Optimal location of DG and FACTS devices under Maximum loading Capacity

Driven by environmental concerns and fluctuations of electric energy cost, the integration of distributed generators (DGs) into distribution networks close to the loads, has gained its importance and emerged as complementary to the conventional central power plants. However, the proliferation of distributed generators (DGs) has altered a distribution network from a passive system to an active one. Hence, modifications in the existing distribution power flow methods are needed to account for a DG unit, which can operate either as a PV or PQ bus. Among these methods, the Sequential Power Flow (SPF) method can readily accommodate for PV buses as long as the DG models in the sequence-component frame can be formulated. However, the convergence of SPF is heavily dependent on the choice of initial conditions. Moreover, when the resistance-to-reactance ratio (or R/X) is high, SPF will also diverge. This paper presents a new robust three-phase distribution power flow, which incorporates a homotopic method into the SPF method to overcome these problems. An IEEE benchmark system is used to test the validity and robustness of the proposed algorithm

To manage the microgrid effectively, it is important to get the power information among multiple distributed generation (DG) units. However, when the droop control method is adopted, as the corresponding DG units cannot be simply modeled as VF or PQ buses, the conventional power flow algorithm may become inapplicable. To solve this issue, a time domain iteration (TDI) based power flow algorithm is hereby proposed. Firstly, a microgrid model is prepared, containing a network model and several DG unit models. Then, the proposed TDI is executed for power flow calculation, which mimics the real-time operation of microgrids. In each iteration, the DG unit models input voltages and currents to the network model; then the network model changes its state accordingly and feeds related parameters back to those DG unit models. As the DG unit models simulate the behavior of actual DG units, the proposed algorithm is not limited to the droop control governed microgrids. Moreover, as the TDI has definite physical meaning, convergence problems can be avoided. Finally, the validity of the proposed power flow algorithm is verified through the Matlab simulation results from an 8-bus microgrid system.

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

The impact of distributed generation (DG) on distribution power losses is closely related to DG technologies and load distribution. DG units are modeled as PQ, PQ(V) and PV nodes in power flow calculation. A revised back/forward power flow method is employed to calculate distribution power flow with various types of DG units. Power losses are computed when different types of DG units are connected to heavy load, general load and light load area apart. Simulation on IEEE 33-bus distribution network shows that connecting DG to heavy load area helps to maximize the loss reduction. And the installation of DG consuming reactive power will increase network losses unless reactive power compensation device is installed accordingly.

Distributed generations (DG) can be used to enhance power generation systems and improve distribution system efficiency. However, the installation of DG units at non-appropriate location and sizing can result in negative impacts such as an increasing in power losses and violations of system constraints. In this paper, a new DG allocation planning tool is developed for determining the optimal location and sizing of DG in practical distribution systems. The proposed planning tool is developed by writing DIgSILENT Programming Language (DPL) script in DIgSILENT PowerFactory software. The optimally placed optimal power flow (OPF) with DG is formulated as a minimization of energy losses function and a maximization of benefit to cost ratio, subjected to system constraints including real and reactive power generation, line and transformer loading, voltage profile, step voltage change, reverse power, energy losses, short circuit level, and DG operating limits. All possible locations and sizes of DG installations are evaluated and then the optimal solution is selected from the best objective function value obtained. Test results on the practical 9-bus and 437-bus distribution systems taken from Provincial Electricity Authority (PEA) of Thailand indicate that the proposed planning tool can be used to determine the optimal DG allocation without violation of system constraints.

Impact of Distributed Generation on the Electric Protection System

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.

Increased loading of power systems, and environmental restrictions, combined with a world wide deregulation of the power industry, require more effective control means for power flow control and voltage support. From the point view of steady-state analysis, this paper concentrates on building up a versatile model for all types of FACTS devices. In this respect, the decomposed power injection model (PIM) is proposed. Based on the model, a novel framework for power flow control, using FACTS devices, is adopted to derive FACTS control parameters. In order to take full advantage of the optimal multiplier Newton-Raphson power flow method (OMNR) for power flow with embedded FACTS devices and interior point linear programming (IPLP) for power flow control, a rectangular-polar hybrid formulation is adopted. Numerical results on a modified IEEE 5-node network are presented to illustrate the proposed approach.

In this paper, a new method to incorporating FACTS devices in optimal power flow (OPF) problem is presented. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and thyristor controlled phase shifters (TCPS), are considered in this method. The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two subproblems' iteration. The first subproblem is a power flow control problem and the second subproblem is a conventional OPF problem. The two subproblems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.

The energy management system (EMS) of an electrical distribution system (EDS), with the integration of distributed generation (DG) and distribution static compensator (DSTATCOM), provides numerous benefits and significantly differs from the existing EDSs. This paper presents an optimal integration of DG based on photovoltaic (PV) solar panels and DSTATCOM in EDS. A single objective function, based on maximizing the active power loss level (APLL) in EDS, is deployed to find the optimal size and location of photovoltaic DG and DSTATCOM simultaneously in different study cases using various particle swarm optimization (PSO) algorithms. These PSO algorithms are the basic PSO, adaptive acceleration coefficients PSO (AAC-PSO), autonomous particles groups for PSO (APG-PSO), nonlinear dynamic acceleration coefficients PSO (NDAC-PSO), sine cosine acceleration coefficients PSO (SCAC-PSO), and time-varying acceleration PSO (TVA-PSO). These algorithms are applied to the standard IEEE 33- and 69-bus EDSs, which are used as test systems to verify the effectiveness of the proposed algorithms. Simulation results prove that the TVA-PSO algorithm exhibits higher capability and efficiency in finding optimum solutions. Comparing the simulation results attained for different study cases leads to the conclusion that DG and DSTATCOM were optimally-allocated simultaneously, which resulted in a significant reduction of power losses and an enhancement of the voltage profile.

Distributed generators (DGs) are nowadays most commonly used in distribution networks to meet the rising demand. The distribution network power flow is altered as more distributed generations are integrated with the distribution network. Analyzing and researching the network's power flow with distributed generation is an essential element of analyzing the distributed generation's influence on the networks. This study proposed new sites in the test network for the purpose of connecting DGs. Good results were obtained in improving the necessary network elements. Firstly, applying a Newton-Raphson method to measure the distribution network's power flow without distributed generations. Then the optimal power flow approach with DGs is set up based on many goals such as minimizing fuel cost, minimizing losses, voltage deviation, increasing efficiency, and the incremental fuel cost in the network appeared. Eventually, applying Particle Swarm to overcome the optimization problems. In MATLAB software environment and programing MATPOWER version 6.0, the simulation is evaluated and validated using the IEEE30 node method. The test results indicate that optimal power flow analysis with distributed generation will accomplish many networks aims and boost system economy.

Distributed generation (DG) units has become an essential part of the smart grid due to increased penetration of renewable energy sources. These units help to improve power quality according to their position and control. Grids could face unexpected faults which affects power quality most especially voltage profile and power losses. This paper presents the potential application of (DG) units as a solution for improving voltage profile and mitigating power losses in loop distribution systems in case of faults for different positions of DG unit. Load flow analysis of a 66/20 kV distribution line network is performed to calculate the various values of voltage and power flow at each bus for different faults in each position of DG unit. Simulation of this distribution substation network with DG units have been developed in the MATLAB/Simulink Environment. The objective of this study is enhancement of voltage profile at various buses and the improvement in power flows in the distribution network with reduction in branch losses in the presence of fault.

In order to solve the low-voltage problem of three-phase four-wire system in the low-voltage distribution network by using distributed generation (DG), this paper establishes the mathematical model of the DG configuration, and presents the DG placing method and configuration principle, considering the influence of unbalanced load, reactive power compensation and the existing DG of the network. For the phase voltage deviation and the phase power flow calculation, the mathematical model and the calculation method are given in this paper. According to the voltage deviation and the power flow under the maximum load condition, the placing site of DG is determined. Based on the expectation of the node voltage deviation percentage wanted to improved, the maximum current and maximum capacity of DG are deduced. In order to ensure the normal operation of the low-voltage distribution network, the voltage distributions are checked under the minimum load after conFigure the DG, and the minimum injection node current and minimum capacity of DG are determined in the case of minimum load. Taking the practical 0. 4kV three-phase four-wire network as an example, the DGs are configured to solve the low voltage problem. By the analysis and comparison of voltage distribution, the proposed placing DG method can be applied to different load cases, and it is an effective method to solve the low-voltage problem.

Several extreme weather disasters have demonstrated that grid resilience is a significant aspect of modern power systems to work upon. In event of grid failure, back up power sources must support the network. Thus, optimal planning of distributed generations (DGs) in power distribution networks need to be done so that in addition to grid power support, ancilliary services such as minimized real power losses and voltage deviations are attained. In the present work, combined resilience evaluation and analytic operational enhancement strategy has been studied in a radial power distribution network. To achieve overall minimal power losses and better voltage control, the Marine Predator (MPA) meta-heuristic optimization technique is adopted. The MPA is utilised to discover the best DG location and size at the same time. The proposed method is applicable to both single and multiple DG unit sizing and siting. Furthermore, to calculate the best size of DG unit, the suggested method just requires the results of the base case load flow. The proposed approach is tested on a IEEE 33-bus radial distribution test system.

Dynamic sub-transmission system expansion planning incorporating distributed generation using hybrid DCGA and LP technique