Control algorithm for a residential photovoltaic system with storage

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 min for the input data (the resolution of most PV production and load data) selfconsumption 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.

Published in:
Applied Energy, 202, 78-87
Oxford, Elsevier Sci Ltd
IMT Number : 868

 Record created 2017-05-30, last modified 2018-01-28

External link:
Download fulltext
Rate this document:

Rate this document:
(Not yet reviewed)