# Information System Architecture for Supervision and Control of Railway Smart Grids

Khaled ALMAKSOUR<sup>(1)</sup>, Mhamad SHMAYSANI<sup>(1)</sup>, Flovic GOSSELIN<sup>(2)</sup>, Hervé CARON<sup>(3)</sup>, Adrien DUCROCQ<sup>(1)</sup>, Christophe SAUDEMONT<sup>(1)</sup> and Benoit ROBYNS<sup>(1)</sup>

<sup>(1)</sup>Univ. Lille, Arts et Metiers Institute of Technology, Centrale Lille, Junia, ULR 2697-L2EP, F-59000 Lille, France

<sup>(2)</sup>Université Catholique de Lille, Lille, France

<sup>(3)</sup>SNCF Réseau, 6 Avenue François Mitterrand, 93574 La Plaine Saint Denis, France
Tel.: +33 328 384 858

E-Mail: khaled.almaksour@junia.com

## ACKNOWLEDGMENT

This work was carried out as part of the RACCOR-D project, funded by the French government as part of FRANCE 2030.

*Index Terms*—DC railway power supply, Industrial information systems, Industrial communications, Battery energy storage system, Battery Management Systems (BMS).

*Abstract*—This paper presents an information system architecture for controlling a storage system connected to a DC railway substation with local photovoltaic production (PV). The proposed architecture collects various information and data measurements necessary for the proper operation of the storage system enabling both day-ahead and real-time control.

## I. INTRODUCTION

The goal of reducing the carbon footprint has been increasingly pursued across various industrial sectors, including electrified railway systems [1]. Integrating local renewable energy production with stationary battery-based storage systems appears to be a viable solution for railway operators to achieve this objective. To address the intermittency of renewable production and the high variability of power substation consumption, several studies have proposed energy management systems (EMS) based on optimization algorithms to achieve specific goals, such as reducing grid consumption or shaving peak demand [2]. The study presented in [3], proposes an EMS for a railway system with local PV production, an energy storage system (ESS), and regenerative braking

capability. The optimization results show a reduction in energy costs by 48.6% and a decrease in energy consumption during peak hours by 24%. However, most studies in the literature are validated using simulation environments like MATLAB, with several assumptions, such as the availability of measurement data and communication with different system components. Real-time simulation, as used in [4], adds realism by analyzing the behavior of a railway lab-scale power converter interacting with a railway system simulated in real time. However, the industrial transfer of research findings to operational systems is rarely discussed in the literature. Furthermore, the use of information technology tools in power systems requires further clarification due to the existing gap between research development and real-world application. This gap is often caused by researchers' lack of consideration for physical system limitations on one hand, and the need for feasible, reliable, and cost-effective solutions for industrial actors on the other hand. This work proposes an information system architecture designed for a SCADA (Supervisory Control and Data Acquisition) system for railway smart grids. The main contribution of this work is to provide an information system architecture with an integration of industrial solutions for an EMS for railway smart grids.

Within the framework of an ongoing project coordinated by the French railway operator SNCF, an energy management system for DC railway sections is being developed. The present work proposes an architecture of the information system of the SCADA that includes day-ahead supervision and real-time control. The connection with different information sources and the choice of database organization are discussed. This information
system is intended to be implemented on an industrial computer capable of interfacing with storage converters and PV converters to send set-points generated by the supervision system. The paper is organized as follows: the second section presents the architecture of the studied system, the third section describes the SCADA system, the fourth section presents the proposed information system architecture. Some details about the Technological implementation of the proposed solution is given in section five. A discussion about the proposed solution and the validation of the studied architecture using real-time simulation is presented in section six and a conclusion is provided in the seventh section.

## II. STUDIED SYSTEM ARCHITECTURE

A DC traction substation (TSS) mainly consists of transformer rectifier unit connected to the catenary. A TSS with local PV production and (ESS) connected to the catenary is considered for this study (see figure 1). The system is powered by a public electricity grid at 63 kV. This voltage is then stepped down to 1.5 kV DC by the TSS. The 1.5 kV DC voltage is then fed to the overhead catenary wires, which are suspended above the railway tracks. The trains collect the power from the catenary wires using pantographs. The pantographs are mounted on the roof of the train and make contact with the catenary wires as the train moves along the track. The ESS and the PV system considered in our research are connected to the 1.5 kV DC bus via DC-DC converters to the substation in the middle. The maximum power at which converters and batteries operates is 1.2 MW in discharge mode and 800 kW in charging mode. The surface of the photovoltaic panels considered is 2000 m2 [11].

## III. SCADA DESCRIPTION

The role of the SCADA in the studied system is to control the ESS and the PV system to achieve specific objectives, such as reducing energy consumption from the grid or minimizing energy costs. This control approach is inspired by the study in [5] and is based on two control steps. The first step involves day-ahead optimization, which uses forecast of TSS consumption and PV production to generate a power profile for the ESS over 24 hours. This profile is applied to the actual system as long as no constraints are present. If constraints arise, the real-time controller adjusts the ESS set-point and may limit PV production in some cases. This real-time control is based on measurements taken directly from the TSS [11]. Figure 2 presents a simplified operational schema of the SCADA system.

<figure><img src="image_1.png" /><figcaption>Fig. 1. Diagram of TSS with local production and ESS</figcaption></figure>

<figure><img src="image_2.png" /><figcaption>Fig. 2. Simplified operational schema of the SCADA</figcaption></figure>

The following section presents the architecture of an industrial system proposed for an implementation of the control algorithm on an actual system.

## IV. INFORMATION SYSTEM ARCHITECTURE

### A. Real-time measurements and communication protocol

Different measurements are needed for SCADA system such as power flow and voltage. These measurements are provided using current and voltage sensors and communicated with SCADA system using a communication protocol. Modbus TCP/IP [6] is a widely used messaging service for industrial applications with different functions that allow information exchange between the sensors and the SCADA system. As illustrated in figure 3, the SCADA system of the studied system uses Power measurements of TSS and the PV system to provide
consumption and production forecast for the day ahead optimization. The real-time controller uses other measurements like TSS voltage and storage data to avoid any constraint in the system. Programmable Logic Controller (PLC) are used in such system as gateway between the low level control of power converters present in the system and the SCADA. The PLC can communicate using Modbus protocol to send measurements to SCADA system and send back set-points to converters low level controller. A central PLC is used for the studied system as a gateway to gather data from different PLCs of ESS, PV and TSS.

## B. Data flow description

The data flow and the different tasks in the SCADA system are managed by python programs. Data flow is presented in figure 4. The data acquisition is done using "Modbus TCP/IP data acquisition program" with a sampling time of 200 ms in order to get different measurements and send it to the database. Data cleaning and correction is done using "Clean data" program which is run one time every 24 hours. An average of the corrected data over ten minutes is performed and saved to a database. The PV production forecast is provided basing on weather forecast and 10-minute historical data. The program uses a Neural Network based model and is run every 24 hours. The weather forecast is provided from an API (Application Programming Interface) of a web-service. By the same, the TSS consumption forecast is provided using Neural Network based model. The program is run every 24 hours. Day-ahead optimization algorithm provide power profile to the ESS based in the different forecasts of the production and the consumption. This program is run every 24 hours. The real-time controller program is run with a step time of 200 ms in order to check the different constraints in the system. If no violation is detected, the day ahead profile will be applied to the ESS. If any constraint is detected, the real-time controller will adjust the ESS set-point and the PV system set-point to reduce the constraints impact on the system.

The real-time measurements data is used for the Graphical User Interface (GUI) in order to monitor the operation of the system by an operator and to allow different analysis of KPI (Key Performance Indicator) basing on historical data.

# V. TECHNOLOGICAL IMPLEMENTATION

This section is dedicated to present the technological choices that are made to implement the SCADA system

<figure><img src="image_3.png" /><figcaption>Fig. 3. SCADA information system architecture</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 4. Data flow diagram</figcaption></figure>

in an industrial environment to ensure a relevant coordination between the different services that need to be provided by the SCADA.

### A. Micro-services architecture

The first choice has been to develop in a micro-services architecture. Micro-services software architecture consists in breaking down the complete software in small pieces, each being in charge of a particular function. This type of architecture is very useful in terms of maintainability and evolvability, which are two very important concerns during the product-life on the industrial side. In addition, developing in micro-services is very convenient when working in teams : Each developer is responsible of his own service and only has to stick to inputs/outputs specifications to meet the global function of the software. In a research environment, this is very

useful to bring together all local contributions that can be made along time by various contributors like research engineers, students or PhD candidates.

### B. Kafka

Kafka is a distributed data streaming platform capable of publishing, subscribing to, storing, and processing real-time data streams. It efficiently manages data flows from multiple sources to multiple consumers, facilitating the simultaneous movement of large volumes of data across various endpoints, not limited to a single path but throughout an extensive network. Kafka combines three key capabilities [7] [8]:

* To publish (write) and subscribe to (read) streams of events, including continuous import/export of data between different parts of a system.

* To store streams of events durably and reliably.

* To process streams of events as they occur or retrospectively. And all this functionality is provided in a distributed, highly scalable, elastic, fault-tolerant, and secure manner.

It has been decided to implement this architecture using Kafka as message broker. Kafka is then in charge of linking micro-services together (see figure 3). Each micro-service produces the result of its computation in a specific topic (as a (producer)) and all various services that need this data can subscribe to this topic (as a (subscriber)) to be automatically informed by Kafka when new data is produced. The main concern as a global architect being then to build consistency in time refresh between the services. In addition to allowing micro-services to communicate between each other, Kafka brings a natural monitoring of the system through an overview of the outputs of each service. It also facilitates the automatic loggings of data through connectors to local database. One of the constraint that still has to be considered with Kafka can be the real time constraints of the system when critical response times need to be met.

### C. Docker

Choice has been made to embed each micro-service in a docker container. A docker container is kind of a micro-environment setup explicitly for the corresponding micro-service. This gives at the same time more freedom of choice for the developer concerning the language or libraries used and an easier integration in the global system, not having to consider consistency between libraries versions for the whole services. It also prepares for an easier deployment on future servers for the industrial phase. The list of used dockers is summarised in Table I
with the function of each docker and the corresponding step-time.

<table><caption>TABLE I
DOCKER LIST OF THE SCADA</caption><thead><tr><td>Program</td><td>Function</td><td>Step-time</td></tr></thead><tbody><tr><td>Real-time measurements</td><td>Communicate using the protocol Modbus TCP/IP.</td><td>200 ms</td></tr><tr><td>Weather forecast</td><td>Get weather forecast using solemnet API</td><td>2 hours</td></tr><tr><td>Clean data</td><td>filter and correct invalid data and save it to the database</td><td>24 hours</td></tr><tr><td>TSS Consumption forecast</td><td>predict TSS consumption for 24 hours based on historical data</td><td>24 hours</td></tr><tr><td>PV production forecast</td><td>predict PV production for 24 hours based on historical data and weather forecast</td><td>24 hours</td></tr><tr><td>day ahead optimization</td><td>provide power profile for ESS for 24 hours based on consumption and production forecast</td><td>24 hours</td></tr><tr><td>Real-time controller</td><td>verify the constraints of TSS and adjust the ESS power and PV power if necessary, else it applies the day ahead set-point</td><td>200 ms</td></tr><tr><td>GUI</td><td>communicate with Kafka to get data for GUI</td><td>1 s</td></tr><tr><td>Storage Power management</td><td>Calculate and send set-points for each storage unit. It manages also the balance between different storage units</td><td>200 ms</td></tr><tr><td>PV Power management</td><td>Calculate and send set-points for each PV converter</td><td>200 ms</td></tr></tbody></table>

## VI. DISCUSSION

The proposed system information architecture suggests the use of Kafka messaging service, PostgreSQL database and docker containers for the implementation of SCADA system on an industrial server and ensure an organised operation of the different programs in automated way. The SCADA system is intended to be implemented on an industrial server. However, the validation of reliable operation of the SCADA still a complicated and risky task since any failure could lead to big consequences on the actual system. Real-time simulation of the TSS suggests a relevant test environment to test a continuous operation of the SCADA and to test the coordination of the different programs with considering the real-time operation. Figure 5 presents

the TSS including the ESS and the PV system simulated in real-time simulator. The PLCs present in the actual system are emulated using Modbus TCP/IP module in the simulated part of the system. The industrial server communicates with the emulated PLCs by the same exact way present in the actual system.

<figure><img src="image_5.png" /><figcaption>Fig. 5. Real-time simulation based validation system</figcaption></figure>

## VII. CONCLUSION

This paper presents an information system architecture for supervision and control of railway Smart Grid. The aim of this work is to provide information for the industrial implementation of SCADA system. The architecture uses an open-source distributed event streaming platform Kafka to manage the data flow between the different function of the SCADA system which uses real-time measurements. The use of Docker to package and run applications in isolated environments is also introduced. A validation of the SCADA system operation using real-time simulation as an intermediate validation step before integrating the actual system is proposed. The future work consists of testing the operation of the SCADA using real-time simulator in which a dynamic
model of the TSS, ESS and PV system is implemented
with an emulation of the TSS consumption using real
traffic tables.

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