Exergoeconomic analysis for a thermoelectric generator using mutation particle swarm optimization (M-PSO)
TEG; Multi-objective optimization; Epsilon-constraint; TOPSIS
Efficiency and cost-effectiveness play dominant roles in the commercialization of thermoelectric generator (TEG) technology. In this paper, the exergy analysis of a TEG module with 199 TE couples was considered. Two objective functions, the exergy efficiency and levelized cost of energy (LCOE), were established for exergoeconomic analysis. The geometric structure and working conditions involving TE couple length, base area ratio, working temperature, and load resistance were varied. The particle swarm optimization (PSO) method has excellent convergence and few parameters need to be adjusted. Mutation can increase randomization for the PSO method, making it possible to improve its search direction. Therefore, the mutation-PSO (M-PSO) algorithm was used to optimize the exergy efficiency and LCOE for the TEG. Through the M-PSO algorithm, the optimum corresponds to an exergy efficiency of 29% and LCOE of 1.93 $US/kWh⋅m2 under a maximum temperature difference of 40 K. In order to achieve a balance between the two exergoeconomic indices, the ξ-constraint combined with the M-PSO method was used to obtain alternatives, named Pareto solutions. Then, these alternatives were ranked to acquire an ideal solution based on a technique for order preference by similarity ideal solution (TOPSIS) method. The TOPSIS ideal solution corresponds to an exergy efficiency of 22.2% and LCOE of 3.02 $US/kWh⋅m2.
Wang, Xi; Henshaw, Paul; and Ting, David S-K. (2021). Exergoeconomic analysis for a thermoelectric generator using mutation particle swarm optimization (M-PSO). Applied Energy, 294 (15).