Model of risk analysis on site selection of biomass power plant based on stochastic robust interval method

Abstract: The transport cost of biomass fuels accounts for a large proportion of the total cost of the operation of biomass power plant. Optimizing biomass power plant site can largely mitigate the transport cost and reduce the pollutant emissions from the transportation process of biomass fuels. Therefore, it is significant to optimize the biomass power plant sit. However, the biomass power system contains many uncertainties, because that many parameters can hardly be acquired as deterministic values but expressed as interval and/or stochastic formats. For example, the supply demand of biomass fuels can be expressed as probability distributions; also, interval values can describe the uncertain parameters such as the biomass fuels price, which fluctuates between lower and upper bounds. Energy systems would become insecurity and with a high risk without considering these uncertainties. Security is a priority in the operation of biomass power plant. In this study, a stochastic robust interval model (SRIM) was developed for the biomass power plant site selection under uncertainties, through incorporating interval-parameter programming (IPP) and robust optimization (RO) within two-stage programming (TSP) framework. In SRIM, decision variables were divided into two subsets: those that must be determined before the realizations of random variables were known, and those that were determined after the realized random variables were available. The SRIM can deal with the uncertainties described in the terms of the interval values and probability distributions, moreover, it can also reflect economic penalties as corrective measures or recourse against any infeasibilities arising due to a particular realization of an uncertain event. In the SRIM modeling formulation, penalties were exercised with the recourse against any infeasibility, and robustness measures were introduced to examine the variability of the second stage costs that were above the expected levels. The SRIM was generally suitable for risk-aversive planners under high-variability conditions. The SRIM method was applied to a hypothetical case of planning biomass power plant (with installed capacity of 15 MW) site selection with considering the uncertainties. A number of solutions under different robustness levels have been generated. The obtained results can help generate desired decision alternatives that will be able to enhance the safety of biomass power system with a low system-failure risk level and particularly useful for risk-aversive decision makers in handling high-variability conditions. The result are beneficial for managers analyzing the results to gain insights into the tradeoff between system's safety and economic, and analyzing the risk of the system. The results of SRIM shows: 1) The construction number of biomass power plant is one; 2) The optimum biomass power plant is from (245, 242) km to (250, 247) km; 3) The optimum allocation scheme for each fuel purchase and storage station; 4) The system costs under different robust levels; 5) The notion of risk in stochastic programming under different robust levels. The modeling results from the RISO can help generate desired decision alternatives that will be able to not only enhance the safety of planning biomass power plant site selection with a low system-failure risk level, but also mitigate pollutant emissions from the transportation process of biomass fuels. The results are reasonable, and could provide a reference for the selection of the biomass power plant site.