Orientador: Carlos Roberto Minussi / Resumo: Esta pesquisa será dedicada ao desenvolvimento de uma metodologia com vistas à compreensão e ao exame do comportamento do hábito de consumo de eletricidade residencial, via análise de similaridade, baseado no uso de uma rede neural da família ART (Adaptive Resonance Theory). Trata-se de uma rede neural composta por dois módulos ART-Fuzzy, cujo treinamento é realizado de modo não supervisionado. No primeiro módulo, serão usadas, como entrada, as informações que caracterizam os hábitos de consumo e a situação socioeconômica. A saída do primeiro módulo junto com os dados referentes aos equipamentos eletroeletrônicos da residência compõem a entrada do segundo módulo que, finalmente, produz informações, na saída, relativas ao diagnóstico pretendido, ou seja, a formação de agrupamentos similares (clusters). Todo o processamento da rede neural modular é realizado com dados binários, os quais são gerados a partir de informações quantitativas e qualitativas. As redes neurais da família ART são estáveis e plásticas. A estabilidade refere-se à garantia de sempre produzir soluções, ou seja, não se observa problemas relativos à má convergência. A plasticidade é uma característica que possibilita a execução do treinamento de forma contínua sem destruir o conhecimento adquirido previamente. É um recurso pouco observado nas demais redes neurais disponíveis na literatura especializada. Com essas propriedades (estabilidade e plasticidade), combinada com o processamento de dados essencialmente ... (Resumo completo, clicar acesso eletrônico abaixo) / Doutor

本研究主要應用基於總體經驗模態分解法(EEMD)之倒傳遞類神經網路(BPNN)預測兩種不同的非線性時間序列數據，包括政大逐時用電量以及逐日歷史黃金價格。透過EEMD，這兩種資料會分別被拆解為數條具有不同物理意義的本徵模態函數(IMF)，而這讓我們可以將這些IMF視為各種影響資料的重要因子，並且可將拆解過後的IMF放入倒傳遞類神經網路中做訓練。 另外在本文中，我們也採用移動視窗法作為預測過程中的策略，另外也應用內插法和外插法於逐時用電量的預測。內插法主要是用於補點以及讓我們的數據變平滑，外插法則可以在某個範圍內準確預測後續的趨勢，此兩種方法皆對提升預測準確度占有重要的影響。 利用本文的方法，可在預測的結果上得到不錯的準確性，但為了進一步提升精確度，我們利用多次預測的結果加總平均，然後和只做一次預測的結果比較，結果發現多次加總平均後的精確度的確大幅提升，這是因為倒傳遞類神經網路訓練過程中其目標為尋找最小誤差函數的關係所致。 / In this paper, we applied the Ensemble Empirical Mode Decomposition (EEMD) based Back-propagation Neural Network (BPNN) learning paradigm to two different topics for forecasting: the hourly electricity consumption in NCCU and the historical daily gold price. The two data series are both non-linear and non-stationary. By applying EEMD, they were decomposed into a finite, small number of meaningful Intrinsic Mode Functions (IMFs). Depending on the physical meaning of IMFs, they can be regarded as important variables which are input into BPNN for training. We also use moving-window method in the prediction process. In addition, cubic spline interpolation as well as extrapolation as our strategy is applied to electricity consumption forecasting, these two methods are used for smoothing the data and finding local trend to improve accuracy of results. The prediction results using our methods and strategy resulted in good accuracy. However, for further accuracy, we used the ensemble average method, and compared the results with the data produced without applying the ensemble average method. By using the ensemble average, the outcome was more precise with a smaller error, it results from the procedure of finding minimum error function in the BPNN training.

Submitted by Cristiane Oliveira (cristiane.oliveira@fgv.br) on 2011-05-24T13:38:06Z No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) / Approved for entry into archive by Gisele Isaura Hannickel(gisele.hannickel@fgv.br) on 2011-05-24T13:40:59Z (GMT) No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) / Approved for entry into archive by Gisele Isaura Hannickel(gisele.hannickel@fgv.br) on 2011-05-24T13:43:37Z (GMT) No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) / Made available in DSpace on 2011-05-24T13:47:00Z (GMT). No. of bitstreams: 1 71060100728.pdf: 12246419 bytes, checksum: cf8249056976b4597f65472aa3e65d6a (MD5) Previous issue date: 2010-04-29 / In order to evaluate the use of Electricity Consumption as a Socioeconomic Status, this research analyzes information in two levels of geographical aggregation. At the first level, under a territorial perspective, it investigates indicators of Income and Electric Energy Consumption aggregated by weighted areas (set of census sectors) in the city of São Paulo and uses the microdata of Demographic Census 2000 jointly with residential consumers’ database of AES Eletropaulo. It applies Spatial Auto-Regressive (SAR) models, Geographically Weighted Regression (GWR), and an unprecedented combined model (GWR+SAR), developed in this study. Several neighborhood matrices were used to assess the influence of space (with Downtown-Suburbs pattern) of the variables under study. The variables showed strong spatial autocorrelation (Moran's I greater than 58% for the Energy Consumption and more than 75% for the Household Income). Relations between Income and Electricity Consumption were very strong (coefficients of determination of Income reached values from 0.93 to 0.98). At the second level, the household one, it uses data collected in the Annual Satisfaction Survey of Residential Customer, coordinated by the Brazilian Electricity Distributors Association (ABRADEE) for the years 2004, 2006, 2007, 2008 and 2009. Weighted Linear Model (WLM), GWR and SAR were applied to survey data with interviews allocated on the centroid and the seat of the districts. For the year 2009, we obtained the actual locations of the households interviewed. Additionally, 6 algorithms of points distribution within the polygons of the districts have been developed. The results from models based on centroids and seats obtained a coefficient of determination R 2 of around 0.45 for the GWR technique, while the models based on scattering points within the polygons of the districts have reduced this account to about 0.40. These results suggest that the algorithms of allocation of points in polygons allow the observation of a more realistic association between the constructs analyzed. The combined use of the findings shows that the billing information of the electricity distributors has great potential to support strategic decisions. Because they are current, available and monthly updated, socioeconomic indicators based on energy consumption can be very useful as an aid to processes of classification, concentration and estimation of household income. / Com o objetivo de avaliar o uso do consumo de energia elétrica como indicador socioeconômico, esta pesquisa analisa informações em dois níveis de agregação geográfica. No primeiro, sob perspectiva territorial, investiga indicadores de Renda e Consumo de Energia Elétrica agregados por áreas de ponderação (conjunto de setores censitários) do município de São Paulo e utiliza os microdados do Censo Demográfico 2000 em conjunto com a base de domicílios da AES Eletropaulo. Aplica modelos de Spatial Auto-Regression (SAR), Geographically Weighted Regression (GWR), e um modelo inédito combinado (GWR+SAR), desenvolvido neste estudo. Diversas matrizes de vizinhança foram utilizadas na avaliação da influência espacial (com padrão Centro-Periferia) das variáveis em estudo. As variáveis mostraram forte auto-correlação espacial (I de Moran superior a 58% para o Consumo de Energia Elétrica e superior a 75% para a Renda Domiciliar). As relações entre Renda e Consumo de Energia Elétrica mostraram-se muito fortes (os coeficientes de explicação da Renda atingiram valores de 0,93 a 0,98). No segundo nível, domiciliar, utiliza dados coletados na Pesquisa Anual de Satisfação do Cliente Residencial, coordenada pela Associação Brasileira dos Distribuidores de Energia Elétrica (ABRADEE), para os anos de 2004, 2006, 2007, 2008 e 2009. Foram aplicados os modelos Weighted Linear Model (WLM), GWR e SAR para os dados das pesquisas com as entrevistas alocadas no centróide e na sede dos distritos. Para o ano de 2009, foram obtidas as localizações reais dos domicílios entrevistados. Adicionalmente, foram desenvolvidos 6 algoritmos de distribuição de pontos no interior dos polígonos dos distritos. Os resultados dos modelos baseados em centróides e sedes obtiveram um coeficiente de determinação R2 em torno de 0,45 para a técnica GWR, enquanto os modelos baseados no espalhamento de pontos no interior dos polígonos dos distritos reduziram essa explicação para cerca de 0,40. Esses resultados sugerem que os algoritmos de alocação de pontos em polígonos permitem a observação de uma associação mais realística entre os construtos analisados. O uso combinado dos achados demonstra que as informações de faturamento das distribuidoras de energia elétrica têm grande potencial para apoiar decisões estratégicas. Por serem atuais, disponíveis e de atualização mensal, os indicadores socioeconômicos baseados em consumo de energia elétrica podem ser de grande utilidade como subsídio a processos de classificação, concentração e previsão da renda domiciliar.

Submitted by Daniela Sbizera Justo null (sbizera@yahoo.com) on 2016-09-20T14:14:51Z No. of bitstreams: 1 Tese-Daniela Sbizera Justo.pdf: 5782774 bytes, checksum: 483d11758263a9d6c3a3d4c89fe66919 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-09-22T19:44:56Z (GMT) No. of bitstreams: 1 justo_ds_dr_ilha.pdf: 5782774 bytes, checksum: 483d11758263a9d6c3a3d4c89fe66919 (MD5) / Made available in DSpace on 2016-09-22T19:44:56Z (GMT). No. of bitstreams: 1 justo_ds_dr_ilha.pdf: 5782774 bytes, checksum: 483d11758263a9d6c3a3d4c89fe66919 (MD5) Previous issue date: 2016-08-25 / Esta pesquisa será dedicada ao desenvolvimento de uma metodologia com vistas à compreensão e ao exame do comportamento do hábito de consumo de eletricidade residencial, via análise de similaridade, baseado no uso de uma rede neural da família ART (Adaptive Resonance Theory). Trata-se de uma rede neural composta por dois módulos ART-Fuzzy, cujo treinamento é realizado de modo não supervisionado. No primeiro módulo, serão usadas, como entrada, as informações que caracterizam os hábitos de consumo e a situação socioeconômica. A saída do primeiro módulo junto com os dados referentes aos equipamentos eletroeletrônicos da residência compõem a entrada do segundo módulo que, finalmente, produz informações, na saída, relativas ao diagnóstico pretendido, ou seja, a formação de agrupamentos similares (clusters). Todo o processamento da rede neural modular é realizado com dados binários, os quais são gerados a partir de informações quantitativas e qualitativas. As redes neurais da família ART são estáveis e plásticas. A estabilidade refere-se à garantia de sempre produzir soluções, ou seja, não se observa problemas relativos à má convergência. A plasticidade é uma característica que possibilita a execução do treinamento de forma contínua sem destruir o conhecimento adquirido previamente. É um recurso pouco observado nas demais redes neurais disponíveis na literatura especializada. Com essas propriedades (estabilidade e plasticidade), combinada com o processamento de dados essencialmente binários, confere ao sistema neural uma ampla capacidade de produzir objetivos que podem ser facilmente modificados visando atender requisitos preestabelecidos pelos usuários (consumidor, empresa do setor elétrico). Neste sentido, o resultado esperado é a obtenção de informações referentes à similaridade de consumidores, à qual pode-se vislumbrar alguns benefícios, por parte dos consumidores, como melhorar o hábito de consumir energia elétrica, oferecendo também, por meio do conhecimento dos consumidores similares, a obtenção de melhores estratégias de negociação com os fornecedores, principalmente, no caso de sistemas smart grids. Neste novo paradigma do setor elétrico, há uma forte tendência do(s) consumidor(es) escolher(em) livremente a empresas fornecedoras de energia elétrica. Além disso, é discutida uma melhor forma para a realização da previsão de carga em pontos da rede elétrica onde há uma maior incerteza, e.g., nos barramentos mais próximos do consumidor (transformadores etc.), i.e., as incertezas no contexto da previsão de carga total do sistema são aumentadas à medida que se adentra a partir da carga global até chegar ao consumidor final, em especial ao usuário residencial. A base de dados, para a fase de treinamento da rede neural, é construída a partir de informações disponibilizadas por consumidores voluntários via o preenchimento de formulário. Realizada a fase de treinamento, a rede neural adquire um conhecimento incipiente afeito de ser aperfeiçoado ao longo do tempo, quando se implementa o recurso da plasticidade. / This work develops a methodology to understand and analyze the behavior of residential electricity consumption by similarity analysis, based on a neural network of ART (Adaptive Resonance Theory) family. The neural network is composed of two Fuzzy-ART modules whose training are non-supervised. At the first module, the inputs are information that characterize the consumption habits and the socio-economic situation. The output of the first module with the data referred to electro-electronic equipment available at the residence compose the input of the second module, which finally produces information at the output related to the diagnosis proposed, i.e. the formation of clusters. All the neural network processing is realized with binary data, which are generated from quantitative and qualitative information. ART family neural networks are stable and plastic. The stability assures that it always produces a solution, i.e. there is no convergence problem. The plasticity is a characteristic that allows executing the processing continuously without losing the knowledge previously learned. Those advantages are seldom observed in other neural networks available at the specialized literature. Considering these properties (stability and plasticity), combined with the data processing exclusively binary, the neural network is capable to be modified when necessary to attend pre-defined requests by the users (consumers, distributers, etc.). Therefore, the expected result is to obtain information referred to the similarity with consumers, and with this information, the consumers can improve their habits or even negotiating with the producers in case of smart grid systems. This new electrical system paradigm, the tendency is that the consumers can arbitrarily choose the electrical distributers. Furthermore, the work discusses the best way to realize load forecasting in points where there is uncertainty, e.g., on the busses near the consumers (transformers), i.e., the uncertainties considering the global forecasting increase if the information of residences is not considered. The database for the training phase of the neural network was built by a quiz form filled by some volunteer consumers. Afterwards, when finishing the training phase, the neural network acquires knowledge that along time can implement the plasticity resource.

Abstract Achieving the objective of a decarbonised economy by 2050 will require massive efforts in the energy sector. Emissions from residential houses will have to be almost completely cut, by around 90% by 2050. Home automation is a potential tool for achieving this goal. However, the environmental and economic benefits of automation technologies first need to be assessed. This thesis evaluates the impact of home automation for electricity management in the residential sector using environmental and economic indicators. To this end, a life cycle assessment was performed to evaluate the impacts of the manufacturing, use and disposal phases. The influences of end-user behaviour, household size and multiple levels of technological deployment were also investigated. A Markov chain simulation tool, built on the MatLab platform, was developed to assess all possible combinations of impacting factors. Dynamic environmental indicators were developed based on the ReCiPe method for aggregating the impacts of processes. All these indicators were then combined to form a single index based on multi-criteria acceptability analysis. The results suggest that home automation can decrease peak load, but that overall electricity consumption may increase due to electricity use by the actual automation system. The effect of home automation was more noticeable in larger households than in one-person households. In addition, use of dynamic environmental indicators proved more relevant than fixed indicators to represent the environmental impact of home automation. Within the life cycle of automation technology, the manufacturing phase had the highest impact, but most of the CO2 emissions originated from the use phase. In conclusion, the most important environmental benefit of home automation is reducing CO2 emissions during peak time by load shifting. / Tiivistelmä Vähähiilisen talouden saavuttaminen vuoteen 2050 mennessä edellyttää valtavia ponnisteluja energia-alalla. Rakennuksista aiheutuvia päästöjä on vähennettävä radikaalisti, jopa 90 % vuoteen 2050 mennessä. Rakennusten energiatehokkuutta edistävä automaatiotekniikka on yksi keino tämän päämäärän saavuttamiseen. Kotiautomaation kautta voidaan sekä vähentää energian kokonaiskulutusta että tasoittaa energiankäyttöprofiilia. On kuitenkin tutkittava myös, mitkä ovat automaatiotekniikan ympäristö- ja taloudelliset vaikutukset. Tässä työssä käsitellään kotiautomaation vaikutusta sähkön kulutuksen hallintaan asuinrakennuksissa käyttämällä ympäristö- ja talousindikaattoreita. Tätä varten suoritettiin kotiautomaation elinkaariarviointi selvittämällä laitteiden valmistus-, käyttö- ja hävittämisvaiheiden ympäristövaikutukset. Työssä tarkasteltiin myös asukkaiden käyttäytymisen, kotitalouden koon ja eri teknologiavaihtoehtojen vaikutuksia ympäristö- ja talousvaikutuksiin. Arviointi suoritettiin Markovin ketjun simulointityökalulla, joka rakennettiin Matlab-alustalle. Dynaamisia ympäristömittareita kehitettiin ReCiPe-menetelmää käyttäen. Indikaattorit on edelleen yhdistetty yhdeksi indeksiksi käyttäen monikriteeriarviointia. Tulokset viittaavat siihen, että huippukuormitusta voidaan vähentää käyttämällä kotiautomaatiota, mutta sähkön kokonaiskulutus voi kasvaa automaatiojärjestelmän sähkönkulutuksen takia. Kotiautomaation vaikutukset ovat eniten havaittavissa suurissa kotitalouksissa. Lisäksi, dynaamiset indikaattorit edustavat paremmin kotiautomaation vaikutusta ympäristöön kuin staattiset indikaattorit. Automaatioteknologian elinkaaressa suurimmat ympäristövaikutukset ovat valmistusvaiheessa, mutta CO2-päästöjä syntyy eniten käyttövaiheessa. Lopuksi voidaan todeta, että kotiautomaation merkittävin ympäristöhyöty on CO2-päästöjen vähentäminen huippukulutuksen aikana siirtämällä kuormitusta toiseen ajankohtaan.

This pilot study consists of two parts. The first part investigates the extent to which the domestic electricity consumers intend to use and use energy efficiently using the Theory of Planned Behaviour. The second part investigates the extent to which the Energy @ Home educational intervention changed the domestic electricity consumers’ behaviour. For the first part of the study an advertisement was published and a convenience stratified sample of 61 domestic electricity consumers were selected from the 290 respondents. Data was collected from the domestic electricity consumers via a questionnaire and a telephone response log. The co-relational research design was used to investigate the relationship between the predictor variables the independent variables in the constructs of the Theory of Planned Behaviour. Simple linear regression analysis resulted in F statistic for the predicted behavioural intention was 29.74 with a p value less than 0.0001 which indicates significant statistical evidence of a linear relation between the predictor variables and the independent variables. The r2 of 0.87 implies that data points that fall closely along the best fit line. Therefore the predictor variables were good predictors of the response variable. All the participants that intended to use electricity efficiently confirmed via the telephone that they were using electricity efficiently. In the second part of the study 11 out of the 61 participants were chosen to participate in the Energy @ Home educational intervention and television program. Data was collected via the Energy audit log and the electricity consumption log. The participants intended to save between 2% and 35% of their electricity consumption and the actual electricity consumption savings were between 2% and 30%. / Science and Technology Education / M. Sc. (Mathematics, Physics & Technology Education (Physics Education))

Tesis por compendio / [ES] La crisis climática hace necesaria una transición energética para reducir el consumo de energía primaria y sustituir los combustibles fósiles por el uso de energías renovables mientras se electrificacan consumos energéticos. Esta transición energética está cambiando el paradigma del sector eléctrico, que pasa de ser un sistema con generación centralizada, demanda pasiva y casi nula capacidad de almacenamiento, a un sistema que tendrá que adaptarse a la variabilidad en el aprovechamiento de los flujos energéticos renovables. El desarrollo de nuevas tecnologías y las mayores necesidades de flexibilidad que aparecen implican un nuevo contexto de generación descentralizada, demanda activa y el desarrollo del almacenamiento como herramienta imprescindible de sistema. Y en esta nueva situación, los agentes consumidores adquiere un rol central en los sistemas eléctricos presentes y futuros. Esta tesis doctoral tiene como objetivo analizar las políticas públicas y la regulación que fomentarán la transición energética dentro del sistema eléctrico donde el consumidor jugará un papel imprescindible y se situará en el centro de mismo. La tesis emplea una serie de metodologías y herramientas transversales para abordar y analizar las tres etapas del proceso de las políticas públicas: formulación, diseño y evaluación. El cuerpo principal de la tesis contiene cuatro aportaciones recogidas en tres bloques. El primer bloque, formulación, responde a los dos primeros objetivos específicos que se centran en analizar programas de respuesta de la demanda a nivel internacional de forma estandarizada y cuantificar el potencial de flexibilidad en el sector residencial. El segundo, diseño, responde al tercer objetivo específico que se centra analizar los impactos que la regulación de fomento del autoconsumo tendría sobre el sistema eléctrico. Y el tercero, evaluación, responde al cuarto objetivo específico de extraer buenas prácticas y mejoras sobre una política pública ya implementada que utiliza la demanda industrial para proporcionar servicios complementarios al sistema. Cada una de las aportaciones contiene casos de estudio y políticas reales que han sido aplicadas o están siendo estudiadas. De esta forma la tesis aborda la problemática regulatoria actual centrada en fomentar un nuevo marco normativo para una transición energética donde la demanda eléctrica incrementa su papel en el sistema. Las cuatro aportaciones presentadas en esta tesis demuestran la necesidad de continuar avanzando en la formulación, diseño y evaluación de políticas con el objetivo de situar a los consumidores en el centro del sistema eléctrico. Mediante una combinación de técnicas y metodologías se muestran diversas formas de abordar la problemática y así mejorar las políticas públicas encaminadas a fomentar la transición energética del sistema energético actual a un sistema renovable y con una mayor participación de la ciudadanía y los agentes consumidores de electricidad. / [CA] La crisi climàtica fa necessària una transició energètica per a reduir el consum d'energia primària i substituir els combustibles fòssils per l'ús d'energies renovables mentre s-electrifiquen els consums energètics. Aquesta transició energètica està canviant el paradigma del sector elèctric, que passa de ser un sistema amb generació centralitzada, demanda passiva i quasi nul·la capacitat d'emmagatzematge, a un sistema que haurà d'adaptar-se a la variabilitat en l'aprofitament dels fluxos energètics renovables. El desenvolupament de noves tecnologies i les majors necessitats de flexibilitat que sorgeixen impliquen un nou context de generació descentralitzada, demanda activa i el desenvolupament de l'emmagatzematge com a eina imprescindible del sistema. I en aquesta nova situació, els agents consumidors adquireix un rol central en els sistemes elèctrics presents i futurs. Aquesta tesi doctoral té com a objectiu analitzar les polítiques públiques i la regulació que fomentaran la transició energètica dins del sistema elèctric on el consumidor jugarà un paper imprescindible i se situarà en el centre del mateix. La tesi empra una sèrie de metodologies i eines transversals per a abordar i analitzar les tres etapes del procés de les polítiques públiques: formulació, disseny i avaluació. El cos principal de la tesi conté quatre aportacions recollides en tres blocs. El primer bloc, formulació, respon als dos primers objectius específics que se centren a analitzar programes de resposta de la demanda elèctrica en l'àmbit internacional de forma estandarditzada i quantificar el potencial de flexibilitat en el sector residencial. El segon, disseny, respon al tercer objectiu específic que se centra analitzar els impactes que la regulació de foment de l'autoconsum tindria sobre el sistema elèctric. I el tercer, avaluació, respon al quart objectiu específic d'extraure bones pràctiques i millores sobre una política pública ja implementada que utilitza la demanda industrial per a proporcionar serveis complementaris al sistema. Cadascuna de les aportacions conté casos d'estudi i polítiques reals que han sigut aplicades o estan sent estudiades actualment. D'aquesta manera la tesi aborda la problemàtica reguladora actual centrada a fomentar un nou marc normatiu per a una transició energètica on la demanda elèctrica incrementa el seu paper en el sistema. Les quatre aportacions presentades en aquesta tesi demostren la necessitat de continuar avançant en la formulació, disseny i avaluació de polítiques amb l'objectiu de situar als consumidors en el centre del sistema elèctric. Mitjançant una combinació de tècniques i metodologies es mostren diverses maneres d'abordar la problemàtica i així millorar les polítiques públiques encaminades a fomentar la transició energètica del sistema energètic actual a un sistema renovable i amb una major participació de la ciutadania i els agents consumidors d'electricitat. / [EN] The climate crisis requires an energy transition to reduce primary energy consumption and replace fossil fuels with renewable energies while electrifying energy consumption. This energy transition is changing the paradigm of the electricity sector from a system with centralised generation, passive demand, and almost no storage capacity, to a system that will have to adapt to the variability in the use of renewable energy flows. The development of new technologies and the greater flexibility needs that arise from this change imply a new context of decentralized generation, active demand, and the development of storage as an essential system tool. And in this new situation, consumers acquire a central role in the present and future electricity systems. This doctoral thesis aims to analyse the public policies and regulations that will promote the energy transition within the electricity system where the consumer will play an essential role being at the center of it. The thesis employs a series of transdisciplinary methodologies and tools to address and analyse the three stages of the public policy process: formulation, design, and evaluation. The main body of the thesis contains four contributions organised in three blocks. The first block, formulation, responds to the first two specific objectives that focus on analysing demand response programs at the international level in a standardised way and quantifying the potential for flexibility in the residential sector. The second, design, responds to the third specific objective, which focuses on analysing the impacts that the regulation to promote self-consumption would have on the electricity system. And the third, evaluation, responds to the fourth specific objective of extracting good practices and improvements on an already implemented public policy that uses industrial demand to provide complementary services to the system. Each of the contributions contains case studies and real policies that have been implemented or are under study. In this way, the thesis addresses the current regulatory issues focused on promoting a new regulatory framework for an energy transition where electricity demand increases its role in the system. The four contributions presented in this thesis demonstrate the need to continue advancing in the formulation, design, and evaluation. Through a combination of techniques and methodologies, the document shows different ways of approaching the problem to improve public policies aimed at promoting the energy transition from the current energy system to a renewable system with greater participation of citizens and electricity consumers. / Ribó Pérez, DG. (2021). Study and Assessment of Energy Policies to Achieve Consumer Centered Power Systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/179514 / TESIS / Compendio

This thesis is a collaboration with Stockholm Globe Arena Fastigher AB (SGAF) and focuses on a case study involving the multi-functional arenas Avicii Arena and Annexet in Stockholm, Sweden. The objective of this study is to investigate Key Performance Indicators (KPI) that can sufficiently measure and evaluate monthly and yearly electricity, heating and cooling consumption while considering events and visitors. Data regarding visitor and event, electricity, heating and cooling were provided by companies in agreement with SGAF, which is handled in a Data Reduction. This study identified four different KPIs to evaluate energy consumption dynamics; KPI 1: Energy consumption per event day, KPI 2: Energy consumption per visitor, KPI 3: Load Factor and KPI 4: Occupancy rate. The results showed that the monthly KPI 1 and 2 values exhibited unpredictable fluctuations hindering its ability to assess energy consumption patterns. In contrast, the annual KPI 1 and 2 were able to effectively evaluate the energy consumption which uncovered that the electricity consumption in the venues is on a downward trend. However, the heating and cooling consumption remained stagnant during the same timeframe. KPI 3 and 4 displayed efficient operation of the energy systems. Moreover, all four KPIs revealed that the energy consumption is influenced by other factor beyond visitors and events. A subsequent Correlation Analysis unveiled two additional factors, outdoor temperature and event types, affects the energy consumption which suggests potential areas for future research. / Detta examensarbete ar ett samarbete med Stockholm Globe Arena Fastigheter AB (SGAF) och fokuserar på en fallstudie som involverar de multi-funktionella arenorna Avicii Arena och Annexet i Stockholm, Sverige. Målet med denna studie är att undersöka Nyckeltal som kan mäta och utvärdera månatlig och årlig elektricitetsförbrukning, värmeförbrukning och kylförbrukning med hänsyn till evenemang och besökare. Data avseende besökare och evenemang, elförbrukning, värmeförbrukning och kylförbrukning tillhandahölls av företag i samförstånd med SGAF som hanterades i en Data Reduktion. Denna studie identifierade fyra olika nyckeltal för utvärdering av energiförbrukningen; Nyckeltal 1: Energiförbrukning per evenemangsdag, Nyckeltal 2: Energiförbrukning per besökare, Nyckeltal 3: Belastningsfaktor och Nyckeltal 4: Beläggningsgrad. Resultaten visar att de månatliga nyckeltalen 1 och 2 uppvisade förutsägbara fluktuationer som hindrade dess förmåga att bedöma energiförbrukningsmönster. Den årliga nyckeltalen 1 och 2 kunde effektivt utvärdera energiförbrukningen vilket avslöjade att elförbrukningen i anläggningarna minskar. Dock, påvisade värmeförbrukningen och kylförbrukningen oförändrade under samma tidsperiod. Nyckeltal 3 och 4 uppvisade att energisystemen i anläggningarna körs på ett effektivt sätt. Vidare, visade samtliga fyra nyckeltal att energiförbrukningen påverkas av andra faktorer utöver besökare och evenemang. En efterföljande korrelationsanalys påvisar att två ytterligare faktorer, utomhus temperatur och evenemangstyper, påverkar energiförbrukningen vilket antyder nya potentiella forskningsområden.

This dissertation details regulation's impact in networked markets, notably in deregulated electricity and internet service markets. These markets represent basic infrastructure in the modern economy; their innate networked structures make for rich fields of economic research on regulatory impact. The first chapter models deregulated electricity industries with a focus on the Texas market. Optimal economic benchmarks are considered for markets with regulated delivery and interrelated network costs. Using a model of regulator, consumer, and firm interaction, I determine the efficiency of the current rate formalization compared to Ramsey-Boiteux prices and two-part tariffs. I find within Texas's market increases to generator surplus up to 55% of subsidies could be achieved under Ramsey-Boiteux pricing or two-part tariffs, respectively. The second chapter presents a framework to analyze dynamic processes and long-run outcomes in two-sided markets, specifically dynamic platform and firm investment incentives within the internet-service platform/content provision market. I use the Ericson-Pakes framework applied within a platform that chooses fees on either side of its two-sided market. This chapter determines the impact of network neutrality on platform investment incentives, specifically whether to improve the platform. I use a parameterized calibration from engineering reports and current ISP literature to determine welfare outcomes and industry behavior under network neutral and non-neutral regimes. My final chapter explores retail firm failure within the deregulated Texas retail electricity market. This chapter investigates determinants of retail electric firm failures using duration analysis frameworks. In particular, this chapter investigates the impact of these determinants on firms with extant experience versus unsophisticated entrants. Understanding these determinants is an important component in evaluating whether deregulation achieves the impetus of competitive electricity market restructuring. Knowing which economic events decrease a market's competitiveness helps regulators to effectively evaluate policy implementations. I find that experience does benefit a firm's duration, but generally that benefit assists firm duration in an adverse macroeconomic environment rather than in response to adverse market conditions such as higher wholesale prices or increased transmission congestion. Additionally, I find evidence that within the Texas market entering earlier results in a longer likelihood of duration. / text