CC BY
4DOI: 10.29141/2658-5081-2022-23-2-5 EDN: BEAXKI JEL classification: M11, L94, L11
Anatoly P. Dzyuba South Ural State University, Chelyabinsk, Russia
Irina A. Solovyeva South Ural State University, Chelyabinsk, Russia
Aleksandr V. Semikolenov South Ural State University, Chelyabinsk, Russia
Prospects of introducing microgrids in Russian industry
Abstract. Improving energy efficiency keeps on being one of the most pressing problems for Russian industry. The paper aims to examine the prospects of using microgrids in Russian regions, including in the old industrial ones, to reduce energy costs of industrial enterprises. The methodological basis of the study comprises theoretical aspects of pricing within the models of retail and wholesale energy markets, tenets of uneven demand for energy under the use of microgrids. The authors apply analysis, synthesis, systematisation and statistical observation, create matrices and positioning maps and explore the parameters of energy consumption schedules at industrial enterprises of various types as well as the values of 'common pot' electricity transmission tariffs introduced in the subjects of the Russian Federation. The researchers develop own system of indicators for assessing the variability in the cost of electricity transmission services and present a map of Russian regions that illustrate the prospects of using microgrids and mechanisms of demand management in industrially developed regions with a view to cutting energy costs.
Keywords: industry; industrial region; microgrids; energy efficiency; energy costs; energy consumption.
For citation: Dzyuba A. P., Solovyeva I. A., Semikolenov A. V. (2022). Prospects of introducing microgrids in Russian industry. Journal of New Economy, vol. 23, no. 2, pp. 80-101. DOI: 10.29141/2658-5081-2022-23-2-5. EDN: BEAXKI. Article info: received January 28, 2022; received in revised form February 21, 2022; accepted March 9, 2022
Introduction
Intensive digitalisation of business processes, robotisation of industrial production
and the growing impact of the global financial crisis stimulate the growth of energy consumption and cause special attention to energy conservation and energy efficiency.
One of the current and promising areas for improving the energy efficiency of industrial production and regions is the use of distributed energy systems. They differ from traditional power generation systems in the following:
• relatively low level of installed capacity of distributed energy facilities compared to the capacity of traditional power plants;
• location of these facilities on the sites of industrial enterprises or in close proximity to them;
• creation of distributed energy facilities to meet the demand for heat and electricity (capacity) of one basic consumer or a group of basic consumers;
• legislated opportunity to sell electricity (capacity) generated by distributed energy at prices determined by agreement of the parties;
• absence of external restrictions from the operators of the technological infrastructure (unified and regional dispatching departments of the Unified Energy System of Russia) regarding the volumes of electricity production (capacity) and their operational changes.
Thus, distributed energy is characterised by autonomy of operation, provision of energy supply to specific basic facilities, the absence of strict subordination to the operators of the technological infrastructure of energy systems, the possibility of selling excess electricity generated into the energy system and freedom of relations between producers and consumers of electricity (capacity). This makes it possible to reduce the cost of energy supply to industrial complexes and increase the energy efficiency of both individual industrial enterprises and entire regions.
The objects of this study are microgrids of industrial enterprises. There are two options for the use of distributed generation systems by these enterprises: 1) connection of this system to the enterprises' sites, without the use of an external electric grid; 2) connection of the enterprise either to a system, the object of which is not included in a single complex of an industrial enterprise, or through electric grids belonging to a third-party organisation.
In modern practice, the second option is more common. In this case, an industrial enterprise is forced to pay the cost of electricity transmission services, even if the technological connection is made directly to the electric grids of its producers. At the same time, microgrids are necessary, among other things, to reduce the cost of energy supply to an industrial enterprise and increase overall energy efficiency.
The purpose of the study is to determine the prospects for the use of microgrids in Russian regions, primarily old industrial ones, and to assess the opportunities for savings in the field of payment for electricity transmission services in the regional context.
The use of distributed energy: A theoretical review
The importance of distributed energy in the development of energy systems in most countries of the world laid the foundation for such areas of research as increasing the efficiency of the operation of the relevant systems, creating distributed energy technologies based on alternative and renewable energy sources, developing low-power distributed energy technologies (microgrids), integrating distributed energy technologies into parallel work with centralised power systems and demand-side management technologies.
A plethora of foreign research is devoted to modeling the operation of distributed generation with a centralised power supply system and improving the sustainability of energy supply to consumers [Beltran et al., 2020, p. 102; Sandhya, Chatterjee, 2021; Anu-radha, Jayatunga, Ranjit Perera, 2021; Baghbanzadeh et al., 2021; Matos, 2021; Martinez et al., 2021]. Particular attention is paid to the integration of distributed generation systems [Howlader, Matayoshi, Senjyu, 2015; Rahiminejad et al., 2016; Kakran, Chanana, 2018, p. 532; Menke, Bornhorst, Braun, 2019; Belmahdi, El Bouardi, 2020; Valencia, Hincapie, Gallego, 2021; Li, Chen, 2022], including energy market mechanisms [Kumar, Kumar, Sandhu, 2018, p. 835; Craig et al., 2018; Yu et al., 2018; Liu et al., 2019; Abdulkareem Saleh Abushamah, Haghifam, Ghanizadeh Bolandi, 2021; Lin et al., 2021].
The digitalisation of the electric power industry has led to the development of demand-side management technologies and the development of practical mechanisms for optimising the electrical loads of distributed generation systems and programs for introducing such tools into the practice of functioning of power systems [Poudineh, Jamasb, 2014; Howlader, Matayoshi, Senjyu, 2015; Nakada, Shin, Managi, 2016; Viana, Junior, Udaeta, 2018; Wang et al., 2018, p. 1077; Nejad et al., 2019].
The advance of domestic scientific developments in this area was seriously stimulated by a change in legislation in the field of using demand-side management technologies and microgrids in Russia1.
The most significant studies on increasing the efficiency of distributed generation systems, taking into account electricity demand-side management, include the works of
1 On amendments to some acts of the Government of the Russian Federation on the functioning of aggregators for managing the demand for electricity in the Unified Energy System of Russia, as well as improving the mechanism for price-dependent reduction in electricity consumption and the provision of services to ensure system reliability: Decree of the Government of the Russian Federation of March 3, 2019 no. 287 http://www.consultant.ru/ document/cons_doc_LAW_320896/. (In Russ.)
Korzittske [2013], Khanaev [2020a, 2020b] and Nekhoroshikh [2019]. Part of Russian publications focuses on reducing energy costs for electricity consumers [Bayramgulova, Goncharova, Varganova, 2017; Veselov, Pankrushina, Zolotova, 2018; Papkov, Osokin, Kulikov, 2018; Hovalova, Zholnerchik, 2018; Varganova et al., 2019; Zatsarinnaya, Logacheva, Grigoryeva, 2019; Shelomentsev, Orlov, 2019] and the prospects for using the achievements of digitalisation for this objective [Glotov, Zaytsev, 2018, pp. 8-9; Rogalev, Molodyuk, Isamukhamedov, 2019, pp. 10-16].
The development of digital control technologies in the fuel and energy industries and demand-side management technologies for electricity consumption leads to the improvement of the functional properties of consumers in the process of participating in the electric power system. This leads to the allocation of such consumers and their properties into a separate economic category and the object of the study - 'active consumer' who have their own sources of distributed generation [Volkova, Gubko, Salnikova, 2013; Golovshchikov, Zakirova, 2016; Voroshilov, Kechkin, Shalukho, 2017; Kucherov et al., 2018, p. 84; Kosarev, Fedorov, Khamitov, 2020].
In the most general form, a microgrid is a facility for the production of electricity with an installed generating capacity of not more than 25 MW, which has direct electric links with power receiving devices of an industrial consumer of electricity (power) and only one electric link with the Unified Energy System, regulation of production and electricity consumption (power) of which is produced within the framework of a single balance-flow, taking into account the pricing parameters for the purchase of electricity (power) and natural gas in modern energy market conditions and the existing technological restrictions on the allowed power consumption by enterprise's power receiving devices, synchronously controlled on the basis of intelligent functionally integrated devices.
An analysis of research in the field of application of distributed energy systems at industrial enterprises shows the insufficient development of practical mechanisms for the use of microgrids in industry. Thus, there is no methodological approach to the integrated regulation of demand and costs for the purchase of electricity and natural gas in the process of managing the functioning of these microgrids at industrial enterprises in Russia.
To develop such an approach, it is necessary to take into account the uneven demand schedules for the consumption of natural gas, changes in the integrated demand for the consumption of electricity and natural gas, the results of the analysis of the pricing principles in the wholesale and retail electricity markets, as well as the price parameters of the cost of purchasing electricity by industrial enterprises directly connected to the grids of electricity producers.
Prospects for the use of microgrids to reduce the cost of electricity transmission services
As noted above, it is essential to take into account the uneven demand schedules of the energy load of enterprises for the effective implementation of microgrids in the activities of industrial enterprises. The aim of this accounting is timely price-dependent regulation of demand for the purchase of energy resources, which will reduce the energy supply costs of an industrial enterprise and increase its overall energy efficiency.
There is a number of traditional indicators that are used to describe electrical load schedules. Here are the formulas for their calculation.
The completion ratio of the daily schedule of electrical loads Rcomp is calculated as follows:
„ _ Vavdaily m
"■comp — j. , (1)
v max daily
where Vavdaily is the average value of electricity consumption for the observable day, kW; Vmaxdaily is the maximum hourly value of electricity consumption for each day, kW. Vavdaily is determined by the formula:
_ £ Vhdaify
Vav daily — 24 '
where Vhdally is hourly value of electricity consumption for each hour in the observable day.
Daily load schedule irregularity factor Firreg is calculated using the formula:
_ ^min daily / q \
irreg — Yt-, (3)
v max daily
whereVmi„dally is the minimum hourly value of electricity consumption for each day, kW. To calculate the daily load shape factor FSh the following formula is used:
FSh = (4)
Vavdaily
where Pstd is standard deviation of power on a daily interval, kW.
Figure 1 shows examples of schedules of electrical loads with various indicators of uneven demand (indicated by letters A-I). All schedules have the same total volume of daily electricity consumption, while the completion ratios of the presented load schedules differ from each other by a consistent change in the maximum value of electricity consumption in the daytime period.
MW 1.5
1.0
0.5
0.0
®
MW 1.5
1.0
0.5
0.0
®
MW 1.5
1.0
0.5
0.0
©
1 5 10 15 20 25
1 5 10 15 20 25
1 5 10 15 20 25
MW 1.5
1.0
0.5
0.0
®
MW 1.5
1.0
0.5
0.0
©
1 5 10 15 20 25
1 5 10 15 20 25
1 5 10 15 20 25
MW 2.5
2.0
1.5
1.0
0.5
0.0
1 5 10 15 20 25
1 5 10 15 20 25
1 5 10 15 20 25
Fig. 1. Graphs of electrical loads with various indicators of uneven demand
Descriptive parameters of the observed daily schedules of electrical loads, presented in Figure 1 are illustrated in Figure 2 and given in Table 1. The analysis revealed that, despite the overall daily electricity consumption, the characteristics of the maximum and minimum per day, as well as the completion ratios, irregularities and shapes are different. At the same time, the investigated parameters of the graphs have an extremum on the "D" graph, which is expressed by equal values in each hour of the observed day.
Table 1 also shows the results of calculating the sums of the energy supply costs of an industrial enterprise, depending on the electrical load shapes, namely the amount of obligations to pay for electric power Tsepm and the amount of obligations to pay for electricity transmission services w„m.
MW 30.0 20.0 10.0 0.0
—I-1-1-1-1-1—
-1-1—
ABCDEFGHI Electricity consumption per day
B C D E F G H Maximum per day
ABCDEFGHI Completion ratio
C D E F G Shape factor
ABCDEFGHI Amount of obligations to pay for electric power
MW 8.0 6.0 4.0 2.0 0.0
MW 15.0
10.0
5.0
0.0
-1-1—
t-1-1-1—
ABCDEFGHI Average daily consumption
B C D E F G H I Minimum per day
Indicator of uneven demand
ABCDEFGHI Exact hour of grid maximum
ABCDEFGHI Amount of obligations to pay for the electricity transmission service
Fig. 2. Parameters of the studied daily schedules of electrical loads
Table 1. Descriptive parameters of the investigated daily schedules of electrical loads
Characteristic Daily schedules of electrical loads*
A B C D E F G H I
Electricity consumption per day 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0 24.0
Average daily consumption 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Maximum per day 1.28 1.10 1.06 1.00 1.07 1.24 1.43 1.93 2.28
Minimum per day 0.57 0.84 0.94 1.00 0.93 0.85 0.72 0.48 0.33
Completion ratio 0.78 0.91 0.95 1.00 0.94 0.81 0.70 0.52 0.44
Indicator of uneven demand 0.44 0.76 0.89 1.00 0.88 0.69 0.50 0.25 0.14
Shape factor 0.26 0.09 0.04 0.00 0.04 0.13 0.26 0.58 0.78
Exact hour of grid maximum 11 11 11 11 11 11 11 11 11
Capacity value during the exact hour of maximum 0.57 0.84 0.94 1.00 1.06 1.24 1.43 1.93 2.28
Amount of obligations to pay for electric power 0.57 0.84 0.94 1.00 1.06 1.24 1.43 1.93 2.28
Amount of obligations to pay for the electricity transmission service 0.85 0.98 1.02 1.00 1.06 1.24 1.43 1.93 2.28
Coefficient of payment for electricity transmission services 0.036 0.041 0.042 0.042 0.044 0.051 0.060 0.080 0.095
*The corresponding graphs are shown in Figure 1.
The analysis shows that the schedules of the amounts of obligations for payment of electric power and payment for electricity transmission services do not have an extrem-um and increase sequentially when moving from schedule "A" to schedule "I". Thus, the use of traditional indicators for assessing the volatility of the daily electricity consumption schedule does not allow us to trace the dependence of the cost of paying for electric power and electricity transmission services.
To determine the impact of the volatility parameters of electricity consumption schedules on the amount of obligations to pay for electricity transmission services, we propose using the indicator that we developed, namely the coefficient of payment for electricity transmission services:
_ max(phworkmonthi £ Ppeakco) Lpaym trans — 744 ' (5)
where Phwork is the average value of the hourly power on working days of the calendar month, corresponding to the intervals of the planned peak load hours of the power system Ppeakc0. Indicators Ppeakco are approved by the System Operator of the Unified Energy System (hereinafter called SO UES) for each month of the calendar year at the
end of the year preceding the year in which electricity is consumed; 744 is the number of hours in the billing month, consisting of 31 days. If the number of days in the calculation month is 30, then the number in the denominator of formula (5) will be 720, and if the month is 28 days long, it will be 672.
The periods Ppeakco approved by SO UES for the first price zone of the wholesale electricity market for 2022 are shown in Figure 3. Differences in the intervals of peak loads by months of the calendar year are due to the change in the seasonal characteristics of the peaks of the electric power system in different regions and the change in the length of the daylight hours.
Month 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
January
February
March
April
May
June
July
August
September
October
November
December
Fig. 3. Periods of planned peak load hours of the power system for the first price zone, 2022
The values of the coefficient of payment for electricity transmission services were calculated (Figure 4) for the typical daily schedules of electrical loads we studied (see Figure 1).
points 0.0035
0.0030 0.0025 0.0020 0.0015 0.0010 0.0005 0.0000
0.00076
0.00113 0-00127 0.00134
III
B
D
0.00166
0.00143 U UU1UU
III
0.00306
0.00260
0.00193 ill
H
Fig. 4. Values of the coefficient of payment for the electricity transmission for the investigated electrical load schedules
The corresponding values are increasing consistently as the shape of the demand schedule changes (namely, an increase in the volume of hourly electricity consumption during the peak period of daily demand) and reflect its impact on the value of average prices of electricity transmission services. The indicator Cpaymtr can be used to predict the cost of purchasing electricity in various scenarios for modeling and managing the characteristics of the daily volatility of electric load schedules.
The amount of payment for electricity transmission services by industrial enterprises operating in various regions of Russia, in addition to the shape of the electricity consumption schedule, is significantly influenced by the regional tariffs for the provision of electricity transmission services. These tariffs are set for each region individually according to the 'common pot principle, i.e., they are the same for the entire territory of the subject of the federation and reflect the costs of electricity transmission by all electric grid companies operating in the region.
Table 2 presents the values of 'common pot' electricity transmission tariffs in the Sverdlovsk oblast and Perm krai in the first half of 2022. It can be seen that the change in tariffs by design voltage levels is non-linear both in terms of individual rates and in the context of the ratios between rates in the regional context.
Table 2. Uniform ('common pot') rates for electricity transmission services through the grids of the Sverdlovsk oblast and the Perm krai, effective in the first half of 2022
No. Tariff groups of consumers of electrical energy (power) Unit of measurement VN CN-I CN-II NN
Sverdlovsk oblast
1. Other consumers (tariffs are indicated excluding VAT)
1.1. Double-rate tariff:
1.1.1. electrical grid maintenance rate ruble/ MW-month 560,931.60 939,969.40 1,228,469.95 1,347,924.14
1.1.2. the rate for payment of technological costs (losses) in electrical ruble/ MW-hour 164.3 278.69 371.46 747.46
1.2. One-part tariff ruble/ kW-hour 1.05763 1.87032 2.83671 3.5564
Perm krai
1. Other consumers (tariffs are indicated excluding VAT)
1.1. Double-rate tariff:
1.1.1. electrical grid maintenance rate ruble/ MW-month 669,370.30 890,346.19 1,008,672.64 973,547.02
Table 2 (concluded)
No. Tariff groups of consumers of electrical energy (power) Unit of measurement VN CN-I CN-II NN
1.1.2. the rate for payment of technological costs (losses) in electrical ruble/ MW-hour 163.88 330.49 514.39 1,074.86
1.2. One-part tariff ruble/ kW-hour 1.17953 1.59708 2.35792 3.47989
Difference between the tariffs in the Sverdlovsk oblast and the Perm krai
Differences between tariffs Double-rate tariff:
electrical grid maintenance rate ruble/ MW-month -108,438.70 49,623.21 219,797.31 374,377.12
the rate for payment of technological costs (losses) in electrical ruble/ MW-hour 0.42 -51.80 -142.93 -327.40
One-part tariff ruble/ kW-hour -0.12 0.27 0.48 0.08
The difference in the tariff components for the provision of electricity transmission services affects the change in the final values of the electricity transmission tariffs with an equivalent change in the coefficient of payment for electricity transmission services and leads to a significant variation in the economic effect of reducing the cost of these services when managing the demand schedules of the microgrids of the industrial enterprises.
Table 3 illustrates the difference in electricity transmission tariffs in the regions with indicators of the coefficient of payment for electricity transmission services for schedules of the "D" and "I" types.
The analysis revealed that with the same change in the coefficient of payment for electricity transmission services, the indicators of tariffs for the provision of these services, approved for various regions of Russia, change disproportionately. Tariffs can vary by more than 2.5 times. Thus, if the specified coefficient changes from schedule "D" to schedule "I", the electricity transmission tariff at the level of design voltage SN-II in the Irkutsk and Omsk oblasts, Khabarovsk krai, the Republic of Bashkortostan increases by no more than 50 %, while at a similar change in the coefficient of payment for electricity transmission services in the Kursk, Smolensk and Leningrad oblasts, it increases by more than 250 %.
We can state that the effect of price-dependent management of the electricity consumption schedule in terms of the cost of electricity transmission services of microgrids in various industrial areas will vary significantly. Thus, the prospects for the use of mi-crogrids in the regional context are also different.
Table 3. Difference in electricity transmission tariffs in the Russian regions with the values of the coefficient of payment for electricity transmission services for schedules of the type "D" and "I" (double-rate tariff CN-II)
No. Federal District Region Tariff difference
1 North Caucasian Federal District Republic of Ingushetia 5.06
2 Central Federal District Kursk oblast 3.85
3 Central Federal District Smolensk oblast 3.64
4 Northwestern Federal District Leningrad oblast 3.54
5 North Caucasian Federal District Republic of North Ossetia 3.46
6 Southern Federal District Republic of Kalmykia 3.44
7 Central Federal District Ryazan oblast 3.33
8 Southern Federal District Rostov oblast 3.31
9 Ural Federal District Kurgan oblast 3.22
10 Ural Federal District Tyumen oblast 3.22
11 Central Federal District Lipetsk oblast 3.21
12 Northwestern Federal District Republic of Karelia 3.12
13 Central Federal District Kostroma oblast 3.11
14 Volga Federal District Samara oblast 3.03
15 Central Federal District Bryansk oblast 3.02
16 Siberian Federal District Tomsk oblast 3.02
17 Northwestern Federal District Saint Petersburg city 2.96
18 Northwestern Federal District Pskov oblast 2.89
19 Volga Federal District Saratov oblast 2.84
20 Central Federal District Kaluga oblast 2.84
21 Northwestern Federal District Vologda oblast 2.81
22 Volga Federal District Republic of Mari El 2.80
23 Central Federal District Tambov oblast 2.79
24 Southern Federal District Republic of Adygea 2.78
25 Southern Federal District Krasnodar krai 2.78
26 Central Federal District Voronezh oblast 2.76
27 Volga Federal District Nizhny Novgorod oblast 2.76
28 Northwestern Federal District Republic of Komi 2.74
29 Volga Federal District Ulyanovsk oblast 2.72
30 Southern Federal District Volgograd oblast 2.71
31 Central Federal District Ivanovo oblast 2.63
32 Far Eastern Federal District Amur oblast 2.61
33 North Caucasian Federal District Kabardino-Balkarian Republic 2.60
34 Central Federal District Vladimir oblast 2.54
35 North Caucasian Federal District Chechen Republic 2.51
36 Volga Federal District Kirov oblast 2.50
37 Far Eastern Federal District Republic of Sakha (Yakutia) 2.46
38 Ural Federal District Chelyabinsk oblast 2.40
Table 3 (concluded)
No. Federal District Region Tariff difference
39 Central Federal District Tver oblast 2.36
40 Central Federal District Orel oblast 2.34
41 Siberian Federal District Republic of Khakassia 2.32
42 Central Federal District Yaroslavl oblast 2.27
43 Far Eastern Federal District Zabaykalsky krai 2.27
44 Far Eastern Federal District Republic of Buryatia 2.23
45 Volga Federal District Republic of Tatarstan 2.23
46 North Caucasian Federal District Republic of Dagestan 2.22
47 Central Federal District Belgorod oblast 2.21
48 Ural Federal District Sverdlovsk oblast 2.18
49 Northwestern Federal District Arkhangelsk oblast 2.17
50 Siberian Federal District Tuva Republic 2.14
51 Volga Federal District Orenburg oblast 2.09
52 Central Federal District Tula oblast 2.06
53 Northwestern Federal District Novgorod oblast 2.05
54 Volga Federal District Udmurt Republic 2.04
55 Northwestern Federal District Kaliningrad oblast 2.03
56 Volga Federal District Republic of Mordovia 2.01
57 Volga Federal District Penza oblast 2.00
58 Far Eastern Federal District Primorsky krai 1.96
59 Siberian Federal District Krasnoyarsk krai 1.86
60 North Caucasian Federal District Stavropol krai 1.86
61 Far Eastern Federal District Jewish Autonomous Oblast 1.84
62 Central Federal District Moscow oblast 1.84
63 Volga Federal District Perm krai 1.80
64 Central Federal District Moscow city 1.76
65 North Caucasian Federal District Karachay-Cherkess Republic 1.74
66 Siberian Federal District Kemerovo oblast 1.74
67 Siberian Federal District Novosibirsk oblast 1.69
68 Siberian Federal District Altai Republic 1.68
69 Siberian Federal District Altai krai 1.68
70 Southern Federal District Crimea Republic 1.58
71 Volga Federal District Chuvash Republic 1.57
72 Northwestern Federal District Murmansk oblast 1.53
73 Southern Federal District Astrakhan oblast 1.49
74 Volga Federal District Republic of Bashkortostan 1.47
75 Far Eastern Federal District Khabarovsk krai 1.46
76 Southern Federal District Sevastopol city 1.35
77 Siberian Federal District Omsk oblast 1.04
78 Siberian Federal District Irkutsk oblast 0.96
For a comparative assessment of relative and absolute indicators of tariffs for electricity transmission services, we propose to use the average electricity transmission tariff in the region (hereinafter referred to as the average tariff) and the coefficient of volatility of prices for electricity transmission of the region (hereinafter referred to as the volatility coefficient).
The average tariff is calculated by the formula:
T _ [ TPvn + TPcNl + TPCN2 + TPNN ]
ltravi - 4 +
\[TCvn + TCcNl + TCcNl + TCnn] .. r 1
+ L---X Lpaym tr^ (6)
4
where TPVN/TPCN1/TPCN2/TPNN is the tariff rate of process consumption (losses) in the double-rate 'common pot' tariff for electricity transmission services of the region i, ruble/kWh; TCVN/TCCN1/TCCN2/TCNN is the tariff rate of maintenance in the doublerate 'common pot' tariff for electricity transmission services of the region i (ruble/ kW*month). When calculating Ttravi the value of the completion ratio Rcomp, equal to 1 (Cpaymtr = 0.00134) will change.
The volatility coefficient is calculated by the formula:
„ TaviossCN2 +TavmaintX Cpaymtr 1=0.0013]
Cftr, = -, (7)
" CN2 + x Cr
where Cpaymtr[Rcomp = 1] ) is the coefficient of payment for electricity transmission services with a completion ratio of the daily electrical load schedule equal to 1 (Cpaymtr = 0.00134); Cpaymtr[Rcomp = 0.44]) is the coefficient of payment for electricity transmission services with a completion ratio of the daily electrical load schedule equal to 0.44 (Cpaymtr = 0.00306).
To visualise the level of differences in the indicators under consideration, a map of the variability in the cost of electricity transmission services in the regions was constructed (Figure 5), illustrating the values of average electricity transmission tariffs in the region (absolute indicator) and the degree of change in transmission tariffs with an equivalent change in the volatility characteristics of daily demand schedules for electricity consumption.
As part of the cluster analysis, the regions were divided into three groups depending on the degree of efficiency in managing the costs of paying for electricity transmission services.
The grouping of regions according to the indicators of the variability in the cost of electricity transmission services and, accordingly, the prospects for the use of microgrids in order to reduce the cost of electricity consumption is presented in Table 4.
3.2
2.7
I
U
N—'
Ö o '5b
<U t-l
at
a
• »-H
to +-»
Ö
0
• 1—I
C/5
s
CO 1=1 aj
>H
£ U
•i-I Ii
<J <D
SP
u
1
2.2
1.7
Group 1
Kursk oblast * Ingushetia • North Ossetia
Lipetsk oblast Kalmykia •• • Smolensk oblast
Rostov oblast
Nizhny Novgorod oblast I
Tver oblast
Mari El1
Krasnodar krai • Adygea Kaluga oblast
Tambov oblast
• Leningrad oblast
Bryansk oblast
• •
Tuva
• Kostroma oblast
• Ryazan oblast Samara oblast
Group 2
^ , ,, Saratov oblast Ulyanovsk oblast •__
Orenburg oblast # • • • VologdacSl^st
Tula oblast # Yaroslavl oblast M Amur - Volgograd oblast\ Orlov oblast* Belgorod oblast (^¿ast Pskov oblast \
T_I Vladimir oblast * I • | • Saint Petersburg cif^
Karelia
Kirov oblast Penza oblast •
Voronezh oblast
Krasnoyarsk km
Kurgan oblasT>
Tyumen oblast • •
Komi
Stavropol krai
~ 0 Tomsk oblast Kabardino-Balkaria Buryatia • Novgorod oblast Mordovia • Chelyabinsk oblast
Arkhangelskobfast ^^^ • Ivanovo oblast ^^ « Karachay-Cherkessia
- * • Dagestan
Sverdlovsk oblast Jewish AO
Chechnya
Perm krai • •—i
Moscow oblast •
Astrakhan oblast Bashkortostan
Novosibirsk oblast w
Chuvashia * '
\
\
Altai Altai krai
N
Sevastopol city
• Tatarstan 'Udmurtia • Primorsky krai Kaliningrad oblast 9 • Khakassia s^ Kemerovo oblast
\ *
\ Moscow city
\
• Crimea
Khabarovsk krai
Irkutsk oblast
Murmansk
oblast
1.80 1.85 1.90 1.95 2.00 2.05 2.10 2.15 Price volatility coefficient for electricity transmission
2.20
2.25
Fig. 5. Map of the variability in the cost of electricity transmission services
in Russian regions
Table 4. Grouping of the regions according to indicators of variability in the cost of electricity transmission services
Group Region Indicators
T 1 travi Cftri
1 Kursk, Smolensk, Leningrad, Ryazan, Rostov, Lipetsk, Kostroma, Samara, Bryansk, Kaluga, Tambov, Nizhny Novgorod oblasts; Krasnodar krai; republics of Ingushetia, North Ossetia, Kalmykia, Karelia, Adygea, Tuva 2.48-3.28 ruble/kWh 2.01-2.21
2 Kurgan, Tyumen, Tomsk, Pskov, Saratov, Vologda, Voronezh, Ulyanovsk, Volgograd, Ivanovo, Amur, Kirov, Vladimir, Chelyabinsk, Tver, Orel, Yaroslavl, Belgorod, Sverdlovsk, Arkhangelsk, Orenburg, Tula, Novgorod, Kaliningrad, Penza, Kemerovo oblasts; Jewish Autonomous Oblast; Saint Petersburg city; Moscow city; Kabardino-Balkarian, Chechen, Karachay-Cherkess, Udmurt republics; republics of Mari El, Komi, Sakha (Yakutia), Khakassia, Buryatia, Tatarstan, Dagestan, Mordovia; Zabaykalsky, Primorsky, Krasnoyarsk krais 1.50-2.53 ruble/kWh 1.88-2.22
3 Stavropol, Perm, Altai, Khabarovsk krais; Moscow, Novosibirsk, Omsk, Murmansk, Astrakhan, Irkutsk oblasts; republics of Altai, Crimea, Bashkortostan, Chuvash Republic; Sevastopol city 0.89-1.92 ruble/kWh 1.21-2.10
The regions of the first group are characterised by relatively high values of the average tariff and a high volatility coefficient. In this case, the prospects for demand management in terms of the cost of electricity transmission services are the most favourable. Industrial enterprises operating in these territories have the opportunity, by managing the schedules of their energy loads, to significantly reduce the cost of paying for electricity transmission services through the use of a microgrid.
In the second group, the level of average tariffs is lower than in the first one, and the values of the volatility coefficient are at the average level. The effectiveness of price-dependent demand management of the microgrid in these regions is slightly lower than in the first group, while the prospects for price-dependent electricity consumption in terms of the cost of electricity transmission services remain.
In the regions of the third group, the average tariffs and the volatility coefficient are significantly lower than in other groups. When making investment decisions on the construction and use of microgrids, a detailed calculation of the economic effect from the use of distributed generation and the calculation of efficiency under various scenarios of price-dependent demand management should be carried out due to the lower prospects for the use of microgrids.
The second group is the most numerous, which indicates a high potential to reduce the cost of electricity transmission services in most regions of Russia and there are prospects for the introduction of microgrids, especially in industrial regions with a significant share of electricity consumption.
Conclusion
Having studied the electricity transmission tariffs and the costs associated with the uneven demand schedules of electricity consumption by industrial consumers, we arrived at the following conclusions.
The effectiveness of the use of a microgrid in terms of price-dependent management of electricity consumption from the energy system with regard to the cost of electricity transmission services is affected by two key factors:
• the absolute value of the unified ('common pot') tariff for the provision of electricity transmission services, approved for a certain voltage level separately for each industrial area;
• the relative change in the final tariff for electricity transmission services of an industrial enterprise when the structure of the electricity consumption schedule changes in the process of demand management.
The system of indicators we developed to assess tariffs for the provision of electricity transmission services and parameters of demand schedules that affect the formation of the amount of obligations to pay for electricity transmission services, as well as the map of variability in the cost of electricity transmission services between regions built on their basis, allow
• making management decisions more objectively and improving the efficiency of using microgrids;
• managing the operation of distributed power generation systems in accordance with the indicators of the cost of electricity transmission services;
• evaluating the economic efficiency of investment projects for the creation of micro-grids connected to electric networks of various classes of the predicted voltage level and located in the territories of different industrial energy regions of Russia.
The results obtained are of particular practical importance for the old industrial regions, which most urgently need productive mechanisms to reduce costs and increase the efficiency of functioning in conditions of economic instability.
The proposed indicators can become the basis for making decisions on the implementation of investment programs for developing the energy sector and improving energy efficiency both at the level of individual industrial enterprises and at the regional and federal scales.
References
Bayramgulova Yu. M., Goncharova I. N., Varganova A. V. (2017). Methods for assessing the effectiveness of the introduction of distributional generation sources. In: Collection of Int. Sci. Essays "Energy and Electric Technical Systems" (pp. 61-66). (In Russ.)
Varganova A. V., Goncharova I. N., Bayramgulova Yu. M., Efimova V. A. (2019). Efficiency estimation method used to evaluate distributed generation sources introduction. Vestnik Yuzh-no-Uralskogo gosudarstvennogo universiteta. Seriya "Energetika" = Bulletin of South Ural State University. Series "Power Engineering", vol. 19, no. 4, pp. 52-58. DOI: 10.14529/power190406. (In Russ.)
Veselov F. V., Pankrushina T. G., Zolotova I. Yu. (2018). Tariff policy of the integrated power grid as a factor of investment attractiveness of the sources of distributed generation in the UES of Russia. Promyshlennaya energetika = Industrial Power Engineering, no. 11, pp. 2-10. (In Russ.)
Volkova I. O., Gubko M. V., Salnikova E. A. (2013). Active consumer: The problem of optimising the consumption of electricity and own generation. Problemy upravleniya = Control Sciences, no. 6, pp. 53-61. (In Russ.)
Voroshilov A. A., Kechkin A. Yu., Shalukho A. V. (2017). On the issue of assessing the optimal distribution of electricity in active-adaptive electrical grids with distributed generation. Proc. 1st Int. Sci.-Tech. Conf. "Problems and Prospects for the Development of Energy, Electrical Engineering and Energy Efficiency" (pp. 89-93). (In Russ.)
Glotov A. V., Zaytsev N. N. (2018). Digital evolution of mobile GTPPs. Energiya edinoy seti = Energy of Common Grid, no. 3 (38). pp. 6-10. (In Russ.)
Golovshchikov V. O., Zakirova S. Z. (2016). Distributed generation (including renewable energy sources) as an element of an intelligent electric power system with an active-adaptive grid. Sovremennye tekhnologii i nauchno-tekhnicheskiy progress = Modern Technologies and Scientific and Technical Progress, vol. 1, pp. 86-87. (In Russ.)
Zatsarinnaya Yu. N., Logacheva A. G., Grigoryeva M. O. (2019). Electricity in retail markets as a commodity in smart grids. Energeticheskie sistemy = Energy Systems, vol. 4, no. 1, pp. 237-242. (In Russ.)
Korzittske R. (2013). Demand management, distributed generation and virtual power plants: Economic signals. Effektivnoe antikrizisnoe upravlenie = Efficient Crisis Management, no. 1 (76), pp. 40-49. (In Russ.)
Kosarev B. A., Fedorov V. K., Khamitov R. N. (2020). Modeling the connection of a prosumer in parallel operation with the power supply system of an oil and gas production enterprise. Iz-vestiya Tomskogo politekhnicheskogo universiteta. Inzhiniringgeoresursov = Bulletin of the Tomsk Polytechnic University. Geo Assets Engineering, vol. 331, no. 8, pp. 144-153. https://doi.org/10.18 799/24131830/2020/8/2776. (In Russ.)
Kucherov Yu. N., Ivanov A. V., Korev D. A., Utkin N. A., Zhuk A. Z. (2018). Development of active consumer technologies and their integration into the public electric network. Energetich-eskaya politika = Energy Policy, no. 5, pp. 73-86. (In Russ.)
Nepomnyashchiy V. A., Ilyushin P. V. (2013). New approaches to ensure the reliability of power supply to consumers of electric energy. Nadezhnost i bezopasnost energetiki = Safety and Reliability of Power Industry, no. 4 (23), pp. 14-25. (In Russ.)
Nekhoroshikh I. N. (2019). Innovative mechanisms of control demand for electric power: A review of international experience and assessment of the prospects of its application in Russia. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya "Ekonomika. Sotsiologiya. Menedzhment" = Bulletin of the Southwest State University. Series "Economics. Sociology. Management", vol. 9, no. 2 (31), pp. 17-25. (In Russ.)
Papkov B. V., Osokin V. L., Kulikov A. L. (2018). About the features of small and distributed generation in the intellectual electric power industry. Vestnik Ufimskogo gosudarstvennogo avi-atsionnogo tekhnicheskogo universiteta = Buleltin of the Ufa State Aviation Technical University, vol. 22, no. 4 (82), pp. 119-131. (In Russ.)
Rogalev N. D., Molodyuk V. V., Isamukhamedov Ya. Sh. (2019). Improving the reliability of the functioning of the electric power industry in the conditions of the development of distributed generation and the digital economy. In: Voropay N. I. (ed.) Methodological issues of research of the reliability of large energy systems (pp. 8-17). Irkutsk: Melentiev Energy Systems Institute (Siberian Branch of RAS). (In Russ.)
Rytsova A. V. (2018). Influence of distributed generation on the mode of operation ofthe power system. Vestnik sovremennykh issledovaniy=Bulletin of Modern Research, no. 12.5 (27), pp. 247-249. (In Russ.)
Khanaev V. V. (2020a). Demand side management - current state and development prospects. Elektroenergiya. Peredacha i raspredelenie = Electric Power. Transmission and Distribution, no. 5 (62), pp. 74-77. (In Russ.)
Khanaev V. V. (2020b). Electricity demand management as an addition to distributed generation. Energeticheskaya politika = Energy Policy, no. 4 (146), pp. 38-51. (In Russ.)
Hovalova T. V., Zholnerchik S. S. (2018). The effects of the introduction of smart grids. Stra-tegicheskie resheniya i risk-menedzhment = Strategic Decisions and Risk Management, no. 2 (105), pp. 92-101. https://doi.org/10.17747/2078-8886-2018-2-92-101. (In Russ.)
Khudyakov K. I., Chirov D. A., Smirnov A. Yu., Yakovlev S. O. (2020). Problems of integration of distributed generation and centralised power supply system. In: Proc. 3rd Int. Sci. Conf. "Priority Directions of Innovative Activity in Industry" (pp. 162-164). (In Russ.)
Shelomentsev V. A., Orlov V. S. (2019). Means and methods for reducing the cost of electro-activity and improving the reliability of electric power systems. Proc. Sci-Prac. Conf. "Energy saving and innovative technologies in the fuel and energy complex" (pp. 363-367). Tyumen: Tyumen Industrial University. (In Russ.)
Abdulkareem Saleh Abushamah H., Haghifam M. R., Ghanizadeh Bolandi T. (2021). A novel approach for distributed generation expansion planning considering its added value compared with centralized generation expansion. Sustainable Energy, Grids and Networks, vol. 25, 100417. DOI: 10.1016/j.segan.2020.100417.
Anuradha K. B. J., Jayatunga U., Ranjit Perera H. Y. (2021). Loss-voltage sensitivity analysis based battery energy storage systems allocation and distributed generation capacity upgrade. Journal of Energy Storage, vol. 36, 102357. DOI: 10.1016/j.est.2021.102357.
Baghbanzadeh D., Salehi J., Samadi Gazijahani F., Shafie-khah M., Catalao J. P. S. (2021). Resilience improvement of multi-microgrid distribution networks using distributed generation. Sustainable Energy, Grids and Networks, vol. 27, 100503. DOI: 10.1016/j.segan. 2021.100503.
Belmahdi B., El Bouardi A. (2020). Simulation and optimization of microgrid distributed generation: A case study of University Abdelmalek Essaadi in Morocco. Procedia Manufacturing, vol. 46, pp. 746-753. DOI: 10.1016/j.promfg.2020.03.105.
Beltran J. C., Aristizabal A. J., Lopez A., Castaneda M., Zapata S., Ivanova Y. (2020). Comparative analysis of deterministic and probabilistic methods for the integration of distributed generation in power systems. Energy Reports, vol. 6, Supplement 3, pp. 88-104. DOI: 10.1016/j. egyr.2019.10.025.
Craig M. T., Jaramillo P., Hodge B.-M., Williams N. J., Severnini E. (2018). A retrospective analysis of the market price response to distributed photovoltaic generation in California. Energy Policy, vol. 121, pp. 394-403. DOI: 10.1016/j.enpol.2018.05.061.
Howlader H. O. R., Matayoshi H., Senjyu T. (2015). Distributed generation incorporated with the thermal generation for optimum operation of a smart grid considering forecast error. Energy Conversion and Management, vol. 96, pp. 303-314. DOI: 10.1016/j.enconman.2015.02.087.
Howlader H. O. R., Matayoshi H., Senjyu T. (2016). Distributed generation integrated with thermal unit commitment considering demand response for energy storage optimization of smart grid. Renewable Energy, vol. 99, pp. 107-117. DOI: 10.1016/j.renene.2016.06.050.
Kakran S., Chanana S. (2018). Smart operations of smart grids integrated with distributed generation: A review. Renewable and Sustainable Energy Reviews, vol. 81, part 1, pp. 524-535. DOI: 10.1016/j.rser.2017.07.045.
Kumar M., Kumar A., Sandhu K. S. (2018). Impact of distributed generation on nodal prices in hybrid electricity market. International Conference on Processing of Materials, Minerals and Energy, vol. 5, issue 1, part 1, pp. 830-840. DOI: 10.1016/j.matpr.2017.11.154.
Li Z., Chen G. (2022). Fixed-time consensus based distributed economic generation control in a smart grid. International Journal of Electrical Power & Energy Systems, vol. 134, 107437. DOI: 10.1016/j.ijepes.2021.107437.
Lin Q., Liu L.-J., Yuan M., Ge L.-J., Wang Y.-H., Zhang M. (2021). Choice of the distributed photovoltaic power generation operating mode for a manufacturing enterprise: Surrounding users vs a power grid. Journal of Cleaner Production, vol. 293, 126199. DOI: 10.1016/j. j clepro.2021.126199.
Liu S., Bie Z., Liu F., Li Z., Li G., Wang X. (2019). Policy implication on distributed generation PV trading in China. Energy Procedia, vol. 159, pp. 436-441. DOI: 10.1016/j. egypro.2018.12.043.
Martinez S. D., Campos F. A., Villar J., Rivier M. (2021). Joint energy and capacity equilibrium model for centralized and behind-the-meter distributed generation. International Journal of Electrical Power & Energy Systems, vol. 131, 107055. DOI: 10.1016/j.ijepes.2021.107055.
Matos S. P. S., Vargas M. C., Fracalossi L. G. V., Encarna^ao L. F., Batista O. E. (2021). Protection philosophy for distribution grids with high penetration of distributed generation. Electric Power Systems Research, vol. 196, 107203. DOI: 10.1016/j.epsr.2021.107203.
Menke J.-H., Bornhorst N., Braun M. (2019). Distribution system monitoring for smart power grids with distributed generation using artificial neural networks. International Journal of Electrical Power & Energy Systems, vol. 113, pp. 472-480. DOI: 10.1016/j. ijepes.2019.05.057.
Nakada T., Shin K., Managi S. (2016). The effect of demand response on purchase intention of distributed generation: Evidence from Japan. Energy Policy, vol. 94, pp. 307-316. DOI: 10.1016/j.enpol.2016.04.026.
Nejad H. C., Tavakoli S., Ghadimi N., Korjani S., Nojavan S., Pashaei-Didani H. (2019). Reliability based optimal allocation of distributed generations in transmission systems under demand response program. Electric Power Systems Research, vol. 176, 105952. DOI: 10.1016/j. epsr.2019.105952.
Poudineh R., Jamasb T. (2014). Distributed generation, storage, demand response and energy efficiency as alternatives to grid capacity enhancement. Energy Policy, vol. 67, pp. 222-231. DOI: 10.1016/j.enpol.2013.11.073.
Rahiminejad A., Vahidi B., Hejazi M. A., Shahrooyan S. (2016). Optimal scheduling of dis-patchable distributed generation in smart environment with the aim of energy loss minimization. Energy, vol. 116, part 1, pp. 190-201. DOI: 10.1016/j.energy.2016.09.111.
Sandhya K., Chatterjee K. (2021). A review on the state of the art of proliferating abilities of distributed generation deployment for achieving resilient distribution system. Journal of Cleaner Production, vol. 287, 125023. DOI: 10.1016/j.jclepro.2020.125023.
Valencia A., Hincapie R. A., Gallego R. A. (2021). Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium-low voltage distribution networks. Journal of Energy Storage, vol. 34, 102158. DOI: 10.1016/j. est.2020.102158.
Viana M. S., Junior G. M., Udaeta M. E. M. (2018). Analysis of demand response and photovoltaic distributed generation as resources for power utility planning. Applied Energy, vol. 217, pp. 456-466. DOI: 10.1016/j.apenergy.2018.02.153.
Wang Y., Huang Y., Zeng M., Li F., Zhang Y. (2018). Energy management of smart micro-grid with response loads and distributed generation considering demand response. Journal of Cleaner Production, vol. 197, part 1, pp. 1069-1083. DOI: 10.1016/j.jclepro.2018.06.271.
Yu H., Hong B., Luan W., Huang B., Semero Y. K., Tesfaye Eseye A. (2018). Study on business models of distributed generation in China. Global Energy Interconnection, vol. 1, issue 2, pp. 162-171. DOI: 10.14171/j.2096-5117.gei.2018.02.008.
Information about the authors
Anatoly P. Dzyuba, Dr. Sc. (Econ.), Sr. Researcher of Economics and Finance Dept., South Ural State University, 76 Lenina Ave., Chelyabinsk, 454080, Russia Phone: +7 (351) 267-91-28, e-mail: dziubaap@susu.ru
Irina A. Solovyeva, Dr. Sc. (Econ.), Associate Prof., Head of Economics and Finance Dept., South Ural State University, 76 Lenina Ave., Chelyabinsk, 454080, Russia Phone: +7 (351) 267-98-17, e-mail: solovevaia@susu.ru
Aleksandr V. Semikolenov, Applicant for Candidate Degree of Economics and Finance Dept., South Ural State University, 76 Lenina Ave., Chelyabinsk, 454080, Russia Phone: +7 (351) 267-91-28, e-mail: avs@msk-energo.ru
© Dzyuba A. P., Solovyeva I. A., Semikolenov A. V., 2022
