@unpublished{BohlayerBuergerFleschutzetal.2020, author = {Bohlayer, Markus and B{\"u}rger, Adrian and Fleschutz, Markus and Braun, Marco and Z{\"o}ttl, Gregor}, title = {Multi-period investment pathways - Modeling approaches to design distributed energy systems under uncertainty}, year = {2020}, abstract = {Multi-modal distributed energy system planning is applied in the context of smart grids, industrial energy supply,and in the building energy sector. In real-world applications, these systems are commonly characterized by existing system structures of different age where monitoring and investment are conducted in a closed-loop, with the iterative possibility to invest. The literature contains two main approaches to approximate this computationally intensive multiperiod investment problem. The first approach simplifies the temporal decision-making process collapsing the multistage decision to a two-stage decision, considering uncertainty in the second stage decision variables. The second approach considers multi-period investments under the assumption of perfect foresight. In this work, we propose a multi-stage stochastic optimization problem that captures multi-period investment decisions under uncertainty and solves the problem to global optimality, serving as a first-best benchmark to the problem. To evaluate the performance of conventional approaches applied in a multi-year setup and to solve the multi-period problem at lower computational effort, we propose a rolling horizon heuristic that on the one hand reveals the performance of conventional approaches applied in a multi-period set-up and on the other hand enables planners to identify approximate solutions to the original multi-stage stochastic problem. Additionally, we consider an open-loop version of the rolling horizon algorithm to evaluate how single-period investments perform with respect to the entire scenario tree and compared to multi-period investments. We conduct a real-world case study and investigate solution quality as well as the computational performance of the proposed approaches. Our findings indicate that the approximation of multi-period investments by two-stage stochastic approaches yield the best results regarding constraint satisfaction, while deterministic multi-period approximations yield better economic and computational performance.}, language = {en} } @unpublished{BohlayerFleschutzBraunetal.2019, author = {Bohlayer, Markus and Fleschutz, Markus and Braun, Marco and Z{\"o}ttl, Gregor}, title = {Energy-intense production-inventory planning with participation in sequential energy markets}, year = {2019}, abstract = {To support the uprise of demand response, especially in the context of industrial processes, we propose a new approach to integrally determine the production-inventory plan and the cost-minimizing bids to participate in sequential reserve and energy-only markets. In particular, our approach considers time-coupling constraints which occur in the context of a production-inventory planning problem. We extend this problem with a comprehensive bidding formulation, which allows evaluating revenues and potential cost from the market participation, considering price uncertainties and uncertain activations of committed reserve capacity. This results in a multistage stochastic mixed-integer linear program, which explicitly considers the stage-wise revelation of information in our setup. To illustrate the capabilities of our approach, we apply our model to a real-world case study in which we investigate the participation of a cement plant in the German energy-only and reserve markets. The results of our case study indicate significant revenues for flexible industrial processes when participating in German spot and reserve markets.}, language = {en} }