Power System Reliability Indices Research Articles

Application of Power System Reliability and Outage Cost Indices on 33/11 kV Distribution Injection Substation: A Case Study of Asaba Business District

The energy satisfaction that consumers can obtain from a power service provider solely depends on how reliable the feeder to which such customers are connected guarantees adequate and secure energy delivery. This research work is, therefore, to assess the reliability indices and the cost of expected electricity not supplied to the Asaba metropolis through the Benin Electricity Distribution Company (BEDC) Asaba 33/11 kV distribution injection substation. The method used consists of computing the System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), Customer Average Interruption Duration Index (CAIDI), Average Service Availability Index (ASAI), and Expected Energy Not Supplied Index (EENS) of the four feeders emanating from the substation and radiating into the metropolis. This is achieved by first collecting the outage hour, frequency of outage, and expected energy at the point of outage from the station's hourly logbook. The input data is fed into a program written in a MATLAB/OCTAVE environment. The values of the most important reliability index, ASAI, or availability, computed on the SPC, Anwai, Express, and Asaba Commercial feeders are 0.24, 0.23, 0.23, and 0.76 p.u., respectively, in 2020, while in 2021, these values are 0.32, 0.31, 0.33, and 0.83 p.u., respectively. Using the ASAI standard value from IEEE Standard 1366-2003, which is 0.999 as a benchmark, shows that all the feeders can be categorized as less reliable in the years under study. The annual cost of expected energy not supplied during power interruptions to Asaba electricity consumers in 2020 is $2,426,187.046 and $2,214,669.91 in 2021, respectively.

Optimal battery energy storage system deployment from perspectives of private investors and system operators for enhancing power system reliability

Due to their intermittent nature, high penetration of renewable energy resources (RES) can deteriorate power system reliability. A battery energy storage system (BESS) offers an opportunity to reduce the uncertainty associated with RES and hence improve power system reliability. In the last decade, the cost of BESS has declined so its deployment has become increasingly feasible, but the decision ultimately depends on its Net-Benefit to the investor. Depending on the perspective of the investor, the deployment decision involves discovering the most optimal location, capacity, and technology of BESS. A sensitivity analysis based on the Z-bus matrix is used for selecting the optimal location. In this paper, from the narrow perspective of a private investor, the Net-Benefit function is the energy arbitrage value of BESS minus its overall cost. However, the system operator additionally values BESS because it enables more reliable system operation at reduced operating costs. After examining multiple power system reliability indices, the product of Expected Unserved Energy (EUE) and Value of Lost Load (VoLL) is incorporated into the Net-Benefit function of the system operator. MATPOWER Optimal Scheduling Tool (MOST) is used for testing the proposed Net-Benefit functions on a standard reliability test system (RTS) known as RTS-GMLC. Three diverse case studies are examined in three exploration phases; the first phase helps determine search space whereas the second one narrows it down and the third phase finds the optimal BESS solution. Presented research results demonstrate that, over a period of 15 years, optimal BESS deployment can offer as much as $M164.8 and $M33.6 Net-Benefits to the system operator and private investor respectively.

An improved sequential importance sampling method for reliability assessment of renewable power systems with energy storage

Due to the temporal coupling constraints of energy storage, the reliability of modern renewable power systems can no longer be assessed by non-sequential methods. However, the existing sequential methods cannot be combined with variance reduction methods as easily as the non-sequential ones. Hence, the power system reliability assessment is faced with incredible computational burden. In this paper, we propose an improved sequential importance sampling method to accelerate the computation of power system reliability indices. The proposed method is composed of two stages, namely the pre-simulation and main simulation stages. In the pre-simulation stage, we design a customized probability distribution parameter optimization process for load curves in the cross entropy based importance sampling method. In the main simulation stage, the derived probability distribution parameters in the pre-simulation stage are converted and further used to carry out a sequential Monte Carlo sampling process to efficiently estimate the reliability indices of renewable power systems with energy storage. The proposed method is validated by numerical tests performed on the modified IEEE-RTS 79 and IEEE-RTS 96 test systems, both of which are integrated with three wind farms, one photovoltaic station and one battery energy storage power station. Results show that the proposed method is much faster than existing sequential importance sampling methods in both test systems because of exploiting the acceleration potential of the load. Therefore, the proposed method can be used to assess the reliability of the renewable power systems with more energy storage devices in the future.

Computing reliability indices of a wind power system via Markov chain modelling of wind speed

Statistical modelling of wind speed is of great importance in the evaluation of wind farm performance and power production. Various models have been proposed in the literature depending on the corresponding time scale. For hourly observed wind speed data, the dependence among successive wind speed values is inevitable. Such a dependence has been well modelled by Markov chains. In this paper, the use of Markov chains for modelling wind speed data is discussed in the context of the previously proposed likelihood ratio test. The main steps for Markov chain based modelling methodology of wind speed are presented and the limiting distribution of the Markov chain is utilized to compute wind speed probabilities. The computational formulas for reliability indices of a wind farm consisting of a specified number of wind turbines are presented through the limiting distribution of a Markov chain. A case study that is based on real data set is also presented.

Comparative results of applying the FDLISR method for rural and urban distribution networks with distributed generations

High penetration of distributed generations (DGs) into a distribution network (DN) could not only enhance the power-system reliability indexes but also reduce power losses. The use of DGs is effective for service restoration plans of the DN to minimize outage customers and power loss. However, the presence of DGs could lead to the protection miscoordination of overcurrent relays/reclosers due to fault isolation and service restoration processes. In light of this assumption, a fault detection, location, isolation, and service restoration (FDLISR) method is proposed to apply to both rural and urban distribution networks with DGs such that the comparative results of this FDLISR method are analyzed and discussed to demonstrate which type of the distribution network has a high risk of protection miscoordination of overcurrent relays/reclosers from service restoration scenarios. The faulted-section isolation and the distribution-network service restoration can be performed by solving a two-constraints-based objective function and seven appropriate performance indices (PI). By using the E-Terra Dispatching Operator Training Simulator (E-DOTS), simulation results of the FDLISR method applied for both the 22 kV rural-type and urban-type distribution networks are presented and compared to demonstrate its effectiveness by showing the possibility of protection miscoordination and the consideration of power losses for each service restoration plan. Consequently, the computation time of the FDLISR method proposed for the DN is within one-and-half minutes. Moreover, two performance indices of power loss and protection miscoordination, LOSS-PI and PRO-PI respectively, can highly impact the priority rank of possible service restoration plans for the rural DN while only the PRO-PI affects multiple service-restoration plans for the urban DG-contained DN.

Comprehensive probabilistic assessment on capacity adequacy and flexibility of generation resources

The increasing penetration of renewable energy in power systems has been playing an increasingly important role on power system reliability. However, traditional reliability indices of power systems, such as the loss-of-load probability and the expected unserved load, can only reflect the generation capacity adequacy but neglect its flexibility. Focusing on this issue, a set of comprehensive assessment indices that can accurately quantify both generation capacity adequacy and flexibility is proposed in this paper. Specifically, the proposed up-ward capacity shortage probability and expectation indices assess both generation capacity and ramp-up capacity shortages that may cause load loss, while the down-ward capacity shortage probability and expectation assess both base-load cycling capacity and ramp down capacity shortages that may cause renewable energy curtailment. A probabilistic assessment method with the time sequential probabilistic production simulation technology is developed to effectively calculate the proposed indices. This method can adequately consider uncertainty factors, such as the random outages of generating units and forecasting errors of loads and renewable energy, as well as dynamic operation statuses of generating units. Numerical examples are presented to verify the effectiveness of the proposed indices and the evaluation method.

Construction of Grid Reliability Assessment Model Based on Sensitivity Analysis

The traditional reliability of power supply only focuses on the continuity of power supply and does not include the impact of power quality on customers in the assessment. In this paper, we use “the ability of the grid to provide continuously available power to customers” as the criterion to reflect more realistically the level of reliability perceived by customers. This reliability assessment, which is closer to the customer’s perspective, can also be used as a basis for quality-based pricing in the electricity market that is acceptable to both the supplier and the consumer, and the difference between the reliability of electricity consumption and the reliability of electricity supply statistics can also be used by power supply companies to evaluate their own power supply quality and service level. This paper uses the AC algorithm to calculate the reliability indexes of the power system in combination and proposes to use the component sensitivity analysis to rank the system components to obtain the key component information for the combination of the power system reliability indexes.

Review on Power System Reliability Indices and Evaluation Techniques

Now a day’s most accountable issue in the power industry is Reliability. power systems reliability using Quantitative analysis can be examined with the help of reliability indices. For a long period of time different methods introduced and developed to assess the reliability indices of the power system. In modern society Uninterrupted and Reliable electric power supply is necessary. The wide use of electricity has been lead to a high vulnerability to electric power failures. This is the reason, reliability of electric power has gained spotlight and it’s considered significant role while planning for a electric power system operation.In this paper, a summary is given of the technique of finding out the reliability of a power system. Readers will get an idea about the possibilities of measuring Reliability indices.The inspiration at the back of this paper is to set up a comprehensive general idea about Analytical and simulation technique for reliability assessment and serving for future research and development in the area of electrical power system.

Reliability of Supply and the Impact of Weather Exposure and Protection System Failures

Extreme weather is known to cause failure bunching in electrical transmission systems. However, protection systems can also contribute to the worsening of the system state through various failure modes—spontaneous, missing or unwanted operation. The latter two types of failures only occur when an initial failure has happened, and thus are more likely to happen when the probability of failure of transmission lines is high, such as in an extreme weather scenario. This causes an exacerbation of failure bunching effects, increasing the risk of blackouts, or High Impact Low Probability (HILP) events. This paper describes a method to model transmission line failure rates, considering both protection system reliability and extreme weather exposure. A case study is presented using the IEEE 24 bus Reliability Test System (RTS) test system. The case study, using both an approximate method as well as a time-series approach to calculate reliability indices, demonstrates both a compact generalization of including protection system failures in reliability analysis, as well as the interaction between weather exposure and protection system failures and its impact on power system reliability indices. The results show that the inclusion of protection system failures can have a large impact on the estimated occurrence of higher order contingencies for adjacent lines, especially for lines with correlated weather exposure.

A reliability-constrained demand response-based method to increase the hosting capacity of power systems to electric vehicles

This paper proposes a reliability-constrained demand response-based method to maximise the permissible penetration level of electric vehicles (EVs) at electric power system buses. The continuous increase of EVs will deteriorate the reliability of power systems if no countermeasures are taken. Implementation of demand response (DR) programs can be an effective solution to reduce or even avoid load curtailments. Also, the mobility of EV loads causes a dynamic change in load curtailments. This dynamic change can be captured through building load profiles for EVs considering drivers’ behaviour and preferences to charge EVs. The proposed method is developed based on a DR program and load profiles of EVs. In the proposed method, existing load profiles are combined with load profiles for initial numbers of EVs at corresponding buses. The initial numbers are calculated based on total interruptible/curtailable (I/C) loads at system buses and peak load of EVs. Drivers’ behaviour and preferences in terms of charging times, periods, and locations are modelled in developing load profiles for EVs. Then, I/C loads-based DR is implemented to restructure load profiles of system buses. Both incentives and penalties are considered to develop the DR program. Finally, the maximum permissible penetration level is calculated by increasing the number of EVs until systems’ prior (without EVs) reliability level is achieved. The proposed method is demonstrated on the Roy Billinton Test System, IEEE Reliability Test System, and IEEE-33 Bus Distribution System. Monte Carlo simulation is performed to evaluate the well-known reliability indices of power systems. The results show that the permissible penetration level of EVs in power systems can be increased significantly using the proposed method.

Importance Sampling Using Multilabel Radial Basis Classification for Composite Power System Reliability Evaluation

Resource additions to electric power grids are planned in advance to maintain reliability of electric power supply. This is achieved by performing reliability studies for calculating reliability indices at the planning stage to ensure that the required levels of reliability will be met. This article proposes a new state classification approach to calculate power system reliability indices using a combination of multilabel radial basis function (MLRBF) networks and importance sampling (IS) within the framework of Monte Carlo simulation process. Multilabel classification algorithms are different from single-label approaches, in which each instance can be assigned to multiple classes. This characteristic gives MLRBF the capability to classify composite power system states (success or failure), at the bus as well as system level. Bus level indices provide useful reliability information for locational reliability, which allows more rational and equitable distribution of resources. MLRBF classification does not require optimal power flow (OPF) analysis; however, OPF is required for the training and cross-entropy optimization phases. This article shows that the scope and computational efficiency to evaluate reliability indices can be significantly increased if the proposed MLRBF classifier is used together with well-known variance reduction technique of IS. As the classifier is trained to recognize the reliability status of a system state, it can also be used in operational planning when a number of scenarios need to be evaluated in a short time for their ability to satisfy load. The proposed method is illustrated using the IEEE reliability test system for different load levels. The outcomes of case studies show that MLRBF algorithm together with IS provides excellent classification accuracy in reliability evaluation while substantially reducing computation time and enhancing the scope of evaluation.

Reliability-Based Metrics to Quantify the Maximum Permissible Load Demand of Electric Vehicles

The continuous increase of electric vehicles (EVs) is expected to introduce several challenges to power systems among which is the deteriorating reliability of power supply. This paper proposes two adequacy metrics to quantify the maximum permissible EV loads for a power system without deteriorating its reliability and the required improvements to power systems to accommodate high penetrations of EVs, which are defined as follows: 1) extra effective available energy for EVs' charging (EEAE-EVs) and 2) extra effective required generation to accommodate EVs (EERG-EVs). The EEAE-EVs provides a measure for the maximum amount of EV loads that a power system can accommodate without adding new generation while maintaining its reliability. The EERG-EVs provides a measure to the minimum amount of new generation that is needed to restore the reliability level of a power system if the load of EVs exceeds the maximum permissible load of the system. These metrics provide a decision aid to power system planners and operators on when and how to perform generation expansion. The importance of the proposed metrics is demonstrated on the IEEE Reliability Test System (IEEE RTS) with real EV charging data. The sequential Monte Carlo simulation method is used in evaluating the well known power system reliability indices, the EEAE-EVs, and the EERG-EVs. The results show that a power system can accommodate EV loads without deteriorating its reliability as long as EV loads do not exceed the EEAE-EVs of the system. For the IEEE RTS, the EERG-EVs shows that the amount of the required generation to maintain system reliability is about half of the added EV loads from 330-660 MW.

A comprehensive approach for wind turbine generation allocation with accurate analysis of load curtailment using nested programming

Utilization of renewable energy based distributed generation such as wind turbine generation (WTG) provides proper opportunity for enhancement of the electric power distribution networks. However, appropriate integration of WTG in distribution networks is crucial for ensuring quality and reliability of power supply. This paper presents a novel and comprehensive solution methodology for optimum allocation of different WTG types. A nested programming based on imperialist competitive algorithm is used to minimize weighted sum of active power losses and system reliability indexes without violating the network constraints. The main optimization problem is formulated as a mixed integer nonlinear programming, while for accurate analysis of load curtailment, expected energy not supplied of the network is calculated by a set of nonlinear optimal power flow programming sub-problems. Four case studies are examined to demonstrate various aspects of the proposed approach using two 33-bus and 9-bus distribution networks. According to results, the case studies elaborate accuracy and validity of the proposed methodology that not only imply more precise results than conventional methods but also shows reliability evaluation satisfy technical constraints during different contingencies. This comprehensive methodology can be used as a suitable tool for different networks with various islanding rules.

Reliability evaluation of PV power systems with consideration of time‐varying factors

With the large-scale grid-connected operation of solar photovoltaic (PV) power systems in recent years, the accurate assessment of its reliability level has received increasing focus. This study obtained a probability model for system power output by analysing the structural characteristics of the PV power system and by examining its component failure mode and the effect of the component failure mode on the output power. The algorithmic model for the reliability evaluation of the PV power system was established based on the sequential Monte Carlo method, the weather conditions, component ageing, and light intensity. Finally, the six states model and their reliability indices of the PV power system were studied considering the resource constraint and the structural characteristics of the PV power system. The results showed that the model can fully reflect the actual operation reliability of a PV power system.

Incorporating Cyber Layer Failures in Composite Power System Reliability Evaluations

This paper proposes a novel approach to analyze the impacts of cyber layer failures (i.e., protection failures and monitoring failures) on the reliability evaluation of composite power systems. The reliability and availability of the cyber layer and its protection and monitoring functions with various topologies are derived based on a reliability block diagram method. The availability of the physical layer components are modified via a multi-state Markov chain model, in which the component protection and monitoring strategies, as well as the cyber layer topology, are simultaneously considered. Reliability indices of composite power systems are calculated through non-sequential Monte-Carlo simulation. Case studies demonstrate that operational reliability downgrades in cyber layer function failure situations. Moreover, protection function failures have more significant impact on the downgraded reliability than monitoring function failures do, and the reliability indices are especially sensitive to the change of the cyber layer function availability in the range from 0.95 to 1.

Consideration effect of uncertainty in the reliability indices of power systems using a scenario-based approach

In power systems, reliability evaluations mainly depend on the precise forecasting of the reliability parameters of the internal components. Nonetheless, the procedure of calculating the reliability parameters such as failure rate and repair rate parameters is based on the failure statistics and measurements which can cause much inherent uncertainty and thus affecting the reliability indices of the system. Therefore, the main purpose of this work is to suggest a sufficient and easy-implemented approach based on scenario production to capture the uncertainty of the reliability parameters including the failure rate and repair rate variables. Here, the scenario generation process is constructed using the roulette wheel mechanism along with the probability density function of the random variables. Then, the proposed scenario based approach makes use of a scenario reduction mechanism to avoid high computational burden. This aspect of the proposed stochastic framework makes it useful for the optimization applications which require iterative analysis. In order to see the feasibility and satisfying performance of the proposed framework, an IEEE standard test system is used as the case study.

Differential Evolution Based Intelligent System State Search Method for Composite Power System Reliability Evaluation

This paper presents a new approach for evaluating the reliability indices of a composite power system that adopts binary differential evolution (BDE) algorithm in the search mechanism to select the system states. These states also called dominant states, have large state probability and higher loss of load curtailment necessary to maintain real power balance. A chromosome of a BDE algorithm represents the system state. BDE is not applied for its traditional application of optimizing a non-linear objective function, but used as tool for exploring more number of dominant states by producing new chromosomes, mutant vectors and trail vectors based on the fitness function. The searched system states are used to evaluate annualized system and load point reliability indices. The proposed search methodology is applied to RBTS and IEEE–RTS test systems and results are compared with other approaches. This approach evaluates the indices similar to existing methods while analyzing less number of system states.

Consideration effect of uncertainty in power system reliability indices using radial basis function network and fuzzy logic theory

Reliability assessment of composite power systems is a critical and important part of power investigations especially in the market-driven environments. Therefore, the reliability indices as criteria for the comparison of the reliability of the power systems should be evaluated precisely and carefully. Because of the nonlinear behavior of the systems as the effect of different parameters like weather conditions, load pattern changes and some others, reliability indices always contain much uncertainty. In this paper a neuro-fuzzy based method is proposed to reduce the degree of the uncertainty in the reliability indices and therefore to evaluate the reliability of the composite power systems precisely. Fuzzy logic theory makes it possible to make use of the human experts knowledge in the reliability evaluations. Also by the use of RBFNN and its powerful characteristic to learn any nonlinear mapping between two states it would be possible to evaluate the reliability indices for every short time interval needed so that reliability evaluation in real time would be achievable and feasible. In this paper the RBFNN is trained by the training patterns that are achieved by the use of fuzzy logic theory, then the results are examined on a standard Reliability Test System (RTS-96).

Power system performance quality indices

The paper summarizes the results of a recent industry supported research and development study in which a novel framework for evaluating meaningful power system reliability and quality indices was developed and applied. The system-wide integrated performance indices are capable of addressing and revealing areas of deficiencies and bottle-necks as well as redundancies in the composite generation–transmission–demand structure of large-scale power grids. The novel technique utilizes a basic linear programming formulation, which offers a general and comprehensive framework to assess the harmony and compatibility of generation, transmission, and demand in a power system. Practical application to a portion of the Saudi power grid is also presented in the paper for demonstration purposes. Copyright © 2010 John Wiley & Sons, Ltd.

Probabilistic Arrival Power Evaluation considering Voltage Stability

Purpose of the electric power system planning and operation is to supply the electric energy to customers continuously and economically. With the mutual exclusive laws of nature between reliability and economic, finding the meeting point is very important but not easy. Commonly the probabilistic reliability indices of the electric power systems are represented with negatively. And the effectiveness of FACTS on the probabilistic reliability could not be reflected with common methods. In this paper, a method to evaluate the probabilistic arrival power at each load point is presented. With this new proposed method, probabilistic reserve margin at load points can be calculated and which can be used with positive reliability index also. Using the P-V analysis, the voltage stability is considered in reliability evaluation. It is expected that the proposed method will be useful expecially in reliability evaluation of electric power system which has voltage restriction.