**1. Introduction**

Perpetration of renewable energy in electric markets has reached an impressive 26.5%, being wind, bio and solar power at the forefront of modern renewables development and integration to electric retail [1]. A crucial parameter when designing renewable energy plants is its load factor, also known as capacity factor or plant (load) factor, which corresponds to the ratio between the generated and rated energy of the plant during a certain amount of time. In UK, PV plants present annual load factors close to 10% [2], which are calculated considering the rated power of the converter. A common practice is to increase annual plant factor by oversizing the power rating of the PV array, with respect to the converter [3]. The ratio between PV array rated power and the inter AC rated output power is known as Inverter Loading Ratio (ILR) [4]; in places with high irradiation variability such as UK, PV array power ILR oversizing can reach as much as 40%, whereas, in places with lower irradiation variability, such as central Chile, oversizing is closer to 15%. Moreover, the continuous drop on PV module prices have encouraged the increase of ILR in PV plants [3]; some authors have even proposed ILR oversizing up to 80% [4].

When an oversized PV array reaches the power rating of the converter, the converter loses the ability to increase its current and therefore is unable to reduce the DC-link voltage and loses the ability to track the Maximum Power Point (MPP). This behavior is called clipping, and it forces the system to waste available PV power. Clipped power is the name assigned to this wasted power. Figure 1

presents a grid-tied central inverter PV plant with PV array oversizing, where the available PV power is truncated at the rating of the inverter (clipped), limiting the exported PV power. Both power curves were normalized to the inverter rating.

**Figure 1.** Central inverter grid-tied PV plant with additional Battery Energy Storage System (BESS).

Generation–demand matching paradigm, where intermittency and high variability of renewable resources play a major role, can be achieved by relying on other systems connected to the electrical network and/or by the addition of an Energy Storage System (ESS). The first solution is not suitable for harnessing clipped power, since it requires the clipped power to be transferred to the electric network through the inverter, which is already operating at its rated power. The latter solution presents a more promising alternative to enhance existing PV plants, enabling them to harness clipped power. This solution has been widely researched as an alternative not only to deal with generation–demand mismatch, but also as means for renewables to provide complementary services, such as load shifting [5], global maximum power point tracking [6] and peak-shaving [7]. Note that the standard location for ESSs is, as shown in Figure 1, beside the transformer (before [8] or after [9]).

Sizing the ESS is a fundamental part of designing a tailored solution to handle clipped power. ESS sizing strategies for PV applications have been previously proposed in the literature: in [10,11], sizing strategies to comply maximum power ramp rate regulation were proposed; in [12], a sizing strategy to provide support for household PV applications; in [5], a sizing strategy to balance the peak and off-peak electricity consumption; and, in [13], a sizing strategy for smoothing power output and storing clipped power at PV plant level. This latter sizing strategy consists on averaging the PV power beyond a certain power limit (clipping level), hence hiding power dynamics to the sizing process. Additionally, the analysis is based on a single sunny day. It must be noted that the power limitation is imposed by contract with the grid operator. In addition, this sizing strategy aims at providing a concentrated solution for a full PV plant, where energy storage is connected at the point of common coupling and inverter ratings are not a limitation for the power exceeding PV plant.

A much wider variety of ESS sizing can be found in the literature related to wind power applications. In [14], a sizing strategy to maximize service-hours per BESS unit cost is presented. The strategy forecasts power generation based in long term historic data and statistical noise; this prediction is then low pass filtered, allowing to obtain an ESS power reference curve, which is later processed by a cost function obtaining the ESS energy rating. Nevertheless, low pass filtering generates phase delay depending on frequency, consequently reshaping the power curve and leading to over or under sizing of the ESS. A sizing strategy to minimize penalties caused by not complying day-ahead power bidding is presented in [15]. The strategy generates 25 initial ESS power references by subtracting bid power from 43-hour-power generation forecasts. The initial references are then presented in a histogram, together with a compliance level, which can be used to generate the ESS sizing. The power generation forecast and bidding strategy are not described in the paper. Moreover, this method considers a 43-hour horizon, which is not ideal for PV systems' daily cycles. A hybrid ESS sizing strategy to comply with maximum power ramp rate regulation is presented in [16]. For this purpose, wind forecast and uncertain load behavior are subtracted, generating a power reference which is later transformed into frequency-domain by Discrete Fourier Transform (DFT). The result is later separated into low, medium and high frequencies, corresponding to the desired power output, the power reference for BESS and the power reference for Supercapacitors, respectively. However, DFT strategy decomposes the full signal into periodic sinusoids losing information regarding the time location of frequencies, therefore leading to a wrong sizing of the ESS. Another strategy to size a hybrid ESS while complying with maximum power ramp rate is proposed in [9]. Here, several historical datasets are filtered by wavelet discrete transform, generating a maximum power ramp rate compliant power curve. ESS power reference curve is obtained by averaging the differences between all original curves and their filtered version. The result is later filtered selecting high and low frequencies as supercapacitors and BESS power references, respectively. The strategy relies on averaging the results, hence masking some dynamic behaviors.

This document presents an analysis of the annual power generated by a PV plant. An analytical model was applied to estimate annual clipping losses. An ESS sizing strategy, based in historic data, was proposed; this strategy considers efficiency of the technology (energy storage technology and power electronics) and provides ESS energy and power sizing, required to recover a certain percentage of the annual clipped power. Additionally, configuration and control strategies were proposed to retrofit an existing PV plant, in order to handle clipped power without modifying the existing MPPT strategy. To validate, at power converter level, the technical feasibility of performing the clipping energy storage service, real PV system and power converters models including control strategies were simulated. The simulations shows specifically that existing central inverter based PV plants can be retrofitted to perform this service (without modifications to the central inverter topology and control). Moreover, the study provides an insight into the daily and seasonal behavior of PV power generation, hence suggesting the advantage of additional usage of ESS, as ancillary services, during idle hours.

To the best knowledge of the authors, the estimation of clipped power, the ESS sizing strategy, the proposed ESS configuration enabling fully usage of a Battery ESS (BESS) and the proposal of a control strategy to harness such power, are novel.

The document is arranged as follows: Section 2 presents a brief description of problem. Section 3 describes the PV model applied to estimate available MPP and a comparison between predicted power and empiric power measurements. Section 4 section presents the ESS sizing strategy. The selection of an Energy Storing Technologies (ESTs), capable of handling clipped power, is presented in Section 5. The control strategy, configuration and simulation of the ESS connected to the PV plant is presented in Section 6.
