A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness

Time-based pricing programs for domestic electricity users have been effective in reducing peak demand and facilitating renewables integration. Nevertheless, high cost, price non-responsiveness and adverse selection may create the possible challenges. To overcome these challenges, it can be fruitful to investigate the ‘high-potential’ users, which are more responsive to price changes and apply time-based pricing to these users. Few studies have investigated how to identify which users are more price-responsive. We aim to fill this gap by comprehensively identifying the drivers of domestic users’ price responsiveness, in order to facilitate the selection of the high-potential users. We adopt a novel data-driven approach, first by a feed forward neural network model to accurately determine the baseline monthly peak consumption of individual households, followed by an integrated machine-learning variable selection methodology to identify the drivers of price responsiveness applied to Irish smart meter data from 2009-10 as part of a national Time of Use trial. This methodology substantially outperforms traditional variable selection methods by combining three advanced machine-learning techniques. Our results show that the response of energy users to price change is affected by a number of factors, ranging from demographic and dwelling characteristics, psychological factors, historical electricity consumption, to appliance ownership. In particular, historical electricity consumption, income, the number of occupants, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are found to be significant drivers of price responsiveness. We also observe that continual price increase within a moderate range does not drive additional peak demand reduction, and that there is an intention-behaviour gap, whereby stated intention does not lead to actual peak reduction behavior. Based on our findings, we have conducted scenario analysis to demonstrate the feasibility of selecting the high potential users to achieve significant peak reduction

PERSONAL DATA PROTECTION RULES! GUIDELINES FOR PRIVACY-FRIENDLY SMART ENERGY SERVICES

Privacy-friendly processing of personal data is proving to be increasingly challenging in today’s energy systems as the amount of data grows. Smart energy services provide value creation and co-creation by processing sensible user data collected from smart meters, smart home devices, storage systems, and renewable energy plants. To address this challenge, we analyze key topics and develop design requirements and design principles for privacy-friendly personal data processing in smart energy services. We identify these key topics through expert interviews, text-mining, and topic modelling techniques based on 149 publications. Following this, we derive our design requirements and principles and evaluate these with experts and an applicability check with three real-world smart energy services. Based on our results and findings, we establish a further research agenda consisting of five specific research directions

Energy disaggregation in NIALM using hidden Markov models

This work presents an appliance disaggregation technique to handle the fundamental goal of the Non-Intrusive Appliance Load Monitoring (NIALM) problem i.e., a simple breakdown of an appliance level energy consumption of a house. It also presents the modeling of individual appliances as load models using hidden Markov models and combined appliances as a single load model using factorial hidden Markov models. Granularity of the power readings of the disaggregated appliances matches with that of the readings collected at the service entrance. Accuracy of the proposed algorithm is evaluated using publicly released Tracebase data sets and UK-DALE data sets at various sampling intervals. The proposed algorithm achieved a success rate of 95% and above with Tracebase data sets at 5 second sampling resolution and 85% and above with UK-DALE data sets at 6 second sampling resolution --Abstract, page iii

Techniques, Taxonomy, and Challenges of Privacy Protection in the Smart Grid

As the ease with which any data are collected and transmitted increases, more privacy concerns arise leading to an increasing need to protect and preserve it. Much of the recent high-profile coverage of data mishandling and public mis- leadings about various aspects of privacy exasperates the severity. The Smart Grid (SG) is no exception with its key characteristics aimed at supporting bi-directional information flow between the consumer of electricity and the utility provider. What makes the SG privacy even more challenging and intriguing is the fact that the very success of the initiative depends on the expanded data generation, sharing, and pro- cessing. In particular, the deployment of smart meters whereby energy consumption information can easily be collected leads to major public hesitations about the tech- nology. Thus, to successfully transition from the traditional Power Grid to the SG of the future, public concerns about their privacy must be explicitly addressed and fears must be allayed. Along these lines, this chapter introduces some of the privacy issues and problems in the domain of the SG, develops a unique taxonomy of some of the recently proposed privacy protecting solutions as well as some if the future privacy challenges that must be addressed in the future.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111644/1/Uludag2015SG-privacy_book-chapter.pd

Smart Grid challenges - Device Trustworthiness

The Power Grid development brings about technological design changes, resulting in increased connectivity and dependency on IoT devices. The changes offer opportunities to manipulate the IoT hardware as the root of trust. Although terrifying, hardware attacks are considered resource-demanding and rare. Nonetheless, Power Grids are attractive targets for resourceful attackers. As such, the Ukraine attacks boosted Power Grid cybersecurity focus. However, physical assurance and hardware device trustworthiness received less attention. Overhead Line Sensors are utilized in Dynamic Line Rating doctrines for Power Grids. They are potentially essential in the future to optimize conductor ampacity. Conductor optimization is crucial for Power Grids because future throughput volatility demands a high level of grid flexibility. However, there may be challenges to the integrity and availability of the data collected using Overhead Line sensors. We believe that in securing the future Smart Grid, stakeholders need to raise attention to device trustworthiness entailing the hardware layer. That said, integrated into cloud-enhanced digital ecosystems, Overhead Line Sensors can also be manipulated through the network, software, and supply chain to impact their trustworthiness

Characterization of new flexible players: Deliverable D3.2

Project TradeRES - New Markets Design & Models for 100% Renewable Power Systems: https://traderes.eu/about/ABSTRACT: The subject matter of this report is the analysis of the electricity markets’ actors’ scene, through the identification of actor classes and the characterisation of actors from a behavioural and an operational perspective. The technoeconomic characterization of market participants aims to support the upcoming model enhancements by aligning the agent-based model improvements with the modern market design challenges and the contemporary characteristics of players. This work has been conducted in the context of task T3.2, which focuses on the factorization of the distinctive operational and behavioural characteristics of players in market structures. Traditional parties have been considered together with new and emerging roles, while special focus has been given on new actors related to flexible technologies and demand-side response. Among the main objectives have been the characterization of individual behaviours, objectives and requirements of different electricity market players, considering both the traditional entities and the new distributed ones, and the detailed representation of the new actors.N/

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book