This study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic system and a reheat thermal unit under three different scenarios. In the first scenario, the frequency response of the algorithm to a 0.1 p.u. load change in both regions was examined. In the second scenario, the algorithm's frequency response to sudden load changes from 0.1 p.u. to 0.4 p.u. was tested. Finally, the algorithm's frequency response was examined against different levels of solar irradiance for sensitivity analysis. This study compared the performance of the SHO-optimized controller with the optimization algorithms reported in the literature, including the Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). In this context, the optimization of PI controller gain parameters based on the ITAE metric resulted in SHO algorithm achieving the best performance with values of 2.5308, followed by WOA at 4.1211, FA at 7.4259, and GA at 12.1244. In tests, SHO significantly outperformed these algorithms in key performance metrics, such as Settling Time, Overshoot (M+), and Undershoot (M-). Specifically, SHO achieved 98.94% better overshoot and 85.25% reduced undershoot than GA, and concluded settling times 52.79% faster than GA in the first scenario. Similar superior outcomes were noted in subsequent tests. These results underline SHO's efficacy in enhancing system stability and control performance, marking it as a significant advancement over conventional LFC methods.

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.

Economic dispatch problem is an optimization problem where objective function is highly nonlinear. In this paper, an efficient method based on hybrid genetic algorithm- particle swarm optimization (GA-PSO) for economic dispatch (ED) problem is proposed. In the proposed method, children created by using similarity measurement between mother and father chromosomes relationship. The feasibility of the proposed approach is demonstrated for solve various types of economic dispatch (ED) problems in power systems such as, economic dispatch with valve point (EDVP) effects, the ED of generators with prohibited operating zones and ED with only fuel options and it is compared in the recent literature. The study results show that the proposed approach is more efficient in finding higher quality solutions in various type ED problems. Ill. 3, bibl. 20, tabl. 10 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.155

Tr en 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. The planning of electricity power systems operation wtih increasing energy demand and its operation under optimum conditions have gained more value in nowadays. The demanded electrical energy in the world and in our country is met by thermal fuel plants with a very high rate. In the case of Economic Dispatch (ED), the fuel costs of thermal power plants should be reduced. In this study, thermal plants should operate by using Crow Search Algorithm (CSA) is one of the heuristic methods, ED analysis of 14 buses and 6 thermal plants with 400 kV has been conducted. In the analyzes, generator capacity constraints, with line loss and production-consumption power balance are taken into consideration and optimum operating conditions of generators are determined in order to minimize the total fuel cost of the system.