Armando M. Leite da Silva

A Method for Ranking Critical Nodes in Power Networks Including Load Uncertainties

This paper presents a new method for ranking critical nodes in bulk power systems. The proposed approach is able to offer a list of nodes or substations, from which system planners can easily identify those facilities with more urgent investment needs. The ranking process includes both static (via optimal power flow) and dynamic (via transient stability) performance analyses to assess deterministic indices. These indices measure the static and dynamic vulnerabilities of the network. As an extension of the deterministic approach, a new ranking based on the same indices is evaluated through a Monte Carlo simulation, considering several load scenarios modeled through uncertainties and the related generating dispatches. Applications in a test system presenting the rank of critical nodes are provided and discussed.

Spinning Reserve Assessment Under Transmission Constraints Based on Cross-Entropy Method

This paper proposes a methodology to evaluate the spinning reserve requirements of generating systems, taking into consideration the loss of load risk due to failures in the generation and transmission network. In order to provide a more flexible and robust tool to assess the risk indices for bulk power systems, a Monte Carlo simulation based tool is proposed. Due to the rarity of the events associated with this particular problem, an unbiased estimator based on importance sampling is applied, and the set of optimal parameters is obtained using a cross-entropy method. The proposed methodology is capable of assessing the operating reserve requirements, especially the one that is synchronized, to cope with failures and capacity restrictions of both generating and transmission systems. The IEEE-RTS (reliability test system) is used to test the proposed methodology and also some modifications of this system are created to fully verify the ability of the proposed approach to satisfactorily respond to the transmission constraints.

Probabilistic Assessment of Spinning Reserve via Cross-Entropy Method Considering Renewable Sources and Transmission Restrictions

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.

Transmission expansion planning based on relaxed N-1 criteria and reliability indices

This paper proposes a new methodology to solve the transmission expansion planning (TEP) problem based on relaxed N-1 criteria. An optimization technique is used to determine the best TEP plans through an adaptive multi-operator evolutionary approach. These plans are obtained by ensuring the N-1 security criterion and also relaxing it to accept pre-specified levels of equipment overload. The major focus is to measure probabilistically the relaxation of TEP plans through traditional reliability indices, and, consequently, the effectiveness of deterministic N-1 based approaches. Discussions are carried out using the results from two test systems: Modified IEEE-RTS and a configuration of the South Brazilian network.

Spinning reserve assessment via quasi-sequential Monte Carlo simulation with renewable sources

This paper presents a new methodology for assessing spinning reserve in generating systems with high penetration of renewable energy. A state-space model is proposed to represent the generation capacity failures and the intermittency of renewable sources based on historical scenarios. The uncertainty in the system supply is captured through risk indices that represent the probability of not meeting the short-term estimated demand. A security strategy associated with the probability distribution of reserve levels is also proposed to avoid the over-sizing of reserve capacity levels to handle unlikely extreme operating points. Risk indices are estimated via quasi-sequential MCS-CE (Monte Carlo Simulation via Cross-Entropy) method, where the corresponding parameters are optimally distorted based on CE concepts. The proposed method is applied to a modified version of the IEEE RTS-79 system to cope with renewable sources.

Support Vector Machine application in composite reliability assessment

This paper presents a methodology for assessing the reliability indices for composite generation and transmission systems based on Support Vector Machines (SVM). The importance of SVMs is its high generalization ability. The SVMs are used to classify data into two distinct classes. These can be named positive and negative. Thus, the basic idea is to classify the system states into success or failure. For this, a pre-classification of states is achieved by performing the proposed SVM-based neural network, where the sampled states during the beginning of the non-sequential Monte Carlo simulation (MCS) are considered as input data for training and validation sets. By adopting this procedure, a large number of states are classified by a simple evaluation of the network, providing significant reductions in computational costs. The proposed methodology is applied to the IEEE Reliability Test System and to the IEEE Modified Reliability Test System.

Evaluation of spare transformer requirements for distribution substations via chronological Monte Carlo simulation

This paper presents a new probabilistic methodology for sizing the number of spare transformers for a group of power distribution substations. Based on a chronological Monte Carlo simulation, the proposed methodology allows the assessment of several reliability indices and expected costs, even considering systems composed by transformers with different ages and non-exponential lifetimes. Real conditions of system operation, such as load transfers between transformers or substations, use of mobile units, and the replacement of failed transformers by spares, are duly modeled. In order to illustrate the proposed concepts, numerical examples using a real system are performed and the corresponding results properly discussed.

Transmission network cost allocation via nodal methodology considering different dispatching scenarios and tariff zones

This paper presents a new methodology for allocation of transmission system costs in electricity markets established in one single area or composed by the interconnection of multiple subsystems. Initially, the proposed method allows the decomposition of the total cost of transmission in two installments: the first, referring to the utilized capacity of the network (estimated at a predetermined operating point), and the second, to the transmission capacity still available in the system. A subsystem decomposition technique of the nodal tariffs also ensures the applicability of the method in interconnected markets. From the decomposition rates, it is possible to assess the responsibility of each participant on the cost of use of any specified set of transmission elements. The flexibility of the method enables an allocation via postage stamp costs (i.e., pro-rata), related to interconnection lines and/or high systemic importance circuits. After the presentation of the mathematical formulation, a procedure for the consideration of multiple operating points (dispatching scenarios) is proposed to evaluate nodal prices and also a new algorithm to determine zonal tariffs, i.e., applicable to all generators or loads at the zone. The proposed approach is described in detail and illustrated by numerical applications to the Brazilian interconnected system.

Spare transformers optimization using Monte Carlo simulation and metaheuristic techniques

This paper presents a new methodology based on metaheuristic optimization techniques for computing optimal distribution substation spare transformers. A chronological Monte Carlo simulation-based algorithm is used in the calculation of performance indices and system costs in a period of interest, considering factors such as load growth, increasing in the number of transformers in operation, and inclusion of reinforcements to the initial inventory. Some numerical experiments are carried out in order to illustrate the potentialities of the proposed method.