A data-driven heuristic for corrective transmission switching

Li, Xingpeng; Balasubramanian, Pranavamoorthy; Hedman, Kory W.

doi:10.1109/naps.2016.7747894

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…Spinning reserve is modeled in this work and it can only be provided by traditional units that are controllable. Constraints (10) and (11) show that the spinning reserve provided by a unit cannot exceed its ramping capability and available generation capacity respectively. As shown in ( 12) and ( 13), the "largest generator" rule is used to set the reserve requirement: the total reserve should be greater than or equal to the largest generation for all scenarios considered in SOPF.…”

Section: Objective Functionmentioning

confidence: 99%

Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables

Xia

2020

2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)

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There has been a significant growth of variable renewable generation in the power grid today. However, the industry still uses deterministic optimization to model and solve the optimal power flow (OPF) problem for real-time generation dispatch that ignores the uncertainty associated with intermittent renewable power. Thus, it is necessary to study stochastic OPF (SOPF) that can better handle uncertainty since SOPF is able to consider the probabilistic forecasting information of intermittent renewables. Transmission network congestion is one of the main reasons for renewable energy curtailment. Prior efforts in the literature show that utilizing transmission network reconfiguration can relieve congestion and resolve congestioninduced issues. This paper enhances SOPF by incorporating network reconfiguration into the dispatch model. Numerical simulations show that renewable curtailment can be avoided with the proposed network reconfiguration scheme that relieves transmission congestion in post-contingency situations. It is also shown that network reconfiguration can substantially reduce congestion cost, especially the contingency-case congestion cost.

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Section: Objective Functionmentioning

confidence: 99%

Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables

Xia

2020

2020 IEEE/PES Transmission and Distribution Conference and Exposition (T&D)

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“…Short-circuit current is one major concern in power system operations; the studies conducted in [23] provide new insights to reduce the short-circuit current with TNTC. As a corrective action, TNTC can substantially reduce postcontingency violations and the proposed vicinity-base local search and data mining heuristics can provide multiple quality solutions [24][25][26][27][28][29]. Authentic ISO New England data and software are used to demonstrate the benefits of using TNTC as a corrective mechanism in response to both the N−1 and N−1−1 events: corrective TNTC can alleviate thermal overloads and achieve economic benefits [30].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity factors based transmission network topology control for violation relief

Korad

Balasubramanian³

2020

IET Generation, Transmission & Distribution

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Transmission networks consist of thousands of branches for large-scale real power systems. They are built with a high degree of redundancy for reliability concern. Thus, it is very likely that there exist various network topologies that can deliver continuous power supply to consumers. The optimal transmission network topology could be very different for different system conditions. Transmission network topology control (TNTC) can provide the operator with an additional option to manage network congestion, reduce losses, relieve violation, and achieve cost saving. This paper examines the benefits of TNTC in reducing post-contingency overloads that are identified by real-time contingency analysis (RTCA). The procedure of RTCA with TNTC is presented and two algorithms are proposed to determine the candidate switching solutions. Both algorithms use available system data: sensitivity factors or shifting factors. The proposed two TNTC approaches are based on the transmission switching distribution factor (TSDF) and flow transfer distribution factor (FTDF) respectively. FTDF based TNTC approach is an enhanced version of TSDF based TNTC approach by considering network flow distribution. Numerical simulations demonstrate that both methods can effectively relieve flow violations and FTDF outperforms TSDF.

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Transmission Expansion Planning with Seasonal Network Optimization

Xia

2020

2020 IEEE Power &Amp; Energy Society Innovative Smart Grid Technologies Conference (ISGT)

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Transmission expansion planning (TEP) is critical for the power grid to meet fast growing demand in the future. Traditional TEP model does not utilize the flexibility in the transmission network that is considered as static assets. However, as the load profile may have different seasonal patterns, the optimal network configuration could be very different for different seasons in the planning horizon. Therefore, this paper proposes to incorporate seasonal network optimization (SNO) into the traditional TEP model. SNO dynamically optimizes the network for each season of each planning epoch. Two TEP-SNO models are proposed to investigate the benefits of optimizing the status of (i) existing branches, and (ii) existing and new branches, respectively. Numerical simulations demonstrate the effectiveness of the proposed TEP-SNO models. It is shown that SNO can improve system operational efficiency, defer investment of new transmission elements, and reduce the total cost.

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A data-driven heuristic for corrective transmission switching

Cited by 9 publications

References 29 publications

Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables

Stochastic Optimal Power Flow with Network Reconfiguration: Congestion Management and Facilitating Grid Integration of Renewables

Sensitivity factors based transmission network topology control for violation relief

Transmission Expansion Planning with Seasonal Network Optimization

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