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Electric power industry is experiencing a movement from the existing conventional electric grid to a more reliable, efficient and secure smart grid. In order to achieve these goals, components such as energy storage will be included, and potentially in large scale. Many feasible applications of energy storage in power systems have been investigated. The major benefits of energy storage include electric energy time-shift, frequency regulation and transmission congestion relief. In this paper, we focus on the reliability improvement of the bulk power system brought by the utilization of energy storage in the local distribution systems integrated with renewable energy generation. An intelligent operation strategy for energy storage which improves reliability considering the renewable energy integration is presented. The smart grid communication and control network is utilized to implement the proposed energy storage operation. A bulk power system reliability evaluation framework is proposed to study the reliability impact brought by the energy storage integration and operation. A detailed case study and sensitivity analysis is performed to demonstrate the effectiveness of the presented operation strategy and evaluation framework, and to provide valuable insights on the power system reliability impact derived from the energy storage integration. [ABSTRACT FROM PUBLISHER]

Generation and transmission unit outages cause MW line overloading and bus voltage magnitude violation problems in the remaining part of the system. This paper presents a new method for bus voltage magnitude screening for transmission line and transformer outages. Classification and ranking errors of sensitivity-based methods are overcome by imposing a non-linear constrained optimization module on the well-known linear feed-forward computational algorithms. Genetic algorithms are preferred as computation tool because of its implementation simplicity as well as for its parallel processing advantage in future computers. Accuracy of the method is tested on IEEE 57-bus test system and IEEE 118-bus test system. The results are compared with those of the full load flow to verify the strength of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a fast and efficient method which combines the Monte Carlo simulation (MCS) and the least squares support vector machine (LSSVM) classifier, for reliability evaluation of composite power system. LSSVM is used to accurately pre-classify the power system operating states as either success or failure states during the Monte Carlo sampling. These pre-classified failure states are then evaluated for adequacy analysis using DC power flow to calculate reliability indices. As a result, the computing time to perform power flow analysis of the system success states is eliminated. The proposed hybrid method is applied to the IEEE Reliability Test System (IEEE-RTS-79) and simulation results obtained using LSSVM with linear and nonlinear kernels are compared with that of nonsequential MCS. These promising results demonstrate the efficacy of the proposed MCS-LSSVM based hybrid method in terms of both classification accuracy and computational time in evaluating the composite power system reliability. [ABSTRACT FROM PUBLISHER]

Abstract: This paper proposes a general planning method to minimize the variance of aggregated wind farm power output by optimally distributing a predetermined number of wind turbines over a preselected number of potential wind farming sites. The objective is to facilitate high wind power penetration through the search for steadier overall power output. Another optimization formulation that takes into account the correlations between wind power outputs and load is also presented. Three years of wind data from the recent NREL/3TIER study in the western US provides the statistics for evaluating each site upon their mean power output, variance and correlation with each other so that the best allocations can be determined. The reliability study reported in this paper investigates the impact of wind power output variance reduction on a power system composed of a virtual wind power plant and a load modeled from the 1996 IEEE RTS. Some traditional reliability indices such as the LOLP are calculated and it is eventually shown that configurations featuring minimal global power output variances generally prove the most reliable provided the sites are not significantly correlated with the modeled load. Consequently, the choice of uncorrelated/negatively correlated sites is favored. [ABSTRACT FROM AUTHOR]

Extreme adverse weather such as a hurricane can have a significant impact on composite power system reliability. Since hurricanes can cause the simultaneous failures of multiple system components, common-cause failures (CCF) should be investigated in the reliability evaluation of composite power systems when the effects of hurricanes are considered. A few techniques have been proposed to evaluate the effects of CCF, but they are not suitable for composite power systems. This paper proposes a method based on Bayesian networks (BN) to solve this problem. Basically, the proposed method uses the noisy OR-gate model to reduce the dimensional dilemma of the conditional probability method. This model also considers the independent failures of transmission lines and generating units during hurricanes. The functionality of a BN is determined by its configuration and the conditional probability distributions (CPD) associated with the nodes. In this paper, the CPD of BN is obtained by using random sampling. Since hurricanes usually last for only a limited period of time, a pseudo-repetitive temporal model is used to calculate the time-specific system reliability indices. The proposed method is applied to the modified IEEE Reliability Test System (RTS). The implementation demonstrates that the proposed method is effective and flexible in its applications. [ABSTRACT FROM AUTHOR]

This paper explores the impact of including component repair on the reliability modeling of all-digital protection systems. It is shown that repairable and nonrepairable assumptions make a remarkable difference in the computed reliability indices of the mean time to failure (MTTF) and the mean time to first failure (MTTFF). Theoretical analysis of basic system structures consisting of two components is first examined. Then, a typical all-digital protection system architecture is modeled and numerically analyzed. Some interesting results are found by comparing reliability indices of MTTF and MTTFF and explanations of these results are provided. [ABSTRACT FROM AUTHOR]

In power system reliability evaluation, alternating renewal process is generally used to model the failure and repair cycle of a component. This means that after repair, the component is assumed to be restored to as good as new condition from the reliability perspective. However, in practice, there may exist an aging trend in some components as they grow old. This paper describes how aging characteristics of components may impact the calculation of commonly used reliability indices such as loss of load expectation (LOLE). In order to construct the system failure and repair history of components, sequential Monte Carlo simulation method using stochastic point process modeling is introduced. Three methods are described for this purpose and this methodology is applied to the Single Area IEEE Reliability Test System. The results are analyzed and compared. [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]

Abstract: The optimum economic operation and planning of electric power generation systems occupies a crucial position in the electric power industry. Unit commitment (UC) is an important function in generation resource management. Moreover, it is nowadays critical to incorporate reliability analysis of the power system into its design of operation strategy. For this purpose, equipment malfunction or failure should be considered in unit commitment. In this paper, first the model for UC considering generator outages is formulated, where the reliability requirement is incorporated into the spinning reserve constraint in the optimization design. Then, a mixed binary- and real-coded particle swarm optimization (PSO) is developed to locate the optimum generation combination. A 10-generator test power system is used to verify the effectiveness of the proposed approach along the 24-h planning horizon. A comparative study is conducted to examine the impact of reliability constraint on the optimal solution obtained. Furthermore, comparison is made between the proposed method and other methods including both analytical and meta-heuristic algorithms. [Copyright &y& Elsevier]

Transfer capability is used to schedule future transactions between areas and arrange commercial activities in multi-area power systems. Probabilistic evaluation can provide more information about transfer capability than deterministic approaches, potentially leading to more efficient operation of the transmission network. Probabilistic methods can also model generating units with variable outputs such as wind farms (WFs). Fluctuating nature of WF creates different impact on transfer capability compared to conventional generators. In this paper, three methods are proposed to compute transfer capability in multi-area power systems containing WF. In all the methods, Monte Carlo simulation is used to assess probabilistic variation of transfer capability. The methods differ in the manner of handling output of WF and load. In the first approach, system load data and power output of WF are directly used in the Monte Carlo simulation. In the second approach clustered power output of WF and system load data are used. In the third one, after clustering of input data, Monte Carlo simulation is run separately for each of the clusters. Incorporating clustering into Monte Carlo simulation can accelerate convergence speed, reduce variance and sometimes provide additional useful information. IEEE-RTS is used to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Available transfer capability (ATC) is a key index of the remaining capability of a transmission system for future transactions. To calculate the ATC between two areas in an interconnected power system, capacity benefit margin (CBM) is one of the indexes that need to be assessed. CBM ensures security of system operation when the system faces generation deficiency in some areas. In this paper, three new methods are introduced which reflect different objectives for CBM assessment. This provides more flexibility of choices for market participants. In the proposed methods, CBM determination is formulated as an optimization problem and particle swarm optimization (PSO) method is used to solve the problem. The numerical results for modified IEEE reliability test system are presented to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Multisource hybrid power generation systems are a type of representative application of the renewables' technology. In this investigation, wind turbine generators, photovoltaic panels, and storage batteries are used to build hybrid generation systems that are optimal in terms of multiple criteria including cost, reliability, and emissions. Multicriteria design facilitates the decision maker to make more rational evaluations. In this study, an improved particle swarm optimization algorithm is developed to derive these nondominated solutions. Hybrid generation systems under different design scenarios are designed based on the proposed approach. First, a grid-linked hybrid system is designed without incoroprating system uncertainties. Then, adequacy evaluation is conducted based on probabilistic methods by accounting for equipment failures, time-dependent sources of energy, and stochastic generation/load variations. In particular, due to the unpredictability of wind speed and solar insolation as well as the random load variation, time-series models are adopted to reflect their stochastic characteristics. An adequacy evaluation procedure including time-dependent sources, is adopted. Sensitivity studies are also carried out to examine the impacts of different system parameters on the overall design performance. [ABSTRACT FROM AUTHOR]