Feature-based time-series analysis

This work presents an introduction to feature-based time-series analysis. The time series as a data type is first described, along with an overview of the interdisciplinary time-series analysis literature. I then summarize the range of feature-based representations for time series that have been developed to aid interpretable insights into time-series structure. Particular emphasis is given to emerging research that facilitates wide comparison of feature-based representations that allow us to understand the properties of a time-series dataset that make it suited to a particular feature-based representation or analysis algorithm. The future of time-series analysis is likely to embrace approaches that exploit machine learning methods to partially automate human learning to aid understanding of the complex dynamical patterns in the time series we measure from the world.Comment: 28 pages, 9 figure

ASEAN grid flexibility: Preparedness for grid integration of renewable energy

In 2015, ASEAN established a goal of increasing its renewable energy share in its energy portfolio from approximately 13–23% by 2025. Renewable electricity, especially intermittent and variable sources, presents challenges for grid operators due to the uncertain timing and quantity of electricity supply. Grid flexibility, the electric grid's ability to respond to changing demands and supply, now stands a key resource in responding to these uncertainties while maximizing the cost-effective role of clean energy. We develop and apply a grid flexibility assessment tool to assess ASEAN's current grid flexibility using six quantitative indicators: grid reliability, electricity market access; load profile ramp capacity; quality of forecasting tools; proportion of electricity generation from natural gas; and renewable energy diversity. We find that ASEAN nations cluster into three groups: better; moderately; and the least prepared nations. We develop an analytical ramp rate calculator to quantify expected load ramps for ASEAN in an integrated ASEAN Power Grid scenario. The lack of forecasting systems and limited electricity market access represent key weaknesses and areas where dramatic improvements can become cost-effective means to increase regional grid flexibility. As ASEAN pursues renewable energy targets, regional cooperation remains essential to address identified challenges. Member nations need to increase grid flexibility capacity to adequately prepare for higher penetrations of renewable electricity and lower overall system costs

(Position Paper) Characterizing the Behavior of Small Producers in Smart Grids:A Data Sanity Analysis

Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid’s operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges.This work is financially supported by the Danish project RemoteGRID, which is a ForskEL program under Energinet.dk with grant agreement no. 2016-1-12399.Peer ReviewedPostprint (published version

A review of the enabling methodologies for knowledge discovery from smart grids data

The large-scale deployment of pervasive sensors and decentralized computing in modern smart grids is expected to exponentially increase the volume of data exchanged by power system applications. In this context, the research for scalable and flexible methodologies aimed at supporting rapid decisions in a data rich, but information limited environment represents a relevant issue to address. To this aim, this paper investigates the role of Knowledge Discovery from massive Datasets in smart grid computing, exploring its various application fields by considering the power system stakeholder available data and knowledge extraction needs. In particular, the aim of this paper is dual. In the first part, the authors summarize the most recent activities developed in this field by the Task Force on “Enabling Paradigms for High-Performance Computing in Wide Area Monitoring Protective and Control Systems” of the IEEE PSOPE Technologies and Innovation Subcommittee. Differently, in the second part, the authors propose the development of a data-driven forecasting methodology, which is modeled by considering the fundamental principles of Knowledge Discovery Process data workflow. Furthermore, the described methodology is applied to solve the load forecasting problem for a complex user case, in order to emphasize the potential role of knowledge discovery in supporting post processing analysis in data-rich environments, as feedback for the improvement of the forecasting performances

Incorporating life cycle external cost in optimization of the electricity generation mix

The present work aims to examine the strategic decision of future electricity generation mix considering, together with all other factors, the effect of the external cost associated with the available power generation technology options, not only during their operation but also during their whole life cycle. The analysis has been performed by integrating the Life Cycle Assessment concept into a linear programming model for the yearly decisions on which option should be used to minimize the electricity generation cost. The model has been applied for the case of Greece for the years 2012-2050 and has led to several interesting results. Firstly, most of the new generating capacity should be renewable (mostly biomass and wind), while natural gas is usually the only conventional fuel technology chosen. If externalities are considered, wind energy increases its share and hydro-power replaces significant amounts of biomass-generated energy. Furthermore, a sensitivity analysis has been performed. One of the most important findings is that natural gas increases its contribution when externalities are increased. Summing-up, external cost has been found to be a significant percentage of the total electricity generation cost for some energy sources, therefore significantly changing the ranking order of cost-competitiveness for the energy sources examined