Feasibility study of using cogeneration plants at Kuzbass coal mines

. The paper considers the problem of reducing greenhouse gas emissions in the process of coal mining during the coal mine methane utilization in power supply systems. An algorithm to form recommendations for the implementation of CMM generation is presented. A simulation model for one of the Kuzbass coal mines was developed in the PowerFactory software application. The simulation model considers the uneven nature of the power consumption of mining equipment. As a result of modeling, daily power consumption profiles and voltage levels in the coal mine power supply system were determined before and after the implementation of the proposed measures. Based on the results, the technical and economic effects was estimated, which consisted in reducing the direct and indirect carbon footprint, electricity and capacity fees. It has been established that the cost of carbon dioxide emission quotas significantly affects the investment attractiveness of cogeneration projects. Based on the results, recommendations are given to stimulate the development of small generation in coal mines.

Introduction. According to the Russian Coal Industry Development Program to 2035, the competitiveness of Russian coal companies will increase due to the technological development of the coal industry: modernization and technical re-equipment; implementation by coal companies of high-performance technologies, digital technologies in the processes of coal mining and processing. In accordance with the Order of the Government of the Russian Federation N 1144-r dated by 11th of May 2022, large investments are aimed at creating a set of technologies that increase the efficiency of coal mining and processing. Serious challenge for the development strategy of the coal industry in Russia is the need to "decarbonize coal mining" to compensate for the negative effects of greenhouse gas emissions into the environment. "Greening of national energy consumption" is one of the possible solutions aimed at reducing the carbon footprint of finished products by increasing the share of "clean" energy sources in the energy balance of a coal mine.
In accordance with the Strategy of social and economic development of the Russian Federation with the low level of greenhouse gas emissions until 2050  among the measures aimed at implementing the strategy, the following are noted: development of distributed generation; creation of additional methane-air mixture extracted by underground degassing wells has been proved. The work [29] is devoted to the issues of using CMM to provide electricity and heat supply to a data processing center created on the basis of a coal mine infrastructure. There are also studies [3,30], aimed at improving the efficiency of using CMM with an efficiency close to 95 % when using gas piston units operating on a cogeneration or trigeneration cycle. The publication [31] considers the idea of joint use of CMM and biogas. It is shown that in this case the payback period will be about two years. The utilization of ventilation methane with a concentration of less than 3.5 % [32,33] is very promising (in China, its utilization can produce up to 2610 GW•h/year [34]).
Nevertheless, the issue of introducing CMM utilization in the conditions of Kuzbass coal mines has not been sufficiently studied and does not allow us to draw conclusions about the advisability of introducing cogeneration at the stages of construction and re-equipment of coal mines.
The efficiency of power generation based on CMM and its inclusion in the power supply system of a coal mine are evaluated using a simulation model developed in the DIgSILENT PowerFactory software application.
Methodology. The feasibility study of introducing distributed generation on the CMM was carried out according to the algorithm (Fig.1), which includes the following steps: • Data acquisition and processing. At this stage, interaction with the enterprise is carried out, the following data is requested: equipment parameters; electrical load and generation profiles (in the presence of distributed energy facilities); information about the technological process (if it needs to be considered when forming recommendations); data on tariffs for electricity (contract on electricity supply); power supply scheme. The data is processed, the equivalent circuit parameters are calculated, which are used when creating a model in PowerFactory.
• Selection of gas-air mixture (GAM) preparation equipment and generating equipment. The composition of the generating equipment is selected based on information about the characteristics of the methane-air mixture transported by the coal mine degassing system. At CMM low concentration (less than 3.5 %), it is necessary to consider the installation of equipment for CMM purification and enrichment using adsorption and membrane technologies.
• Creation of a simulation model in PowerFactory using the received data. To generate a consumption profile for a shearer and a scraper conveyor, we used the results from studies [35,36]. Finally, the model is verified using actual measurements of load current and voltage levels.
• Feasibility study. Two software modules have been developed: for the electricity cost calculation and the financial and economic model construction. The feasibility study also considers the reduction of the carbon footprint in the process of CMM utilization  (Fig.2) and the reduction of electricity consumption  .   • Report preparation. The report contains information on the issue of the proposed recommendations effectiveness.
Results and discussion. The considered mine operates around the clock. The power supply system of the considered coal mine includes the following power facilities: power transformers (transformers of main step-down substation (2  16 MVA), underground mine substations (1250-3150 kVA); high-voltage and low-voltage cable network; shearer (cutting drive 2  825 kW and combine feed 2  150 kW); crusher (crusher drive 1  400 kW); reloader (reloader drive 1  400 kW); belt conveyor (belt conveyor drive 3  500 kW); generalized load (underground mine substations, auxiliary power, ventilation, heater); reactive power compensation devices placed on the surface.
Estimation of CMM generation. CMM power generation depends on the potential amount of energy contained in cubic meter of the GAM, which ventilates from the mine. Indicative methane capture level is about 16.3 m 3 /min, methane concentration -44 %.
The calorific value of the GAM can be obtained with the use of D.I.Mendeleev's formula (1) where LCVi -lower heating value of methane, MJ/m 3 ; ai -share of the i-gas in the GAM; Pg -useful heat which is equivalent to electric power generation, kW; R -gas yield, m 3 ; 0.28 -percentage of energy contained in methane that can be converted into effective power. Data on the scope of GAM issuance and methane concentrations (Fig.3) used to estimate electricity generation are shown in Fig.3.
To transfer GAM consumption into the mass of the evolved gas, the formula is used where V -GAM flow rate, m 3 /min; ρ -GAM density, kg/m 3 ; s -GAM concentration, %. Based on the calculated CMM generation profile, it can be concluded that the average energy production  for CMM will not exceed 2.4-2.6 MW. Based on the nature of the fuel (natural gas) and the operation mode which implies the loading of generating equipment within 30-80 % in 95 % operating time, a gas-piston power plant of the block-container type AG-3000S-T400-3RN was chosen. The AG-3000 includes 10 gas piston units (GPUs) with a rated capacity of 315 kW for a rated voltage of 0.4 kV. The methane-air mixture is supplied through the main pipeline to 10 generator sets. In the simulation model, a gas-piston power plant is implemented using two GPU models, the parameters of which are equal to the parameters of five parallel GPUs. Two step-up transformers 6/0.4 kV are used to output GPUs' power.
At the modelling stage, only the normal mode was considered (Fig.4). In this case, the calculations were performed for each hour based on a real schedule of electrical loads for one of the Kuzbass coal mines (Fig.5).
Results of the implementation of the CMM utilization system: maximum load of substation transformers is reduced by 9.51 % (from 69.23 to 59.72 %); minimum voltage level at underground power distribution station N 5 is increased by 0.83 % (from 6.12 to 6.17 kV).
Electrical energy cost calculation. Electrical energy and power costs are calculated for the 4th price category based on estimated daily demand profiles, considering the medium voltage level  and maximum load power being 10 MW at least. Tariffs are taken, according to PAO "Kuzbassenergosbyt". Electrical power cost is calculated, considering peak, and planned peak load hours, according to AO "ATS" and AO "SO UPS", respectively. A power and energy cost calculation module is developed, based on the data mentioned above. The option to sell the power to the external power grid is not considered because the power demand of the coal mine is always higher than its CMM generation.
Carbon footprint estimation. CO2 emissions from stationary fuel combustion are calculated using default methods, based on different sources, sources groups, or organization in total: where j -fuel type, used in combustion; n -amount of fuel type, used within a period (in this paper only methane is considered -n = 1); FCj year -j-fuel consumption over a year, t (thousand m 3 , tons of conventional fuel or TJ); EFCO 2 j year -CO2 emissions coefficient of j-fuel combusting over a year, which is determined by methane concentration, carbon dioxide density and moles of carbon, created after combustion, t; OFj year -j-fuel oxidation factor (taken equal 1 for gaseous fuels). Indirect carbon footprint is estimated according to a carbon dioxide emission, which is presented on the ATS website [38], E(CO 2 j year) ind = Pcons j E(CO 2 j) plan, where Pcons j -power, consumed from the external grid over an hour j; E(CO 2 j) plan -planned CO2 emissions coefficient, t/MW• h. The effect of CMM utilization is calculated by the following expression: where ECO 2 year -emissions level after applying CMM generation, t; E(CO 2 year) util -emissions level before applying CMM generation, t. Quota payment reduction is estimated by the following expression: where ETSyear -greenhouse gases emission payment; PETS CO 2 -CO2 emissions payment (16 rub./t); PETS CH 4 -CH4 emissions payment (108 rub./t) * .
In quota payment reduction calculation, it is necessary to consider that, while CMM utilization, the GPU creates 2.75 kg of CO2 for 1 kg of methane [39].   (1) and with CMM generation (2) Pg, Pl -electrical energy, generated and consumed from the external grid Economic profit estimation. In terms of income, the economic model considers the following: electrical energy payment reduction, emissions payment reduction, and heating cost reduction due to generation of GPU thermal power (estimated, based on coal buying cost, which combustion power is equal to heating power of the GPU). Heat outcome over a year is 12971.7 Gcal. In terms of expenses, the economic model considers the following: capital investments for buying GPUs and operational costs, which include regular maintenance, oil replacement, and major overhaul. In total, capital investments, incomes, and operating expenses are 165750; 52489; 13282 thousand rub.
A result of the net present value (NPV) calculation is a payback period of five years, considering the discount rate as 7.5 % (taken, based on the author's forecast of refinancing rate at the end of the 2022). A detailed list of technical and economic indicators is shown in the Table 1 and 2, respectively. For the calculation of electrical power cost the following tariffs are taken: power capacity -804110.8 rub./MW·month; services for energy transmission -794057.7 rub./MW·month. On Fig.6 NPV dynamic is shown for the different values of CO2 emissions tariffs. Data shows that CO2 emission tariff affects a project's investment attractiveness greatly: the current tariff gives a payback period of 5.2 years; in the case of emissions trading mechanism integrating (analogically to EU countries) payback period could be lowered to 1.2 years.