A two-stage stochastic-robust optimization for a hybrid renewable energy CCHP system considering multiple scenario-interval uncertainties

The optimal device capacity can impose the hybrid combined cooling, heating and power (CCHP) system integrated with renewable energy on both the significant energy, economy, and environment benefits and the low dependence on the grid. However, the multiple uncertainties pose enormous challenges in the modeling framework and corresponding solving strategy. This paper proposes a two-stage multi-objective stochastic-robust hybrid optimization model for the optimal device capacity of a hybrid CCHP system. The multiple uncertainties are incorporated into this model, and they are modeled by integrating the stochastic hierarchy scenario-generation method into the robust box uncertainty set. The problems of multi-objective optimization and multi-stage coupling are handled by the ε-constraint method and column constraint generation, respectively. Eventually, the vital parameter influence of this model is analyzed by an industrial park case. The results show that the befitting scenario number can make scenario-based stochastic optimization save 97.71% of computation time and ensure the reliability of the optimized results. Moreover, the uncertain budget value significantly influences the successive device capacity while hardly impacting discrete device numbers. Besides, the installed capacities of the wind turbine, photovoltaic system and electrical energy storage considerably determine the economic performance of the hybrid system.