Abstract
Renewable distributed generators (RDGs) are widely embedded in electrical distribution networks due to their economic, technological, and environmental benefits. However, the main problem with RDGs, photovoltaic generators, and wind turbines, in particular, is that their output powers are constantly changing due to variations in sun irradiation and wind speed, leading to power system uncertainty. Such uncertainties should be taken into account when selecting the optimal allocation of RDGs. The main innovation of this paper is a proposed efficient metaheuristic optimization technique for the sizing and placement of RDGs in radial distribution systems considering the uncertainties of the loading and RDG output power. A Monte Carlo simulation method, along with the backward reduction algorithm, is utilized to create a set of scenarios to model these uncertainties. To find the positions and ratings of the RDGs, the artificial gorilla troops optimizer (GTO), a new efficient strategy that minimizes the total cost, is used to optimize a multiobjective function, total emissions, and total voltage deviations, as well as the total voltage stability boosting. The proposed technique is tested on an IEEE 69-bus network and a real Egyptian distribution grid (East Delta Network (EDN) 30-bus network). The results indicate that the proposed GTO can optimally assign the positions and ratings of RDGs. Moreover, the integration of RDGs into an IEEE 69-bus system can reduce the expected costs, emissions, and voltage deviations by 28.3%, 52.34%, and 66.95%, respectively, and improve voltage stability by 5.6%; in the EDN 30-bus system, these values are enhanced by 25.97%, 51.1%, 67.25%, and 7.7%, respectively.
Highlights
Incorporating distributed generators (DGs) can enhance system performance and voltage profiles, reducing both the total production cost of electricity and harmful greenhouse gas emissions
Renewable distributed generators (RDGs), including a solar PV unit, and wind turbines (WTs)-based DGs are incorporated into IEEE 69-bus and 30-bus EDN systems
The results were compared with those obtained using particle swarm optimization (PSO) [28], the ant lion optimizer (ALO) [29], the whale optimization algorithm (WOA) [30], and the sine cosine algorithm (SCA) [31] to verify the effectiveness of the proposed gorilla troops optimizer (GTO)
Summary
Incorporating distributed generators (DGs) can enhance system performance and voltage profiles, reducing both the total production cost of electricity and harmful greenhouse gas emissions. Determining the optimal allocation of DGs provides a number of benefits, including lower energy costs, reduced emissions, and improved voltage profiles [4,5,6]. In [3], the authors employed various bio-inspired methods, such as grey wolf optimization, manta ray foraging optimization, satin bowerbird optimization, and whale optimization (WOA), as well as Monte Carlo simulation, to determine the optimal position of DGs. In [14], an effective algorithm was presented, the equilibrium optimizer, with the purpose of resolving the energy management issue of a microgrid and optimizing DG location and size. The application of an efficient metaheuristic optimization technique called GTO, to determine the optimal sizes and placement of the RDGs. The allocation problem of RDGs is solved, taking into account the uncertainties of the load and the output power of RDGs. The uncertainties of the system are addressed using the Monte Carlo simulation method along with the backward reduction algorithm.
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