Optimal Installation of Multiple DG using Chaotic Moth-flame Algorithm and Real Power Loss Sensitivity Factor in Distribution System

Distributed energy resources (DERs) can improve the performance of radial distribution systems. The nonlinear power flow constraints, multi-objective trade-offs, and network reconfiguration scenarios for DER placement and sizing call for the formulation of optimization problems. Most of the times optimization algorithms suffer from premature convergence and poor exploration-exploitation balance. These problems exhibit an inherent internal structural symmetry. In order to overcome the above problem, this study uses the Multi-Objective Archimedes Optimization Algorithm (MAOA) to optimally allocate DERs in the Radial Distribution Networks (RDNs), moreover the performance of the proposed MAOA is compared with the other well established algorithms including Particel Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Shuffled Frog Leaping Algorithm (SFLA), Atom Search Optimization (ASO), and Butterfly Optimization Algorithm (BOA) on the IEEE-33 RDN. The comparison is made for the four cases (S1: DER Only), (S2—Network Reconfiguration Only), (S3—DER Followed by Reconfiguration), and (S4—Reconfiguration Followed by DER) considering factors like voltage profile, network reconfiguration, active and reactive power loss reduction, carbon emission DER utilization and Cost reduction. The MAOA is observed to provide better results among all the other benchmark algorithms. In S3, the active power loss is reduced by 68.41%, whereas the reactive power loss is reduced by 57.44% and the MAOA algorithm improves the voltage by 3.98%. The minimum voltage of the network is also improved by 6.28%. The algorithm improves convergence with a percentage of 18.50% enhancing the system’s operational symmetry and stability, while satisfying all constraints. At Bus 3 and Bus 6 of IEEE-33 bus radial distribution network (Baran–Wu test system), DG capacity is allocated to be 3.8 MW and 2.1 MW, respectively.

This paper utilize two novel optimization techniques namely Particle Swarm Optimization (PSO) and Craziness based Particle Swarm Optimization (CRPSO) to find the optimum location and optimum size of single and two Distributed generator (DG) in distribution system. Minimization of active power loss in distribution network is carried out through DG placement. The method is tested with two standard IEEE test cases namely 33-node radial distribution network and 69-node radial distribution network. The node with minimum system power loss is considered as the optimum location for DG placement which in turn enhances the voltage profile of overall system. The results of two optimization techniques for optimum size and optimum location of DG placement are found competitive.

Flexible AC Transmission Systems (FACTS) are essential devices used for the efficient performance of modern power systems and many developing countries lack these devices. Due to the non-existence of these advanced technologies, the national grid remains weak and vulnerable to power stability issues that can jeopardize system stability. This study proposes novel research to solve issues of an evolving national grid through the installation of FACTS devices. FACTS devices play a crucial role in minimizing active power losses while managing reactive power flows to keep the voltages within their respective limits. Due to the high costs of FACTS, optimization must be done to discover optimal locations as well as ratings of these devices. However, due to the nonlinearity, it is a challenging task to find the optimal locations and appropriate sizes of these devices. Shunt VARs Compensators (SVCs) and Thyristor-Controlled Series Compensators (TCSCs) are the two FACTS devices considered for the study. Optimal locations for SVCs and TCSCs are determined by Voltage Collapse Proximity Index (VCPI) and Line Stability Index (Lmn), respectively. Particle Swarm Optimization (PSO) is employed to find the ideal rating for FACTS devices to minimize the system operating cost (cost due to active power loss and capital cost of FACTS devices). This technique is applied to IEEE (14 and 30) bus systems. Moreover, reliable operation of the electricity grid through the placement of FACTS for developing countries has also been analysed; Pakistan being a developing country has been selected as a case study. The planning problem has been solved for the present as well as for the forecasted power system. Consequently, in the current national network, 6.21% and 6.71% reduction in active and reactive power losses have been observed, respectively. Moreover, voltage profiles have been improved significantly. A detailed financial analysis covering the calculation of Operation Cost (OC) of the national grid before and after the placement of FACTS devices is carried out.

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.

Optimal placement of distributed generators is essential, as inappropriate placement may increase the system losses. This article proposes a novel methodology for finding the optimal size and location for installation of Distributed Generators (DGs) so as to minimize total power losses in radial distribution systems. Distribution load flow is used to find the power loss in the system. The Proposed methodology is tested with 69-node radial distribution system with single and multiple DGs and the efficacy is compared with the Acharya's method and classical grid search algorithm. Effect of optimal DGs placement on system voltage profile and branch power losses are also computed and reported.

A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems

This paper develops an improved version of the chemical reaction optimization (CRO) algorithm based on the opposition-based learning (OBL) strategy named quasi-oppositional CRO (QOCRO) for optimal reconfiguration of a power system to minimize power loss of the network. Furthermore, to avoid suboptimal solutions and to increase the convergence rate, chaotic behavior is mapped with QOCRO, which results in chaotic QOCRO (CQOCRO). The reconfiguration technique can minimize power loss up to a certain level. Further power loss reduction may be accomplished by locating the capacitor in the optimal location. To investigate the performance of the proposed CQOCRO, QOCRO, and CRO approaches, they are successfully implemented on two test systems, namely 33-bus and 69-bus radial distribution systems. Moreover, the numerical results are compared with other population-based optimization techniques like krill herd (KH) algorithm, oppositional krill herd (OKH) algorithm, and fuzzy approach. The computational results reveal that CQOCRO is superior to QOCRO, CRO, and other algorithms available in the literature in this domain. Finally, a convergence graph is given to identify the convergence superiority of CQOCRO.

Voltage profile and real power loss are the criteria in determining performance of Radial Distribution System (RDS). Presence of Distributed Generators in RDS is assessed by ‘Penetration Ratio’. The use of PR fails in the analysis of the implications of the presence of a Microgrid on RDS as it is zero in islanded mode and sometimes in grid-connected mode. An attempt has been made to introduce a new term ‘Relief Factor’ to overcome this lacuna. This Factor is evaluated by developing an algorithm based on power flow analysis using Backward Forward Sweep method. The developed algorithm is tested on a 34 bus RDS by integration of a two-shift industrial Microgrid. The power flow analysis is run for 24 snapshots of the day. Relief Factor is found to be suitable to analyze the impact of a Microgrid on the performance of RDS in both the modes of Microgrid operation viz. grid-connected mode and islanded mode.

In this paper, the impact of distribution STATCOM (DSTATCOM) allocation on radial distribution systems is presented. A DSTATCOM is placed at each bus of a distribution network, one at a time, and its impact on system power loss is investigated. A new mathematical modelling for DSTATCOM is done to suitably incorporate it in the forward-backward sweep load flow algorithm of radial distribution systems. It is observed that there is appreciable reduction in power loss with DSTATCOM allocation at certain locations. The IEEE-69 bus radial distribution system is used as the test system.

Insertion of distributed generation (DG) in existing distribution system has effectively improved its performance and operation. Many different approaches for the planning of distribution system with DG insertion are presented by researchers. In this paper a multi objective approach has been proposed to maximize the mutual benefits of both the distribution system operator and DG owner. The contradictory relationship between reduction in MVA rating of DGs and reduction of power losses of the system is the motivation for this multi-objective approach. The best compromised size of DGs in MVA, their operating power factors and positions are obtained to reduce the system active power loss along with the reduction of DGs size. The 69-bus and 85-bus radial distribution system are considered as test systems. The Pareto-front of non-dominated solutions is obtained by using multi-objective differential evolution (MODE) optimization algorithm. The performance of MODE algorithm is also compared with that of multi-objective particle swarm optimization (MOPSO) algorithm. The different system operating indices such as active power loss, reactive power loss and voltage deviation are evaluated to show the effect of the best compromised solution of DGs placement in distribution system.

This study presents a pelican optimization algorithm (POA) based technique for optimizing the integration of capacitor banks in radial power distribution systems. The proposed approach adopts a hybrid approach that combines the loss sensitivity factors (LSF) and the POA to determine the appropriate placement and sizing of the capacitor banks, respectively, for best system performance. The proposed methodology is tested on the IEEE 33-bus distribution system using MATLAB software. The performance for integrating two capacitor banks is compared to that of the particle swarm optimization (PSO) and the genetic algorithm (GA) methods. The proposed POA method achieved a significant improvement in system performance. Specifically, it reduced active power losses (PLoss) by 68.08%, which surpasses the reductions achieved by PSO (61.79%) and GA (57.73%). In the case of reactive power loss (QLoss), the POA-based approach also proved superior with a 39.035% reduction, while PSO and GA-based methods achieved 28.50% and 22.59% reductions, respectively. These results underscore the effectiveness of the POA-based approach in capacitor bank placement and sizing, making it a promising candidate for enhancing voltage profiles and minimizing power losses in radial distribution systems.

The distribution system has high r/x ratio results into higher power loss. To minimize the active power loss optimal placement of DG is an important factor. This will improve the voltage profile of each node. In this paper network reconfiguration and DG placements are considered. The minimization of operating cost of distribution network operator is considered. To find out the optimal location of multiple DG two optimization techniques are applied. The method is tested with 33-node radial distribution network.

Rapid depletion of fossil based oil, coal and gas reserves and its greater demand day by day necessitates the search for other alternatives. Severe environmental impacts caused by the fossil fuel based power plants and the escalating fuel costs are the major challenges faced by the electricity supply industry. Integration of Distributed Generators (DG) especially, wind and solar systems to the grid can offer viable solutions to address these challenges. This paper focuses on a new simple and fast load flow algorithm for finding the voltage profile and power losses in a radial distribution system (RDS). Backward Forward Sweep Algorithm (BFSA) has been practiced to find the voltage profile and power losses with the integration of various size of DG at different locations. Genetic Algorithm (GA) based BFSA is adopted in finding the optimal location and sizing of DG to attain an improved voltage profile and considerable reduced power loss. Simulation results show that the proposed algorithm is more efficient in finding the optimal location and sizing of DG in 15-bus radial distribution system. The obtained results are compared with the system modeled in Simulink.

Flexible alternating current transmission systems (FACTS) devices are emerging technologies that serve as a means of providing reactive power support to mitigate issues of active power loss and voltage instability on existing power grid. The optimal placement, locations and sizes of these devices influence its performance on the grid. This paper presents a comparative analysis of performance evaluation of SVC and TCSC for transmission loss reduction on Nigerian 330kV grid system using self- adaptive firefly algorithm. The results of the analysis showed that with the system reinforced with SVC, the total system loss reduced from 98.21MW to 92.44MW which is about 5.88% reduction. The reduction in active power loss with the optimal location of TCSCs is 94.259MW which amount to 4.02% reduction. An appreciable voltage enhancement occurred at Ayede, New Haven, Onitsha and Kano due to the system reinforcement with SVCs and TCSCs. In all, SVC gives better result than TCSC in term of active power reduction and voltage profile enhancement.

The power distribution system has difficulties with regard to power loss and unacceptable voltage drops as a result of the rapidly expanding power system network, rising electrical energy consumption, and longer distances of power distribution. A typical strategy to address the issues with the distribution system is to perform distribution system reconfiguration. The study focuses on distribution feeder reconfiguration of the Katunje Feeder of Bhaktapur, Nepal where optimization problem is formulated to minimize the system active power loss and investment cost of the system. Genetic algorithm is employed in a co-simulation framework to solve the optimization problem where states of 26 different tie switches are to be altered to achieve the desired optimum results. Two cases are formulated: in case I active power loss is assigned more weight than investment cost whereas, in case II equal weights are assigned for active power loss and investment cost. The results showed the reduction in active power loss and investment cost for both the cases. Case I resulted in more active power loss reduction compared to case II, and case II resulted in more investment cost reduction compared to case I. From this, decision makers can obtain insights in adopting one of the cases for distribution feeder reconfiguration based on technical consideration (active power loss reduction) or economic consideration (investment cost reduction).