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Efficiency and reliability enhancement of a multi-source DC microgrid dedicated to residential applications by integrating bi-Level managements – EREMITE

ANR JCJC EREMITE

Efficiency and Reliability Enhancement of a Multi-Source DC Microgrid Dedicated to Residential Applications by Integrating Bi-Level Managements

Development of both advanced energy management strategy and health management strategy for the efficient and reliable operations of DC microgrids in both short-term and long-term timescales

Despite of its numerous advantages and extensive applications, DC microgrid is still relatively a novel technology and its grid architectures, control strategies, stabilization techniques and so on deserve tremendous research efforts. In both European and French scales, as more and more renewable DC microgrids being and to be installed, the efficiency, the monitoring and maintenance, the durability of those systems are becoming more and more important concerns. <br />As DC microgrids involve numerous energy sources, storage units and different types of loads according to the involved applications, the efficiency and reliability of the whole system is dependent on multiple factors, as the characteristics of the adopted sources, climatic conditions, and load profiles. Numerous studies have been proposed to improve the efficiency and reliability of the DC microgrids. However, the studies on efficiency improvement are usually in the condition that all the elements in the system are in healthy state; the studies on reliability improvement are oriented to recover the functionality of the system when an occurred fault has been detected and identified.<br />This project aims to improve the efficiency and reliability of a multi-source (Photovoltaic /Wind/Batteries/Supercapacitors) DC microgrid by integrating both energy and health management strategies. More specifically, the projects objectives are threefold. <br />(1) Development of an advanced energy management strategy (EMS) to improve the system operational efficiency. More concretely, an artificial intelligence-based EMS taking into consideration the forecasting of both the sources and the load demand response will be developed. <br />(2) Development of an online health management strategy (HMS) to enhance the system reliability and durability. An HMS of the distributed sources will be integrated in the initial design phase of the microgrid to perform a real-time health monitoring and diagnostic methodology. <br />(3) Interaction of the EMS and HMS for both short-term and long-term efficient and reliable operation. The health information and the degradation factors of each source will further complement the existing microgrid performance indexes to enhance the system efficiency, reliability, and durability.

1. An improved fuzzy logic controller, in combination with the reservoir computing (RC) forcasting method for the microgrid EMS design
Fundamentally, a real-time EMS could be formulated as a stochastic multi-objective optimal power flow problem. The decision-making module which deals with the RES generation, the load demand (e.g., load shedding), the electricity market (in grid-connected mode), as well as the forecasting information is highly necessary.
One of the major difficulties for real-time realization is the computational complexity considering the hierarchical control layers involving the multiple different sources, loads and time-varying operating conditions. In addition, conventional control methods such as model-based one cannot be adapted flexibly to the situations such as the plug-and-play operation and can fail if unexpected changes in topology occur. Fuzzy controllers and artificial neural networks (ANN) can provide potential solutions to handle the computational complexity and achieve the desired adaptiveness. However, further research is still needed to determine adequate design and calibration procedures for such controllers. In this project, an improved fuzzy logic controller with uncertainty-dealing capability, less computational complexity and good generalization capability, is developed for the microgrid EMS design.
In addition, forecasting ability under dynamical operating conditions (e.g., changing irradiance, temperature, wind speed and loads) covering both short-/long-time scales (e.g., minutes for instantaneous power demand, days for energy scheduling) is a critical element. In this project, a novel framework of artificial neural network (ANN), named RC is chosen for both short-term and long-term prediction (adaptive prediction) under different operating conditions. The principal reason is its strong capability to cope with temporal signals of a surprising wide variety of types (from deeper memory deterministic chaotic systems to shorter memory sequences).
2. EIS function integrated DC/DC converters for microgrid real-time health management
The basic principle is to integrate the functionality of online EIS characterization of the microgrid components without human intervention or expensive hardware. EIS has been proven as a powerful technique for the characterization of various electrochemical devices, e.g., batteries, fuel cells and solar cell. As a non-destructive tool, it provides a high resolution of fundamental processes of the target device by obtaining the impedance information in the frequency domain. Nevertheless, due to the cost (>20 k€/kW) and volume (>10kg/kW) factors, the spectrometers are highly constrained for real-time characterization uses.

1. Design and implementation of EMS
The characteristics of each component of the multi-source DC microgrid (PV-wind-hybrid storage system) was firstly studied. This part was followed by the dynamic modeling of each part, including the PV and wind generation parts, the storage part, the controller part, and the loads. A simulation platform of the whole DC microgrid was further developed by integrating each part. Based on the platform, two different EMS have been designed and initially validated, following a comprehensive literature review on the existing EMS for DC microgrids. Both the two EMS can achieve the main objectives, including the DC bus stability under both static and dynamic operating conditions and the state of charge level control for both batteries and supercapacitors.
2. Integrated HMS of the distributed sources
In parallel with the EMS part, HMS is integrated in the initial design phase of the DC microgrid. Particularly, a real-time health monitoring and diagnostic methodology based on the DC/DC converters is proposed. This part began with literature research on the fault types and fault diagnosis methods for PV system used in DC microgrids. The electrochemical impedance spectroscopy (EIS) method has been selected considering its capacity for characterization and fault detection, while there is no need of human intervention and expensive hardware. The simulation phase was realized to study the influence of ac signal injection region, the ac signal amplitude, and the selection of the equivalent circuit model. In addition, an original coordination control method was proposed to switch the PV system between maximal power point tracking (MPPT) mode and EIS mode, without influencing the microgrid normal operation. In parallel, a new version of DC/DC converter has been designed and realized in the laboratory, namely magnetically coupled boost converter with assisted recovery stage.
3. Development of experimental platforms
The experimental platforms have been constructed and initially tested, including a PV generation platform, a wind generation platform, and the real-time controller for implementing both the designed EMS and HMS. The PV generation platform includes 16 PV panels installed on the roof of IUT de Thionville-Yutz, 8 self-designed high-step ratio DC/DC converters in the laboratory and a data supervision system. The wind generation platform has been developed with the collaboration of department GIM of the IUT. The platform has received a co-financing of the department for both research and teaching uses. Besides the installed wind generator in the campus, the same type of generator was installed inside for studying the wind generation behavior and connection with the DC microgrid without the limitation of weather conditions. The platform includes a wind generator, a DC motor, a self-developed electrical box for driving the motor and imitating the real wind behavior and corresponding converters.

1. Concerning the EMS part, the originality and novelty of this project consists mainly in the design and realization of a multi-objective EMS based on artificial intelligent methods, considering both the actual and future RES availability, demand side management, and the electricity market.
For the next step, an improved fuzzy logic controller with uncertainty-dealing capability, less computational complexity, and good generalization capability, in combination with the RC method for forecasting will be developed for the microgrid EMS design. Performance of the proposed EMS will be further evaluated and compared with the existing strategies in terms of energy efficiency, forecasting accuracy, computational complexity, etc.
2. HMS of the DC microgrid constitutes another important aspect for the reliable and durable operation of the whole system. So far, most research contribute to the EMS issues with comparatively little focus on the HMS issues. More precisely, the HMS includes the diagnosis, prognosis and the targeted control dedicated to enhancing the reliability and durability in both the component and system level. Lots of present studies on HMS have been carried out for one single component in the microgrids. A systematical HMS which can coordinate different faults and degradation issues is still missing.
In the next step, a real-time health monitoring and diagnostic methodology based on the DC-DC converters will be realized. The basic principle is to integrate the functionality of online EIS characterization of the microgrid components without human intervention or expensive hardware. Another potential benefit of this impedance-based monitoring technique is to facilitate the stabilization analysis of the whole system especially under dynamical operating conditions. The online EIS technique will be explored for both health monitoring and stability analysis uses.
3. The interaction of the two strategies, i.e., the EMS and HMS on the system long-term performances will be studied. It should be noted that an efficiency-oriented EMS could aggregate the degradation rate of the system components. Meanwhile, the online health monitoring such as EIS which obtains the sources’ impedances by introducing small sinusoidal perturbations on the DC/DC converter’s control loops, could complicate the controlling strategies and have adverse effects on the system stability (e.g., DC bus stability). In the next step, the interaction of the EMS and HMS will be further explored.

International journals
1. S. Ali, Z. Zheng, M. Aillerie, J.P. Sawicki, M.C. Péra, D. Hissel, 2021, “A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications”. MDPI Energies, 2021, 14(14): 4308 (Open-access journal) ?hal-03346267?.
2. F.Z. Naama, A. Zegaoui, A. Djahbar, M. Aillerie, “Simulation and modeling of a small permanent magnet synchronous generator wind turbine directly from its datasheet”, Przeglad Elektrotechniczny 1 (7), 10-14, 2020 < hal-02942213>.

International conferences
1. X. Wang, Z. Zheng, M. Aillerie, J.P. Sawicki, M.C. Péra and D. Hissel, 2021, “Online Spectroscopy Imped-ance Fault Diagnosis Method for Photovoltaic Panels in Microgrids”, 47th Annual Conference of the IEEE In-dustrial Electronics Society (IECON 2021), Toronto, Canada (1-6 pages).
2. S. Ali, Z. Zheng, M. Aillerie, J.P. Sawicki, M.C. Péra and D. Hissel, 2021, “DC microgrid energy management systems dedicated to residential applications: A review study”, TMREES 2021 International Conference Ath-ens-Greece, Oral presentation, Online, 28-30 May 2021.
3. Z. Zheng, “Tutorial T05.02 Diagnosis, prognosis and fault tolerance control for fuel cell systems”, IEEE IE-CON 2020, Singapore, Oral presentation, online, 18-21 October 2020.
4. Z. Zheng, “Energy management of a Fuel cell/ultracapacitor/battery hybrid electric vehicle based on fuzzy logic”, TOP 10 EMS solution of IEEE VTS motor vehicles data challenge, IEEE VPPC 2020, 26-29 October 2020.

Orgnization of conferences
1. IEEE IECON 2021, Organization of a special session SS23 on “Energy and health management of DC mi-crogrids”, Toronto, Canada. (Online, ieeeiecon.org/approved-special-session/)
2. IEEE ISIE 2021, Organization of a special session SS20 on “Reliability Analysis and Improvement Methods for Industrial Systems”, Kyoto, Japan. (Online, www.isie2021.org/special_session.html)

Microgrid provide a promising and efficiency solution to increase the penetration of renewable energy sources (RES) and storage systems, and to improve the resilience and reliability of the utility grid. With the emergence of more DC-type RES and loads, DC microgrid has been gaining an increased popularity over the past decade. With respect to its AC counterpart, it has principle advantages such as higher reliability and efficiency, simpler control and no issues of skin effects, harmonics and synchronization. In both European and French scales, as more and more renewable DC microgrids being and to be installed, the efficiency, the monitoring and maintenance, the durability of those systems are becoming more and more important concerns. This project aims to improve the efficiency and reliability of a multi-source (Photovoltaic /Wind/Batteries/ Supercapacitors) DC microgrid by integrating both energy and health management strategies.

Project coordination

Zhixue ZHENG (LABORATOIRE MATÉRIAUX OPTIQUES, PHOTONIQUE ET SYSTÈMES)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

LMOPS LABORATOIRE MATÉRIAUX OPTIQUES, PHOTONIQUE ET SYSTÈMES

Help of the ANR 208,366 euros
Beginning and duration of the scientific project: January 2020 - 42 Months

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