Chen Zhongying, Wang Jun, Han Qing-Long
IEEE Trans Neural Netw Learn Syst. 2023 Sep;34(9):5464-5475. doi: 10.1109/TNNLS.2022.3160645. Epub 2023 Sep 1.
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.
本文探讨基于协作神经动力学优化的事件触发式最优负荷分配。针对热能和电力系统中的事件触发式负荷分配，提出了两个基数约束全局优化问题，并定义了两个事件触发函数。在具有多个投影神经网络和元启发式更新规则的协作神经动力学优化框架中，开发了一种事件触发式调度方法。阐述了实验结果，以证明该方法相对于空调系统和发电系统中许多现有最优负荷分配方法的有效性和优越性。
