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The still growing share of intermittent renewable sources in the electric energy matrix is a factor that greatly increases the complexity of modelling electrical power systems. Although its exploitation is conditioned to the geographic location where the natural resource emerges itself, when this location coincides with the grid‐edge of the system, close to the consumer, the composite reliability assessment is exposed to the combination of two problems: (i) representation of the intermittent energy availability of generators; and (ii) failure event rarity. To address the first problem, a fast Monte Carlo tool capable of representing chronological aspects can be used. For the second problem, an efficient variance reduction technique that adapts well to the composite problem of generation and transmission must be established. Therefore, a quasi‐sequential Monte Carlo simulation tool aided by importance sampling via the cross‐entropy method is proposed as an efficient and robust procedure for evaluating the composite reliability of systems with renewable participation at grid‐edge. The method is evaluated through a modified version of the IEEE Reliability Test System ‐ 1996, which presents renewable generation in consumer load buses and rarity in the composite failure. [ABSTRACT FROM AUTHOR]

This paper presents a new method for allocating transmission system costs in hydro prevailing energy markets. The proposed approach is based on nodal pricing, and it considers multiple hydrological scenarios obtained from optimized energy models. These models are much closer to the operational reality of hydro dominant systems and more adequate to the transmission operation planning. The nodal pricing strategy comprises two steps to recover the total costs of the transmission system. The first one calculates the charges corresponding to the utilized network capacity, estimated at a specified operating condition, and it brings the locational signal to the tariff. In the second step, the charges related to the transmission capacity available but not used in the system are assessed by a postage stamp. This latter step accomplishes the task of recovering the total transmission charges and defining the final tariff for specific operating dispatching. On the other hand, the optimized energy models, based on stochastic dual dynamic programming, are used to minimize the operating costs while penalizing possible energy spillages. These models create monthly sequences of hydrothermal coordinated generating scenarios and dispatching conditions, which are used by the nodal pricing strategy to produce the generation/load charges and tariffs and the corresponding statistics. The proposed method, combining the nodal pricing strategy and the optimized energy model, is applied to a Brazilian network configuration and the results are fully discussed. Moreover, probabilistic analyses are also performed to describe the annual costs of transmission of thermoelectric and hydroelectric plants and industrial electrointensive consumers installed in different areas of the network. [ABSTRACT FROM AUTHOR]

• A new quasi-sequential Monte Carlo simulation (QS-MCS) method properly designed to the reliability assessment of distribution grids with renewable DG and other time-dependent events is proposed. • ew test functions are described and discussed to calculate the usual distribution reliability indices. • The computational efficiency of the proposed QS-MCS is similar to non-sequential-based tools, but its accuracy and modeling flexibility are at the same level as sequential-MCS tools. • The reliability indices of two distribution systems, a test system and a real distribution network from Brazil, are evaluated under the presence of renewable DG sources. • The algorithm is built using object-oriented programming and all the results obtained are compared and validated to those evaluated by a Sequential-MCS tool. The increasing insertion of distributed generation (DG) in distribution systems has profoundly altered the typical structure of these grids. In general, renewable DG resources are intermittent and make the reliability performance evaluation of distribution networks more complex. Thus, it is essential to have an efficient method and models capable of adequately representing the stochastic characteristics of load, generation and grid, including the protection system response. The proposed quasi-sequential Monte Carlo simulation (QS-MCS) aims to capture in detail the behavior of these characteristics, just like sequential or chronological simulations do, but with the same simplicity and performance of non-sequential simulations. Based on new unbiased test functions, distribution reliability indices are calculated to cope with the insertion of DG considering two systems, and discussions are raised about the flexibility, accuracy and computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the static transmission expansion planning (TEP) problem is solved for real large electrical networks. Different generation and load scenarios are considered and the "N‐1" security criterion is ensured. The solution of the problem is achieved based on a constructive metaheuristic algorithm (CMA‐TEP) specially developed for large systems. The proposed method performs a gradual and parallel process of constructing solutions (expansion plans), which incorporates heuristics and procedures generally used by planning engineers. Operational information for the intact network condition and under contingencies is used during the constructive process, to identify reinforcements to be added (most attractive) to or removed (less effective) from the network being set up. Moreover, mechanisms of recombination, perturbation, selection, refinement, and depuration of solutions are also employed during the constructive process. The linear DC network model, including ohmic transmission losses, is used throughout the entire constructive process. The robustness and efficiency of the proposed CMA‐TEP method is tested in a configuration of the Brazilian network, considering two hydrological scenarios: dry and humid. Comparisons based on statistical performance indices are also carried out with specialized genetic algorithms. [ABSTRACT FROM AUTHOR]

This work presents a new method to evaluate generation reserve margins in systems with renewable sources. In assessing the adequacy of generation reserve amounts, besides failures in generating units, their capacity intermittencies, unavailability, and capacity limits of the transmission system are duly considered. Risk indices are evaluated using quasi-sequential Monte Carlo simulation techniques. The cross-entropy method is used to treat rare events and also to identify critical equipment for operation in each scenario. The proposed method is applied to the original IEEE RTS system and to a modified configuration with insertion of wind power plants. A subsystem of the Brazilian interconnected network is also used to illustrate the practicality of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a simple and computationally efficient methodology for computing robust dynamic equivalents. While simple to implement, the methodology involves many factors, and these are discussed, explained and justified throughout the paper. It classifies clusters of generators to be equivalenced by their electrical distances, and identifies second order reduced models for them. It maps them into classical synchronous machine models and aggregates them. Reduction of the network is based on the Ward PV method. The quality of the dynamic equivalent, in terms of flexibility and reduced dependence on disturbances, is automatically verified by performance indices. The methodology can easily be embedded in time domain simulation packages. Two large real power system networks are used for testing the performance of the approach. [ABSTRACT FROM AUTHOR]

This paper proposes a new evolutionary algorithm to solve transmission expansion planning problems in electric power systems. In order to increase the robustness of the search process and to facilitate its use by planners on different networks and operation conditions, the proposed method uses multi-operators and a mechanism for dynamic adaptation of the selection probabilities of these operators. Two sets of search operators are proposed: evolutionary and specialized. The mathematical formulation considers a DC network model including transmission losses and the “N-1” deterministic criterion. The proposed method is applied to a well known academic test system and a configuration of the Brazilian network. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to evaluate loss of load indices in composite generation and transmission systems. The main idea is to combine a Cross-Entropy (CE)-based optimization process and nonsequential Monte Carlo Simulation (MCS) to obtain an auxiliary sampling distribution, which can minimize the variance of the reliability index estimators. This auxiliary sampling distribution will properly modify the original unavailabilities of both generation and transmission equipment, so that important failure events are sampled more often. As a result, the MCS algorithm can reach convergence faster and with fewer samples, leading to significant gains in computational performance, especially when dealing with very reliable system configurations. The proposed method is tested using several composite power systems, including the IEEE RTS 79, IEEE RTS 96, and a configuration of the Brazilian system. [ABSTRACT FROM PUBLISHER]

• A new method that allows the effects of generator aging to be included in the reliability analysis of power systems is proposed. • A novel model for the failure rate capable of representing the stages of normal operation and wear-out is described. • Catastrophic failures and equipment refurbishing actions are also included in the proposed method. • The new method is built using a chronological Monte Carlo simulation and variance reduction techniques. • The aging of components can cause significant reductions in the reliability of generating systems. Equipment aging has been a major concern among electric utilities' planners, since quality of service can be put at risk. This paper proposes a new method that allows the effects of the aging of generators to be properly included into the reliability analysis of power systems. First, a new model for the failure rate capable of representing the stages of normal operation and wear-out, replacing the adoption of constant failure rates, is described. The effects of repairable and non-repairable (i.e., catastrophic) failures on the reliability assessment of generation systems are also discussed. The proposed method is built from the previous models using a sequential Monte Carlo simulation and variance reduction techniques. To validate the proposed method, the IEEE Reliability Test System is used with modifications in which different ages are defined for existing generating units that are progressively aging over the years. Several tests are described together with strategies for adopting preventive refurbishments and the corresponding results are duly discussed. [ABSTRACT FROM AUTHOR]

• The GMS problem is assessed from the ISO point of view based on GENCOs schedules. • The generation maintenance scheduling problem is assessed from both points of view, ISO and GENCOs. • Production cost and load shedding are minimized ensuring reliability constraints. • Uncertainties of load, generating capacity unavailability and renewable energy are duly modeled. • Non-sequential Monte Carlo simulation and cross entropy methods are properly combined. • The evolution strategy metaheuristic is used as the main tool to solve the problem. This work proposes an efficient method for solving the generation maintenance scheduling (GMS) problem, defined through an interactive process carried out between the independent system operator (ISO) and generation companies (GENCOs). In this problem, starting from a schedule previously informed by GENCOs, it is desired to minimize the expected costs of production and system load shedding for the period of analysis from the ISO point of view. The Evolution Strategy metaheuristic is used to solve the resulting optimization problem. The non-sequential Monte Carlo simulation and Cross-Entropy methods are combined to efficiently assess the maintenance schedules searched during the solving process. Uncertainties related to the behavior of load, unavailability of generation equipment, and variability of renewable energy sources are taken into account in the modeling and solution of the GMS problem. The performance of the proposed method is tested with the IEEE-RTS modified with the inclusion of renewable sources. [ABSTRACT FROM AUTHOR]

In light of the additional needs imposed by high penetration level of renewable sources on the planning of electric networks, this paper introduces new models and procedures for obtaining more robust and flexible networks. A new methodology, computational algorithm, and a new set of performance indices are presented based on the concept of chronological power flow (CPF). In view of the presence of intermittent energy resources, aspects related to the operation of the system during moments of high energy availability will be duly evaluated, since that is when there might be a waste of renewable energy, and also during low renewable production conditions, when the system may be more fragile. The results obtained for the IEEE Reliability Test System—1996 will also be presented and discussed. [ABSTRACT FROM PUBLISHER]

Abstract: This paper discusses the development of a Multi-Agent Systems (MAS) technology-based platform with potential applications in management and simulation processes in power systems. In order to explore some of the features of MAS, a new methodology is proposed to assess power systems reliability based on Monte Carlo simulation (MCS), exploiting the benefits of the distributed artificial intelligence area and, mainly, the use of the distributed capacity in two ways: building autonomous behaviors to the applications and mitigating computational effort. Through the use of this technology, it was possible to divide the MCS algorithm into distinct tasks and submit them to the agents’ processing. Two different approaches to solve generating capacity reliability problems based on chronological MCS illustrate the potential of MAS in power systems reliability assessment. [Copyright &y& Elsevier]

This paper proposes a new methodology to evaluate system generating capacity reliability indices considering time-dependent power sources and loads. Based on sequential Monte Carlo simulation (MCS) and the cross-entropy (CE) method, the basic idea is to find an optimal distortion for the equipment transition rates using the CE concepts. A chronological simulation is then carried out using these newly found optimal reference parameters. This process suitably modifies the chronological evolution of the system in order to improve its statistical efficiency and convergence properties. As a result, the computational efforts of the sequential simulation can be greatly reduced while retaining many of its main advantages. Comparisons with the chronological, pseudo-chronological, and quasi-sequential MCS are carried out using the IEEE-RTS-96 (Reliability Test System) and modifications of this system that include renewable sources. [ABSTRACT FROM PUBLISHER]

Abstract: This paper proposes a new methodology to solve transmission expansion planning (TEP) problems in power system, based on the metaheuristic ant colony optimisation (ACO). The TEP problem includes the search for the least cost solution, bearing in mind investment cost and reliability worth. Reliability worth is considered through the assessment of the interruption costs represented by the index LOLC – loss of load cost. The focus of this work is the development of a tool for the multi-stage planning of transmission systems and how reliability aspects can influence on the decision-making process. The applications of the proposed methodology are illustrated through case studies carried out using a test system and a real sub-transmission network. [Copyright &y& Elsevier]

This paper presents a new Monte Carlo simulation (MCS) approach based on cross-entropy (CE) method to evaluate generating capacity reliability (GCR) indices. The basic idea is to use an auxiliary importance sampling density function, whose parameters are obtained from an optimization process that minimizes the computational effort of the MCS estimation approach. In order to improve the performance of the CE-based method as applied to the GCR assessment, various aspects are considered: system size, rarity of the failure event, number of different units, unit capacity sizes, and load shape. The IEEE Reliability Test System is used to test the proposed methodology, and also various modifications of this system are created to fully verify the ability of the proposed approach against both, a crude MCS and an extremely efficient analytical technique based on discrete convolution. A configuration of the Brazilian South-Southeastern generating system is also used to demonstrate the capability of the proposed CE-based MCS method in real applications. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology based on Monte Carlo chronological simulation to evaluate the operating reserve requirements of generating systems with large amounts of renewable energy sources. The main idea is to build a new probabilistic approach to assess several performance indices bearing in mind the long-term planning. Besides the conventional indices used in generating capacity reliability assessment and also those obtained from the well-being framework, a new set of indices are proposed to measure the availability of different types of operating reserve. The relationship between the proposed approach and the traditional PJM method is also discussed. Renewable in this study mainly comprises wind power sources and for that the IEEE RTS-96 is adapted to illustrate the proposed methodology. Finally, different planning criteria are discussed to deal with the design of generation system capacity, static and operational, in the long-term planning. [ABSTRACT FROM AUTHOR]

This paper presents new probabilistic methodologies for computing the optimal number of transformer spares for power distribution substations. The basic idea consists of three steps: 1) the reliability evaluation of a given system of transformers with inventory of spares; 2) the calculation of investment and operational costs of the system for different alternatives of inventory composition; and 3) the identification of the number of spares that minimizes the total cost. Two new models are proposed for the reliability evaluation step. In the first one, the system operational states are represented by a Markov process. The second one uses a chronological Monte Carlo simulation model to assess the reliability performance of a system with inventory of spares. Both models are able to provide indices such as probability, frequency, and duration of failures, as well as estimates of energy not supplied and the corresponding costs. The proposed methodologies are applied to a 72-ky distribution transformer system, and the obtained results are compared to those from a widely used model based on a Poisson distribution. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on nonsequential Monte Carlo simulation (MCS) and artificial neural network (ANN) concepts. ANN techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named Group Method Data Handling (GMDH), is used, and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providing significant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e., loss of load probability, frequency, duration, and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the WEE-RTS 96, and to a configuration of the Brazilian South-Southeastern System. [ABSTRACT FROM AUTHOR]

Over the past decade, electric power systems have been restructured based on different models to introduce competition among market participants. Transmission open access for suppliers and customers is one of the most prevailing unbundling services. Usually, a connection tariff is established based on different market factors, including the actual usage of the transmission network. In Brazil, most vertically integrated companies owned by the federal and state governments were unbundled, and new transmission companies are being created. These companies make their assets available to the independent system operator, designated as Operador Nacional do Sistema Elétrico (ONS). One of the main objectives of ONS is to manage transmission services, over a certain voltage level, to ensure nondiscriminatory access to the grid. All transmission users, including regional distribution companies, pay wheeling transmission charges at each delivery or connection point with the major Brazilian network administrated by ONS. This paper presents a methodology for determining the optimal amount of transmission system usage (ATSU) that must be contracted by a regional distribution company, at each connection point. In the proposed approach, the maximum flow at each connection is represented as a random variable in order to obtain the distribution probability of the transmission charges as a function of the ATSU parameter. Besides providing the optimal amounts to be contracted, the proposed probabilistic methodology allows quantifying the risks of violation of each contract and estimating possible penalties. All methodology development steps are described and illustrated through numerical examples. An application to a typical Brazilian regional distribution company is also discussed. [ABSTRACT FROM AUTHOR]

Presents a study which described a new probabilistic method for determining the available transfer capability (ATC) between areas of an interconnected system of electric power transaction activities. Determination of ATC probability distribution function; Determination of ATC with additional power injections.

Presents a study which defined cost related reliability measures for power system equipment. Concepts of Markov chains; Cost consideration in Markov models; Solution of transient Markov chains.

Presents a study which extended the concepts and evaluation techniques for composite generation and transmission reliability assessment of the static and dynamic aspects in electric power systems. Status of the competition in the electric power utility industry; Conclusions.

Presents a study which discussed a methodology to evaluate loss of load indices with emphasis on loss of load cost assessment for composite generation and transmission systems. Bases of the results of composite generation and transmission system reliability evaluations; Conclusions.

• More realistic system representation when compared to previous assessment methods for distribution transformer stock sizing. • The effects of using load transfers and mobile unit substations on the reliability indices are demonstrated. • Any probability distribution can be used to represent operating and waiting times. • Exponential and non-exponential times to failure are tested and the results discussed. • Aging exerts a significant influence on reliability indices and operating costs. • The proposed method is tested in a real system through several examples to demonstrate its flexibility and practicality. This paper proposes a new and practical probabilistic method for calculating reliability indices and costs associated with distribution substations that share spare transformers of a same group. Based on Monte Carlo simulation, the proposed algorithm considers the chronological representation of failure occurrences, and enhances previous methods by modeling emergency actions, such as transferring load to neighboring feeders and connecting a mobile unit substation to ensure electricity supply to customers during the replacement of failed transformers. Thus, spare transformer requirements with new acquisitions to replenish the stock or even to expand the system are probabilistically assessed. All waiting times associated to these actions can be duly represented by any probability distribution, including the usual exponential model. A more accurate calculation of reliability indices and costs associated with the interruption of consumers is achieved. The corresponding computational algorithm is used in several numerical applications, considering a class of transformers of a Brazilian electricity utility. The results of these applications are deeply discussed to demonstrate the practical relevance of the proposed method. [ABSTRACT FROM AUTHOR]

This article discusses various issues related to electric power transmission loss allocation, and presents a mathematical analysis in this regard. The article makes specific reference to the article ""Transmission Loss Allocation: Part I-Single Energy Market" that was previously published in the November 2003 issue of the periodical "IEEE Transactions on Power Systems." In the article, Transmission Loss Allocation, the authors have presented an equation to calculate transmission loss factors independently of the slack bus designation used by the power flow solution. This article discusses this equation.

In power system planning studies that involve the search for optimal investments with low risks for the power grid, probabilistic reliability assessments provide very useful tools and indices. A challenge faced in the application of these tools is related to the computational effort demanded by the evaluation process, especially when the combined effects of failure of generation and transmission equipment are considered. In this context, the present work proposes a new method for efficient estimation of the main composite reliability indices by combining Binary Logistic Regression (BLR) technique, a machine learning tool used for binary data classification, with the non-Sequential Monte Carlo simulation (NS-MCS) method. In addition, a computational parallelization strategy is incorporated to the proposed method to improve even more the efficiency of the composite reliability assessment. The performance of the proposed approach is analyzed by evaluating composite reliability indices for the IEEE-RTS considering two different generation and load scenarios, in addition to a real large-scale power system. The results obtained are compared with those using the NS-MCS method in its conventional version. • Method based on a machine learning tool and the use of computational parallelization. • Use of knowledge related to the unavailability of critical transmission equipment. • Sensitivity analysis in relation to the main parameters that define the method. • Analysis of the method performance in the evaluation of a real and large-scale system. [ABSTRACT FROM AUTHOR]

This paper proposes improvements in the sequential Monte Carlo (MC) simulation based approaches for reliability evaluation of composite generation and transmission systems. The main idea is to compute a threshold load level to facilitate the process of state evaluation, which is very time-consuming mainly due to optimal power flow computations. Two main sampling techniques namely random sampling (RS) and Latin Hypercube sampling (LHS) are applied to the proposed sequential MC simulation technique. Their performances in terms of simulation time as well as solution accuracy are compared. Case studies using the IEEE Reliability Test System (IEEE RTS) are utilized to demonstrate the performance of the proposed approach. Reliability indices including loss of load probability, expected unserved energy, loss of load frequency and duration are estimated. It is shown that the proposed approach considerably reduces the computational requirements during state evaluation, and that the application of LHS can further decrease the simulation time and required sample size to reach convergence. [ABSTRACT FROM PUBLISHER]

Summary: Composite generation and transmission system reliability evaluation allows the assessment of the risks of system operation failure, taking into account the uncertainties associated with the availability of equipment. One of the great challenges faced in the use of techniques based on probabilistic assessment during the planning stages is related to the required computational costs. Depending on the reliability levels of the system under study and on the grid size, a large number of operation performance analyzes are necessary. In this sense, this article proposes a new and simple method to efficiently evaluate the composite reliability of electrical power networks. The nonsequential Monte Carlo simulation (MCS) method is combined with unsupervised machine learning (UML) techniques to reduce the computational effort involved in the process of estimating composite reliability indices. The proposed approach allows different unsupervised techniques to be employed, in order to obtain significant reductions in CPU times, without losing the accuracy of the estimated indices. The IEEE‐RTS system, considering the original load and generation and its modified version with the transmission network stressed, in addition to a real large system, is used for evaluating the performance of the proposed method. The results obtained with the use of three different classification techniques (Kohonen self‐organizing map, K‐means, and K‐medoids) are presented and analyzed. [ABSTRACT FROM AUTHOR]

This paper presents an application of evolutionary particle swarm optimization (EPSO)-based methods to evaluate power system reliability. Population-based (PB) methods appear as competitors to the traditional Monte Carlo simulation (MCS), because they are computationally efficient in estimating a variety of reliability indices. The work reported in this paper demonstrates that EPSO variants can focus the search in the region of the state space where contributions to the formation of a reliability index may be found, instead of conducting a blind sampling of the space. The results obtained with EPSO are compared to MCS and with other PB methods. [ABSTRACT FROM AUTHOR]