Energy storage sizing for wind power: impact of the autocorrelation of day‐ahead forecast errors

Haessig, Pierre; Multon, Bernard; Ahmed, Hamid Ben; Lascaud, S.; Bondon, Pascal

doi:10.1002/we.1680

Cited by 69 publications

(55 citation statements)

References 19 publications

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“…e storage is used to mitigate the uctuations of the wind power plant (see [10,11] for previous work on this context). Figure 1 shows the variable and energy ows of this system, with the three main ones highlighted in red:…”

Section: Wind-storage Systemmentioning

confidence: 99%

“…Also important, it exhibits persistence (e.g. positive correlation) along several hours [10]. We capture both the randomness and the persistence using an Autoregressive AR(1) model:…”

Section: Forecast Error Persistencementioning

confidence: 99%

“…the RMS forecast error) which is linked to the variance of w, and the AR coe cient ϕ which is the correlation between two successive hours. Both must be estimated on actual time series [10].…”

Section: Forecast Error Persistencementioning

confidence: 99%

“…is penalty with a free tolerance band is inspired by a grid code for windstorage systems in French islands [10] and can be similarly found in the Hungarian grid code [13, §5.A].…”

Section: Control Optimizationmentioning

confidence: 99%

“…is farm has a mean production of 0.343 pu and RMS forecast error is σ P = 0.195 pu. e AR coe cient ϕ, which gives the correlation of forecast errors between two successive hours, is estimated at 0.79. is high positive temporal correlation is typically observed with day-ahead forecasts and it adversely impacts the system performance [10]. On gures 3 and 4, we represent a 10 days extract of this input data (forecast in gray, production in light blue).…”

Section: Input Datamentioning

confidence: 99%

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Energy storage control with aging limitation

Haessig

Ahmed

Multon

2015

2015 IEEE Eindhoven PowerTech

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Energy Storage Systems (ESS) are o en proposed to mitigate the uctuations of renewable power sources like wind turbines. In such a context, the main objective for the ESS control (its energy management) is to maximize the performance of the mitigation scheme.However, most ESS, and electrochemical ba eries in particular, can only perform a limited number of charge/discharge cycles over their lifetime. is limitation is rarely taken into account in the optimization of the energy management, because of the lack of an appropriate formalization of cycling aging.We present a method to explicitly embed a limitation of cycling aging, as a constraint, in the control optimization. We model cycling aging with the usual "exchanged energy" counting method. We demonstrate the e ectiveness of our aging-constrained energy management using a publicly available wind power time series. Day-ahead forecast error is minimized while keeping storage cycling just under an acceptable target level.

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Section: Wind-storage Systemmentioning

confidence: 99%

“…Also important, it exhibits persistence (e.g. positive correlation) along several hours [10]. We capture both the randomness and the persistence using an Autoregressive AR(1) model:…”

Section: Forecast Error Persistencementioning

confidence: 99%

Section: Forecast Error Persistencementioning

confidence: 99%

“…is penalty with a free tolerance band is inspired by a grid code for windstorage systems in French islands [10] and can be similarly found in the Hungarian grid code [13, §5.A].…”

Section: Control Optimizationmentioning

confidence: 99%

Section: Input Datamentioning

confidence: 99%

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Energy storage control with aging limitation

Haessig

Ahmed

Multon

2015

2015 IEEE Eindhoven PowerTech

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Methodology for integration of wind resource forecasts based on artificial neural networks

Carneiro

Lima²,

Carvalho

et al. 2022

Intl J of Energy Research

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Summary An adaptation of the portfolio theory (PT) is proposed in this article, denoted as PrevPT, “Previsão” (in Portuguese) by PT, to integrate the three artificial neural networks, namely multilayer perceptron (MLP) backpropagation, radial basis function (RBF), and self‐organizing map (SOM), based forecasting techniques, aiming to analyze the impact of wind speed forecasting errors and achieve more accurate results. In its first use, the PT goal was to maximize a financial return, at any risk, through the diversification of securities or investments that are not positively correlated. Based on the development of PrevPT, which was used until this work only for solar forecasting, the proposed technique is applied in this paper to integrate and improve the results of individual wind forecasts. Four‐year wind speed data (January 2007 to December 2010) from two different locations (Algeciras, Spain and Petrolina, Brazil) were used. Our methodology develops a topology that integrates the forecasts obtained by MLP, RBF, and SOM aiming to obtain smaller forecast errors. By diversifying the forecasted asset, when one of the assets has negative prediction errors, another compensates for them and, thus, the total or partial cancellation of the errors occurs. PrevPT obtains a mean absolute percentage error of 1.13% for Spain and 2.35% for Brazil. PrevPT surpassed the results obtained by the three techniques applied individually in the two locations. The main innovations of the methodology are the significant reduction of errors and optimization of resource planning, and the beneficial features compared to other predictor integration techniques.

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Predictive control and sizing of energy storage to mitigate wind power intermittency

Peng

Sun

2015

Wind Energy

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This paper studies how the control algorithm impacts the required capacity of battery energy storage system (BESS) to mitigate wind intermittency. We study a futuristic scenario in which wind generation is traded in the energy market on an hourly basis, and the wind power producer has to procure reserves to handle wind fluctuations. The wind power producer can avoid paying for conventional reserves by charging/discharging BESS to compensate for wind surplus and deficit. We develop control algorithms using the model predictive control (MPC) methodology, which incorporates wind forecasts for the next few hours when determining wind scheduling. The MPC algorithm is developed by solving a non‐linear optimization problem to minimize operation costs to the wind power producer. In addition to operation costs, the MPC algorithm considers two practical aspects: the efficiency loss of BESS and the smoothness in wind power scheduling. BESS sizing is studied by parametric analyses. Simulations show that BESS is more effective in reducing operation costs and reducing wind curtailment than conventional reserves. In addition, MPC is a horizon‐based control algorithm and can preview future information in its control action. Simulations also show that MPC consistently outperforms an instantaneous heuristic algorithm that does not use future information. Therefore, we confirm that MPC can reduce the BESS capacity required to cover wind uncertainties. Copyright © 2015 John Wiley & Sons, Ltd.

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Energy storage sizing for wind power: impact of the autocorrelation of day‐ahead forecast errors

Cited by 69 publications

References 19 publications

Energy storage control with aging limitation

Energy storage control with aging limitation

Methodology for integration of wind resource forecasts based on artificial neural networks

Predictive control and sizing of energy storage to mitigate wind power intermittency

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