René Henrion, Nicolas Perkowski, Caren Tischendorf
Maximilian Schade (HU)
01.01.2019 – 31.12.2021
The project aims to support decision making for the design and control of energy networks like power networks or gas transport networks or couplings of them. Particular focus of the investigations are the uncertainties caused by fluctuations in demand and supply. Neglecting uncertainties, the transient behaviour of such networks can be described by systems of partial differential equations (e.g. the Euler equations for gas networks) coupled via algebraic constraints. A well-established approach for analyzing such systems is the formulation as an operator DAE of the form
A*(Dv(t))’ + B(v(t)) = r(t), t∈ I
with linear operators A*: Z*→ V*, D: V → Z and a nonlinear operator B: V → V*, where I is a time interval and V, Z, H are Banach spaces such that Z ⊂ H ≡ H* ⊂ Z* forms an evolution triple. To include uncertainties in the demand and supply, the function r on the right-hand side shall be modeled as a stochastic process Rt which leads to a stochastic operator DAE of the form
A*d(DVt) + B(Vt) = C(t,Vt)dRt, t∈ I.
The first challenge for the existence of solutions is the proper choice of the stochastic process Rt. Existence of solutions is not guaranteed if we allow Rt to be a Wiener process because the algebraic part needs a proper treatment. As long as the system does not contain hidden constraints, an appropriate choice for Rt would be the use of an integrated diffusion, say an integrated mean-reverting Ornstein-Uhlenbeck process, for the algebraic part and the Wiener process for the differential part. This needs a separation of both parts which can be treated by a decoupling approach.
A second direction of research will add the optimization aspect to the consideration of PDAEs under uncertainty. The latter is assumed to be given as before by stochastic loads in the nodes of the network, however in the simpler setting of a random vector ξ (for instance, after a KarhunenLoève expansion of the original stochastic process). The decision u to be optimized could represent a certain design variable. The aim is to find optimal decisions which are robust in a probabilistic sense with respect to later actions of the random parameter. This results in the formulation of an optimization problem subject to a probabilistic constraint:
min f(u): A*(Dv(t))’ + B(v(t)) = r(ξ, t), (t ∈ I), ℙ[g(u, v, ξ, t)≤ 0 ∀t ∈ I] ≥ p
In this model, a decision u is declared to be feasible whenever the probability of satisfying the infinite stochastic inequality system g(u, v, ξ, t) ≤ 0 ∀t ∈ I is satisfied with a probability larger than a specified level p ∈ (0, 1). The inequalities might represent, for instance, pressure bounds in a gas network. Probabilistic constraints represent a major model within stochastic optimization which is widely applied in engineering sciences, first of all in power management. Traditionally, they are considered in the framework of operations research, i.e., of finite-dimensional optimization.
A first goal will be to obtain a (possibly local) solution theory for the considered equations. For that purpose we will first study monotone parabolic SPDAEs and we will extend existing work on coupled systems to the stochastic setting. By extending the analysis to “locally monotone” coefficients, we hope to treat interacting networks of Navier-Stokes equations. Networks of Euler equations could then be derived in the vanishing viscosity limit. Additionally, we aim to exploit the port-Hamiltonian structure of the network equations for the construction of proper numerical schemes. In order to address the optimization problem, we plan to start with the question of feasibibility of solutions. On one hand we want to exploit ideas of the perturbation analysis that allows us to develop sufficient criteria for the validity of the inequality constraints for transient solutions. On the other hand, we intend to incorporate for the first time probabilistic constraints (moreover of implicit type) into optimization problems subject to uncertain PDAEs.
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