Dynamic Synthesis/Design and Operation/Control Optimization Under Uncertainty of a PEMFC System

2008 ◽  
Author(s):  
Kihyung Kim ◽  
Meng Wang ◽  
Michael R. von Spakovsky ◽  
Douglas J. Nelson
Keyword(s):  
Dynamic System ◽  
Alternative Energy ◽  
Large Scale ◽  
Proton Exchange ◽  
Optimization Under Uncertainty ◽  
Cost Models ◽  
System Synthesis ◽  
Control Optimization ◽  
Synthesis Design ◽  
Operation Control

Proton exchange membrane fuel cells (PEMFCs) are one of the leading candidates in alternative energy conversion devices for transportation, stationary, and portable power generation applications. Such systems with their own fuel conversion unit typically consist of several subsystems: a fuel processing subsystem, a fuel cell stack subsystem, a work recovery-air supply subsystem, and a power electronics subsystem. Since these subsystems have different physical characteristics, their integration into a single system/subsystem level unit make the problems of optimal dynamic system synthesis/design and operation/control highly complex. Thus, dynamic system/subsystem/component modeling and highly effective optimization strategies are required. Furthermore, uncertainties in the results of system synthesis/design and operation/control optimization can be affected by any number of sources of uncertainty such as the load profiles and cost models. These uncertainties can be taken into account by treating the problem probabilistically. The difficulty with doing this, particularly when large-scale dynamic optimization with a large number of degrees of freedom is being used to determine the optimal synthesis/design and operation/control of the system, is that the traditional probabilistic approaches (e.g., Monte Carlo Method) are so computationally intensive that combined with large-scale optimization it renders the problem computationally intractable. This difficulty can be overcome by the use of approximate approaches such as the response sensitivity analysis (RSA) method based on Taylor series expansion. Thus, in this paper, a stochastic modeling and uncertainty analysis methodology for energy system synthesis/design and operation/control which uses the RSA method is proposed and employed for calculating the uncertainties on the system outputs. Their effects on the synthesis/design and operation/control optimization of a 5kWe PEMFC system are assessed by taking the uncertainties into account in the objectives and constraints. It is shown that these uncertainties significantly affect the reliability of being able to meet certain constraints (e.g., that on the CO concentration) during the synthesis/design and operation/control optimization process. These and other results are presented.

Multi- Versus Single-Level Dynamic Synthesis/Design and Operation/Control Optimizations of a PEMFC System

2008 ◽  
Author(s):  
Meng Wang ◽  
Kihyung Kim ◽  
Michael R. von Spakovsky ◽  
Douglas J. Nelson
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Proton Exchange ◽  
Optimization Techniques ◽  
Optimization Strategy ◽  
Single Level ◽  
Control Optimization ◽  
Synthesis Design ◽  
Gradient Based ◽  
Operation Control

As primary tools for the development of energy systems, optimization techniques have been studied for decades. However, for large-scale synthesis/design and operation/control optimization problems, it may turn out that it is impractical to solve the entire problem as a single optimization problem. In this paper, a multi-level optimization strategy, dynamic iterative local-global optimization (DILGO), is utilized for the synthesis/design and operation/control optimization of a 5 kWe PEMFC (Proton Exchange Membrane Fuel Cell) system. The strategy decomposes the system into three subsystems: a stack subsystem (SS), a fuel processing subsystem (FPS), and a work and air recovery subsystem (WRAS) and, thus, into three optimization sub-problems. To validate the decomposition strategy, the results are compared with a single-level dynamic optimization, in which the whole system is optimized together. In addition, for the purpose of comparison between different optimization algorithms, gradient-based optimization results are compared with those for a hybrid heuristic/gradient-based optimization algorithm.

Stochastic Modeling and Uncertainty Analysis With Multi-Objective Optimization Strategies for the Synthesis/Design and Operation/Control of a PEMFC Fuel Processing Subsystem

2008 ◽  
Author(s):  
Kihyung Kim ◽  
Meng Wang ◽  
Michael R. von Spakovsky ◽  
Douglas J. Nelson
Keyword(s):  
Uncertainty Analysis ◽  
Large Scale ◽  
Operating Cost ◽  
Energy System ◽  
Cost Models ◽  
Multi Objective Optimization ◽  
Fuel Processing ◽  
Synthesis Design ◽  
Operation Control ◽  
Processing Subsystem

A stochastic modeling and uncertainty analysis methodology for energy system synthesis/design is proposed in this paper and applied to the development of the fuel processing subsystem (FPS) of a proton exchange membrane fuel cell (PEMFC) system. The FPS consists of a steam methane reformer, both high and low temperature water-gas shift reactors, a CO preferential oxidation reactor, a steam generator, a combustor, and several heat exchangers. For each component of the system, detailed thermodynamic, geometric, chemical kinetic, and cost models are developed and integrated into an overall model for the subsystem. Conventionally, in energy system synthesis/design, such models are treated deterministically, using a specific set of non-probabilistic input variable values that produce a specific set of non-probabilistic output variable values. Even though these input values, which include the specific load profile (i.e. electrical, thermal, and/or aerodynamic) for which the system or subsystem is synthesized/designed, can have significant uncertainties that inevitably propagate through the system to the outputs, such deterministic approaches are unable to quantify these uncertainties and their effect on the final synthesis/design and operation/control. This deficiency can, of course, be overcome by treating the inputs and outputs probabilistically. The difficulty with doing this, particularly when large-scale dynamic optimization with a large number of degrees of freedom is being used to determine the optimal synthesis/design and operation/control of the system, is that the traditional probabilistic approaches (e.g., Monte Carlo Method) are so computationally intensive that combined with large-scale optimization it renders the problem computationally intractable. This difficulty can be overcome by the use of approximate approaches such as the response sensitivity analysis (RSA) method based on Taylor series expansion. In this study, RSA is employed and developed by the authors for use with dynamic energy system optimization. Load profile and cost models are treated as probabilistic input values and uncertainties in output results investigated. The results for the uncertainty analysis applied to the optimization of the FPS synthesis/design and operation/control are compared with those found using a Monte Carlo approach with good results. In this paper, the FPS synthesis/design and operation optimization is treated as a multi-objective optimization problem to minimize the capital cost and operating cost simultaneously, and uncertainty effects on the optimization are assessed by taking uncertainties into account in the objectives and constraints. Optimization results show that there is little effect on the objective (the operating cost and capital cost), while the constraints (e.g., that on the CO concentration) can be significantly affected during the synthesis/design and operation/control optimization.

Use of State Space in the Dynamic Synthesis/Design and Operation/Control Optimization of a PEMFC System

2008 ◽  
Author(s):  
Meng Wang ◽  
Kihyung Kim ◽  
Michael R. von Spakovsky ◽  
Douglas J. Nelson
Keyword(s):  
Design Optimization ◽  
State Space ◽  
High Efficiency ◽  
Traditional Approach ◽  
Proton Exchange ◽  
Decision Variable ◽  
Control Optimization ◽  
Synthesis Design ◽  
Partial Load ◽  
Operation Control

An often used approach to the synthesis/design optimization of energy systems is to only use steady state operation and high efficiency (or low total life cycle cost) at full load as the basis for the synthesis/design. Transient and partial load operations are considered secondarily by system and control engineers once the synthesis/design is fixed (i.e. system testing with standard load profiles). This paper considers the system dynamics from the very beginning of the synthesis/design process by developing the system using a set of transient thermodynamic, kinetic, geometric as well as cost models developed and implemented for the components of a 5 kW PEMFC (Proton Exchange Membrane Fuel Cell) system. The system is composed of three subsystems: a stack subsystem (SS), a fuel processing subsystem (FPS), and a work and air recovery subsystem (WRAS). In addition, state space is used in a looped set of optimizations to illustrate the effect of the control system on the synthesis/design optimization and to develop a set of optimal multi-input, multi-output (MIMO) controllers consistent with the optimal synthesis/design of the PEMFC system. It is shown that these MIMO controllers correspond to the ones found in a non-looped optimization in which the gains for the controllers are part of the decision variable set for the overall synthesis/design and operation/control optimization. These last set of results are then compared with the optimizations results found with the traditional approach of using a single load point in order to show the advantage of the dynamic optimization.

Optimal Synthesis/Design of a Pem Fuel Cell Cogeneration System for Multi-Unit Residential Applications–Application of a Decomposition Strategy

2004 ◽  
Vol 126 (1) ◽  
pp. 30-39 ◽  
Author(s):  
Borja Oyarza´bal ◽  
Michael R. von Spakovsky ◽  
Michael W. Ellis
Keyword(s):  
Fuel Cell ◽  
Design Optimization ◽  
Large Scale ◽  
Unit Cost ◽  
Pem Fuel Cell ◽  
Energy System ◽  
Cell System ◽  
Proton Exchange ◽  
Fuel Cell System ◽  
Synthesis Design

The application of a decomposition methodology to the synthesis/design optimization of a stationary cogeneration proton exchange membrane (PEM) fuel cell system for residential applications is the focus of this paper. Detailed thermodynamic, economic, and geometric models were developed to describe the operation and cost of the fuel processing sub-system and the fuel cell stack sub-system. Details of these models are given in an accompanying paper by the authors. In the present paper, the case is made for the usefulness and need of decomposition in large-scale optimization. The types of decomposition strategies considered are conceptual, time, and physical decomposition. Specific solution approaches to the latter, namely Local-Global Optimization (LGO) are outlined in the paper. Conceptual/time decomposition and physical decomposition using the LGO approach are applied to the fuel cell system. These techniques prove to be useful tools for simplifying the overall synthesis/design optimization problem of the fuel cell system. The results of the decomposed synthesis/design optimization indicate that this system is more economical for a relatively large cluster of residences (i.e. 50). Results also show that a unit cost of power production of less than 10 cents/kWh on an exergy basis requires the manufacture of more than 1500 fuel cell sub-system units per year. Finally, based on the off-design optimization results, the fuel cell system is unable by itself to satisfy the winter heat demands. Thus, the case is made for integrating the fuel cell system with another system, namely, a heat pump, to form what is called a total energy system.

scholarly journals Sensors Integrated Control of PEMFC Gas Supply System Based on Large-Scale Deep Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 349
Author(s):  
Jiawen Li ◽  
Tao Yu
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Integrated Control ◽  
Supply System ◽  
Proton Exchange ◽  
Coordination Problem ◽  
Hydrogen Flow ◽  
Model Free ◽  
Integrated Controller ◽  
Gas Supply

In the proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor influencing the output characteristics of PEMFC, and there is a coordination problem between their flow controls. Thus, the integrated controller of the PEMFC gas supply system based on distributed deep reinforcement learning (DDRL) is proposed to solve this problem, it combines the original airflow controller and hydrogen flow controller into one. Besides, edge-cloud collaborative multiple tricks distributed deep deterministic policy gradient (ECMTD-DDPG) algorithm is presented. In this algorithm, an edge exploration policy is adopted, suggesting that the edge explores including DDPG, soft actor-critic (SAC), and conventional control algorithm are employed to realize distributed exploration in the environment, and a classified experience replay mechanism is introduced to improve exploration efficiency. Moreover, various tricks are combined with the cloud centralized training policy to address the overestimation of Q-value in DDPG. Ultimately, a model-free integrated controller of the PEMFC gas supply system with better global searching ability and training efficiency is obtained. The simulation verifies that the controller enables the flows of air and hydrogen to respond more rapidly to the changing load.

Techno-Economic Assessment of Utilizing Wind Energy for Hydrogen Production Through Electrolysis

2017 ◽  
Author(s):  
Reza Ziazi ◽  
Kasra Mohammadi ◽  
Navid Goudarzi
Keyword(s):  
Hydrogen Production ◽  
Wind Energy ◽  
Wind Turbines ◽  
Alternative Energy ◽  
Large Scale ◽  
Fossil Fuels ◽  
Combustion Engine ◽  
Economic Assessment ◽  
Large Cities ◽  
North East

Hydrogen as a clean alternative energy carrier for the future is required to be produced through environmentally friendly approaches. Use of renewables such as wind energy for hydrogen production is an appealing way to securely sustain the worldwide trade energy systems. In this approach, wind turbines provide the electricity required for the electrolysis process to split the water into hydrogen and oxygen. The generated hydrogen can then be stored and utilized later for electricity generation via either a fuel cell or an internal combustion engine that turn a generator. In this study, techno-economic evaluation of hydrogen production by electrolysis using wind power investigated in a windy location, named Binaloud, located in north-east of Iran. Development of different large scale wind turbines with different rated capacity is evaluated in all selected locations. Moreover, different capacities of electrolytic for large scale hydrogen production is evaluated. Hydrogen production through wind energy can reduce the usage of unsustainable, financially unstable, and polluting fossil fuels that are becoming a major issue in large cities of Iran.

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