Currently, the world is increasingly using electrical energy storage systems (EES). This is due to the development of electric transport, the growth of "green" energy, and the need to regulate the load of large energy systems. The growth of the battery market in the last decade was 20-30%.

K. V. Dobrego1), S. A. Fursov1), S. S. Dubnovitsky2), V. L. Chervinsky3)

1) 1AK -GROUP LLC «Aktiv OMZ» (Minsk, Republic of Belarus)

2) 1AK -GROUP LLC «Zubr Energy» (Pinsk, Republic of Belarus)

3) Belarusian National Technical University (Minsk, Republic of Belarus),

According to BlumbergNEF [1], the capacity and capacity of electricity storage systems in the world increased by 16 GW / 35 GWh in 2022.

In Belarus, in 2022, the Concept of using energy storage systems based on lithium-ion batteries in the Belarusian energy system was developed [2]. The document indicates the need to modernize the regulatory framework for the use of ESS, create competence centers and implement pilot projects.

The most important condition for the practical application of ESS in industry and public utilities is the availability of scientifically sound methods for calculating the economic efficiency of their use.

The works [3,4,5] consider the basic aspect of using ESS in the power system — load schedule alignment. In [6] a comparative analysis of the economic efficiency of competing projects is carried out: the introduction of ESS and the use of electric boilers to level the load schedule of the power system under the operating conditions of the Belarusian NPP. In a more modern work [7] a functional and economic analysis of the use of ESS at thermal power plants was carried out. First of all, the savings on the costs of the TPP’s own needs were assessed, subject to the market value of the electricity spent on these purposes.

The report on the research work «The concept of using energy storage systems based on lithium-ion batteries in the Belarusian energy system» [2] contains sections devoted to a generalized analysis of the economic feasibility of using ESS in various systems: at thermal power plants; substations of electrical networks; in distribution networks of industrial enterprises; in a system with renewable energy sources; in the charging infrastructure for electric transport. The methodology is based on the assessment of fuel and energy savings and does not contain instructions on calculating the effect for specific enterprises.

1AK-GROUP specialists have developed for the first time in the republic a methodological basis for calculating the economic efficiency of using SNE at industrial enterprises. A brief summary of the methodological approach is given below.

The initial data for calculating the efficiency of using SNE can be divided into blocks: 1) the configuration of the consumer system, 2) the economic conditions of power supply, 3) data on normal load modes (generation) and statistics of power supply failures, 4) technical and economic parameters of SNE, conditions and limitations of its operation, 5) other essential conditions.

If we talk about an industrial enterprise, then the configuration of the system is the power supply scheme, information on the number of blocks of homogeneous consumers (with the same requirements for the quality of power supply, similar load schedules), the presence and type of their own generation sources, etc. Economic conditions of electricity supply — first of all, tariffs and other conditions stipulated by the electricity supply agreement. Data on the load mode (generation) include load schedules and other statistics for each block of homogeneous consumers (winter working and weekend days, summer working and weekend days) and each generator (if any). Data on power supply interruption statistics are used to calculate the economic effect associated with ensuring the reliability and uninterruptible power supply of the enterprise.

As an example, let us consider a typical enterprise in the mechanical engineering industry, characterized by a simple configuration — the enterprise operates according to one load schedule and with the same requirements for the quality of power supply, does not have its own generation sources.

The enterprise is supplied with electricity as an energy-intensive enterprise with electricity consumption of up to 25 million kWh inclusive. Basic fee (for capacity) — Cmax = 31.43 [rubles/kW]. The reduction coefficient for the basic fee Km=0.5 is set if during the accounting month the evening peak does not exceed the morning peak and in the absence of cases of exceeding the maximum allocated capacity. Differentiated tariff for electricity for two time periods: Сд = 0.19478 rubles / kW h (from 6.00 to 23.00) and Сн = 0.16936 rubles / kW h (from 23.00 to 6.00). There are no other essential conditions.

Functions of the SNE in the system

The most important element of the methodology for calculating the economic efficiency of using the SNE is the definition of the SNE functions that have an economically significant effect. For the Enterprise in question, four functions are relevant, listed in Table 1.

Table 1. SNE functions that have an economically significant effect.

We will call functions of a binary nature (fulfillment / non-fulfillment) rigid or qualitative, and functions that are always fulfilled to a greater or lesser extent — soft or quantitative.

An important methodological point of the problem under consideration is the statistical nature of both the load schedules of industrial enterprises and the expected economic effect from the implementation of the SNE functions. In this regard, the values of the economic effect from the implementation of each of the functions (St1, St2, St3 and St4) should be interpreted as expected statistical values. An assessment of the standard deviations of these and other calculated values is necessary to assess the reserve capacity of the SNE required to perform the established functions under conditions of statistical fluctuations in energy consumption parameters.

It is advisable to rank the SNE functions by priority. This can be done on the basis of assessments of their economic effect.

Calculation of the economic effect

The calculation of the economic effect is carried out in several stages.

1. Based on the initial data (Fig. 1 and others), calculations of typical, averaged consumption characteristics and other preliminary calculations are made.

2. The economic effect of each of the considered functions of the energy storage system is consistently assessed depending on the energy capacity allocated for its implementation.

3. The correspondence between the capacities allocated for each function and the total capacity of the energy storage system is determined. It should be borne in mind that due to the joint implementation of some functions and the features of the load schedules, the total capacity of the energy storage system is less than the sum of the capacities allocated for the implementation of each function.

4. Calculation of economic indicators for the use of the energy storage system. Thus, the simple payback period without taking into account operating costs is found as the quotient of the investment cost and the total economic effect.

Additionally, an assessment of the standard deviation of the economic effect from the expected value can be performed. Based on the assessments made, an informed decision can be made on the feasibility or inexpediency of implementing the energy storage system at the enterprise.

Fig. 1. Typical enterprise load schedule on winter working (left) and weekend (right) days. Blue columns — minimum tariff time, red — morning and evening peak loads

Consumer tariff maneuvering.

The monthly economic effect of tariff maneuvering is generally calculated taking into account the energy capacity used for this function 〖Q1〗_i

Ст1=∑_i▒〖〖Q1〗_i (Кэф∙Сдень-Снож)〗, rubles/month, (1)

where i is the numbering of the days of the month. The same for the average monthly values of the quantities

Ст1=30∙〖Q1〗_ (Кэф∙Сдень-Снож), rubles/month.

The average value of Q1 should take into account the weekly work schedule of the enterprise and be based on the actual load schedules.

For the Enterprise under consideration, with an insignificant (~ 13%) relative range of the differentiated tariff, the consumer tariff maneuvering function is of little importance. It is estimated that with an energy efficiency (E) of the SNE of 95% (for lithium-ion batteries) and a capacity of 1000 kW h, the monthly economic effect will be St1 = 1000 * 0.0156 * 30 = 468 rubles, and the annual one — and assuming 11 full working months — 5.148 rubles per year.

Reduction of consumption peaks.

Maneuverable use of the SNE to compensate for short-term peaks allows for an effective reduction in the maximum peak power consumption of electricity by the Enterprise. The specific value of peak power reduction is calculated taking into account the actual load schedule of the enterprise. The graphs in Fig. 2 show that with a reserve of energy capacity of 500 kWh, the peak load of electricity consumption by the enterprise on a winter working day can be reduced by more than 150 kW.

Fig. 2. Reduction of the peak load ΔPmax depending on the energy of compensation of peak consumption Q2. a) — winter working day — b) — winter day off,

The economic effect of other functions of the SNE is calculated in a similar way, and

the total annual economic effect of using the SNE is obtained by summing the effects of all the functions taken into account

St = St1 + St2 + St3 + St4.

The figure shows the calculated values

Fig. 3. Dependence of the total economic effect [rubles/year] on the capacity of the SNE, [kWh]. 1, 2, 3, 4 — contribution of functions 1, 2, 3, 4 respectively

The simple payback period of investments without taking into account depreciation and operating costs is calculated as the quotient of the total investments and the total annual effect, in Fig. 6. Also in Fig. 4 are presented the options for calculating the simple payback period with an increased difference between the day and night tariffs, as well as under the condition of the absence of the reduction coefficient Km.

Fig. 4. Simple payback period (years) as a function of the energy capacity of the SNE. 1 — basic calculation for the Enterprise; 2 — calculation for Km = 1; 3 — calculation under the condition of Sden / Snoch = 2

As can be seen, the payback period of investments is a complex function of the energy capacity of the SNE with a minimum of about 4 years. In the case of the full cost of the tariff for the connected capacity (Km = 1) and maintaining other operating conditions of the enterprise, the minimum effective simple payback period investment decreases to 3.2 years. In the case of the enterprise under consideration, the range of the differentiated tariff is small (~13%) and the effect of the corresponding SNE function is insignificant. With a twofold ratio of the maximum and minimum tariffs and other equal conditions, the minimum effective payback period will be 3.5 years.

List of references

1 World energy storage market. [Electronic resource]. — Access mode: https://about. bnef. com/blog/1h-2023-energy-storage-market-outlook. Access date: 04/07/2023.

2 Molochko A. F., Privalov A. S., Zhuchenko E. A., Ivashko E. V. et al. «The concept of using energy storage systems based on lithium-ion batteries in the Belarusian energy system» Final R&D report. GPO «Belenergo» RUE «BelTEI» Volume 1,2. Minsk 2022. No. B 22- 3/4

3 Gurtovtsev A. A., Zabello E. P. Alignment of power system electric load graphs. Power and Fuel and Energy Complex. 2008. No. 7/8. P. 13-20

4 Dobrego K. V. On the justification of the economic feasibility of using electrochemical energy storage devices in the power system. Energy Strategy, 2022, No. 5 (89), pp. 28-32.

5 Chernetsky A. M. Assessment of the economic efficiency of using electricity storage devices in the power system. Power Engineering. News of Universities and Energy Associations of the CIS 2013, Vol. 4, pp. 21-28.

6 Voronov E. O., Kovalev D. V., Sivak A. V., Kudryavtsev D. I., Negodko A. Z., Dragun A. A. On the issue of using electrochemical energy storage devices in the conditions of the Belarusian energy system. Energy Strategy. No. 4 2017, pp. 14-17.

7 Mendeleev D. I., Rossikhin D. A., Galimzyanov L. A., Fedotov A. Yu. Analysis of the use of electricity storage systems at thermal power plants 2022, pp. 73-78