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| Optimal Capacity Configuration of P2H Equipment Considering Dynamic Power Range and Hydrogen Production Efficiency |
| Li Junzhou, Zhao Jinbin, Chen Yiwen, Mao Ling, Qu Keqing |
| College of Electrical Engineering Shanghai University of Electric Power Shanghai 200090 China |
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Abstract With the rapid development of wind power generation, the inherent characteristics of intermittent, fluctuating, and randomness lead to high wind curtailment rates, and power-to-hydrogen (P2H) equipment converts excess wind power into clean hydrogen energy that can be stored and reused. However, traditional P2H equipment planning research ignores external power fluctuation, only analyzing its static characteristics. As an electrical load with a fixed energy conversion efficiency, P2H equipment is too ideal to operate within any range under the rated capacity. The load range is affected by wind power fluctuation, and exceeding this allowable range will influence the safety and service life of P2H equipment. Recently, the dynamic power range has been studied, but most have yet to give specific control strategies and unified models. Therefore, this paper proposes a power range selection strategy to smoothly track the external power fluctuation by segmented response current and voltage.
Firstly, an alkaline electrolysis cell (AEC) is selected as the research object, and the correlation between wind power and hydrogen production is studied by considering its hydrogen production efficiency characteristic. Then, based on realizing high-quality hydrogen production, an optimal configuration model combining operation range planning and economic planning is constructed. The range selection strategy ensures the operating ability of AEC to act as a load, realizing the stable operation of the whole system. In economic planning, the model comprehensively considers the whole life cycle cost, power purchase cost, hydrogen sales revenue, and wind curtailment penalty cost as the objective function, and uses the chaotic particle swarm optimization (CPSO) algorithm to solve it. Finally, the method proposed simultaneously has a lower wind curtailment rate and higher hydrogen production quality than the existing configuration models, thus leading to optimal economics.
By selecting historical annual wind speed data at 10 min intervals, the two-parameter Weibull distribution function is applied to build typical scenarios. Simulation results show that, compared with traditional models, the proposed configuration model reduces the capacity by 22 % and the wind curtailment rate by 23 %, greatly improving the equipment investment cost. When the safety device detects that the hydrogen to oxygen (HTO) is higher than 2 %, P2H equipment will shut down automatically, thereby reducing the explosion risk of mixing hydrogen and oxygen. Furthermore, the influence of hydrogen selling price on the capacity planning results is separately analyzed. The price of 7.2 EUR/kg is a key node in this example, directly related to the curtailed wind power.
The main conclusions are as follows: (1) The proposed range selection strategy can track fluctuating wind power, which ensures the safety of mixed gas and overload problems. (2) By analyzing the hydrogen production efficiency curve of AEC, the equipment can operate near the maximum efficiency point, and the marginal cost of hydrogen production is reduced. (3) Compared with traditional models, the optimal configuration model combines operating range planning and economic planning, considering equipment safety, hydrogen production quality, and economy. It greatly increases the hydrogen production revenue, reduces the total planning cost, and can better adapt to wind power fluctuation. The CPSO algorithm can also converge to a better solution with fewer iterations than the PSO algorithm. (4) The hydrogen selling price will influence the capacity configuration results of P2H equipment. As the hydrogen price rises, the equipment capacity and hydrogen quality increase linearly. However, due to curtailed wind power limitations, the configuration results remain the same after the price reaches 7.2 EUR/kg.
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Received: 04 July 2022
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2023, Vol. 38 
