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

Conceptual Modeling of Collaborative Intelligent Manufacturing For Customized Products: An Ontological Approach

Mass customization is implemented to provide outstanding service to customers with diverse tastes and preferences. However, mass customization has limitations in the traditional value chain and production paradigm. Taking advantage of advanced information technology, such as manufacturing grid and virtual enterprise, to facilitate mass customization and improve the customer perceived valued of mass customization raises a challenge issue. To achieve an ideal mass customization, the customer\u27s needs should be identified and met by the collaborative manufacturing from several manufacturers. Comprehensive conceptual models corresponding to the collaborative manufacturing for customized products are essential to understand how the collaborative process can apply in customized production, and facilitate early detection and correction of system development errors. In this paper, an ontology is described via a customized bicycle buying scenario to describe how to use an ontology for collaborative manufacturing. This ontological approach provides understanding of the domain, which can be used as a unifying framework to represent the selected phenomena for conceptual model

Travel Agencies: From online channel conflict to multi-channel harmony

The adoption of Internet as a distribution channel and a privileged e-commerce tool has pressed Travel Agencies (TAs) to a latent channel conflict. Our main interest is to understand how the traditional independent travel agencies in Portugal deal with the online channel. We suggest that TAs have to develop an innovative business model based on the online and offline complementary channels, in order to achieve a multi-channel harmony

The Economics of Social Data

A data intermediary pays consumers for information about their preferences and sells the information so acquired to firms that use it to tailor their products and prices. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that reduces the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and show that it preserves the privacy of the consumers’ identities while providing precise information about market demand to the firms. This enables the intermediary to capture the entire value of information as the number of consumers grows large

The Economics of Social Data

We propose a model of data intermediation to analyze the incentives for sharing individual data in the presence of informational externalities. A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market in which firms and consumers can tailor their choices to the demand data. The social dimension of the individual data - whereby an individual’s data are predictive of the behavior of others - generates a data externality that can reduce the intermediary’s cost of acquiring information. We derive the intermediary’s optimal data policy and establish that it preserves the privacy of consumer identities while providing precise information about market demand to the firms. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large

The Economics of Social Data

We propose a model of data intermediation to analyze the incentives for sharing individual data in the presence of informational externalities. A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers can tailor their choices to the demand data. The social dimension of the individual data-whereby an individual's data are predictive of the behavior of others -- generates a data externality that can reduce the intermediary's cost of acquiring the information. We derive the intermediary's optimal data policy and establish that it preserves the privacy of consumer identities while providing precise information about market demand to the firms. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large

A programmable structure for pervasive computing

This exstended abstract presents an asymmetric and programmable (extensible) approach to pervasive computing. The idea is to off-load computations from light portable clients into a back-bone of seamlessly integrated servers. This way, a user can extend and personalize his pervasive computational environment by installing computations following his trajectory throughout the day. Focus on this extended abstract is on structural issues related to the back-end servers running mobile code off-loaded from the mobile user