The IEEE Reliability Test System - Extensions to and Evaluation of the Generating System

Allan, R.N.; Billinton, R.; Abdel-Gawad, Nagat M. K.

doi:10.1109/tpwrs.1986.4335006

Cited by 125 publications

(57 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As is shown in Table 3, as for the indices EENS, LOLP, and total energy generation, the proposed approach is as accurate as that in [29], and more accurate than serialized PPS approach. This result verifies the correctness and the effectiveness of the proposed approach.…”

Section: Verification Of the Correctness Of The Proposed Approachmentioning

confidence: 75%

See 1 more Smart Citation

Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Zhou

Wang

et al. 2017

Energies

View full text Add to dashboard Cite

Due to the volatile and correlated nature of wind speed, a high share of wind power penetration poses challenges to power system production simulation. Existing power system probabilistic production simulation approaches are in short of considering the time-varying characteristics of wind power and load, as well as the correlation between wind speeds at the same time, which brings about some problems in planning and analysis for the power system with high wind power penetration. Based on universal generating function (UGF), this paper proposes a novel probabilistic production simulation approach considering wind speed correlation. UGF is utilized to develop the chronological models of wind power that characterizes wind speed correlation simultaneously, as well as the chronological models of conventional generation sources and load. The supply and demand are matched chronologically to not only obtain generation schedules, but also reliability indices both at each simulation interval and the whole period. The proposed approach has been tested on the improved IEEE-RTS 79 test system and is compared with the Monte Carlo approach and the sequence operation theory approach. The results verified the proposed approach with the merits of computation simplicity and accuracy.

show abstract

Section: Verification Of the Correctness Of The Proposed Approachmentioning

confidence: 75%

“…The simulation period is 8760 h with 1 h resolution. The comparison among the results given by the proposed approach in this paper, precise results by analytical method in [29], and the results obtained by the serialized PPS approach in [11] is shown in Table 3.…”

Section: Verification Of the Correctness Of The Proposed Approachmentioning

confidence: 97%

Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Zhou

Wang

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…6 obscures the dysfunctional lack of connectivity for these buses. Indeed, the unusually exhaustive system description in [83], building on [94,95], nowhere notes or anticipates voltage problems for these buses.…”

Section: ) Ieee Rts96 Test Systemmentioning

confidence: 99%

Visualizing the Electrical Structure of Power Systems

Cuffe

Keane

2017

IEEE Systems Journal

108

View full text Add to dashboard Cite

“…To confirm the strength and confidence of the DEGA, a series of 100 runs of the algorithm have been made in the same conditions as previously discussed, and the results are listed in Table 2. The results from DEGA are compared with results reported in [25]. The computational burden for the proposed method is evaluated against the MCS, unit addition, and MSGA algorithms as listed in Table 3.…”

Section: Case (1): Ieee Rts-79mentioning

confidence: 99%

Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

et al. 2017

View full text Add to dashboard Cite

Abstract:The reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evolution is used to enhance the performance of the probability of mutation in a genetic algorithm (GA) by incorporating features from the paradigm into the disparity theory. The DEGA is based on metaheuristic searching for the truncated sampling of state-space for the reliability assessment of power generation system adequacy. Two reliability test systems (IEEE-RTS-79 and (IEEE-RTS-96) are used to demonstrate the effectiveness of the proposed algorithm. The simulation result shows the DEGA can generate a larger variety of the individuals in an early stage of the next population generation. It is also able to estimate the reliability indices accurately.

show abstract

The IEEE Reliability Test System - Extensions to and Evaluation of the Generating System

Cited by 125 publications

References 6 publications

Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Universal Generating Function Based Probabilistic Production Simulation Approach Considering Wind Speed Correlation

Visualizing the Electrical Structure of Power Systems

Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory

Contact Info

Product

Resources

About