303 research outputs found
Coalition Formation Algorithm of Prosumers in a Smart Grid Environment
In a smart grid environment, we study coalition formation of prosumers that
aim at entering the energy market. It is paramount for the grid operation that
the energy producers are able to sustain the grid demand in terms of stability
and minimum production requirement. We design an algorithm that seeks to form
coalitions that will meet both of these requirements: a minimum energy level
for the coalitions and a steady production level which leads to finding
uncorrelated sources of energy to form a coalition. We propose an algorithm
that uses graph tools such as correlation graphs or clique percolation to form
coalitions that meet such complex constraints. We validate the algorithm
against a random procedure and show that, it not only performs better in term
of social welfare for the power grid, but also that it is more robust against
unforeseen production variations due to changing weather conditions for
instance.Comment: 6 pages, 4 figures, 1 table. submited to ICC 201
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy
management mechanism for the smart grid that enables each prosumer of the
network to participate in energy trading with one another and the grid. This
poses a significant challenge in terms of modeling the decision-making process
of each participant with conflicting interest and motivating prosumers to
participate in energy trading and to cooperate, if necessary, for achieving
different energy management goals. Therefore, such decision-making process
needs to be built on solid mathematical and signal processing tools that can
ensure an efficient operation of the smart grid. This paper provides an
overview of the use of game theoretic approaches for P2P energy trading as a
feasible and effective means of energy management. As such, we discuss various
games and auction theoretic approaches by following a systematic classification
to provide information on the importance of game theory for smart energy
research. Then, the paper focuses on the P2P energy trading describing its key
features and giving an introduction to an existing P2P testbed. Further, the
paper zooms into the detail of some specific game and auction theoretic models
that have recently been used in P2P energy trading and discusses some important
finding of these schemes.Comment: 38 pages, single column, double spac
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
Prosumer communities and relationships in smart grids: A literature review, evolution and future directions
Smart grids are robust, self-healing networks that allow bidirectional propagation of energy and information within the utility grid. This introduces a new type of energy user who consumes, produces, stores and shares energy with other grid users. Such a user is called a "prosumer." Prosumers' participation in the smart grid is critical for the sustainability and long-term efficiency of the energy sharing process. Thus, prosumer management has attracted increasing attention among researchers in recent years. This paper systematically examines the literature on prosumer community based smart grid by reviewing relevant literature published from 2009 to 2018 in reputed energy and technology journals. We specifically focus on two dimensions namely prosumer community groups and prosumer relationships. Based on the evaluated literature, we present eight propositions and thoroughly describe several future research directions
Blockchain and artificial intelligence enabled peer-to-peer energy trading in smart grids
Peer-to-peer (P2P) energy trading allows smart grid-connected parties to trade renewable energy with each other. It is widely considered a scheme to mitigate the supplydemand imbalances during peak-hour. In a P2P energy trading system, users (e.g., prosumers, Electric Vehicles (EV)) increase their utility by trading energy securely with each
other at a lower price than that of the main grid. However, three challenges hinder the
development of secured P2P energy trading systems. First, there is a lack of implicit trust
and transparency between trading participants because they do not know each other. Second, P2P energy trading systems cannot offer an intelligent trading strategy that could
maximize users’ (agents’) utility. This is because the agents may lack previous trading
experience data that enable them to select an optimal trading strategy. Third, the current
energy trading platforms are mainly centralized, which makes them vulnerable to malicious
attacks and Single point of failure (SPOF). This may interrupt the transaction validation
mechanism when the system is compromised, and the central database is unavailable. [...
Exploring auction based energy trade with the support of MAS and blockchain technology
This document describes a simulation of the local energy market with support of multi-agent approach and blockchain technology. The investigated points include blockchain technology and its applications, Ethereum platform and smart contracts as a tool for storing data of operations and creating assets, multi-agent approach to model the local energy market. The document explores building a solution for proposed problem with blockchain technology, agent interactions on the simulated market and auction models, that provide sustainability and profit for the local energy market overall
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