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 a hybrid combined heat and power system to determine the optimal capacities and operational strategies of components for minimizing the total cost, which includes investment, operation, and CO2 emission costs. Hybrid optimization algorithms, in genetic algorithm and particle swarm optimization, are employed to solve the two-layer optimization, respectively. Typical scenarios with probability distributions are generated in Monte Carlo simulations and a clustering approach, which demonstrate the influences of the uncertainties of renewable energies and electrical and thermal loads. The simulation results validate the effectiveness of the proposed optimization model. When considering the CO2 emission cost, the renewable energy penetration resulting from the larger capacities of renewable power technologies reaches 30%, which is 11.5% higher than the optimal case without considering the emission cost. This optimal integration increases the fossil energy utilization efficiency by 2.5% and the revenue from excess electricity sales by 2.7 times. The levelized capital cost, however, increases by 33.0%, and the utility grid integration and the net interaction also increase by 1.1% and 21.5%, respectively.