On the relationship between battery power capacity sizing and solar variability scenarios for industrial off-grid power plants

Abstract

Due to its high short-term variability, solar-photovoltaic power in isolated industrial grids faces a challenge of grid reliability. Storage systems can provide grid support but come at a high cost that requires carefully evaluating power capacity needs. Battery sizing methodologies are now the focus of many studies, with a global upward trend in detailed modelling and complex optimization. However, although solar variability can be the source of uncertainties and battery oversizing, it rarely features as an input in scenarios. This study proposes several solar variability scenarios thanks to the wavelet-variability model and two variability metrics. These scenarios are employed as inputs in two sizing methodologies to compare the resulting battery capacity and draw conclusions on the role of modelling complexity and scenario identification. Results show that neglecting the photovoltaic power plant smoothing effect leads to an overestimation of the battery power support of 51%. In the other hand, complex dynamic modelling may reduce the battery power capacity by 25%. The economic analysis shows that a proper combination of variability scenario and battery sizing methodology may reduce the levelized costs of electricity by 3%. Highlights• Modelling photovoltaic plant geographical smoothing avoids over-investments.• Identifying variability scenarios is crucial to ensure continuity of supply.• Combining ramp-detection and variability index spares the use of day-long timeseries.

Allocation of spinning reserves for autonomous grids subject to frequency stability constraints and short-term solar power variations

“…Worst-case short-term variations can be approximated by a maximum amplitude ∆p RES , a minimum duration ∆T RES , and a constant rate of change rr RES = ∆p RES /∆T RES . This assumption has been corroborated by recently published analysis of short-term wind [39] and solar [28] variability, the determination of these parameters from highresolution wind speed and solar irradiance measurements also being discussed in detail in these papers.…”

“…Fig. A.4 summarizes a procedure for extracting ramp scenarios from irradiance sensor time series, as was recently proposed in [28] and applied in section 4.…”

“…[27] integrated the power drops observed in solar PV, within 15-minute time windows, into the constraints of an optimal planning problem, this accounting for the stochastic variation of solar irradiance. A recent work [28],…”

“…A MILP algorithm is used to solve a rolling frequencyconstrained UC problem, and to optimally size the installed capacity of solar PV and battery ESS. The RES short-term variability is evaluated by using a method recently published by [28], that identifies equivalent solar ramping scenarios during cloud passage. The proposed frequency stability constraints are then applied to the optimization problem.…”

Allocation of Spinning Reserves in Autonomous Grids Considering Frequency Stability Constraints and Short-Term Solar Power Variations

“…[27] integrated the power drops observed in solar PV, within 15-minute time windows, into the constraints of an optimal planning problem, this accounting for the stochastic variation of solar irradiance. A recent work [28], however, highlights the varying duration and magnitude of the shortterm variability of PV plants, and also that maximum perturbation may rise to 60 % of the plant's rated power in less than 30 s. These short-term variations must be taken into account if realistic decisions on storage investment or unit scheduling in optimization algorithms are to be achieved. [14] furthermore provide an updated review of the frequencyconstrained UC problem, a classification of the different approaches documented in the literature, and a discussion of their many shortcomings.…”