Improving Energy Efficiency by Household Consumers in the Republic of Tajikistan Based on the Developed Forecasting Method

The proposed methods of forecasting power consumption in the works of many authors [14, 15] are not fully applicable in the conditions of the RT terrain.

Therefore, in this paper, the task is to offer a method that allows you to predict power consumption by household consumers, taking into account the above features and factors.

To offer a method that allows predicting power consumption by household consumers of the Republic of Tajikistan, it is necessary to assess the dynamics of power consumption in previous years.

This dynamic of power consumption can be built on the data of the readings of electricity metering devices installed in household consumers.

To assess the specific electricity consumption, taking into account the above, according to the monthly readings of electricity metering from consumers of the household sector, received from OJSC "City Electric Networks" and OJSC "Pamir Energy", electricity consumption charts were built by months for 2017 climatic factors and territorial location, as well as the provision of consumers in the household sector with heat supply, temporary norms of specific power consumption were identified.

For comparison, the most typical consumers of the household sector in Dushanbe, receiving power from the Aviator substations, and the cities of the Gorno-Badakhshan Autonomous Region of Khorog and Rushon, were taken, which made it possible to establish the compliance of the monthly electricity consumption with the norms of specific power consumption or exceeding them.

Figures 1, 2 show the dependences of the average monthly electricity consumption for groups of household consumers with standard projects for the cities of Dushanbe and Khorog of the Republic of Tajikistan for 2017.

From the obtained annual power consumption graphs for household consumers, Figure 1, 2 for 2 cities of the Republic of Tajikistan, it can be seen that the electricity consumption by household consumers is different for each city. Electricity consumption is influenced by both their territorial location and landscaping. Thus, to establish the dependence of power consumption on climatic-meteorological and recommendation of power consumption norms, it is necessary to build a mathematical model taking into account the terrain conditions of the Republic of Tajikistan. This model should allow the establishment of electricity consumption standards that do not go beyond the permitted power output by city and district power grids.

Based on the data from electricity metering devices for the considered household consumers (see Figures 1, 2), the dependences of the total average monthly power consumption of all household consumers are plotted, which receives power from the Aviator 110/10 kV substation in Dushanbe and for one of the substations in Khorog with different climates -meteorological conditions of the terrain and the geographical location of the object of the Republic of Tajikistan Figures 3, 4.

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Figure 1. Average monthly electricity consumption by residential consumers with a typical design receiving power from the Aviator substation in Dushanbe (RT) for 2017

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Figure 2. Average monthly electricity consumption by household consumers with a typical project in Khorog (RT) for 2017

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Figure 3. The total average monthly power consumption of all household consumers for one of the substations in Khorog

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Figure 4. The total average monthly power consumption of all household consumers, powered by the 110/10 kV Aviator substation in Dushanbe

To establish the influence of peculiarities of the terrain conditions of the RT on energy consumption, annual schedules of electricity consumption by household consumers was built for two characteristic conditions of the area of the cities of the RT, namely in the city of Khorog with the location at an altitude of 2123 m. above sea level and Dushanbe at an altitude of 706 m. above sea level (see Figures 3, 4).

When developing methods for predicting power consumption by household consumers, taking into account the climate-meteorological features of the terrain conditions and the geographical location of the consumers in question, the results of comparison with the results shown in Figures 3, 4.

For another proof, the fact that the real data of power consumption, depending on the specific electrical load, does not correspond to the real conditions developed in Soviet times and factors influencing these conditions [16-18].

Based on the results shown in Figures 1-4, we present the results of comparing the standard values of the specific load [19, 20] corresponding to the number of household consumers. These dependencies, presented in the form of histograms (Figures 5, 6), allow showing a real picture of the compliance of regulatory data with the measurement results, which, in turn, will allow us to assess the degree of energy efficiency of the considered household consumers and assume their influence on the operating parameters of urban electrical networks 10 / 0.4 kV.

From the results of the comparison of Figures 5, 6 it can be seen that due to the increased energy consumption of the considered consumers in Dushanbe during the evening winter maximum load, the exceeded value of the exceeded standardized value, and in summer there is no excess. For the city of Khorog during the hours of winter evening maximum loads, the measured values are 2 times higher than the normalized value, and in summer there is no excess.

Based on a comparison of the results obtained in Figures 5, 6 of the results of tests carried out to improve the energy efficiency of urban networks of the considered urban networks, it is necessary to propose a new method of comparison without violating the normative values of load balancing due to optimization of power consumption. At the same time, electrical networks consume all external energy consumption [16-18].

The proposed following method for increasing the energy efficiency of the considered electrical networks, taking into account all the stated factors.

W_{power consumption forecasting}=W_{temp.  pow.  consum.  stand.}∙(1-α_{terrain conditions})         (1)

where, W_{temporary power consumption standards} =P_allowed.∙t_{time of the month}, kW / h., $P_{\text {allowed.}} \cdot t_{\text {time of the month}}$-   installed permitted capacity of Barki Tajik, 4-5 kW, $t_{\text {time of the month}}$-   time in a month, h.

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Figure 5. Comparison of the measurement results with the normative considered types of high-quality household consumers in Dushanbe

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Figure 6. Comparison of measurement results with standard data corresponding to the considered number of household consumers in Khorog

The coefficient that characterizes the terrain conditions ($\alpha_{\text {terrain conditions}}\quad$)   is determined by the expression:

$\alpha_{\text {terrain conditions}}=\frac{\left(t_{\text {av. mon. am. tem.}}\quad\quad+ t_{\text {tem.diff.}}\quad\right)}{\left(t_{\text {av. mon. am. tem. }}\quad\quad+ t_{\text {av.mon. add. tem.}} \quad\quad\right.)}$，      (2)

From expressions (2) $t_{\text {av.mon. add. tem.}}$   is determined by the following method:

$t_{\text {average incremental.}}\begin{array}{l}\quad=t_{a v . \text { mon. am. tem.}} \quad\pm t_{\text {int. sur. temp.}} \quad\pm t_{\text {ind. temp}}\end{array}$           (3)

where, t_{av. mon. am. tem.} - average monthly ambient temperature, ℃. This value corresponds to the data obtained from the hydrometeorological station for the forecast year and the territorial locations of the considered cities of the Republic of Tajikistan above sea level;

t_{tem. diff.} - temperature changes in different points of the location of the cities under consideration for which it is planned to predict monthly power consumption, ℃;

t_{av. mon. am. tem.} - data on changes in the average monthly temperature at 0 points above sea level in relation to the forecast point of power consumption of the corresponding month, ℃;

t_{av. mon. add. tem.} - average monthly temperature difference inside and outside the room, ℃. (This temperature indicator allows you to estimate the inside room temperature by changing the outside temperature for the months in question).

t_{int. sur. temp.} - the temperature of the inner surfaces of the walls of the construction of houses [21], ℃;

t_{ind. temp.}- the temperature inside the room (apartment), ℃, a plus t_{ind. temp.} at minus the average monthly ambient temperature, that is, in order to achieve the optimum room temperature +22℃ [21], it is necessary to increase the power of the electric heater taking into account the factor in our in the case of lack of heating, minus t_{ind. temp.} at a positive average monthly ambient temperature in order to achieve the optimum room temperature of +22℃, it is necessary to cool the premises using an air conditioner.

Taking into account the proposed method for increasing energy efficiency and the coefficient characterizing the conditions of the area, the equations for determining the average electrical load are proposed:

$P_{\text {Average electrical load}}\begin{array}{l}\quad=\frac{W_{\text {power consumption forecasting}}}{t_{\text {maximum load time}}} \cdot \alpha_{\text {terrain conditions}}\end{array}$           (4)