Chronological Monte Carlo-Based Assessment of Distribution System Reliability

Silva, A.M.L. da; Cassula, Agnelo Marotta; Nascimento, Luiz Carlos do; Freire, José Celso; Sacramento, Cleber E.; Guimaraes, Ana Carolina R.

doi:10.1109/pmaps.2006.360423

Cited by 26 publications

(21 citation statements)

References 17 publications

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“…This procedure allows the modeling of any chronological aspect involved in the process. Therefore, the algorithm proposed in [6] is changed [21], [23] to concurrently cover equipment of all involved feeders. There are a number of aspects related to the chronological simulation process, which depend on some system operating assumptions.…”

Section: ) Chronological Methodologymentioning

confidence: 99%

“…3) System Decomposition: Before describing the data calibration procedure based on the previous algorithm, a scheme of decomposing the distribution network into smaller sub-networks is used in order to deal with real size systems [17], [23]. This scheme consists in completely separating each of the feeders, and the normally open (NO) switches that interconnect the feeders are replaced by fictitious energy sources.…”

Section: ) Chronological Methodologymentioning

confidence: 99%

“…Both non-sequential and sequential MCS have been extensively used in composite and distribution systems. There are also some applications of the sequential MCS in distribution systems [6]- [8], [12]- [13], [21], [23]. Next subsections will, firstly, provide the basic concepts of chronological simulations and, secondly, describe an efficient variant being used in the reliability assessment of distribution networks.…”

Section: B Monte Carlo Simulation Approachmentioning

confidence: 99%

“…1) Basic Concepts of Chronological Simulations [5], [23]: Sequential MCS is very convenient due to its flexibility, as it allows the representation of non-exponential residence times, useful when dealing with chronological processes. The operation history of system states, for a simulation period T, is based on stochastic models of the components and on the load model.…”

Section: B Monte Carlo Simulation Approachmentioning

confidence: 99%

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Distribution reliability: Data calibration based on Monte Carlo simulation and evolutionary optimization

Silva

Guimaraes²,

Nascimento

2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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Power system failures are random events and their prevention can be managed by the regulatory agencies through pre-set performance targets. Once an acceptable target is established for a reliability index, it is assumed to be the limit value that must not be exceeded; otherwise the utility may receive a penalty that creates an extra operational cost. To maintain these targets under control, the utilities must regularly evaluate their system, and the reliability indices should be predicted using accurate system information. Most utilities do not have accurate information about their own system parameters (e.g., failure rates, etc.), and may use similar data for assessing reliability performance. Consequently, the predicted indices will not reflect the exact status performance of the distribution system. The solution can be reached by validating the database. This process includes detection, localization of the incorrect data, and, finally, the parameter correction. The validation methodology proposed in this paper uses Chronological Monte Carlo-based techniques to assess the probability distributions of system reliability indices, statistical evaluation of samples, and a metaheuristic-based optimization approach. Case studies on a simple distribution network and on a real system are presented and discussed.

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Section: ) Chronological Methodologymentioning

confidence: 99%

Section: ) Chronological Methodologymentioning

confidence: 99%

Section: B Monte Carlo Simulation Approachmentioning

confidence: 99%

Section: B Monte Carlo Simulation Approachmentioning

confidence: 99%

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Distribution reliability: Data calibration based on Monte Carlo simulation and evolutionary optimization

Silva

Guimaraes²,

Nascimento

2014

2014 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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“…A Monte Carlo simulation was proposed to simulate chronological behaviours of multiple components for evaluating the probability density functions of reliability distribution indices [6]. Recently, a hybrid approach combining several different analysis methods was introduced to examine the impacts of different maintenance policies on reliability of individual components as well as bulk electric system [7].…”

Section: Introductionmentioning

confidence: 99%

Optimisation of maintenance schedules and extents for composite power systems using multi-objective evolutionary algorithm

Yang

Chang

2009

IET Generation, Transmission &Amp; Distribution

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Reducing the overall cost and improving the reliability are the two primary but often conflicting objectives in power system. Preventive-maintenance schedules thus need to be optimised to trade-off among multiple objectives. An integrated methodology with three functional blocks is proposed in this study. The first block models the stochastic deterioration process of individual component with a continuous-time Markov model, of which transition rates are influenced by different maintenance extents and aging of components. The second block evaluates the reliability of a composite power system, taking into account the configuration and failure dependence of the system. Particularly, this block identifies the minimum cut sets with consideration of protection trip and operational switching. The third block employs the Pareto-based multiobjective evolutionary algorithm to find the optimal solutions in a large search space and provide a holistic view of relationships among conflicting multiple objectives. A novel representation of maintenance activities is introduced in this study specifying both the maintenance timings and extents, and is proven to outperform the authors' previous representation, specifying the maintenance frequencies only. Optimisation of the reliability, maintenance failure costs is carried out on the Roy Billinton Test System (RBTS) demonstrating the potential of this approach in handling complex systems. Nomenclature RBTSRoy Billiton Test System MTTF mean time to failure MTTR mean time to repair EUE expected unserved energy C o,a operation cost for component a C ins cost of inspection of component a T min,s times of minor maintenance in state s for one component T maj,s times of major maintenance in state s for one component C min,s average cost of minor maintenance for component a in state s C maj,s average cost of major maintenance for component a in state sC f,a expected failure cost of component a p i,f failure probability in interval i C avef,a average cost of failure for component a l cs and m cs failure rate and duration of one cut set l A , l B , l C and l D failure rate of events A, B, C and D r A , r B , r C and r D duration of events A, B, C, and D U cs annual duration of one cut set l top and U top failure rate and annual duration of top event in one interval n number of minimum cut sets leading to the top event C sysO overall operation cost C sysF expected failure cost of system 930

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Improving distribution system reliability calculation efficiency using multilevel Monte Carlo method

Huda

Živanović

2017

Int. Trans. Electr. Energ. Syst.

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Power distribution system reliability is generally evaluated by sequential Monte Carlo simulation (MCS). To obtain a high accuracy, we found that sequential MCS technique needs long execution time. In this paper, we show that reliability indices could be evaluated using a novel sequential multilevel Monte Carlo technique that improves the computational efficiency of MCS. The key idea behind the multilevel Monte Carlo method is to use computationally cheaper low-accuracy solutions of coarse grids as control variates for high-accuracy solutions of fine grids. Therefore, the proposed method can construct multilevel estimators of reliability indices with lower variance. Reliability indices are modelled based on stochastic differential equations and exponential probability distributions. The Milstein path discretisation is used to approximate the numerical solution of stochastic differential equations. Case studies are performed on a small distribution system. Numerical results are presented to demonstrate the computational cost-effectiveness of the proposed method in comparison with the sequential MCS.

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Chronological Monte Carlo-Based Assessment of Distribution System Reliability

Cited by 26 publications

References 17 publications

Distribution reliability: Data calibration based on Monte Carlo simulation and evolutionary optimization

Distribution reliability: Data calibration based on Monte Carlo simulation and evolutionary optimization

Optimisation of maintenance schedules and extents for composite power systems using multi-objective evolutionary algorithm

Improving distribution system reliability calculation efficiency using multilevel Monte Carlo method

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