Evaluating the Economic Benefits of a Smart-Community Microgrid with Centralized Electrical Storage and Photovoltaic Systems

Jura Arkhangelski, Pierluigi Siano, Abdou-Tankari Mahamadou and Gilles Lefebvre
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Jura Arkhangelski: Centre for Studies and Thermal, Environment and Systems Research, University Research Institute of Creteil-Vitry, University Paris-Est, 61, General de Gaulle Avenue, 94000 Creteil, France
Pierluigi Siano: Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano, Italy
Abdou-Tankari Mahamadou: Centre for Studies and Thermal, Environment and Systems Research, University Research Institute of Creteil-Vitry, University Paris-Est, 61, General de Gaulle Avenue, 94000 Creteil, France
Gilles Lefebvre: Centre for Studies and Thermal, Environment and Systems Research, University Research Institute of Creteil-Vitry, University Paris-Est, 61, General de Gaulle Avenue, 94000 Creteil, France

Energies, 2020, vol. 13, issue 7, 1-21

Abstract: 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.

Keywords: microgrid; deep learning; optimal power flow; mixed-integer nonlinear programming; long short-term memory; Monte Carlo simulation; centralized electrical storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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