A simulation method (SM), linear programming method (LPM), project evaluation methods (PEMs), and whole farm modeling (WFM) were applied to analyze the investment appeal of a biogas project on a Russian farm. The biogas project was evaluated for constant input parameters. The project efficiency evaluation procedure was elaborated to evaluate and maximize biogas investment project efficiency. The procedure to evaluate the project efficiency includes defining the optimal state of the farm for the situations “with project” and “without project.” The main elements for optimization are the equipment for anaerobic digestion, substrate blend structure, fertilizing plan, cost plan, and farm production structure. The optimization was fulfilled by simulation modeling (SM) and LPM. The situations “with project” and “without project” were compared by using PEMs, the main indicators of project efficiency: net present value (NPV), internal rate of return (IRR), payback period (PBP), and profitability index (PI). The optimal substrate blend structure was defined by the direct search method (DSM) to select the probe providing the highest NPV afterward. The procedure to maximize biogas project efficiency was applied to justify the benefits of biogas production on the farm under corresponding conditions and to work out the recommendations for businesses and municipalities.

The purpose of this research is to evaluate the biogas potential of agriculture in the typical Russian region. The design of this study was completed using the main kinds of agricultural production of the Tambov region as the feedstocks for biogas production. Average amounts of the feedstocks were calculated on the base of data for the period 2009–2018. The quantities and revenues of electricity, heat, and biofertilizers from biogas produced from various substrates were estimated and mapped for each of the twenty-three municipal districts of the region. Results revealed an average total monetary biogas potential of 88.52 × 109 RUB for the Tambov region per year, where 75.43% are provided by electricity and heat energy and the remaining 24.57%—by biofertilizers, therefore, biogas potential of the Tambov region is comparable with biogas potential of a European country. Such feedstocks as sunflower silage, cereal grain, and cereal straw were defined as the most attractive substrates in the region. At the same time, the most of feedstocks being the main farmers’ commodity production are debatable to be used as substrates; as for Russian farmers, biogas production is a new and not well-known technology. Nevertheless, the developed calculation method can now be applied by local authorities of the Tambov region and other regions of the Russian Federation as the base to develop the biogas sector in the most promising areas by supporting farmers and business structures and attracting investments in biogas technology.

The dynamic model of the biogas project was created with changing parameter values over time and compared to the static model of the same project based on constant values of the same parameters. For the dynamic model, the same methods were used to evaluate the biogas project as for the static model to calculate substrate mix volumes, costs, farm production volumes, number of biogas plant equipment, driers, and other numerical characteristics of the farm. Project risks were evaluated by the sensitivity analysis and Monte Carlo simulation. The study was conducted for four scenarios regarding the substrate mix structure and the possibility of selling electricity on the market. In the scenarios, the scale of the project was determined by the size and structure of agricultural and biogas production. The results have shown that when only wastes are used as substrates, net present values (NPVs) of the project are equal to 29.45 and 56.50 M RUB in dependence on the possibility to sell electricity on the market. At the same time, when the substrate mix is diversified, the project NPVs are equal to 89.17 and 186.68 M RUB depending on the ability to sell all the produced electricity to the common power grid. The results of the sensitivity analysis defined that the values of elasticity coefficients are less than 3.14%. Results of the Monte Carlo simulation have shown a probability distribution of positive NPVs for each scenario. This study was conducted to make recommendations for business and municipalities.