Smart green applications: From renewable energy management to intelligent transportation systems

Smart green applications: From renewable energy management to intelligent transportation system

A Distributed Software Solution for Demand Side Management with Consumer Habits Prediction

Future smart grids will open the marketplace to novel services for grid management, such as Demand Side Management (DSM). To achieve energy saving in distribution systems, DSM aims at modifying load profile patterns of electricity demand by involving actively customers. In particular, residential customers can participate to this service by shifting their energivourous appliances (e.g. washing machine and dishwasher). In this paper, we present a novel DSM service to manage a day ahead balance. It exploits a human-in-the-loop approach to provide suggestions on shifting their appliances based on Latent Dirichlet Allocation algorithm combining both i) the probability density function of each customer’s appliance usage and ii) the cost function. To assess our DSM service, we present our experimental results performed in a realistic environment where we simulated a virtual population of about 1′000 families

A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy

SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

The smart grid, by the integration of advanced control and optimization technologies, provides the traditional grid with an indisputable opportunity to deliver and utilize the electricity more efficiently. Building smart grid applications is a challenging task, which requires a formal modeling, integration, and validation framework for various smart grid domains. The design flow of such applications must adapt to the grid requirements and ensure the security of supply and demand. This dissertation, by proposing a formal framework for customers and operations domains in the smart grid, aims at delivering a smooth way for: i) formalizing their interactions and functionalities, ii) upgrading their components independently, and iii) evaluating their performance quantitatively and qualitatively.The framework follows an event-driven demand response program taking no historical data and forecasting service into account. A scalable neighborhood of prosumers (inside the customers domain), which are equipped with smart appliances, photovoltaics, and battery energy storage systems, are considered. They individually schedule their appliances and sell/purchase their surplus/demand to/from the grid with the purposes of maximizing their comfort and profit at each instant of time. To orchestrate such trade relations, a bilateral multi-issue negotiation approach between a virtual power plant (on behalf of prosumers) and an aggregator (inside the operations domain) in a non-cooperative environment is employed. The aggregator, with the objectives of maximizing its profit and minimizing the grid purchase, intends to match prosumers' supply with demand. As a result, this framework particularly addresses the challenges of: i) scalable and hierarchical load demand scheduling, and ii) the match between the large penetration of renewable energy sources being produced and consumed. It is comprised of two generic multi-objective mixed integer nonlinear programming models for prosumers and the aggregator. These models support different scheduling mechanisms and electricity consumption threshold policies.The effectiveness of the framework is evaluated through various case studies based on economic and environmental assessment metrics. An interactive web service for the framework has also been developed and demonstrated