MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri

Customized normalization clustering meth-odology for consumers with heterogeneous characteristics

The increasing use and development of renewable energy sources and distributed generation, brought several changes to the power system operation. Electricity markets worldwide are complex and dynamic environments with very particular characteristics, resulting from their restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players` benefits. This paper proposes a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, applied to load values, submitted to different types of normalization process, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data sub-groups that brings the most added value for the decision making process is found, according to the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 30 bus, including residential and commercial consumers, photovoltaic generation and storage unit

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

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

Load Forecasting and Synthetic Data Generation for Smart Home Energy Management System

A number of recent trends, such as the increased power consumption in developed and developing countries, the dangers associated with greenhouse gases, the potential shortages of fossil fuels, and the increasing availability of solar and wind energy act as motivating factors for the development of more intelligent and efficient systems both on the power provider as well as the consumer side. One of the most important prerequisites for making efficient energy management decisions is the ability to predict energy production and consumption patterns. While long-term forecasting of average consumption had been extensively used to direct investments in the energy grid, short-term predictions of energy consumption became practical only recently. Most of the existing work in this domain operates at the level of individual households. However, the availability of historical power consumption data can be an issue due to concerns such as privacy, data size or data quality. Researchers have been provided with synthetic smart home energy management systems that mimic the statistical and functional properties of the actual smart grid in order to improve their access to public system models. Through developing time series to represent different operating conditions of these synthetic systems, the potential of artificial smart home energy management system applications will be further enhanced. The work described in this dissertation extends the ability to predict and control power consumption to the level of individual devices in the home. This work is made possible by several recent developments. Internet of things technologies that connect individual devices to the internet allows the remote tracking of energy consumption and the remote control and scheduling of the devices. At the same time, progress in artificial intelligence and machine learning techniques improve the accuracy of predictions. These components often form the basis of smart home energy management systems (HEMS). One of our insights that facilitates the prediction of the energy consumption of individual devices is that the history of consumption contains important information about future consumption. Thus, we propose to use a long short-term memory (LSTM) recurrent neural network for prediction. In a second contribution, we extend this model into a sequence-to-sequence model which uses several interconnected LSTM cells on both the input and the output sides. We show that these approaches produce better predictions compared to memoryless machine learning techniques. The prediction of energy consumption delivers maximum value when it is integrated with the active component of a HEMS. We design a reinforcement learning-based technique where a Q-learning model is trained offline based on the prediction results. This system is then validated only using real data from PV power generation and load consumption. Considering the scarcity of data among the smart grid users, in our third contribution, we propose the Variational Autoencoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model capable of learning various types of data distributions, such as electrical load consumption, PV power production and electric vehicles charging load consumption, and generating plausible sample data from the same distribution without first performing any pre-training analysis on the data. Our extensive experiments have shown the accuracy of our approach in synthesizing smart home datasets. There is a high degree of resemblance between the distribution of VAE-GAN synthetic data and the distribution of real data. The next step will be to incorporate Q-learning for offline optimization of HEMS using synthetic data and to test its performance with real test data

K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

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