Chen Fu

With the increasing maturity of renewable energy technologies and the pressing need to address climate change, urban power systems are striving to integrate a higher proportion of low-carbon renewable energy sources. However, the inherent variability and intermittency of wind and solar power pose significant challenges to the stability and reliability of urban power grids. Existing research has primarily focused on short-term energy storage solutions or small-scale integrated energy systems, which are insufficient to address the long-term, large-scale energy storage needs of urban areas with high renewable energy penetration. This paper proposes a mid-to-long-term capacity expansion model for hydrogen energy storage in urban-scale power systems, using Shanghai as a case study. The model employs mixed-integer linear programming (MILP) to optimize the generation portfolios from the present to 2060 under two scenarios: with and without hydrogen storage. The results demonstrate that by 2060, the installed capacity of hydrogen electrolyzers could reach 21.5 GW, and the installed capacity of hydrogen power generators could reach 27.5 GW, accounting for 30% of the total installed capacity excluding their own. Compared to the base scenario, the electricity–hydrogen collaborative energy supply system increases renewable penetration by 11.6% and utilization by 12.9% while reducing the levelized cost of urban comprehensive electricity (LCOUCE) by 2.514 cents/kWh. These findings highlight the technical feasibility and economic advantages of deploying long-term hydrogen storage in urban grids, providing a scalable solution to enhance the stability and efficiency of high-renewable urban power systems.

Against the backdrop of the global transition towards clean energy, deep-sea wind-power hydrogen production integrates offshore wind with green hydrogen technology. Addressing the technical coupling complexity and the impact of uncertain hydrogen prices, this paper develops a capacity optimization model. The model incorporates floating wind turbine output, the technical distinctions between alkaline (ALK) electrolyzers and proton exchange membrane (PEM) electrolyzers, and the synergy with energy storage. Under three hydrogen price scenarios, the results demonstrate that as the price increases from 26 CNY/kg to 30 CNY/kg, the optimal ALK capacity decreases from 2.92 MW to 0.29 MW, while the PEM capacity increases from 3.51 MW to 5.51 MW. Correspondingly, the system’s Net Present Value (NPV) exhibits an upward trend. To address the limitations of traditional methods in handling multi-dimensional benefit correlations and information ambiguity, a comprehensive benefit evaluation framework encompassing economic, technical, environmental, and social synergies was constructed. Sensitivity analysis indicates that the comprehensive benefit level falls within a relatively high-efficiency interval. The numerical characteristics, an entropy value of 3.29 and a hyper-entropy of 0.85, demonstrate compact result distribution and robust stability, validating the applicability and stability of the proposed offshore wind–hydrogen benefit assessment model.

In order to solve the problem of large-scale offshore wind power consumption, the development of an offshore wind power hydrogen supply chain has become one of the trends. In this study, 10 feasible options are proposed to investigate the economics of an offshore wind hydrogen supply chain for offshore hydrogen refueling station consumption from three aspects: offshore wind hydrogen production, storage and transportation, and application. The study adopts a levelized cost analysis method to measure the current and future costs of the hydrogen supply chain. It analyses the suitable transport modes for delivering hydrogen to offshore hydrogen refueling stations at different scales and distances, as well as the profitability of offshore hydrogen refueling stations. The study draws the following key conclusions: (1) the current centralised wind power hydrogen production method is economically superior to the distributed method; (2) gas-hydrogen storage and transportation is still the most economical method at the current time, with a cost of CNY 32.14/kg, which decreases to CNY 13.52/kg in 2037, on a par with the cost of coal-based hydrogen production using carbon capture technology; and (3) at the boundaries of an operating load factor of 70% and a selling price of CNY 25/kg, the offshore hydrogen refueling station. The internal rate of return (IRR) is 21%, showing good profitability; (4) In terms of the choice of transport mode for supplying hydrogen to the offshore hydrogen refueling station, gas-hydrogen ships and pipeline transport will mainly be used in the near future, while liquid organic hydrogen carriers and synthetic ammonia ships can be considered in the medium to long term.

Offshore wind-to-hydrogen production is an effective means of solving the problems of large-scale grid-connected consumption and high power transmission costs of offshore wind power. Site selection is a core component in planning offshore wind-to-hydrogen facilities, involving careful consideration of multiple factors, and is a classic multi-criteria decision-making problem. Therefore, this study proposes a multi-criteria decision-making method based on the two-dimensional linguistic cloud model to optimize site selection for offshore wind-to-hydrogen bases. Firstly, the alternative schemes are evaluated using two-dimensional linguistic information, and a new model for transforming two-dimensional linguistic information into a normal cloud is constructed. Then, the cloud area overlap degree is defined to calculate the interaction factor between decision-makers, and a multi-objective programming model based on maximum deviation-minimum correlation is established. Following this, the Pareto solution of criteria weights is solved using the non-dominated sorting genetic algorithm II, and the alternatives are sorted and selected through the cloud-weighted average operator. Finally, an index system was constructed in terms of resource conditions, planning conditions, external conditions, and other dimensions, and a case study was conducted using the location of offshore wind-to-hydrogen production bases in Shanghai. The method proposed in this study demonstrates strong robustness and can provide a basis for these multi-criteria decision-making problems with solid qualitative characteristics.

With the rapid development of a social economy, the yearly increase in air conditioning load in the winter and summer seasons may bring serious challenges to the safe and economic operation of the power grid during the peak period of electricity consumption. So, how we reasonably adjust the set temperature of air conditioning so as to cut down the load during peak periods is very important. In this paper, considering the thermal inertia of air-conditioned buildings and the adaptability of human thermal comfort to temperature changes, the air conditioning load is regarded as virtual energy storage, the air conditioning temperature adjustment range for different users is determined based on the adaptive thermal comfort model of different geographic locations and climatic conditions, and a compensation mechanism is set up based on air conditioning users’ level of participation. Then, an optimal scheduling strategy for a microgrid was constructed with the objectives of user satisfaction, carbon emissions, and microgrid operation benefits, as well as regulating the users’ electricity consumption behavior, and the strategy was solved by using a multi-objective JAYA algorithm. Finally, winter and summer are used as case studies to analyze the results, which demonstrate that regulating the virtual energy storage of air conditioning can effectively improve the economy and environmental friendliness of a microgrid operation and reduce the cost of electricity consumption for the users, taking into account the comfort of the users.