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In response to challenges in constructing charging and hydrogen refueling facilities during the transition from conventional fuel vehicles to electric and hydrogen fuel cell vehicles, this paper introduces an innovative method for siting and capacity determination of Electric Hydrogen Charging Integrated Stations (EHCIS). In emphasizing the calculation of vehicle charging and hydrogen refueling demands, the proposed approach employs the Voronoi diagram and the particle swarm algorithm. Initially, Origin–Destination (OD) pairs represent car starting and endpoints, portraying travel demands. Utilizing the traffic network model, Dijkstra’s algorithm determines the shortest path for new energy vehicles, with the Monte Carlo simulation obtaining electric hydrogen energy demands. Subsequently, the Voronoi diagram categorizes the service scope of EHCIS, determining the equipment capacity while considering charging and refueling capabilities. Furthermore, the Voronoi diagram is employed to delineate the EHCIS service scope, determine the equipment capacity, and consider distance constraints, enhancing the rationality of site and service scope divisions. Finally, a dynamic optimal current model framework based on second-order cone relaxation is established for distribution networks. This framework plans each element of the active distribution network, ensuring safe and stable operation upon connection to EHCIS. To minimize the total social cost of EHCIS and address the constraints related to charging equipment and hydrogen production, a siting and capacity model is developed and solved using a particle swarm algorithm. Simulation planning in Sioux Falls city and the IEEE33 network validates the effectiveness and feasibility of the proposed method, ensuring stable power grid operation while meeting automotive energy demands.

The fault current limiter (FCL) is an effective measure for improving system stability and suppressing short-circuit fault current. Because of space and economic costs, the optimum placement of FCLs is vital in industrial applications. In this study, two objectives with the same dimensional measurement unit, namely, the total capital investment cost of FCLs and circuit breaker loss related to short-circuit currents, are considered. The circuit breaker loss model is developed based on the attenuation rule of the circuit breaker service life. The circuit breaker loss is used to quantify the current-limiting effect to avoid the problem of weight selection in a multi-objective problem. The IEEE 10-generator 39-bus system in New England is used to evaluate the performance of the proposed genetic algorithm (GA) method. Comparative and sensitivity analyses are performed. The results of the optimized plan are validated through simulations, indicating the significant potential of the GA for such optimization.

In the process of building a new power system with new energy sources as the mainstay, wind power and photovoltaic energy enter the multiplication stage with randomness and uncertainty, and the foundation and support role of large-scale long-time energy storage is highlighted. Considering the advantages of hydrogen energy storage in large-scale, cross-seasonal and cross-regional aspects, the necessity, feasibility and economy of hydrogen energy participation in long-time energy storage under the new power system are discussed. Firstly, power supply and demand production simulations were carried out based on the characteristics of new energy generation in China. When the penetration of new energy sources in the new power system reaches 45%, long-term energy storage becomes an essential regulation tool. Secondly, by comparing the storage duration, storage scale and application scenarios of various energy storage technologies, it was determined that hydrogen storage is the most preferable choice to participate in large-scale and long-term energy storage. Three long-time hydrogen storage methods are screened out from numerous hydrogen storage technologies, including salt-cavern hydrogen storage, natural gas blending and solid-state hydrogen storage. Finally, by analyzing the development status and economy of the above three types of hydrogen storage technologies, and based on the geographical characteristics and resource endowment of China, it is pointed out that China will form a hydrogen storage system of “solid state hydrogen storage above ground and salt cavern storage underground” in the future.

The use of clean electricity to replace fossil energy burning for heating is an important emission reduction way to achieve carbon neutrality. Without a good business model, it is very difficult to promote electric heating as a replacement for coal-fired systems in some areas with abundant coal resources. This study proposed a new business model for electric heating with stakeholders that included the government, the power grid company, and heat users. Based on this model, a specific tri-objective optimization model was proposed with the electric heating promotion power, heating electricity price, and government subsidy as variables to characterize the game relationship of the stakeholders, and the components of the stakeholder benefit function were analyzed in detail. A classical multi-objective genetic algorithm was used to solve the model. Finally, an “electrical heating” project for a typical area in China was analyzed, and four promotion cases were examined. The results showed that the power grid company and users had an antagonistic relationship in relation to the electricity price, but the best solution was found for all three stakeholders due to the high financial subsidies provided by the government.

Hydrogen energy leads us in an important direction in the development of clean energy, and the comprehensive utilization of hydrogen energy is crucial for the low-carbon transformation of the power sector. In this paper, the demand for hydrogen energy in various fields is predicted based on the support vector regression algorithm, which can be converted into an equivalent electrical load when it is all produced from water electrolysis. Then, the investment costs of power generators and hydrogen energy equipment are forecast considering uncertainty. Furthermore, a planning model is established with the forecast data, initial installed capacity and targets for carbon emission reduction as inputs, and the installed capacity as well as share of various power supply and annual carbon emissions as outputs. Taking Gansu Province of China as an example, the changes of power supply structure and carbon emissions under different scenarios are analysed. It can be found that hydrogen production through water electrolysis powered by renewable energy can reduce carbon emissions but will increase the demand for renewable energy generators. Appropriate planning of hydrogen storage can reduce the overall investment cost and promote a low carbon transition of the power system.

To utilize heat and electricity in a clean and integrated manner, a zero-carbon-emission micro Energy Internet (ZCE-MEI) architecture is proposed by incorporating non-supplementary fired compressed air energy storage (NSF-CAES) hub. A typical ZCE-MEI combining power distribution network (PDN) and district heating network (DHN) with NSF-CAES is considered in this paper. NSF-CAES hub is formulated to take the thermal dynamic and pressure behavior into account to enhance dispatch flexibility. A modified DistFlow model is utilized to allow several discrete and continuous reactive power compensators to maintain voltage quality of PDN. Optimal operation of the ZCE-MEI is firstly modeled as a mixed integer nonlinear programming (MINLP). Several transformations and simplifications are taken to convert the problem as a mixed integer linear programming (MILP) which can be effectively solved by CPLEX. A typical test system composed of a NSF-CAES hub, a 33-bus PDN, and an 8-node DHN is adopted to verify the effectiveness of the proposed ZCE-MEI in terms of reducing operation cost and wind curtailment.

This paper introduces a new rotor design for the easy insertion and removal of rotor windings. The shape of the rotor is optimized based on a surrogate method in order to achieve low power loss under the maximum power output. The synchronous machine with the new rotor is evaluated in 2-D finite element software and validated by experiments. This rotor shows great potential for reducing the maintenance and repair costs of synchronous machines, making it particularly suited for low-cost mass production markets including gen-sets, steam turbines, wind power generators, and hybrid electric vehicles.

This paper presents a doubly fed induction generator (DFIG) wind power system with hydrogen energy storage, with a focus on its virtual inertia adaptive control. Conventionally, a synchronous generator has a large inertia from its rotating rotor, and thus its kinetic energy can be used to damp out fluctuations from the grid. However, DFIGs do not provide such a mechanism as their rotor is disconnected with the power grid, owing to the use of back-to-back power converters between the two. In this paper, a hydrogen energy storage system is utilized to provide a virtual inertia so as to dampen the disturbances and support the grid’s stability. An analytical model is developed based on experimental data and test results show that: (1) the proposed method is effective in supporting the grid frequency; (2) the maximum power point tracking is achieved by implementing this proposed system; and, (3) the DFIG efficiency is improved. The developed system is technically viable and can be applied to medium and large wind power systems. The hydrogen energy storage is a clean and environmental-friendly technology, and can increase the renewable energy penetration in the power network.

This paper presents a wind-methanol-fuel cell system with hydrogen storage. It can manage various energy flow to provide stable wind power supply, produce constant methanol, and reduce CO2 emissions. Firstly, this study establishes the theoretical basis and formulation algorithms. And then, computational experiments are developed with MATLAB/Simulink (R2016a, MathWorks, Natick, MA, USA). Real data are used to fit the developed models in the study. From the test results, the developed system can generate maximum electricity whilst maintaining a stable production of methanol with the aid of a hybrid energy storage system (HESS). A sophisticated control scheme is also developed to coordinate these actions to achieve satisfactory system performance.

With the increasing penetration of renewable energy in power systems, fluctuation of renewable energy power plants has great influence on stability of the system, and renewable power curtailment is also becoming more and more serious due to the insufficient consumptive ability of local power grid. In order to maximize the utilization of renewable energy, this paper focuses on the generation scheduling optimization for a wind-solar-thermal hybrid system considering that the produced energy will be transmitted over a long distance to satisfy the demands of the receiving end system through ultra-high voltage (UHV) transmission lines. Accordingly, a bilevel optimization based on a non-cooperative game method is proposed to maximize the profit of power plants in the hybrid system. Users in the receiving end system are at the lower level of the bilevel programming, and power plants in the transmitting end system are at the upper level. Competitive behavior among power plants is formulated as a non-cooperative game and the profit of power plant is scheduled by adjusting generation and bidding strategies in both day-ahead markets and intraday markets. In addition, generation cost, wheeling cost, and carbon emissions are all considered in the non-cooperative game model. Moreover, a distributed algorithm is presented to obtain the generalized Nash equilibrium solution, which realizes the optimization in terms of maximizing profit. Finally, several simulations are implemented and analyzed to verify the effectiveness of the proposed optimization method.

This paper presents a wind-methanol-fuel cell system with hydrogen storage. It can manage various energy flow to provide stable wind power supply, produce constant methanol, and reduce CO2 emissions. Firstly, this study establishes the theoretical basis and formulation algorithms. And then, computational experiments are developed with MATLAB/Simulink (R2016a, MathWorks, Natick, MA, USA). Real data are used to fit the developed models in the study. From the test results, the developed system can generate maximum electricity whilst maintaining a stable production of methanol with the aid of a hybrid energy storage system (HESS). A sophisticated control scheme is also developed to coordinate these actions to achieve satisfactory system performance. [ABSTRACT FROM AUTHOR]