Uncertainty Quantification And Economic Dispatch Models For The Power Grid

The modern power grid is constrained by several challenges, such as increased penetration of Distributed Energy Resources (DER), rising demand for Electric Vehicle (EV) integration, and the need to schedule resources in real-time accurately. To address the above challenges, this dissertation offers solutions through data-driven forecasting models, topology-aware economic dispatch models, and efficient optional power flow calculations for large scale grids. Particularly, in chapter 2, a novel microgrid decomposition scheme is proposed to divide the large scale power grids into smaller microgrids. Here, a two-stage Nearest-Generator Girvan-Newman (NGGN) algorithm, a graphicalclustering-based approach, followed by a distributed economic dispatch model, is deployed to yield a 12.64% cost savings. In chapter 3, a deep-learning-based scheduling scheme is intended for the EVs in a household community that uses forecasted demand, consumer preferences and Time-of-use (TOU) pricing scheme to reduce electricity costs for the consumers and peak shaving for the utilities. In chapter 4, a hybrid machine learning model using GLM with other methods was designed to forecast wind generation data. Finally, in chapter 5, multiple formulations for Alternating Current Optimal Power Flow (ACOPF) were designed for large scale grids in a high-performance computing environment. The ACOPF formulations, namely, power balance polar, power balance Cartesian, and current balance Cartesian, are tested on bus systems ranging from a 9-bus to 25,000. The current balance Cartesian formulation had an average of 23% faster computational time than two other formulations on a 25,000 bus system

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

User behaviour models to forecast electricity consumption of residential customers based on smart metering data

This paper presents a novel approach to forecast day-ahead electricity consumption for residential households where highly irregular human behaviour plays a significant role. The methodology requires data from fiscal smart meters, which makes it applicable to real scenarios where personal data gathering is not feasible. These data are rarely complete; therefore, a robust combination of machine-learning techniques is used to handle missing data and outliers. The novelty of this method relies on identifying and predicting user electricity consumption behaviour as a procedure to improve the forecasting of the overall electricity consumption of each individual customer. The methodology uses Gaussian mixture clustering to identify behaviour clusters and an eXtreme Gradient Boosting classification (XGBoost) model to predict the day-ahead behaviour pattern. This predicted user behaviour cluster is fed into an Artificial Neural Network (ANN) to enable an improved capturing of the highly unpredictable user conduct for the forecast of electricity consumption. A novel metric, namely the Euclidean Distance-based Accuracy (EDA), is finally proposed to enable a more thorough evaluation of time series classification algorithms. The whole development is tested over 500 residential users placed in a southeastern region of Spain. The results showed that, when the novel approach was used, the MAPEd and NRMSEd were reduced by 7% and 9% respectively, increasing to a 20% and 17% respective reduction for the best cases according to EDA. This methodology sets the basis for massive user-centred analyses, very profitable to any electricity company.This work was developed during the PhD thesis of F. Lazzari. D. Chemisana thanks ICREA for the ICREA Acadèmia. Dr. J. Cipriano thanks the Ministerio de Ciencia e Innnovación, Spain for the Juan de la Cierva Incorporación grant. This work was also supported by the Project PID2020-113614RB-C22, funded by MCIN/AEI/10.13039/501100011033, Spain, and by the European regional Fund, through the POCTEFA program and the project EKATE-EFA 312/19. All authors approved the version of the manuscript to be publishe

A novel feature set for low-voltage consumers, based on the temporal dependence of consumption and peak demands

This paper proposes a novel feature construction methodology aiming at both clustering yearly load profiles of low-voltage consumers, as well as investigating the stochastic nature of their peak demands. These load profiles describe the electricity consumption over a one-year period, allowing the study of seasonal dependence. The clustering of load curves has been extensively studied in literature, where clustering of daily or weekly load curves based on temporal features has received the most research attention. The proposed feature construction aims at generating a new set of variables that can be used in machine learning applications, stepping away from traditional, high dimensional, chronological feature sets. This paper presents a novel feature set based on two types of features: respectively the consumption time window on a daily and weekly basis, and the time of occurrence of peak demands. An analytic expression for the load duration curve is validated and leveraged in order to define the the region that has to be considered as peak demand region. The clustering results using the proposed set of features on a dataset of measured Flemish consumers at 15-min resolution are evaluated and interpreted, where special attention is given to the stochastic nature of the peak demands

Enhancing household-level load forecasts using daily load profile clustering

Forecasting the electricity demand for individual households is important for both consumers and utilities due to the increasing decentralized nature of the electricity system. Particularly, utilities often have very little information about their consumers except for aggregate building level loads, without knowledge of interior details about the household appliance sets or occupants. In this paper, we explore the possibility of enhancing the day-ahead load forecasts for hundreds of individual households by clustering their daily load profile history to obtain each consumer's specific typical consumption patterns. The clustering method is based on load profile shape using the Earth Mover's Distance metric to calculate similarity between load profiles. The forecasting methods then predict the next day shape from the empirical probability of previous cluster transitions in the consumer's load history and estimate the magnitude either by using historical load relationships with temperature and forecast temperatures or previous day consumption levels. The generated forecasts are compared to a benchmark Multiple Linear Regression (MLR) day-ahead forecast and persistence forecasts for all individuals. While at the aggregate level the MLR method represents a significant improvement over persistence forecasts, on an individual level we find that the best forecasting model is specific to the individual. In particular, we find that the MLR model produces lower errors when consumers have a consistent daily temperature response and the cluster model with previous day magnitude produces lower errors for consumers whose consumption changes abruptly in magnitude for several days at a time. Our work adds to the state of knowledge surrounding individual household load forecasting and demonstrates the potential for cluster-based methodologies to enhance short term load forecasts

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries