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• W3204347663 abstract "Distributed Energy Systems (DESs) are undoubtedly the backbone of the energytransition. Within these systems, the heating, cooling, and electricity sector arecommonly coupled, resulting in multi-modal systems characterized by temporaldependencies induced by storages. Under continuously changing and uncertainframework conditions, the identification of operating and investment strategiesfor these systems presents a challenge for decision-makers. In order to addressthis challenge while facing the requirements and opportunities posed by the transition towards a carbon-neutral energy supply, this thesis investigates quantitativedecision support tools to identify investment and operating strategiesfor multi-modal DESs.Following a brief introduction in Chapter 1, Chapter 2 proposes a Mixed-Integer Linear Programming (MILP) formulation for the economic optimizationof the synthesis, design, and operation of a multi-modal energy system servinglocal electricity demands as well as thermal demands of different quality. Thismodel focuses on the coupling of the heating, cooling, and electricity sectorcomplementing the existing literature by the inclusion of detailed heat source andsink modeling. The approach enables the planner to quantify dynamic effects onthe efficiency of heating and cooling devices. Through the explicit considerationof temperature requirements in the model, waste heat recovery from cooling andrefrigeration cycles via direct heat integration or heat upgrading technologiescan be evaluated. To illustrate the practicability of the presented modelingapproach, it is applied within a real-world case study.Chapter 3 investigates models for the identification of long-term investmentstrategies for DESs under uncertainty. First, the problem is described by a multistagestochastic optimization problem that captures multi-period investmentdecisions under uncertainty and can be solved to global optimality, serving as afirst-best benchmark. To evaluate the performance of conventional approachesfrom the literature applied in a multi-year setup and to solve the multi-period problem at lower computational effort, a rolling horizon heuristic is proposed.To further reduce the computational complexity, clustering algorithms are appliedto select representative load profiles within the considered years and toaggregate hourly time intervals to multi-hour segments. Uncertain system parameters are modeled as multivariate stochastic processes. Within a real-worldcase study, the solution quality as well as the computational performance of theproposed approaches are investigated. The findings indicate that the approximationof multi-period investments by two-stage stochastic approaches yield thebest results regarding constraint satisfaction, while deterministic multi-periodapproximations yield better economic and computational performance.Chapter 4 proposes a new approach to integrally determine the production inventoryplan and the cost-minimizing bids to participate in sequential reserveand energy-only markets. In particular, this approach considers time-couplingconstraints that occur in the context of a production-inventory planning problembut also in the context of DES modeling. A comprehensive bidding formulationis proposed to evaluate revenues and potential costs from the participation insequential markets, considering price uncertainties and uncertain activations ofcommitted reserve capacity. This results in a multistage stochastic MILP, whichexplicitly considers the stage-wise revelation of information when participatingin sequential markets. Probabilistic electricity and reserve price forecasts, aswell as reserve demand forecasts, are obtained by fitting autoregressive-moving averagemodels with exogenous regressors to historic market data. To generatean analytically tractable set of scenarios, a large set of sample paths is firstgenerated and then reduced to a subset of representative scenarios using anadapted version of the partitioning around medoids algorithm. The model isapplied to a real-world case study in which the participation of a cement plant inthe German energy-only and reserve markets is investigated. The results indicatesignificant cost reductions for flexible industrial processes when participating inGerman spot and reserve markets.Concerning the actual control of multi-modal DESs characterized by slowsystem dynamics and complex interactions of (storage) technologies, Chapter5 presents a whole-year simulation study on nonlinear mixed-integer ModelPredictive Control (MPC) for a complex thermal energy supply system whichconsists of a heat pump, stratified water storages, free cooling facilities, and alarge underground thermal storage. For the solution of the arising Mixed-IntegerNon-Linear Programs (MINLPs) an existing general and optimal control-suitabledecomposition approach is applied. The MPC performance for this study, whichconsists of more than 50,000 real-time suitable MINLP solutions, is comparedto an elaborate conventional control strategy for the system. It is shown thatMPC can significantly reduce yearly energy consumption while providing a similar degree of constraint satisfaction, and autonomously identify previouslyunknown, beneficial operation modes." @default.
• W3204347663 created "2021-10-11" @default.
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• W3204347663 date "2021-01-01" @default.
• W3204347663 modified "2023-09-26" @default.
• W3204347663 title "Investment and Operating Strategies for Distributed Energy Systems - Model-Based Assessment of Integrated Energy Systems, Market Opportunities, and Uncertainty" @default.
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