@article{GrimmMartinSchmidtetal.2016, author = {Grimm, Veronika and Martin, Alexander and Schmidt, Martin and Weibelzahl, Martin and Z{\"o}ttl, Gregor}, title = {Transmission and generation investment in electricity markets: The effects of market splitting and network fee regimes}, series = {European Journal of Operational Research}, volume = {254}, journal = {European Journal of Operational Research}, number = {2}, doi = {10.1016/j.ejor.2016.03.044}, pages = {493 -- 509}, year = {2016}, abstract = {We propose an equilibrium model that allows to analyze the long-run impact of the electricity market design on transmission line expansion by the regulator and investment in generation capacity by private firms in liberalized electricity markets. The model incorporates investment decisions of the transmission system operator and private firms in expectation of an energy-only market and cost-based redispatch. In different specifications we consider the cases of one vs. multiple price zones (market splitting) and analyze different approaches to recover network cost—in particular lump sum, generation capacity based, and energy based fees. In order to compare the outcomes of our multilevel market model with a first best benchmark, we also solve the corresponding integrated planner problem. Using two test networks we illustrate that energy-only markets can lead to suboptimal locational decisions for generation capacity and thus imply excessive network expansion. Market splitting heals these problems only partially. These results are valid for all considered types of network tariffs, although investment slightly differs across those regimes.}, language = {en} } @unpublished{GugatKrugMartin2021, author = {Gugat, Martin and Krug, Richard and Martin, Alexander}, title = {Transient gas pipeline flow: Analytical examples, numerical simulation and a comparison to the quasi-static approach}, doi = {10.1007/s11081-021-09690-4}, year = {2021}, abstract = {The operation of gas pipeline flow with high pressure and small Mach numbers allows to model the flow by a semilinear hyperbolic system of partial differential equations. In this paper we present a number of transient and stationary analytical solutions of this model. They are used to discuss and clarify why a pde model is necessary to handle certain dynamic situations in the operation of gas transportation networks. We show that adequate numerical discretizations can capture the dynamical behavior sufficiently accurate. We also present examples that show that in certain cases an optimization approach that is based upon multi-period optimization of steady states does not lead to approximations that converge to the optimal state.}, language = {en} } @article{GugatLeugeringMartinetal.2018, author = {Gugat, Martin and Leugering, G{\"u}nter and Martin, Alexander and Schmidt, Martin and Sirvent, Mathias and Wintergerst, David}, title = {Towards Simulation Based Mixed-Integer Optimization with Differential Equations}, series = {Networks}, journal = {Networks}, doi = {10.1002/net.21812}, pages = {24}, year = {2018}, abstract = {We propose a decomposition based method for solving mixed-integer nonlinear optimization problems with "black-box" nonlinearities, where the latter, e.g., may arise due to differential equations or expensive simulation runs. The method alternatingly solves a mixed-integer linear master problem and a separation problem for iteratively refining the mixed-integer linear relaxation of the nonlinear equalities. The latter yield nonconvex feasible sets for the optimization model but we have to restrict ourselves to convex and monotone constraint functions. Under these assumptions, we prove that our algorithm finitely terminates with an approximate feasible global optimal solution of the mixed integer nonlinear problem. Additionally, we show the applicability of our approach for three applications from optimal control with integer variables, from the field of pressurized flows in pipes with elastic walls, and from steady-state gas transport. For the latter we also present promising numerical results of our method applied to real-world instances that particularly show the effectiveness of our method for problems defined on networks.}, language = {en} } @article{KrugLeugeringMartinetal.2021, author = {Krug, Richard and Leugering, G{\"u}nter and Martin, Alexander and Schmidt, Martin and Weninger, Dieter}, title = {Time-Domain Decomposition for Optimal Control Problems Governed by Semilinear Hyperbolic Systems with Mixed Two-Point Boundary Conditions}, series = {Control and Cybernetics}, journal = {Control and Cybernetics}, pages = {20}, year = {2021}, abstract = {In this article, we continue our work (Krug et al., 2021) on time-domain decomposition of optimal control problems for systems of semilinear hyperbolic equations in that we now consider mixed two-point boundary value problems and provide an in-depth well-posedness analysis. The more general boundary conditions significantly enlarge the scope of applications, e.g., to hyperbolic problems on metric graphs with cycles. We design an iterative method based on the optimality systems that can be interpreted as a decomposition method for the original optimal control problem into virtual control problems on smaller time domains.}, language = {en} } @article{KrugLeugeringMartinetal.2020, author = {Krug, Richard and Leugering, G{\"u}nter and Martin, Alexander and Schmidt, Martin and Weninger, Dieter}, title = {Time-Domain Decomposition for Optimal Control Problems Governed by Semilinear Hyperbolic Systems}, series = {SIAM Journal on Control and Optimization}, journal = {SIAM Journal on Control and Optimization}, pages = {28}, year = {2020}, abstract = {In this article, we extend the time-domain decomposition method described by Lagnese and Leugering (2003) to semilinear optimal control problems for hyperbolic balance laws with spatio-temporal varying coefficients. We provide the design of the iterative method applied to the global first-order optimality system, prove its convergence, and derive an a posteriori error estimate. The analysis is done entirely on the continuous level. A distinguishing feature of the method is that the decomposed optimality system can be interpreted as an optimality system of a local "virtual" optimal control problem. Thus, the iterative time-domain decomposition of the optimality system can be interpreted as an iterative parallel scheme for virtual optimal control problems on the subintervals. A typical example and further comments are given to show the range of potential applications. Moreover, we provide some numerical experiments to give a first interpretation of the role of the parameters involved in the iterative process.}, language = {en} } @article{BaermannLiersMartinetal.2015, author = {B{\"a}rmann, Andreas and Liers, Frauke and Martin, Alexander and Merkert, Maximilian and Thurner, Christoph and Weninger, Dieter}, title = {Solving network design problems via iterative aggregation}, series = {Mathematical Programming Computation}, volume = {7}, journal = {Mathematical Programming Computation}, number = {2}, doi = {10.1007/s12532-015-0079-1}, pages = {189 -- 217}, year = {2015}, abstract = {In this work, we present an exact approach for solving network design problems that is based on an iterative graph aggregation procedure. The scheme allows existing preinstalled capacities. Starting with an initial aggregation, we solve a sequence of network design master problems over increasingly fine-grained representations of the original network. In each step, a subproblem is solved that either proves optimality of the solution or gives a directive where to refine the representation of the network in the subsequent iteration. The algorithm terminates with a globally optimal solution to the original problem. Our implementation uses a standard integer programming solver for solving the master problems as well as the subproblems. The computational results on random and realistic instances confirm the profitable use of the iterative aggregation technique. The computing time often reduces drastically when our method is compared to solving the original problem from scratch.}, language = {en} } @unpublished{LiersMartinMerkertetal.2020, author = {Liers, Frauke and Martin, Alexander and Merkert, Maximilian and Mertens, Nick and Michaels, Dennis}, title = {Solving Mixed-Integer Nonlinear Optimization Problems using Simultaneous Convexification - a Case Study for Gas Networks}, pages = {36}, year = {2020}, abstract = {Solving mixed-integer nonlinear optimization problems (MINLPs) to global optimality is extremely challenging. An important step for enabling their solution consists in the design of convex relaxations of the feasible set. Known solution approaches based on spatial branch-and-bound become more effective the tighter the used relaxations are. Relaxations are commonly established by convex underestimators, where each constraint function is considered separately. Instead, a considerably tighter relaxation can be found via so-called simultaneous convexification, where convex underestimators are derived for more than one constraint function at a time. In this work, we present a global solution approach for solving mixed-integer nonlinear problems that uses simultaneous convexification. We introduce a separation method that relies on determining the convex envelope of linear combinations of the constraint functions and on solving a nonsmooth convex problem. In particular, we apply the method to quadratic absolute value functions and derive their convex envelopes. The practicality of the proposed solution approach is demonstrated on several test instances from gas network optimization, where the method outperforms standard approaches that use separate convex relaxations.}, language = {en} } @unpublished{AignerBurlacuLiersetal., author = {Aigner, Kevin-Martin and Burlacu, Robert and Liers, Frauke and Martin, Alexander}, title = {Solving AC Optimal Power Flow with Discrete Decisions to Global Optimality}, abstract = {We present a solution framework for general alternating current optimal power flow (AC OPF) problems that include discrete decisions. The latter occur, for instance, in the context of the curtailment of renewables or the switching of power generation units and transmission lines. Our approach delivers globally optimal solutions and is provably convergent. We model AC OPF problems with discrete decisions as mixed-integer nonlinear programs. The solution method starts from a known framework that uses piecewise linear relaxations. These relaxations are modeled as as mixed-integer linear programs and adaptively refined until some termination criterion is fulfilled. In this work, we extend and complement this approach by problem-specific as well as very general algorithmic enhancements. In particular, these are mixed-integer second-order cone programs as well as primal and dual cutting planes. For example objective cuts and no-good-cuts help to compute good feasible solutions as where outer approximation constraints tighten the relaxations. We present extensive numerical results for various AC OPF problems where discrete decisions play a major role. Even for hard instances with a large proportion of discrete decisions, the method is able to generate high quality solutions efficiently. Furthermore, we compare our approach with state-of-the-art MINLP. Our method outperforms all other algorithms.}, language = {en} } @article{AbedChenDisseretal.2017, author = {Abed, Fidaa and Chen, Lin and Disser, Yann and Groß, Martin and Megow, Nicole and Meißner, Julie and Richter, Alexander T. and Rischke, Roman}, title = {Scheduling Maintenance Jobs in Networks}, year = {2017}, abstract = {We investigate the problem of scheduling the maintenance of edges in a network, motivated by the goal of minimizing outages in transportation or telecommunication networks. We focus on maintaining connectivity between two nodes over time; for the special case of path networks, this is related to the problem of minimizing the busy time of machines. We show that the problem can be solved in polynomial time in arbitrary networks if preemption is allowed. If preemption is restricted to integral time points, the problem is NP-hard and in the non-preemptive case we give strong non-approximability results. Furthermore, we give tight bounds on the power of preemption, that is, the maximum ratio of the values of non-preemptive and preemptive optimal solutions. Interestingly, the preemptive and the non-preemptive problem can be solved efficiently on paths, whereas we show that mixing both leads to a weakly NP-hard problem that allows for a simple 2-approximation.}, language = {en} } @unpublished{AignerSchaumannvonLoeperetal., author = {Aigner, Kevin-Martin and Schaumann, Peter and von Loeper, Freimut and Martin, Alexander and Schmidt, Volker and Liers, Frauke}, title = {Robust DC Optimal Power Flow with Modeling of Solar Power Supply Uncertainty via R-Vine Copulas}, abstract = {We present a robust approximation of joint chance constrained DC Optimal Power Flow in combination with a model-based prediction of uncertain power supply via R-vine copulas. It is applied to optimize the discrete curtailment of solar feed-in in an electrical distribution network and guarantees network stability under fluctuating feed-in. This is modeled by a two-stage mixed-integer stochastic optimization problem proposed by Aigner et al. (European Journal of Operational Research, (2021)). The solution approach is based on the approximation of chance constraints via robust constraints using suitable uncertainty sets. The resulting robust optimization problem has a known equivalent tractable reformulation. To compute uncertainty sets that lead to an inner approximation of the stochastic problem, an R-vine copula model is fitted to the distribution of the multi-dimensional power forecast error, i.e., the difference between the forecasted solar power and the measured feed-in at several network nodes. The uncertainty sets are determined by encompassing a sufficient number of samples drawn from the R-vine copula model. Furthermore, an enhanced algorithm is proposed to fit R-vine copulas which can be used to draw conditional samples for given solar radiation forecasts. The experimental results obtained for real-world weather and network data demonstrate the effectiveness of the combination of stochastic programming and model-based prediction of uncertainty via copulas. We improve the outcomes of previous work by showing that the resulting uncertainty sets are much smaller and lead to less conservative solutions while maintaining the same probabilistic guarantees.}, language = {en} }