**1. Introduction**

Over the past few decades, the views on the role of traditional hydrocarbon energy changed dramatically. An increasing stress in concepts of development of a human society of the future is made on power engineering on the basis of using the renewable energy sources, as Melas et al., Augutis et al., Bhattacharya et al., and Umbach et al. have consistently shown through examples of various countries across the world [1–4]. The answer to the question of why this is happening lies not only in the consideration of actions of economic mechanisms and incentives in the energy sector. One can see that the answer also partly lies in a political subtext of energy development based on renewable energy, its scientific, environmental, and social aspects and so on. All of them form the outer visible part of the energy economic and policy iceberg.

Within the core of the hidden processes of close attention of governments of many countries to the development of renewable energy generation one can find careful attempts to find the scale and depth of the next energy crisis. Its occurrence is only a matter of time, due to many factors, the main of which are the following two: (i) physically limited amount of extracted energy sources, and (ii) the constant growth of energy consumption by human civilization, as Qureshi et al., Uz, or Ang et al. point out in their papers in the relevant energy-focused journals [5–7]. Particularly noteworthy is the fact that electricity is no longer just an additional benefit. As a result of the serious growth of the world's population and the concomitant restructuring of all vital industries nowadays, energy has become the key to the survival of billions of people all around the world.

In an attempt to avoid the consequences of a social and economic shock as a result of the energy crisis, various countries took steps to break the vicious circle and provide the basis for ensuring the minimum necessary level of power generation within the national energy systems [8,9]. This explains the emergence of keen interest in renewable energy based on the use of natural energy flows. Their integration into power systems is one of the priority tasks of the energy policy of a number of countries aimed at ensuring energy security, despite the fact that renewable energy sources in many cases are characterized by low economic e fficiency and form many problems associated with the optimization and managemen<sup>t</sup> of operating modes of power systems. For instance, some researchers [10–13] explain this by the uneven production of renewable energy and its inconsistency with the electricity consumption schedule. Moreover, the introduction of renewable energy often leads to an increase in the cost of energy for the end consumer. Some other researchers [14–16] note that the increase in the cost of electricity is primarily due to an increase in the grid tari ff due to a decrease in electricity consumption from the grid and an increase in the demand for peak power capacities.

Most recently, academic research and engineering projects conducted in the field of development of renewable energy have started to gain serious attention.

Serious state support is provided for scientific and technological developments of renewable energy power plants, the emphasis of which is shifted towards increasing their installed capacity. As noted in the papers [17–19], an increase in the unit capacity of power plants leads to a reduction in the cost of electricity production.

Another direction in the field of the use of renewable energy sources which is developing no less intensively is the development, creation and operation of small power plants. According to many authors [20–23], they focus on the needs of private households or small settlements. It is worth noting that the military departments of most countries are strongly interested in creating such autonomous systems, which ensures a constant flow of funds to finance such projects.

To achieve the goals of national strategies for the development of renewable energy and increase their investment attractiveness at the level of governments and energy departments of countries, it is necessary to develop state tari ff policy tools aimed at introducing and using renewable energy sources in energy supply systems. They should include economic incentives to optimize energy consumption through the introduction of autonomous energy sources and be based on di fferentiated pricing by hour area of the day. At the same time, as practice shows, di fferentiated tari ffs are often less profitable for the consumer than a fixed tari ff during the billing period [14,15]. This is due to the discrepancy between the fixed values of the tari ff rates established for the time zones and the actual daily energy consumption schedule. Taking into account the development of automated systems for commercial metering of electricity, it is necessary to develop a pricing method that takes into account real-time changes in energy consumption and stimulates consumers and manufacturers to introduce and e ffectively use renewable energy sources.

This paper aims to solve the following important problems and issues:


#### **2. Overview of the Global Renewable Energy Market**

According to the data from 2016, the renewable energy market is represented by power plants with a total capacity of more than 2000 GW (Figure 1) [24–26]. The main suppliers of renewable energy are hydropower and wind as well as solar energy and bioenergy.

**Figure 1.** World production capacity of renewable energy sources, GW.

At the same time, solar and wind energy markets are the fastest growing in the world. The world wind power capacity over the past 7 years increased 2.5 times, reaching a total capacity of 487 GW, while the global solar photovoltaic power capacities increased more than 10 times and amounted to 303 GW (Figure 2) [24–26].

Additionally, there is a significant reduction in the cost of individual technologies of renewable energy. This contributes to the innovative development of the industry, in particular, reducing the cost of installation and production of solar photovoltaic cells, as well as the modernization of wind turbine structures and the emergence of new materials. In terms of the cost, the renewable energy sources in many countries are now close to competing with new fossil-fired power plants (Figure 3) [27–29].

**Figure 2.** World wind and solar production capacity, GW.

**Figure 3.** Estimated cost of electricity on renewable energy installations, US dollars per kWh.

One of the most competitive renewable energy sources is coastal wind installations, primarily for countries where energy resources are expensive. Where fossil fuels are fairly cheap (for example, fairly low gas prices in the United States and Russia), wind turbines can compete with traditional energy sources only under favorable weather conditions and state support programs for the development of the industry. At the same time, the increased interest of many countries in environmental protection and energy security leads to the active development of renewable energy [30–34].

Nowadays, more than 160 countries of the world (82%) pursue a policy of supporting the sector, of which in 20 countries the share of renewable energy sources in the total energy balance exceeds 20%. According to the EU energy strategy, by 2030 countries should ensure a 40% reduction in greenhouse gas emissions and an increase to 32% of the share of renewable energy [35]. In the longer term, many countries will likely go significantly further. In particular, Germany plans to reach 60% of the share of renewable energy in the country's total energy balance by 2050. Figure 4 presents a generalized model of factors influencing the development of renewable energy in the world.

**Figure 4.** Factors renewable energy development in the world.

Overall, it becomes apparent that high rates of growth in production of renewable energy are largely due to significant state support. It allows in a number of countries to make renewable energy attractive, even in cases where its initial economic indicators are more than 50% worse than when using fossil fuel resources. At the same time, renewable energy is successfully developing in the world in spite of the crisis phenomena in the global economy, and, in fact, is one of the most effective ways to overcome the energy crisis.

#### **3. Analysis of the Methods and Consequences of State Support for the Development of RES**

Over the past few decades, the development of renewable energy sources in the world has achieved grea<sup>t</sup> success in increasing production efficiency and reducing equipment cost. Due to the development of new technologies, renewable energy has become capable of the cost of production to compete with traditional power generation in countries with favorable for the use of natural energy. However, in spite of all their achievements, today renewable energy technologies face grea<sup>t</sup> challenges in ensuring the required level of competitiveness due to the peculiarities of energy production and the high cost of building an installed capacity unit, which makes it impossible to develop them without state support [36,37].

Government programs to support renewable energy sources are usually aimed at solving several problems at once [38–42]:


Many countries use several RES support tools at once. At the same time, it is possible to trace the relationship between the number of tools used to support renewable energy sources and the country's income. Low-income countries use an average of 2.2 different mechanisms, while high-income countries use 4.8 [43]. Table 1 presents a summary of the use of renewable energy support mechanisms in the world.


**Table 1.** Application of state support mechanisms for renewable energy sources (RES) in the world.

In recent years, more and more political tools have been created for the development of RES. For 2018, 164 countries are already implementing state programs for the development of RES. At the same time, half of them are countries with developing or transitional economies [43,44]. Tax relief and earmarked loans are commonly used to support renewable energy. In high-income countries investment subsidies and grants are mainly used.

The dominant mechanisms for the support of renewable energy are schemes for the introduction of a special fixed (green) tariff (feed-in tariff) and netting of electricity (net metering), applied in both developing and developed countries. Today, about 66% of countries use these tools.

Net metering assumes that in windy or sundial hours the owner of an installation of renewable energy sources delivers surplus electricity to the grid, which he cannot consume himself, and during windless or night hours he consumes electricity from the grid to offset the volumes supplied earlier. If according to the results of the billing period, such a participant in energy supply consumed more electricity than he supplied, then he compensates for the difference at the usual rate. If, on the contrary, he consumes less energy than he supplied, he may use the surplus in subsequent periods or even receive payments from the power utility. When using net metering, only one meter is required, which will show the difference between the volumes of electricity taken and delivered to the grid. A feed-in tariff allows owners of a power plant for renewable energy to receive compensation for electricity supplied to the network that exceeds the retail tariff; however, as the number of owners of such installations grows, the feed-in tariff usually decreases and ultimately compares to the retail tariff. This scheme requires the installation of two separate meters.

Despite the advantages, the use of these tools to support renewable energy often leads to the emergence of cross-subsidization of consumers and generation, as well as an increase in the tariff for electricity transmission throughout the network. So for the period 2008–2016, Australia, Spain, Portugal and Germany were characterized by an exceptional increase in electricity prices (a 112% price increase) [14,15,43]. This is due to the fact that in these countries, generation and sale of electricity are competitive activities, while transmission and distribution networks are structurally separated and regulated by the monopolies. Reducing the demand for electricity from the grid and reducing the uniformity of the electricity consumption schedule due to the spread of generation to renewable energy sources led to an increase in the network tariff, which was to provide the necessary regulated revenue to grid companies and compensate them for the investments made over the past five years to develop the network infrastructure. In turn, consumers responded to the growth of tariffs by reducing consumption, which led to an increase in network tariff rates for the next year in order to provide the necessary gross revenue to grid companies and the second wave of changes in electricity demand from the grid.

The described negative processes in the energy supply system aggravated the state programs for subsidizing consumers who commissioned installations for renewable energy sources, in particular, solar photovoltaic panels, which were compensated by raising prices for power grid supply. The sharp increase in fixed-rate tariff obligations for RES (feed-in tariff) led to the third wave of network tariff increases.

Another consequence of the wrong state policy in support of renewable energy is the emergence of indirect cross-subsidization, when consumers who have not installed or cannot afford installations on renewable energy, in fact, pay the grid company for consumers who switch to renewable energy, forced to raise the network tariff, and generation company, which increases the uneven loading of power equipment and the number of hours of use of expensive peak power while reducing the amount of electricity generation [44–46].

The way out of the situation described is the development of a tari ff that stimulates the consumer to equalize the load during the day period of electricity consumption [47–49]. This suggests the use of a di fferentiated tari ff for the zones of the day, the key disadvantage of which in this situation is its static nature, when the selected zones may not correspond to a dynamically changing load schedule and, thus, instead of reducing the price of electricity for the consumer, lead to its increase. It is necessary to develop a methodology for dynamic di fferentiation of the electricity tari ff, which with the development of automated systems for commercial metering of electricity and their intellectualization becomes applicable in practice.

#### **4. Proposal for the Method of Dynamic Di** ff**erentiation of Electricity Tari** ff**s**

To solve the problem of reducing power supply costs, reducing the need for expensive peak capacity of the power system and increasing the e fficiency of using autonomous power plants for renewable energy, it is necessary to manage power consumption by encouraging consumers to use measures to level the daily consumption schedule. These measures should encourage consumers to introduce power storage equipment into the autonomous power supply system (for example, based on solar photovoltaic panels), allowing them to distribute the produced energy over a wide range of hours during the day, as well as to change their institutional behavior, which will be expressed in adapting the daily pattern of electricity consumption to a production schedule of available autonomous energy sources.

Such a measure to stimulate the optimization of power consumption and the e ffective use of autonomous sources is the formation of an economically sound tari ff according to the zonal principle. The zones of base, semi-peak and peak load on the power system are distinguished. For each zone, depending on the amount of electricity consumption, its own tari ff rate is set.

At the same time, as shown in the next part of the paper, the zonal di fferentiation of the tari ff does not always contribute to improving the e fficiency of electricity supply and may even be less beneficial for the consumer than the single-value undi fferentiated tari ff. This is due to the fixed values of the peak and half-peak tari ff rates and transition time zones, which do not correspond to the actual daily load curve of consumers. Also, a contribution is made by the existing tari ff di fferentiation in many countries in terms of amount of energy consumption, which is often poorly founded.

The development of automated systems for commercial metering of electricity along the path of using advanced (smart) electricity meters, equipped with communication tools for monitoring and data transmission on energy consumption in real time, allows us to develop pricing methods for electricity with much greater di fferentiation of time zones consumption and tari ff rates than the existing ones [50–52]. Thus, it is possible to solve the problem of inconsistency of the tari ff di fferentiation system and real daily energy consumption schedules.

The following algorithm is proposed for dynamic pricing of electricity, taking into account the fact that consumer load charts on weekends and working days di ffer significantly:

	- a. Minimum power consumption per unit of time:

$$P\_w^{\min} = \min P\_w(t) , P\_h^{\min} = \min P\_h(t)$$

b. Average power consumption per unit of time:

$$
\overline{P}\_w = \frac{\sum\_{t=0}^{n-1} P\_w(t)}{n}, \\
\overline{P}\_h = \frac{\sum\_{t=0}^{n-1} P\_h(t)}{n}.
$$

c. Half-peak power consumption per unit of time:

$$P\_w^{pp} = \overline{P}\_w + 2\sqrt{\frac{\sum\_{t=0}^{n-1} \left(P\_w(t) - \overline{P}\_w\right)^2}{n}},\\P\_h^{pp} = \overline{P}\_h + 2\sqrt{\frac{\sum\_{t=0}^{n-1} \left(P\_h(t) - \overline{P}\_h\right)^2}{n}}$$

d. Maximum power consumption per unit of time:

$$P\_w^{\max} = \max P\_w(t), P\_h^{\max} = \max P\_h(t)$$

	- a. Basic power consumption (B):

$$P\_w^B(t) \in [0, P\_w^{\min}]\_\prime\\P\_h^B(t) \in [0, P\_h^{\min}]\_\prime t = \overline{0, 23}$$

b. Half-peak power consumption (PP):

$$\begin{split} P\_{\boldsymbol{\upnu}}^{\mathrm{PP}}(t\_{\boldsymbol{\upnu}}) \in (P\_{\boldsymbol{\upnu}}^{\mathrm{min}}, P\_{\boldsymbol{\upnu}}^{\mathrm{pp}}], P\_{\boldsymbol{h}}^{\mathrm{PP}}(t\_{\boldsymbol{h}}) \in (P\_{\boldsymbol{h}}^{\mathrm{min}}, P\_{\boldsymbol{h}}^{\mathrm{pp}}), t\_{\boldsymbol{\upnu}} = \overline{t\_{P\_{\boldsymbol{\upnu}}^{\mathrm{min}}}, t\_{P\_{\boldsymbol{\upnu}}^{\mathrm{pp}}}}, t\_{\boldsymbol{h}} = \overline{t\_{P\_{\boldsymbol{h}}^{\mathrm{min}}}, t\_{P\_{\boldsymbol{h}}^{\mathrm{pp}}}}, P\_{\boldsymbol{w}}(t\_{P\_{\boldsymbol{\upnu}}^{\mathrm{min}}}) = P\_{\boldsymbol{\upnu}}^{\mathrm{min}}, \\ P\_{\boldsymbol{h}}(t\_{P\_{\boldsymbol{h}}^{\mathrm{min}}}) = P\_{\boldsymbol{h}}^{\mathrm{min}}, P\_{\boldsymbol{w}}(t\_{P\_{\boldsymbol{w}}^{\mathrm{pp}}}) = P\_{\boldsymbol{w}}^{\mathrm{pp}}, P\_{\boldsymbol{h}}(t\_{P\_{\boldsymbol{h}}^{\mathrm{pp}}}) = P\_{\boldsymbol{h}}^{\mathrm{pp}} \end{split}$$

c. Peak power consumption (P):

$$\begin{split} &P\_w^P(t\_w) \in \left(P\_w^{pp}, P\_w^{\max}\right), P\_h^P(t\_h) \in \left(P\_h^{pp}, P\_h^{\max}\right), t\_w = \overline{t\_{P\_w^{pp}}, t\_{P\_w^{\max}}}, t\_h = \overline{t\_{P\_h^{pp}}, t\_{P\_h^{\max}}}, P\_w(t\_{P\_w^{pp}}) = P\_w^{pp}, \\ &P\_h(t\_{P\_h^{pp}}) = P\_h^{pp}, P\_w(t\_{P\_w^{\max}}) = P\_w^{\max}, P\_h(t\_{P\_h^{\max}}) = P\_h^{\max} \end{split}$$

	- a. Basic power consumption (B):

$$\mathbf{C}^B(t) = \mathbf{C}^B, t = \overline{0, 23}$$

b. Half-peak power consumption (PP):

$$\mathbb{C}^{pp}(t) \in [\mathbb{C}^{pp}\_{\min}, \mathbb{C}^{pp}\_{\max}], \mathbb{C}^{pp}\_{\min} = \mathbb{C}^{pp}(P(t) = P\_{\min}), \mathbb{C}^{pp}\_{\min} > \mathbb{C}^{B}, \mathbb{C}^{pp}\_{\max} = \mathbb{C}^{pp}(P(t) = P^{pp}), t = \overline{t\_{\text{pria}}, t\_{\text{prib}}}$$

c. Peak power consumption (P):

$$\mathbb{C}^{\mathbb{P}}(t) \in \left[\mathbb{C}^{\mathbb{P}}\_{\text{min}}, \mathbb{C}^{\mathbb{P}}\_{\text{max}}\right], \mathbb{C}^{\mathbb{P}}\_{\text{min}} = \mathbb{C}^{\mathbb{P}}(P(t) = P^{\text{PP}}), \mathbb{C}^{\mathbb{P}}\_{\text{min}} > \mathbb{C}^{\text{PP}}\_{\text{max}}, \mathbb{C}^{\mathbb{P}}\_{\text{max}} = \mathbb{C}^{\text{PP}}(P(t) = P^{\text{max}}), t = \overline{t\_{P^{\text{P}}}, t\_{P^{\text{max}}}}$$

5. We believe that the increase or decrease in energy consumption proportionally reduces or increases the cost of electricity in the corresponding zone with the coe fficient *K*:

a. Half-peak power consumption (PP):

$$K^{PP} = \frac{C\_{\text{max}}^{PP} - C\_{\text{min}}^{PP}}{t\_{P^{PP}} - t\_{P^{\text{min}}}}$$

b. Peak power consumption (P):

$$K^P = \frac{C\_{\text{max}}^p - C\_{\text{min}}^p}{t\_{P^{\text{max}}} - t\_{P^{\text{PP}}}}$$

	- a. Half-peak power consumption (PP):

$$G^{PP}(t) = \frac{P^{PP}(t)}{P^{PP}(t-1)}$$

b. Peak power consumption (P):

$$G^P(t) = \frac{P^P(t)}{P^P(t-1)}$$

7. A dynamic calculation of the cost of electricity for the consumers at each time point for working days and days off are produced. In case of increase in energy consumption in the half-peak and peak zones, an additional penalty is imposed [*G*(*t*) − <sup>1</sup>]*<sup>K</sup>*. Accordingly, when energy consumption decreases in comparison with the previously registered value, the tariff rate is decreased:

$$\begin{split} TC\_{\varpi}(t) &= \begin{cases} \begin{aligned} & \mathcal{C}^{B}P\_{\varpi}(t) , P\_{\varpi}(t) = P\_{\varpi}^{\min} \\ & \mathcal{C}^{PP}(t)P\_{\varpi}(t) + [\mathcal{G}^{PP}(t) - 1]K^{\mathcal{P}} , P\_{\varpi}(t) \in (P\_{\varpi}^{\min} , P\_{\varpi}^{\textup{PP}}) \\ & \mathcal{C}^{P}(t)P\_{\varpi}(t) + [\mathcal{G}^{P}(t) - 1]K^{\mathcal{P}} , P\_{\varpi}(t) \in (P\_{\varpi}^{\textup{PP}} , P\_{\varpi}^{\textup{max}} \end{aligned} \end{cases} \end{split}$$

$$\begin{split} TC\_{h}(t) = \begin{cases} \begin{aligned} & \mathcal{C}^{B}P\_{h}(t) , P\_{h}(t) = P\_{h}^{\min} \\ & \mathcal{C}^{PP}(t)P\_{h}(t) + [\mathcal{G}^{PP}(t) - 1]K^{\mathcal{P}} , P\_{h}(t) \in (P\_{h}^{\min} , P\_{h}^{\textup{PP}}) \\ & \mathcal{C}^{P}(t)P\_{h}(t) + [\mathcal{G}^{P}(t) - 1]K^{\mathcal{P}} , P\_{h}(t) \in (P\_{h}^{\textup{PP}} , P\_{h}^{\textup{max}} \end{aligned} \end{cases}$$

8. The total cost of electricity per day is calculated, taking into account the discrete values of the indications of smart meters:

$$TC\_w^D = \sum\_{i=1}^n TC\_w(t\_i)(t\_i - t\_{i-1}), \\ TC\_h^D = \sum\_{i=1}^n TC\_h(t\_i)(t\_i - t\_{i-1})$$

The above algorithm implements the principle of dynamic pricing of electricity for the consumer, when at each moment in time it is determined in which energy consumption zone (base, half-peak, peak) the consumer load curve is found. Based on the calculation of the chain load growth rates for each energy consumption zone, a system of tariff rates is formed. The growth of energy consumption in the semi-peak zone and its coverage due to the use of energy system resources, rather than renewable energy, is punished by higher tariff rates, reduction of energy consumption is encouraged by a dynamic reduction in rates.

#### **5. Empirical Model: A Case Study of Krasnodar Region of Russia**

Our empirical model focuses on the Krasnodar region which is one of the largest regions-consumers of electricity in Russia. At the same time, it is characterized by a high level of energy deficiency. Hence, the development of territorial energy system in the region is given grea<sup>t</sup> importance [53,54].

In 2016, electricity production amounted to about 7 billion kWh with a consumption volume of 22 billion kWh. From 2006 to 2016 electricity consumption in the region increased by more than two times. The increase in consumption in the territorial energy system was mainly due to the growth of electricity consumption by industrial enterprises, the intensive development of the resort and recreation complex and the buildup of electrical loads during the construction of Olympic facilities in Sochi.

The shortage of generating capacity of the territorial energy system of the Krasnodar region was covered by the flow of electricity and power through the intersystem power lines from adjacent power systems. In January 2017, the total flow of electricity into the power system of the Krasnodar region amounted to more than 1 billion kWh, including 130 million kWh via interstate lines. In total, the Krasnodar region satisfies its electricity needs by 35% due to preferential energy production at thermal power plants [55].

Krasnodar region is one of the most attractive in Russia for the development of generation based on the use of renewable energy sources in terms of its natural and climatic characteristics. According to existing estimates, by realizing the potential of renewable energy in the Krasnodar region, it is possible to produce up to 1300 MW instead of burning hydrocarbon fuels. The most important types of renewable resources for the Krasnodar region are solar and wind energy, which account for 80% of the energy potential of renewable energy sources in the region [52,56,57].

Special attention in the region is paid to the development of distributed solar energy, which should make it possible to reduce capital expenditures on the construction of new sources of traditional energy, as well as on the construction of new kilometers of transmission lines. The potential of solar energy in the region is about 1400 kWh/m2, which corresponds to the level of countries that actively use solar energy today, such as the United States and Australia [14,28,52].

Solar energy is used primarily for the needs of households using electricity generators based on photovoltaic cells and panels [58]. Figures 5 and 6 show the averaged graphs of the daily load of a household on a typical working day and on a day o ff with a graph of energy generated by solar installations.

**Figure 5.** Household electrical load graph on a working day with a superimposed graph of electricity generated by solar installations.

As can be seen from the graphs presented, the peak of the electricity produced by solar power plants does not correlate with the time of day with the peaks of household electricity consumption, especially on working days. The significant di fference between daily power consumption and autonomous power generation of households with solar panels leads to ine fficient use of solar generation. Thus, the economic potential of individual solar installations is not fully disclosed, which leads to low efficiency of state programs to subsidize the development of solar energy.

**Figure 6.** Household electrical load graph on a day off with a superimposed graph of electricity generated by solar installations.

One of the ways to increase the efficiency of using solar installations is to use energy storage technologies, which make it possible to distribute electricity generated from solar power plants during the day (Figure 7) [38,59–61].

**Figure 7.** Household electrical load graph on a working day with a superimposed graph of electricity produced by solar installations with energy storage.

As can be seen from the graph, as a result of the use of energy storage, it becomes possible to use electricity generated from solar installations more evenly throughout the day. Consumption during peak hours is reduced by about 30% and household energy consumption at night is reduced by 80%.

The use of solar power and energy storage by households is largely related to the current tariff menu for electricity from the network in the region. Today, one-rate tariffs are applied in Kranodar region, differentiated by two and three zones of the day, as well as the volume of consumption (Table 2) [59].

According to the current tariff menu and the "dynamic" differentiated tariff proposed in the previous part of the paper, the cost of electricity per month was estimated for households consuming electricity only from the grid, households with solar panels and households with solar panels and storage using given electrical load graphs. The calculation results are shown in Table 3.



**Table 3.** Comparison of the results of calculating the cost of electricity for households according to the current tariff menu and the proposed dynamic tariff differentiation.


As can be seen from the data, the cost of electricity when applying the existing tariff differentiation scheme for two and three times zones of the day is the highest for households that do not use solar power. These results are also true for households with solar panels and storage, which suggests that the existing tariff menu does not encourage consumers to use solar energy efficiently. This is due to the discrepancy between the time zones used in the existing differentiated tariffs and the actual peak and half-peak areas of household electricity consumption, as well as the high values of household electricity consumption in the morning and evening hours. Thus, it is beneficial for the consumer to use the one-rate undifferentiated tariff regardless of the availability of renewable energy sources and the use of energy storage devices.

This problem can be solved using the proposed method of dynamic tariff differentiation. For households that do not use solar installations, the simplest one-rate tariff still remains the cheapest. However, for households that use solar installations, and the solar systems with stored energy, there are obvious advantages for a "dynamic" differential tariff. Reducing the cost of electricity for households will stimulate the population to more active and, most importantly, rational use of solar installations, will reduce the load on the energy system, as well as reduce the need for building new generating capacity of traditional energy with high capital costs.
