Delivery Reliability Assessment for a Multistate Smart-Grid Network with Transmission-Loss Effect
Source abstract
Abstract
Assessing the performance of the smart-grid system (SGS) under uncertainty is essential for ensuring a reliable energy supply from the perspective of the grid operator. In this study, a multistate smart-grid network (MSGN) is developed to evaluate the delivery capability of the SGS. An MSGN consists of multiple interconnected facilities, where nodes represent energy sources or converters and arcs denote feeders. The output of each facility in the MSGN is modeled as multistate, as maintenance activities and partial failures can result in multiple possible output levels. During power delivery, transmission losses may arise due to heat dissipation and feeder aging, potentially resulting in insufficient power supply at the demand side. From a smart-grid management perspective, delivery reliability, defined as the probability that the MSGN can successfully deliver sufficient power from energy sources to the destination under transmission loss, is adopted as a performance index for evaluating SGS capability. To compute delivery reliability, a minimal-path-based algorithm is developed. A practical SGS is presented to demonstrate the applicability of the proposed model and to provide managerial insights into smart-grid performance and operational decision-making.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Power System Reliability and Maintenance · Integrated Energy Systems Optimization · Electric Power System Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial Engineering and Management, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413, Taiwan
Department of Business Administration, Asia University, Taichung 413, Taiwan
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 411, Taiwan
Funding
National Science and Technology Council
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