Giulio Binetti

Bio: Giulio Binetti is an academic researcher from Polytechnic University of Bari. The author has contributed to research in topics: Economic dispatch & Consensus. The author has an hindex of 11, co-authored 23 publications receiving 793 citations. Previous affiliations of Giulio Binetti include Instituto Politécnico Nacional & University of Bari.

Distributed Consensus-Based Economic Dispatch With Transmission Losses

Abstract: A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints. The proposed approach is based on two consensus algorithms running in parallel. The first algorithm is a first-order consensus protocol modified by a correction term which uses a local estimation of the system power mismatch to ensure the generation-demand equality. The second algorithm performs the estimation of the power mismatch in the system using a consensus strategy called consensus on the most up-to-date information. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. Simulations performed on standard test cases demonstrate the effectiveness of the proposed approach for both small and large systems.

A Distributed Auction-Based Algorithm for the Nonconvex Economic Dispatch Problem

Abstract: This paper presents a distributed algorithm based on auction techniques and consensus protocols to solve the nonconvex economic dispatch problem. The optimization problem of the nonconvex economic dispatch includes several constraints such as valve-point loading effect, multiple fuel option, and prohibited operating zones. Each generating unit locally evaluates quantities used as bids in the auction mechanism. These units send their bids to their neighbors in a communication graph that supports the power system and which provides the required information flow. A consensus procedure is used to share the bids among the network agents and resolves the auction. As a result, the power distribution of generating units is updated and the generation cost is minimized. The effectiveness of this approach is demonstrated by simulations on standard test systems.

Scalable Real-Time Electric Vehicles Charging With Discrete Charging Rates

Abstract: Large penetration of electric vehicles (EVs) can have a negative impact on the power grid, e.g., increased peak load and losses, that can be largely mitigated using coordinated charging strategies. In addition to shifting the charging process to the night valley when the electricity price is lower, this paper explicitly considers the EV owner convenience that can be mainly characterized by a desired state of charge at the departure time. To this end, the EV charging procedure is defined as an uninterruptible process that happens at a given discrete charging rate and the coordinated charging is formulated as a scheduling problem. The scalable real-time greedy (S-RTG) algorithm is proposed to schedule a large population of EVs in a decentralized fashion, explicitly considering the EV owner criteria. Unlike the majority of existing approaches, the S-RTG algorithm does not rely on iterative procedures and does not require heavy computations, broadcast messages, or extensive bi-directional communications. Instead, the proposed algorithm schedules one EV at a time with simple computations, only once (i.e., at the time the EV connects to the grid), and only requires low-speed communication capability making it suitable for real-time implementation. Numerical simulations with significant EVs penetration and comparative analysis with scheduling policies demonstrate the effectiveness of the proposed algorithm.

An end stage kidney disease predictor based on an artificial neural networks ensemble

Abstract: IgA Nephropathy (IgAN) is a worldwide disease that affects kidneys in human beings and leads to end-stage kidney disease (ESKD) thus requiring renal replacement therapy with dialysis or kidney transplantation. The need for new tools able to help clinicians in predicting ESKD risk for IgAN patients is highly recognized in the medical field. In this paper we present a software tool that exploits the power of artificial neural networks to classify patients' health status potentially leading to ESKD. The classifier leverages the results returned by an ensemble of 10 networks trained by using data collected in a period of 38years at University of Bari. The developed tool has been made available both as an online Web application and as an Android mobile app. Noteworthy to its clinical usefulness is that its development is based on the largest available cohort worldwide.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

DC Microgrids—Part I: A Review of Control Strategies and Stabilization Techniques

Abstract: This paper presents a review of control strategies, stability analysis, and stabilization techniques for dc microgrids (MGs). Overall control is systematically classified into local and coordinated control levels according to respective functionalities in each level. As opposed to local control, which relies only on local measurements, some line of communication between units needs to be made available in order to achieve the coordinated control. Depending on the communication method, three basic coordinated control strategies can be distinguished, i.e., decentralized, centralized, and distributed control. Decentralized control can be regarded as an extension of the local control since it is also based exclusively on local measurements. In contrast, centralized and distributed control strategies rely on digital communication technologies. A number of approaches using these three coordinated control strategies to achieve various control objectives are reviewed in this paper. Moreover, properties of dc MG dynamics and stability are discussed. This paper illustrates that tightly regulated point-of-load converters tend to reduce the stability margins of the system since they introduce negative impedances, which can potentially oscillate with lightly damped power supply input filters. It is also demonstrated that how the stability of the whole system is defined by the relationship of the source and load impedances, referred to as the minor loop gain. Several prominent specifications for the minor loop gain are reviewed. Finally, a number of active stabilization techniques are presented.

Distributed Consensus-Based Economic Dispatch With Transmission Losses

Abstract: A distributed algorithm is presented to solve the economic power dispatch with transmission line losses and generator constraints. The proposed approach is based on two consensus algorithms running in parallel. The first algorithm is a first-order consensus protocol modified by a correction term which uses a local estimation of the system power mismatch to ensure the generation-demand equality. The second algorithm performs the estimation of the power mismatch in the system using a consensus strategy called consensus on the most up-to-date information. The proposed approach can handle networks of different size and topology using the information about the number of nodes which is also evaluated in a distributed fashion. Simulations performed on standard test cases demonstrate the effectiveness of the proposed approach for both small and large systems.