An Extended IEEE 118-Bus Test System With High Renewable Penetration

Peña, Ivonne; Martínez-Anido, Carlo Brancucci; Hodge, Bri-Mathias

doi:10.1109/tpwrs.2017.2695963

Cited by 170 publications

(81 citation statements)

References 15 publications

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“…An illustrative example built on that described in [10] is first analyzed. The second case study is based on a modified version of the extended IEEE 118-bus system [27]. Finally, a benchmark based on the Polish 2383-bus system [28] is examined.…”

Section: Numerical Resultsmentioning

confidence: 99%

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Robust Transmission Network Expansion Planning Under Correlated Uncertainty

Roldán

Mı́nguez

García-Bertrand

et al. 2019

IEEE Trans. Power Syst.

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This paper addresses the transmission network expansion planning problem under uncertain demand and generation capacity. A two-stage adaptive robust optimization framework is adopted whereby the worst-case operating cost is accounted for under a given user-defined uncertainty set. This work differs from previously reported robust solutions in two respects. First, the typically disregarded correlation of uncertainty sources is explicitly considered through an ellipsoidal uncertainty set relying on their variance-covariance matrix. In addition, we describe the analogy between the corresponding second-stage problem and a certain class of mathematical programs arising in structural reliability. This analogy gives rise to a relevant probabilistic interpretation of the second stage, thereby revealing an undisclosed feature of the worst-case setting characterizing robust optimization with ellipsoidal uncertainty sets. More importantly, a novel nested decomposition approach based on results from structural reliability is devised to solve the proposed robust counterpart, which is cast as an instance of mixed-integer trilevel programming. Numerical results from several case studies demonstrate that the effect of correlated uncertainty can be captured by the proposed robust approach.

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Section: Numerical Resultsmentioning

confidence: 99%

“…Increase the iteration counter ν ← ν + 1. 5) Solution of problem (26)- (27). Solve problem (26)- (27) for given d (ν−1) and η (ν−1) through the equivalent analytical expression (28).…”

Section: ) Update Of the Iteration Counter Of The Inner Loopmentioning

confidence: 99%

Robust Transmission Network Expansion Planning Under Correlated Uncertainty

Roldán

Mı́nguez

García-Bertrand

et al. 2019

IEEE Trans. Power Syst.

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show abstract

“…In order to prove the improvement degree of the NSGA-II algorithm on network optimization results of different node systems, the IEEE 33-, 69-and 118-bus systems are introduced. The parameters of the IEEE 69-and 118-bus systems are described in References [33,34]. Table 4 shows the optimization results of the INSGA-II algorithm on three examples.…”

Section: Case Analyses Of Ieee 33- 69- and 118-bus Systemsmentioning

confidence: 99%

“…To this end, this section optimizes the configuration scheme to consider the DG annual output change and load change trend and the revised IEEE 33-bus system-related data reference [34]. The data was recorded every hour, so it is assumed that the DG output is equal every hour and that the load demand is constant.…”

Section: Experiments On Load and Dg Output Considering Annual Time Sementioning

confidence: 99%

Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II

Liu

Luo²,

Liu

et al. 2019

Processes

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With the development of distributed generation technology, the problem of distributed generation (DG) planning become one of the important subjects. This paper proposes an Improved non-dominated sorting genetic algorithm-II (INSGA-II) for solving the optimal siting and sizing of DG units. Firstly, the multi-objective optimization model is established by considering the energy-saving benefit, line loss, and voltage deviation values. In addition, relay protection constraints are introduced on the basis of node voltage, branch current, and capacity constraints. Secondly, the violation constrained index and improved mutation operator are proposed to increase the population diversity of non-dominated sorting genetic algorithm-II (NSGA-II), and the uniformity of the solution set of the potential crowding distance improvement algorithm is introduced. In order to verify the performance of the proposed INSGA-II algorithm, NSGA-II and multiple objective particle swarm optimization algorithms are used to perform various examples in IEEE 33-, 69-, and 118-bus systems. The convergence metric and spacing metric are used as the performance evaluation criteria. Finally, static and dynamic distribution network planning with the integrated DG are performed separately. The results of the various experiments show the proposed algorithm is effective for the siting and sizing of DG units in a distribution network. Most importantly, it also can achieve desirable economic efficiency and safer voltage level. Processes 2019, 7, 955 2 of 22The optimal planning of distributed generation sizing and siting is critical to ensure the operational performance of a distribution network in terms of power quality, voltage stability, reliability and profitability. DG planning problems are often defined as multi-objective and multi-constrained optimization problems. A number of review papers [8][9][10] have surveyed the optimization techniques for optimal DG planning in power distribution networks. The aforementioned review papers mainly focused on the discussion of various computational methods and metaheuristic algorithms.Nowadays, industrial systems, such as papermaking, steelmaking, petrochemical, and power generation, are becoming more and more complex. In such cases, data-driven models based on novel nonlinear signal processing and data analysis techniques may provide an attractive alternative [11]. Therefore, it is paramount but challenging to develop effective techniques in modelling, monitoring, and control for complex industrial systems [12,13].Among many optimization methods, multi-objective heuristic algorithms are the main means to solve multi-objective optimization problems today because they can effectively balance multiple objectives for optimal search [14]. At present, the main objectives of DG planning include the lowest investment cost of DG, the lowest environmental pollution, the optimal voltage quality of the power grid, and the minimum power loss. Partha Kayal et al. aimed to minimize the reactive power loss of the DN, to maximi...

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“…The proposed indices are applied to the IEEE 118-bus test system to demonstrate the performance of indices in large-scale systems. This test system has been used in many well-known publications in the field of power system operation and planning [38][39][40][41]. The remainder of this paper is organized as follows.…”

mentioning

confidence: 99%

Evaluation of power system robustness in order to prevent cascading outages

Hajbani¹,

Seyedi²,

Zare³

2019

Turk J Elec Eng & Comp Sci

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Power system robustness against overload condition is a challenging issue in the fields of power system planning and operation. In this paper, two indices are proposed to evaluate power system robustness. The proposed indices are used to identify critical lines whose failure is due to overload, leading the power system to cascading outages and blackout. The first proposed index is a linear index. The second index is based on graph theory metrics. To prevent cascading outages, system robustness is calculated for all N − 1 and N − 2 contingencies. The lines whose outages lead to the smallest robustness values are considered as critical lines. The proposed indices are validated by applying to the IEEE 118-bus test system. Simulation results demonstrate the capability of both indices in identifying critical lines. Therefore, the purposed indices could be used as a reliable metric to apply preventive actions against possible cascading outages and blackouts.

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An Extended IEEE 118-Bus Test System With High Renewable Penetration

Cited by 170 publications

References 15 publications

Robust Transmission Network Expansion Planning Under Correlated Uncertainty

Robust Transmission Network Expansion Planning Under Correlated Uncertainty

Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II

Evaluation of power system robustness in order to prevent cascading outages

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