Carbon emission flow oriented multitasking multi‐objective optimization of electricity‐hydrogen integrated energy system

Abstract

Abstract A grid‐connected electricity‐hydrogen integrated energy system (EH‐IES) is proposed here for the cooperative analysis of electricity, heat, cooling and hydrogen energy flows, where carbon emission flow (CEF) is introduced to allocate the carbon emission. Simultaneously, an improvement multitasking multi‐objective optimization (MT‐MOO) algorithm is applied to optimize the operation of this EH‐IES. The proposed EH‐IES optimizes the operational cost, carbon dioxide emission and energy loss while considering the uncertainties of future energy demand, wind and photovoltaic (PV) power outputs. The CEF model is introduced here to allocate the carbon emission among the EH‐IES, which can calculate the carbon emission in the energy flow. In order to solve the MOO model, a MT‐MOO algorithm is proposed to utilize the implicit information of different optimization tasks. However, there are harmful interactions between the different optimization tasks when the relativity between different optimization tasks is low. Therefore, the paper introduces an online learning random mating probability method which calculates the transformation extent of implicit information online between different optimization tasks. Simulation results show that the proposed EH‐IES has good feasibility and efficiency, and the proposed algorithm has better convergence performance than comparing intelligence algorithms.