The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69-bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.
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.
The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies more essential given its advantages such as reducing power losses and enhancing voltage profiles. This study addresses the optimal sizing and setting of Photovoltaic Distributed Generator (PVDG) connected to Radial Distribution Network (RDN) using various novel optimization algorithms. These algorithms are implemented to minimize the Multi-Objective Function (MOF), which devoted to optimize the Total Active Power Loss (TAPL), the Total Voltage Deviation (TVD), and the overcurrent protection relays (OCRs)?s Total Operation Time (TOT). The effectiveness of the proposed algorithms is validated on the test system standard IEEE 33-bus RDN. In this paper is presented a recent meta-heuristic optimization algorithm of the Slime Mould Algorithm (SMA), where the results reveal its effectiveness and robustness among all the applied optimization algorithms, in identifying the optimal allocation (locate and size) of the PVDG units into RDN for mitigating the power losses, enhance the RDN system's voltage profiles and improve the overcurrent protection system. Accordingly, the SMA approach can be a very favorable algorithm to cope with the optimal PVDG allocation problem.
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