Continuous Time Multi-Stage Stochastic Reserve and Unit Commitment

Hreinsson, Kari; Analui, Bita; Scaglione, Anna

doi:10.23919/pscc.2018.8442490

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…Recently, the application of riskbased assessment techniques for power system operation with WPG has attracted high interest from electrical power industry [11], [12] and [13]. The literature on the continuous-time operation model can be reached in [14], [15] and [16]. However, the model proposed in this paper differs from the above references in five aspects: -The continuous-time model for ESU has been not presented.…”

Section: B Nmmentioning

confidence: 99%

Minimizing Wind Power Curtailment Using a Continuous-Time Risk-Based Model of Generating Units and Bulk Energy Storage

Nikoobakht¹,

Aghaei

Shafie‐khah

et al. 2020

IEEE Trans. Smart Grid

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Section: B Nmmentioning

confidence: 99%

Minimizing Wind Power Curtailment Using a Continuous-Time Risk-Based Model of Generating Units and Bulk Energy Storage

Nikoobakht¹,

Aghaei

Shafie‐khah

et al. 2020

IEEE Trans. Smart Grid

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“…In (11), the first term represents generation cost; the second term represents load shedding cost; and the third term represents over-generation cost. For simplicity, this is shown in (12).…”

Section: B Formulationmentioning

confidence: 99%

“…This improvement in solution quality, however, comes at the cost of substantial computational burden [1], [9]- [11]. Since the introduction of stochastic unit commitment (SUC), many researchers have developed a variety of methods to model more uncertainties and improve SUC's computational efficiency [12]- [16]. The computational demands of the problem can vary based on the type of uncertainty, e.g., generation dispatch or transmission status, and the uncertainty distribution, e.g., normal or Boolean.…”

Section: Introductionmentioning

confidence: 99%

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Mohammadi

2020

IEEE Access

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Many foreseeable natural hazards, including extreme weather events, lead to an outage of multiple transmission lines. Although such outages can be predicted in advance, there is a great deal of uncertainty in these predictions. To appropriately use the failure estimations in power system scheduling, this paper formulates a stochastic unit commitment (SUC) problem with explicit modeling of the predicted outages. The formulated problem, however, is extremely computationally-demanding, as the uncertainty is placed on the binary status of transmission lines. This paper, then, develops a computationally efficient algorithm to solve the formulated SUC for large-scale systems. The algorithm employs generation shift factors to enable rapid calculation of power flows. Additionally, flow canceling transactions are used to model multiple line outages without having to recalculate shift factors. Finally, critical constraints are iteratively detected and added to the problem. This approach substantially reduces the size of the problem, which helps computational tractability. The effectiveness of the developed algorithm is demonstrated through simulation studies on the Texas 2000-bus test system. The algorithm is used to minimize the lost load during a hypothetical hurricane. The results show that the algorithm is computationally tractable and can effectively identify a preventive dispatch, leading to a substantial reduction in power outages.

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“…Renewable generations have the uncertainty of power output; that is why the introduction of stochastic UC programming is becoming very necessary [48,49]. Most of the recent papers have considered two or multistage stochastic UC [50][51][52][53][54][55]. Some articles considered hybrid scholastic UC to manage uncertainty on the expected net load [56].…”

Section: Stochastic Uc Problemmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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Continuous Time Multi-Stage Stochastic Reserve and Unit Commitment

Cited by 8 publications

References 13 publications

Minimizing Wind Power Curtailment Using a Continuous-Time Risk-Based Model of Generating Units and Bulk Energy Storage

Minimizing Wind Power Curtailment Using a Continuous-Time Risk-Based Model of Generating Units and Bulk Energy Storage

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Energy Storage System Analysis Review for Optimal Unit Commitment

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