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A series resistance induced by cell interconnections will lead to a worse TCPMPP of solar modules compared with cells.

Recent improvements in light-emitting diode (LED) technology has allowed for the use of LEDs for solar simulators with excellent characteristics. In this paper, we present a solar simulator prototype fully based on LEDs. Our prototype has been designed specifically for light soaking and current-voltage (I(V )) measurements of amorphous silicon solar cells. With 11 different LED types, the spectrum from 400 to 750 nm can be adapted to any reference spectrum-such as AM1.5g-with a spectral match corresponding to class A+ or better. The densely packed LEDs provide power densities equivalent to 4 suns for AM1.5g or 5 suns with all LEDs at full power with no concentrator optics. The concept of modular LED blocks and electronics guarantees good uniformity and easy up-scalability. Instead of cost-intensive LED drivers, lowcost power supplies were used with current control, including a feedback loop on in-house developed electronics. This prototype satisfies the highest classifications (better than AAA from 400 to 750 nm) with an illuminated area of 18 cm × 18 cm. For a broader spectrum, the spectral range could be extended by using other types of LEDs or by adding halogen lamps. The space required for this can be saved by using LEDs with higher power or by reducing the maximum light intensity.

High penetration of photovoltaic (PV) electricity could affect the stability of the low-voltage grid due to over-voltage and transformer overloading at times of peak production. Residential battery storage can smooth out those peaks and hence contribute to grid stability. A feed-in limit allows for the easy setting of a maximum power injection cap and motivates PV owners to increase their self-consumption. A simple control strategy for a residential battery system coupled with a PV system that maximizes selfconsumption and minimizes curtailment losses due to a feed-in limit is presented. The algorithm used in this strategy does not require a forecast of insulation conditions. The performance of this algorithm is compared to a second algorithm-a control strategy based on linear optimization using a forecast. Assuming an exact forecast, this second algorithm is very close to the maximum self-consumption and minimum curtailment losses achievable and can be used to benchmark the simple strategy. The results show that the simple strategy performs as well as the second algorithm with exact forecasts and performs significantly better than the second algorithm using real forecasts. Moreover, it is shown that this result is valid for a large range of storage capacities and PV sizes. Furthermore, it is shown that with a time resolution of 15 minutes for the input data (the resolution of most PV production and load data) self-consumption is overestimated by about 3 % and curtailment losses are underestimated by the same amount. Load sensitivity simulations show that different load curve shapes do not fundamentally change the results. Finally, to assess the effect of load aggregation, the case where the strategy is applied separately to 44 households with storage is compared to the case where it is applied to a centralized storage system of the same size as the total storage of the 44 households. The reduction of the curtailment losses with the number of aggregated houses is showed.Keywords: Photovoltaic (PV), Home storage system, Battery management strategy, PV integration into the electrical grid Highlights• A control strategy for a battery system coupled with a PV system is presented.• With a feed-in limit this strategy does not need a PV production forecast.• This strategy performs as well as a strategy relying on an exact forecast.• A relatively small storage size allows peak injection reduction of 50 %.