Optimal coordination of static VAR compensators, fixed capacitors, and distributed energy resources in Egyptian distribution networks

Abstract

This article suggests a bi‐stage methodology for optimal allocation of static VAR compensating (SVC) systems in integration with fixed capacitors (FCs) and distributed energy resources (DERs). The proposed methodology is based on an improved Grey Wolf algorithm (IGWA). Multifarious objectives are comprised to minimize the investment costs of the new devices installation, the costs of the power generation from the grid, the active power losses, the system voltage deviations, and to enhance the power transfer capability through minimizing the load balancing index. These disparate objectives are incorporated together and handled based on IGWA by merging simultaneously various daily loadings through light, intermediate and nominal load levels. The proposed methodology is applied on two various topologies of real‐world grids belong to the Egyptian electrical unified network. The simulation results demonstrate noticeable technical and economical features with eminent capabilities of the utilized algorithm. The compensation of the reactive power via SVCs based on the proposed methodology leads to significant improvement in the whole quality of the nodes voltage with the load variations. In light loading conditions, the SVCs regulate their output, in an optimal way, in according to the reactive power requirements in the neighboring nodes and they help in relieving the negative impacts of nodes overvoltage due to the DERs and FCs that may be existed.

Adequate Operation of Hybrid AC/MT-HVDC Power Systems Using an Improved Multi- Objective Marine Predators Optimizer

“…Various modern and efficient algorithms are utilized for comparison purposes such as BAT [37,38]; CSOA [39,40]; DA [41]; MVO [42]; SSO [43]; PSO; GWO [44]. They are performed with fitness evaluations of 15000 times where Maxit is taken 300.…”

“…The proposed IMMPO is developed in MATLAB language and applied on a modified IEEE 30-bus, and a practical part of the Egyptian system of WDRPN to solve the considered problem in AC/MTHVDC Grids. For such implementations, the suggested algorithm is comparatively assessed with different modern algorithms of BAT [37,38]; CSOA [39,40]; DA [41]; MVO [42]; SSO [43]; PSO; GWO [44]. Simulation results that are carried out on two test systems show the capability of the proposed solution methodology in finding diversified Pareto solutions with several possible operating points.…”

Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

“…The rapid power demand increase with limited generation and transmission expansion is a big challenge for numerous electrical grids. Therefore, the conventional distribution systems suffer from excessive losses, poor voltage regulation, continuous overloading, unreliability, and service insecurity [1], [2]. Commonly, the enhancement of distribution systems requires addition of active and reactive power resources and controlling these injected active and reactive power.…”

“…In addition to this, optimal DNR and DGs placement are manifested in literature based on various algorithms such as the dataset approach and water cycle algorithm [4], improved elitist-Jaya algorithm [7], and improved sine-cosine algorithm [20]. Furthermore, optimal DGs and CBs placement in distribution systems has been manifested in various articles using meta-heuristic techniques such as the water cycle algorithm [2], PSO [3], genetic algorithm [6], and enhanced grey wolf algorithm [21]. In [22], tabu search and PSO algorithms have been presented in a comparative manner for DNR with the allocation of different DG types to improve the voltage profile and minimize the losses, but the daily load variations have not been included.…”

Multi‐objective manta ray foraging algorithm for efficient operation of hybrid AC/DC power grids with emission minimisation

“…The applications are carried out on two standard tests systems namely the modified IEEE 30‐bus, the modified IEEE 57‐bus test power systems and a one practical network from the Egyptian Network at the West Delta Region. To prove the proposed MO‐MRFA capability for such purposes, the obtained simulation results are compared with some modern algorithms such as: grey wolf optimiser (GWO)[47]; particle swarm optimizer (PSO); salp swarm algorithm (SSA) [48]; multi verse optimizer (MVO) [49]; dragonfly optimization algorithm (DA) [50]; crow search optimization algorithm (CSOA) [51, 52]; bat optimization algorithm (BAT) [53, 54]; marine predators optimization algorithm (MPO) [55]. The main contributions of this study are summarized as follow:…”