An Energy Storage System Sizing Method for Wind  Power Integration

Wang, Wei; Mao, Chengxiong; Lu, Jiming; Wang, Dan

doi:10.3390/en6073392

Cited by 24 publications

(16 citation statements)

References 22 publications

(19 reference statements)

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“…And we also relates the average wind power since that value can affect level of the WP fluctuation as described in Equation (5). From these considerations of average wind speed and capacity of an ESS in the formulation of parameter k d , we can adapt this formulation to different size of WT or ESS using above Equation (8). Figure 3 illustrates the ESS control strategy that was previously introduced in [24].…”

Section: Proposed Control Strategymentioning

confidence: 99%

“…Energy storage system (ESS) can be used to mitigate the renewable power fluctuation, shave the load peak, and level the load, as discussed in [7][8][9]. Some authors [10] also studied single or hybrid ESSs and reported that the performance was improved when using a hybrid ESS compared to a single ESS, even when compared to a previous study that investigated a single type ESS control [11].…”

Section: Introductionmentioning

confidence: 99%

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Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

2017

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This paper proposes a method for the coordinated control of a wind turbine and an energy storage system (ESS). Because wind power (WP) is highly dependent on wind speed, which is variable, severe stability problems can be caused in power systems, especially when the WP has a high penetration level. To solve this problem, many power generation corporations or grid operators have begun using ESSs. An ESS has very quick response and good performance for reducing the impact of WP fluctuation; however, its installation cost is high. Therefore, it is important to design the control algorithm by considering both the ESS capacity and WP fluctuation. Thus, we propose a control algorithm to mitigate the WP fluctuation by using the coordinated control between the wind turbine and the ESS by considering the ESS capacity and the WP fluctuation. Using de-loaded control, according to the WP fluctuation and ESS capacity, we can expand the ESS lifespan and improve grid reliability by avoiding the extreme value of state of charge (SoC) (i.e., 0 or 1 pu). The effectiveness of the proposed method was validated via MATLAB/Simulink by considering a small power system that includes both a wind turbine generator and conventional generators that react to system frequency deviation. We found that the proposed method has better performance in SoC management, thereby improving the frequency regulation by mitigating the impact of the WP fluctuation on the small power system.

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Section: Proposed Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

2017

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“…C E = E max E /u min = P max e × ∆t/u min (6) Noted that the main effect of the ESS is to compensate for the power deviation from the forecast; as a result, the largest forecasting error is the total capacity of the ESSs.…”

Section: Electrical Energy Forecastingmentioning

confidence: 99%

“…Studies [3,4] show that an optimal planning of locations and sizes of ESSs in power systems can reduce a power system's costs and enhance a its reliability and power quality. A novel method has been presented in [5] for designing an energy storage system dedicated to the reduction of the uncertainty of short-term wind power forecasts up to 48 h. Wang et al in [6] proposed a determination methodology for optimizing the capacity of an ESS that enables a wind power generator to meet the requirements of grid integration. To improve regulation effects, the segmentation method and automatic segmentation…”

Section: Introductionmentioning

confidence: 99%

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

et al. 2017

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Due to the increasingly serious energy crisis and environmental pollution problem, traditional fossil energy is gradually being replaced by renewable energy in recent years. However, the introduction of renewable energy into power systems will lead to large voltage fluctuations and high capital costs. To solve these problems, an energy storage system (ESS) is employed into a power system to reduce total costs and greenhouse gas emissions. Hence, this paper proposes a two-stage method based on a back-propagation neural network (BPNN) and hybrid multi-objective particle swarm optimization (HMOPSO) to determine the optimal placements and sizes of ESSs in a transmission system. Owing to the uncertainties of renewable energy, a BPNN is utilized to forecast the outputs of the wind power and load demand based on historic data in the city of Madison, USA. Furthermore, power-voltage (P-V) sensitivity analysis is conducted in this paper to improve the converge speed of the proposed algorithm, and continuous wind distribution is discretized by a three-point estimation method. The Institute of Electrical and Electronic Engineers (IEEE) 30-bus system is adopted to perform case studies. The simulation results of each case clearly demonstrate the necessity for optimal storage allocation and the efficiency of the proposed method.

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“…As the power grid fails to accommodate the fast deployment of wind power, wind curtailment phenomena occur frequently, which is against the original intention of "energy conservation and emission reduction". In order to enhance the controllability of wind farms, matched energy storage devices are constructed so as to facilitate the wind power integration, which has become the consensus within this field [6][7][8]. In [9], the authors used an energy storage system (ESS) to improve the wind energy integration and grid voltage stability.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

Jiang

Han

et al. 2016

Energies

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Abstract:In order to deal with the uncertainties of wind power, wind farm and electric vehicle (EV) battery switch station (BSS) were proposed to work together as an integrated system. In this paper, the collaborative scheduling problems of such a system were studied. Considering the features of the integrated system, three indices, which include battery swapping demand curtailment of BSS, wind curtailment of wind farm, and generation schedule tracking of the integrated system are proposed. In addition, a two-stage multi-objective collaborative scheduling model was designed. In the first stage, a day-ahead model was built based on the theory of dependent chance programming. With the aim of maximizing the realization probabilities of these three operating indices, random fluctuations of wind power and battery switch demand were taken into account simultaneously. In order to explore the capability of BSS as reserve, the readjustment process of the BSS within each hour was considered in this stage. In addition, the stored energy rather than the charging/discharging power of BSS during each period was optimized, which will provide basis for hour-ahead further correction of BSS. In the second stage, an hour-ahead model was established. In order to cope with the randomness of wind power and battery swapping demand, the proposed hour-ahead model utilized ultra-short term prediction of the wind power and the battery switch demand to schedule the charging/discharging power of BSS in a rolling manner. Finally, the effectiveness of the proposed models was validated by case studies. The simulation results indicated that the proposed model could realize complement between wind farm and BSS, reduce the dependence on power grid, and facilitate the accommodation of wind power.

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An Energy Storage System Sizing Method for Wind Power Integration

Cited by 24 publications

References 22 publications

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

Electrical Energy Forecasting and Optimal Allocation of ESS in a Hybrid Wind-Diesel Power System

Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

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