jailcd2020072: Optimal Design of CCHP System under Energy Demand Uncertainty: A Two-Stage Stochastic Programming Approach

Abstract

Uncertainty causes significant complexity and inaccuracy to the design process of Combined Cooling Heating and Power (CCHP) system. Therefore, it is important to consider the uncertainty in the design process of CCHP. In this paper, the CCHP optimization model under uncertainty is presented as a two-stage stochastic mixed integer linear programming model. In terms of uncertain parameters, energy demand, including cooling demand, heating demand and electricity demand, is considered and probabilistic scenarios are used to describe the uncertainty of energy demand in this paper. To generate probabilistic energy demand scenarios, the Latin Hypercube Sampling (LHS) method was employed, and feature-based k-medoids clustering algorithm was applied for scenario reduction. The model takes the annual total cost as the optimization objective and seeks the configuration portfolio with the lowest cost in all scenarios. To illustrate the application of proposed model the design of a region-scale CCHP system is investigated. The output of the model is compared with that obtained from deterministic optimization model. The comparison indicated that the optimal annual total cost of stochastic model is higher than the deterministic model's estimation. Additionally, the optimal CCHP configurations of stochastic model deviate significantly from the deterministic design.