Series of Articles: Interoperability – Measuring Interoperability

Integrating controllable loads into the energy system is one of the current topics in the energy industry. The smart charging of electric vehicles is crucial in this context and at the same time poses new challenges. Interoperability plays a central role in enabling smart charging with different components. This was investigated at the FfE as part of the unIT-e² research project.

The first part of this series defined the concept of interoperability, identified existing challenges, and analyzed the potential benefits of its implementation. This second part focuses on an in-depth examination of methods for measuring interoperability. Assessing interoperability is crucial for determining the level of cooperation and integration between different systems. This helps enhance the efficiency and effectiveness of the technologies in use and ensures seamless integration.

Measuring and evaluating interoperability is essential for a thorough analysis and improvement of systems. A transparent evaluation allows for setting priorities and deploying resources efficiently where they are most needed. An interoperability assessment model provides valuable insights into the current status and forms a basis for targeted optimization. In socio-technical systems, interoperability goes beyond mere information exchange and includes a system’s ability to exhibit emergent behaviors in real-world processes. Measurement is relevant not only for assessing existing systems but also for achieving a certain level during development. Thus, interoperability measurement is a vital step for promoting and securing effective communication and collaboration between different systems.

What methods are there for measuring interoperability?

Given the various definitions and applications of interoperability, numerous measurement methods have been developed. Rezaei et al. (2014) provide a comprehensive overview of these approaches. One example is the Quantification of Interoperability Methodology (QIM), a military approach for measuring three main parameters: wide-area surveillance, over-the-horizon targeting, and electronic warfare. The Levels of Information Systems Interoperability (LISI) model, an extension of QIM, has broader applicability and assesses interoperability using a matrix with five levels: isolated, connected, functional, domain-specific, and enterprise-wide. The Organizational Interoperability Maturity Matrix builds on LISI and describes the maturity of organizations in a more abstract framework. Another significant model is the Enterprise Interoperability Maturity Model developed by the European Commission, which includes various maturity levels and sub-aspects [1].

The LCIM-Model

A model that has proven particularly suitable for evaluating interoperability in the energy sector and electromobility is the Levels of Conceptual Interoperability Model (LCIM). Originally developed by Tolk (2003) and further refined in subsequent years by him and others (e.g., Tolk et al., 2007; Axelsson, 2020), the LCIM encompasses seven levels of interoperability. These levels range from no interoperability (Level 0) to the technical connectivity level (Level 1), which focuses on basic network connections, up to the conceptual level (Level 6), which describes the use and interpretation of data in a comprehensive context. The higher levels, which emphasize the conceptual and dynamic use of data, are particularly relevant as they represent a system’s ability not only to process data but to use it meaningfully and efficiently [2,3]

The LCIM levels are defined as follows [2]:

1. Level 0 describes isolated systems with no interoperability.

1. Level 1, technical interoperability, enables data exchange through clearly defined communication protocols and network infrastructures.
2. On Level 2, syntactic interoperability, a common data structure is used, ensuring that the data exchange format is clearly defined.
3. Level 3, semantic interoperability, is achieved when data meaning is exchanged through a common reference model, allowing the content of the transmitted data to be uniformly interpreted.
4. On Level 4, pragmatic interoperability, systems understand the context of the data being exchanged and are aware of the methods and procedures employed by other participating systems.
5. Level 5, dynamic interoperability, requires that systems are capable of recognizing and adapting to changes in state to adjust data usage to altered conditions. This level is particularly important for capturing operational effects.
6. The highest level, Level 6 or conceptual interoperability, is reached when the conceptual models of systems, including assumptions and constraints, are aligned. This necessitates the creation of fully specified implementation-independent models that allow for interpretation and evaluation by engineers.

The LCIM model has established itself as a valuable tool for assessing interoperability in system-of-systems contexts. It enables the analysis of both technical and conceptual levels of interoperability, thus providing a comprehensive foundation for identifying weaknesses and implementing targeted improvements. This versatility makes LCIM a preferred approach for measuring interoperability in the energy and electromobility sector.

The third article in the series on interoperability explains how the LCIM model was used within the unIT-e² research project for measuring and evaluating interoperability. It analyzes how the model was applied to assess the degree of interoperability at various interfaces. This investigation enables well-founded conclusions about existing challenges and potentials necessary for seamless integration between sectors. The analysis using the LCIM model provides valuable insights into current interoperability practices and offers starting points for optimizing systems and processes to ensure more efficient collaboration.

Literature

[1] Rezaei, R., Chiew, T. K., Lee, S. P. & Shams Aliee, Z. (2014). Interoperability evaluation models: A

systematic review. Computers in Industry, 65(1), 1–23. https://doi.org/10.1016/j.compind.2013.09.00

[2] Tolk, A., Turnitsa, C. D. & and Diallo, S. Y. (2006). Ontological Implications of the Levels of

Conceptual Interoperability Model. Modeling, Simulation & Visualization Engineering Faculty Publications., 33. https://digitalcommons.odu.edu/msve_fac_pubs/33

[3] Axelsson, J. (2020). Achieving System‐of‐Systems Interoperability Levels Using Linked Data and

Ontologies. INCOSE International Symposium, 30(1), 651–665. https://doi.org/10.1002/j.2334-5837.2020.00746.x