OPTIMAL LOCATION AND SIZING OF MULTIPLE DISTRIBUTED GENERATORS IN RADIAL DISTRIBUTION NETWORK USING METAHEURISTIC OPTIMIZATION ALGORITHMS

. 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

Optimal design of wind energy generation in electricity distribution network based on technical-economic parameters

In order to satisfy electricity customers and avoid some environmental constraints and problems, the transition to renewable energy sources has become increasingly important given their advantages and benefits, such as reducing pollution and improving the reliability of the targeted distribution system. In this paper, several state-of-the-art metaheuristic optimisation algorithms are used to investigate the optimal setting and sizing of wind turbines (WTs) when connected to the electricity distribution network (EDN). The selected algorithms were implemented to optimise and minimise a multi-objective function (MOF) considered as the sum of the techno-economic parameters of total active power loss (TAPL), total voltage deviation (TVD) and investment cost of the WTG (ICWTG) when the daily uncertainties and variations of the load-source powers are taken into account. The effectiveness of the selected algorithms was validated on the two standard test systems IEEE 33-bus and 69-bus. The simulation results in this paper showed the superiority of the Gorilla Troops Optimizer (GTO) algorithm compared to other new metaheuristic optimisation algorithms in terms of providing the best optimised results. Accordingly, the GTO algorithm showed excellent effectiveness and robustness in determining the optimal setting and sizing of the WTG units in EDN. Thus, the daily active power losses were reduced to 1,415 MWh for the first test system and 1,072 MWh for the second test system, while also improving the bus voltage profiles and favouring the investment costs of the installed WTG units, all with daily uncertainties in terms of load demand and WTG power variations

Chaos Game Optimization Algorithm for Parameters Identification of Different Models of Photovoltaic Solar Cell and Module

In order to achieve the optimum feasible efficiency, the electrical parameters of the photovoltaic solar cell and module should always be thoroughly researched. In reality, the quality of PV designs can have a significant impact on PV system dynamic modeling and optimization. PV models and calculated parameters, on the other hand, have a major effect on MPPT and production system efficiency. Because a solar cell is represented as the most significant component of a PV system, it should be precisely modeled. For determining the parameters of solar PV modules and cells, the Chaos Game Optimization (CGO) method has been presented for the Single Diode Model (SDM). A set of the measured I-V data has been considered for the studied PV design and applied to model the RTC France cell, and Photowatt-PWP201 module. The objective function in this paper is the Root Mean Square Error (RMSE) between the measured and identified datasets of the proposed algorithm. The optimal results that have been obtained by the CGO algorithm for five electrical parameters of PV cell and model have been compared with published results of various optimization algorithms mentioned in the literature on the same PV systems. The comparison proved that the CGO algorithm was superior

Fault detection in photovoltaic systems using the inverse of the belonging individual Gaussian probability

This article addresses the problem of fault early detection in photovoltaic systems. In the production field, solar power plants consist of many photovoltaic arrays, which may suffer from many different types of malfunctions over time. Hence, fault early detection before it affects PV systems and leads to a full system failure is essential to monitor these systems. The fields of control and monitoring of systems have been extensively approached by many researchers using various fault detection methods. Despite all this research, to early detect and locate faults in a very large photovoltaic power plant, we must, in particular, think of an effective method that allows us to do so at the lowest costs and time. Thus, we propose a new robust technique based on the inverse of the belonging individual Gaussian probability (IBIGP) to early detect and locate faults in the power curve as well as in the Infrared image of the photovoltaic systems. While most fault detection methods are well incorporated in other domains, the IBIGP technique is still in its infancy in the photovoltaic field. We will show, however, in this work that the IBIGP technique is a very promising tool for fault early detection enhancement

Exploring LBWO and BWO Algorithms for Demand Side Optimization and Cost Efficiency: Innovative Approaches to Smart Home Energy Management

Demand side management (DSM) involves technologies and strategies that allow customers to actively participate in the optimization of their energy usage patterns, ultimately contributing to a more sustainable and efficient energy system. In this paper, leader beluga whale optimization improvement (LBWO) and original beluga whale optimization (BWO) are used to implement a DSM scheme that enables lower peak-to-average ratio (PAR) and decreasing the expenses associated with electricity consumption. In the context of this research, electricity consumers decide to store, buy, or sell the electricity to maximize profits while minimizing its costs and PAR. Electricity consumers make their decisions based on the amount of electricity generated from their mini-grid, electricity prices and demand from the public network. The mini-grid is a combination of a photovoltaic (PV) panel and a wind turbine connected to an energy storage system (ESS). An ESS is used for maintaining power system stability because the power generated from renewable energy source (RES) has intermittent characteristics depending on environmental conditions. The proposed scheme is tested on three different cases from a study, the first case is the traditional house, the second case is the smart house with DSM, and the last case is the smart house with its mini-grid and DSM. Simulation results indicate that in case 2, LBWO and BWO achieved a remarkable reduction in electricity cost by 61% and 51% respectively. In case 3, the reduction was even more significant, with LBWO and BWO lowering the cost by 76% and 64% respectively. Moreover, LBWO generated a revenue of 154 (cents), while BWO generated a revenue of 118 (cents). The results confirm the effectiveness and robustness of the suggested scheme in reducing electricity costs and the PAR (Peak to Average Ratio), while simultaneously increasing profits for electricity consumers

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

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

Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm

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

Optimizing the hybrid PVDG and DSTATCOM integration in electrical distribution systems based on a modified homonuclear molecules optimization algorithm

Abstract The increasing use of non‐linear loads in electrical distribution systems (EDS) led to a greater need for reactive power compensation, losses minimization, improved voltage and stability. This paper proposes the optimal integration of hybrid photovoltaic distributed generation (PVDG) and distribution static synchronous compensator (DSTATCOM) into IEEE 33 and 69‐bus EDS. A modified version of homonuclear molecules optimization (mHMO) is developed to determine the optimal allocation of the devices, while minimizing a multi‐objective function (MOF) based on total active power losses (TAPL), total voltage deviation (TVD), and investment cost of integrated devices (ICPVDG and ICDSTATCOM). The primary objective of the mHMO is to enhance the equilibrium between exploration and exploitation in the original HMO by implementing a fresh exploration stage. The effectiveness of mHMO was assessed using CEC17 benchmark functions. The findings demonstrate that mHMO achieved excellent results, including high‐quality solution and a favourable convergence rate. Additionally, results demonstrate that mHMO outperforms its basic version in reducing TAPL by 94.27% and 97.87% for the two EDS, while improving voltage profiles and reducing the cost of integrated devices. This study shows the potential of hybrid PVDG‐DSTATCOM in improving the performance of EDS and highlights the effectiveness of mHMO in optimizing their integration