Dynamic price optimization of a solar integrated cogeneration system considering uncertainties of building demands

Introducing renewable and high-efficiency devices into conventional cogeneration systems has the potentials to reduce fossil fuel consumption and mitigate environmental issues. This study aims to incorporate solar power and thermal energy into the power and waste heat generated by the gas turbine, respectively, while utilizing a double-effect absorption heat pump to meet the total heat demands. Following the creation of thermodynamic models, a trading mechanism is introduced involving an operating participant known as the product seller, which is analogous to electricity companies in power trading models. To maximize the interests of the participants with different confidence levels, the master-slave game is employed, using Genetic and Quadratic programming algorithms. The hourly outputs of devices and responded demands are evaluated based on the dynamic purchasing and selling price of products. The results indicate that the optimal interest of the cogeneration system is the lowest among all the participants. The power proportions from gas turbine and power grid exceed 60 %, while the contribution of gas turbine decreases by 11.0 % due to the lower heat demands caused by reduced confidence level. Overall, this study offers valuable insights into the market-oriented development of solar assisted cogeneration systems.