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

Technology and Economics of Smart Grids and Sustainable Energy https://doi.org/10.1007/s40866-022-00128-z (2022) 7:3 ORIGINAL PAPER Improving the Techno-Economic Pattern for Distributed GenerationBased Distribution Networks via Nature-Inspired Optimization Algorithms Ahmed S. Hassan1 · ElSaeed A. Othman2 · Fahmy M. Bendary3 · Mohamed A. Ebrahim3 Received: 5 October 2020 / Accepted: 19 January 2022 © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 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. Keywords Distributed generation · Distribution networks · Multi-objective · Optimization techniques · Voltage stability index · Power losses Introduction The increased load demand, the global direction towards a clean environment by reducing the CO2 emissions and the enormous development of RERs technologies have played an important role in the expansion of the Distributed * Ahmed S. Hassan ; 1 Ministry of Electricity and Renewable Energy (MOERE), Cairo, Egypt 2 Department of Electrical Engineering, Faculty of Engineering, Al Azhar University, Cairo, Egypt 3 Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt Generation (DG) into the Electric Distribution Networks (EDNs) [1]. Generally, DGs, unlike big central power plants, are connected to or in close to load centers by the governmental utilities or the private sector via different investment schemes to supply customers with electricity locally and to reduce the total investments required for new huge power plants or transmission lines projects [2]. New DG-integrated EDNs are no longer passive as the power injected from the DGs changes the magnitude and even direction of network power flows. Thus, EDNs protection scheme, reliability, stability, power losses, voltage profile and power quality is changed due to DGs integration according to their types, locations, capacities, and operating power factor [3]. 13 Vol.:(0123456789) 3 Page 2 of 25 Technology and Economics of Smart Grids and Sustainable Energy Literature Review Generally, nature-inspired optimization algorithms are very effective in addressing and solving new problems in different fields of science and engineering by formulating them to optimization problems, subjected to complex nonlinear constraints. In the science field, many researches are introduced in different aspects. For example, in [4], the authors have employed Whale Optimization Algorithm (WOA) for modeling the daily reference evapotranspiration to achieve water resource management goals such as irrigation scheduling. In addition, the authors of [5] have employed a hybrid model of bio-inspired metaheuristic optimization algorithms to assess the soil temperature impact on plant germination and growth. Also, predicting river streamflow time series is presented in [6] by Shuffled Frog Laping Algorithm (SFLA) for water resources planning and management. While, the Krill Herd Algorithm (KHA) is introduced in [7] as a tool to forecast, analyze, and monitor the solar radiation time series in different climatic zones. Furthermore, to optimize the use of the lake, precise prediction of the lake water level fluctuations the researchers presented this study [8], depending on the Grey Wolf Algorithm (GWO). However, in the Engineering field, especially the renewable distributed generation resources integration, many researchers have addressed the implementation of distributed generation resources within the distribution networks by introducing several techniques for enhancing their performance. These techniques can be divided into three acting categories: heuristic, numerical, and analytical based [9]. The authors of [10] presented the DG allocation problem to optimize system losses, voltage stability and voltage deviations using the Monte Carlo simulation (MCS) integrated with some bio-inspired algorithms, which are, Manta-ray Foraging Optimization (MRFO), Grey wolf optimizer (GWO), WOA and Satin Bird Optimization (SBO) under load uncertainties. The study was implemented and applied to IEEE 33 and 69-bus radial systems and resulted in determining the optimal locations which provide an improvement in the system’s monitored parameters. However, the study didn’t compare between algorithms using graphics. In addition, the study [11] introduced the PSO algorithm for solving the location and sizing of DGs to decrease the power loss, enhance the voltage profile, and reduce the line current of a reconfigured radial IEEE-33 bus distribution system. The simulation results demonstrated that the proposed technique is comparatively efficient in reaching the objective function. In addition, a new method based on Coyote Algorithm (COA) is presented in [12] for distributed generation resources 13 (2022) 7:3 placement with a single objective of minimizing the real power loss. The effectiveness of this method is assessed while applying it on two distribution systems 69-node and 119-node at two proposed scenarios the first is depending on reconfiguration only and the second supposes simultaneous reconfiguration and DG placement. The findings proved the effectiveness of COA in sol (...truncated)