Giorgio Graditi

Bio: Giorgio Graditi is an academic researcher from ENEA. The author has contributed to research in topics: Photovoltaic system & Smart grid. The author has an hindex of 34, co-authored 196 publications receiving 3427 citations. Previous affiliations of Giorgio Graditi include University of Palermo.

Heuristic-based shiftable loads optimal management in smart micro-grids

Abstract: In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original extension of a glowworm swarm particles optimization algorithm, with algorithmic enhancements to treat multiple objective formulations. The performance of the algorithm is compared to the NSGA-II on the considered power systems application.

Is the distribution grid ready to accept large-scale photovoltaic deployment? State of the art, progress, and future prospects

Abstract: The installed capacity of photovoltaic (PV) systems has recently increased at a much faster rate than the development of grid codes to effectively and efficiently manage high penetrations of PV within the distribution system. In a number of countries, PV penetrations in some regions are now raising growing concerns regarding integration. Management strategies vary considerably by country—some still have an approach that PV systems should behave as passive as possible, whereas others demand an active participation in grid control. This variety of grid codes also causes challenges in learning from “best practice.” This paper provides a review of current grid codes in some countries with high PV penetrations. In addition, the paper presents a number of country-specific case studies on different approaches for improved integration of PV systems in the distribution grid. In particular, we consider integration approaches using active and reactive power control that can reduce or defer expensive grid reinforcement while supporting higher PV penetrations. Copyright © 2011 John Wiley & Sons, Ltd.

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.

Demand response and smart grids—A survey

Abstract: The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects.

A review of data-driven building energy consumption prediction studies

Abstract: Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.