The purpose of this research is to identify the need of Smart Grid Technologies in communication between industrial plants with co-generation capability and the electric utilities in providing the most optimum scheme for buying and selling of electricity in such a way that the fuel consumption is minimized, reliability is increased, and time to restore the system is reduced. A typical industrial plant load profile based on statistical mean and variance of industrial plants' load requirement is developed, and used in determining the minimum cost of producing the next megawatt-hours by a typical electric utility. The 24-hour load profile and optimal power flow program are used to simulate the IEEE 39 Bus Test System. The methodology for the use of smart grid technology in fuel saving is documented in the thesis. The results obtained from this research shall be extended to include several industrial plants served by electric utilities in future work by the UNO research team.

[EN] The electricity sector is currently undergoing a process of liberalization and separation of roles, which is being implemented under the regulatory auspices of each Member State of the European Union and, therefore, with different speeds, perspectives and objectives that must converge on a common horizon, where Europe will benefit from an interconnected energy market in which producers and consumers can participate in free competition. This process of liberalization and separation of roles involves two consequences or, viewed another way, entails a major consequence from which other immediate consequence, as a necessity, is derived. The main consequence is the increased complexity in the management and supervision of a system, the electrical, increasingly interconnected and participatory, with connection of distributed energy sources, much of them from renewable sources, at different voltage levels and with different generation capacity at any point in the network. From this situation the other consequence is derived, which is the need to communicate information between agents, reliably, safely and quickly, and that this information is analyzed in the most effective way possible, to form part of the processes of decision taking that improve the observability and controllability of a system which is increasing in complexity and number of agents involved. With the evolution of Information and Communication Technologies (ICT), and the investments both in improving existing measurement and communications infrastructure, and taking the measurement and actuation capacity to a greater number of points in medium and low voltage networks, the availability of data that informs of the state of the network is increasingly higher and more complete. All these systems are part of the so-called Smart Grids, or intelligent networks of the future, a future which is not so far. One such source of information comes from the energy consumption of customers, measured on a regular basis (every hour, half hour or quarter-hour) and sent to the Distribution System Operators from the Smart Meters making use of Advanced Metering Infrastructure (AMI). This way, there is an increasingly amount of information on the energy consumption of customers, being stored in Big Data systems. This growing source of information demands specialized techniques which can take benefit from it, extracting a useful and summarized knowledge from it. This thesis deals with the use of this information of energy consumption from Smart Meters, in particular on the application of data mining techniques to obtain temporal patterns that characterize the users of electrical energy, grouping them according to these patterns in a small number of groups or clusters, that allow evaluating how users consume energy, both during the day and during a sequence of days, allowing to assess trends and predict future scenarios. For this, the current techniques are studied and, proving that the current works do not cover this objective, clustering or dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users are developed. These techniques are tested and validated on a database of hourly energy consumption values for a sample of residential customers in Spain during years 2008 and 2009. The results allow to observe both the characterization in consumption patterns of the different types of residential energy consumers, and their evolution over time, and to assess, for example, how the regulatory changes that occurred in Spain in the electricity sector during those years influenced in the temporal patterns of energy consumption. / [ES] El sector eléctrico se halla actualmente sometido a un proceso de liberalización y separación de roles, que está siendo aplicado bajo los auspicios regulatorios de cada Estado Miembro de la Unión Europea y, por tanto, con distintas velocidades, perspectivas y objetivos que deben confluir en un horizonte común, en donde Europa se beneficiará de un mercado energético interconectado, en el cual productores y consumidores podrán participar en libre competencia. Este proceso de liberalización y separación de roles conlleva dos consecuencias o, visto de otra manera, conlleva una consecuencia principal de la cual se deriva, como necesidad, otra consecuencia inmediata. La consecuencia principal es el aumento de la complejidad en la gestión y supervisión de un sistema, el eléctrico, cada vez más interconectado y participativo, con conexión de fuentes distribuidas de energía, muchas de ellas de origen renovable, a distintos niveles de tensión y con distinta capacidad de generación, en cualquier punto de la red. De esta situación se deriva la otra consecuencia, que es la necesidad de comunicar información entre los distintos agentes, de forma fiable, segura y rápida, y que esta información sea analizada de la forma más eficaz posible, para que forme parte de los procesos de toma de decisiones que mejoran la observabilidad y controlabilidad de un sistema cada vez más complejo y con más agentes involucrados. Con el avance de las Tecnologías de Información y Comunicaciones (TIC), y las inversiones tanto en mejora de la infraestructura existente de medida y comunicaciones, como en llevar la obtención de medidas y la capacidad de actuación a un mayor número de puntos en redes de media y baja tensión, la disponibilidad de datos sobre el estado de la red es cada vez mayor y más completa. Todos estos sistemas forman parte de las llamadas Smart Grids, o redes inteligentes del futuro, un futuro ya no tan lejano. Una de estas fuentes de información proviene de los consumos energéticos de los clientes, medidos de forma periódica (cada hora, media hora o cuarto de hora) y enviados hacia las Distribuidoras desde los contadores inteligentes o Smart Meters, mediante infraestructura avanzada de medida o Advanced Metering Infrastructure (AMI). De esta forma, cada vez se tiene una mayor cantidad de información sobre los consumos energéticos de los clientes, almacenada en sistemas de Big Data. Esta cada vez mayor fuente de información demanda técnicas especializadas que sepan aprovecharla, extrayendo un conocimiento útil y resumido de la misma. La presente Tesis doctoral versa sobre el uso de esta información de consumos energéticos de los contadores inteligentes, en concreto sobre la aplicación de técnicas de minería de datos (data mining) para obtener patrones temporales que caractericen a los usuarios de energía eléctrica, agrupándolos según estos mismos patrones en un número reducido de grupos o clusters, que permiten evaluar la forma en que los usuarios consumen la energía, tanto a lo largo del día como durante una secuencia de días, permitiendo evaluar tendencias y predecir escenarios futuros. Para ello se estudian las técnicas actuales y, comprobando que los trabajos actuales no cubren este objetivo, se desarrollan técnicas de clustering o segmentación dinámica aplicadas a curvas de carga de consumo eléctrico diario de clientes domésticos. Estas técnicas se prueban y validan sobre una base de datos de consumos energéticos horarios de una muestra de clientes residenciales en España durante los años 2008 y 2009. Los resultados permiten observar tanto la caracterización en consumos de los distintos tipos de consumidores energéticos residenciales, como su evolución en el tiempo, y permiten evaluar, por ejemplo, cómo influenciaron en los patrones temporales de consumos los cambios regulatorios que se produjeron en España en el sector eléctrico durante esos años. / [CAT] El sector elèctric es troba actualment sotmès a un procés de liberalització i separació de rols, que s'està aplicant davall els auspicis reguladors de cada estat membre de la Unió Europea i, per tant, amb distintes velocitats, perspectives i objectius que han de confluir en un horitzó comú, on Europa es beneficiarà d'un mercat energètic interconnectat, en el qual productors i consumidors podran participar en lliure competència. Aquest procés de liberalització i separació de rols comporta dues conseqüències o, vist d'una altra manera, comporta una conseqüència principal de la qual es deriva, com a necessitat, una altra conseqüència immediata. La conseqüència principal és l'augment de la complexitat en la gestió i supervisió d'un sistema, l'elèctric, cada vegada més interconnectat i participatiu, amb connexió de fonts distribuïdes d'energia, moltes d'aquestes d'origen renovable, a distints nivells de tensió i amb distinta capacitat de generació, en qualsevol punt de la xarxa. D'aquesta situació es deriva l'altra conseqüència, que és la necessitat de comunicar informació entre els distints agents, de forma fiable, segura i ràpida, i que aquesta informació siga analitzada de la manera més eficaç possible, perquè forme part dels processos de presa de decisions que milloren l'observabilitat i controlabilitat d'un sistema cada vegada més complex i amb més agents involucrats. Amb l'avanç de les tecnologies de la informació i les comunicacions (TIC), i les inversions, tant en la millora de la infraestructura existent de mesura i comunicacions, com en el trasllat de l'obtenció de mesures i capacitat d'actuació a un nombre més gran de punts en xarxes de mitjana i baixa tensió, la disponibilitat de dades sobre l'estat de la xarxa és cada vegada major i més completa. Tots aquests sistemes formen part de les denominades Smart Grids o xarxes intel·ligents del futur, un futur ja no tan llunyà. Una d'aquestes fonts d'informació prové dels consums energètics dels clients, mesurats de forma periòdica (cada hora, mitja hora o quart d'hora) i enviats cap a les distribuïdores des dels comptadors intel·ligents o Smart Meters, per mitjà d'infraestructura avançada de mesura o Advanced Metering Infrastructure (AMI). D'aquesta manera, cada vegada es té una major quantitat d'informació sobre els consums energètics dels clients, emmagatzemada en sistemes de Big Data. Aquesta cada vegada major font d'informació demanda tècniques especialitzades que sàpiguen aprofitar-la, extraient-ne un coneixement útil i resumit. La present tesi doctoral versa sobre l'ús d'aquesta informació de consums energètics dels comptadors intel·ligents, en concret sobre l'aplicació de tècniques de mineria de dades (data mining) per a obtenir patrons temporals que caracteritzen els usuaris d'energia elèctrica, agrupant-los segons aquests mateixos patrons en una quantitat reduïda de grups o clusters, que permeten avaluar la forma en què els usuaris consumeixen l'energia, tant al llarg del dia com durant una seqüència de dies, i que permetent avaluar tendències i predir escenaris futurs. Amb aquesta finalitat, s'estudien les tècniques actuals i, en comprovar que els treballs actuals no cobreixen aquest objectiu, es desenvolupen tècniques de clustering o segmentació dinàmica aplicades a corbes de càrrega de consum elèctric diari de clients domèstics. Aquestes tècniques es proven i validen sobre una base de dades de consums energètics horaris d'una mostra de clients residencials a Espanya durant els anys 2008 i 2009. Els resultats permeten observar tant la caracterització en consums dels distints tipus de consumidors energètics residencials, com la seua evolució en el temps, i permeten avaluar, per exemple, com van influenciar en els patrons temporals de consums els canvis reguladors que es van produir a Espanya en el sector elèctric durant aquests anys. / Benítez Sánchez, IJ. (2015). Dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59236 / TESIS

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-02-08T12:58:24Z No. of bitstreams: 1 Ademilson dos Santos Xavier_.pdf: 9916385 bytes, checksum: 8bcbfe8f2b36b5a58781ce2518c1bb83 (MD5) / Made available in DSpace on 2017-02-08T12:58:24Z (GMT). No. of bitstreams: 1 Ademilson dos Santos Xavier_.pdf: 9916385 bytes, checksum: 8bcbfe8f2b36b5a58781ce2518c1bb83 (MD5) Previous issue date: 2016-10-13 / Nenhuma / Estudos recentes mostraram que uma grande parte da energia elétrica consumida no Brasil destina-se a edificações do setor Comercial e Público. Observando-se que o sistema de HVAC (Heating, Ventilation, and Air Conditioning) apresenta relevante papel no quadro final do consumo energético em edificações, a determinação de seus requisitos mínimos de eficiência energética torna-se o fator chave para o sucesso de qualquer programa de certificação energética em construções. Esse trabalho tem como objetivo avaliar de que forma as condições climatológicas brasileiras podem influenciar o desempenho de um sistema condicionador de ar com tecnologia VRF (Variable Refrigerant Flow) em Condições de Carga Parcial (Part Load Conditions), para uma edificação comercial. Para isso, foi desenvolvida uma metodologia capaz de identificar as localidades que foram objetos desse estudo. As cidades selecionadas foram: São Paulo (SP), Rio de Janeiro (RJ), Fortaleza (CE) e Brasília (DF). Após esta etapa, a caracterização do edifício modelo de referência (hotel) foi concretizada. Características físicas, valores de carga térmica externa e interna, e o perfil de uso (taxa de ocupação) da edificação, foram tópicos abordados. O software de simulação utilizado foi o EnergyPlus e as suas respectivas curvas e equações de desempenho foram ajustadas com base nos dados de performance dos aparelhos condicionadores de ar que foram selecionados. O fabricante Toshiba foi escolhido. A performance dos equipamentos VRF foi analisada através de resultados como Carga Térmica global, Condição de Carga Plena, Condições de Carga Parcial, PLR (Part Load Ratio), COP (Coefficient of Performance) e ICOP (Coeficiente Integrado de Performance). Os resultados mostraram que apesar da Condição de Carga Plena (Full Load Condition) ser recomendada para dimensionar o sistema de HVAC, quando o objetivo principal for avaliar o desempenho energético desse sistema (HVAC) para um período mais longo de operação essa análise deve ser realizada através das Condições de Carga Parcial (Part Load Conditions). Para as quatro localidades estudadas os valores de desempenho obtidos através das simulações mostram que os equipamentos de ar condicionado VRF utilizados atingiram o seu coeficiente máximo de performance (COPmáx) na condição de 50% de PLR e um ICOP de 6,8, contra um COP de 3,4 na condição nominal e um ICOP de 3,7 de acordo com o Regulamento Técnico da Qualidade do Nível de Eficiência Energética de Edifícios Comerciais, de Serviço e Públicos, RTQ-C. / Currently studies have been shown that a large part of the electricity consumed in Brazil is intended to buildings Commercial and Public Sector. The HVAC system (Heating, Ventilation, and Air Conditioning) shows a significant role in the final frame of the energy consumption in buildings, determining its minimum requirements for energy efficiency becomes the key factor for the success of any program energy certification of buildings. This work aims to analyze how the Brazilian climatic conditions can influence the performance of air conditioner systems with VRF technology (Variable Refrigerant Flow) in the Part-Load Condition for a commercial building. Therefore a methodology was developed to identify the locations that were the subject of this study. The cities selected are: São Paulo (SP), Rio de Janeiro (RJ), Fortaleza (CE) and Brasília (DF). In addition the building characteristics have completed, external and internal thermal load values with their use profile have been analyzed. The software applied was EnergyPlus and their performance curves and equations have been adjusted according with the performance data of air conditioning units selected. The manufacturer Toshiba has been chosen. The VRF equipment performance was analyzed through results as Full-Load Condition, Part-Load Conditions, Part Load Ratio (PLR), COP and COP (Integrated Coefficient of Performance). The main results have showed that despite the Full-Load Condition is recommended to dimension the HVAC system, when the objective is the evaluation of the energy performance for a longer operation period this analysis should be carried out through Part-Load Conditions. For all of the four cities have studied the performance values obtained from the simulations show that the air-conditioning equipment VRF used reached their maximum performance coefficient (COPmáx) at 50% of PLR and ICOP 6.8, against a COP 3.4 in nominal condition and ICOP of 3.72 according to the Quality Technical Regulation of the Energy Efficiency Level Commercial Buildings, and Public Service, RTQ-C.

Power industry restructuring has created many opportunities for customers to reduce their electricity bills. In order to facilitate the retail choice in a competitive power market, the knowledge of hourly load shape by customer class is necessary. Requiring a meter as a prerequisite for lower voltage customers to choose a power supplier is not considered practical at the present time. In order to be used by Energy Service Provider (ESP) to assign customers to specific load profiles with certainty factors, a technique which bases on load research and customers¡¦ monthly energy usage data for a preliminary screening of customer load profiles is required. Distribution systems supply electricity to different mixtures of customers, due to lack of field measurements, load point data used in distribution network studies have various degrees of uncertainties. In order to take the expected uncertainties in the demand into account, many previous methods have used fuzzy load models in their studies. However, the issue of deriving these models has not been discussed. To address this issue, an approach for building these fuzzy load models is needed. Load aggregation allows customers to purchase electricity at a lower price. In some contracts, load factor is considered as one critical aspect of aggregation. To facilitate a better load aggregation in distribution networks, feeder reconfiguration could be used to improve the load factor in a distribution subsystem. To solve the aforementioned problems, two data mining techniques, namely, the fuzzy c-means (FCM) method and an Artificial Neural Network (ANN) based pattern recognition technique, are proposed for load profiling and customer class assignment. A variant to the previous load profiling technique, customer hourly load distributions obtained from load research can be converted to fuzzy membership functions based on a possibility¡Vprobability consistency principle. With the customer class fuzzy load profiles, customer monthly power consumption and feeder load measurements, hourly loads of each distribution transformer on the feeder can be estimated and used in distribution network analysis. After feeder models are established, feeder reconfiguration based on binary particle swarm optimization (BPSO) technique is used to improve feeder load factors. Test results based on several simple sample networks have shown that the proposed feeder reconfiguration method could improve customers¡¦ position for a good bargain in electricity service.

Operational performance of grid-connected microgrid with integrated solar photovoltaic (PV) electricity production and battery energy storage (BES) is investigated.  These distributed energy resources (DERs) have the potential to reduce conventionally produced electrical power and contribute to reduction of greenhouse gas emissions.  This investigation is based upon the DER’s techno-economic specifications and theoretical performance, consumer load data and electrical utility retail and distribution data.  Available literature provides the basis for DER specification and performance.  Actual consumer load profile data is available for residential and commercial consumer sector customers.  The electrical utility data is obtained from Mälarenergi, AB.  The aim is to investigate how to use simulations to specify a grid connected microgrid with DERs (PV production and a BES system) for two consumer sectors considering a range of objectives.  An open-source, MATLAB-based simulation tool called Opti-CE has successfully been utilized.  This package employs a genetic algorithm for multi-objective optimization.  To support attainment of one of the objectives, peak shaving of the consumer load, a battery operational strategy algorithm has been developed for the simulation.  With respect to balancing peak shaving and self-consumption one of the simulations supports specification of a commercial sector application with 117 kWp PV power rating paired with a lithium ion battery with 41.1 kWh capacity.  The simulation of this system predicts the possibility to shave the customer load profile peaks for the month of April by 20%.  The corresponding self-consumption ratio is 88%.  Differences in the relationship between the load profiles and the system performance have been qualitatively noted.  Furthermore, simulation results for lead-acid, lithium-ion and vanadium-redox flow battery systems are compared to reveal that lithium ion delivers the best balance between total annualized cost and peak shaving performance for both residential and commercial applications.

The aim of this study is to investigate, analyse and evaluate the installed off-grid PV system in Mavuno girls’ secondary school that is located in a rural area in northwest Tanzania. The original motivation behind this study was the rapid degradation of the installed battery bank within less than 3 years. The PV system was installed before the actual operation of the school, so the study aimed to answer a very pressing question which is "What is the actual load profiles in the school?". There was a high need to identify the actual school load profiles to enable several concerned social actors to evaluate the system and to decide for future extensions. Therefore, the study aimed to analyse the implementation of electricity in the school by creating actual load profiles, analysing the system performance versus the users’ needs and evaluating the sustainability and utilization of implementation. The study followed a multi-disciplinary approach combining the social and technical aspects of PV systems implementation to seek further understanding of the users’ consumption behaviours. It thus included a 1-month of field work in June 2018 during which participant observations and semi-structured interviews together with load measurements were carried out so as to create load profiles that are considering the patterns and deviations in users’ behaviours. During the field work, 2/3 of the students were in holidays so the taken measurements corresponded to the school at 30 % capacity. That is why the study also included 4 days of inverter data logging after the 1-month field work by the technical head of the school to overcome the limitations in held measurements. The observations showed that the actual installed system was slightly different from the documentation. In addition, the local installation practices are not fully appropriate from the technical point of view, and are affected by local social norms, as will be discussed. Besides, the participant observations and held interviews with relevant social actors showed that the daily behaviours of energy users do not exactly follow the school daily routine. Consequently, the social study was important to create actual effective load profiles. The observations and responses from interviews together with measurements were used to categorize the school loads into 29 different units. Those units can be used for current load prioritizations and for future load extrapolations. The created load profiles also represent a useful addition to load databases used by energy researchers who work on similar rural electrification projects. After the field work, several characteristics were calculated by Microsoft Excel such as apparent power consumptions, active power consumptions, battery bank state of charge, load power factor and PV generated energy. The characteristics were used in calculations evaluating the energy balance in the system. The results of held calculations showed that lighting during dark hours accounted for on around 78 % of the logged daily apparent energy use, as it has a low a low average power factor of 0.28. It also showed that some loads if time-bounded, they will significantly decrease the daily energy consumption. The calculations were also used to run PVSyst simulations to evaluate the system sizing which resulted in the recommendation that either the array size should be doubled, or the apparent energy consumption should be decreased to half. The study included suggestions for possible improvements such as decreasing the reactive consumed energy by either replacing the currently used light bulbs with ones that have higher power factor ( ≥0.8 for example) or by installing a capacitive compensation for power factor correction. In addition, it was recommended to quantify the school loads according to their priority or importance and to regulate observed time-unbounded loads such as "pumping water" and "ironing". Lastly, the study discussed how generated electricity is utilized in the school and what opportunities for women empowerment have become potentially possible with the provision of electricity.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-02T12:24:39Z No. of bitstreams: 1 annaclaudiamancinidasilvacarneiro.pdf: 1333903 bytes, checksum: a7b3819bb5b0e1adb8efd07bca0f9aa2 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-06T19:35:55Z (GMT) No. of bitstreams: 1 annaclaudiamancinidasilvacarneiro.pdf: 1333903 bytes, checksum: a7b3819bb5b0e1adb8efd07bca0f9aa2 (MD5) / Made available in DSpace on 2017-03-06T19:35:55Z (GMT). No. of bitstreams: 1 annaclaudiamancinidasilvacarneiro.pdf: 1333903 bytes, checksum: a7b3819bb5b0e1adb8efd07bca0f9aa2 (MD5) Previous issue date: 2014-09-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A previsão de cargas elétricas é fundamental para o planejamento das empresas de energia. O foco deste estudo são as previsões a curto prazo; assim, aplicamos métodos univariados de previsão de séries temporais a uma série real de cargas elétricas de 104 semanas no Rio de Janeiro, nos anos de 1996 e 1997, e experimentamos várias combinações dos métodos de melhor desempenho. As combinações foram feitas pelo método outperformance, uma combinação linear simples, com pesos fixos. Os resultados das combinações foram comparados ao de simulações de redes neurais artificiais que solucionam o mesmo problema, e ao resultado de um método de amortecimento de dupla sazonalidade aditiva. No geral, este método de amortecimento obteve os melhores resultados, e talvez seja o mais adequado e confiável para aplicações práticas, embora necessite de melhorias para garantir a extração completa da informação contida nos dados. / Forecasting the demand for electric power is crucial for the production planning in energy utilities. The focus of this study are the short-term forecasts. We apply univariate time series methods to the forecasting of a series containing observations of the energy consumption of 104 weeks in Rio de Janeiro, in 1996 and 1997, and experiment with several combinations of the methods which have the best performance. These combinations are done by the outperformance method, a simple linear combination with fixed weights. The results were compared to those obtained by neural networks on the same problem, and with the results of a exponential smoothing method for dual additive seasonality. Overall, the exponential smoothing method achieved the best results, and was shown to be perhaps the most reliable and suitable for practical applications, even though it needs improvements to ensure complete extraction of the information contained in the data.

Laut der Bundesregierung soll bis 2050 der Anteil umweltschädlicher Treibhausgase um bis zu 95 % verringert werden (BMWi 2010). Dies kann nur gelingen, wenn auf der einen Seite der Energieverbrauch reduziert wird, z. B. durch Verbesserung der Energieeffizienz technischer Anlagen und die energetische Gebäudesanierung, und auf der anderen Seite der Beitrag regenerativer Energien am Energieverbrauch gesteigert wird. Zur Umsetzung und Bewältigung der damit einhergehenden Herausforderungen kommt den Städten und Gemeinden eine Schlüsselrolle zu. Findet in diesen doch der Großteil des Energieverbrauchs und des umweltschädlichen anthropogenen Ausstoßes von Treibhausgasen statt (Pichler, P.-P. et al. 2017). Vielerorts wurden diesbezüglich erste Weichen gestellt und Maßnahmen zur Reduzierung des Energieverbrauchs aber auch zur Integration regenerativer Energien eingeleitet (Aretz, A. et al. 2009). So entstanden u. a. im Rahmen der Nationalen Klimaschutzinitiative (BMU) in den letzten Jahren eine Vielzahl an Energiekonzepten für Gemeinden und Quartiere, allen voran für die Metropolen und Großstädte. Doch neben den Großstädten und den Metropolen kommt gerade den Klein- und Mittelstädten aufgrund ihrer Anzahl und Verankerung in der Fläche eine zentrale Bedeutung bei der Umsetzung der Energiewende in der Breite zu. Aufgrund der vielfältigen Herausforderungen, denen sich Klein- und Mittelstädte gegenübersehen (z. B. Demografischer Wandel, Bevölkerungsabwanderung, Daseinsvorsorge, Klimaanpassung, Energiewende, usw.), bedürfen diese zukünftig einer steigenden Unterstützung bei der Transformation hin zu einer nachhaltigen Energieversorgung. Voraussetzung hierfür ist neben finanziellen und personellen Ressourcen insbesondere auch die Bereitstellung von Informationen, die für eine Entwicklung von Maßnahmen hin zur Transformation notwendig sind, von besonderer Bedeutung. Während für Großstädte und Metropolen bereits eine Vielzahl an Daten und Informationen existieren und immer neue hinzukommen, fehlt es in Klein- und Mittelstädten bzw. in der Fläche an relevanten Informations- und Datenquellen für die Erarbeitung von Energiekonzepten. Infolgedessen werden bei der Erstellung von Konzepten für Kleinund Mittelstädte, insbesondere ländlich geprägten Regionen, häufig regionale oder auch nationale Datenquellen und Durchschnittswerte herangezogen. Die hierbei verwendeten Verfahren und Softwarelösungen liefern eine breite Spanne unterschiedlicher Ergebnisse, wodurch ein Vergleich zwischen Konzepten und Ergebnissen aber auch zwischen den Gemeinden nur bedingt möglich ist. Trotz des fluktuierenden Charakters der Mehrheit der regenerativen Energieformen werden in den Konzepten nur vereinzelt die zeitlichen Verläufe von Energienachfrage und Energieangebot berücksichtigt. Hierbei beeinflussen gerade diese das Potenzial einer Gemeinde große Teile der Energienachfrage mit regenerativen Energien zu versorgen und damit unabhängiger von Energieimporten zu werden. Im Rahmen der Arbeit wurde dahingehend ein Verfahren erarbeitet, mit dessen Hilfe das regenerative Energiepotenzial für die Energieversorgung der Wohnbebauung ermittelt werden kann. Exemplarisch wurden hierfür die Solarenergie und die in organischen Restund Abfallstoffen gespeicherte Energie ausgewählt. Dem Angebot solarer und in Biomasse gespeicherter Energie (Kapitel 5) wird der Energiebedarf der Privaten Haushalte für Strom, Warmwasser und Raumwärme gegenübergestellt (Kapitel 4). Gegenüber derzeit gängigen Untersuchungen auf kommunaler Ebene werden hierbei insbesondere die zeitlichen Unterschiede in der Energienachfrage und dem regenerativen Energieangebot durch Last- und Bereitstellungsprofile berücksichtigt. Grundlage der Berechnungen von Energiebedarf und regenerativem Energieangebot bildet das Siedlungsmodell (Kapitel 3), welches den Wohngebäudebestand und die damit verbundene Flächennutzung u. a. aufBasis von amtlichen Statistiken und frei zugänglichen Daten beschreibt. Anhand eines Fallbeispiels wird der Ansatz demonstriert (Kapitel 6) und mittels Sensitivitätsstudien der Einfluss sowie Wirkungszusammenhänge ausgewählter Parameter näher beleuchtet. Abschließend werden im Kapitel 7 die Ergebnisse ausgewertet, interpretiert sowie ein Fazit gezogen. Aus den gewonnenen Erkenntnissen zeigt sich, dass Berechnungen zum regenerativen Energiepotenzial auf Jahresbasis nur eine vergleichsweise geringe Aussagekraft bezüglich des Beitrags zur Energieversorgung zulassen. Demgegenüber weisen Modellierungen auf stündlicher Basis darauf hin, in welchen Zeiträumen, die hier betrachteten, regenerativen Energiequellen (Solarenergie, organische Rest- und Abfallstoffe) einen Beitrag zur Energieversorgung leisten können. Weiterhin zeigt sich, dass das bilanzierte regenerative Energiepotenzial für ein Jahr etwa um den Faktor 3 über dem modellierten Potenzial auf stündlicher Basis liegt.:Abbildungsverzeichnis Tabellenverzeichnis Abkürzungsverzeichnis Kurzfassung Abstract 1 Einleitung 1.1 Forschungsfrage und Zielstellung 1.2 Forschungskonzept und Aufbau der Arbeit 2 Grundlagen und Definitionen 2.1 100 %-Erneuerbare Eigenversorgung 2.2 Nutzenergie, Endenergie, Verbrauch und Bedarf 2.2.1 Nutz- und Endenergie 2.2.2 Energieverbrauch und Energiebedarf 2.3 Lastgang der Energienachfrage und Volatilität regenerativer Energien 2.4 Wirkungszusammenhänge zwischen Siedlungsstruktur und Energie 2.4.1 Struktur und Morphologie von Siedlungen 2.4.2 Siedlungsstrukturen im Kontext von Energieverbrauch und Energieeffizienz 2.4.3 Siedlungsstrukturen und Energiebereitstellung 3 Das Siedlungsmodell 3.1 Der Strukturtypenansatz 3.1.1 Die Strukturtypen 3.1.2 Merkmale und Merkmalsausprägungen der Strukturtypen 3.1.3 Herleitung der Strukturtypen 3.2 Gebäudevertreter 3.2.1 Systematik der Gebäudevertreter 3.2.2 Merkmale und Merkmalsausprägungen der Gebäudevertreter 3.2.3 Herleitung der Gebäudevertreter 4 Energiebedarfsmodell 4.1 Heizwärmebedarf privater Haushalte 4.1.1 Verfahren zur Berechnung des Heizwärmebedarfs auf städtischer bzw. teilstädtischer Ebene 4.1.2 Energiekennwerte 4.1.3 Modelle zur Modellierung von Heizlastprofilen 4.1.4 Berechnung des Heizwärmebedarfs und Modellierung der Wärmelast im Modell 4.2 Energieaufwand für die Bereitstellung von Warmwasser in privaten Haushalten 4.2.1 Einflussfaktoren des häuslichen Warmwasserkonsums und dem damit verbundenen Energieverbrauch 4.2.2 Methode zur Berechnung des Energiebedarfs für Warmwasser 4.2.3 Lastprofil des Warmwasserbedarfs 4.2.4 Berechnung und Modellierung des Warmwasserbedarfs im Modell 4.3 Stromverbrauch privater Haushalte 4.3.1 Einflussfaktoren 4.3.2 Methoden zur Ermittlung des Strombedarfs privater Haushalte 4.3.3 Ansätze und Konzepte zur Modellierung von Lastprofilen privater Haushalte 4.3.4 Berechnung und Modellierung des Stromverbrauchs privater Haushalte im Modell 5 Potenziale regenerativer Energien und Energiespeicher 5.1 Aktive Nutzung direkter solarer Strahlungsenergie 5.1.1 Berechnung der solaren Strahlungsenergie 5.1.2 Energetische Nutzung direkter Solarstrahlung mittels Photovoltaik 5.1.3 Energetische Wärmenutzung direkter Solarstrahlung 5.1.4 Flächen für eine aktive solare Strom- und Wärmebereitstellung 5.1.5 Berechnung des PV- und Solarthermiepotenzial im Modell 5.2 Energetische Nutzung organischer Abfälle aus dem Siedlungsbereich 5.2.1 Aufkommen organischer Siedlungsabfälle privater Haushalte 5.2.2 Berechnung des Aufkommens organischer Siedlungsabfälle im Modell 5.2.3 Energiebereitstellung aus organischen Siedlungsabfällen privater Haushalte 5.2.4 Berechnung des energetischen Potenzials im Modell 5.3 Energiespeicher 5.3.1 Stromspeicher 5.3.2 Wärmespeicher 5.3.3 Integration der Speicher im Modell 6 Anwendung des Verfahrens an einem Fallbeispiel 6.1 Das Fallbeispiel 6.1.1 Bestand und Struktur der Wohngebäude 6.1.2 Einwohner- und Haushaltsdaten 6.1.3 Eingangsdaten und Annahmen zu den Strukturtypen 6.1.4 Eingangsdaten regenerativer Energien 6.1.5 Ergebnisse des Siedlungsmodells 6.2 Energiebedarf im Fallbeispiel 6.2.1 Gesamtbilanz 6.2.2 Warmwasserbedarf 6.2.3 Strombedarf 6.2.4 Heizenergie 6.3 Potenzial regenerativer Energien im Fallbeispiel 6.3.1 Biomasse 6.3.2 Solarenergie (Photovoltaik) 6.3.3 Solarenergie (Solarthermie) 6.4 Bilanzierung der stündlichen Last und der Energiebereitstellung 6.4.1 Winter 6.4.2 Übergang 6.4.3 Sommer 6.5 Sensitivitätsanalysen 6.5.1 Extremer Winter und extremer Sommer 6.5.2 Bauliche Dichte 6.5.3 Thermische Verbesserung von Wohngebäuden 6.5.4 Speicherdimension 6.5.5 Zusammenfassung der Sensitivitätsanalysen 7 Auswertung und Interpretation 7.1 Beitrag regenerativer Energien zur Energieversorgung 7.2 Das Verfahren – Umsetzung, Grenzen und Übertragbarkeit 7.3 Zukünftige Aufgaben und weitere Forschungsfelder 8 Literatur 9 Anhang 9.1 Anhang Kapitel „Lastprofile privater Haushalte“ 9.2 Anhang Kapitel 7. - „Datengrundlage Fallbeispiel“

Increasing concern about global climate change has led to a growing interest in energy usage and water consumption. It is well known that changes in consumption habits lead to more efficient use of energy and water sources. Nowadays, globalization, environmental concerns, and the shortage of resources have led to an increase of stakeholder pressure on companies to expand their focus to sustainability. Also, the high impact that the savings can have in the financial status of the company. It is encouraging the headboards to study and improve the ways water and energy are being used within the processes. Significant economic savings and benefits for the environment could be achieved with slight changes in the company. As an overview, this project starts with the extraction of data from a platform for energy management in an industrial company. Then, it goes through the understanding of the energy and water usage data set. Later, a methodology to handle and process the data will be set. It is intending to extract relevant information using clustering. The idea is to compare the usage profiles between different factories, using key performance indicators and reducing the initial data set. Once the benchmarking is performed, some critical parameters will be selected to support the decision-making process related to investments to reduce the energy usage and water consumption in a specific location. Finally, the case of study will be implemented with the measurements from Alfa Laval. We will study how, from daily measurements with a very low investment and using the proper algorithms and methodologies, the main behaviours and features in an industrial location can be extracted from the utilization data. These characteristics can be used to develop strategies or productions schemes based on the interests of the energy manager and the company.