Annika Eichler

Modeling for optimal operation of PEM fuel cells and electrolyzers

This contribution presents and analyzes modeling and minimum cost operation of proton exchange membrane (PEM) fuel cells and electrolyzers. First, detailed thermoelectric models of the electrochemical technologies based on a first-principle approach are presented. Then, as the detailed nonlinear models developed are intractable for use in online optimal control computation, a mixed integer linear program (MILP) is formulated with a piecewise affine approximation of the conversion efficiency and linear temperature dynamics for the devices. The outputs of the simplified linear models are compared with the detailed ones, when optimally producing and consuming a fixed quantity of hydrogen gas. Comparisons are performed for a variety of price scenarios and efficiency approximations, for both the fuel cell and electrolyzer.

A Data-Driven Stochastic Optimization Approach to the Seasonal Storage Energy Management

Several studies in the literature have shown the potential energy savings emerging from the cooperative management of the aggregated building energy demands. Sophisticated predictive control schemes have recently been developed that achieve these gains by exploiting the energy generation, conversion, and storage equipment shared by the building community. A common difficulty with all these methods is integrating knowledge about the long term evolution of the disturbances affecting the system dynamics (e.g., ambient temperature and solar radiation). In this context, the seasonal storage capabilities of the system are difficult to be optimally managed. This letter addresses this issue by exploiting available historical data to: (i) construct bounds that confine with high probability the optimal charging trajectory of the seasonal storage and (ii) generate a piece-wise affine approximation of the value function of the energy stored in the seasonal storage at each time step. Using these bounds and value functions, we formulate a multistage stochastic optimization problem to minimize the total energy consumption of the system. In a numerical study based on a realistic system configuration, the proposed method is shown to operate the system close to global optimality.

Humans in the Loop: A Stochastic Predictive Approach to Building Energy Management in the Presence of Unpredictable Users * *This project is supported by the ETH Zurich Foundation, the Swiss Competence Centers for Energy Research under the project FEEB&D and NanoTera.ch under the project HeatReserves.

Abstract Efficient building energy management has attracted a great deal of academic interest with significant potential energy savings to be envisaged. Social scientists strive to achieve these savings by employing behavior-based approaches, while engineers investigate control strategies for the efficient operation of the building devices. This work can be seen as a first step towards bridging these two approaches by proposing a control scheme that encapsulates building occupant behavior into the energy management system. In particular, the occupants willingness to tolerate comfort bound violations is modeled as a random measurable uncertainty and incorporated into the building energy management system through disturbance feedback control policies. The respective optimal control problem is formulated as a mixed-integer stochastic optimization problem, and a computationally tractable approximation of it is derived by restricting the disturbance feedback control policies to admit an affine structure. An extensive numerical study verifies that the proposed approach can significantly reduce the energy consumption of the buildings.

Humans in the Loop: A Stochastic Predictive Approach to Building Energy Management in the Presence of Unpredictable Users

Abstract Efficient building energy management has attracted a great deal of academic interest with significant potential energy savings to be envisaged. Social scientists strive to achieve these savings by employing behavior-based approaches, while engineers investigate control strategies for the efficient operation of the building devices. This work can be seen as a first step towards bridging these two approaches by proposing a control scheme that encapsulates building occupant behavior into the energy management system. In particular, the occupants willingness to tolerate comfort bound violations is modeled as a random measurable uncertainty and incorporated into the building energy management system through disturbance feedback control policies. The respective optimal control problem is formulated as a mixed-integer stochastic optimization problem, and a computationally tractable approximation of it is derived by restricting the disturbance feedback control policies to admit an affine structure. An extensive numerical study verifies that the proposed approach can significantly reduce the energy consumption of the buildings.

Parameter identification of canalyzing Boolean functions with ternary vectors for gene networks

In gene dynamics modeling, parameters of Boolean networks are identified from continuous data under various assumptions expressed by logical constraints. These constraints may restrict the dynamics of the network to the subclass of canalyzing functions, which are known to be appropriate for genetic networks. This paper introduces a high performance algorithm, which solves the parameter identification problem by so called Zhegalkin identification and exploits the restriction to canalyzing functions resulting in reduced calculation time. The canalyzing constraint is formulated in terms of orthogonal ternary vector lists — which are intrinsically used in a Branch-and-Cut algorithm obeying this constraint. The algorithm is applied to mRNA micro array data from mice under different contaminant conditions and good correspondence to a known apoptotic pathway can be shown.

Fixed Mode Elimination by Minimum Communication Within an Estimator-Based Framework for Distributed Control

A distributed control scheme is considered, where each subsystem estimates a part of the state space and uses a state feedback-based controller to actuate a subset of the system’s inputs. The estimated parts of the state space are overlapping and thus provide some information about the neighboring subsystems to the local controllers. The number of communication links which are added to improve performance is a design choice. However, a minimum of communication is needed if there are fixed modes (FMs), which are eigenvalues of the closed-loop system that cannot be changed under the given structural constraints. Mild conditions on the overlapping structure are given under which there exists a communication topology of measurements or estimates to eliminate all FMs. The problem of finding the minimum communication to eliminate all FMs is formulated as a minimum cost coverage problem with submodular constraints. Based on this result, an algorithm for the FM elimination is proposed and compared to the greedy algorithm. A numerical example illustrates the results.

Parameter Identification of Canalyzing and Nested Canalyzing Boolean Functions with Ternary Vectors for Gene Networks

In gene dynamics modeling, parameters of Boolean networks are identified from continuous data under various assumptions expressed by logical constraints. These constraints may restrict the dynamics of the network to the subclass of canalyzing or nested canalyzing functions, which are known to be appropriate for genetic networks. This paper introduces high performance algorithms, which solve the parameter identification problem by so called Zhegalkin identification and exploit the restriction to canalyzing or nested canalyzing functions resulting in reduced calculation time. The constraints are formulated in terms of orthogonal ternary vector lists, which offer an efficient representation for Boolean functions. The canalyzing constraints can be intrinsically incorporated in an existing Branch-and-Cut algorithm, which lead to a natural restriction of the search space and thus of the calculation time. For nested canalyzing constraints this is not possible. Instead, an identification algorithm based on enumeration is proposed. The algorithms are applied to mRNA micro array data from mice under different contaminant conditions and good correspondence to a known apoptotic pathway can be shown.