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

The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network and water transport delay, which make the problem of finding global optimum difficult using standard optimization methods. This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using shuffled complex evolution (SCE-UA) method. The proposed method introduces the new concept of competitive evolution and complex shuffling, which ensure that the information on the parameter space gained by each of individual complexes is shared throughout the entire population. This conducts an efficient search of the parameter space. In this study, the hydrothermal scheduling is formulated as an objective problem that maximizes the social welfare. Penalty function is proposed to handle the equality, inequality constraints especially active power balance constraint and ramp rate constraints. The simulation results reveal that SCE-UA effectively overcomes the premature phenomenon and improves the global convergence and optimization searching capability. It is a relatively consistent, effective and efficient optimization method in solving the short-term hydrothermal scheduling problem.