In der vorliegenden Arbeit wird ein Modell zur Beschreibung des Energiebedarfs elektrischer Fahrzeuge des ÖPNV auf Basis von Wahrscheinlichkeitsdichten entwickelt, das insbesondere eine Dimensionierung von fahrzeugseitigem Energiespeichersystem und wegseitiger Energieversorgungsinfrastruktur in einem fahrleitungsfreien Betriebskonzept (DockingPrinzip) erlaubt. Im Gegensatz zur deterministischen Energiebedarfsbestimmung ermöglicht die stochastische Modellierung mit einer Kombination aus Markov-Kette und Semi-Markov-Prozess die Berücksichtigung von Zuverlässigkeitsvorgaben im Sinne einer Missionserfüllung. Schließlich kann so die Größe hybrider Fahrzeugenergiespeichersysteme und die Lage von Nachladestationen entlang der Strecke optimiert werden. Die Wirksamkeit der Modellierung wird anhand einer Fallstudie basierend auf Messdaten für ein Straßenbahnfahrzeug demonstriert. Für die Auslegung der wegseitigen Energieversorgungsinfrastruktur werden die Belastungsgänge des Nachladeprozesses in Form von zeitgewichteten Belastungsdauerkurven für charakteristische Netztopographien hergeleitet. Ein Laden des fahrzeugseitigen Energiespeichers aus einer wegseitigen Energie-Vorsammel-Station (Docking-Station) bringt einerseits eine erhebliche Glättung des Leistungsverlaufs beim Energiebezug. Andererseits ist ein elektrischer Anschluss dieser Station an das Niederspannungsnetz in gewöhnlichen städtischen Siedlungsstrukturen innerhalb weniger hundert Meter möglich.

Tesis por compendio / [ES] La energía, la comunicación y la informática son componentes fundamentales de la sociedad moderna, ya que sientan las bases para el desarrollo tecnológico y el crecimiento económico. La estrecha interrelación entre estos pilares se ha hecho cada vez más evidente en los últimos años, a medida que los avances en computación y análisis de datos han permitido nuevos enfoques de gestión y sostenibilidad de la energía. En este contexto, el uso eficiente de la energía se ha convertido en un objetivo clave para los investigadores, los responsables políticos y las empresas por igual. Al aprovechar el poder de las técnicas informáticas y de aprendizaje automático (ML), es posible destacar los desafíos de asegurar los sistemas de energía y optimizar el uso de la energía, lo que lleva a la necesidad de técnicas avanzadas como algoritmos bio-inspirados y redes neuronales. Esta tesis doctoral tiene como objetivo analizar los programas y estrategias de gestión de la carga, el consumo y la demanda en el panorama energético actual. El núcleo central presenta un estudio exhaustivo sobre la integración de algoritmos bio-inspirados, como la optimización de enjambres de partículas (PSO) y los modelos de redes neuronales artificiales (ANN) en los sistemas de gestión de la carga para hacer frente a los retos de la gestión de la carga y utilizar la energía de forma eficiente y segura. El cuerpo principal de esta tesis comprende tres publicaciones científicas, cada una de las cuales corresponde a una etapa distinta dentro del marco general de investigación de este estudio: la primera etapa propone un sistema de monitorización de bajo coste para aplicaciones energéticas que introduce un sistema SCADA basado en web rentable que era un del 80% más barato que una solución similar. La arquitectura de bajo coste propuesta, diseñada para bancos de pruebas de microrredes, ofrece monitorización en tiempo real, accesibilidad remota y control fácil de usar para aplicaciones académicas y de investigación. La segunda etapa combina la optimización híbrida de enjambre de partículas (PSO) en cascada con redes neuronales feed-forward para pronosticar y optimizar con precisión la demanda de energía en una microrred en AC, mejorando la integración de fuentes de energía renovables como gasificación de biomasa. Los resultados muestran que el modelo PSO-ANN propuesto tiene un rendimiento un 23,2% mejor en términos de MSE que los modelos de RNA de retropropagación feed-forward (FF-BP) y propagación directa en cascada (CF-P). La tercera y última etapa se centró en un sistema inteligente de gestión de la carga reforzado con criptografía híbrida para garantizar la comunicación protegida y la privacidad de los datos, abordando así de manera efectiva los desafíos de seguridad energética en entornos residenciales. Los resultados mostraron que el modelo propuesto de Gestión de Carga aplicado a Sistemas Residenciales de Seguridad (SRS-LM) fue un 37% mejor en rendimiento (costo de energía, utilización de energía, tiempo computacional) y con una reducción de carga máxima del 60% en comparación con un modelo de Medidor de Energía Inteligente Universal (USEM). / [CA] L'energia, la comunicació i la informàtica són components fonamentals de la societat moderna, ja que establixen les bases per al desenvolupament tecnològic i el creixement econòmic. L'estreta interrelació entre estos pilars s'ha fet cada vegada més evident en els últims anys, a mesura que els avanços en computació i anàlisi de dades han permés nous enfocaments de gestió i sostenibilitat de l'energia. En este context, l'ús eficient de l'energia s'ha convertit en un objectiu clau per als investigadors, els responsables polítics i les empreses per igual. En aprofitar el poder de les tècniques informàtiques i d'aprenentatge automàtic (ML), és possible destacar els desafiaments d'assegurar els sistemes d'energia i optimitzar l'ús de l'energia, la qual cosa porta a la necessitat de tècniques avançades com a algorismes bio-inspirats i xarxes neuronals. Esta tesi doctoral té com a objectiu analitzar els programes i estratègies de gestió de la càrrega, el consum i la demanda en el panorama energètic actual. El nucli central presenta un estudi exhaustiu sobre la integració d'algorismes bio-inspirats, com l'optimització d'eixams de partícules (PSO) i els models de xarxes neuronals artificials (ANN) en els sistemes de gestió de la càrrega per a fer front als reptes de la gestió de la càrrega i utilitzar l'energia de manera eficient i segura. El cos principal d'esta tesi comprén tres publicacions científiques, cadascuna de les quals correspon a una etapa diferent dins del marc general d'investigació d'este estudi: la primera etapa proposa un sistema de monitoratge de baix cost per a aplicacions energètiques que introduïx un sistema SCADA basat en web rendible que era un del 80% més barat que una solució similar. L'arquitectura de baix cost proposada, dissenyada per a bancs de proves de microxarxes, oferix monitoratge en temps real, accessibilitat remota i control fàcil d'usar per a aplicacions acadèmiques i d'investigació. La segona etapa combina l'optimització híbrida d'eixam de partícules (PSO) en cascada amb xarxes neuronals feed-forward per a pronosticar i optimitzar amb precisió la demanda d'energia en una microxarxa en AC, millorant la integració de fonts d'energia renovables com a gasificació de biomassa. Els resultats mostren que el model PSO-ANN proposat té un rendiment un 23,2% millor en termes de MSE que els models d'RNA de retropropagació feed-forward (FF-BP) i propagació directa en cascada (CF-P). La tercera i última etapa es va centrar en un sistema intel·ligent de gestió de la càrrega reforçat amb criptografia híbrida per a garantir la comunicació protegida i la privacitat de les dades, abordant així de manera efectiva els desafiaments de seguretat energètica en entorns residencials. Els resultats van mostrar que el model proposat de Gestió de Càrrega aplicat a Sistemes Residencials de Seguretat (SRS-LM) va ser un 37% millor en rendiment (cost d'energia, utilització d'energia, temps computacional) i amb una reducció de càrrega màxima del 60% en comparació amb un model de Mesurador d'Energia Intel·ligent Universal (USEM). / [EN] Energy, communication, and computing are critical components of modern society, providing the foundation for technological development and economic growth. The close interrelation between these pillars has become increasingly apparent in recent years, as computing and data analysis advances have enabled new energy management and sustainability approaches. In this context, efficient energy usage has become a key focus for researchers, policymakers, and businesses alike. By harnessing the power of computing and machine learning (ML) techniques, it is possible to highlight the challenges of securing energy systems and optimizing energy usage, leading to the need for advanced techniques such as bio-inspired algorithms and neural networks. This doctoral thesis aims to analyse load consumption and demand management programs and strategies in the current energy landscape. The central core presents an study on integrating bio-inspired algorithms, such as particle swarm optimization (PSO) and artificial neural networks (ANN) models in load management systems to meet load management challenges and use energy efficiently and securely. The main body of this thesis comprises three scientific publications, each corresponding to a distinct stage within the overarching research framework of this study: the first stage covers the proposal of a low-cost architecture in energy systems introducing a cost-effective web-based SCADA system that was over 80% cheaper than a similar solution. The proposed low-cost architecture, tailored for microgrid testbeds, offers real-time monitoring, remote accessibility, and user-friendly control for academic and research applications. The second stage combined a cascade hybrid Particle Swarm Optimization (PSO) with feed-forward neural networks to accurately forecast and optimize energy demand in an AC microgrid, notably enhancing the integration of renewable energy sources like biomass gasification. The results showed that the proposed PSO-ANN model performs 23.2% better in terms of MSE than Feedforward Backpropagation (FF-BP) and Cascade forward propagation (CF-P) ANN models. The third and final stage focused on a smart load management system fortified with hybrid cryptography to ensure protected communication and data privacy, thereby effectively addressing energy security challenges in residential settings. Results showed that the proposed Security Residential System Load Management (SRS-LM) model was 37% better in performance (power cost, power utilization, computational time) and with a 60% peak load reduction compared to a Universal Smart Energy Meter (USEM) model. / Chiñas Palacios, CD. (2024). Bio-Inspired Algorithms and Artificial Neural Networks Applied to Smart Load Management Systems to Optimize Energy Usage [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203128 / Compendio

Detta arbete var beställt av Sjöfartshögskolan i Kalmar. Skolan ville ha en enhet som kunde mäta aktiv-, reaktiv- och skenbar effekt, ström, spänning, frekvens och cosϕ på en generator och som kommunicerade vidare dessa värden till en PLC. Detta för att kunna lastfördela lasten mellan ett antal generatorer i kursen Tillämpad elteknik 15 hp där studenterna bygger en generatorinstallation med tre generatorer. Ett funktionsblock för lastfördelning skulle också programmeras. Prototypen som konstruerades baserades på en Arduino Ethernet och kommunikationen löstes med Modbus TCP/IP över Ethernet. Ett lastfördelningsprogram programmerades i form av ett funktionsblock som studenterna kunde importera till CoDeSys v2.3 och använda i sina installationer. Prototypen kunde läsa av värdena med ungefär samma noggrannhet som ett kommersiellt instrument som använder sig av samma mätteknik som prototypen. Uppdateringsfrekvensen var dock lägre än hos ett kommersiellt instrument. Kommunikationen med PLC:n fungerade utan problem. Då ingen undervisning hölls i arbetets slutskede kunde inte lastfördelningen testas på en fullskalig anläggning. Lastfördelningsprogrammet klarade dock av att hålla rätt frekvens på en ensam generator och fungerade som tänkt när programmet testades i en simulator. Prototypen gav fel mätvärden vid kapacitiv last. Vid jämförelse med en kommersiell tångamperemeter visade sig mätfelet bero på mätmetoden då båda gav liknande resultat. Som referens användes en professionell elkvalitetsanalysator. Alla uppdragsgivarens krav blev uppfyllda och arbetet kommer att kunna användas i undervisningen. / This thesis was ordered by Kalmar Maritime Academy. The request was for a device that could measure active, reactive and apparent power, as well as frequency, voltage, current and cosϕ on a generator. The measured values would be communicated to a PLC for use in a load sharing program between a number of generators in the course Tillämpad elteknik, 15 ECTS. In that course the students constructs a three-generator electric power grid. Included in the request was also to program a load sharing program. The prototype being constructed was based on the Arduino Ethernet, and the communication was enabled by means of the Modbus TCP/IP protocol over Ethernet. A load sharing program was created in the form of a function block which the student could import into the CoDeSys for use in the generator systems. The prototype could measure values with close to the same accuracy as a commercial available instrument that were using the same technique for measuring. The refresh rate was however lower than the commercial available instrument. Communication with the PLC worked without any issues. No full-scale testing could be done since no course was held during the final stages of the thesis, however the load sharing program could keep frequency on a single generator alone and worked in a simulated soft environment. Measuring errors occurred when measuring a capacitive load. When comparing to a commercial available clamp meter, the same errors occurred. As a reference a professional power and energy quality analyser was used. All the requests were fulfilled and the result of this thesis will be used in the educational programme at the Academy.

In der vorliegenden Arbeit wird ein Modell zur Beschreibung des Energiebedarfs elektrischer Fahrzeuge des ÖPNV auf Basis von Wahrscheinlichkeitsdichten entwickelt, das insbesondere eine Dimensionierung von fahrzeugseitigem Energiespeichersystem und wegseitiger Energieversorgungsinfrastruktur in einem fahrleitungsfreien Betriebskonzept (DockingPrinzip) erlaubt. Im Gegensatz zur deterministischen Energiebedarfsbestimmung ermöglicht die stochastische Modellierung mit einer Kombination aus Markov-Kette und Semi-Markov-Prozess die Berücksichtigung von Zuverlässigkeitsvorgaben im Sinne einer Missionserfüllung. Schließlich kann so die Größe hybrider Fahrzeugenergiespeichersysteme und die Lage von Nachladestationen entlang der Strecke optimiert werden. Die Wirksamkeit der Modellierung wird anhand einer Fallstudie basierend auf Messdaten für ein Straßenbahnfahrzeug demonstriert. Für die Auslegung der wegseitigen Energieversorgungsinfrastruktur werden die Belastungsgänge des Nachladeprozesses in Form von zeitgewichteten Belastungsdauerkurven für charakteristische Netztopographien hergeleitet. Ein Laden des fahrzeugseitigen Energiespeichers aus einer wegseitigen Energie-Vorsammel-Station (Docking-Station) bringt einerseits eine erhebliche Glättung des Leistungsverlaufs beim Energiebezug. Andererseits ist ein elektrischer Anschluss dieser Station an das Niederspannungsnetz in gewöhnlichen städtischen Siedlungsstrukturen innerhalb weniger hundert Meter möglich.

The concepts of 'data warehousing' and 'on-line analytical processing' have seen a growing interest in the research and commercial product community. Today, the trend moves away from complex centralized data warehouses to distributed data marts integrated in a common conceptual schema. However, as the first part of this paper demonstrates, there are many problems and little solutions for large distributed decision support systems in worldwide operating corporations. After showing the benefits and problems of the distributed approach, this paper outlines possibilities for achieving performance in distributed online analytical processing. Finally, the architectural framework of the prototypical distributed OLAP system CUBESTAR is outlined.

In the context of electricity, arbitrage trading involves taking advantage of existing price variations within electricity markets. The report conducted financial modelling for energy storage systems and demand-side load management for electricity arbitrage trading in single-family homes. The analysis included two different energy storage systems: a thermal energy storage system and a battery energy storage system. Additionally, electricity spot cost reduction was compared between electricity arbitrage trading and traditional energy efficiency measures such as air-to-water and ground-source heat pumps. The report's findings indicated that air-to-water and ground-source heat pumps emerged as the most economically viable choices for reducing electricity spot costs, irrespective of the studied electricity price area. The thermal energy storage system, employing an insulated hot water storage tank, ranked the third most efficient in achieving cost savings. The battery energy storage system, represented by a lithium home battery system, demonstrated the lowest rate of cost saving among the analyzed energy efficiency measures.  The financial modelling highlighted the economic potential for thermal energy storage systems, particularly in southern Sweden's electricity price areas SE3 and SE4. On the other hand, no economically viable options for battery energy storage systems were identified, regardless of the studied electricity price area. As a results, the report recommends utilizing thermal energy storage systems and implementing demand-side load management as strategies to hedge against future electricity price volatility.

No description available.

Le souhait d’augmenter la part des énergies renouvelables dans le mix énergétique entraine une augmentation des parts des énergies volatiles et non pilotables, et rend donc l’équilibre offre-demande difficile à satisfaire. Une façon d’intégrer ces énergies dans le réseau électrique actuel est d’utiliser de petits moyens de production, de consommation et de stockage répartis sur tout le territoire pour compenser les sous ou sur productions. Afin que ces procédés puissent être intégrés dans le processus d’équilibre offre-demande, ils sont regroupés au sein d’une centrale virtuelle d’agrégation de flexibilité, qui est vue alors comme une centrale virtuelle. Comme pour tout autre moyen de production du réseau, il est nécessaire de déterminer son plan de production. Nous proposons dans un premier temps dans cette thèse une architecture et un mode de gestion pour une centrale d’agrégation composée de n’importe quel type de procédés. Dans un second temps, nous présentons des algorithmes permettant de calculer le plan de production des différents types de procédés respectant toutes leurs contraintes de fonctionnement. Et enfin, nous proposons des approches pour calculer le plan de production de la centrale d’agrégation dans le but de maximiser son gain financier en respectant les contraintes réseau. / The desire to increase the share of renewable energies in the energy mix leads to an increase inshare of volatile and non-controllable energy and makes it difficult to meet the supply-demand balance. A solution to manage anyway theses energies in the current electrical grid is to deploy new energy storage and demand response systems across the country to counter balance under or over production. In order to integrate all these energies systems to the supply and demand balance process, there are gathered together within a virtual flexibility aggregation power plant which is then seen as a virtual power plant. As for any other power plant, it is necessary to compute its production plan. Firstly, we propose in this PhD thesis an architecture and management method for an aggregation power plant composed of any type of energies systems. Then, we propose algorithms to compute the production plan of any types of energy systems satisfying all theirs constraints. Finally, we propose an approach to compute the production plan of the aggregation power plant in order to maximize its financial profit while complying with all the constraints of the grid.

Indiana University-Purdue University Indianapolis (IUPUI) / The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in terms of energy consumption. Therefore, it becomes one of the primary targets when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production. / 2019-12-05

<div>The compressed air system is widely used throughout the industry. Air compressors are one of the most costly systems to operate in industrial plants in therms of energy consumption. Therefore, it becomes one of the primary target when it comes to electrical energy and load management practices. Load forecasting is the first step in developing energy management systems both on the supply and user side. A comprehensive literature review has been conducted, and there was a need to study if predicting compressed air system’s load is a possibility. </div><div><br></div><div>System’s load profile will be valuable to the industry practitioners as well as related software providers in developing better practice and tools for load management and look-ahead scheduling programs. Feed forward neural networks (FFNN) and long short-term memory (LSTM) techniques have been used to perform 15 minutes ahead prediction. Three cases of different sizes and control methods have been studied. The results proved the possibility of the forecast. In this study two control methods have been developed by using the prediction. The first control method is designed for variable speed driven air compressors. The goal was to decrease the maximum electrical load for the air compressor by using the system's full operational capabilities and the air receiver tank. This goal has been achieved by optimizing the system operation and developing a practical control method. The results can be used to decrease the maximum electrical load consumed by the system as well as assuring the sufficient air for the users during the peak compressed air demand by users. This method can also prevent backup or secondary systems from running during the peak compressed air demand which can result in more energy and demand savings. Load management plays a pivotal role and developing maximum load reduction methods by users can result in more sustainability as well as the cost reduction for developing sustainable energy production sources. The last part of this research is concentrated on reducing the energy consumed by load/unload controlled air compressors. Two novel control methods have been introduced. One method uses the prediction as input, and the other one doesn't require prediction. Both of them resulted in energy consumption reduction by increasing the off period with the same compressed air output or in other words without sacrificing the required compressed air needed for production.</div><div><br></div>