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

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.

Reinforcement learning based two‐timescale energy management for energy hub

“…With the growing energy crisis and pollution, a new economic, low-carbon energy system is urgently needed, and integrated energy system (IES) that incorporate multiple energy sources, including renewable energy, are seen as a promising solution [1]. As shown in Figure 1, structurally, the IES consists of interconnected energy hubs (EH) as core components, the EH can achieve complementary conversion and storage of multiple energy sources through scheduling algorithms for energy management.…”

“…However, in terms of the operating characteristics of the equipment within EH, the heating and cooling equipment have slow response time, frequent adjustment of equipment such as micro gas turbines and absorption chillers can easily cause damage, reduce lifespan [3]. Implementing real-time regulation for them is FIGURE 1 The EH-based integrated energy system. challenging, while the opposite is true for electric energy storage (EST), so a conventional single timescale energy management strategy is no longer sufficient to take into account the operating characteristics of all equipment, two-timescale energy management is necessary.…”

Multi‐objective optimal planning of a residential energy hub based on multi‐objective particle swarm optimization algorithm

“…Implementing this framework would result in low carbon emissions, low operational costs, and improved renewable energy consumption. A multi-objective (MO) optimization problem is developed in [18] that obtains the optimal operation of an energy hub by simultaneously optimizing operation costs, carbon emission, and energy losses in the integrated energy system. A novel framework for obtaining optimal operation of interconnected energy hubs is developed in [19] that benefits from the flexibility provision of such interconnected energy systems to tackle power network congestion issues.…”

Optimal operation of multiple integrated energy systems based on a hybrid Taguchi‐compact salp swarm algorithm

“…How to improve energy efficiency and promote energy structure transformation has been widely concerned by the society [3,4]. Integrated energy system (IES) is a multienergy network that combines electricity, heat, cold, natural gas and other renewable energy, which achieves the advantage of multi-energy complementation and collaboration [5,6]. Therefore, it is significant to study the optimal operation of IES in order to achieve the economic and environmental benefits [7].…”