Direct Control Strategy of Real-Time Tracking Power Generation Plan for Wind Power and Battery Energy Storage Combined System

Bin, Li; Mo, Xinmei

doi:10.1109/access.2019.2946453

Cited by 23 publications

(13 citation statements)

References 13 publications

(9 reference statements)

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“…The DOD of a complete discharge is 100%. Figure 1 shows the attenuation relationship between the DOD of a lithium-iron-phosphate battery and its cycle life (Li et al, 2019).…”

Section: Impacts Of State Of Charge To the Cycle Life Of Batterymentioning

confidence: 99%

Coordinated Control of a Wind Turbine and Battery Storage System in Providing Fast-Frequency Regulation and Extending the Cycle Life of Battery

et al. 2022

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Fast-frequency regulation (FFR) is becoming a key measure to enhance the frequency stability of power systems as the penetration of renewables and power electronics continues to grow and the system inertia declines. Although different control methods have been proposed to provide a wind turbine generator (WTG) with a limited capability of virtual inertia and frequency support, the coordination between the WTG and a battery energy storage system (BESS), as well as the potential optimization benefits, have not been fully studied. This study proposes a coordinated control of WTG and BESS that provides FFR to the AC system and at the same time extends the cycle life of the battery. First, a cost effective and SOC-based FFR strategy of BESS alone was proposed. Then, a coordinated FFR method for the WTG–BESS hybrid system under all wind speeds was proposed by analyzing the operational characteristics of WTG. The proposed coordinated strategy improves the FFR performance with a longer cycle life and lower cost of battery under different operating conditions. Simulation results based on varying wind speeds indicate that the proposed FFR strategy raises the frequency nadir and avoids the frequency secondary dip.

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“…The DOD of a complete discharge is 100%. Figure 1 shows the attenuation relationship between the DOD of a lithium-iron-phosphate battery and its cycle life (Li et al, 2019).…”

Section: Impacts Of State Of Charge To the Cycle Life Of Batterymentioning

confidence: 99%

Coordinated Control of a Wind Turbine and Battery Storage System in Providing Fast-Frequency Regulation and Extending the Cycle Life of Battery

et al. 2022

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“…In this paper, the wind farm self-discipline level (SDL) can be evaluated by F-measure. SDL is defined as the weighted harmonic average of PICP and WIDTH, as shown in formula (13).…”

Section: Evaluation Of the Self-discipline Levelmentioning

confidence: 99%

“…In [12], a mathematical model and an optimization method is proposed to compensate the forecast error of wind power and reduce the uncertainty of wind power output. In [13], a direct control strategy is proposed to track the deviation of the wind power plan, the control strategy in this paper can change the charge and discharge power of energy storage in real-time according to the deviation of wind power and the state of charge.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wind Farm Self-Discipline Interval and Energy Storage System Configuration

Zhang

Dong

et al. 2020

IEEE Access

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The uncertain and stochastic output of the wind farm results in a lot of problems when it is connected to the power grid. In order to improve the wind power's friendship to the grid, the wind farm should has a certain self-discipline level. In this paper, it is studied from the perspective of the wind farm self-discipline interval. First, the concept of wind farm self-discipline interval is proposed, followed by a comprehensive index which is used to evaluate the wind farm self-discipline level by comprehensively considering the interval width and the interval accuracy. Second, an optimization method is discussed to obtain the optimal self-discipline interval. This method has general applicability, not only suitable for normal distribution and other known distributions but also for arbitrary distributions (such as non-symmetric, multi-peak distributions). Finally, the size of the energy storage system (ESS) in the wind farm is optimized to guarantee a suitable wind farm self-discipline level. Simulation results show that the proposed method not only effectively improves the self-discipline level of the wind farm but also has general applicability. INDEX TERMS Wind power, self-discipline, energy storage system (ESS), optimization.

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“…The Monte Carlo method (a statistical simulation method) solves these problems with a large number of random simulations. When the problem be solved is based on the probability of the occurrence of a certain random event, according to the means of random trials, this method takes the frequency of the occurrence of the event as the probability of the random event; thereby, the numerical characteristics of a certain random variable are obtained and then taken as the solution of the problem [15]. The Monte Carlo method requires a probability distribution consistent with the actual conditions.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification for a Power Distribution Network Based on MCMC Algorithm

et al. 2021

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The calculation and analysis of a power distribution network (PDN) require accurate device parameters. However, a PDN has many points, and the distribution area is very wide. The PDN parameters are influenced by manual entry, and most are relatively random. Additionally, these parameters are affected by the operating status. Thus, this paper proposes an algorithm that accurately identifies PDN parameters based on the Markov chain and Monte Carlo (MCMC) method. The algorithm assumes that the PDN parameters conform to a nonlinear probability space. The parameters are the line resistance L R , line reactance L X , short-circuit loss k P , short-circuit voltage percentage % k U , no-load loss 0 P , no-load current percentage 0 % I , etc. The algorithm in this paper uses the Monte Carlo method to provide parameter values that conform to the initial probability distribution and then combines the data collected from the actual feeder to perform power flow calculations to obtain the loss function. The data include the head and end voltages and active and reactive power on the low voltage side. The Markov chain and loss function update the initial parameter probability distribution. The low voltage side voltage of the power flow calculation is iteratively calculated under the new given parameters to obtain the new loss function, and finally, the PDN line and transformer parameter values are identified. Actual feeder data verification results show that this MCMC PDN parameter identification method can obtain high-precision parameter values without phase angle information; additionally, this method is insensitive to the initial values and exhibits fast convergence.

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Direct Control Strategy of Real-Time Tracking Power Generation Plan for Wind Power and Battery Energy Storage Combined System

Cited by 23 publications

References 13 publications

Coordinated Control of a Wind Turbine and Battery Storage System in Providing Fast-Frequency Regulation and Extending the Cycle Life of Battery

Coordinated Control of a Wind Turbine and Battery Storage System in Providing Fast-Frequency Regulation and Extending the Cycle Life of Battery

Optimization of Wind Farm Self-Discipline Interval and Energy Storage System Configuration

Parameter Identification for a Power Distribution Network Based on MCMC Algorithm

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