A two-layer optimization of design and operational management of a hybrid combined heat and power system

This article proposes a two-layer collaborative stochastic optimization model of hybrid combined heat and power system to determine the optimal capacities operational strategies components for minimizing total cost, which includes investment, operation, CO 2 emission costs. Hybrid algorithms, in genetic algorithm particle swarm optimization, are employed solve respectively. Typical scenarios with probability distributions generated Monte Carlo simulations clustering approach, demonstrate influences uncertainties renewable energies electrical thermal loads. The simulation results validate effectiveness proposed model. When considering energy penetration resulting from larger technologies reaches 30%, is 11.5% higher than case without cost. integration increases fossil utilization efficiency by 2.5% revenue excess electricity sales 2.7 times. levelized capital however, 33.0%, utility grid net interaction also increase 1.1% 21.5%,