Wang Tiancai, He Xing, Huang Tingwen, Li Chuandong, Zhang Wei
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China; Key laboratory of Machine Perception and Children's Intelligence Development, Chongqing University of Education, Chongqing, 400067, China.
Department of Mathematics, Texas A&M University at Qatar, Doha, P.O.Box 23874, Qatar.
Neural Netw. 2017 Sep;93:126-136. doi: 10.1016/j.neunet.2017.05.004. Epub 2017 May 15.
The economic emission dispatch (EED) problem aims to control generation cost and reduce the impact of waste gas on the environment. It has multiple constraints and nonconvex objectives. To solve it, the collective neurodynamic optimization (CNO) method, which combines heuristic approach and projection neural network (PNN), is attempted to optimize scheduling of an electrical microgrid with ten thermal generators and minimize the plus of generation and emission cost. As the objective function has non-derivative points considering valve point effect (VPE), differential inclusion approach is employed in the PNN model introduced to deal with them. Under certain conditions, the local optimality and convergence of the dynamic model for the optimization problem is analyzed. The capability of the algorithm is verified in a complicated situation, where transmission loss and prohibited operating zones are considered. In addition, the dynamic variation of load power at demand side is considered and the optimal scheduling of generators within 24 h is described.
经济排放调度（EED）问题旨在控制发电成本并减少废气对环境的影响。它具有多个约束条件和非凸目标。为了解决该问题，尝试采用结合启发式方法和投影神经网络（PNN）的集体神经动力学优化（CNO）方法，对具有十台热力发电机的微电网进行调度优化，并使发电和排放成本之和最小化。由于考虑阀点效应（VPE）时目标函数存在不可微点，因此在引入的PNN模型中采用微分包含方法来处理这些点。在一定条件下，分析了优化问题动态模型的局部最优性和收敛性。在考虑输电损耗和禁止运行区域的复杂情况下验证了该算法的性能。此外，考虑了需求侧负荷功率的动态变化，并描述了24小时内发电机的最优调度。
