AAGLMES: an intelligent expert system realization of adaptive autonomy using generalized linear models

Abstract—We earlier introduced a novel framework for realization of Adaptive Autonomy (AA) in human-automation interaction (HAI). This study presents an expert system for realization of AA, using Support Vector Machine (SVM), referred to as Adaptive Autonomy Support Vector Machine Expert System (AASVMES). The proposed system prescribes proper Levels of Automation (LOAs) for various environmental conditions, here modeled as Performance Shaping Factors (PSFs), based on the extracted rules from the experts’ judgments. SVM is used as an expert system inference engine. The practical list of PSFs and the judgments of GTEDC’s (the Greater Tehran Electric Distribution Company) experts are used as expert system database. The results of implemented AASVMES in response to GTEDC’s network are evaluated against the GTEDC experts’ judgment. Evaluations show that AASVMES has the ability to predict the proper LOA for GTEDC’s Utility Management Automation (UMA) system, which changes in relevance to the changes in PSFs; thus providing an adaptive LOA scheme for UMA. Keywords-Support Vector Machine (SVM); Adaptive Autonomy (AA); Expert System; Human Automation Interaction (HAI); Experts’ Judgment; Power System; Distribution Automation; Smart Grid

AAHES: A hybrid expert system realization of Adaptive Autonomy for smart grid

Abstract--Smart grid expectations objectify the need for optimizing power distribution systems greater than ever. Distribution Automation (DA) is an integral part of the SG solution; however, disregarding human factors in the DA systems can make it more problematic than beneficial. As a consequence, Human-Automation Interaction (HAI) theories can be employed to optimize the DA systems in a human-centered manner. Earlier we introduced a novel framework for the realization of Adaptive Autonomy (AA) concept in the power distribution network using expert systems. This research presents a hybrid expert system for the realization of AA, using both Artificial Neural Networks (ANN) and Logistic Regression (LR) models, referred to as AAHES, respectively. AAHES uses neural networks and logistic regression as an expert system inference engine. This system fuses LR and ANN models' outputs which will results in a progress, comparing to both individual models. The practical list of environmental conditions and superior experts' judgments are used as the expert systems database. Since training samples will affect the expert systems performance, the AAHES is implemented using six different training sets. Finally, the results are interpreted in order to find the best training set. As revealed by the results, the presented AAHES can effectively determine the proper level of automation for changing the performance shaping factors of the HAI systems in the smart grid environment

Cyber security for smart grid: a human-automation interaction framework

Abstract-- Power grid cyber security is turning into a vital concern, while we are moving from the traditional power grid toward modern Smart Grid (SG). To achieve the smart grid objectives, development of Information Technology (IT) infrastructure and computer based automation is necessary. This development makes the smart grid more prone to the cyber attacks. This paper presents a cyber security strategy for the smart grid based on Human Automation Interaction (HAI) theory and especially Adaptive Autonomy (AA) concept. We scheme an adaptive Level of Automation (LOA) for Supervisory Control and Data Acquisition (SCADA) systems. This level of automation will be adapted to some environmental conditions which are presented in this paper. The paper presents a brief background, methodology (methodology design), implementation and discussions. Index Terms—smart grid, human automation interaction, adaptive autonomy, cyber security, performance shaping facto

Analysis of Emergent Behavior of Reliability in the System of Systems Including Energy Hubs

Abstract: System of systems is a collection of independent systems that pool their resources and capabilities together to create a new, more functional system. One of the basic features of any system of systems is emergent behavior that arises out of the interactions between components of a system and cannot easily be predicted or extrapolated from the behavior of those individual components. The purpose of this study is to provide an analysis of reliability-emergent behavior in the system of systems involving energy hubs. Based on this, the effect of the electrical interaction of energy hubs is examined, in order to improve reliability and economic indices in the management of the microgrid system. In order to evaluate the proposed model, the method has been implemented on IEEE 33-bus 12.66 kV test system. Numerical experiments confirm the performance and effectiveness of the proposed method.Keywords: Systems of systems, Emergent behavior, Microgrid operation scheduling, Smart energy hubs, Reliabilit

Data-Driven Flexibility-oriented Energy Management Strategy for Building Cluster Meso Energy Hubs

Buildings owned by a single person or set of occupants can be identified as a cluster, while they may not be located in the same area. Market-related interconnection of buildings enables a company or operator to benefit from the energy flexibility of the buildings by managing them as a whole cluster. This paper proposes a clustering method that divides multiple buildings into different clusters based on building cluster Pearson correlations coefficient (PCC). Further, this research introduces a building cluster as a meso energy hub (MEEH) as an integrated and synergistic managerial framework in multi-carrier energy systems. The proposed data-driven clustering method aims to increase the PCC between the energy generations and demands of buildings divided in the same cluster MEEH. Besides PCC, on-site energy ratio (OER), on-site mismatch ratio (OMR), maximum hourly surplus (MHS), and maximum hourly deficit (MHD) are employed as energy flexible building cluster indicators to demonstrate the effectiveness of the proposed approach towards net zero energy communities (NZECs) The numerical results show that the proposed method yields 33% decrement in operation cost, 70% increment in the average value of PCC, and significant improvements in OER, OMR, MHS, and MHD as energy flexible building cluster indicators

An expert system realization of adaptive autonomy in Electric Utility Management Automation

Abstract: Earlier we introduced a novel framework for implementation of Adaptive Autonomy (AA). This study presents an expert system realization of the AA framework, referred to as Adaptive Autonomy Expert System (AAES). The proposed AAES is based on the extracted rules from the Expert’s Judgment on proper Levels of Automation (LOA) for various environmental conditions, modeled as Performance Shaping Factors (PSFs). Decision fusion method is used as expert system inference engine, where eight decision fusion methods are developed as prospective ones. The AAES is realized in the practical case of electric power Utility Management Automation (UMA) for the Greater Tehran Electricity Distribution Company (GTEDC). The practical list of PSFs and the judgments of GTEDC’s experts are used as the expert system rule base in this research. The results of implementing the proposed AAES to GTEDC’s network are evaluated according to two criteria: average error and error margin. Five out of eight decision fusion methods are proven to be suitable inference engines, due to both criteria. Evaluation of the results shows that the proposed AAES can estimate proper LOAs for GTEDC’s UMA system, which change due to the changes in PSFs; thus providing a dynamic (adaptive) LOA scheme for UMA

AAPNES: A Petri Net expert system realization of adaptive autonomy in smart grid

Interaction of human and computer agents should be harmonized by adapting the automation level of the IT systems to maintain a high performance for the system in changing environmental conditions. This research presents an expert system for the realization of adaptive autonomy (AA), using Petri Net (PN), referred to as AAPNES, based on practical list of environmental conditions and superior experts' judgments. As revealed by the results, the presented AAPNES can effectively determine the proper level of automation for changing the performance shaping factors of the human-automation interaction systems in the smart grid

Exploring Social Capital in Situation-Aware and Energy Hub-Based Smart Cities: Towards a Pandemic-Resilient City

Although the severity of the COVID-19 pandemic has appears to have subsided in most parts of the world, nevertheless, in addition to six million deaths, it has yielded unprecedented challenges in the economy, energy, education, urban services, and healthcare sectors. Meanwhile, based on some reports, smart solutions and technologies have had significant success in achieving pandemic-resilient cities. This paper reviews smart city initiatives and contributions to the prevention and treatment of coronavirus disease, as well as reducing its destructive impact, leading towards pandemic-resilient economic and health systems. Furthermore, the situational awareness contributions are reviewed in pandemic-resilient governance. The main contribution of this study is to describe the construction of social capital in smart cities as a facilitator in creating a pandemic-resilient society in crisis through two analyses. Moreover, this research describes smart cities’ energy as interconnection of energy hubs (EHs) that leads to a high level of resiliency in dealing with the main challenges of the electricity industry during the pandemic. Energy-hub-based smart cities can contribute to designing pandemic-resilient energy infrastructure, which can significantly affect resilience in economic and health infrastructure. In brief, this paper describes a smart city as a pandemic-resilient city in the economic, energy, and health infrastructural, social, and governmental areas