Energy hub-based optimal planning for integrated energy systems considering part-load characteristics and synergistic effect of equipment

Integrated energy systems (IESs) represent a promising energy supply model within the energy internet. However, multi-energy flow coupling in the optimal configuration of IES results in a series of simplifications in the preliminary planning, affecting the cost, efficiency, and environmental performance of IES. A novel optimal planning method that considers the part-load characteristics and spatio-temporal synergistic effects of IES components is proposed to enable a rational design of the structure and size of IES. An extended energy hub model is introduced based on the "node of energy hub" concept by decomposing the IES into different types of energy equipment. Subsequently, a planning method is applied as a two-level optimization framework-the upper level is used to identify the type and size of the component, while the bottom level is used to optimize the operation strategy based on a typical day analysis method. The planning problem is solved using a two-stage evolutionary algorithm, combing the multiple-mutations adaptive genetic algorithm with an interior point optimization solver, to minimize the lifetime cost of the IES. Finally, the feasibility of the proposed planning method is demonstrated using a case study. The life cycle costs of the IES with and without consideration of the part-load characteristics of the components were $4.26 million and $4.15 million, respectively, in the case study. Moreover, ignoring the variation in component characteristics in the design stage resulted in an additional 11.57% expenditure due to an energy efficiency reduction under the off-design conditions.