The current environmental challenges require the implementation of environmentally friendly energy production systems. In this context, proton exchange membrane fuel cell stacks (PEMFC) represent, due to their high electrical efficiency and their low level of $CO_{2}$ emissions, a promising alternative technology. However, there are still many technical aspects that need to be improved before they become a commercial reality. One of them is the temperature control of the stack, since its electrical efficiency and its lifetime depend on the performance of this control. In this work, we design a multiloop PID control of the temperature of a PEMFC stack and validate it experimentally. The stack is the prime mover of a micro combined heat and power system (micro-CHP). For this task, we use a previously developed nonlinear model and apply a multiobjective optimization methodology. To assess its performance, the PID control is compared to a second PID control designed with a linearized model. The results show, on the one hand, the importance of having a nonlinear model valid in a wide operation range for the correct design of the temperature control of a PEMFC stack and, on the other hand, the advantages of applying a multiobjective optimization methodology to this problem.

Micro-combined heat and power systems (μ-CHP) based on proton exchange membrane fuel cell stacks (PEMFC) are capable of supplying electricity and heat for the residential housing sector with a high energy efficiency and a low level of CO ₂ emissions. For this reason, they are regarded as a promising technology for coping with the current environmental challenges. In these systems, the temperature control of the stack is crucial, since it has a direct impact on its durability and electrical efficiency. In order to design a good temperature control, however, a dynamic model of the μ-CHP cooling system is required. In this paper, we present a model of the cooling system of a PEMFC-based μ-CHP system, which is oriented to the design of the temperature control of the stack. The model has been developed from a μ-CHP system located in the laboratory of our research team, the predictive control and heuristic optimization group (CPOH). It is based on first principles, dynamic, non-linear, and has been validated against the experimental data. The model is implemented in Matlab/Simulink and the adjustment of its parameters was carried out using evolutionary optimization techniques. The methodology followed to obtain it is also described in detail. Both the model and the test data used for its adjustment and validation are accessible to anyone who wants to consult them. The results show that the model is able to faithfully represent the dynamics of the μ-CHP cooling system, so it is appropriate for the design of the stack temperature control.

Commercial PEMFC-based micro-CHP systems are operated by rule-based energy management strategies. Each of these strategies constitutes a different way to meet the household energy demand (following the heat demand, following the electricity demand, the maximum of the two, etc.). Previous studies demonstrate that which of them is the best —i.e. the one that manages to meet the demand at the lowest operating cost— depends on the particular scenario in which the micro-CHP system works (gas and electricity prices, annual energy demands, ability to export electricity to the grid, etc.). This paper aims to explore this dependence relationship and to deepen our understanding of it. To this end, a parametric analysis is conducted and the performances achieved by four rule-based operating strategies are compared. The parameters whose influence is studied, and through which the scenario is jointly characterized, are: (1) energy prices (electricity and natural gas), (2) feed-in tariff, (3) stack degradation, (4) climate and (5) heat to power ratio of the demand. The results show this dependence relationship in a clear and more comprehensive way, and offer a better understanding of its nature. From this improved understanding it can be inferred, among other things, that adapting the strategy to the scenario can generate annual savings of up to 14.5 percentage points. Moreover, this enhanced characterization of that dependence relationship can be useful for the design of a new operating strategy, a strategy that, without falling into the complexity that an optimal energy management approach (based on linear programming) involves, manages to exploit the savings potential of micro-CHP systems, thus facilitating their future mass commercialization.

Commercial proton exchange membrane fuel cell (PEMFC)-based micro-combined heat and power (micro-CHP) systems are operated by rule-based energy management systems (EMSs). These EMSs are easy to implement but do not perform an explicit economic optimization. On the other hand, an optimal EMS can explicitly incorporate an economic optimization, but its implementation is more complex and may not be viable in practice. In a previous contribution, it was shown that current rule-based EMSs do not fully exploit the economic potential of micro-CHP systems due to their inability to adapt to changing scenarios. This study investigates the economic performance and behavior of an optimal EMS in 46 scenarios within the European framework. This EMS is designed using a model predictive control approach, and it is formulated as a mixed integer linear programming problem. The results reveal that there are only four basic optimal operating patterns, which vary depending on the scenario. This finding enables the design of an EMS that is computationally simpler than the optimal EMS but capable of emulating it and, therefore, is able to adapt effectively to changing scenarios. This new EMS would improve the cost-effectiveness of PEMFC-based micro-CHP systems, reducing their payback period and facilitating their mass market uptake.