Power system dispatching and operation optimization including distributed power sources

Abstract

The increasing severity of energy shortage and environmental pollution issues has led to widespread attention being given to distributed power sources, such as wind and photovoltaic power. However, the integration of these sources into the grid has resulted in changes to the original operation mode and system structure of the distribution network, which has impacted the stability and economic efficiency of power system operation. This thesis has focused on distributed power systems as the subject of research. The wind and photovoltaic power predictions have been utilized as distributed power sources. Two optimization methods, network reconfiguration and reactive power compensation, have been combined and the operation of the distribution network with distributed power has been optimized from the perspectives of model construction and algorithm solving, with the aim of enhancing the performance of the distributed power system. The operation optimization of distribution grids containing distributed power sources has been achieved, leading to improvements in the economic efficiency, energy efficiency, and power quality of these grids. The specific content and outcomes of the research are as follows: (1) A multi-objective firefly algorithm (MOFSA) based on a hybrid strategy was designed to address the optimization model of the distribution network operation with distributed power sources. Initially, the MOFSA was proposed by incorporating a thirdorder chaotic mapping strategy and a nonlinear decreasing population proportion factor to enhance population diversity. It also utilized a Cauchy variation strategy and a positional perturbation strategy to improve the algorithm's ability to escape local optima. Subsequently, a non-dominated sorting mechanism and an external archiving mechanism were employed to achieve multi-objective optimization. The solution performance of the algorithm was then verified using test functions. The results demonstrated that the MOFSA algorithm had superior convergence accuracy to other tested algorithms, and the resulting solution set was more uniformly distributed. （2）In response to the economic, energy, and power quality issues of power systems with distributed power supply, a static integrated optimization model for power systems with distributed power supply was constructed by combining static reconfiguration and reactive power compensation. The model was established with the objective of minimizing the economic cost of the power system, node voltage deviation, and power loss, while considering the operational constraints of the power system. The MOFSA algorithm was then used to solve the model, and the optimal compromise solution was 河北工业大学博士学位论文 DOCTORAL DISSERTATION OF HEBEI UNIVERSITY OF TECHNOLOGY IV selected based on the similarity ranking technique's ideal solution. The results indicated that the Pareto front obtained by the MOFSA algorithm had broader coverage, a more uniform distribution, and a higher degree of completeness compared to other algorithms. The static integrated optimization model was shown to significantly improve the energy efficiency and power quality of the system while ensuring its economic efficiency. （3）For the dynamic integrated optimization problem of the power system with distributed power supply considering time slot division, a time slot division method based on Euclidean distance was proposed. A dynamic integrated optimization model of the power system with distributed power supply was constructed on the basis of the static integrated optimization model. The dynamic comprehensive optimization model was established by considering constraints on the number of switching actions and the coupling between different time sections, and by combining dynamic reconfiguration and reactive power compensation. A time period division method based on Euclidean distance was proposed to dynamically divide the optimization time periods. Finally, the MOFSA algorithm was used to solve the dynamic integrated optimization model with time slot division, and the effectiveness and advancement of the proposed algorithm on the model with the time slot division method were verified in the test system. The results confirmed that the optimization scheme obtained by the MOFSA algorithm was more competitive than other algorithms. The dynamic integrated optimization model was capable of effectively improving the economic efficiency, energy efficiency, and power quality of the power system. Additionally, the dynamic division of optimization periods using the proposed time division method significantly reduced the number of switching operations by 78.72%. Example analyses have verified the effectiveness of the proposed algorithm, model, and time slot division method, providing a reasonable optimization scheme for the operation optimization of power systems with distributed power sourc