Search arXivSearch

arXiv · 2110.11806

Multi-Horizon Planning of Multi-Energy Systems

Abstract

In order to reach EU's goal of zero emissions in 2050, the energy system will go through a significant transition over the next decades. To substitute fossil energy carriers, renewable energy sources will be mainly integrated in the power system. Thereby, sector coupling will play a major role by making flexibility from other sectors such as heat or transport accessible to the power system. Planning the cost optimal transition requires a whole-system view over multiple horizons and across all sectors. This imposes the need for multi-energy system (MES) models coupled with multi-horizon investment models. This paper presents two multi-horizon planning approaches to determine the cost optimal pathway of a MES. As a major contribution, we propose a new method to incorporate technology-dependent learning cost curves in the planning problem and show that the resulting mixed-integer linear programming problem can be solved faster with a Benders decomposition technique as compared to a closed optimization. As a further contribution, we demonstrate the usefulness of our approach by showing the MES expansion pathway for a small German test system.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tim Felling, Oliver Levers, Philipp Fortenbacher. 2021-10-22. Multi-Horizon Planning of Multi-Energy Systems. https://arxiv.org/abs/2110.11806

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Ensuring Stability of Non-Minimal Modes in Input-Output Data-Driven Representation

Many recent data-driven control approaches for linear time-invariant systems are based on output trajectory prediction using input-output data matrices. The system dynamics described by this predictor, which we refer to as the input-output data-driven representation, yields non-unique autoregressive with exogenous inputs (ARX) models having possibly unstable non-minimal modes. In this note, we show that the stability of these non-minimal modes is ensured by a certain choice of ARX model, which coincides with the minimum-norm least-squares predictor using the Moore-Penrose inverse of the data matrix. This stability guarantee holds regardless of the underlying system's stability. Moreover, the stability persists under sufficiently small noise in data when a suitably truncated Moore-Penrose inverse is used. Consequently, the ARX model need not be reduced to the true system order in order to avoid unstable additional modes.

eess.SY

Optimization-Based Formation Flight on Libration Point Orbits

A model predictive control (MPC) framework is developed for station-keeping in spacecraft formation flight along libration point orbits. At each control period, the MPC policy solves a multi-vehicle optimal control problem (MVOCP) that tracks a reference trajectory, while enforcing path constraints on the relative motion of the formation. The control policy makes use of a limited set of control nodes consistent with operational constraints that allow only a small number of maneuver opportunities per revolution. To promote recursive feasibility, path constraints are progressively tightened across the prediction horizon. An isoperimetric reformulation of the constraints is used to prevent inter-sample violations. The resulting MVOCP is a nonconvex program, which is solved via sequential convex programming. The proposed approach is evaluated in a high-fidelity ephemeris model under uncertainties for a formation along the near-rectilinear halo orbit (NRHO), and subject to path constraints on inter-spacecraft separation and relative Sun phase angle. The results demonstrate maintenance of a spacecraft formation that satisfies the path constraints with realistic cumulative propellant consumption.

eess.SY

Certificates Synthesis for A Class of Observational Properties in Stochastic Systems: A Unified Approach

In this paper, we investigate the probabilistic formal verification of stochastic dynamical systems over continuous state spaces. Motivated by problems in state estimation and information-flow security, we introduce the notion of observational properties, which characterize the inferences an external observer can draw from system outputs. These properties are formulated as probabilistic hyperproperties based on HyperLTL over finite traces, yielding a unified framework that subsumes several existing notions studied separately in the literature. We reduce the verification problem to reachability analysis over an augmented structure that integrates the system dynamics with an automaton representation of the specification. Building on this construction, we develop stochastic barrier certificates that provide probabilistic guarantees for property satisfaction while avoiding explicit state-space discretization. The effectiveness of the proposed framework is demonstrated through a case study.

eess.SY