Microgrids for Service Restoration to Critical Load in a Resilient Distribution System

Xu, Yin; Liu, Chen‐Ching; Schneider, Kevin P.; Tuffner, Francis K.; Ton, Dan

doi:10.1109/tsg.2016.2591531

Cited by 382 publications

(271 citation statements)

References 22 publications

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“…Great progress has been made in the utilization of stationary resources for service restoration in distribution systems after major blackouts [3], [8]. Microgrids can consolidate and manage a wide range of distributed energy resources to alleviate the hazardous impacts of extended outages [6].…”

Section: Setsmentioning

confidence: 99%

“…Previous studies generally assume that all the faults have been identified at t r [8], [25] and the restoration starts with accurate damage information. However, the extreme events may involve multiple stages, i.e., after the major strikes, the extreme events can still last and cause subsequent damage.…”

Section: A Problem Statementmentioning

confidence: 99%

“…A distribution network is modeled as a graph G D = (N D , E D ) [8], where N D is the set of distribution system buses, indexed by i and E D is the set of distribution system branches, indexed by (i, j). The mapping G D : M → N D indicates the microgrid locations in the distribution network.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…For example, there are two faults in distribution system #3 at t = 5: substation fault and damage to branch (9,10). The opening line switches are (6, 7), (8,9), (27,28), (30,31), as shown in Fig. 10-(a).…”

Section: (B) Impact Of Damage and Repair To Branchesmentioning

confidence: 99%

“…10-(c), branch (9, 10) is repaired at t = 10, and the topology is reconfigured by opening switches (9,15), (12,22), (27,28). At t = 20, branch (24, 25) is repaired, the distribution network takes the new topology and switches (6,26), (8,21), (9, 10), (12,22) are open.…”

Section: (B) Impact Of Damage and Repair To Branchesmentioning

confidence: 99%

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Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Yao

Wang

Liu

et al. 2020

IEEE Trans. Smart Grid

191

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in scenario s, otherwise set to 0. P t,s G,i , Q t,s G,i Real/reactive power generation at bus i in interval t in scenario s. P t,s r,i , Q t,s r,i Load restoration at bus i in interval t in scenario s. P t,s ij , Q t,s ij Real/reactive power of branch (i, j) in scenario s. v t,s i Voltage magnitude at bus i in interval t in scenario s. P t,s G,m , Q t,s G,m Aggregated active/reactive power in microgrid m in scenario s. P t,s DG,m , Q t,s DG,m Active/reactive power generation of equivalent dispatchable DG in microgrid m in scenario s. P t,s d,m and Q t,s d,m Active/reactive load in microgrid m in scenario s. E t,s DG,m Energy of equivalent dispatchable DG by the end of interval t in scenario s.

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Section: Setsmentioning

confidence: 99%

Section: A Problem Statementmentioning

confidence: 99%

Section: Mathematical Formulationmentioning

confidence: 99%

Section: (B) Impact Of Damage and Repair To Branchesmentioning

confidence: 99%

Section: (B) Impact Of Damage and Repair To Branchesmentioning

confidence: 99%

See 3 more Smart Citations

Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Yao

Wang

Liu

et al. 2020

IEEE Trans. Smart Grid

191

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Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Choopani

Hedayati

Effatnejad

2020

Int Trans Electr Energ Syst

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Summary Self‐healing refers to the specific ability of the smart grid that takes precautionary actions before the fault. It also performs fault location, isolation, and service restoration to minimize network damage after the fault. This process is performed with the help of communications infrastructure and remote control. The main components of this article include the use of graph theory and binary particle swarm optimization algorithm for optimal switching, the use of particle swarm optimization algorithm and forward‐backward sweep load flow for load shedding and distributed generation rescheduling, investigating the effect of self‐healing on improved reliability, power losses reduction, and load shedding reduction and the present a new approach for self‐healing optimization and a new mathematical model. The problem of self‐healing due to nonlinear and unbounded constraints is one of the nonconvex mixed integer nonlinear programming optimization problems. The optimization problem becomes a convex mixed integer linear programming optimization problem using the proposed scheme. Due to the nonlinearity and the nonconvexity of the problem, commercial solvers are not able to solve this problem. Even the Baron solver, which is used to solve nonconvex nonlinear problems, cannot achieve the optimal global solution. However, the proposed method is easily able to achieve the optimal solution by eliminating the nonlinear and nonconvex element. A modified RBTS‐4 distribution system is used as a test system. The IEEE 33‐bus distribution system is used to validate the proposed method. The results of case studies demonstrate the effectiveness of the proposed methodology.

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Dynamics and control of microgrids as a resiliency source

Lee

Liu

et al. 2020

Int Trans Electr Energ Syst

Self Cite

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Microgrids can be operated in a utility‐connected mode or an islanded mode in separation with the transmission or distribution system. As major disasters occur, intentional islanding of a microgrid is a possible solution to serve critical loads, within or outside the microgrids, until the utility service is restored. Through a field test, the technical feasibility to utilize microgrids as a resiliency resource is evaluated. Waveforms of the system dynamics are recorded, and the acquired data and measurements are analyzed and compared with simulation results. Technical issues arising from the starting of an islanded microgrid have been identified. The parallel operation of distributed generators is a major issue in the field test. With different droop characteristics, the dispatch rule of real and reactive power between the DGs is established. Based on the field test experience, a control scheme integrating droop control and feedback control is designed for proper power dispatch among DGs and frequency/voltage control in a microgrid. During the parallel operation, regulation of the frequency and voltage within an acceptable range is provided by the proposed control method. The proposed dispatch and control capabilities would significantly improve the dynamic performance of the microgrid. Simulation results based on a modified IEEE 13‐node test feeder validate the performance of the control strategy.

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Microgrids for Service Restoration to Critical Load in a Resilient Distribution System

Cited by 382 publications

References 22 publications

Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Self‐healing optimization in active distribution network to improve reliability, and reduction losses, switching cost and load shedding

Dynamics and control of microgrids as a resiliency source

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