A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty

Abstract

A novel method is proposed for designing an energy storage system (ESS) which is dedicated to the reduction of the uncertainty of short-term wind power forecasts up to 48 h. The investigation focuses on the statistical behavior of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions (horizon, quality) on the distribution of SOC. With this knowledge, an optimized sizing of the ESS can be done with a well-defined uncertainty limit. For this study, one-year time series of power output measurements and forecasts were available for two wind farms. As a reference, different forecast quality degrees are simulated based on a persistence approach. With the forecast data, empirical probability density functions (pdfs) are generated which are the basis of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy represents the remaining forecast uncertainty. As a consequence, the proposed probabilistic method permits the sizing of energy storage systems as a function of the desired remaining forecast uncertainty, reducing simultaneously power and energy capacity.Index Terms-Energy storage sizing, probability density function, short-term forecast error, state of charge, wind power.

Economic Evaluation of ESS in Urban Railway Substation for Peak Load Shaving Based on Net Present Value

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“…To enable renewable energy sources (RESs), such as solar and wind power, ESSs are applied to power systems for various purposes. ESSs reduce the uncertainty in the operation of RESs [1] and make it possible to accommodate high penetration levels of RESs [2]. The authors in [3] proposed a method to determine the optimal ESS operation to increase the value of wind-power generation.…”

Optimal siting and sizing of Energy Storage System for power systems with large-scale wind power integration

“…For the studies on optimal sizing problem, many methodologies have been proposed [7]- [9]. The sizing problem can be modeled as an optimization problem.…”

Profit maximizing storage allocation in power grids

“…References [2], [3], [4] and [5] indicate factors that drive energy storage investment. References [6] and [7] couple energy storage with wind farm operations to reduce forecasting errors and spinning reserve requirements. However these studies neglect the effects of losses and network congestion, which may lead to underestimation in the value of storage.…”