Stochastic Network-Constrained Unit Commitment to Determine Flexible Ramp Reserve for Handling Wind Power and Demand Uncertainties

Naghdalian, Salar; Amraee, Turaj; Kamali, Sadegh; Capitanescu, Florin

doi:10.1109/tii.2019.2944234

Cited by 35 publications

(24 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the wind speed is considered as an uncertain parameter with normal PDF 42 . The formulation of wind speed's PDF and the relationship between the power output of wind farms and wind speed can be found in Reference 42. The estimated points by HPEM can be formulized as follows 36 :

x_{j, m} = μ_{x_{j}} + φ_{j, m} σ_{x_{j}}

where

φ_{j . m}

μ_{x_{j}}

, and

σ_{x_{j}}

are the standard location, average, and SD of the uncertain variable

x_{j . m}

respectively.…”

Section: Three‐point Estimate Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Probabilistic multiobjective reconfiguration considering the optimal location of shunt capacitors and distributed generations in distribution network

Beirami

Naghdalian

Aliabadi

2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

Reconfiguration is one of the most important functions in the distribution network's automation system. Reconfiguration is formulated as an optimization problem with a large number of scenarios, which demands high central processing unit time to check all of them. Therefore, it is necessary to utilize a high-efficiency optimization method. In this paper, the minimization of active power losses, total voltage deviations of buses, and maximization of system loading margin are integrated as three objective functions of the proposed reconfiguration model. Also, to improve the voltage profile, reduce power losses, and increase system loading margin, shunt capacitors (SCs) and distributed generations (DGs) are located. Seasonal daily load curves are applied to better simulate networks' real conditions. This paper uses the nondominated sorting genetic algorithm II, which generates a set of nondominated solutions. This set includes a wide range of solutions with different weighting coefficients. The multicriteria decision-making (MCDM) algorithm, as a powerful and flexible decision-making tool, is utilized to select the best solution based on tuning parameters. Also, it is assumed that DGs are wind farms, thus the uncertainty of DGs' power output is taken into the account, and the threepoint estimate method (3PEM) is utilized to reduce the number of subscenarios generated by 3PEM. Finally, the subscenario aggregation method is utilized to extract the value of objective functions in subscenarios. The developed model determines the optimal location of SCs and DGs in conjunction with the optimal network reconfiguration. The proposed method is implemented on the typical 33 and 69 bus radial distribution systems.

show abstract

x_{j, m} = μ_{x_{j}} + φ_{j, m} σ_{x_{j}}

where

φ_{j . m}

μ_{x_{j}}

, and

σ_{x_{j}}

are the standard location, average, and SD of the uncertain variable

x_{j . m}

respectively.…”

Section: Three‐point Estimate Methodsmentioning

confidence: 99%

“…In this paper, it is assumed that the DGs are wind farms. Therefore, the wind speed is considered as an uncertain parameter with normal PDF 42 . The formulation of wind speed's PDF and the relationship between the power output of wind farms and wind speed can be found in Reference 42.…”

Section: Three‐point Estimate Methodsmentioning

confidence: 99%

Probabilistic multiobjective reconfiguration considering the optimal location of shunt capacitors and distributed generations in distribution network

Beirami

Naghdalian

Aliabadi

2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…The rate of load change when participating in a DRP shall not exceed its permissible limit in the consecutive time intervals according to the load structure, so similar to the ramp-up/down limits which have been defined for thermal units, (39) and ( 40) are defined for DRP participant below. 41)- (44).…”

Section: Idrp Constraintsmentioning

confidence: 99%

“…Active and reactive power balance are modeled in (45) and (46), which indicate the total amount of generated active (reactive) power should be equal to consumed active (reactive) power [28]. Voltage magnitude and voltage angle of the system buses should not exceed a predefined value, which is represented in ( 47) and ( 48) [44]. Equation (49) shows that the loading limit for a transmission line shall be its thermal loading limit [28].…”

Section: Ac-network Constraintsmentioning

confidence: 99%

Evaluating the effect of electric vehicle parking lots in transmission-constrained AC unit commitment under a hybrid IGDT-stochastic approach

Ahrabi

Abedi

Nafisi

et al. 2021

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

Power network operators have recently faced new challenges due to an increase in the penetration of non-dispatchable renewable energy sources in power grids.Incorporating emerging flexible resources like electric vehicle parking lots (EVPLs) and demand response programs (DRPs) into power systems, could be a good solution to deal with inherent uncertainties imposed by these resources to the power grid. EVPLs can improve power system operating conditions by active and reactive power injection capabilities. The participation of consumers in DRPs can also improve energy consumption management by decreasing or shifting loads to other periods. This paper proposes a hybrid information gap decision theory (IGDT)stochastic method to solve a transmission-constrained AC unit commitment model integrated with electric vehicle (EV), incentive-based DRP, and wind energy. The behavioural uncertainty related to EV owners is modelled using a scenario-based method. Additionally, an IGDT method is applied to manage wind energy uncertainty under a two-level optimization model. Verification of the proposed model is done under several case studies. Based on the results achieved, the proposed risk-based hybrid model allows the operator to differentiate between the risk level

show abstract

“…Response sets are an excellent way to ascertain the availability of the capacity without the stochastic model. SCUC is solved considering the uncertainty of the load demand and wind power in [44], [45], [46]. Also, ramping and spinning reserves are used for the mitigation of the uncertainties of wind and load demand.…”

Section: Previous Work On Security Constrained Unit Commitmentmentioning

confidence: 99%

Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles

Panda

Das

2021

Renewable and Sustainable Energy Reviews

View full text Add to dashboard Cite

Stochastic Network-Constrained Unit Commitment to Determine Flexible Ramp Reserve for Handling Wind Power and Demand Uncertainties

Cited by 35 publications

References 27 publications

Probabilistic multiobjective reconfiguration considering the optimal location of shunt capacitors and distributed generations in distribution network

Probabilistic multiobjective reconfiguration considering the optimal location of shunt capacitors and distributed generations in distribution network

Evaluating the effect of electric vehicle parking lots in transmission-constrained AC unit commitment under a hybrid IGDT-stochastic approach

Economic operational analytics for energy storage placement at different grid locations and contingency scenarios with stochastic wind profiles

Contact Info

Product

Resources

About