Modified sunflower optimization for network reconfiguration and distributed generation placement

This paper proposed modified sunflower optimization (MSFO) for the combination of network reconfiguration and distributed generation placement problem (NR-DGP) to minimize power loss of the electric distribution system (EDS). Sunflower optimization (SFO) is inspired form the ideal of sunflower plant motion to get the sunlight and its reproduction. To enhance the performance of SFO, it is modified to MSFO wherein, the pollination and mortality techniques have been modified by using Levy distribution and mutation of the best solutions. The results are evaluated on two test systems. The efficiency of MSFO is compared with that of the original SFO and other algorithms in literature. The comparisons show that MSFO outperforms to SFO and other methods in obtained optimal solution. Furthermore, MSFO demonstrates the better statistical results than SFO. So, MSFO can be a powerful approach for the NR-DGP problem

Voltage Profile improvement by optimal DG allocation using atom search optimisation in radial and mesh distribution system

Recent years have seen a considerable increase in the generation of power using renewable energy sources. This paper deals with the allocation of distributed generations (DG) in radial as well as mesh distribution network (RDSm&MDSm, respectively). The allocation of DG is been done through a meta-heuristic technique known as Atom Search Optimisation. In the paper, the power loss is reduced, along with simultaneous improvement of voltage profile of the system. The proposed algorithm has been tested on IEEE 33 & 69 radial and weakly meshed system with 5 tie lines is considered for power loss minimisation using single and multiple DGs. Two power factors are considered for the simulation 0.8 lag and 0.9 pf lag. The simulation results are carried out in MATLAB

OPTIMAL POWER MANAGEMENT OF DGS AND DSTATCOM USING IMPROVED ALI BABA AND THE FORTY THIEVES OPTIMIZER

In this study an improved Ali Baba and the forty thieves Optimizer (IAFT) is proposed and successfully adapted and applied to enhance the technical performances of radial distribution network (RDN). The standard AFT governed by two sensible parameters to balance the exploration and the exploitation stages. In the proposed variant a modification is introduced using sine and cosine functions to create flexible balance between Intensification and diversification during search process. The proposed variant namely IAFT applied to solve various single and combined objective functions such as the improvement of total power losses (TPL), the minimization of total voltage deviation and the maximization of the loading capacity (LC) under fixed load and considering the random aspect of loads. The exchange of active powers is elaborated by integration of multi distribution generation based photovoltaic systems (PV), otherwise the optimal management of reactive power is achieved by the installation of multi DSTATCOM. The efficiency and robustness of the proposed variant validated on two RDN, the 33-Bus and the 69-Bus. The qualities of objective functions achieved and the statistical analysis elaborated compared to results achieved using several recent metaheuristic methods demonstrate the competitive aspect of the proposed IAFT in solving with accuracy various practical problems related to optimal power management of RDN

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

Improvement of active distribution systems with high penetration capacities of shunt reactive compensators and distributed generators using Bald Eagle Search

This work proposes an intelligent allocation of distributed generation (DG) units and shunt reactive compensators (SRC) with high penetration capacities into distribution systems for power loss mitigation using the Bald Eagle Search (BES) optimization algorithm. The intelligent allocation causes a reduction in voltage variations and enhances the voltage stability of the systems. The SRC units include shunt capacitors (SC), Static Var Compensators (SVC), and Distribution Static Compensators (DSTATCOM), which are determined according to their capacities. The optimization study includes the 33-bus and the 118-bus distribution systems as medium to large systems. Performance parameters, including the reactive power loss, Total Voltage Deviation (TVD), and Stability Index (SI), besides the power loss, are recorded for each optimization case study. When the BES algorithm optimizes 1, 2, and 3 DG units operating at optimal power factor (OPF) into the 33-bus systems, percentage reductions of power loss reach 67.84%, 86.49%, and 94.44%, respectively. Reductions of 28.26%, 34.47%, 35.24%, and 35.44% are achieved in power loss while optimizing 1, 3, 5, and 7 SRC units. With a combination of DG/SRC units, the power loss reductions achieve 72.30%, 93.89%, and 97.49%, optimizing 1, 3, and 5 pairs of them. Similar reductions are achieved for the rest of the performance parameters. With high penetration of compensators into the 118-bus system, the percentage reductions of power loss are 29.14%, 73.27%, 83.72%, 90.14%, and 93.41% for optimal allocations of 1, 3, 5, 7, and 9 DG units operating at OPF. The reduction reaches 11.15%, 39.08% with 1 and 21 devices when optimizing the SRC. When DG SRC units are optimized together, power loss turns out to be 32.83%, 73.31%, 83.32%, 88.52%, and 91.29% with 1, 3, 5, 7, and 9 pairs of them. The approach leads to an enhanced voltage profile near an acceptable range of bus voltages, reduces the voltage fluctuation substantially, and enhances the system stability. The study also ensures the BES algorithm’s capability to solve these nonlinear optimization problems with high decision-variable numbers

Role of Metaheuristics in Optimizing Microgrids Operating and Management Issues::A Comprehensive Review

The increased interest in renewable-based microgrids imposes several challenges, such as source integration, power quality, and operating cost. Dealing with these problems requires solving nonlinear optimization problems that include multiple linear or nonlinear constraints and continuous variables or discrete ones that require large dimensionality search space to find the optimal or sub-optimal solution. These problems may include the optimal power flow in the microgrid, the best possible configurations, and the accuracy of the models within the microgrid. Metaheuristic optimization algorithms are getting more suggested in the literature contributions for microgrid applications to solve these optimization problems. This paper intends to thoroughly review some significant issues surrounding microgrid operation and solve them using metaheuristic optimization algorithms. This study provides a collection of fundamental principles and concepts that describe metaheuristic optimization algorithms. Then, the most significant metaheuristic optimization algorithms that have been published in the last years in the context of microgrid applications are investigated and analyzed. Finally, the employment of metaheuristic optimization algorithms to specific microgrid issue applications is reviewed, including examples of some used algorithms. These issues include unit commitment, economic dispatch, optimal power flow, distribution system reconfiguration, transmission network expansion and distribution system planning, load and generation forecasting, maintenance schedules, and renewable sources max power tracking

Distribution network reconfiguration considering DGs using a hybrid CS-GWO algorithm for power loss minimization and voltage profile enhancement

This paper presents an implementation of the hybrid Cuckoo search and Grey wolf (CS-GWO) optimization algorithm for solving the problem of distribution network reconfiguration (DNR) and optimal location and sizing of distributed generations (DGs) simultaneously in radial distribution systems (RDSs). This algorithm is being used significantly to minimize the system power loss, voltage deviation at load buses and improve the voltage profile. When solving the high-dimensional datasets optimization problem using the GWO algorithm, it simply falls into an optimum local region. To enhance and strengthen the GWO algorithm searchability, CS algorithm is integrated to update the best three candidate solutions. This hybrid CS-GWO algorithm has a more substantial search capability to simultaneously find optimal candidate solutions for problem. Furthermore, to validate the effectiveness and performances of the proposed hybrid CS-GWO algorithm is being tested and evaluated for standard IEEE 33-bus and 69-bus RDSs by considering different scenarios

Scientific research trends about metaheuristics in process optimization and case study using the desirability function

This study aimed to identify the research gaps in Metaheuristics, taking into account the publications entered in a database in 2015 and to present a case study of a company in the Sul Fluminense region using the Desirability function. To achieve this goal, applied research of exploratory nature and qualitative approach was carried out, as well as another of quantitative nature. As method and technical procedures were the bibliographical research, some literature review, and an adopted case study respectively. As a contribution of this research, the holistic view of opportunities to carry out new investigations on the theme in question is pointed out. It is noteworthy that the identified study gaps after the research were prioritized and discriminated, highlighting the importance of the viability of metaheuristic algorithms, as well as their benefits for process optimization

Multi-objective distributed generation integration in radial distribution system using modified neural network algorithm

This paper introduces a new approach based on a chaotic strategy and a neural network algorithm (NNA), called chaotic-based NNA (CNNA), to solve the optimal distributed generation allocation (ODGA), in the radial distribution system (RDS). This consists of determining the optimal locations and sizes of one or several distributed generations (DGs) to be inserted into the RDS to minimize one or multiple objectives while meeting a set of security limits. The robustness of the proposed method is demonstrated by applying it to two different typical RDSs, namely IEEE 33-bus and 69-bus. In this regard, simulations are performed for three DGs in the cases of unity power factor (UPF) and optimal power factor (OPF), considering single and multi-objective optimization, by minimizing the total active losses and improving the voltage profile, voltage deviation (VD) and voltage stability index (VSI). Compared to its original version and recently reported methods, the CNNA solutions are more competitive without increasing the complexity of the optimization algorithm, especially when the RDS size and problem dimension are extended