Under the influence of changing legal and economic conditions, the use of electricity storage systems in industrial companies is becoming increasingly interesting. We have already dealt with the conceivable applications in an article. Under certain conditions, electricity storage systems can already make a positive contribution to optimized energy supply and reduced energy costs.
In the following, we would like to show which framework conditions essentially influence the use of electricity storage systems in industry and under which circumstances a more detailed consideration of such systems can be indicated.
In particular, the applications of self-consumption optimization and electricity cost reduction through peak load reduction are taken into account.
The following formula can be used as a rule of thumb for estimating the economic efficiency of battery operation:
The cash flow generated from the increase in own consumption depends above all on the labour price and the feed-in tariff. The figure below compares the development of the feed-in tariff for photovoltaic systems with the average industrial electricity price for SMEs in Germany. It should be noted that a pro-rata EEG-Umlage (levy for renewable energy law) of 40 % must also be paid for self-consumed electricity from own generation plants which were commissioned from 2014 onwards and whose output exceeds 10 kWp.
If the electricity production costs of storage, i.e. the costs per kWh stored related to the service life of the battery, are above the difference between the working price and the feed-in remuneration (plus pro-rata EEG-Umlage), the increase in own consumption results in a negative cash flow (payments). If, on the other hand, the electricity production costs are lower, payments are generated.
If your company still profits from high feed-in tariffs, the increase of your own consumption by battery storage systems probably makes no sense, at least from an economic point of view. However, this situation can be completely different with the withdrawal of the own generation systems from the subsidy, since the price for the electricity supplied is then determined by the market. On the credit side in the form of payments are savings from avoided electricity procurement costs, which in industrial companies usually consist of a labour price and a performance price. The power costs are calculated on the basis of the maximum load in a billing period, averaged over a quarter of an hour (power price [€/kW] * peak load [kW]).
In addition to the economic parameters, the individual operational load profile also represents a decisive factor with regard to the use of storage systems. Load profiles whose highest loads have a low fluctuation range are particularly suitable for closer examination. The annual load duration line of the adjacent loads should therefore be as flat as possible in the upper range. Unusually high, stochastically occurring load peaks, on the other hand, indicate a volatile load profile that is difficult to estimate and increase the risk of losing significant payments from peak load reduction.
The left figure shows a comparatively flat annual duration line of the active power reference. In the upper value range, for example, there is a maximum difference of 20 kW between the loads for a duration of 100 hours. In the figure on the right, on the other hand, large differences in active power can be seen in the upper range. The loads therefore occur only sporadically and with a high variance at the peak, and the load profile therefore tends to be less suitable for the use of battery storage systems, as shown. The regular occurrence of active energy output allows the accumulator to be filled from surplus and low-cost, self-generated electricity and thus kept ready for operation. If, on the other hand, it is not regularly possible to fill the storage tank from own generation, it may be necessary to purchase more active power to maintain the operational capability of the storage tank.
The basis of every intelligent battery control is a solid and high-resolution database. This makes it possible to react appropriately to the present situation and to operate the battery optimally both technically and economically. If, for example, the load threatens to increase the peak load memory, i.e. the average 15-minute load, to a new maximum within the billing period and thus generate increased power costs, this can be prevented by using a battery („peak shaving“). In the future, it will also be possible to combine information from different sources, which will make it possible to make a prognosis of the load. By linking high-resolution real-time energy data, information from the PPS system and weather data, load and generation can be predicted and thus the battery management can be optimally adapted to the expected conditions.
In summary, it can be said that battery storage systems will become even more attractive in the future as a result of technical developments, the associated further cost degression and optimised control systems. In addition to drivers such as image or pioneering spirit, the economic potential of such systems usually plays a decisive role in the investment decision. As shown, the economic viability of such systems depends essentially on the external and individual energy industry conditions and the operational load profile.
Are you interested in the use of battery storage systems? If so, we recommend carrying out an initial potential assessment before designing such a system. This will provide you with information as to whether it is worth taking a closer look at the use of battery storage systems under the underlying conditions and parameters.