Control algorithm for a residential photovoltaic system with storage

Abstract

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 %.

Managing PV power injection and storage, enabling a larger direct consumption of renewable energy: A case study for the Belgian electricity system

“…For wind deployment, it means to install a yearly capacity from 2018 on, between 250 and 500 MW for offshore and for onshore wind turbines. For PV deployment, some plans predict a more conservative growth, but values up to 20 GWp are in line with the outlooks. The scope of Table is to calculate how the share of consumed renewable energy would increase beyond phase 2, if for each phase, an additional 10 GWp of PV and 0.5 kWh/kWp of batteries are installed.…”

“…In Figure (right), the fraction of lost (not injected) energy of the PV‐generated annual energy (as a percentage of the maximum possible energy yield at 1 kW/kWp injection level) is calculated. At 0.5 kW/kWp, the annual energy loss for this orientation is 12% . For all the other orientations, there is less loss, because these orientations have less favorable conditions for high power generation levels.…”

“…In Table , the night injection level is shown as function of the net battery capacity. A round trip efficiency of 90% for the net battery capacity is assumed . Therefore, the battery will inject during nighttime only 90% of its energy accumulated during daytime.…”

“…Results of these choices can be calculated using this model. In the Belgian plan for renewables, a considerable amount of wind deployment is already foreseen, and phases 1 and 2 are increasing the wind capacities up to the medium wind scenario, considered as realistic levels. PV deployment is complementing this wind deployment in phases 1 and 2 up to 20 GWp.…”

“…At 0.5 kW/kWp, the annual energy loss for this orientation is 12%. 29 For all the other orientations, there is less loss, because these orientations have less favorable conditions for high power generation levels. For example, for a 1-kWp installation in the horizontal plane in Belgium, the condition of 1 kW generation can never occur, because the sun position is never vertical in Belgium.…”