PROFILING - CONCEPTS AND APPLICATIONS

Profiling is an approach to put a label or a set of labels on a subject, considering the characteristics of this subject. The New Oxford American Dictionary defines profiling as: “recording and analysis of a person’s psychological and behavioral characteristics, so as to assess or predict his/her capabilities in a certain sphere or to assist in identifying a particular subgroup of people”. This research extends this definition towards things demonstrating that many methods used for profiling of people may be applied for a different type of subjects, namely things. The goal of this research concerns proposing methods for discovery of profiles of users and things with application of Data Science methods. The profiles are utilized in vertical and 2 horizontal scenarios and concern such domains as smart grid and telecommunication (vertical scenarios), and support provided both for the needs of authorization and personalization (horizontal usage).:The thesis consists of eight chapters including an introduction and a summary. First chapter describes motivation for work that was carried out for the last 8 years together with discussion on its importance both for research and business practice. The motivation for this work is much broader and emerges also from business importance of profiling and personalization. The introduction summarizes major research directions, provides research questions, goals and supplementary objectives addressed in the thesis. Research methodology is also described, showing impact of methodological aspects on the work undertaken. Chapter 2 provides introduction to the notion of profiling. The definition of profiling is introduced. Here, also a relation of a user profile to an identity is discussed. The papers included in this chapter show not only how broadly a profile may be understood, but also how a profile may be constructed considering different data sources. Profiling methods are introduced in Chapter 3. This chapter refers to the notion of a profile developed using the BFI-44 personality test and outcomes of a survey related to color preferences of people with a specific personality. Moreover, insights into profiling of relations between people are provided, with a focus on quality of a relation emerging from contacts between two entities. Chapters from 4 to 7 present different scenarios that benefit from application of profiling methods. Chapter 4 starts with introducing the notion of a public utility company that in the thesis is discussed using examples from smart grid and telecommunication. Then, in chapter 4 follows a description of research results regarding profiling for the smart grid, focusing on a profile of a prosumer and forecasting demand and production of the electric energy in the smart grid what can be influenced e.g. by weather or profiles of appliances. Chapter 5 presents application of profiling techniques in the field of telecommunication. Besides presenting profiling methods based on telecommunication data, in particular on Call Detail Records, also scenarios and issues related to privacy and trust are addressed. Chapter 6 and Chapter 7 target at horizontal applications of profiling that may be of benefit for multiple domains. Chapter 6 concerns profiling for authentication using un-typical data sources such as Call Detail Records or data from a mobile phone describing the user behavior. Besides proposing methods, also limitations are discussed. In addition, as a side research effect a methodology for evaluation of authentication methods is proposed. Chapter 7 concerns personalization and consists of two diverse parts. Firstly, behavioral profiles to change interface and behavior of the system are proposed and applied. The performance of solutions personalizing content either locally or on the server is studied. Then, profiles of customers of shopping centers are created based on paths identified using Call Detail Records. The analysis demonstrates that the data that is collected for one purpose, may significantly influence other business scenarios. Chapter 8 summarizes the research results achieved by the author of this document. It presents contribution over state of the art as well as some insights into the future work planned

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading

Towards flexibility trading at TSO-DSO-customer levels : a review

The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.fi=vertaisarvioitu|en=peerReviewed

Peer-to-Peer Energy Trading in Smart Residential Environment with User Behavioral Modeling

Electric power systems are transforming from a centralized unidirectional market to a decentralized open market. With this shift, the end-users have the possibility to actively participate in local energy exchanges, with or without the involvement of the main grid. Rapidly reducing prices for Renewable Energy Technologies (RETs), supported by their ease of installation and operation, with the facilitation of Electric Vehicles (EV) and Smart Grid (SG) technologies to make bidirectional flow of energy possible, has contributed to this changing landscape in the distribution side of the traditional power grid. Trading energy among users in a decentralized fashion has been referred to as Peer- to-Peer (P2P) Energy Trading, which has attracted significant attention from the research and industry communities in recent times. However, previous research has mostly focused on engineering aspects of P2P energy trading systems, often neglecting the central role of users in such systems. P2P trading mechanisms require active participation from users to decide factors such as selling prices, storing versus trading energy, and selection of energy sources among others. The complexity of these tasks, paired with the limited cognitive and time capabilities of human users, can result sub-optimal decisions or even abandonment of such systems if performance is not satisfactory. Therefore, it is of paramount importance for P2P energy trading systems to incorporate user behavioral modeling that captures users’ individual trading behaviors, preferences, and perceived utility in a realistic and accurate manner. Often, such user behavioral models are not known a priori in real-world settings, and therefore need to be learned online as the P2P system is operating. In this thesis, we design novel algorithms for P2P energy trading. By exploiting a variety of statistical, algorithmic, machine learning, and behavioral economics tools, we propose solutions that are able to jointly optimize the system performance while taking into account and learning realistic model of user behavior. The results in this dissertation has been published in IEEE Transactions on Green Communications and Networking 2021, Proceedings of IEEE Global Communication Conference 2022, Proceedings of IEEE Conference on Pervasive Computing and Communications 2023 and ACM Transactions on Evolutionary Learning and Optimization 2023

Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts

The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above

Understanding Deregulated Retail Electricity Markets in the Future: A Perspective from Machine Learning and Optimization

On top of Smart Grid technologies and new market mechanism design, the further deregulation of retail electricity market at distribution level will play a important role in promoting energy system transformation in a socioeconomic way. In today’s retail electricity market, customers have very limited ”energy choice,” or freedom to choose different types of energy services. Although the installation of distributed energy resources (DERs) has become prevalent in many regions, most customers and prosumers who have local energy generation and possible surplus can still only choose to trade with utility companies.They either purchase energy from or sell energy surplus back to the utilities directly while suffering from some price gap. The key to providing more energy trading freedom and open innovation in the retail electricity market is to develop new consumer-centric business models and possibly a localized energy trading platform. This dissertation is exactly pursuing these ideas and proposing a holistic localized electricity retail market to push the next-generation retail electricity market infrastructure to be a level playing field, where all customers have an equal opportunity to actively participate directly. This dissertation also studied and discussed opportunities of many emerging technologies, such as reinforcement learning and deep reinforcement learning, for intelligent energy system operation. Some improvement suggestion of the modeling framework and methodology are included as well.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145686/1/Tao Chen Final Dissertation.pdfDescription of Tao Chen Final Dissertation.pdf : Dissertatio

Realizing the potential of distributed energy resources and peer-to-peer trading through consensus-based coordination and cooperative game theory

Driven by environmental and energy security concerns, a large number of small-scale distributed energy resources (DERs) are increasingly being connected to the distribution network. This helps to support a cost-effective transition to a lower-carbon energy system, however, brings coordination challenges caused by variability and uncertainty of renewable energy resources (RES). In this setting, local flexible demand (FD) and energy storage (ES) technologies have attracted great interests due to their potential flexibility in mitigating the generation and demand variability and improving the cost efficiency of low-carbon electricity systems. The combined effect of deregulation and digitalization inspired new ways of exchanging electricity and providing management/services on the paradigm of peer-to-peer (P2P) and transparent transactions. P2P energy trading enables direct energy trading between prosumers, which incentivizes active participation of prosumer in the trading of electricity in the distribution network, in the meantime, the efficient usage of FD and ES owned by the prosumers also facilitates better local power and energy balance. Though the promising P2P energy trading brings numerous advancements, the existing P2P mechanisms either fail to coordinate energy in a fully distributed way or are unable to adequately incentivize prosumers to participate, preventing prosumers from accessing the highest achievable monetary benefits and/or suffering severely from the curse of dimensionality. Therefore, this thesis aims at proposing three P2P energy trading enabling mechanisms in the aspect of fully distributed efficient balanced energy coordination through consensus-based algorithm and two incentivizing pricing and benefit distribution mechanisms through cooperative game theory. Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of DER due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of FD and ES resources. This thesis demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability. Moreover, this thesis proposes two computationally efficient pricing and benefit distribution mechanisms to construct a stable grand coalition of prosumers participating in P2P trading, founded on cooperative game-theoretic principles. The first one involves a benefit distribution scheme inspired by the core tatonnement process while the second involves a novel pricing mechanism based on the solution of single linear programming. The performance of the proposed mechanisms is validated against state-of-the-art mechanisms through numerous case studies using real-world data. The results demonstrate that the proposed mechanisms exhibit superior computational performance than the nucleolus and are superior to the rest of the examined mechanisms in incentivizing prosumers to remain in the grand coalition.Open Acces

Big data analytics in smart grids: a review

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