Use and economic efficiency of electricity storage systems in manufacturing industries
What influencing factors need to be taken into account?
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 discussed the possible applications in an article on battery storage systems.
Under certain conditions, electricity storage systems can already make a positive contribution to an optimized energy supply and reduced energy costs. In the following, we want to show which framework conditions essentially influence the use of electricity storage systems in industry and under which conditions a more detailed consideration of such systems can be useful. In particular, the use cases of optimizing self-consumption and reducing electricity costs by lowering peak loads are taken into account.
As a rule of thumb for estimating the economic efficiency of battery operation, the following formula can be applied:
Profit / Loss
(input / output)
avoided electricity purchase costs - electricity production costs of the storage system -
foregone feed-in tariff - pro rata EEG levy
Electricity purchase costs and feed-in tariff
The cash flow generated from the increase in self-consumption depends primarily on the energy price and the feed-in tariff. The figure below compares the development of the feed-in tariff for photovoltaic systems and the average industrial electricity price for SMEs in Germany. It should be noted that a pro-rata EEG levy of 40% is also payable for self-consumed electricity from self-generation plants commissioned from 2014 onwards with an output of more than 10 kWp.
If the electricity production costs of the storage, i.e. the costs per stored kWh related to the lifetime of the battery, are above the difference between the energy price and the feed-in tariff (plus the pro-rata EEG levy), the increase in self-consumption results in a negative cash flow (payouts)
If your company is still benefiting from high feed-in tariffs, increasing self-consumption with battery storage systems probably makes no sense, at least from an economic point of view. However, this situation can be completely different when self-generation systems are no longer subsidized, as the price for the electricity supplied is then determined by the market.
On the credit side, in the form of payments, there are savings from avoided electricity procurement costs, which in the case of industrial companies are usually made up of a labor price and a capacity price. The power costs are determined on the basis of the maximum quarter-hourly average load in a billing period (power price [€/kW] * peak load [kW]).
In addition to the economic parameters, the individual operational load profile thus 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 a more detailed investigation. The annual duration curve of the applied 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 assess and increase the risk of losing substantial payments from the load peak reduction.
The left figure shows a comparatively flat annual duration curve of the active power reference. Thus, in the upper value range for a duration of 100 hours, a maximum difference of 20 kW is present between the loads. In the right figure, on the other hand, large differences in active power can be seen in the upper range. Accordingly, the loads occur only sporadically and with a high variance in the peak and the load profile therefore tends to be less suitable for the use of battery storage systems, as shown.
In addition to the behavior of the load in the upper value range, self-generation should regularly exceed own demand. Regular occurrence of active power output allows the storage system to be filled from surplus and low-cost self-generated electricity and thus to be kept operational. If, on the other hand, the possibility to fill the storage from self-generation is not regularly available, an increased active power purchase may have to be accepted in order to maintain the operability of the storage.
Battery control and database
The basis of any intelligent battery control is a solid and high-resolution database. This makes it possible to react appropriately to the situation at hand and to operate the battery optimally, both technically and economically. If, for example, the load threatens to increase the peak load storage, i.e. the load applied on a 15-minute average, to a new maximum within the billing period and thus generate increased power costs, this occurrence can be prevented by using a battery ("peak shaving"). The more accurate the energy data basis is, the more precise the operation management of the battery system can be.
In the future, it will also be possible to combine information from different sources, which will make it possible to forecast the load. By linking high-resolution real-time energy data, information from the PPS system, and weather data, it will be possible to forecast load and generation and thus optimally adapt battery operation management to the expected conditions.
In summary, it can be stated that battery storage systems will continue to gain in attractiveness in the future due to technical developments, an associated further cost degression and optimized control systems. In addition to drivers such as image or pioneering spirit, the economic potential of such systems usually plays the decisive role in the investment decision. As shown, the economic viability essentially depends on the external and individual energy industry conditions as well as on 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 actually designing such a system. This will tell you whether it is worthwhile to take a closer look at the use of battery storage systems under the underlying conditions and parameters.
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