Magda Foti
@uth.gr
University of Thessaly
Scopus Publications
Scholar Citations
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Scopus Publications
Menelaos Kokaras and Magda Foti
Elsevier BV
Magda Foti
IEEE
Transactive energy paradigm targets creating a way for producers and consumers to match and balance energy supply and demand on a real time basis. An approach towards realizing this target is through price signals generated by energy market mechanisms. Pilot projects have confirmed that transactive control technology is effective in various ways, such as improving energy efficiency and reliability, reducing energy cost and encouraging integration of renewable energy sources. However, a main challenge towards the broad adoption of smart grid technologies, such as transactive energy, is the protection of the participants’ privacy. This paper presents the design and the implementation of a uniform price double auction mechanism which preserves the privacy of its participants leveraging homomorphic encryption. Market participants exchange encrypted information among them and the market entity. The auction clearing is jointly computed and the computations are performed on encrypted data. Large-scale experimentation, simulating the Olympic Peninsula pilot project, enable us to measure the effectiveness and scalability of the proposed distributed mechanism. The analysis of the results suggests that the proposed protocol provides an effective mechanism for the implementation of a privacy preserving real-time energy market.
Magda Foti and Manolis Vavalis
Elsevier BV
Costas Mavromatis, Magda Foti, and Manolis Vavalis
Institute of Electrical and Electronics Engineers (IEEE)
The Alternating Direction Method of Multipliers (ADMM) is widely utilized to solve the distributed Optimal Power Flow (OPF) problem, providing convergence under certain assumptions. ADMM relies on a penalty parameter <inline-formula><tex-math notation="LaTeX">$\\rho$</tex-math></inline-formula> to accelerate its convergence. The selection of appropriate values of <inline-formula><tex-math notation="LaTeX">$\\rho$</tex-math></inline-formula> is crucial for the quality of the final solution and for the efficiency of the iterative process. In this paper, we propose a weighted-<inline-formula><tex-math notation="LaTeX">$\\rho$</tex-math></inline-formula> ADMM, with its weights automatically determined by leveraging the nature of the optimal power flow problem. Specifically, the affinity matrix, a combination of the admittance matrix and Hessian matrix of the Lagrangian function of the associated OPF problem, is utilized to determine the penalty parameters. The convergence of the iterative scheme is analyzed and the effectiveness of the proposed methodology is validated through simulation experiments which show that the weighted-<inline-formula><tex-math notation="LaTeX">$\\rho$</tex-math></inline-formula> ADMM overcomes the necessity for suitable initial parameter selection.
Magda Foti, Costas Mavromatis, and Manolis Vavalis
Elsevier BV
Abstract We design, implement and analyze a decentralized consensus algorithm based on the blockchain technology for the solution of the Optimal Power Flow problem. The proposed algorithm enables independent power grid nodes, without prior trust on each other, to reach an agreement on the Optimal Power Flow solution. We decompose the Optimal Power Flow problem using a blockchain-based Alternating Direction Method of Multipliers and we comment on efficiency, trust, security and transparency issues. The successive iterants of the solution of the power flow problem are securely stored on the blockchain, removing the need for a central operating authority, while allowing network nodes to verify the validity and optimality of the solution. We illustrate the effectiveness of the consensus algorithm presented through simulation experiments on the 39-bus New England transmission system, and IEEE-57 and IEEE-118 benchmark systems. We systematically compare the effectiveness of the proposed algorithm with well established blockchain consensus algorithms. The results show that our method maintains the convergence characteristics of the Alternating Direction Method of Multipliers iterative scheme and enables independent network nodes to reach consensus.
Magda Foti and Manolis Vavalis
Elsevier BV
Abstract The energy sector is undergoing a massive transformation that greatly affects and is being affected by its market mechanisms. This paper presents the design, the prototype implementation and the analysis of a decentralized, real-time, uniform-price double auction energy market. The proposed market is a distributed and decentralized application and its rules may be specified through a smart contract. Market participants interact with the smart contract sending their asks and offers and the distributed application clears the market based on a uniform-price economic dispatch model. We propose and analyze three different implementation approaches on an Ethereum blockchain network. A model representing a physical demonstration project conducted in Seattle, USA, is utilized to simulate the power grid and to provide real-life scenarios for our analysis. We systematically evaluate our three implementation approaches and elucidate several blockchain issues regarding the decentralization of next-generation energy markets. In particular, we identify that certain computation modules may be installed on smart consuming devices in order to increase efficiency, security and decentralization and reduce the overall blockchain overhead cost. Furthermore, we show that in order to fulfill the market’s high demand for step timing, we ought to have the block generation time several magnitudes smaller compared to the auction period.
Magda Foti, Dimitrios Greasidis, and Manolis Vavalis
IEEE
This paper considers the design, the development and the experimental evaluation, of a blockchain based smart contract specifying the operating rules of a real time, uniform-price double auction energy market. Producers and consumers interact with this contract sending their offers and bids accordingly and the contract clears the market based on a double auction model. We propose four different approaches for implementing, through the Ethereum platform, both the P2P network as well as the smart contract. We systematically compare the above approaches on the basis of their decentralization nature, operating costs, computational costs, effectiveness, security, privacy and beyond. This comparison is achieved through large scale, real time simulations based on the GridLAB-D platform.
Nihla Akram, Sanjaya De Silva, Magda Foti, Malith Jayasinghe, Miyuru Dayarathna, Manolis Vavalis, and Srinath Perera
IEEE
The main objective of this paper is to elucidate issues that concern the integration of existing software systems and platforms into an open-source, open-architecture simulation engine that delivers large scale data analytics for the efficiency and stability of next generation power distribution grids. Our objective is achieved through the design and the implementation of a prototype simulation engine that couples state-of-the-art software from both the electrical engineering and information sciences. The effectiveness of our system is examined through two realistic large and detailed simulation scenarios which also involve machine learning and game theory.
Magda Foti and Manolis Vavalis
IGI Global
This paper has two aims. Firstly, to briefly present overall objectives and expected outcome of an on-going effort concerning design, implementation and the analysis of next generation energy systems based on anticipatory control and a set of ICT emerging technologies and innovations. Secondly, to describe an early proof-of-concept implementation and the associated experimentation of a simulation platform focused on holistic detailed studies of electric energy markets. The proposed platform allows us to elucidate issues related to the open and smart participation of producers and consumers on large-scale e-markets. Based on an existing simulation system, the authors present the required theoretical studies, the enabling technologies, and the practical tools that contribute to the development of such a platform capable of truly large scale simulations. Elements of game theory are utilized to solve the optimization problem related to the maximization of the social welfare of producers and consumers. Selected simulation results associated with the basic required characteristics are presented.
Magda Foti and Manolis Vavalis
IEEE
In this paper we consider the design and the implementation of a machine learning approach and its integration with a widely used energy simulation platform. We focus on auction based energy markets which require their participants to bid for their energy demands or offers at small time intervals. Our agent based system utilize weather data to teach both consuming devices and renewable energy sources to bid in an effective manner. We simulate realistic case studies of a residential distribution power grid with a total of more than 600 households with varying energy requirements. Photovoltaic panels as well as wind turbines are the regional energy resources. Our experimentation exhibit the effectiveness of the learning procedure both in term of power consumption and cost.
Elias Z. Tragos, Magda Foti, Manolis Surligas, George Lambropoulos, Stelios Pournaras, Stefanos Papadakis, and Vangelis Angelakis
IEEE
Ambient Assisted Living (AAL) describes an ICT based environment that exposes personalized and context-aware intelligent services, thus creating an appropriate experience to the end user to support independent living and improvement of the everyday quality of life of both healthy elderly and disabled people. The social and economic impact of AAL systems have boosted the research activities that combined with the advantages of enabling technologies such as Wireless Sensor Networks (WSNs) and Internet of Things (IoT) can greatly improve the performance and the efficiency of such systems. Sensors and actuators inside buildings can create an intelligent sensing environments that help gather realtime data for the patients, monitor their vital signs and identify abnormal situations that need medical attention. AAL applications might be life critical and therefore have very strict requirements for their performance with respect to the reliability of the devices, the ability of the system to gather data from heterogeneous devices, the timeliness of the data transfer and their trustworthiness. This work presents the functional architecture of SOrBet (Marie Curie IAPP project) that provides a framework for interconnecting efficiently smart devices, equipping them with intelligence that helps automating many of the everyday activities of the inhabitants. SOrBet is a paradigm shift of traditional AAL systems based on a hybrid architecture, including both distributed and centralized functionalities, extensible, self-organising, robust and secure, built on the concept of “reliability by design”, thus being capable of meeting the strict Quality of Service (QoS) requirements of demanding applications such as AAL.
Sofoklis Kyriazakos, George Labropoulos, Nikos Zonidis, and Magda Foti
IEEE
In this paper we present a novel Building Management System (BMS) that is based on advanced pattern matching algorithms and is utilizing a cloud-based Service Oriented Architecture (SOA) to offer a number of rich personalized applications for Ambient Living. The novelty presented in this paper is deriving from the evolution of a proprietary BMS product, namely Ecosystem, which is enhanced with the features presented in this paper, to address high demand for personalization and service adaptation in the new era of Information and Communication Technologies.