ABSTRACT
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.
ABSTRACT
This paper presents a new approach for the multiobjective optimal design of robust controllers in systems with stochastic parametric uncertainty. Traditionally, uncertainty is incorporated into the optimization process. However, this can generate two problems: (1) low performance in the nominal scenario; and (2) high computational cost. For the first point, it is possible to ensure that the controllers produce an acceptable performance for the nominal scenario in exchange for being lightly robust. For the second point, the methodology proposed in this work reduces the computational cost significantly. This approach addresses uncertainty by analyzing the robustness of optimal and nearly optimal controllers in the nominal scenario. The methodology guarantees obtaining controllers that are similar/neighboring to lightly robust controllers. Two examples of controller design are shown: one for a linear model and another for a nonlinear model. Both examples demonstrate the usefulness of the proposed new approach.