A multi-objective approach to determine time series aggregation strategies for optimal design of multi-energy systems

Abstract

The optimal topology and operation of multi-energy systems (MES) can be determined by mathematical programming models, which are commonly difficult to solve due to the complex structures of the models and large-scale time-series data. The time series aggregation (TSA) strategies are usually adopted to reduce the complication of the data input and thus improve the solution efficiency. Nevertheless, for the optimal design of MES, significant differences exist in the structures and performances of the systems resulting from different TSA strategies. It is crucially important to determine appropriate TSA strategies in the optimal design of MES. In this work, a two-step method was proposed to determine TSA strategies based on a multi-objective optimization framework. In the first step, the preliminary design for the multi-energy system was obtained by different TSA strategies for the optimal design and operation of MES. In the second step, the total annual cost and loss of load probability (LSLP) of the system were taken as two objectives, and a multi-objective optimization approach was established to determine the appropriate TSA strategy for the optimal design of the MES, where the structure and performance of the system were considered simultaneously. The implementation and effectiveness of the proposed method were demonstrated by the optimal design of an MES with energy storage. The deviations of the design scheme and system performance obtained by different TSA strategies were comprehensively compared and analyzed. The results indicate that the feasibility of the optimal design schemes of the MES can be verified by using the original full-time series data, and the deviation of the results obtained by different TSA strategies can be quantified. The reduction of LSLP is at the expense of the increase of the costs. In the multiple time grids strategy (TG1) with the number of time steps (TS) 730, the TAC of the system increases from $9.79 × 107 to $1.56 × 108 when the LSLP of the system decreases from 7.90% to zero. The determination of the strategy based on the result of the minimum LSLP is critical to meet the demand in the energy system. The appropriate TSA strategies for the optimal design of MES can be selected and determined in compliance with specific requirements.