Markov and Monte Carlo Simulation of Waste-to-Energy Power Plants Considering Variable Fuel Analysis and Failure Rates

In the present work, a method has been developed to estimate the annual capacity factor of waste to energy power plants. The case study is an under-construction power plant located in an area of high precipitation rate. Existing data of the waste analysis in the region shows that its content, including the mass fractions of the paper, food, and plastic, changes daily and seasonally. The variation of the waste analysis in addition to components’ failure and the off-design performance of the steam cycle make the behavior of the system highly stochastic. To deal with this stochastic nature, the probability distribution function (PDF) of waste analysis is constructed to consider a range of possible inputs. Moreover, time-varying failure rates are used in Markov analysis to calculate the system’s availability by considering infant failures, random failures, and aging. Also, the off-design performance of the power plant is simulated by thermoflex to capture nonlinearities caused by steam flow reduction in the steam turbine. The PDFs of the municipal solid waste (MSW) physical analysis are estimated by sampling from a pit near the location. The Monte Carlo simulation has been used to couple the stochastic nature of the MSW content to the simulation and operation of the power plant, modeled by thermoflex. The results show 2300 kW for power generation in full load condition in the first year, highlighting 30% power derate due to municipal solid waste quality reduction.