Improving the Techno-Economic Pattern for Distributed Generation-Based Distribution Networks via Nature-Inspired Optimization Algorithms

Abstract

The massive increase in the utilization of Distributed Generation (DG) units in the traditional Electric Distribution Networks (EDNs) enforces the distribution companies' operators to enhance the technical performance of EDNs while considering economic perspectives. This challenge paves the way for developing a multi-objective optimization platform to tackle the techno-economic problems while respecting system uncertainties as well as the operational policy of the distribution companies. As a motivating solution for this multi-objective problem, this paper introduces the application of three natureinspired algorithms as multi-objective optimization techniques for enhancing the techno-economic performance of EDNs through the integration of multiple Renewable Energy Resources (RERs). Grasshopper Optimization Algorithm (GOA), Salp Swarm Algorithm (SSA) and Moth Flame Optimization Algorithm (MFO), have been employed in this comparative study to minimize the active power losses, enhance the Fast Voltage Stability Index (FVSI) and reduce the total costs, considering the penetration level specified margin as well as and the framework of the DG units' operating power factor constraints. The proposed algorithms have been implemented in the MATLAB environment and applied on various benchmark IEEE test systems (33-bus, 57-bus and 300-bus) as a mimic, small and large EDNs. A realistic part of the Egyptian distribution network (171-bus) is also introduced as a practical, applicable case study. The attained results show that the suggested optimization platform especially using MFO, is more effective and successful in determining and finding the optimal locations and capacities of different DG types for getting the optimal value of the objective function in minimum time within a minimum number of iterations.

An Improved Analysis of Distributed Resource Access Location and Capacity on Voltage Stability of Distribution Networks in Smart Grid

“…In order to produce more precise and trustworthy findings, this method considers a number of variables, including voltage and thermal limitations. Enhancing the efficiency and cost of power distribution through the use of nature-inspired optimization algorithms is referred to as "improving the techno-economic pattern" for distributed generation-based distribution networks in the discussion by Hassan, A. S., et al [22]. By optimizing the distribution network's architecture and functioning, these algorithms imitate the behavior of natural systems, improving system performance and lowering operating expenses.…”

Intelligent Composite Compensation Under Load Variation in Power Transmission System.

“…Conversely, the lines congestion and obstruction are greatly reduced by the Optimal DG Placement. Many studies are primarily concerned with verifying these DG sources so that the placement and size of the renewable energy generators reduce power loss and save generator costs [9]- [10].…”

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

“…Reddy et al (2018) presented a method based on an Ant Lion Optimization algorithm to optimize DG in radial distribution systems and minimize losses from the point of view of subscribers. Hassan et al (2022) presented the nature-inspired optimization algorithm as well as the techno-economic pattern to determine the optimal size and location of DG in distribution systems. By examining the research in this field, we notice a new perspective called the combination of two reconfiguration problems at the same time as the optimal allocation of DG units.…”