=Paper=
{{Paper
|id=Vol-2638/paper7
|storemode=property
|title=A software platform to support the energy system resilience study
|pdfUrl=https://ceur-ws.org/Vol-2638/paper7.pdf
|volume=Vol-2638
|authors=Alexei Edelev,Evgeniy Fereferov
|dblpUrl=https://dblp.org/rec/conf/iccs-de/EdelevF20
}}
==A software platform to support the energy system resilience study==
https://ceur-ws.org/Vol-2638/paper7.pdf
A software platform to support the energy system resilience
study
A V Edelev1 [0000-0003-2219-9754] and E S Fereferov2 [0000-0002-7316-444X]
1
Melentiev Energy Systems Institute of Siberian Branch of SB RAS, Lermontov Str.
130, Irkutsk, Russia, 664033
2
Matrosov Institute for System Dynamics and Control Theory of SB RAS, Lermontov
Str. 134, Irkutsk, Russia, 664033
E-mail: alexedelev@gmail.com, fereferov@icc.ru
Abstract. The paper addresses the problem of supporting research of the energy
systems resilience. The resilience is concerned with the energy system ability to
withstand large disturbances. To reach the goal of creating user-friendly software for
decision making support in that field, a software platform is considered. It includes
some original frameworks for retrieving, gathering, querying and analysing data, and
building thematic maps to produce disturbance scenarios and evaluate consequences
of their impact. The main advantage of the proposed platform is its applicability for all
the stages of the energy system resilience research starting from risk mitigation
assessment and ending with comparing alternative recovering or
reconstruction strategies.
1. Introduction
The resilience of Energy System (ES) can be understood as the ability of critical infrastructures to
prevent damage before disturbance events, mitigate losses during the events, and improve the recovery
capability after the events [1, 2].
The main stages of the ES resilience research scheme are presented in Figure 1. The resilience
research starts with collecting information about the ES current state and development strategies,
choosing the disturbance types and measures to enhance the ES resilience.
The central role in the ES resilience research plays the vulnerability analysis [3]. The vulnerability
characterizes the scale of negative system consequences caused by a disturbance impact on ES.
The vulnerability analysis begins with the generation of a set of the possible ES states that reflect
the most representative characteristic combinations of external conditions of the ES development and
functioning during the given time period. Also according to the chosen types, the disturbance
scenarios are created.
Further, the vulnerability analysis is performed from three perspectives: global vulnerability,
critical components, and spatial vulnerability. [4, 5]. Each of the perspectives is characterized by its
own methods of modelling disturbances and evaluating the consequences of their impact. A
disturbance is generally modelled as a deformation of the ES structure and degradation of the
functional capabilities of the ES elements.
In general, the efficiency assessment of the resilience enhancement measures and checking their
acceptability are the same for all vulnerability analysis subclasses. One of the main effects of
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
resilience enhancement measures is reducing direct and indirect damage due to the energy
resources shortage.
Finally, experts choose the most effective measures that are invariant respect to some subset of
conditions and disturbance types and makes decisions about their implementation.
Energy system Resilience
Disturbance types development and enhancement
functionality data measures
Generation of the possible ES states and disturbance scenarios
Global vulnerability Determination of Spatial vulnerability
analysis critical elements analysis
Effectiveness assessment of the resilience enhancement measures and check of
their acceptability
The selection of invariant resilience enhancement steps and decision-making on
their implementation
Figure 1. The main stages of the ES resilience research scheme.
In order to accurately represent standalone and integrated ESs when assessing their resilience, some
issues have to be taken into account [6]. For example, a particular ES should be considered at the most
detailed technological level while an integrated ES can use the more aggregated representation.
A software platform (Figure 2) for the ES resilience research should enable accurate and
comprehensive scenario of awareness by integrating data from both the surrounding environment and
the behaviour and functional state of systems. Also, it should provide scenario analysis to i) assess
risks to mitigate impacts, ii) compare repair and reconstruction strategies, iii) support the business case
for investing into the ES resilience [7].
The creation of the software platform implies the adoption of the specific and suitable ES models,
retrieving geospatial data from multiple sources. Thus, the Geographic Information System (GIS) and
Database Management System (DBMS) are the key information technologies to build a visual
representation of the disturbances consequences and graphical user interface for the resilience
enhancement measures efficiency assessment [8].
The rest of the paper is structured as follows. In Section 2, we give a brief overview of the ES
resilience assessment frameworks and the correspondent decision making assistance software. Section
3 presents a framework for preparing and transformation semi-structured data as the first part of the
software platform. A framework for automate creation of a database application as the second part of
the software platform is proposed in Section 4. Section 5 describes the application of the software
platform to develop a workbench to find the ES critical components. Section 6 concludes the paper.
Figure 2. The architecture of the software platform providing the ES resilience study.
2. Related work
A range of approaches have been proposed in the literature for the resilience assessment [9, 10].
Resilience assessment frameworks are different in objective and scope based on type of the
assessment, type of the system, and type of the disruptive event [11]. Ouyang et al. developed a three-
stage decision-making framework [12] to protect system components against intentional [13] and
spatially localized [14] attacks. Fang and Zio formulated resilience enhancement problem as a robust
optimization [15, 16]. Ouyang et al. introduced four mathematical models and their solution
algorithms for exactly identifying the optimal protection strategies against worst-case malicious
attacks and natural hazards [17]. Sandia National Laboratories have developed a general framework
and complex software for assessing the resilience of all types of engineered and economic systems to
help developers and policy makers identify critical system vulnerabilities [18-20].
Nowadays there are several efforts to create the software frameworks for the resilience assessment
[21]. A software for the systemic seismic vulnerability and risk assessment of complex systems has
been developed in the SYNERG-G project [22]. A new concept of decision support system for all
phases of the risk and vulnerability analysis capable to work in both the operational and simulation
modes has been presented in the CIPCast [23]. The HAZUS platform was developed for estimating
risks from natural hazards on the built-environment [24].
Thus the resilience assessment process is quite complicated and particularly a number of challenges
for modeling and simulating ES exist [4]. Our proposed approach to deal with them is the software
platform that enables the flexible prototyping and fast implementation of applications so-called
workbenches. It should be noted that this platform is of a general character and must be adapted to the
particular task of the resilience research for example vulnerability analysis. In other words, an
application prototype before usage has to be fulfilled with expert knowledge, specific tools and
ES models.
3. Framework for extracting and transforming subject data
Usually, the initial data for the ES resilience study is the statistical information presented in Microsoft
Excel or Comma-Separated Values (CSV) format. The subject data in its form are semi-structured.
Despite the machine-readable format, there is no meta-information in the input data describing the
roles of the values. It is difficult to identify the headers and data placement and the relationships
between data sections. The researcher must make additional efforts to extract, clean and convert the
input data into the target format.
We developed algorithms and framework for marking up and transforming data from semi-
structured sources into target formats of scientific applications. The framework prototype includes a
markup tool and transformation modules. The markup tool provides the researcher the ability to
visually customize and specify the process of transforming data needed to solve a particular class of
problems. Transformation modules provide the creation and transformation of data into target
structures of specific types. The general scheme of marking up and transforming is shown in Figure 3.
Figure 3. Marking up and data transformation scheme.
The markup and transform scheme is as follows:
1. Loading semi-structured data. The framework allows one to load semi-structured files in MS
Excel and CSV formats. After that the researcher can view data was a spreadsheet form and visually
shape the structural model of the source data.
2. Defining entities names. Using the markup tools the researcher determines the names of entities.
Entity names can be selected from the corresponding cells or written by the user.
3. Defining ranges of entities in semi-structured data. The researcher visually sets the position of
the beginning (upper left cell) and end (lower right cell) for each entity on the sheet. It is assumed that
a semi-structured data sheet can have either one or several tables. Moreover, the user can split one
table into several ones by setting boundaries for each table.
4. Defining attributes of entities and domains. The user visually define which cells from the range
specified in the step 4 are attribute names. Also domains of attributes can be specified.
5. Defining references between entities. The user can specify the references between some tables
defining what attributes implement these references.
6. Setting transformation rules. At this step, transformation rules are configured. For example, user
can specify a rule for splitting attributes into several attributes or assign conditional processing of
values using the construction «if … then … else …».
7. Creating structural specification. The description of the initial data scheme can be extended with
additional structures to provide the formation of complex target structures. For example, such an
extension can maintain a hierarchical structure. The knowledge model about the markup and
transformation rules are saved to a structure specification file. Next, the specification can be processed
by data transformation modules into specific target formats or loaded into a markup tool system for
correction.
8. Transforming source data. Transformation modules usually start execution from markup tool.
The Application Programming Interface (API) for interacting external modules is implemented in the
markup tool. API methods provide access to semi-structured data via the structures of the created
model. The API allows us to support interaction with different transformation modules without
modification of the markup tool. As of today, we developed translation modules into XML and JSON
formats. These modules allows us to create target structures taking access to the generated model and
data by means of API. Schemes of target structures for XML and JSON are set using templates in the
same file formats. In addition to other constructions, the templates contain specific tags. Values from
table fields are associated with tags.
The specification created using the markup tool is enough to generate a database. We implemented
a module that provides the generation of relational database schemas based on the specifications and
filling it with data via API. The structural specifications can be applied many times in solving typical
problems of data extraction and transformation, for example, in statistical information processing for
different periods are used.
4. Framework for Database Application Develop
For ES resilience assessment, an expert needs to be able to interact with source data, prepare ES model
data for calculations, configure and run computing modules, and view calculation results. We used the
GeoARM [25-26] framework to create an information system for ES resilience study provision. The
GeoARM allows one to device a full-featured database application by developing Specifications of
Database Applications (SDA). However, such specifications are rather small and do not contain code
duplicates. The SDA is to provide the minimum required information in its pure form about database
tables, their fields, links between them and their usage in the database application. All the other tasks
are performed by general algorithms, controlled by SDA. GeoARM allows us to interactively create
specifications for database applications and execute specification-driven algorithms. The general
SDA-directed algorithms for generation of user interfaces, interactive query building, report
generation, GIS interaction have been implement. Some nonstandard tasks can further be solved by
plugin modules, which extend the capabilities of the main application. GeoARM allows programmer
to provide access to most modern DBMSs (MS SQL Server, Oracle, Interbase, FireBird, PostgreSQL,
MySQL, SQLite, etc.).
The Workbenches being created with the help of GeoARM provides interaction with the
entities of the database as tables and individual records, and also allows one to build user queries. The
composition of controls and the style of working with fields of tables or views is determined
automatically based on information from the specification. For example, in tabular mode, lists of
values are automatically generated for the lookup fields, and if we work with several fields of the
source table, the sequence of their selection can be figured out. In form mode, visual interface
elements are automatically created depending on the specified fields types in the SDA. The user can
also work with records of details tables if the corresponding relationships of the “master-detail” type
were specified for the table in the SDA.
The GIS module is integrated into the GeoARM. This module is implemented using the GIS
ToolKit package from the GIS Panorama. Standard map viewing mechanisms, such as loading a map,
scaling, dragging and dropping a map, drawing, deleting an object, receiving information about an
object are implemented in the module. GIS module allows one to create and load digital maps, create
objects on maps, view information about objects and bind objects with database records. Each
application created using GeoARM has GIS functionality.
5. A workbench to find the ES critical components
The applicability of the proposed approach is demonstrated on the example of supporting the critical
components perspective of ES vulnerability analysis.
An ES sample is the Unified Natural Gas Supply System of Russia. Its network consists of the 382
nodes (including 28 sources, 64 consumers, 24 underground storages, and 266 compressor nodes) and
486 arcs representing main pipelines and branches to distribution networks.
The network is represented as a graph G ( N , E ) , where N is a set of nodes,
E i, j : i, j N, i j is a set of oriented arcs, the arc i, j E represents energy transport, i
and j are the starting and ending nodes of the arc i, j , respectively. The flow over the arc i, j E
and its capacity (the maximal flow) are denoted by yij and bij , respectively. We consider the
minimum cost flow problem that is related to determining the maximum flow z st with lowest cost
between a common source (node with the index s ) and sink (node with the index t ). This problem is
formulated as follows [27]:
kij yij min ,
i , j E
(1)
z st , if j s,
yij y ji zst , if j t , (2)
iN j iN j 0 otherwise ,
0 yij bij , (3)
where kij is the flow cost over i, j E , N j is the subset of input arcs for node j , N j is the subset
of output arcs of node j . Equation (2) ensures that the input flow and output flow for any node will be
equal. The flow constraints in equation (3) ensure that the flow over any arc will be non-negative and
will not exceed its capacity.
The network configuration is stored in a set of text files, thus the ES model initial data source is
semi-structured. We use the Framework for extracting and transforming subject data for import the ES
model initial data into a database. The Workbench of researcher (Figure 4) being created with the help
of GeoARM provides interaction with the entities of the ES database as tables and individual records,
and also allows one to build user queries and display information as digital maps. Researcher can
model critical situations by changing the characteristics of the ES objects and calling computational
modules. The tables of the solutions data are filled in as a result of the execution of external modules
which are launched by means of the plugin engine. Further, the researcher can generate a map with the
simulation results if the database has fields with the coordinates of the objects. We can visually build
queries on resultant data tables and display the query results in a GIS (Figure 5). In this case, we set
the color of objects on the map depending on the values of a specific field of the query result. We also
built a view for aggregating solutions data using DBMS tools and implemented access to it using
GeoARM (Figure 6).
Figure 4. The workbench for the ES resilience assessment.
Figure 5. The GIS module.
Figure 6. The aggregated view of the solution data.
6. Conclusions
In the paper, we considered the problems concerned supporting ES resilience research. Conducting the
studies requires a lot of efforts to prepare data, process data, perform calculations and analyze the
results. Often the source data are semi-structured and requires conversion into target formats for
further calculations and analysis. The amount of data describing ES is quite big and requires the use of
modern DBMS. Also, the research support requires user-friendly software that allows one to process
data, run calculation modules, analyze and display the results.
We have developed a software platform to support ES resilience research. The platform consists of
two main information systems: a system for extracting and transforming data from semi-structured
sources and a researcher's workbench. Both systems are based on structural specifications that allow
standardized solutions to common problems. The developed platform allows us to solve the
following tasks:
• Extract source data from semi-structured sources and load them into the database,
• Interact with the database through a convenient user interface,
• Execute external calculation modules,
• Conduct data analysis: build queries to the database, build thematic maps in the GIS.
An example of the Workbench to support finding the ES critical components was considered. It is
shown that the described software platform provides the flexible prototyping and fast implementation
of applications. And in comparison with other approaches, the main advantage of proposed platform is
that it is applicable for all stages of the ES resilience research starting from risk mitigation assessment
and ending with comparing alternative recovering or reconstruction strategies.
Acknowledgments
The research was supported by the Program of the Fundamental Research of the Siberian Branch of
the Russian Academy of Sciences, project no. III.17.5.1 (reg. no. AAAA-A17-117030310451-0). The
study in developing the GeoARM framework and creating Researcher workbench was provided within
the same program, project no. IV.38.1.2 (reg. no. АААА-А17-117032210079-1). The algorithms and
framework for marking and transforming data from semi-structured sources were developed with
support by the Russian Foundation for Basic Research, project no. 19-07-00097-a (reg. no. АААА-
А19-119062590002-7).
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