This study deals with the urban microgrid energy management that is dedicated to individual and collective self-consumption by providing flexibility to the distribution grid (DG). The proposed urban community microgrid is interconnected to a DG and it consists of an association of centralized storage units (called community energy storage system), community intermittent renewable generation, and intelligent energy management system (EMS). One of the main advantages of urban microgrid is that, in case of faults in the DG, it can cut existing interconnections and continue to supply the responsible community in the island mode. In this study, the developed urban microgrid EMS is based on the predictive control management through the day-ahead optimal power flow (DA-OPF) strategy. The main contributions of this work can be defined by two points. The first point is related to a development of the DA-OPF strategy for the urban microgrid based on the intelligent deep learning data forecasting and the mixed-integer nonlinear programming optimization methods. The second point concerns a development of an optimization function integrating the concept of ancillary services of DG flexibility. Experimental results and economical evaluation are presented in this article. By using the proposed strategies, it results in an important electricity price reduction for the considered urban microgrid, compared to a conventional distribution system and basic operation schemes.

This paper studies a Hybrid Renewable Energy System (HRES) as a reliable source of the power supply in the case of the connection to the grid. The grid connection imposes restrictions to the power delivered and harmonic content on the HRES. This causes the HRES to use multiple control systems and subsystems, as the normalization of the measurements, the current control, active harmonic compensation, synchronization, etc., described in this paper. Particular attention was paid to interactions in the storage system of the HRES. The durability of the HRES can be increased by the combination of the supercapacitors and batteries. This requires a power management solution for controlling the energy storage system. The aim of the supercapacitors is to absorb/inject the high-frequency fluctuations of the power and to smooth out the power of the batteries system of the HRES. This can be possible owing to the use of a low-pass second order filter, explained in this paper, which separates the high-frequency component of the storage system reference for the supercapacitor from the low-frequency component for the batteries system. This solution greatly increases the reliability and durability of the HRES.

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.

This paper presents a methodology of the battery modeling based on experimental tests results. As main contribution, authors conducted an analysis of the main constraints and effects of each parameter of the batteries model on its behavior and lifetime. This can help to improve the accuracy of the model. Due to its capability to model the recovery effect and the rate-capacity one, the Kinetic Battery Model serves as basis of the study. Results of experimental tests are used to realize the analysis and to define the suitable methodology of batteries parameters identification. These last can serve for prediction and estimation of the battery lifetime according to the actual operating conditions, particularly in microgrid and distributed systems.