Paper Info
Title
Robust Optimization for Virtual Power Plants.
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.
Year
DOI
Venue
2017
10.1007/978-3-319-70169-1_2
AI*IA 2017 ADVANCES IN ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Virtual Power Plants,Robust optimization,Forecast uncertainty
Conference
10640
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Allegra De Filippo102.03
Michele Lombardi227028.86
Michela Milano3111797.67
Alberto Borghetti4468.05