Economic dispatch issues in power system aim to try getting an optimal plan for the power generators to minimize the fuel cost (FC) in parallel with satisfying system constraints. This paper proposes a new enhancement based on particle swarm optimization (PSO) algorithm called multiple inertia weight PSO (MIW-PSO) to solve the combined economic and emission load dispatch (CEELD) issues in the modern electrical power systems. Two electrical test systems are investigated in this study to validate the competence of the proposed algorithm. The obtained results for CEELD case using MIW-PSO compared with MOCPSO indicate a promising performance in terms of minimizing FC and pollutant emission (PE) are reduced 84.96 $/h and 12.01 kg/h for the first test system. As well as, for the second test system, compared with NSGA-RL are reduced 0.241 $/h and 3.15 kg/h. Moreover, the proposed algorithm has more accuracy, better convergence time, and higher quality solutions for the minimum CEELD compared with other methods.

Contemporary researches offer that most researchers have concentrated on either network reconfiguration or Distributed Generation (DG) units insertion for boosting the performance of the distribution system (DS). However, very few researchers have been studied optimum simultaneous distributed generation units insertion and distribution networks reconfiguration (OSDGIR). In this paper, the stochastic meta-heuristic technique belong to swarm intelligence algorithms is proposed. Salp Swarm Algorithm (SSA) is inspired by the behavior of salps when navigating and foraging in the depth of the ocean. It utilized in solving OSDGIR. The objective function is to reduce power loss and voltage deviation in the Distribution System. The SSA is carried out on two different systems: IEEE 33-bus and local Iraqi radial (AL-Fuhood distribution network). Three cases are implemented; only reconfiguration, only DG units insertion, and OSDGIR. Promising results were obtained, where that power loss reduced by 93.1% and recovery voltage index enhanced by 5.4% for the test system and by 78.77% reduction in power loss and 8.2% improvement in recovery voltage for AL-Fuhood distribution network after applying OSDGIR using SSA. Finally, SSA proved effectiveness after an increase in test system loads by different levels in terms of reduced power loss and voltage deviation comparison with other methods

<span>During the last few decades, electrical power demand enlarged significantly whereas power production and transmission expansions have been brutally restricted because of restricted resources as well as ecological constraints. Consequently, many transmission lines have been profoundly loading, so the stability of power system became a Limiting factor for transferring electrical power. Therefore, maintaining a secure and stable operation of electric power networks is deemed an important and challenging issue. Transient stability of a power system has been gained considerable attention from researchers due to its importance. The FACTs devices that provide opportunities to control the power and damping oscillations are used. Therefore, this paper sheds light on the modified particle swarm optimization (M-PSO) algorithm is used such in the paper to discover the design optimal the Proportional Integral controller (PI-C) parameters that improve the stability the Multi-Machine Power System (MMPS) with Unified Power Flow Controller (UPFC). Performance the power system under event of fault is investigating by utilizes the proposed two strategies to simulate the operational characteristics of power system by the UPFC using: first, the conventional (PI-C) based on Particle Swarm Optimization (PI-C-PSO); secondly, (PI-C) based on modified Particle Swarm Optimization (PI-C-M-PSO) algorithm. The simulation results show the behavior of power system with and without UPFC, that the proposed (PI-C-M-PSO) technicality has enhanced response the system compared for other techniques, that since it gives undershoot and over-shoot previously existence minimized in the transitions, it has a ripple lower. Matlab package has been employed to implement this study. The simulation results show that the transient stability of the respective system enhanced considerably with this technique.</span>

In the present days, power systems are operated favourably close to their stability limit. System stability can be safeguarded by employing an emergency control technique, known as load shedding; this technique is affected by shedding some loads. Such a technique implemented only when the voltage or frequency deterioration below a specific voltage or frequency threshold. In this work, an advanced emergency load shedding model is forwarded based on the voltage stability indicator aimed at enhancing voltage. This indicator facilitates online monitoring of voltage stability and predicts the voltage problem of the system with sufficient accuracy, fast and simple numerical calculation, also can work well in the steady-state as well as during the transient process. The proposed model and algorithm have been tested firstly on the IEEE 14 bus system as a standard system and then implemented on the Iraqi National Power Grid (INPG) as a particular reference system. The obtained results for the voltage profile has recovered by 11.1% for the standard system and 2.03% for the particular system.

Many of the power cuts in the electricity system in the last decade indicate that there is much work to be done to address the voltage instability and subsequent collapse. This study presents a method to find and select the optimum location of FACTs device using the hybrid line stability index (HLSI) that is appropriate for the prediction voltage collapse in power system networks. Such the HLSI was obtained by deriving expressions basics equivalent Line Stability Index (Lmn), and Fast Voltage Stability Index (FVSI) and mix theirs through a switch logic based on the voltage angle difference Where indicate the nearness voltage collapse. The HLSI has tested in Iraqi National Super Grid System (INSGS) it gives the same results as the other indicators (Lmn & FVSI). For the base state, INSGS was found to be stable with all the three indicators have approximately equal values least than 1 for all lines. The contingency state detects that ranks of bus 24 the weakest bus in the system with the lower maximum allowable reactive load of (490.371 Mvar) and the line critical concerning bus 24, the line connecting bus 20 to bus 24. The values the three indicators, Lmn, FVSI, and HSLI, approximately equal, increasing the accuracy of HLSI.