between PV electricity generation and the load curve for energy consumption. To overcome this incompatibility between production and consumption, energy storage and demand response are seen as effective solutions. Smart meters and the installation of intelligent smart appliances in homes have paved the way for efficient energy consumption monitoring and active household load control in the residential sector.  The aim of the thesis work is to develop a dynamic energy management algorithm tailored to optimize the energy consumption pattern of controllable household assets to maximize PV selfconsumption. A rolling horizon algorithm based dynamic model was designed using mixedinteger linear programming (MILP) and later compared with the baseline model to understand the real-time operational benefits of the rolling horizon approach.  Analyzing device scheduling patterns based on the feed-in-tariff showed considerable differences in the scheduling approach for both optimization models. A comparative analysis was conducted to understand the system benefits offered by both optimization models under different feed-in-tariff structures. Higher self-consumption rates were achieved through annual scheduling approach, but it does not reflect the real-time operation of the systems in the household. A rolling horizon optimization reflects the real-time operation of the energy system and has a lower self-consumption rate due to a limited optimization horizon. The method indicates the significant potential of self-consumption specially in lieu of decreasing feed-in tariffs. / En viktig utmaning som de decentraliserade installationerna av solcellssystem i bostadssektorn innebär är att elproduktionen från solcellerna inte stämmer överens med belastningskurvan för energiförbrukningen. För att komma till rätta med denna oförenlighet mellan produktion och konsumtion ses energilagring och efterfrågeflexibilitet som effektiva lösningar. Smarta mätare och installation av intelligenta smarta apparater i hemmen har banat väg för effektiv övervakning av energiförbrukningen och aktiv styrning av hushållens belastning i bostadssektorn.  Syftet med avhandlingen är att utveckla en dynamisk energihanteringsalgoritm som är skräddarsydd för att optimera energiförbrukningsmönstret för kontrollerbara hushållstillgångar för att maximera självförbrukningen av solceller. En dynamisk modell baserad på en algoritm med rullande horisont utformades med hjälp av blandad linjär programmering (MILP) och jämfördes senare med basmodellen för att förstå de operativa realtidsfördelarna med metoden med rullande horisont. Analysen av schemaläggningsmönster för enheter baserat på inmatningstariffen visade att det fanns betydande skillnader i schemaläggningsmetoden för båda optimeringsmodellerna. En jämförande analys genomfördes för att förstå de systemfördelar som erbjuds av de båda optimeringsmodellerna under olika strukturer för inmatningstariffer. Högre självförbrukningsnivåer uppnåddes genom den årliga schemaläggningsmetoden, men den återspeglar inte realtidsdriften av systemen i hushållet. En optimering med rullande horisont återspeglar energisystemets drift i realtid och har en lägre självförbrukningsgrad på grund av en begränsad optimeringshorisont. Metoden visar på den betydande potentialen för självförbrukning speciellt i stället för minskande inmatningstariffer.

Tesis por compendio / [ES] La innovación en los métodos de trabajo con nuevos sistemas de producción ajustados, sostenibles y resilientes, así como la gestión tecnológica eficaz son las tendencias actuales para mejorar el rendimiento en las organizaciones. Así, esta tesis doctoral investiga las contribuciones teóricas y formula un modelo conceptual con el uso de herramientas de fabricación ajustada o lean manufacturing (LM) y de la industria 4.0 (I4.0) para procesos de planificación de la producción en las cadenas de suministro. Además, se utiliza la optimización como tecnología facilitadora de la I4.0 para desarrollar la propuesta de solución. La metodología que se utiliza es bibliográfica, exploratoria y experimental; se aplican técnicas de investigación operativa, normalización de datos y casos de estudio en empresas industriales. Como punto de partida se revisa la literatura existente relacionada con la LM y las tecnologías de la I4.0 en el contexto de la cadena de suministro. Seguidamente, se diseña un modelo conceptual denominado LSCP 4.0 para facilitar la toma de decisiones en los niveles de decisión estratégico, táctico y operativo. Se trata de una relación estructurada entre los paradigmas lean, ágil, sostenible, resiliente y flexible para mejorar el rendimiento de las cadenas de suministro mediante la aplicación de las tecnologías facilitadoras de I4.0. Basado en esto, se propone un modelo matemático de optimización lineal entera-mixta, denominado LSCP 4.0, para maximizar los beneficios y planificar simultáneamente la producción, inventario de materiales y productos terminados satisfaciendo la demanda proveniente de previsiones y pedidos en firme en una cadena de suministro de cinco niveles. Novedosamente, se combinan enfoques de producción de just in time (JIT) y planificación de requerimientos de materiales (MRP). Ambos modelos para LSCP 4.0, i.e., conceptual y matemático, se validan en casos de estudio reales de la industria de calzado. Adicionalmente, se considera el uso de otra tecnología facilitadora de la I4.0 como la computación en nube para abordar el problema del intercambio de información entre los nodos de la cadena de suministro. Así, se propone un modelo de datos normalizado para la fabricación colaborativa en la nube aplicado a la industria del calzado. / [CA] La innovació en els mètodes de treball amb nous sistemes de producció ajustats, sostenibles i resilients, així com la gestió tecnològica eficaç són les tendències actuals per a millorar el rendiment en les organitzacions. Així, aquesta tesi doctoral investiga les contribucions teòriques i formula un model conceptual amb l'ús d'eines de fabricació ajustada o lean manufacturing (LM) i de la indústria 4.0 (I4.0) per a processos de planificació de la producció en les cadenes de subministrament. A més, s'utilitza l'optimització com a tecnologia facilitadora de la I4.0 per a desenvolupar la proposta de solució. La metodologia que s'utilitza és bibliogràfica, exploratòria i experimental; s'apliquen tècniques d'investigació operativa, normalització de dades i casos d'estudi en empreses industrials. Com a punt de partida es revisa la literatura existent relacionada amb laLM i les tecnologies de la I4.0 en el context de la cadena de subministrament. Seguidament, es dissenya un model conceptual denominat LSCP 4.0 per a facilitar la presa de decisions en els nivells de decisió estratègic, tàctic i operatiu. Es tracta d'una relació estructurada entre els paradigmes lean, àgil, sostenible, resilient i flexible per a millorar el rendiment de les cadenes de subministrament mitjançant l'aplicació de les tecnologies facilitadores d'I4.0. Basat en això, es proposa un model matemàtic d'optimització lineal entera-mixta, denominat LSCP 4.0, per a maximitzar els beneficis i planificar simultàniament la producció, inventari de materials i productes acabats satisfent la demanda provinent de previsions i comandes en ferma en una cadena de subministrament de cinc nivells. Novament, es combinen enfocaments de producció de just in time (JIT) i planificació de requeriments de materials (MRP). Tots dos models per a LSCP 4.0, i.e., conceptual i matemàtic, es validen en casos d'estudi reals de la indústria de calçat. Addicionalment, es considera l'ús d'una altra tecnologia facilitadora de la I4.0 com la computació en núvol per a abordar el problema de l'intercanvi d'informació entre els nodes de la cadena de subministrament. Així, es proposa un model de dades normalitzat per a la fabricació col·laborativa en el núvol aplicat a la indústria del calçat. / [EN] Innovation in working methods with new lean, sustainable and resilient production systems, as well as effective technology management, are current trends to improve performance in organisations. This PhD thesis investigates the theoretical contributions and formulates a conceptual model with the use of lean manufacturing (LM) and Industry 4.0 (I4.0) tools for production planning processes in supply chains (SCs). In addition, optimisation is employed as an enabling technology of I4.0 to develop the proposed solution. The applied methodology is bibliographic, exploratory and experimental; operational research techniques, data standardisation and case studies in industrial companies are applied. As a starting point, the existing literature related to LM and I4.0 technologies in the SC context is reviewed. Then a conceptual model, known as LSCP 4.0, is designed to facilitate decision making at the strategic, tactical and operational decision levels. It is a structured relation among lean, agile, sustainable, resilient and flexible paradigms to improve SCs' performance via the application of I4.0 enabling technologies. Based on this, an integer-mixed linear optimisation mathematical model, termed LSCP 4.0, is proposed to maximise profits and to simultaneously plan production, material inventory and finished goods by satisfying the demand from forecasts and firm orders in a five-tier SC. Novel just-in-time (JIT) production and material requirements planning (MRP) approaches are combined. Both models for LSCP 4.0, i.e., conceptual and mathematical, are validated in real case studies from the footwear industry. The use of another I4.0 enabling technology, such as cloud computing, is considered to address the problem of information exchange between SC nodes. Thus a standardised data model for collaborative manufacturing in the cloud applied to the footwear industry is proposed. / This thesis has been developed at the Research Centre on Production Management and Engineering (CIGIP) of the Universitat Politècnica de Valencia, within the framework of the projects: "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)" funded by the European Union H2020 Programme with grant agreement No. 958205; the MCIN/AEI/10.13039/501100011033 and by European Union Next GenerationEU/PRTR with grant agreement PDC2022-133957-100); "Industrial production and loginics optimization in industry 4.0 (i40PT)" funded by the Generalitat Valenciana within the framework of the research groups of excellence project PROMETEO/2021/065; "Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)" funded by the Spanish Ministry of Science, Innovation and Universities with grant agreement RTI2018-101344-B-100; "Operational Programme of the European Regional Development Fund (ERDF) of the Autonomous Valencian Community 2014-2020" (Ref. IDIFEDER/2018/025); "Resilient, Sustainable and People-Oriented Supply Chain 5.0 Optimisation Using Hybrid Intelligence" (RESPECT) (Ref. CIGE/2021/159); and a PhD grant from the Technical University of Ambato. / Reyes Vásquez, JP. (2024). Conceptual and optimisation modelling for lean supply chain planning in Industry 4.0 [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203818 / Compendio

Afin de répondre à la problématique de la régulation multi-objectif et multi-mode des carrefours à feux, nous proposons trois modèles de programmation linéaire mixte en nombres entiers constituant les moteurs d'un système de régulation pleinement adaptatif, ainsi que deux procédures interactives d'optimisation multi-objectif permettant d'adapter itérativement une “politique de régulation” à la situation de trafic. Les critères pris en compte, tous à minimiser, sont le temps d'attente et le nombre d'arrêts des véhicules particuliers, et un critère dédié aux transports en commun permettant de fixer un temps d'attente souhaité pour chaque bus. Des expérimentations ont montré qu'un des trois modèles, dit hybride, se démarque positivement des deux autres. Ce modèle a alors été mis en œuvre avec une des deux procédures interactives, permettant de contrôler un trafic simulé sur une période d'une heure dans différents scénarios types, et comparé à un système de régulation semi-adaptatif. / In order to answer the multi-objective and multi-mode adaptive traffic control problem, we propose three models of mixed integer linear programming, usable with two multi-objective optimization interactive methods, allowing to adapt a “traffic control policy” iteratively to the current traffic situation. The considered criteria, all of them to be minimized, are the total waiting time and the number of stops for private vehicles and a criterion dedicated to public transports allowing to set a target waiting time for every bus. Experiments showed that one of the three models, called hybrid model, distinguishes itself positively from the others. This model was implemented with one of the two interactive methods, allowing to control a traffic simulated over one hour in different scenarios, and was compared to a semi-adaptive traffic control system.