Probing the new IEEE Reliability Test System (RTS-96): HL-II assessment

Pinheiro, J.M.S.; Dornellas, C.R.R.; Schilling, M.T.; Melo, A.C.G.; Mello, J.C.O.

doi:10.1109/59.651632

Cited by 120 publications

(39 citation statements)

References 10 publications

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“…The algorithm has been tested using two (small-and medium-size) IEEE-based power systems. The IEEE-RTS96 [26] builds on a basis of three IEEE-RTS24, each corresponding to one area. …”

Section: Numerical Resultsmentioning

confidence: 99%

Inter-Utilities Power-Exchange Coordination: A Market-Oriented Approach

Aguado

Quintana²

2001

IEEE Power Eng. Rev.

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Abstract-A decentralized operation of the transmission grid for scheduling inter-utilities power exchanges is proposed. This approach is well suited for market-oriented environments; it achieves a market equilibrium while the operational independence of each interconnected utility is preserved. We employ decomposition-coordination techniques in combination with an Interior-Point/Cutting-Plane method in order to reduce the number of iterations of the decomposition algorithm. The paper includes test results on IEEE-based systems.

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“…The algorithm has been tested using two (small-and medium-size) IEEE-based power systems. The IEEE-RTS96 [26] builds on a basis of three IEEE-RTS24, each corresponding to one area. …”

Section: Numerical Resultsmentioning

confidence: 99%

Inter-Utilities Power-Exchange Coordination: A Market-Oriented Approach

Aguado

Quintana²

2001

IEEE Power Eng. Rev.

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“…Therefore, the system peak load will be assumed to follow the hourly load shape of the IEEE-RTS (reliability test system). Based on this assumption, the load will be divided into ten levels using a clustering technique, utilizing the centroid sorting process, developed in [37], which verifies that ten chosen equivalent load levels, with different probabilities, provides a reasonable trade-off between accuracy and fast numerical evaluation [38,39]. In addition, recent studies demonstrate the characteristics of the load according to standard deviation function similar to electricity prices, with the advantages of being closer to the value of actual load when using previous survey data [40].…”

Section: Electrical Heating and Cooling Demandsmentioning

confidence: 99%

Optimal operation of energy hub in competitive electricity market considering uncertainties

Thang

Zhang

et al. 2018

Int J Energy Environ Eng

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This paper proposes a novel energy hub model for areas using both heat and cold demands that arise due to the major changes in environmental temperature in different periods of the year. The energy demand and the electrical price in a competitive electricity market are uncertain with stochastic values which are usually performed by a probability distribution function. Therefore, a stochastic mathematical model representing an optimal operation of energy hub is based on the objective function of minimization of energy costs (including electricity and gas). Several constraints such as energy balance, limited capacity of the transformer, air conditioners, gas boilers, absorption chillers, combined heat, and power and battery energy storage system are also incorporated into the model to guarantee the required specifications. The high-level algebraic modeling software, general algebraic modeling system has been employed to undertake calculations. Finally, numerical results have illustrated the efficiency and capability of the proposed models.

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“…The UT approach allows the unreliability performance of a power system expressed by basic reliability indices [2,3], such as the loss of load probability (LOLP), loss of load expectation (LOLE), frequency of loss of load (FLOL), and expected energy not supplied (EENS), to be traced or allocated to every component in the system. Considerable work has been done [1][2][3][4][5][6][7][8][9][10] in developing power system reliability models and algorithms and reliability application and risk analysis, but there has been relatively little research on UT techniques. The relevant research is mainly focused on reliability analysis considering the class, order of contingencies, and the component failure causes.…”

Section: Introductionmentioning

confidence: 99%

Tracing the Unreliability Contributions of Power System Components

Xie

Billinton

Zhou

2008

Electric Power Components and Systems

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In many situations, a utility would like to know how much each component in the system contributes to the unreliability consequences associated with load point and system outages. This article proposes an unreliability tracing principle, model, and an approach to address this question. A proportional sharing principle has been developed to track reliability indices. The unreliability tracing sharing factors are derived so that they can be used to recognize the major unreliability contributions in a power system. The case studies indicate that the developed technique can be extensively applied to power systems for unreliability tracing and recognizing the major unreliability contributions.

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Probing the new IEEE Reliability Test System (RTS-96): HL-II assessment

Cited by 120 publications

References 10 publications

Inter-Utilities Power-Exchange Coordination: A Market-Oriented Approach

Inter-Utilities Power-Exchange Coordination: A Market-Oriented Approach

Optimal operation of energy hub in competitive electricity market considering uncertainties

Tracing the Unreliability Contributions of Power System Components

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