Frequency control framework of power system with high wind penetration considering demand response and energy storage

Li, Hanyu; Jian, Wang; Zhang, Du; Zhao, Fang; Liang, Huabin; Zhou, Baorong

doi:10.1049/joe.2017.0510

Cited by 11 publications

(15 citation statements)

References 4 publications

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“…This slows the rotor which in turn the kinetic energy stored in the rotating wind turbine blades will be released a similar fashion to a synchronous generator [17], [18]. The frequency model is shown as follows [19]:…”

Section: Charactristics Of Inertia Resourcesmentioning

confidence: 99%

Event-Based Under-Frequency Inertia Emulation Scheme for Severe Conditions

Eliassi

Paulino

Torkzadeh

et al. 2018

2018 7th International Conference on Renewable Energy Research and Applications (ICRERA)

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The existing UFLS schemes suggested for conventional power systems need to be reassessed in the presence of renewable energy sources and storage systems equipped with grid-supporting convertors. A new Event-Based Under-Frequency Inertia Emulation (EB-UFIE) plan is proposed in this paper as a substitute of traditional UFLS program to protect the system against the risk of generation trip and subsequent blackout. Trajectory sensitivities are used as a popular technique to predict the future behavior of the system through a mixed integer programming optimization problem. The proposed scheme has been tested on the modified New England 39-bus system including renewables and storages.

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Section: Charactristics Of Inertia Resourcesmentioning

confidence: 99%

Event-Based Under-Frequency Inertia Emulation Scheme for Severe Conditions

Eliassi

Paulino

Torkzadeh

et al. 2018

2018 7th International Conference on Renewable Energy Research and Applications (ICRERA)

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“…The research by Toma et al [18] considered BESS to provide virtual inertia and PFC in a two-area power system considering 100% renewable generation. However, the battery state-of-charge (SOC) was completely overlooked in the earlier studies [14,15,[18][19][20]. ESS provides IE and PFC to regulate grid frequency with high wind penetration [20].…”

Section: Introductionmentioning

confidence: 99%

Battery Energy Storage System for Aggregated Inertia-Droop Control and a Novel Frequency Dependent State-of-Charge Recovery

2020

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To deal with the technical challenges of renewable energy penetration, this paper focuses on improving the grid voltage and frequency responses in a hybrid renewable energy source integrated power system following load and generation contingency events. A consolidated methodology is proposed to employ a battery energy storage system (BESS) to contribute to voltage regulation through droop-type control and frequency regulation by assimilated inertia emulation (IE) and droop-type control. In addition, a novel frequency-dependent state-of-charge (SOC) recovery (FDSR) is presented to regulate BESS power consumption within the FDSR constraints and recharge the battery during idle periods whenever needed. The efficacy of the proposed BESS controller is demonstrated in an IEEE-9 bus system with a 22.5% photovoltaics (PV) and wind penetration level. The simulation results obtained manifest the satisfactory performance of the proposed controller in regulating simultaneous voltage and frequency in terms of lower rate of change of frequency and better frequency nadir. Furthermore, the proposed FDSR demonstrates its superiority at the time of SOC recovery compared to the conventional approach.

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“…1,2 For the latter, a large amount of active power is transmitted across regions through ultrahigh voltage direct/alternating current, which aggravates the system disturbance when an N-1 fault occurs and reduces the emergency control capability of the receiving power grid. For the former, the integration of renewable List of symbols: a i , The weight vector connecting the input layer to the i-th hidden node; b i , The bias of the i-th hidden node; β i , The output weight of the i-th hidden node; H, The hidden-layer output matrix of the neutral network; N, The total number of training samples; L, The number of hidden nodes; g, Activation function; ΔH, The hidden-layer output matrix corresponding to the newly incremented data; W, The training samples of the prechanged system; Y, The training samples of the postchanged system; T 0 , The initial samples containing only key features; M 0 , The initial frequency prediction model; f 1 , Maximum frequency deviation; f 2 , Time at maximum frequency deviation; f 3 , Steady-state frequency; D, The determined samples; δ, The variance of the RMSE in several updates energy replaces the conventional supplies and makes the system moment of inertia gradually decrease, which reduces the transient stability of the power system after a disturbance.…”

Section: Introductionmentioning

confidence: 99%

“…energy replaces the conventional supplies and makes the system moment of inertia gradually decrease, which reduces the transient stability of the power system after a disturbance. 1,2 For the latter, a large amount of active power is transmitted across regions through ultrahigh voltage direct/alternating current, which aggravates the system disturbance when an N-1 fault occurs and reduces the emergency control capability of the receiving power grid. 3 Therefore, as an important method to ensure the security and stability of the power system, online transient frequency prediction is facing great challenges.…”

mentioning

confidence: 99%

Data inheritance–based updating method and its application in transient frequency prediction for a power system

Wang

Zhang

Ying³

et al. 2019

Int Trans Electr Energ Syst

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Summary Transient frequency prediction helps formulate and take emergency control measures in a timely way, which is of great significance to the security and stability of a power system. Conventional frequency prediction methods encounter challenges when taking both speed and accuracy into account. Machine–learning‐based prediction methods cannot readily achieve satisfactory prediction accuracy based on historical samples of transient faults; only by updating the prediction model online can these methods accommodate a transient fault, which may occur at any time. Aiming at the online update of the prediction model, the concept of data inheritance is presented and applied to the process of model updating. Based on this update method, a transient frequency prediction method considering model inheritance is proposed. In this prediction method, the historical transient fault samples of the power system are used to train the initial prediction model; in this way, the prediction performance can be quickly improved according to the newly incremented transient fault samples by inheriting the historical prediction model. Validations on the New England 39‐bus and NPCC 140‐bus systems demonstrate the effectiveness of the proposed method.

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Frequency control framework of power system with high wind penetration considering demand response and energy storage

Cited by 11 publications

References 4 publications

Event-Based Under-Frequency Inertia Emulation Scheme for Severe Conditions

Event-Based Under-Frequency Inertia Emulation Scheme for Severe Conditions

Battery Energy Storage System for Aggregated Inertia-Droop Control and a Novel Frequency Dependent State-of-Charge Recovery

Data inheritance–based updating method and its application in transient frequency prediction for a power system

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