Optimal Allocation of DGs with network reconfiguration using Improved Spotted Hyena Algorithm

This paper contributes the optimal allocation of DSTATCOM (distribution static compensator) and DG (distributed generation) in radial distribution systems using exhaustive search method (ESM) to reduce the power loss and improvement of the voltage profile. The certain range of active and reactive powers have been injected simultaneously at each node by incorporating the corresponding size of DSTATCOMs and DGs respectively into the radial distribution system (RDS). On the basis of best reduction in power loss, the Size of DSTATCOM and DG are determined. Forward-Backward sweep load flow algorithm was used for the load flow solutions. The results obtained by proposed approach shows the optimal allocation and sizing of DSTATCOM and DG in RDS efficaciously reduces the power loss and improves the voltage profile. The IEEE-30bus RDS was used as a test system.

Due to the enhanced price of electricity, the gradual depletion of fossil fuels, and the global warming concerns, power loss minimization through deployment of distributed generators (DGs) has attracted significant attention in recent decades. This paper proposes a genetic algorithm (GA) based strategy for minimization of active and reactive power losses through optimal location and size of DGs. It also quantifies and tallies the total network power losses for the cases with random as well as optimal allocation of DGs. To validate the accuracy of the obtained results from GA, another nature‐inspired optimization algorithm, cuckoo search, is also deployed. The simulation results on IEEE 30 and 118 bus systems indicate that the proposed strategy not only can effectively reduce the total network active and reactive power losses but also lead to the improvement of network voltage profile.

In the presented work, the optimal sizing and placement of renewable DGs with optimal reconfiguration of 85 bus distribution network using adaptive PSO is considered. The “multi-objective function problem” is minimize for the formulated problem as minimization based on the system performances such as system losses (active and reactive), system voltage profile, and the reliability. The minimization of objective function gives the optimum value of system performances by considering in terms of indices. The work proposed contains different cases of 85 bus distribution system with various combination of DGs and system reconfiguration simultaneously. The method utilizes for power flow of network based on BIBC and BCBV system matrices is backward–forward sweep method. The result analysis of the system shows better performances are achieved with optimal allocation of renewable DGs and reconfiguration of system simultaneously. Finally, the comparative analysis of system performances of system with proposed methodology and other available methods are presented which reveals that the proposed methodology performed better than other method.KeywordsRenewable Distributed Generation (DGs)Network ReconfigurationTie-Switching (TS)Adaptive Particle Swarm Optimization Technique (APSO)

Now a days the usage of electrical power is increasing enormously and it is also becoming highly impossible in imagining the world without the electricity. So here is one of the techniques for maintaining the continuous supply i.e., by generating the electrical power in the distribution premises. Distribution Generation has been growing rapidly in power systems due to their capable solution for the issues like power system deregulation, to meet the demand of electric power and shortages in the electric power transmission capacities. Concept of Distributed generation (DG) plays an effective key role in the distribution networks. Among many of their merits, real power loss reduction, THD reduction voltage sag and voltage swell reduction and voltage profile improvement can be the salient merits of DG. Improper location of DG sources in to the electrical power system would lead to the increase energy or power loss, and also jeopardize the system operation. In this paper, determination of DG location is done by using Sensitivity Analysis and corresponding DG size is found by using Particle Swarm Optimization (PSO) to mitigate the real power losses in the distribution networks. Standard IEEE 15 bus test system is chosen to test the proposed PSO — Sensitivity Analysis approach. The results so obtained show the enhancement of voltage profile, reduction of real power loss using MATLAB.

In this paper, a hybrid methodology is implemented for optimal allocation of synchronous generator-based dispersed generation, for minimization of power losses in distribution system considering voltage dependent practical loads. The proposed hybrid approach is a combination of equivalent current injection-based sensitivity factor and particle swarm optimization. Sensitivity factors advantages in reducing the search space and optimal allocation of synchronous generator-based DG are obtained by particle swarm optimization for the diminution of power losses. The suggested method is validated on 33-bus and 69-bus radial distribution system. The results show significant improvement in voltage profile of all buses after optimal allocation of DG in distribution system.

<p>The rapid growth of distributed generation (DG) units has necessitated their optimization to address the increasing complexity of power grids and reduce power losses. The need for optimization of distributed generation (DG) units has been growing rapidly over the past few years. To minimize such losses, the optimal allocation of DG units needs to be correctly identified and applied. On the other hand, Garra Rufa optimization (GRO) is a mathematical optimization technique that is used to determine the high effective and efficient way to solve very complex problems to achieve optimal results. In this work, Garra Rufa optimization is used to identify the optimal placement and size of DG units in order to meet specific power loss requirements. A comparison between genetic algorithm (GA), particle swarm optimization (PSO), and GRO is done using MATLAB to validate the proposed method. The comparison shows that GRO is better than the other methods in DG allocation, especially in more than two DGs. The optimization techniques are evaluated using the IEEE standard power system case, specifically the 30-bus configuration.</p>

The optimized network reconfiguration and Distributed Generations (DG) sizing with allocation instantaneously via Particle Swarm Optimization (PSO) proposed a new way of allocation DG based on low voltage profile. This method consists of three steps. It started with categorized the switching sequences for radial network configuration while observe the P losses and the profile of voltage without DG. The second step is reconfiguration feeder for reduce losses via DGs allocation based on substations geographical location. The final step is sizing and allocation DGs at each bus with low voltage profile produced from the first step, used to mend the voltage profile and minimize the P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">losses</sub> also compared the result with the geographical based allocation results. The objective of this study is to mend the voltage profile while decreasing the P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">losses</sub> by using optimization technique considering network reconfiguration, DGs Sizing and allocation concurrently. Four cases are compared which is case 1 is the initial case and taken as a reference. All three stages are tested on standards IEEE 33 bus system by using Particle Swarm Optimization (PSO) technique in MATLAB software. This method proved that improvement of Plosses and voltage profile has been made by change of the switching topology with DGs sizing and allocation technique respectively.

Distributed generation (DG), including photovoltaics (PVs), wind turbines (WTs) are becoming vital for Active Distribution Networks (ADN). Therefore, optimal sizing and allocation of these units can improve voltage profiles and reduce active power losses. This study concentrates on optimal allocation and sizing of two kinds of DG units (PVs and WTs) with 1 MW maximum size limits, due to the regulations in Turkey. The Whale optimization algorithm (WOA) and Grey wolf optimization algorithm (GWO) are used as optimization tools to minimize active power losses of 33 and 69 Bus Test Systems. The performance analysis of the methods are performed through simulations and the numerical results are compared in terms of optimal OF values and convergence characteristics.

Nowadays, the penetration of distributed generation (DG) units in power systems is increasing because of their benefits on the power systems. Place, type and size of distributed generators play an important role in power loss reduction, power quality improvement, security enhancement, and cost reduction. Therefore, optimal placement and sizing of DG units in electric power systems are one of the most important problems that should be evaluated carefully. DG allocation is a constrained optimization problem with different important objectives such as power loss minimization, voltage profile improvement, reliability enhancement, investment and operation cost reduction, etc. In this paper, regarding higher distribution active losses compared to transmission and generation losses and investment limitation, DG allocation problem is solved for photovoltaic units, aiming minimization of energy and investment costs considering generation uncertainty and load variation. Due to high uncertainties of solar energy resource, the problem is evaluated under different scenarios of solar radiation under a stochastic programming approach. Tests were carried out using the 33-node distribution system and the obtained results demonstrate the advantage of optimal DG allocation as well as the efficiency of the adopted mathematical to find the optimal solution.

This chapter presents Non-dominated Sorting Genetic Algorithm-II (NSGA-II), a swarm intelligence-based optimization technique, to solve Multi-Objective Reactive Power Management (MORPM) problem for minimization of active power losses, improvement of voltage profile, and minimization of total capacity of Reactive Power Sources (RPS) in Radial Distribution Systems (RDS). In an RDS having Distributed Generators (DGs), reactive power management problem can be solved by regulating reactive powers of DGs and of the reactive power compensation devices like FACTS devices, capacitor banks, etc. Efficacy of the proposed NSGA-II has been established by solving MORPM problem in IEEE 33-bus RDS penetrated with DGs and RPS units and by comparing the multi-objective reactive power management results with those obtained using multi-objective dragonfly algorithm, multi-objective differential evolution algorithm, and modified differential evolution algorithm.

Distributed Generation (DG) generally means power generation in consumption locality. But sometimes, it Told technologies that use renewable energies for power generation. This paper presents a method for optimal sitting and sizing of DG in distribution systems. In this paper, our aim would be optimal distributed generation allocation and sizing for several parameters, include: voltage profile improvement, loss reduction, and THD (Total Harmonic Distortion) reduction in distribution networks. Particle Swarm Optimization (PSO) is used as the solving tool, which referring determined aim, the problem is defined and the objective function is introduced. Considering the fitness values sensitivity in PSO algorithm process, it is needed to apply load flow and harmonic calculations for decision-making. Finally, the feasibility of the proposed method is demonstrated for typical distribution network, and it is compared with the GA method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method is indeed capable of obtaining higher quality solutions efficiently.

Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.

Distributed Generation (DG) is commonly used to reduce active power losses on a distribution network. The optimal location and size of DG will result in minimum active power losses and voltage profile improvement. This research proposes Novel Voltage Sensitivity Index (NVSI) and Stability Index (SI) methods to determine the optimal location and analytical expression to find optimal size and location of DG in Makassar distribution system, Feeder Kima, 76 buses to minimize active power losses and to improve voltage profile. Therefore, DG interconnection has a significant effect on improving the quality of the distribution network. The results show that the sensitivity method does not lead to the best placement DG in reducing active power losses. However, it is an analytical expression, which is very effective in determining optimal location and size of DG to reduce active power losses and to improve voltage profile in Makassar distribution system, Feeder Kima, 76 buses. The most optimal location for DG placement is on Bus 73 (Mega Sakti Pyramid), with a DG size of 0.8515MW. These combinations reduce 45.77% active power losses and increase 1.7336% voltage profile.

The world is transitioning to utilise distributed generation (DG) to reduce social, economic and environmental effects. As DG penetration increases, determining the associated impacts on distribution networks is vital. This paper proposes a concept around sphere of influence of single and multiple DG units through line current analysis and integrated Sankey and Geospatial visualisation approaches. With the increasing integration of renewable energy sources and the need for grid stability, it becomes imperative to analyse the sphere of influence of DGs in geographic context. The main contribution of the paper is to present the visualisation techniques to determine the sphere of influence of DGs within power networks by considering single and multiple DGs. The proposed study combines the power of Sankey visualisation, a flow diagram technique, with geospatial data to gain insights into the DG sphere. The advantage of the proposed method is it acts as a visual lens to determine DG clusters, flow of power, coherency analysis, voltage support, resource allocation, operational planning, and grid resilience assessment. The proposed method is tested using 33 bus distribution network and visualisation on a representative geospatial map is carried out using Python library and Tableau.

Efficient reduction of power losses by allocating various DG types using the ZOA algorithm