Journal
ENERGIES
Volume 13, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/en13071764
Keywords
microgrid; deep learning; optimal power flow; mixed-integer nonlinear programming; long short-term memory; Monte Carlo simulation; centralized electrical storage
Categories
Funding
- Centre for Studies and Thermal, Environment, and Systems Research (CERTES)
- IUT of Creteil-Vitry, University Paris-Est, France
- University of Paris-Est Doctoral School SIE, France
- Smart Grids and Smart Cities Laboratory (SMARTLab), Department of Management & Innovation Systems, University of Salerno, Italy
Ask authors/readers for more resources
In this paper, an innovative method for managing a smart-community microgrid (SCM) with a centralized electrical storage system (CESS) is proposed. The method consists of day-ahead optimal power flow (DA OPF) for day-ahead SCM managing and its subsequent evaluation, considering forecast uncertainties. The DA OPF is based on a data forecast system that uses a deep learning (DL) long short-term memory (LSTM) network. The OPF problem is formulated as a mathematical mixed-integer nonlinear programming (MINLP) model. Following this, the developed DA OPF strategy was evaluated under possible operations, using a Monte Carlo simulation (MCS). The MCS allowed us to obtain potential deviations of forecasted data during possible day-ahead operations and to evaluate the impact of the data forecast errors on the SCM, and that of unit limitation and the emergence of critical situations. Simulation results on a real existing rural conventional community endowed with a centralized community renewable generation (CCRG) and CESS, confirmed the effectiveness of the proposed operation method. The economic analysis showed significant benefits and an electricity price reduction for the considered community if compared to a conventional distribution system, as well as the easy applicability of the proposed method due to the CESS and the developed operating systems.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available