Event-Triggered Cardinality-Constrained Cooling and Electrical Load Dispatch Based on Collaborative Neurodynamic Optimization

Zhongying Chen, Jun Wang, Qing Long Han

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This article addresses event-triggered optimal load dispatching based on collaborative neurodynamic optimization. Two cardinality-constrained global optimization problems are formulated and two event-triggering functions are defined for event-triggered load dispatching in thermal energy and electric power systems. An event-triggered dispatching method is developed in the collaborative neurodynamic optimization framework with multiple projection neural networks and a meta-heuristic updating rule. Experimental results are elaborated to demonstrate the efficacy and superiority of the approach against many existing methods for optimal load dispatching in air conditioning systems and electric power generation systems.

Original languageEnglish
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes

Keywords

  • Costs
  • Dispatching
  • Dynamic load dispatch
  • HVAC
  • Heuristic algorithms
  • Neurodynamics
  • Optimization
  • Power systems
  • and air conditioning (HVAC) systems
  • electric power systems
  • event-triggered mechanisms
  • heating
  • neurodynamic optimization
  • ventilation

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