For the future development of an integrated energy system (IES) with ultra-high penetration of renewable energy, a planning model for an electricity-hydrogen integrated energy system (EH-IES) is proposed with the considerations of hydrogen production and storage technologies. In this EH-IES, a reasonable power to heat and hydrogen (P2HH) model with startup/shutdown constraints and a novel model of seasonal hydrogen storage (SHS) are proposed for the first time. To cope with the challenges faced by EH-IES, we use a combination of stochastic and robust optimization approaches to address the generation-load uncertainties, making the planning results robust. The N-1 contingency of crucial devices is further considered to make the system reliable. Additionally, the effects of the annual ratios of different seasons on the optimal allocation and operation of SHS are investigated. The min-max-min model with binary variables is solved by a nested column-and-constraint generation algorithm. Finally, case studies verified the advantages and effectiveness of the proposed model and planning method.

To retain intact state information of the district heating network (DHN) as possible in the optimization of heat and electricity integrated energy system (HE-IES), this paper combines the transient heat flow and steady-state electric power flow to formulate the dynamic optimal energy flow (OEF) model of HE-IES. For efficient and standardized solution, the finite difference method is applied to convert the partial differential equation constraint (introduced by the temperature dynamics in DHN) into a set of linear equality constraints. The structure of applicable difference schemes for system optimization is analyzed, based on which, a scheme with balanced performances in stability, convergence, simulation accuracy, and computation burden is developed. Moreover, with the proposed multi-objective optimization based method to select proper spatial and temporal calculation step sizes, a compromise between model precision and solution complexity can be reached. Case studies validate the feasibility and benefits of our proposed dynamic OEF computing method, which can further provide support for making optimal planning and operating strategies.

To quantify the cost and low-carbon competitiveness of electrolytic hydrogen in China, this paper presents a detailed assessment of the levelized cost of electrolytic hydrogen produced by a photovoltaic and grid-based hydrogen system (PGHS). This assessment considers the provincial differences in electricity prices, shares of renewables in power grids, and solar radiations in China. We find that in 2019, the levelized cost of hydrogen (LCOH₂) in 31 provinces varies from 31.5 ¥ per kg to 46.8 ¥ per kg in the single electricity price scenario, and reduces by up to 5.5 ¥ per kg when implementing a time-of-use price. In terms of CO₂ emissions, we demonstrate that electrolytic hydrogen has greater CO₂ emissions than the widespread coal-based hydrogen in 21 out of 31 provinces of China. The results suggest that implementing time-of-use electricity prices and subsidizing photovoltaics, instead of electricity prices and electrolyzers, can improve the performance of electric hydrogen production in terms of cost and CO₂ emissions. Further provincial analysis considering hydrogen demand potentials shows that, under stricter CO₂ emission constraints, substituting fossil fuel with electrolytic hydrogen can reduce CO₂ emissions. However, its popularization is impeded by fossil fuels with low costs. Due to the regional differentiation in the cost competitiveness of electrolytic hydrogen, different types and levels of subsidies should be carefully adopted depending on the provincial characteristics in electricity prices, solar radiations, renewable integrations, fossil fuel prices, and hydrogen demand potentials. In the end, when comparing seven pathways towards electrolytic hydrogen development from 2020 to 2050, we find that the production cost of the electrolytic hydrogen is unbundled from the restriction of CO₂ emission requirements after 2030, and a high carbon price may accelerate the cost competitiveness of electrolytic hydrogen by decades.

The heat and electricity integrated energy system (HE-IES) has high energy efficiency and bright application prospects. In the HE-IES, the heat transmission in the heating network and the indoor temperature variation in the buildings are governed by (partial) differential equations, which bring the heat dynamics problem in the system operation. In this paper, we propose a comprehensive approach for the operation of the HE-IES to coordinate the electricity and heat dynamics, including modeling, evaluation, and dispatch procedure. Firstly, we formulate a high resolution model for the heating network and buildings to describe the heat dynamics; Two indices are proposed to measure the impact of heat dynamics, which is then used to select the time resolution. Secondly, an optimal dispatch model with high resolution for the heat dynamics is formulated for the HE-IES; A decentralized and parallel solution method is proposed based on the alternating direction method of multipliers (ADMM). Thirdly, a two-stage procedure for the time resolution selection is proposed, which consists of a dispatch decision stage and a time resolution evaluation stage. A small illustrative case is studied to verify the effectiveness of the proposed method. A big case based on a real heating network in Jilin Province, China is also simulated.

Power-to-hydrogen systems have the potential for long-term energy storage and diversified terminal utilization, contributing to high renewable integration in power systems. To explore the potential energy arbitrage of integrated power-to-hydrogen systems (IP2HSs) across power systems and hydrogen supply chains, we propose a long-duration robust optimization (LDRO) framework for IP2HSs considering energy, ancillary services, and hydrogen markets. A unified capacity bidding model in the ancillary service market is established for fuel cells and electrolyzers of IP2HSs to simulate the bidding strategy in typical markets. The hybrid hydrogen demand model is based on the Cournot model considering the cost-competitiveness of hydrogen in different sectors. A direct solution method combining the column-and-constraint generation algorithm and strong duality theory is proposed for the LDRO framework with quadratic terms. Uncertainty sets are constructed based on day-ahead prices in typical markets. A case study investigates the feasibility of energy arbitrage and the ability of the LDRO framework to deal with long-duration price uncertainties in 15 markets. The results highlight the advantages of hybrid hydrogen demand and the sensitivity of energy arbitrage by IP2HSs to different parameters.