Integrating quantum and classical computing for multi-energy system optimization using Benders decomposition

During recent years, quantum computers have received increasing attention, primarily due to their ability to significantly increase computational performance for specific problems. Computational performance could be improved for mathematical optimization by quantum annealers. This special type of quantum computer can solve quadratic unconstrained binary optimization problems. However, multi-energy systems optimization commonly involves integer and continuous decision variables. Due to their mixed-integer problem structure, quantum annealers cannot be directly used for multi-energy system optimization. To solve multi-energy system optimization problems, we present a hybrid Benders decomposition approach combining optimization on quantum and classical computers. In our approach, the quantum computer solves the master problem, which involves only the integer variables from the original energy system optimization problem. The subproblem includes the continuous variables and is solved by a classical computer. For better performance, we apply improvement techniques to the Benders decomposition. We test the approach on a case study to design a cost-optimal multi-energy system. While we provide a proof of concept that our Benders decomposition approach is applicable for the design of multi-energy systems, the computational time is still higher than for approaches using classical computers only. We therefore estimate the potential improvement of our approach to be expected for larger and fault-tolerant quantum computers

Gotta catch 'em all: Modeling All Discrete Alternatives for Industrial Energy System Transitions

Industrial decision-makers often base decisions on mathematical optimization models to achieve cost-efficient design solutions in energy transitions. However, since a model can only approximate reality, the optimal solution is not necessarily the best real-world energy system. Exploring near-optimal design spaces, e.g., by the Modeling All Alternatives (MAA) method, provides a more holistic view of decision alternatives beyond the cost-optimal solution. However, the MAA method misses out on discrete in-vestment decisions. Incorporating such discrete investment decisions is crucial when modeling industrial energy systems. Our work extends the MAA method by integrating discrete design decisions. We optimize the design and operation of an industrial energy system transformation using a mixed-integer linear program. First, we explore the continuous, near-optimal design space by applying the MAA method. Thereafter, we sample all discrete design alternatives from the continuous, near-optimal design space. In a case study, we apply our method to identify all near-optimal design alternatives of an industrial energy system. We find 128 near-optimal design alternatives where costs are allowed to increase to a maximum of one percent offering decision-makers more flexibility in their investment decisions. Our work enables the analysis of discrete design alternatives for industrial energy transitions and supports the decision-making process for investments in energy infrastructure.Comment: 6 pages, 2 figures, Annual International Conference of the German Operations Research Society (GOR) 202

Design of low-carbon multi-energy systems in the SecMOD framework by combining MILP optimization and life-cycle assessment

Decarbonizing complex industrial energy systems is an important step to mitigate climate change. Designing the transition of such sector-coupled industrial energy systems to low-carbon designs is challenging since both cost-efficient operation and the reduction of environmental impacts over the whole life cycle need to be considered in the system design. Optimal system designs can be identified using software: Recently, the open-source framework SecMOD was introduced for the linear optimization of multi-energy system models, considering environmental impacts by fully integrating life-cycle assessment. In this work, we extend SecMOD to allow mixed-integer decisions that are vital to model industrial energy systems. Thereby, we provide the first open-source mixed-integer linear program framework with full integration of life-cycle assessment. We use SecMOD to investigate the benefits of a pumped-thermal energy storage system in a sector-coupled industrial energy system and identify trade-offs regarding the system design by comparing the economic and climate optimum.ISSN:0098-1354ISSN:1873-437

Analysis of the Influence of Jaw Periosteal Cells on Macrophages Phenotype Using an Innovative Horizontal Coculture System

Jaw periosteum-derived mesenchymal stem cells (JPCs) represent a promising cell source for bone tissue engineering in oral and maxillofacial surgery due to their high osteogenic potential and good accessibility. Our previous work demonstrated that JPCs are able to regulate THP-1-derived macrophage polarization in a direct coculture model. In the present study, we used an innovative horizontal coculture system in order to understand the underlying paracrine effects of JPCs on macrophage phenotype polarization. Therefore, JPCs and THP-1-derived M1/M2 macrophages were cocultured in parallel chambers under the same conditions. After five days of horizontal coculture, flow cytometric, gene and protein expression analyses revealed inhibitory effects on costimulatory and proinflammatory molecules/factors as well as activating effects on anti-inflammatory factors in M1 macrophages, originating from multiple cytokines/chemokines released by untreated and osteogenically induced JPCs. A flow cytometric assessment of DNA synthesis reflected significantly decreased numbers of proliferating M1/M2 cells when cocultured with JPCs. In this study, we demonstrated that untreated and osteogenically induced JPCs are able to switch macrophage polarization from a classical M1 to an alternative M2-specific phenotype by paracrine secretion, and by inhibition of THP-1-derived M1/M2 macrophage proliferation