Probabilistic multicriteria analyses for optimal biomass power plant design

Abstract

The design of biomass power plants is traditionally performed by using a deterministic approach. The deterministic model takes into account energetic, local and social factors to maximize the plant economic profit. When dealing with renewable energy applications, uncertainty, which involves unpredictable factors having a major influence, has recently been recognized as an important factor. In order to take into account the stochastic nature of uncertainty, probabilistic approaches have been widely applied to electric power system design and management [G.J. Anders, Probability Concepts in Electric Power Systems, Wiley-Interscience, 1990]. In this paper, a stochastic approach to optimal biomass plant design is proposed. The approach relates the plant economic index to the technological design. The paper extends the deterministic approach previously proposed in the literature [M. Fiala, G. Pellizzi, G. Riva, A model for the optimal dimensioning of biomass-fuelled electric power plants, J. Agric. Eng. Res. 67 (1997) 17–25, A. Cano, F. Jurado, Optimum location of biomass-fuelled gas turbines in an electric system, in: IEEE Power Engineering Society General Meeting 2006, 18–22 June, 2006, p. 6], and characterizes the uncertainty which concern the model by introducing random variables and probability functions. The stochastic model is formalized, the plant profitability index (PI) in an uncertain scenario is assessed. The results of the performed numerical applications are expressed in terms of probability density functions. Observing that, under circumstances characterized by uncertainty, the traditional evaluation methods, like cost-benefit analysis, can result ill-suited, a suitable tool is proposed. Thus, a stochastic multicriteria discriminat approach, able to focus on the features of the stochastic model to compare technological solutions in terms of alternative design criteria, is proposed and performed.