Information Integration and Knowledge Representation

Knowledge representation (KR) reinforces the possibility that the smart grid could herald our evolution in energy management from the ‘‘Age of Information’’ into the ‘‘Age of ‘Intelligence.’’ This vision, shared by the State Grid Corporation of China in their ‘‘Framework and Roadmap for Strong and Smart Grids’’ [83] would bring energy management within the realm of ‘‘Internet of Things’’ [2].

The pivotal importance of a semantic model to support understanding within KR is underlined by its central position in the GWAC Stack and therefore inter­operability. Whether it is to provide a standard means for message exchange between PSAs operating with heterogeneous perspectives of the smart grid, or a standardized interface specification, the CIM’s platform independence and ability to support information integration is strengthened as a domain ontology. Neumann et al. recognize that the rapid growth of the CIM gives rise to questions about its scope and how best to apply it to a variety of roles ranging from information management and systems integration to information exchanges and application modeling [84]. It could eventually be viewed as a combination of ontologies made from the packages of UML classes of which it is composed, or as part of a federation of ontologies when considered amongst other smart grid standards as well as OPC and MultiSpeak. Either way, it has a range of applications that depend to a greater or lesser extent on the richness of the semantic language to convey the meaning of vocabulary and conceptualizations.

Quirolgico et al. in [85] assert self-managing systems in a domain comprising disparate applications, devices, components, and subsystems depend on a formal ontology to support knowledge interoperability and reasoning. While they were referring in this case to a computing and networks environment, these are some useful pointers to the evolving role of ICT within the smart grid. Not least the importance of full and formal semantic definitions within the vocabulary of the CIM as well as the capability of the languages used for construction and messaging to convey the intended meaning and knowledge representations within the ontology. This is in the interest of reducing the burden of a priori knowledge and reasoning on the part of the participating PSAs. In [86] Tang et al. make the point that the presence of ontology not only serves to promote knowledge sharing across different departments but also makes knowledge reuse available when there are changes to domain tech­nologies through innovation. In [87] Sourouni et al. say ontologies can be employed at different levels of understanding. Examples of these range from contributing to the specification, reliability, and reusability of systems, through making data exchange easier, up to full functional interoperability of data and function.

Referring to the role of the IEC CIM within the ‘‘Semantic Understanding” layer of the GWAC Stack, we may then consider the need for richer information transport not simply supporting information interoperability but knowledge interoperability in future smart grid systems. The latter will depend on the ability of the encoding language to support the knowledge and reasoning constructs intended by the semantics and metadata of the ontology. Semantics are supported by the formality of the CIM descriptions and are combined with metadata using the schema defi­nitions carried by the schema language for machine interpretation. The XML schema definition (XSD) is used to specify the structure and contents of an XML file, and therefore also serves to validate its contents. OWL is designed to explicitly represent the meanings of terms and their relationships in the vocabularies of ontologies. Thus for purposes requiring a higher degree of knowledge representa­tion it may be necessary to consider as schema language, the use of the more powerful Web Ontology Language (OWL) over CIM RDFS expressions in future.

The value of framing the CIM as a metamodel in recognized terms is that we can utilize established methods from related domains such as Artificial Intelligence and Computer Science. For example we can envisage the CIM occupying ‘‘Level M2’’ of OMG’s ‘‘Four Layer Hierarchy’’ [88]. Thus metadata models derived from the CIM become instances of the data models belonging to PSAs at ‘‘Level M1’’, which in turn are composed of instances of data at ‘‘Level M0’’. Each level higher is an abstraction of the level below it and supports opportunities for integration of a wider range of conceptualizations of smart grid reality. Hargreaves et al. report on a methodology using this convention to create a CIM-based metamodel repository as a means of smart grid knowledge representation and development [89]. Alignment of different PSA conceptualizations of the same electrical network helps to optimize power utility processes and understanding of smart grid reality. As the repository integrates CIM-based metadata models aligned over boundaries marked by semantically common power system resources, a fuller knowledge representation of the smart grid reality comes into focus. While semantic com­monality is a requisite for boundary alignment, identification of the same power system resource, derived from different PSA meta-conceptualizations, usually differs due to the different processes for data manipulation employed in each PSA. The issue of multiple identities attributed to the same object is a common feature in human nature where understanding the distinction between one object identity and another often depends on context. As the PSA metadata models are encoded in CIM RDF XML, the use of an XML namespace to “contextualise” each PSA representation provides the means to maintain resource identities in their original form while at the same time rendering them receptive to alignment within the repository. In this way an integrated metamodel repository can offer a rich envi­ronment for information integration and knowledge extraction across utility business domains as well as forming the basis of a central network model man­agement system [59]. Such a resource will become of increasing importance with the integration of large-scale renewable power generation and storage facilities as the smart grid develops.

3 Conclusion

This chapter began by explaining the importance of the smart grid for integrating novel energy processes and technologies to deliver sustainable energy. Depending on interoperability to be reflexive to the changes in supply and demand as well as deliver energy with optimum reliability and economy we examined the centrality of the IEC CIM within model-driven interoperability processes. The value of semantic modeling in building ontology was then discussed leading to the proposal that combined with syntactic agreement provided by schema definitions and management of context provided by namespace, a metadata model repository can leverage the value of PSA data models into KR for better business understanding of smart grid reality. Using this design pattern, we can advance in accordance with interoperability at all levels, including data to data (D2D), model to model (M2M), application to application (A2A), and enterprise-to-enterprise (E2E) in building the vision for the smart grid to move from the age of information toward the age of intelligence.

[1] Field-oriented control of the primary reactive power and electromagnetic torque is inherently decoupled in both the BDFRG and DFIG [20], but not in the BDFIG [8, 10, 26].

[2] A good literature review on control of the BDFIG can be found in [7-10, 18], and of the DFIG in [24, 27].

Updated: October 23, 2015 — 12:40 pm