# Research on energy-saving algorithm of HVAC multi-agent system consensus based on event-triggered mechanism

## TL;DR

This paper introduces an energy-saving algorithm for HVAC systems using event-triggered communication to reduce network load and achieve economic dispatch.

## Contribution

A novel event-triggered algorithm is proposed for distributed economic dispatch in HVAC multi-agent systems.

## Key findings

- The algorithm successfully equalizes incremental costs across generators.
- Event-triggered communication reduces redundant information transmission.
- Discrete unequal trigger times confirm on-demand execution of control tasks.

## Abstract

This paper designs an energy-saving algorithm for HVAC multi-agent systems based on event-triggered mechanism. In this algorithm, the incremental cost of the generator set and the incremental benefit of the flexible load are taken as the consistency variables, and the discrete time consistency algorithm is used to realize the distributed economic dispatch. The algorithm controls the on-demand transmission of information by designing a trigger control condition to avoid transmitting redundant information in the network, thereby reducing the network transmission pressure. In addition, this paper also discusses how to use graph theory to describe the communication topology between agents, and how to use consensus algorithm to analyze the stability and convergence of the system. This paper verifies that the designed trigger conditions are sent to the interacting neighbor generators on demand in the form of discrete unequal periods. The simulation results show that the incremental cost of all generators in the system eventually tends to the same value, and the optimization goal of economic dispatch is realized. At the same time, the trigger time of each generator is discrete and unequal cycle, which proves that the algorithm based on event trigger mechanism can determine whether the control task is executed according to the preset trigger condition, so as to realize the on-demand execution.