Reliability assessment of distribution system with distributed generation

Assessment of customer power supply reliability is an important part of distribution system operation and planning. Distribution system reliability assessment is a measure of continuity and quality of power supply to the consumers, which mainly depends on interruption profile, based on system topology and component reliability data. The paper mainly describes about the radial Distribution system reliability is evaluated in two methods. One by placing capacitor at weak voltage nodes for improvement of voltage profiles, reducing the total losses. Second way by improving reliability indices by placing protective equipment (isolators) in the feeder. This paper present an effective approach for real time evaluation of distribution power flow solutions with an objective of determining the voltage profiles and total losses. To improve the voltage profiles and reducing losses by placing capacitors at weak voltage profile nodes using Particle Swarm Optimization (PSO) technique. The Distribution System Reliability Indices are also calculated for the existing radial distribution system before and after placement of isolator. In this paper we have considered the load diversity factor for analysis of load data for real time system. Two matrices the bus-injection to branch-current matrix (BIBC), the branch-current to bus voltage matrix (BCBV) and a simple matrix multiplication are used to obtain power flow solutions. This paper also presents an approach that determines optimal location and size of capacitors on existing radial distribution systems to improve the voltage profiles and reduce the active power loss. The performance of the method was investigated on an 11kV real time Upadyayanagar radial distribution feeder as system of case study. A matlab program was developed and results are presented.

Since a typical distribution system accounts for 40% of the cost to deliver power and 80% of customer reliability problems, distribution system design and operation is critical for financial success and customer satisfaction. To address this situation, Commonwealth Edison is integrating reliability assessment techniques into distribution planning and engineering. Just as equipment loading and voltage regulation are treated with analytical rigor with power flow models, interruptions and outages can be treated with analytical rigor with reliability models. This is made possible through the use of predictive reliability assessment tools that are able to predict customer reliability characteristics based on system topology and component reliability data. Reliability software predicts expected interruption frequencies and duration in a manner analogous to current and voltage evaluations in traditional power flow models. This article describes the distribution system's reliability assessment model jointly developed by Commonwealth Edison and ABB. This effort modeled, calibrated, and assessed the reliability of more than 3300 feeders. After completing the model, an intelligent system was used to automatically identify potential reliability problems and recommend reliability improvement projects based on expected benefits and costs.

Increased non-linear residential loads in today's distribution system is a concern due to the harmonics related power quality issues. The situation gets worsened by the harmonic resonance introduced by the installation of power factor correction (PFC) capacitor banks in the distribution network. At the same time, more and more renewable energy-based distributed generation (DG) units are being installed in the residential area. These DG systems can be used as an effective way to mitigate the harmonic related power quality problems introduced by the nonlinear residential loads. In literature, very limited work has been done to identify harmonic compensation priorities that should be assigned to different DGs operating at different locations of the distribution system for improved compensation performance. This issue is addressed in this paper. A selective harmonic compensation scheme based on modal analysis is developed to assign compensation priorities on DGs operating at different distribution system nodes for improved compensation performance. A modeled residential distribution system containing distribution components such as distribution line, PFC capacitors, transformers, and household appliances along with DG units is used to verify the improvement of compensation performance. Experimental verification of the proposed method is also provided.

In electrical systems the most critical disturbance is the interruption of the power service. However, the main concern for many commercial and industrial users is the mal-operation produced by voltage sags. This paper presents a study about performance of a distribution system in presence of voltage sags using a distribution generation (DG) unit and a distribution static compensator (DSTATCOM). The analysis is performed by using a stochastic generation of disturbances method and ATP/EMTP software tool. Several case studies in the IEEE-13 bus test feeder taking to account different DG locations and penetration levels for DG unit are presented. In addition, the relation between the injected DSTATCOM reactive power, the type of sag and the location of affected nodes is considered. Simulations show that for IEEE-13 system the DSTATCOM is the best alternative for sags mitigation. However, the DG unit is an alternative that can be considered in larger distribution systems to reduce the effects of voltage sags.

Recent deployment of distributed generation has led to a revolution in distribution systems and the emergence of “smart grid” concepts. The smart grid mainly intends to facilitate integration of renewable energy sources and to achieve higher system reliability and efficiency. Different distributed generation types found in modern distribution systems can be classified into two main categories: intermittent- and dispatchable-based distributed generation; the latter has a unique capability of enabling successful islanding operation, thus improving system reliability. This article proposes a methodology for allocating dispatchable distributed generation units in distribution systems to economically improve system reliability. Distributed generation installation and operation costs are to be optimized against the reliability value, expressed as customers’ “willingness to pay” (WTP) to avoid power interruptions. Therefore, the main goal of this research work is to determine the optimal combination between distributed generation units to be installed and loads to be shed during all possible contingencies. A probabilistic load model is adopted to determine the optimal size of allocated distributed generation units.

This paper reports the results from a survey conducted in 2010/2011 among 14 Norwegian distribution system operators (DSOs) concerning their experiences with integration of distributed generation (DG) in distribution networks. The survey shows what kind of problems DSOs experience caused by DG connected to their networks, how the DSOs approach the problems and what kind of planning methodologies and tools the DSOs are using to overcome the problems. The results of the survey show that most of the DSOs have experienced problems with DG units connected to their network, despite the network reinforcements that have been necessary to integrate many of the DG units. The survey also shows that several of the DSOs are expecting a considerable increase in the number of DG units in their distribution networks in the years to come. This increase will probably be accelerated by the introduction of green certificates by 01.01.2012[1], which is expected to increase the profitability of many DG projects. Some of the DSOs have started to address the challenges in their planning methodologies, improving their planning procedures in order to optimize the DG integration process and decrease the need of expensive grid reinforcements. Still, only a few go beyond the normal planning procedures for networks without DG.

Energy audit and reliability assessment of distribution systems are important to keep track of power system's performance. This will in turn help to minimize power interruptions to customers, boost industrialization, research, and economic development in any nation. In this paper, we present the reliability assessment of power distribution in Afe Babalola University, Ado-Ekiti, Nigeria which is a private University with an estimated population of about 10,500 and having a peak power demand of 2MW. A critical assessment of the power distribution system was carried out with data obtained from the campus's central Substation, taking into account various reliability indices. Furthermore, the load consumption of each substation was obtained from the period of January, 2017 to December, 2017. The results obtained show that the University attained a power availability (ASAI) of 0.99984 because of the presence of strategically placed distributed generators (DGs). The reliability assessment revealed SAIDI as 1.4347 hours/customer year, CAIDI as 0.6620 hours/customer interruption, ASUI as 0.00016, and SAIFI as 2.16712 failures/customer year. Furthermore, the study revealed that the university spends more on diesel generators than on the electricity supplied from Benin Electricity Distribution Company (BEDC). The aforementioned reliability results reveal that power distribution in Afe Babalola University is very reliable, efficient and stable.

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 integration of small-scale renewable energy has demonstrated a considerable impact on distribution systems, mitigating power losses and enhancing system reliability. This article proposes a methodology for identifying the optimal locations of distributed generation units along a long medium- voltage electrical network, in order to, minimize energy losses, improve voltage levels, and facilitate the planning of the electrical distribution system. Utilizing existing distribution system data in ArcGIS, load flow analysis was conducted using the unbalanced Newton-Raphson method within the CYME simulation software.Through the Power Loss Index method and the Flower Pollination Algorithm, this study identified the optimal locations and power levels for distributed generation units within the feeder. The results of various study scenarios indicate that placing one or multiple distributed generation units along the long feeder reduces total power losses and improves voltage level. In particular, the incorporation of distributed generation units led to a 39.62% reduction in active energy losses and a 41.88% reduction in reactive energy losses. Furthermore, the voltage profile was improved compared to the base case.

This paper addresses the usefulness of Artificial Bee Colony (ABC) optimization algorithm for assessment of distribution system reliability. The penalty cost functions are formulated which is related to the failure rate and repair time of each distribution segment. And also, satisfy the reliability constraint such as SAIFI, SAIDI, CAIDI and AENS. The finest values of failure rate and repair time cost function are evaluated using ABC algorithm for reliability enhancement. The performance comparison between ABC and Particle Swarm Optimization (PSO) algorithm also has been done in this paper. In addition, for showing the effectiveness of proposed methodology the numerical results have been compared to the different intelligent techniques that are available in the published literature.

Reliability assessment of modern distribution networks embedded with renewable and distributed resources

With the increasing dependencies on Information and Communication Technologies (ICTs), the smart distribution system has similar characteristics to Cyber-Physical System (CPS) and is seen as the so-called “cyber-physical power system” (CPPS). However, the impact of upstream substation is becoming one of the main factors impacting on distribution reliability. To avoid the too optimistic conclusion, the Cyber-Induced Dependent Failures (CDFs) of upstream substation are important to be considered. Thus the cyber security risk brought by both the malfunction of ICT components and the cyber-attack should be taken into account. This paper provided a methodology to incorporate both the two sides of CDFs in upstream substation into cyber-enabled distribution system reliability assessment. In the sequential Monte Carlo simulation (MCS), the game process between attackers and defenders can be modelled as the semi-Markov Chain (SMP), and indices are calculated by the minimal path method. The effects of CDFs are quantified and converted onto the joint feeders, based on the Breadth-First-Search (BFS), as well. Some conclusions for the distribution and substation automation planning have been made and verified by the cyber-extended Roy Billinton Test System (RBTS).

This paper discusses and identifies the effect of Distributed Generation (DG) units on delivery loss reduction and efficiency improvement of future electric systems. It demonstrates that optimal placement and utilization of DGs in distribution feeders and radial distribution networks can lead to significant efficiency improvement through different parts of electric power systems. To this end, Utilities and Distribution System Operators (DSO) need to encourage DG owners to participate in optimization process. This can be done by implementing encouragement mechanisms such as price signal or feed-in tariffs. Furthermore, this paper investigates and optimizes the effect of μ-level DG units on delivery loss reduction and efficiency improvement of distribution feeders. The paper also proposes two major optimization algorithms for optimum placement and utilization of medium-size and MW-scale DGs in LV distribution networks. Finally, the paper closes with a discussion of policies needed to support efficiency improvement and delivery loss minimization of future distribution systems with large penetration of DG units.

This study presents a new stochastic method to estimate the locational marginal price (LMP) in the distribution networks containing distributed generation (DG). In the proposed method, LMP at each bus is calculated according to the contribution of the DG to the reduced amount of loss and emission. An iterative algorithm which is based on the game theory is proposed to allocate the loss and emission reduction among DG units. Also, a stochastic framework based on the point estimate method is considered to capture uncertainty in the market price and load demand. The proposed policy will provide a robust state estimation tool for distribution companies in the next step of operation. The state estimation tool provides the decision maker with the ability to exert its control over private DG units when loss and emission are minimised. The performance of the proposed algorithm is tested on a realistic distribution network in the deterministic and stochastic frameworks. The obtained results are encouraging.

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.