A New Method for the Evaluation of Expected Energy Generation and Loss of Load Probability

Schenk, K. F.; Misra, R.B.; Vassos, S.; Wen, Wu

doi:10.1109/tpas.1984.318228

Cited by 79 publications

(16 citation statements)

References 8 publications

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“…4> (I; n k , ol) denotes the cumulative distribution for the normal random variable which represents category k. Cumulative distribution for the sum of outage capacity of n generating units dispatched is also approximated by the linear combination of normal distributions [8], i.e., F 4 U^ ~ r 5 0 *""' r ^M ; /Vr> a "-rR where E 7r n r = 1. r=0 (15) In Eq. (15), u n denotes the sum of n outage capacity random variables.…”

Section: Mixture Of Normal Approximation Methodsmentioning

confidence: 99%

Comparison and improvement of power system production simulation methods

Chen

Huang

1994

Journal of the Chinese Institute of Engineers

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Production simulation is effective in evaluating the production cost, fuel demand and reliability of generation systems. Among the most important methodologies that presently exist for production simulation, one numerical and three analytical methods (including the Mixture of Normal Approximation, MONA) are reviewed. In this paper, reasons for the high computing efficiency of the analytical methods and the relatively high simulation accuracy of the MONA method are identified. In this paper, the MONA method's load categorization is improved. The MONA with an improved load categorization has been compared numerically with the four aforementioned existing methods, including the conventional MONA, by testing on two benchmark test systems. The results demonstrate the characteristics of the four existing methods and the effectiveness of the proposed load categorization technique.••nmm&xm&jmz&tiim&m m±m* %mm x^m^&M^s&j-mmamm&R^mmmmm&mfx^ti ° mum >

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Section: Mixture Of Normal Approximation Methodsmentioning

confidence: 99%

Comparison and improvement of power system production simulation methods

Chen

Huang

1994

Journal of the Chinese Institute of Engineers

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“…The reliability of electric networks has been addressed through indicators, such as Loss of Load Probability (Schenk et al, 1984), to measure the likelihood of demand exceeding installed capacity over a period of time. This method cannot be transposed to steam networks due to the complicated interactions between pressure levels, turbines, letdowns, and desuperheaters, which must be modeled for each unit of time.…”

Section: Existing Work and Literature Reviewmentioning

confidence: 99%

Optimal Operations and Resilient Investments in Steam Networks

2016

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Steam is a key energy vector for industrial sites, most commonly used for process heating and cooling, cogeneration of heat and mechanical power as a motive fluid or for stripping. Steam networks are used to carry steam from producers to consumers and between pressure levels through letdowns and steam turbines. The steam producers (boilers, heat and power cogeneration units, heat exchangers, chemical reactors) should be sized to supply the consumers at nominal operating conditions as well as peak demand. First, this paper proposes an Mixed Integer Linear Programing formulation to optimize the operations of steam networks in normal operating conditions and exceptional demand (when operating reserves fall to zero), through the introduction of load shedding. Optimization of investments based on operational and investment costs are included in the formulation. Though rare, boiler failures can have a heavy impact on steam network operations and costs, leading to undercapacity and unit shutdowns. A method is therefore proposed to simulate steam network operations when facing boiler failures. Key performance indicators are introduced to quantify the network's resilience. The proposed methods are applied and demonstrated in an industrial case study using industrial data. The results indicate the importance of oversizing key steam producing equipments and the value of industrial symbiosis to increase industrial site resilience.

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“…Geographical databases with RES potential are linked to the TIMES model to identify classes of the same RES technology with different costs on the regional level. In order to handle the stochastic aspects introduced by the large scale penetration of RES, the TIMES model was combined with a model for Probabilistic Production Simulation (ProPSim), [12,13] which was developed by CRES and the Public Power Corporation of Greece. ProPSim calculates residual load duration curves (RLDC) from hourly values of customer load and hourly values of non-dispatchable generation (wind, PV, small hydro and combined heat and power-CHP) which are provided as input.…”

Section: Overall Approachmentioning

confidence: 99%