Optimization of Power System Scheduling Based on Shuffled Complex Evolution Metropolis Algorithm

Abstract

IntroductionSince the joint power system scheduling optimization is stochastic, dynamic, and involves time-delay, studies at home and abroad have been carried out on the development of power generation schemes and power system scheduling [1]. Commonly used methods include the equal incremental method, dynamic programming, linear programming, Lagrangian relaxation, the genetic algorithm, and the particle swarm optimization (PSO) algorithm. However, these algorithms all have their own limitations on solving the problem of scheduling optimization of hydrothermal power systems. The equal incremental method only satisfies the necessary conditions for the objective function to take the minimum value, not the sufficient conditions. Dynamic programming [2] suffers from the curse of dimensionality. Linear programming [3] requires linear simplification of the problems to be solved, thereby reducing the accuracy of the calculation. The Lagrangian relaxation [4] method has oscillations, even singular points, in the solution process. The genetic algorithm [5] and the PSO [6] algorithm have weak global search capability, and may easily fall into a local optimal solution. Consequently, none of these algorithms can accurately solve the problem of optimal power system scheduling.In order to overcome the shortcomings of power system scheduling optimization and its corresponding solutions of traditional models [7], this paper proposes a power system scheduling optimization model which takes the economic benefits, energy efficiency and environmental benefits into consideration. A new objective function, i.e. objective function of pollution emissions, is added to the objective function based on the conventional coal consumption costs. In this way, under the premise of effectively ensuring safe operation of the power system, the number of thermoelectric generator starts and stops can be minimized, and water resources can be used efficiently. This may also reduce pollutant emissions from electric power companies. This paper also uses the SCEM-UA [8] global optimization algorithm to solve the model. The SCEM-UA algorithm is a global optimization algorithm that combines the advantages of Shuffled Complex Evolution (SCE-UA) algorithm and Markov chain Monte Carlo (MCMC) method.With the SCEM-UA algorithm, in the process of evolution the complexes are not partitioned into multiple sub-complexes. Instead, a Markov chain is constructed so that parameters evolve toward the target posterior probability distribution [9]. SCEM-UA algorithm is

Volterra Series identification Based on State Transition Algorithm with Orthogonal Transformation

“…Those algorithms can overcome the drawbacks of the traditional identification methods, such as the requirement on the continuous differentiable objective function, and the sensitivity the measurement noise. However, they still have their own limitations on solving the problem of optimization [10]. Consequently, none of these algorithms can accurately solve the problem of Volterra series identification.…”