No abstract

No abstract

Enerjinin talebinin artması ile elektrik güç sistemlerinin çalışmasının planlanması ve en uygun koşullarda işletilmesi son zamanlarda artarak önem kazanmaktadır. Ülkemizde ve dünya genelinde talep edilen elektrik enerjisi oldukça yüksek oranla termik yakıtlı santraller tarafından karşılanmaktadır. Ekonomik Yük Dağıtım (EYD) probleminde termik yakıtlı santrallerin yakıt maliyetleri azaltılarak şebekenin işletilmesi amaçlanmaktadır. Bu çalışmada sezgisel yöntemlerden biri olan Karga Arama Algoritması (KAA) uygulanarak Türkiye'de kullanılan 400 kV'luk, 6 adet termik yakıtlı santrali bulunan 14 baralı bir güç sisteminde EYD problemi çözülmüştür. Yapılan çalışmada, jeneratörlerin üretim limit değerleri, hat kayıpları ve üretim-tüketim güç dengesi dikkate alınılarak sistemin toplam yakıt maliyetini en aza indirgemek için jeneratörlerin optimum çalışma şartları belirlenmiştir.

Load Frequency Control (LFC) is essential to ensure the stability and performance of power systems. In this study, a novel optimization algorithm, the Sea Horse Optimizer (SHO) algorithm, was proposed for optimizing controller parameters in LFC problems of power system. The proposed SHO algorithm was tested on a two-area power system with photovoltaic system and reheat thermal units, under three different scenarios: a 10% load change in both area, large load disturbances, and varying solar radiation. The proposed algorithm optimizes the gain parameters of PI/PID controllers using performance metrics such as Integral of Absolute value of the Error (IAE), Integral of Square Error (ISE), Integral of Time multiplied by Square Error (ITSE), and Integral of Time multiplied by Absolute Error (ITAE). The study compared the performance of the SHO-optimized controller with other reported optimization algorithms such as Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). The study also evaluated the accuracy and effectiveness of the controllers using performance metrics such as Settling Time, Overshoot (M+), and Undershoot (M-). The results show that the SHO-tuned controller significantly reduces overshoot, undershoot, and settling time of the system oscillations compared to other algorithms. The study provides valuable insights into the optimization of LFC controllers and presents a significant contribution to the literature with its novel algorithm. The proposed SHO algorithm can be considered an effective alternative solution method for LFC in power systems.

This paper presents a method based on meta-heuristic to solve Dynamic Economic Dispatch (DED) problem in a power system. In this paper, Crow Search Algorithm (CSA), which is one of the heuristic methods is proposed to solve the DED problem in a power system. In this study, line losses, generation limit values of generators, generation-consumption balance, valve-point effect and ramp rate limits of generator are included as constraints. The proposed algorithm was implemented on two different test cases. Finally, the CSA results were compared with the results of well-known heuristics in the literature such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Symbiotic Organism Search (SOS) algorithm, Artificial Bee Colony (ABC) algorithm, Simulated Annealing (SA), Imperial Competitive Algorithm (ICA), Modified Ant Colony Optimization (MACO) algorithm. The results show that the proposed algorithm has a better operating cost. With the results of the algorithm proposed in the test system 1, a profit of $2,056,5931 per day and $751,751,4815 per year is obtained. It is seen that with the results of the algorithm proposed in the test system 2, a daily profit of $12,279,7328 and a yearly profit of $4,482,102,472 are obtained. Test systems are operated by using less fuel with the results of the proposed algorithm and thus the harmful gas emissions released by thermal production units to the environment are also reduced.