Bibliography on the Application of Probability Methods In Power System Reliability Evaluation

Allan, R.N.; Billinton, R.; Breipohl, A.M.; Grigg, C.

doi:10.1109/tpas.1972.293251

Cited by 230 publications

(56 citation statements)

References 3 publications

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“…An excellent annotated bibliography on the application of probability methods in power system reliability evaluation has been prepared by Billinton (1972). He divides his bibliography into six areas: He gives the historical development in each area and then cites references.…”

Section: Literature Searchmentioning

confidence: 99%

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CHANCE: a probabilistic model for electrical energy planning

Wakeland¹

2000

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The energy resource planning process for electric utilities in the Northwestern United States is unique because the region relies upon a mix of hydro and thermal resources. Consequently, methods used to study predominantly thermal or predominantly hydro systems are not applicable. Methods are needed to determine if a particular configuration of resources will be adequate to meet future energy load, taking into account various sources of uncertainty. A literature survey of presently available methods is described, and one method is studied in some detail. A new method for analyzing systems that rely upon a mix of hydro and thermal resources is then described.The primary contributors to uncertainty in a hydro/thermal electricity supply system are: (1) uncertain rainfall and snowfall, which results in uncertain availability of hydro energy; (2) the uncertain arrival times of planned nuclear and coal plants; (3) uncertain capacity factors for thermal plants; and (4) the uncertain amount of energy that customers will require in future years. The new model, called CHANCE, characterizes each of these ·uncertain phenomena with a probability density function. Mathematical convolution and an algorithm developed by the author are then used to determine the probability density function for the energy margin--the difference between supply and demand. Measures of the "energy adequacy" of the supply system are then computed from the energy margin probability density function.A computer program was designed so that the conceptual model can be easily applied to any desired electrical supply system. Once an appropriate set of input assumptions h2ve been determined, they are easily entered into the computer via a question and answer sequence and CHANCE has been applied to Pacific Power and Light Company, Portland, Oregon. At the present time, about half of their energy is generated by hydro plants and the other half is generated by coal plants.Only a small fraction is generated by nuclear plants. All planned additional generation is either coal-fired or nuclear-fired. The results of applying CHANCE indicate that planned resources are not likely to be adequate to meet the needs in the early 1980's.Several evaluation exercises have also been carried out. First, CHANCE was calibrated against other electrical energy planninb models used by Pacific Power and Light. Next, the sensitivity of the CHANCE model to changes in input assumptions was measured. As was anticipated, the model is highly sensitive to the assumed energy load forecast, to the assumed potential delays in the arrival of resources, and to the assumed thermal plant capacity factors. Thus, more research in these areas is warranted.Research that might lead to improvements in the CHANCE model is then outlined, and final conclusions are drawn. The final conclusions are that the CHANCE model: (1) is valid relative to the outlined scope, (2) is quite versatile and flexible, and (3) fulfills an important need in electrical energy planning.

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Section: Literature Searchmentioning

confidence: 99%

“…Some key references are cited by Billinton (1972). Other references are Dillard and Baldwin (1963), Holditch (1967), Cohn (1973), andVolkenau (1972).…”

Section: Literature Searchmentioning

confidence: 99%

CHANCE: a probabilistic model for electrical energy planning

Wakeland¹

2000

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“…The chromosomes of each new generation are produced by the reproduction process. Reproduction of the new generation involves three operations applied to the current generation [6][7][8][9]. These operations are selection, crossover and mutation.…”

Section: Introductionmentioning

confidence: 99%

Reliability Modeling and Evaluation of Electrical Power Generation using Genetic Algorithm

2017

IJAERD

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“…This feature led to the development of techniques based on probabilities, considering the probabilistic or stochastic nature of the composite system behavior. There were thus carried out: techniques for reliability analysis [5]; probabilistic power flow [6]. This type of tools considers probabilistic models for: components failures, generation and load in the buses.…”

Section: Introductionmentioning

confidence: 99%