TY - JOUR A1 - Grimm, Veronika A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Weibelzahl, Martin A1 - Zöttl, Gregor T1 - Transmission and generation investment in electricity markets: The effects of market splitting and network fee regimes JF - European Journal of Operational Research N2 - 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. KW - Electricity market modeling KW - Mixed-integer nonlinear optimization KW - Multilevel programming KW - Network expansion KW - Transmission management Y1 - 2016 U6 - https://doi.org/10.1016/j.ejor.2016.03.044 VL - 254 IS - 2 SP - 493 EP - 509 ER - TY - JOUR A1 - Gugat, Martin A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Sirvent, Mathias A1 - Wintergerst, David T1 - Towards Simulation Based Mixed-Integer Optimization with Differential Equations JF - Networks N2 - 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. KW - Mixed-Integer Optimization KW - Simulation Based Optimization KW - Optimization with Differential Equations KW - Decomposition Method KW - Gas Transport Networks Y1 - 2018 U6 - https://doi.org/10.1002/net.21812 ER - TY - JOUR A1 - Gugat, Martin A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Sirvent, Mathias A1 - Wintergerst, David T1 - MIP-Based Instantaneous Control of Mixed-Integer PDE-Constrained Gas Transport Problems JF - Computational Optimization and Applications N2 - We study the transient optimization of gas transport networks including both discrete controls due to switching of controllable elements and nonlinear fluid dynamics described by the system of isothermal Euler equations, which are partial differential equations in time and 1-dimensional space. This combination leads to mixed-integer optimization problems subject to nonlinear hyperbolic partial differential equations on a graph. We propose an instantaneous control approach in which suitable Euler discretizations yield systems of ordinary differential equations on a graph. This networked system of ordinary differential equations is shown to be well-posed and affine-linear solutions of these systems are derived analytically. As a consequence, finite-dimensional mixed-integer linear optimization problems are obtained for every time step that can be solved to global optimality using general-purpose solvers. We illustrate our approach in practice by presenting numerical results on a realistic gas transport network. KW - Mixed-integer optimal control KW - Instantaneous control KW - Partial differential equations on graphs KW - Gas networks KW - Mixed-integer linear optimization Y1 - 2017 U6 - https://doi.org/10.1007/s10589-017-9970-1 VL - 70 IS - 1 SP - 267 EP - 294 ER - TY - JOUR A1 - Burlacu, Robert A1 - Egger, Herbert A1 - Groß, Martin A1 - Martin, Alexander A1 - Pfetsch, Marc A1 - Schewe, Lars A1 - Sirvent, Mathias A1 - Skutella, Martin T1 - Maximizing the storage capacity of gas networks: a global MINLP approach N2 - In this paper, we study the transient optimization of gas networks, focusing in particular on maximizing the storage capacity of the network. We include nonlinear gas physics and active elements such as valves and compressors, which due to their switching lead to discrete decisions. The former is described by a model derived from the Euler equations that is given by a coupled system of nonlinear parabolic partial differential equations (PDEs). We tackle the resulting mathematical optimization problem by a first-discretize-then-optimize approach. To this end, we introduce a new discretization of the underlying system of parabolic PDEs and prove well-posedness for the resulting nonlinear discretized system. Endowed with this discretization, we model the problem of maximizing the storage capacity as a non-convex mixed-integer nonlinear problem (MINLP). For the numerical solution of the MINLP, we algorithmically extend a well-known relaxation approach that has already been used very successfully in the field of stationary gas network optimization. This method allows us to solve the problem to global optimality by iteratively solving a series of mixed-integer problems (MIPs). Finally, we present two case studies that illustrate the applicability of our approach. KW - Mixed-Integer Nonlinear Programming KW - Transient Gas Transport Optimization KW - Storage Capacity Maximization KW - Power-to-Gas KW - First-Discretize-Then-Optimize Y1 - 2019 U6 - https://doi.org/10.1007/s11081-018-9414-5 VL - 20 SP - 543 EP - 573 ET - Optimization and Engineering ER - TY - JOUR A1 - Krug, Richard A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Weninger, Dieter T1 - Time-Domain Decomposition for Optimal Control Problems Governed by Semilinear Hyperbolic Systems with Mixed Two-Point Boundary Conditions JF - Control and Cybernetics N2 - 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. KW - Time-domain decomposition KW - Optimal control KW - Semilinear hyperbolic systems KW - Convergence Y1 - 2021 ER - TY - JOUR A1 - Krug, Richard A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Weninger, Dieter T1 - Time-Domain Decomposition for Optimal Control Problems Governed by Semilinear Hyperbolic Systems JF - SIAM Journal on Control and Optimization N2 - 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. KW - Time-domain decomposition KW - Optimal control KW - Semilinear hyperbolic systems KW - Convergence KW - A posteriori error estimates Y1 - 2020 ER - TY - INPR A1 - Aigner, Kevin-Martin A1 - Burlacu, Robert A1 - Liers, Frauke A1 - Martin, Alexander T1 - Solving AC Optimal Power Flow with Discrete Decisions to Global Optimality N2 - 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. KW - Mixed-Integer Nonlinear Programming KW - Second-Order Cone Programming KW - AC Optimal Power Flow KW - Discrete Decisions KW - Piecewise Linear Relaxation Y1 - 2020 ER - TY - INPR A1 - Gugat, Martin A1 - Krug, Richard A1 - Martin, Alexander T1 - Transient gas pipeline flow: Analytical examples, numerical simulation and a comparison to the quasi-static approach N2 - 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. Y1 - 2021 U6 - https://doi.org/10.1007/s11081-021-09690-4 ER - TY - JOUR A1 - Hante, Falk A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schewe, Lars A1 - Schmidt, Martin T1 - Challenges in optimal control problems for gas and fluid flow in networks of pipes and canals: From modeling to industrial applications N2 - We consider optimal control problems for the flow of gas or fresh water in pipe networks as well as drainage or sewer systems in open canals. The equations of motion are taken to be represented by the nonlinear isothermal Euler gas equations, the water hammer equations, or the St.~Venant equations for flow. We formulate model hierarchies and derive an abstract model for such network flow problems including pipes, junctions, and controllable elements such as valves, weirs, pumps, as well as compressors. We use the abstract model to give an overview of the known results and challenges concerning equilibria, well-posedness, controllability, and optimal control. A major challenge concerning the optimization is to deal with switching on-off states that are inherent to controllable devices in such applications combined with continuous simulation and optimization of the gas flow. We formulate the corresponding mixed-integer nonlinear optimal control problems and outline a decomposition approach as a solution technique. KW - Networks KW - pipes KW - optimal control KW - Euler and St. Venant equations KW - hierarchy of models Y1 - 2016 ER - TY - JOUR A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Sirvent, Mathias T1 - Nonoverlapping Domain Decomposition for Optimal Control Problems governed by Semilinear Models for Gas Flow in Networks N2 - We consider optimal control problems for gas flow in pipeline networks. The equations of motion are taken to be represented by a first-order system of hyperbolic semilinear equations derived from the fully nonlinear isothermal Euler gas equations. We formulate an optimal control problem on a network and introduce a tailored time discretization thereof. In order to further reduce the complexity, we consider an instantaneous control strategy. The main part of the paper is concerned with a nonoverlapping domain decomposition of the optimal control problem on the graph into local problems on smaller sub-graphs - ultimately on single edges. We prove convergence of the domain decomposition method on networks and study the wellposedness of the corresponding time-discrete optimal control problems. The point of the paper is that we establish virtual control problems on the decomposed subgraphs such that the corresponding optimality systems are in fact equal to the systems obtained via the domain decomposition of the entire optimality system. KW - Optimal control, Gas networks, Euler's equation, Semilinear PDE, Nonoverlapping domain decomposition Y1 - 2017 VL - 46 IS - 3 SP - 191 EP - 225 PB - Control and Cybernetics ER - TY - INPR A1 - Aigner, Kevin-Martin A1 - Schaumann, Peter A1 - von Loeper, Freimut A1 - Martin, Alexander A1 - Schmidt, Volker A1 - Liers, Frauke T1 - Robust DC Optimal Power Flow with Modeling of Solar Power Supply Uncertainty via R-Vine Copulas N2 - 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. KW - chance constrained programming KW - optimal power flow KW - robust optimization KW - conditional uncertainty set KW - R-vine copula Y1 - ER - TY - INPR A1 - Grimm, Veronika A1 - Martin, Alexander A1 - Sölch, Christian A1 - Weibelzahl, Martin A1 - Zöttl, Gregor T1 - Market-Based Redispatch May Result in Inefficient Dispatch N2 - In this paper we analyze a uniform price electricity spot market that is followed by redispatch in the case of network congestion. We assume that the transmission system operator is incentivized to minimize redispatch cost and compare a cost-based redispatch (CBR) to a market-based redispatch (MBR) mechanism. For networks with at least three nodes we show that in contrast to CBR, in the case of MBR the redispatch cost minimizing allocation may not be short-run efficient. As we demonstrate, in case of MBR the possibility of the transmission system operator to reduce redispatch cost at the expense of a reduced welfare may be driven by the electricity supply side or the electricity demand side. If, however, the transmission system operator is obliged to implement the welfare maximizing (instead of the redispatch cost minimizing) dispatch by regulation, this will result in an efficient dispatch also in case of MBR. KW - Electricity Markets, Redispatch, Congestion Management, Computational Equilibrium Models Y1 - 2019 ER - TY - INPR A1 - Schmidt, Martin A1 - Hiller, Benjamin A1 - Koch, Thorsten A1 - Pfetsch, Marc A1 - Geißler, Björn A1 - Henrion, René A1 - Joormann, Imke A1 - Martin, Alexander A1 - Morsi, Antonio A1 - Römisch, Werner A1 - Schewe, Lars A1 - Schultz, Rüdiger A1 - Steinbach, Marc C. T1 - Capacity Evaluation for Large-Scale Gas Networks N2 - Natural gas is important for the energy turnaround in many countries like in Germany, where it serves as a "bridging energy" towards a fossil-free energy supply in the future. About 20% of the total German energy demand is provided by natural gas, which is transported through a complex pipeline network with a total length of about 30000 km and the efficient use of the given transport infrastructure for natural gas is of political, economic, and societal importance. As a consequence of the liberalization of the European gas market in the last decades, gas trading and transport have been decoupled. This has led to new challenges for gas transport companies, and mathematical optimization is perfectly suited for tackling many of these challenges. However, the underlying mathematical problems are by far too hard to be solved by today's general-purpose software so that novel mathematical theory and algorithms are needed. The industrial research project "ForNe: Research Cooperation Network Optimization" has been initiated and funded by Open Grid Europe in 2009 and brought together experts in mathematical optimization from seven German universities and research institutes, which cover almost the entire range of mathematical optimization: integer and nonlinear optimization as well as optimization under uncertainty. The mathematical research results have been put together in a software package that has been delivered to Open Grid Europe at the end of the project. Moreover, the research is still continuing - e.g., in the Collaborative Research Center/Transregio 154 "Mathematical Modelling, Simulation and Optimization using the Example of Gas Networks" funded by the German Research Foundation. Y1 - 2019 ER - TY - INPR A1 - Aigner, Kevin-Martin A1 - Clarner, Jan-Patrick A1 - Liers, Frauke A1 - Martin, Alexander T1 - Robust Approximation of Chance Constrained DC Optimal Power Flow under Decision-Dependent Uncertainty N2 - We propose a mathematical optimization model and its solution for joint chance constrained DC Optimal Power Flow. In this application, it is particularly important that there is a high probability of transmission limits being satisfied, even in the case of uncertain or fluctuating feed-in from renewable energy sources. In critical network situations where the network risks overload, renewable energy feed-in has to be curtailed by the transmission system operator (TSO). The TSO can reduce the feed-in in discrete steps at each network node. The proposed optimization model minimizes curtailment while ensuring that there is a high probability of transmission limits being maintained. The latter is modeled via (joint) chance constraints that are computationally challenging. Thus, we propose a solution approach based on the robust safe approximation of these constraints. Hereby, probabilistic constraints are replaced by robust constraints with suitably defined uncertainty sets constructed from historical data. The uncertainty sets are calculated by encompassing randomly drawn scenarios using the scenario approach proposed by Margellos et al. (IEEE Transactions on Automatic Control, 59 (2014)). The ability to discretely control the power feed-in then leads to a robust optimization problem with decision-dependent uncertainties, i.e. the uncertainty sets depend on decision variables. We propose an equivalent mixed-integer linear reformulation for box uncertainties with the exact linearization of bilinear terms. Finally, we present numerical results for different test cases from the Nesta archive, as well as for a real network. We consider the discrete curtailment of solar feed-in, for which we use real-world weather and network data. The experimental tests demonstrate the effectiveness of this method and run times are very fast. Moreover, on average the calculated robust solutions lead only to a small increase in curtailment, when compared to nominal solutions. KW - OR in energy KW - optimal power flow KW - chance constrained programming KW - robust optimization KW - decision-dependent uncertainty Y1 - 2020 ER - TY - JOUR A1 - Gatzert, Nadine A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Seith, Benjamin A1 - Vogl, Nikolai T1 - Portfolio Optimization with Irreversible Long-Term Investments in Renewable Energy under Policy Risk: A Mixed-Integer Multistage Stochastic Model and a Moving-Horizon Approach JF - European Journal of Operational Research N2 - Portfolio optimization is an ongoing hot topic of mathematical optimization and management science. Due to the current financial market environment with low interest rates and volatile stock markets, it is getting more and more important to extend portfolio optimization models by other types of investments than classical assets. In this paper, we present a mixed-integer multistage stochastic model that includes investment opportunities in irreversible and long-term infrastructure projects in the context of renewable energies, which are also subject to policy risk. On realistic time scales for investment problems of this type, the resulting instances are by far too large to be solved with today's most evolved optimization software. Thus, we present a tailored moving-horizon approach together with suitable approximations and simplifications of the model. We evaluate these approximations and simplifications in a computational sensitivity analysis and derive a final model that can be tackled on a realistic instance by our moving-horizon approach. KW - Mixed-integer optimization KW - Multistage stochastic optimization KW - Portfolio optimization KW - Illiquid investments KW - Policy risk Y1 - 2019 ER - TY - INPR A1 - Krug, Richard A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Weninger, Dieter T1 - A Consensus-Based Alternating Direction Method for Mixed-Integer and PDE-Constrained Gas Transport Problems N2 - We consider dynamic gas transport optimization problems, which lead to large-scale and nonconvex mixed-integer nonlinear optimization problems (MINLPs) on graphs. Usually, the resulting instances are too challenging to be solved by state-of-the-art MINLP solvers. In this paper, we use graph decompositions to obtain multiple optimization problems on smaller blocks, which can be solved in parallel and which may result in simpler classes of optimization problems since not every block necessarily contains mixed-integer or nonlinear aspects. For achieving feasibility at the interfaces of the several blocks, we employ a tailored consensus-based penalty alternating direction method. Our numerical results show that such decomposition techniques can outperform the baseline approach of just solving the overall MINLP from scratch. However, a complete answer to the question of how to decompose MINLPs on graphs in dependence of the given model is still an open topic for future research. KW - Gas transport networks KW - Mixed-integer nonlinear optimization KW - Alternating direction methods KW - Graph decomposition KW - Penalty methods Y1 - 2022 ER - TY - JOUR A1 - Abed, Fidaa A1 - Chen, Lin A1 - Disser, Yann A1 - Groß, Martin A1 - Megow, Nicole A1 - Meißner, Julie A1 - Richter, Alexander T. A1 - Rischke, Roman T1 - Scheduling Maintenance Jobs in Networks N2 - 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. Y1 - 2017 ER - TY - JOUR A1 - Gugat, Martin A1 - Keimer, Alexander A1 - Leugering, Günter A1 - Wang, Zhiqiang ED - Piccoli, Benedetto T1 - Analysis of a system of nonlocal conservation laws for multi-commodity flow on networks JF -  Networks and Heterogeneous Media N2 - We consider a system of scalar nonlocal conservation laws on networks that model a highly re-entrant multi-commodity manufacturing system as encountered in semiconductor production. Every single commodity is mod-eled by a nonlocal conservation law, and the corresponding PDEs are coupled via a collective load, the work in progress. We illustrate the dynamics for two commodities. In the applications, directed acyclic networks naturally occur, therefore this type of networks is considered. On every edge of the network we have a system of coupled conservation laws with nonlocal velocity. At the junctions the right hand side boundary data of the foregoing edges is passed as left hand side boundary data to the following edges and PDEs. For distributing junctions, where we have more than one outgoing edge, we impose time dependent distribution functions that guarantee conservation of mass. We provide results of regularity, existence and well-posedness of the multi-commodity network model for L p-, BV-and W 1,p-data. Moreover, we define an L 2-tracking type objective and show the existence of minimizers that solve the corresponding optimal control problem. KW - conservation laws on network KW - nonlocal conservation laws KW - optimal nodal control KW - systems of hyperbolic pdes Y1 - 2016 U6 - https://doi.org/DOI: 10.3934/nhm.2015.10.749 VL - 10 IS - 4 SP - 749 EP - 785 ER - TY - JOUR A1 - Bärmann, Andreas A1 - Liers, Frauke A1 - Martin, Alexander A1 - Merkert, Maximilian A1 - Thurner, Christoph A1 - Weninger, Dieter T1 - Solving network design problems via iterative aggregation JF - Mathematical Programming Computation N2 - 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. KW - Aggregation KW - Network design KW - Combinatorial optimization KW - Mixed-integer programming KW - Branch-and-cut Y1 - 2015 U6 - https://doi.org/10.1007/s12532-015-0079-1 VL - 7 IS - 2 SP - 189 EP - 217 ER - TY - GEN A1 - Lang, Jens A1 - Leugering, Günter A1 - Martin, Alexander A1 - Tischendorf, Caren T1 - Gasnetzwerke: Mathematische Modellierung, Simulation und Optimierung N2 - Im Mai 2014 wurde seitens der DFG der Transregio 154 Mathematische Modellierung, Simulation und Optimierung am Beispiel von Gasnetzwerken bewilligt. Die Forschungsarbeiten an den beteiligten Standorten, der Friedrich-Alexander-Universität Erlangen-Nürnberg (Sprecheruniversität; Sprecher: Alexander Martin), der Technischen Universität Darmstadt (stellvertretender Sprecher: Jens Lang), der Technischen Universität Berlin, der Humboldt Universität (stellvertretende Sprecherin: Caren Tischendorf) sowie den Partnerinstitutionen Weierstraß-Institut (Berlin), Konrad-Zuse-Zentrum (Berlin) und Universität Duisburg-Essen haben im Oktober 2014 begonnen. Y1 - 2015 U6 - https://doi.org/10.1515/dmvm-2015-0013 ER - TY - INPR A1 - Liers, Frauke A1 - Martin, Alexander A1 - Merkert, Maximilian A1 - Mertens, Nick A1 - Michaels, Dennis T1 - Solving Mixed-Integer Nonlinear Optimization Problems using Simultaneous Convexification - a Case Study for Gas Networks N2 - 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. KW - Mixed-Integer Nonlinear Programming KW - Simultaneous Convexification KW - Convex Envelope KW - Gas Network Optimization Y1 - 2020 ER - TY - INPR A1 - Hümbs, Lukas A1 - Martin, Alexander A1 - Schewe, Lars T1 - Exploiting complete linear descriptions for decentralized power market problems with integralities N2 - It is well known that linear prices supporting a competitive equilibrium exist in the case of convex markets, however, in the presence of integralities this is open and hard to decide in general. We present necessary and sufficient conditions for the existence of such prices for decentralized market problems where market participants have integral decision variables and their feasible sets are given in complete linear description. We utilize total unimodularity and the aforementioned conditions to show that such linear prices exist and present some applications. Furthermore, we compute competitive equilibria for two classes of decentralized market problems arising in energy markets and show that competitive equilibria may exist regardless of integralities. KW - Mixed-integer programming KW - Competitive equilibrium KW - Linear prices KW - Total unimodularity Y1 - 2019 N1 - Former title: "On the existence of solutions to market equilibrium problems with integralities" ER - TY - INPR A1 - Göß, Adrian A1 - Martin, Alexander A1 - Pokutta, Sebastian A1 - Sharma, Kartikey T1 - Norm-induced Cuts: Optimization with Lipschitzian Black-box Functions N2 - Optimal control problems usually involve constraints which model physical states and their possible transitions. These are represented by ordinary or partial differential equations (ODEs/PDEs) which add a component of infinite dimension to the problem. In recent literature, one method to simulate such ODEs/PDEs are physics-informed neural networks. Typically, neural networks are highly non-linear which makes their addition to optimization problems challenging. Hence, we leverage their often available Lipschitz property on a compact domain. The respective Lipschitz constants have to be computed only once and are accessible thereafter. We present a method that, based on this property, iteratively adds cuts involving the violation of the constraints by the current incumbent and the Lipschitz constant. Hereby, the “shape” of a cut depends on the norm used. We prove the correctness of the method by showing that it either returns an optimal solution when terminating or creates a sequence with optimal accumulation points. This is complemented by a discussion about the termination in the infeasible case, as well as an analysis of the problem complexity. For the analysis, we show that the lower and upper iteration bound asymptotically coincide when the relative approximation error goes to zero. In the end, we visualize the method on a small example based on a two-dimensional non-convex optimization problem, as well as stress the necessity of having a globally optimal oracle for the sub-problems by another example. KW - Global Optimization KW - Lipschitz Optimization KW - Black-box Optimization KW - Derivative-free Optimization Y1 - ER -