Philip Voll

Selecting CO2 Sources for CO2 Utilization by Environmental-Merit-Order Curves

Capture and utilization of CO2 as alternative carbon feedstock for fuels, chemicals, and materials aims at reducing greenhouse gas emissions and fossil resource use. For capture of CO2, a large variety of CO2 sources exists. Since they emit much more CO2 than the expected demand for CO2 utilization, the environmentally most favorable CO2 sources should be selected. For this purpose, we introduce the environmental-merit-order (EMO) curve to rank CO2 sources according to their environmental impacts over the available CO2 supply. To determine the environmental impacts of CO2 capture, compression and transport, we conducted a comprehensive literature study for the energy demands of CO2 supply, and constructed a database for CO2 sources in Europe. Mapping these CO2 sources reveals that CO2 transport distances are usually small. Thus, neglecting transport in a first step, we find that environmental impacts are minimized by capturing CO2 first from chemical plants and natural gas processing, then from paper mills, power plants, and iron and steel plants. In a second step, we computed regional EMO curves considering transport and country-specific impacts for energy supply. Building upon regional EMO curves, we identify favorable locations for CO2 utilization with lowest environmental impacts of CO2 supply, so-called CO2 oases.

Multi-objective synthesis of energy systems: Efficient identification of design trade-offs

Abstract The synthesis of energy systems usually has to consider several conflicting objectives leading to a large set of Pareto-optimal solutions with multiple trade-offs. From this large set of solutions, good compromise solutions have to be identified which is a complex and computationally demanding task. We therefore propose a method to reduce both the set of objectives and the solution space: First, the set of objectives is reduced by employing a method from the literature to determine the objectives best representing the design trade-offs. However, in practice, aggregated costs are the decisive criterion. Thus, in a second step, the solution space of the synthesis problem is restricted to an acceptable deviation from minimal aggregated costs. Thereby, only relevant solutions are obtained. The two steps significantly reduce the effort for multi-objective optimization focusing on the most relevant part of the solutions. The proposed method is applied to a real-world case study.

An adaptive discretization MINLP algorithm for optimal synthesis of decentralized energy supply systems

Abstract Decentralized energy supply systems (DESS) are highly integrated and complex systems designed to meet time-varying energy demands, e.g., heating, cooling, and electricity. The synthesis problem of DESS addresses combining various types of energy conversion units, choosing their sizing and operations to maximize an objective function, e.g., the net present value. In practice, investment costs and part-load performances are nonlinear. Thus, this optimization problem can be modeled as a nonconvex mixed-integer nonlinear programming (MINLP) problem. We present an adaptive discretization algorithm to solve such synthesis problems containing an iterative interaction between mixed-integer linear programs (MIPs) and nonlinear programs (NLPs). The proposed algorithm outperforms state-of-the-art MINLP solvers as well as linearization approaches with regard to solution quality and computation times on a test set obtained from real industrial data, which we made available online.

SPREAD - Exploring the decision space in energy systems synthesis

Abstract A method is presented to systematically analyze the decision space in the synthesis of energy supply systems. Commonly, synthesis problems are solved by mathematical optimization yielding a single optimal design. However, optimization is based on a model which never represents reality to perfection. Thus, the designer will be forced to revise parts of the optimal solution. We therefore support the design process by automatically identifying important features of good solutions. For this purpose, we analyze near-optimal solutions. To explore the decision space, we minimize and maximize both the number and the capacity of units while keeping the costs within a specified range. From this analysis, we derive insight into correlations between decisions. To support the decision maker, we represent the range of good design decisions and their correlations in the flowsheet of the energy system. The method is illustrated for the synthesis of an energy system in the pharmaceutical industry.

An Adaptive Normal Constraint Method for Bi-Objective Optimal Synthesis of Energy Systems

Abstract A novel approach is proposed for the efficient generation of the Pareto front for bi-objective optimal synthesis of energy systems. To avoid computationally expensive calculationsof solutions not relevant to the decision maker, the proposed method adapts the computation ofthe Pareto front to the part relevant for practical energy systems. The algorithm produces an evenly distributed set of Pareto optimal solutions employing a modified normal constraint method. In contrast to the classical normal constraint method, the algorithm is no more initialized at the – usually computationally most expensive – single-objective optima but uses an aggregated objective function as starting point for an adaptive exploration of the Pareto front. The presented approach is applied to a real-world synthesis problem of a distributed energy supply system. It is shown that the adaptive normal constraint algorithm automatically generates the most relevant part of the Pareto front for the bi-objective optimal synthesis of an energy system computationally more efficient than the weighted sum method or the e-constraint method.

Synthesis and Optimization of Distributed Energy Supply Systems using Automated Superstructure and Model Generation

Abstract A novel approach is presented for the automated generation of models representing superstructures for the synthesis and optimization of distributed energy supply systems (DESS). Based on a basic problem description (load cases, available technologies, and topographical constraints), the proposed algorithm automatically generates a model accounting for time-varying load profiles and part-load dependent operating efficiencies. Building upon the P-graph approach, the derived superstructure is extended to include multiple redundant conversion units as required for DESS optimization. In the present implementation, a GAMS model is generated that can be readily optimized. The approach is applied to the retrofit synthesis of the energy supply system of an industrial site. It is shown that the automated procedure provides a convenient and efficient optimization framework for DESS.

CO2 vs Biomass: Identification of Environmentally Beneficial Processes for Platform Chemicals from Renewable Carbon Sources

Conventionally, platform chemicals are produced from fossil feedstock. In recent years, the production of chemicals from renewable feedstock has been considered. One possible renewable carbon source is biomass. Another option is carbon dioxide (CO2). A rigorous comparison of processes employing renewable carbon sources is missing today. We therefore identify the environmentally most beneficial process routes for platform chemicals. All processes are integrated into a single superstructure, which is analyzed using linear optimization minimizing global warming impacts. The optimal solutions exploit synergies between the different process routes and feedstocks.

Looking for Alternatives: Optimization of Energy Supply Systems without Superstructure

We investigate different evolutionary algorithm (EA) variants for structural optimization of energy supply systems and compare them with a deterministic optimization approach. The evolutionary algorithms enable structural optimization avoiding to use an underlying superstructure model. As result of the optimization, we are interested in multiple good alternative designs, instead of the one single best solution only. This problem has three levels: On the top level, we need to fix a structure; based on that structure, we then have to select facility sizes; finally, given the structure and equipment sizing, on the bottom level, the equipment operation has to be specified to satisfy given energy demands. In the presented optimization approach, these three levels are addressed simultaneously. We compare EAs acting on the top level (the lower levels are treated by a mixed-integer linear programming (MILP) solver) against an MILP-only-approach and are highly interested in the ability of both methods to deliver multiple different solutions and the time required for performing this task.

The Good, the Bad, and Your Real Choices – Decision Support for Energy Systems Synthesis through Near-Optimal Solutions Analysis

Abstract An optimisation-based decision support methodology is proposed for the synthesis of energy supply systems. Given that mathematical models never perfectly represent the real world and that decision makers are often not aware of all practical constraints, the mathematically optimal solution is usually only an approximation of the real-world optimum. Therefore, in this paper, a synthesis approach is proposed that supports the decision maker through the generation of a set of near-optimal solution alternatives, which can be evaluated in more detail a posteriori. We study two very different synthesis problems at the district and the industrial scale. In both test cases, rich near- optimal solution spaces are identified that exhibit practically identical objective function values. Considering the many uncertainties and constraints arising in practice, a ranking of the generated solutions based on a single objective function value is not significant. Instead, the near-optimal solutions are analysed to support the synthesis process by extracting common features and differences. The obtained information provides deeper understanding of the synthesis problem enabling engineers to reach more rational synthesis decisions.