Refine
Document Type
- Doctoral thesis (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
- Modellreduktion (3) (remove)
Stochastic optimal control problems of residential heating systems with a geothermal energy storage
(2023)
In this thesis we consider a residential heating system equipped with several heat production and consumption units and investigate the stochastic optimal control problem for its cost-optimal management. As a special feature the manager has access to a geothermal storage (GS) which allows for inter-temporal transfer of heat energy by storing leftover solar thermal energy generated in summer for satisfying demand later. It is charged and discharged via heat exchanger pipes filled with a moving fluid. Further, the manager of that system faces uncertainties about the future fuel price and heat demand. The main goal is to minimize the expected aggregated cost for generating heat and running the system. This leads to a challenging mathematical optimization problem. The problem is formulated first as a non-standard continuous-time stochastic optimal control problem for a controlled state process whose dynamics is described by a system of ordinary differential equations (ODEs), stochastic differential equations and a partial differential equation (PDE). The PDE, which describes the temperature distribution in the GS, is first converted into a high-dimensional system of ODEs by semi-discretizing the space variables and its stability is investigated. This makes it possible to compute some aggregated characteristics which are useful for the operation of the GS embedded in the residential heating system. Second, the linear time-varying system of ODEs is approximated by a suitable linear time-invariant system. This allows the Lyapunov balanced truncation model order reduction method to be applied. Finally, we investigate the solution of the resulting standard optimal control problem for a controlled multi-dimensional diffusion process using dynamic programming methods and derive the corresponding Hamilton-Jacobi-Bellman (HJB) equation. However, no analytical solution of the HJB equation can be expected for the control problem under investigation. Therefore, we transform the continuous-time optimal control problem into a discrete-time control problem for a controlled Markov chain with finitely many states by discretizing both the time and the states. After determining the transition probabilities, the problem is solved using methods from the theory of Markovian decision processes. The thesis presents results of extensive numerical experiments carried out with the developed methods which reveal typical properties of the value function and the optimal strategy of the optimization problem. We end this thesis by describing some alternative methods to overcome the curse of dimensionality.
In this work, we consider non-reversible multi-scale stochastic processes, described by stochastic differential equations, for which we review theory on the convergence behaviour to equilibrium and mean first exit times. Relations between these time scales for non-reversible processes are established, and, by resorting to a control theoretic formulation of the large deviations action functional, even the consideration of hypo-elliptic processes is permitted. The convergence behaviour of the processes is studied in a lot of detail, in particular with respect to initial conditions and temperature. Moreover, the behaviour of the conditional and marginal distributions during the relaxation phase is monitored and discussed as we encounter unexpected behaviour. In the end, this results in the proposal of a data-based partitioning into slow and fast degrees of freedom. In addition, recently proposed techniques promising accelerated convergence to equilibrium are examined and a connection to appropriate model reduction approaches is made. For specific examples this leads to either an interesting alternative formulation of the acceleration procedure or structural insight into the acceleration mechanism. For the model order reduction technique of effective dynamics, which uses conditional expectations, error bounds for non-reversible slow-fast stochastic processes are obtained. A comparison with the reduction method of averaging is undertaken, which, for non-reversible processes, possibly yields different reduced equations. For Ornstein-Uhlenbeck processes sufficient conditions are derived for the two methods (effective dynamics and averaging) to agree in the infinite time scale separation regime. Additionally, we provide oblique projections which allow for the sampling of conditional distributions of non-reversible Ornstein-Uhlenbeck processes.
Diese Arbeit untersucht Methoden der Ordnungsreduktion schwach nichtlinearer Finite-Elemente und Finite-Differenzen Systeme. Die Nichtlinearitäten werden in einheitlicher Weise mittels einer Taylor Reihenentwicklung parametrisiert. Diese Darstellung dient als Normalform, auf der die Methoden der Ordnungsreduktion operieren. Die Modelle hoher Ordnung werden mittels projektionsbasierter Verfahren auf Systeme mit nur noch wenigen Freiheitsgraden reduziert. Diese Verfahren beruhen auf Krylov-Unterraum Methoden (Arnoldi-Verfahren) und Methoden der Zustandsauswahl (Guyan-Verfahren) für den linearen Fall. Darüberhinaus werden zwei weitere Methoden der nichtlinearen Ordnungsreduktion vorgestellt, das Verfahren der adiabatischen Elimination schnell relaxierender Variablen und das Verfahren der nichtlinearen Frequenzgangsanpassung. Die Eignung der projektiven Reduktionsmethoden wird anhand von akademischen und industrierelevanten Beispielen demonstriert