Controlled islanding using transmission switching and load shedding for enhancing power grid resilience

Amraee, Turaj; Saberi, Hossein

doi:10.1016/j.ijepes.2017.01.029

Cited by 59 publications

(35 citation statements)

References 17 publications

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“…Moreover, the calculation time required for finding a candidate islanding solution is approximately 0.1 second. It is found that the presented stochastic controlled islanding strategy is computationally efficient and fast in comparison with other comparative algorithms such as OBDD, ant search mechanism, spanning tree‐based breadth first search, angle modulated particle swarm optimization, and Bender's decomposition technique based 2‐stage mixed integer linear programming . In addition, Figure illustrates total expected values of 3 objectives for 5 various values of β .…”

Section: Simulation Results and Discussionmentioning

confidence: 96%

“…It is found that the presented stochastic controlled islanding strategy is computationally efficient and fast in comparison with other comparative algorithms such as OBDD, 19 ant search mechanism, 13 spanning tree-based breadth first search, 15 angle modulated particle swarm optimization, 11 and Bender's decomposition technique based 2-stage mixed integer linear programming. 6 In addition, Figure 13 illustrates total expected values of 3 objectives for 5 various values of β. Obviously, the amount of total power mismatch is decreasing monotonically as the value of β increases.…”

Section: Stochastic Optimization Processmentioning

confidence: 99%

“…Over the last decade, some scholars have focused on optimal preventive splitting of bulk power systems using different scenario generation and reduction techniques. Amraee and Saberi solved a Bender's decomposition technique based 2‐stage mixed integer linear programming problem considering the slow coherency of synchronous generators, inter‐connectivity of each island, lower number of line switching, and the frequency stability constraint. Each individual island consists of a coherent group of generators, and its boundaries are determined with the aim of minimizing total load shedding.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective optimal preventive islanding based on stochastic backward elimination strategy considering uncertainties of loads and wind farms

Jabari

Seyedi

Ravadanegh

et al. 2017

Int Trans Electr Energ Syst

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Summary Nowadays, occurrence of severe contingencies may cause an interconnected power system to lose stability and lead to the partial or wide‐spread cascading failures. Hence, intentional islanding is the last countermeasure to mitigate the system vulnerability and avoid the catastrophic wide area blackout. This paper proposes a novel probabilistic splitting strategy for generating all possible islanding solutions and evaluating different static and dynamic constraints in reduced power system graph. The proposed stochastic scenario generation algorithm investigates the steady‐state stability of all partitions in each generated solution taking into account the uncertainties of loads and wind farms. Multi‐objective binary imperialistic competitive algorithm is then developed to find the optimum line switching points that minimizes load‐generation mismatch and probability of islands' partial blackout, maximizes voltage stability security margin, and satisfies the slow coherency, connectivity, voltage, and the transmission capacity constraints. Monte Carlo simulation and point estimation method are applied in the stochastic programming model to investigate the islands' stability, calculate the optimization error, and determine the critical stressed transmission lines and PQ‐busses under uncertain operating condition. The validity and speed of the proposed approach are revealed using simulation on IEEE 39‐bus standard system.

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Section: Simulation Results and Discussionmentioning

confidence: 96%

Section: Stochastic Optimization Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐objective optimal preventive islanding based on stochastic backward elimination strategy considering uncertainties of loads and wind farms

Jabari

Seyedi

Ravadanegh

et al. 2017

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…Thus, it is necessary to make a trade-off between the requirement of handling the stability issues and the requirement of real-time decision. It is commonly agreed that the sudden active power imbalance is the primary and the severest threat to the island transient stability after system splitting [18,[20][21][22]27]. A large power imbalance disturbance caused by the splitting action would possibly lead to insufferable frequency dip/rise on the islands, and severely threaten the island transient process.…”

Section: Formulation For Static Stability Marginmentioning

confidence: 99%

Active splitting strategy searching approach based on MISOCP with consideration of power island stability

Zhou

Min

et al. 2019

J. Mod. Power Syst. Clean Energy

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Active splitting control utilizes real-time decision and system-level splitting to prevent cascading blackouts and to maintain power supply under severe disturbances. Splitting strategy searching (SSS) is one of the most crucial issues in active splitting control for deciding ''where to split''. SSS determines the splitting surface in real time to properly divide the asynchronous generators into isolated islands with an optimal control effect. In this paper, an SSS approach that focuses on island stability is presented. The proposed SSS approach is designed to ensure a rational stability margin and regulation ability on each island during and after the transient process of system splitting. This method includes the active/reactive power flow feasibility constraints and voltage/angle stability constraints in the steady state, as well as the frequency response capability constraints in the transient process. By considering the island stability constraints in the SSS, the proposed approach can avoid the splitting strategies with poor stability performance. Therefore, the major advantage of the proposed approach is ensuring better island static and transient stability during and after the splitting control. In addition, the entire model is formulated as a mixedinteger second-order cone programming (MISOCP) model. Thus, it can be rapidly solved by using commercial optimization solvers. Numerical simulation of a realistic provincial power system in central China demonstrates the validity of the proposed approach and the necessity of considering the island stability issues.

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“…In both regards, the same-voltage subgraphs of power grid graphs stand in stark contrast. Using standard asymptotic notation, 3 Table 3, lists some of these for well-known random graph models under various mild assumptions. In this table, n denotes the number of vertices in the graph.…”

Section: Distances In Random Graph Modelsmentioning

confidence: 99%

A generative graph model for electrical infrastructure networks

Aksoy

Purvine

Cotilla‐Sanchez

et al. 2018

Journal of Complex Networks

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We propose a generative graph model for electrical infrastructure networks that accounts for heterogeneity in both node and edge type. To inform the model design, we analyze the properties of power grid graphs derived from the U.S. Eastern Interconnection, Texas Interconnection, and Poland transmission system power grids. Across these datasets, we find subgraphs induced by nodes of the same voltage level exhibit shared structural properties atypical to small-world networks, including low local clustering, large diameter, and large average distance. On the other hand, we find subgraphs induced by transformer edges linking nodes of different voltage types contain a more limited structure, consisting mainly of small, disjoint star graphs. The goal of our proposed model is to match both these inter and intra-network properties by proceeding in two phases: the first phase adapts the Chung-Lu random graph model, taking desired vertex degrees and desired diameter as inputs, while the second phase of the model is based on a simpler random star graph generation process. We test the model's performance by comparing its output across many runs to the aforementioned real data. In nearly all categories tested, we find our model is more accurate in reproducing the unusual mixture of properties apparent in the data than the Chung-Lu model. We also include graph visualization comparisons, a brief analysis of edge-deletion resiliency, and guidelines for artificially generating the model inputs in the absence of real data.

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Controlled islanding using transmission switching and load shedding for enhancing power grid resilience

Cited by 59 publications

References 17 publications

Multi‐objective optimal preventive islanding based on stochastic backward elimination strategy considering uncertainties of loads and wind farms

Multi‐objective optimal preventive islanding based on stochastic backward elimination strategy considering uncertainties of loads and wind farms

Active splitting strategy searching approach based on MISOCP with consideration of power island stability

A generative graph model for electrical infrastructure networks

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