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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]

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 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]

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]

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]

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.

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.

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]

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]