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Robust Optimization for Virtual Power Plants

TitleRobust Optimization for Virtual Power Plants
Publication TypeBook Chapter
Year of Publication2017
AuthorsDe Filippo, A., M. Lombardi, M. Milano, and A. Borghetti
EditorEsposito, F., R. Basili, S. Ferilli, and F. A. Lisi
Book TitleAI*IA 2017 Advances in Artificial Intelligence: XVIth International Conference of the Italian Association for Artificial Intelligence, Bari, Italy, November 14-17, 2017, Proceedings
Series TitleLecture Notes in Computer Science
Pagination17–30
PublisherSpringer International Publishing
CityCham
ISBN Number978-3-319-70169-1
Abstract

Virtual Power Plants (VPP) are one of the main components of future smart electrical grids, connecting and integrating several types of energy sources, loads and storage devices. A typical VPP is a large industrial plant with high (partially shiftable) electric and thermal loads, renewable energy generators and electric and thermal storages. Optimizing the use and the cost of energy could lead to a significant economic impact. This work proposes a VPP Energy Management System (EMS), based on a two-step optimization model that decides the minimum-cost energy balance at each point in time considering the following data: electrical load, photovoltaic production, electricity costs, upper and lower limits for generating units and storage units. The first (day-ahead) step models the prediction uncertainty using a robust approach defining scenarios to optimize the load demand shift and to estimate the cost. The second step is an online optimization algorithm, implemented within a simulator, that uses the optimal shifts produced by the previous step to minimize, for each timestamp, the real cost while fully covering the optimally shifted energy demand. The system is implemented and tested using real data and we provide analysis of results and comparison between real and estimated optimal costs.

URLhttps://doi.org/10.1007/978-3-319-70169-1_2
DOI10.1007/978-3-319-70169-1_2