Optimal DG Placement for Power System Reliability based on N-1 Contingency Analysis

Distributed generation (DG) significantly improves power system reliability and voltage profile, and reduces power losses. These benefits can be maximized by optimizing the DG utilization, which greatly depends on adequate number, size, and placement of DG. This study proposes a high-convergence optimization technique for optimal DG placement in distribution networks, aiming to reduce power losses and improve voltage profile and voltage stability index. The technique is tested on a 7-bus system before being verified on the IEEE 33-bus system. After validation, it is tested on a 71-bus distribution network (DN). The estimated ideal size for a single DG is used as a limit for determining the optimal placement and size of multiple DG units. The results show that there is a loss reduction of 70.09%, 61.43%, and 71.15% for the 7-bus, IEEE 33-bus, and 71-bus systems, respectively, from their base case loss. The average absolute error of voltage of the aforementioned buses also shows similar enhancement, decreasing from 0.0144, 0.0561, and 0.0527, to 0.0058, 0.0209, and 0.0184, respectively. The voltage stability index of each DN has increased significantly in comparison to the base case. The comparison demonstrates that the proposed technique can provide better results for any size of distribution network.

A novel severity performance index for optimal allocation and sizing of photovoltaic distributed generations

In a large scale power network, maintaining the magnitude of bus voltages within nominal operating level at all buses is a vital requirement. With such condition, the system will have an optimal transmission line performance with minimum power losses and no overloading issue. To achieve this, the power flow along transmission line ought to be properly controlled using special devices like the Unified Power Flow Controller (UPFC) - a multipurpose device which can control power flow by modifying the voltage magnitude, phase angle and impedance of transmission lines. Hence, this paper proposes a technique for mitigating voltage level and real power losses in transmission system using optimal placement of UPFC and Evolutionary Programming (EP). The implementation of the proposed technique involved IEEE 14-bus reliability test system. Experiment and analysis has justified the capability of EP-based UPFC placement for voltage stability improvement and power loss minimization as well.

Minimizing power loss and improving voltage stability are crucial aspects of power systems, driven by transmission line contingencies, financial losses for utilities, and potential power system blackouts. Optimal allocation comprising the sizing and operating power factor—of Distributed Generation (DG) units and capacitor banks (CBs) significantly enhances power system efficiency. Efforts by power system operators and researchers focus on addressing issues related to power loss, energy loss, voltage profiles, and voltage stability through the strategic placement of DGs and CBs. Additionally, optimal DG and CB allocation protects the distribution system from unforeseen events and enables operators to run the system in islanding mode when necessary. The integration of DG units and CBs in distribution systems aims to enhance overall system performance. This research paper introduces a Water Cycle Algorithm (WCA) for the optimal placement and sizing of DGs and CBs. The proposed method targets both technical and economic benefits, considering multiple objective functions: minimizing power losses, reducing voltage deviation, lowering total electrical energy costs, and improving the voltage stability index. The WCA emulates the natural water cycle, from streams to rivers and rivers to the sea. Five different operational scenarios are evaluated to test the performance of this methodology. Simulations are conducted on distribution systems: the IEEE 69-bus test system and the Sankhu feeder network, a real system. The results demonstrate the superior performance of the proposed WCA compared to other optimization algorithms. The findings highlight the WCA's flexibility, efficiency, and significant improvements in economic benefits, establishing it as a promising approach for optimizing the placement of DG and CB in distribution systems.

This paper describes the effectiveness of solving the coordination of Distributed Generation (DG) and capacitor simultaneously to minimize power loss in the distribution system. Previously, the solution is executed separately which means the coordination of DG is done without the presence of capacitor, and vice versa. Hence, possibility to obtain better results will be limited. A hybrid optimization based on Artificial Bee Colony (ABC) and Artificial Immune System (AIS) will be used to determine the optimal DG and capacitor coordination simultaneously. The proposed method was tested on a 33-bus test system with several cases studies by using MATLAB programming. The simulation results show that the simultaneous approach provides better power loss reduction and voltage profile enhancement compared to a separate analysis.

Distributed Generator (DG) is a small-capacity power plant located within the electrical distribution system and is usually placed on buses directly connected to the load. The placement of distributed generators is a technical effort to reduce voltage drops and power losses in the system. Additionally, Power flow analysis is conducted to plan and determine the power levels within an electrical power system. The results in terms of power losses after adding a DG were achieved in the fifth experiment on bus 149, where the system's total active power loss (P) decreased from 720.822 kW to 682.939 kW, and the total reactive power loss (Q) decreased from 530.02 kVar to 405.835 kVar. These results were obtained from power flow calculations using Electrical Transient Analyzer Program (ETAP) software.Therefore, it can be concluded that the electrical network system is operating efficiently. The results show that the more wind turbine generators are added to the buses, the more the voltage drop is reduced. After simulation, the overall voltage drops still meet the standards according to the text report in ETAP.

Transmission congestion management became a grievous issue with the increase of competitiveness in the power systems. Competitiveness arises due to restructuring of the utilities along with the penetration of auxiliary services. The present study depicts a multi objective technique for achieving the optimal capacities of distributed generators (DG) such as solar, wind and biomass in order to relieve congestion in the transmission lines. Objectives like transmission congestion, real power loss, voltages and investment costs are considered to improve the technical and economical performances of the network. Multi objective particle swarm optimization algorithm is utilized to achieve the optimal sizes of unity power factor DG units. The insisted methodology is practiced on IEEE-30 and IEEE-118 bus systems to check the practical feasibility. The results of the proposed approach are compared with the genetic algorithm for both single and multi-objective cases. Results revealed that the intimated method can aid independent system operator to remove the burden from lines in the contingency conditions in an optimal manner along with the improvement in voltages and a reduction in real power losses of the network.

Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

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.

The power losses in distribution system are high, which form 70 – 80% of total transmission and distribution losses. High losses have severe impact on stability, reliability as well as economy. Therefore, minimization of these losses is very necessary. In this paper proposed various schemes to reduce the active power losses in distribution network, given as:- Optimum reconfiguration network,- Optimum Distributed Generation (DG) placement and- Optimum reconfiguration with optimum (DG) placement.Using Cymdist software to implement the optimal reconfiguration algorithm and proposed Genetic Algorithm (GA) to find the size and location, which programmed under MATLAB software package. Whereon the proposed methodology (GA) simplifies the problem by dividing it in two phases, namely Placement Planning Model (PPM) and Size Planning Model (SPM) thereby reducing the search space. It was the integration of the two methods were used after each method individually to obtain minimum real power losses with better bus voltage (better efficiency for network).To verify the proposed algorithms, IEEE 33-bus system and al – jihad neighborhood distribution system (Baghdad distribution sector) are tested. The simulation results are compared with proposed works in literature.

Distributed Generation (DG) is an auspicious solution to many power system problems such as voltage regulation, power loss minimization, effective operating power factor, etc. This paper presents a modified Artificial Bee Colony (ABC) algorithm to suit the searching for the optimal DG sizing and placement in a distribution network. The problem formulation and the proposed technique are tested on a 28-bus Malaysian distribution system. In confirming the effectiveness of this method to determine the minimum power loss which respects to its optimal size and placement, the proposed population-based meta-heuristic approach is compared with the Evolutionary Programming (EP) method for validation and accuracy. The analysis is focused on the sizing impact when considering the cost factor without neglecting the importance to maintain the minimal loss. The results have shown comparatively the same with EP in terms of the computational efficiency. Significance of savings could be achieved while having a low investment of DG installation with a minimum power loss.

PurposeThe paper aims to present a grid-connected multi-inverter for solar photovoltaic (PV) systems to enhance reliability indices after selected the placement and level of PV solar.Design/methodology/approachIn this study, the associated probability is calculated based on the solar power generation capacity levels and outages conditions. Then, based on this probability, dependability indices like average energy not supplied (AENS), expected energy not supplied and loss of load expectations (LOLE) are computed, also, another indices have been computed such as (customer average interruption duration index (CAIDI), system average interruption frequency index (SAIFI) and system average interruption duration index (SAIDI)) addressing by affected customers with distribution networks reliability assessment, including PV. On the basis of their dependability indices and active power flow, several PV solar modules installed in several places are analyzed. A mechanism for assessing the performance of the grid's integration of renewable energy sources is also under investigation.FindingsThe findings of this study based on data extracted form a PV power plant connected to the power network system in Diyala, Iraq 132 kV, attempts to identify the system's weakest points in order to improve the system's overall dependability. In addition, enhanced reliability indices are given for measuring solar PV systems performance connected to the grid and reviewed for the benefit of the customers.Originality/valueThe main contributions of this study are two methods for determining the reliability of PV generators taking into consideration the system component failure rates and the power electronic component defect rates in a PV system which depend on the power input and the power loss using electrical transient analysis program (ETAP) program.

Small electric generating units known as distributed generators (DG) resources are capable of supplying homes, businesses, and industrial sites with electricity through distribution feeders. We can decrease power loss and upgrade the voltage profile by placing DG in the best possible location. However, because they were installed in distribution feeders, the types, sizes, and locations of DGs have a critical effect on their values. Yet, in order for the power system to maximise the gains from such an installation, it is crucial to choose the correct size and location for a DG. In this research, we investigate the ideal positioning and scale of distributed generation (DG) for lowering losses and enhancing voltage profile in distribution networks. The optimal position and size of the DG are determined using the Gray Wolf Optimization (GWO) algorithm. A thorough investigation of IEEE 33-bus distribution systems is conducted. The results show a significant decrease in real power loss, reactive power loss and voltage profile improvement.

Distribution Network Reconfiguration (DNR) is required to identify the best topology network in order to fulfill the power demand with minimum power losses. This paper proposes a new method which is called as Rank Evolutionary Particle Swarm Optimization (REPSO). The proposed method is a combination of the Particle Swarm Optimization (PSO) and the traditional Evolutionary Programming (EP) algorithm with a rejuvenation of the additional of ranking element. The main objective of this paper is to reduce the power losses while improving the convergence time. The proposed method will be implemented and the real power losses in the IEEE 33-bus test system will be investigated and analyzed accordingly. The results are compared to the conventional PSO and hybridization EPSO method and it is hoped to help the power system engineer in securing the network with the less power loss in the future.

The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods.