Optimization technologies have drawn considerable interest in power system research. The success of an optimization process depends on the efficient selection of method and its parameters based on the problem to be solved. Firefly algorithm is a suitable method for power system operation scheduling. This paper presents a modified firefly algorithm to address unit commitment issues. Generally, two steps are involved in solving unit commitment problems. The first step determines the generating units to be operated, and the second step calculates the amount of demand-sharing among the units (obtained from the first step) to minimize the cost that corresponds to the load demand and constraints. In this work, the priority list method was used in the first step and the second step adopted the modified firefly algorithm. Ten generators were selected to test the proposed method, while the values of the cost function were regarded as criteria to gauge and compare the modified firefly algorithm with the classical firefly algorithm and particle swarm optimization algorithms. Results show that the proposed approach is more efficient than the other methods in terms of generator and error selections between load and generation.

The balance of the power supply and demand (frequency control) is one of the most ancient approaches for the power systems, which is considered as a highly complex system.The power systems frequency response is a perfect indicator of the resilience to the multi-disturbances. In this work, the fuzzy logichas been scaledusing PSO segmentation (SePSO) and suggested to get high performance of frequency stability. PSO has participated into multi-segments for calculating the scald-fuzzy membership with basic rules. Two identical interconnectedpower areas wereselected to exam the new scaled fuzzy method. The time response of the results has undertaken the effectiveness of the controller reactionusing the MATLAB Simulink. The work feed back proved that the proposed SePSO optimization for the controlhas significantly faster with low undershot concerningthe classical controllers in differenttime schedules and disturbance values.

Load forecasting is a significant element in the energy management system of power systems. Precise load forecasting aids electric utilities to conduct decisions of unit commitment, reduction of spinning reserve capacity, and schedule device maintenance plan. Furthermore, load forecasting contributes to reducing the generation cost, and it is fundamental to the reliability of the power systems. On the other hand, short-term load forecasting is substantial for economic running. The forecasting precision directly affects the reliability, economy running and supplying power quality of the power system. Hence, finding the required load forecasting method to enhance the accuracy is valuable for forecasting precision. This paper proposed particle swarm optimization (PSO) to improve working support vector machine (SVM), SVM regression model is derived; also derived SVM with PSO. Support vector machine (SVM) model is adopted with and without PSO based on the historical load data and meteorological data of Tajikistan country, analysis the various factors affecting the forecast. The historical data and the load forecasting factors to be considered are normalized. The two parameters of SVM significantly influenced the model, and therefore it optimized using evolutionary algorithm.

Izvestiya Vysshikh Uchebnysh Zavedeniy. Electromechanics. Scientific and Technical Journal

One of the oldest methodologies for power systems, which is regarded a very complex system, is the balance of supply and demand (frequency control). The frequency response of power systems is a perfect measure of multi-disturbance resilience. The fuzzy logic has been scaled utilising PSO segmentation (SePSO) in this study, and it is advised to achieve high frequency stability performance. With fundamental criteria, PSO has participated in multisegments for computing scald-fuzzy membership. To test the novel scaled fuzzy approach, two identical interconnected power zones were chosen. The time response of the results has undertaken the effectiveness of the controller reactionusing the MATLAB Simulink. The results of the investigation show that the proposed SePSO optimization for control is substantially faster and has a lower undershot than classical controllers in various time schedules and disturbance values. The classical controllers have been used in different power system operation and control topics due to its simplicity.