Optimisation of residential battery integrated photovoltaics system: analyses and new machine learning methods

Modelling and optimisation of battery integrated photovoltaics (PV) systems require a certain amount of high-quality input PV and load data. Despite the recent rollouts of smart meters, the amount of accessible proprietary load and PV data is still limited. This thesis addresses this data shortage issue by performing data analyses and proposing novel data extrapolation, interpolation, and synthesis models. First, a sensitivity analysis is conducted to investigate the impacts of applying PV and load data with various temporal resolutions in PV-battery optimisation models. The explored data granularities range from 5-second to hourly, and the analysis indicates 5-minute to be the most suitable for the proprietary data, achieving a good balance between accuracy and computational cost. A data extrapolation model is then proposed using net meter data clustering, which can extrapolate a month of 5-minute net/gross meter data to a year of data. This thesis also develops two generative adversarial networks (GANs) based models: a deep convolutional generative adversarial network (DCGAN) model which can generate PV and load power from random noises; a super resolution generative adversarial network (SRGAN) model which synthetically interpolates 5-minute load and PV power data from 30-minute/hourly data. All the developed approaches have been validated using a large amount of real-time residential PV and load data and a battery size optimisation model as the end-use application of the extrapolated, interpolated, and synthetic datasets. The results indicate that these models lead to optimisation results with a satisfactory level of accuracy, and at the same time, outperform other comparative approaches. These newly proposed approaches can potentially assist researchers, end-users, installers and utilities with their battery sizing and scheduling optimisation analyses, with no/minimal requirements on the granularity and amount of the available input data

Sistema inteligente para gestão de energia em edifícios com renováveis e carregamento de veículo para rede

Renewable energies have recently seen a strong development. The awareness of the masses regarding the pollution due to fossil fuels is rising and with it, the use of electric vehicles (EVs). Hence, there is an increasing effort to keep energy distribution sustainable and to find ways of reducing its price. The aim of this study is to build a decision algorithm that will help minimize the electrical bill of a household, making use of V2H (Vehicle-to- Home) chargers. In this approach EVs can be used to store energy, which can then be supplied to the household during periods of high demand. One of the inputs that the designed algorithm requires is the household’s energy consumption forecast. Therefore, a energy consumption predictor was developed in this work altogether with a version that does not require past information of the specific household. This predictor is useful while there is not enough past data to train a more reliable model. The decision algorithm was tested in a simulated environment against a baseline decision algorithm. In the several scenarios and test houses, the proposed approach attained an average of 19.29% decrease in the energy expenses of the household.Nos últimos anos, as energias renováveis têm sido alvo de um forte desenvolvimento. A conscientização sobre a poluição por combustível fósseis tem vindo a aumentar e, com isso, o uso de veículos elétricos (EVs). Neste sentido, tem havido um esforço para manter a distribuição de energia sustentável e encontrar formas de reduzir o seu preço. O objetivo deste estudo é construir um algoritmo de decisão que ajude a minimizar os custos de energia elétrica de uma residência, fazendo uso de carregadores V2H (Vehicleto- Home). Assim, os EVs podem ser usados como uma forma de armazenar energia que pode ser fornecida de volta à casa durante os períodos de maior necessidade. Uma das informações que o algoritmo proposto requer é a previsão do consumo energético da casa. Portanto, um modelo de previsão de consumo de energia doméstica foi também desenvolvido neste trabalho, incluindo uma versão que não requer informação histórica. Este modelo é útil enquanto não há informação histórica suficiente para treinar um modelo mais confiável. O algoritmo de decisão foi testado num ambiente simulado e comparado com um algoritmo de decisão base. Nos vários cenários e casas testadas, a abordagem proposta obteve uma redução média de 19.29% nas despesas energéticas da casa.Mestrado em Engenharia Informátic

Residential Demand Response using Electricity Smart Meter Data

The electricity industry is currently undergoing changes in a transitioning period characterised by Energy 3D: Digitalisation, Decentralisation, and Decarbonisation. Smart meters are the vital infrastructure necessary to digitalise the energy system as well as enable advancements in decentralisation and decarbonisation. As of today, more than 500 million smart meters have been installed worldwide, with that number expected to rise to several billion installations over the decade. Smart meters enable electricity load to be measured with half-hourly granularity, providing an opportunity for demand-side management innovations that are likely to be advantageous for both utility companies and customers. Among these innovations, time-of- use (TOU) tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector, however actual use is still limited. The objective of this thesis is to investigate opportunities and problems related to TOU tariffs utilising smart meter data at the national level. The authors have identified four major research gaps which need to be filled in order to expand commercial applications of TOU tariffs. These gaps are the described and addressed in the following chapters: the "TOU load adaptation forecasting problem", the "TOU winner detection problem", the "TOU public dataset problem", and the "excess generation forecasting problem". This thesis demonstrates three modelling approaches and one new TOU dataset (CAMSL). A significant contribution to the field is through the discover of new summary statistical features (statistical moments) and assesses the capacity of these to encapsulate other more widely used explanatory variables of demand response. The thesis is concluded by discussing future works and policy implications, such as the necessity of the more tailored modelling works and public live-stream of smart meter data, which could accelerate the roll-out of the demand side management at the residential sector.EPC

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Deep learning for time series forecasting: The electric load case

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non-linear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different-also traditional-architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short-term forecast (one-day-ahead prediction). Specifically, the focus is on feedforward and recurrent neural networks, sequence-to-sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one