Renewable Energy Resources (RES) can supply a clean and a smart solution to satisfy the rapidly growing load demand. The Photovoltaic (PV) and wind turbines are considered as resources of Distributed Generation (DG). The integration of PV - DG units into distribution systems has a significant effect on reducing the system active power losses. In this paper, a new technique based on the Moth Flame Optimization (MFO) algorithm, is used to optimally allocate the PV -DG units in Radial Distribution Systems (RDSs) and determine the optimal size of each unit. The objective of the technique is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed optimization algorithm is tested using three different IEEE standard RDSs of various sizes, which are 33, 69 and 118-bus systems. In order to demonstrate the applicability and the performance of the MFO algorithm, numerical simulations are obtained when applying the MFO algorithm, and compared with different existing optimization techniques. This comparison shows the validity of the proposed MFO algorithm.

Goal. The integration of photovoltaic distributed generations in the active distribution network has raised quickly due to their importance in delivering clean energy, hence, participating in solving various problems as climate change and pollution. Adding the battery energy storage systems would be considered as one of the best choices in giving solutions to the mentioned issues due to its characteristics of quick charging and discharging, managing the quality of power, and fulfilling the peak of energy demand. The novelty of the proposed work is the development of new multi-objective functions based on the sum of the three technical parameters of total active power loss, total voltage deviation, and total operation time of the overcurrent protection relay. Purpose. This paper is dedicated for solving the allocation problem of hybrid photovoltaic distributed generation and battery energy storage systems integration in the standard IEEE 33-bus and IEEE 69-bus active distribution networks. Methodology. The optimal integration of the hybrid systems is formulated as minimizing the proposed multi-objective functions by applying a newly developed metaheuristic technique based on various chaotic grey wolf optimization algorithms. The applied optimization algorithms are becoming increasingly popular due to their simplicity, lack of gradient information needed, ability to bypass local optima, and versatility in power system applications. Results. The simulation results of both test systems confirm the robustness and efficiency of the chaotic logistic grey wolf optimization algorithm compared to the rest of the algorithms in terms of convergence to the global optimal solution and in terms of providing the best and minimum multi-objective functions-based power losses, voltage deviation and relay operation time values. Practical significance. Recommendations have been developed for the use of optimal allocation of hybrid systems for practical industrial distribution power systems with the renewable energy sources presence.

In the last few years, the integration of renewable distributed generation (RDG) in the electrical distribution network (EDN) has become a favorable solution that guarantees and keeps a satisfying balance between electrical production and consumption of energy. In this work, various metaheuristic algorithms were implemented to perform the validation of their efficiency in delivering the optimal allocation of both RDGs based on multiple photovoltaic distributed generation (PVDG) and wind turbine distributed generation (WTDG) to the EDN while considering the uncertainties of their electrical energy output as well as the load demand’s variation during all the year’s seasons. The convergence characteristics and the results reveal that the marine predator algorithm was effectively the quickest and best technique to attain the best solutions after a small number of iterations compared to the rest of the utilized algorithms, including particle swarm optimization, the whale optimization algorithm, moth flame optimizer algorithms, and the slime mold algorithm. Meanwhile, as an example, the marine predator algorithm minimized the seasonal active losses down to 56.56% and 56.09% for both applied networks of IEEE 33 and 69-bus, respectively. To reach those results, a multi-objective function (MOF) was developed to simultaneously minimize the technical indices of the total active power loss index (APLI) and reactive power loss index (RPLI), voltage deviation index (VDI), operating time index (OTI), and coordination time interval index (CTII) of overcurrent relay in the test system EDNs, in order to approach the practical case, in which there are too many parameters to be optimized, considering different constraints, during the uncertain time and variable data of load and energy production.

In this article is proposed a new approach based on an Arithmetic optimization Algorithm (AOA) that can notably envisage with diverse problems for optimal installation (locate and size) of Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). An approach applied for optimal installation of DSTATCOM units in EDS is used as the new multi-objective functions to minimize the Active Power Loss (APL) and various voltage stability indices. The voltage stability indices applied are the Voltage Stability Index (VSI), the Fast Voltage Stability Index (FVSI), the Line Stability Factor (LPQ), and the Power-Voltage Stability Index (PVSI). The methodology is tested on IEEE 33-, and 69-bus EDSs. The results of the simulation confirm that the optimal installation of DSTATCOM plays a significant role in EDS, where excellently reduced the power losses and enhanced the voltage profiles.