Stochastic Aproach to Economical Analysis of Biomass Power Plants

The global community is motivated to move towards renewable energy supply and market based operation in power systems. In the core of this change is an aspiration of the reduced the influence of power generation on the environment and the increased power supply efficiency, which is achieved through market conditions and free competition. These new conditions define significant changes in the design and planning of the operation of the biomass power plant (BPP). First step in the power plant development project is pre-feasibility study. As a result of study, a developer would choose a place of construction, fuel type, the power grid connection scheme, would estimate the necessary capital expenses, the produced energy, production costs and profits from the energy sales. Economic analysis is an important part of the pre-feasibility study of the power plant (PP). Usually, PP design is evaluated in terms of the profit maximization and the following economic criteria, e.g. NPV, IRR, payback time. The introduction of the markets in power systems substantially changes the approach to economic analysis of the PP profitability. Consequently, the power producer’s energy prices and revenues are subject to significant fluctuations. The criteria for profit maximization for the stakeholders shall be reformulated. The uncertain and random parameters, as the ambient temperature, thermal energy consumption, price of energy has to be taken into account. The problem becomes much more complicated and requires new algorithms and software tools to solve it. This paper proposes a procedure to estimate the average value of these criteria taking into account random nature of the variables and using the historic values of parameters. Besides, we use neural network based algorithm to predict processes in future from the historical data. A case study is conducted on a realistic project of the cogeneration BPP (2 MW) and demonstrates the advantages of the stochastic approach. It is concluded that the proposed approach to profitability analysis, developed algorithms, the data capture from Internet, enhanced user friendly interface shall support and enable improved decision making