Multi-agent systems are well-known for their expressiveness to explore interactions and knowledge representation in complex systems. Multi-agent systems have been applied in the energy domain since the 1990s. As more applications of multi-agent systems in the energy domain for advanced functions, the interoperability raises challenge raises to an increasing requirement for data and information exchange between systems. Therefore, the application of ontology in multi-agent systems needs to be emphasized and a systematic approach for the application needs to be developed. This study aims to investigate literature on the application of ontology in multi-agent systems within the energy domain and map the key concepts underpinning these research areas. A scoping review of the existing literature on ontology for multi-agent systems in the energy domain is conducted. This paper presents an overview of the application of multi-agent systems (MAS) and ontologies in the energy domain with five aspects of the definition of agent and MAS; MAS applied in the energy domain, defined ontologies in the energy domain, MAS design methodology, and architectures, and the application of ontology in the MAS development. Furthermore, this paper provides a recommendation list for the ontology-driven multi-agent system development with the aspects of 1) ontology development process in MAS design, 2) detail design process and realization of ontology-driven MAS development, 3) open standard implementation and adoption, 4) inter-domain MAS development, and 5) agent listing approach.

https://hal.archives-ouvertes.fr/hal-02268957/document

The Application of Ontologies in Multi-Agent Systems in the Energy Sector: A Scoping Review

Traditional parallel computing for power management systems has prime challenges such as execution time, computational complexity, and efficiency like process time and delays in power system condition monitoring, particularly consumer power consumption, weather data, and power generation for detecting and predicting data mining in the centralized parallel processing and diagnosis. Due to these constraints, data management has become a critical research consideration and bottleneck. To cope with these constraints, cloud computing-based methodologies have been introduced for managing data efficiently in power management systems. This paper reviews the concept of cloud computing architecture that can meet the multi-level real-time requirements to improve monitoring and performance which is designed for different application scenarios for power system monitoring. Then, cloud computing solutions are discussed under the background of big data, and emerging parallel programming models such as Hadoop, Spark, and Storm are briefly described to analyze the advancement, constraints, and innovations. The key performance metrics of cloud computing applications such as core data sampling, modeling, and analyzing the competitiveness of big data was modeled by applying related hypotheses. Finally, it introduces a new design concept with cloud computing and eventually some recommendations focusing on cloud computing infrastructure, and methods for managing real-time big data in the power management system that solve the data mining challenges.

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Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

With the development of modern technology, mobile technology is widely used on many electric fields as its convenience and low-cost advantages. Since the grassroots electricians are equipped mobile terminals for higher efficiency, the electric enterprises reinforce the construction of mobile system and professional software application demand is increasing. In this paper, the fault record analysis display technology is raised to solve the concurrent request, data compression, information extraction and high-performance curve drawing when application runs in multi-client mode. Practice has proven that the application with adaptive display technology satisfy the requirements for relay protection routine maintenance and fault analysis which effectively increase the efficiency of electric operation and management.

The Fault Record Analysis Display Technology Based on Mobile Terminal

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.

Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions

The term smart grid refers to a next-generation electrical grid that uses advanced information, communication, and computing technologies to operate more efficiently. These technologies also provide tremendous economic and environmental benefits to the electrical grid. With emerging smart grid technologies, the deployment of smart devices and applications has been significantly increased, leading to the development of a variety of smart grid-related standards and protocols. These standards and protocols have become a necessity for the seamless integration of and interoperability among smart devices and applications. With emerging smart grid technologies, a variety of standards development organizations (SDOs) have been started to develop standards and protocols including all aspects of the smart grid, for example, enterprise and control center, wide area monitoring, substations automation, distributed energy resources (DERs), demand response (DR), metering, electric vehicles (EVs), cybersecurity, and home/building automation. These SDOs include the Institute of Electrical and Electronics Engineers (IEEE), International Electrotechnical Commission (IEC), International Telecommunication Union (ITU), Internet Engineering Task Force (IETF), National Institute of Standards and Technology (NIST), International Organization for Standardization (ISO), and others. The objective of this chapter is to briefly review and discuss major standards, protocols, and challenges in the smart grid domain.

Rafael Santodomingo

Papers

IEC 61970 for Energy Management System Integration