An External Archive-Based Constrained State Transition Algorithm for Optimal Power Dispatch

This paper proposes an external archive-based constrained state transition algorithm (EA-CSTA) with a preference trade-off strategy for solving the power dispatch optimization problem in the electrochemical process of zinc (EPZ). The optimal power dispatch problem aims to obtain the optimal current density schedule to minimize the cost of power consumption with some rigorous technology and production constraints. The current density of each production equipment in different power stages is restricted by technology and production requirements. In addition, electricity price and current density are considered comprehensively to influence the cost of power consumption. In the process of optimization, technology and production restrictions are difficult to be satisfied, which are modeled as nonconvex equality constraints in the power dispatch optimization problem. Moreover, multiple production equipment and different power supply stages increase the amount of decision variables. In order to solve this problem, an external archive-based constrained state transition algorithm (EA-CSTA) is proposed. The external archive strategy is adopted for maintaining the diversity of solutions to increase the probability of finding the optima of power dispatch optimization problem. Moreover, a preference trade-off strategy is designed to improve the global search performance of EA-CSTA, and the translation transformation in state transition algorithm is modified to improve the local search ability of EA-CSTA. Finally, the experimental results indicate that the proposed method is more efficient compared with other approaches in previous papers for the optimal power dispatch. Furthermore, the proposed method significantly reduces the cost of power consumption, which not only guides the production process of zinc electrolysis but also alleviates the pressure of the power grid load.

[1]  Xiaojun Zhou,et al.  Set-Point Tracking and Multi-Objective Optimization-Based PID Control for the Goethite Process , 2018, IEEE Access.

[2]  Yuren Zhou,et al.  An Adaptive Tradeoff Model for Constrained Evolutionary Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[3]  Xiaojun Zhou,et al.  Dynamic optimization based on state transition algorithm for copper removal process , 2017, Neural Computing and Applications.

[4]  K. Teo,et al.  A new exact penalty function method for continuous inequality constrained optimization problems , 2010 .

[5]  Yuren Zhou,et al.  Multi-objective and MGG evolutionary algorithm for constrained optimization , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[6]  Xiaojun Zhou,et al.  A Two-stage State Transition Algorithm for Constrained Engineering Optimization Problems , 2018 .

[7]  Y. Fung,et al.  A Theory of Elasticity of the Lung , 1974 .

[8]  Carlos A. Coello Coello,et al.  Constraint-handling in nature-inspired numerical optimization: Past, present and future , 2011, Swarm Evol. Comput..

[9]  Gary G. Yen,et al.  An Adaptive Penalty Formulation for Constrained Evolutionary Optimization , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  Yong Wang,et al.  A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems , 2009, Frontiers of Computer Science in China.

[11]  Xiaojun Zhou,et al.  A Novel Cognitively Inspired State Transition Algorithm for Solving the Linear Bi-Level Programming Problem , 2018, Cognitive Computation.

[12]  Xiaojun Zhou,et al.  State Transition Algorithm , 2012, ArXiv.

[13]  王勇 Constrained Evolutionary Optimization by Means of (μ + λ)-Differential Evolution and Improved Adaptive Trade-Off Model , 2011 .

[14]  Erkki Paatero,et al.  Copper removal by chelating adsorption in solution purification of hydrometallurgical zinc production , 2010 .

[15]  Jing J. Liang,et al.  Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization , 2006 .

[16]  Yong Wang,et al.  An improved (μ + λ)-constrained differential evolution for constrained optimization , 2013, Inf. Sci..

[17]  Weihua Gui,et al.  Modeling, optimization, and control of solution purification process in zinc hydrometallurgy , 2018, IEEE/CAA Journal of Automatica Sinica.

[18]  Yang Chunhua Optimization of time-sharing power supply for zinc electrolytic process based on improved PSO algorithm , 2007 .

[19]  Xiaojun Zhou,et al.  A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm , 2018, IEEE Transactions on Cybernetics.

[20]  Xiaojun Zhou,et al.  Discrete state transition algorithm for unconstrained integer optimization problems , 2012, Neurocomputing.

[21]  K. Deb An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[22]  W. Gui,et al.  A new multi-threshold image segmentation approach using state transition algorithm , 2017 .

[23]  Xiaojun Zhou,et al.  Fractional-order PID controller tuning using continuous state transition algorithm , 2018, Neural Computing and Applications.

[24]  Mustafa Servet Kiran,et al.  A modification of tree-seed algorithm using Deb's rules for constrained optimization , 2018, Appl. Soft Comput..

[25]  J. C. Balarini,et al.  Importance of roasted sulphide concentrates characterization in the hydrometallurgical extraction of zinc , 2008 .

[26]  Weihua Gui,et al.  An optimal power-dispatching system using neural networks for the electrochemical process of zinc depending on varying prices of electricity , 2002, IEEE Trans. Neural Networks.

[27]  Weihua Gui,et al.  An optimal power-dispatching control system for the electrochemical process of zinc based on backpropagation and Hopfield neural networks , 2003, IEEE Trans. Ind. Electron..

[28]  Xiaojun Zhou,et al.  A dynamic state transition algorithm with application to sensor network localization , 2015, Neurocomputing.