Analysis of a phosphoric acid fuel cell-based multi-energy hub system for heat, power, and hydrogen generation

Park, Chungi; Jung, Yoonju; Lim, Kisung; Kim, Bong-Hyun; Kang, Yoonseung; Ju, Hyunchul

doi:10.1016/j.applthermaleng.2021.116715

Cited by 24 publications

(4 citation statements)

References 30 publications

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“…The optimal operation of multi-carrier energy systems is one of the prominent research topics in recent years, with an extensive focus on minimization of the total operating cost of the EHs while serving the electrical, heating, and, cooling loads. Some papers have been published on the modeling of EHs with deterministic load demand [1]- [3]. However, there are still challenging issues regarding the mathematical modeling and problem formulation of hub assets and their functionality, such as the computational efficiency of the model and the capability to address uncertainties.…”

Section: B Literature Reviewmentioning

confidence: 99%

Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization

et al. 2022

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This paper develops a robust chance-constrained model for handling the uncertainties of generation and consumption in multi-carrier energy hubs. The proposed model incorporates corresponding loading factors for each type of electrical, heating, and cooling loads. This is done to assess the maximum loadability of the whole system. In this respect, the chance-constrained approach is implemented for the feasibility assessment of the operation problem with uncertainties. The uncertainties which are assumed here include the forecast errors of electrical, heating, and cooling load demands, and the volatile solar power generation. The overall problem formulation is developed in the mixed-integer linear programming (MILP) framework. The standard chance-constrained approach is converted to a deterministic optimization model by utilizing the Big M method. The main objective of the proposed model is to maximize the loadability index with uncertainties while addressing the permissible risk index of the decision-maker. The studied energy hub comprises electrical, heating, and cooling loads, and the energy flow technique is adopted in this paper to model the load balance equations. The simulation results are presented for different scenarios while addressing features of the proposed model for the summer and winter seasons. Furthermore, the developed model is evaluated for different scenarios and a comparison is made with the information-gap decision theory

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Section: B Literature Reviewmentioning

confidence: 99%

Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization

et al. 2022

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“…So, in [15], the authors implemented the phosphoric acid fuel stacks for hospital and commercial household heating systems. Here, the liquid-type electrolyte is used, and the catalyst is designed by utilizing platinum material.…”

Section: Introductionmentioning

confidence: 99%

A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems

Touti,

Aoudia,

Hussaian Basha

et al. 2024

International Transactions on Electrical Energy Systems

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Now, the present electric vehicle industry is focusing on the fuel cell technology because its features are high flexibility, continuous power supply, less atmospheric pollution, fast startup, and rapid response. However, the fuel cell gives nonlinear power versus current characteristics. Due to this nonlinear behavior, the maximum power extraction from the fuel stack is quite difficult. So, in this work, an adaptive genetic algorithm with an adaptive neuro-fuzzy inference system (ACS with ANFIS) MPPT controller is introduced for finding the MPP of the fuel stack system thereby extracting the peak power from the fuel stack. The proposed hybrid maximum power point tracking (MPPT) controller is compared with the other MPPT controllers which are enhanced incremental conductance-fuzzy logic controller (EIC with FLC), improved hill climb with fuzzy logic controller (IHC with FLC), adaptive beta with FLC, enhanced differential evolutionary with FLC (EDE with FLC), and marine predators optimization with FLC (MPO with FLC). Here, these hybrid controllers’ comprehensive investigations have been carried out in terms of tracking speed of the MPP, oscillations across the MPP, settling time of the converter voltage, maximum power extraction from the fuel stack, and working efficiency of the MPPT controller. The fuel stack generates a very low output voltage which is improved by using the boost DC-DC converter, and the overall fuel stack-fed boost converter system is designed by utilizing the MATLAB/Simulink tool. From the simulation results, the AGA with ANFIS MPPT controller gives high MPP tracking efficiency when compared to the other hybrid controller.

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“…However, with the continuous development of the IoT and new energy utilization technology, the comprehensive utilization of clean energy has been brought into full play, and the energy management and monitoring mode has also undergone fundamental changes [9][10][11][12][13][14][15]. The market has unanimously recognized and promoted the multi-energy collaborative heat collection technology, such as solar energy combined with wind power generation, water source heat pump, ground-source heat pump, gas-fired heat energy and so on [16][17][18][19][20][21]. Starting from the actual application requirements, there is certain theoretical and practical application value to design and develop a remote real-time monitoring and management system for multienergy heat collection, as well as to manage and control the temperature data information of the system in a remote, efficient, real-time and accurate way.…”

Section: Introductionmentioning

confidence: 99%

Remote Temperature Control Strategy of Multi-energy Heat Collection Based on the Internet of Things

Liu¹,

Wu²,

Wu³

et al. 2023

IJHT

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With the continuous development of the Internet of Things (IoT) and new energy utilization technology, the energy management and monitoring mode has undergone fundamental changes. Design and development of remote real-time monitoring and management system for multi-energy heat collection has certain theoretical and practical application value. The existing remote temperature control model fully considers neither the impact of multi-energy collaborative heat collection on remote temperature control strategy, nor the interactions among multiple objective factors, such as climate and location. Therefore, this paper studied the remote temperature control strategy of multienergy heat collection based on the IoT. First, the paper gave the application architecture of multi-energy heat collection system functions, and developed a multi-energy heat collection system model without simulation performance loss using stochastic modeling ideas. Second, this paper made full use of the error information of the process, thus correcting the predicted value of the remote temperature control output. Finally, the experimental results verified the effectiveness of the model and the control strategy.

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Analysis of a phosphoric acid fuel cell-based multi-energy hub system for heat, power, and hydrogen generation

Cited by 24 publications

References 30 publications

Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization

Scheduling of Energy Hub Resources Using Robust Chance-Constrained Optimization

A Novel Design and Analysis Adaptive Hybrid ANFIS MPPT Controller for PEMFC-Fed EV Systems

Remote Temperature Control Strategy of Multi-energy Heat Collection Based on the Internet of Things

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