Model compendium, data, and optimization benchmarks for sector-coupled energy systems

Decarbonization and defossilization of energy supply as well as increasing decentralization of energy gen- eration necessitate the development of efficient strategies for design and operation of sector-coupled energy systems. Today, design and operation of process and energy systems rely on powerful numeri- cal methods, in particular, optimization methods. The development of such methods benefits from re- producible benchmarks including transparent model equations and complete input data sets. However, to the authors’ best knowledge and with respect to design and optimal control of sector-coupled en- ergy systems, there is a lack of available benchmarks. Hence, this article provides a model compendium, exemplary realistic data sets, as well as two case studies (i.e., optimization benchmarks) for an in- dustrial/research campus in an open-source description. The compendium includes stationary, quasi- stationary, and dynamic models for typical components as well as linearization schemes relevant for optimization of design, operation, and control of sector-coupled energy systems

On modelling effects in the battery and thermal storage scheduling problem

The growing use of intermittent renewable energy sources requires an increased amount of storage capacity to match uncertain generation with uncertain demand. A possible solution is the use of thermal and electrical storages. This paper compares several model formulations: mixed integer linear programs (MILPs), nonlinear programs (NLPs), mixed integer nonlinear programs (MINLPs) for optimizing the operation of a multi-modal home energy system comprising heating and electricity subsystems. The respective optimization problems are then resolved within a model predictive control scheme and the final solutions are compared in terms of runtime and optimality. The results indicate that a thermocline-based thermal storage model leads to the overall lowest costs while not significantly impeding computing times. Additionally, the results show that a continuous heat pump model leads to reduced computing times without affecting the modelling accuracy

Robust energy system planning for decarbonization under technological uncertainty: From transport electrification to power system investments

This work develops energy system modeling tools that identify features of a robust energy policy: a policy that performs well relative to alternatives. The tools are based on the Open Souce Modeling System (OSeMOSYS), are named the Multipurpose OSeMOSYS-based Framework (MOMF), and are applied to Costa Rica´s energy transition through the lens of its National Decarbonization Plan (NDP). The MOMF can support energy decarbonization planning exercises, and it is suitable to address the uncertainty involved in a decades-long process. It compares possible NDP futures -quantitative combinations of uncertainties and sectoral policy objectives- to a business-as-usual (BAU) scenario without decarbonization. The MOMF also evaluates actors within a country, including the fiscal impacts of decarbonization, following the best practices of applied energy modeling for policy support. This work finds that the NDP has high economic benefits (avoided costs relative to the BAU) in the long term, equivalent to 5.5% of GDP yearly in the 2041-2050 decade. In 2031-2040, the benefits are 0.8% of GDP yearly; in 2022-30, the NDP faces net costs (more costs than the BAU) of 0.9% of GDP yearly. These results are averages across futures and can be higher or lower. The government will have lower direct tax revenue of about 0.87% of GDP yearly in 2041-2050 and will need to redistribute benefits to compensate for this. It can use vehicle-kilometer taxes (VKT), property taxes, or energy taxes for the redistribution, mainly taxing private transport owners -who have the highest benefits-. However, to facilitate the decarbonization of freight firms in 2022-2030 and 2031-2040, the government could subsidize their zero-emission vehicles (ZEV) adoption. High benefits, low emissions complying with net-zero targets, and low electricity and public transport prices are desirable policy outcomes. Low costs for ZEVs and energy infrastructure -including renewables and storage- are crucial uncertain conditions for desirable outcomes. The robust levers the government can adopt to achieve desirable outcomes must decouple economic growth from transport activity. The specific levers include public transport investments, digitalization, non-motorized transport, ride-sharing, logistics hubs, and city densification. Moreover, low electricity prices need a low cost of capital to finance investments in the power sector.UCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Ingeniería::Maestría Académica en Ingeniería Eléctric

Model compendium, data, and optimization benchmarks for sector-coupled energy systems

Decarbonization and defossilization of energy supply as well as increasing decentralization of energy gen- eration necessitate the development of efficient strategies for design and operation of sector-coupled energy systems. Today, design and operation of process and energy systems rely on powerful numeri- cal methods, in particular, optimization methods. The development of such methods benefits from re- producible benchmarks including transparent model equations and complete input data sets. However, to the authors’ best knowledge and with respect to design and optimal control of sector-coupled en- ergy systems, there is a lack of available benchmarks. Hence, this article provides a model compendium, exemplary realistic data sets, as well as two case studies (i.e., optimization benchmarks) for an in- dustrial/research campus in an open-source description. The compendium includes stationary, quasi- stationary, and dynamic models for typical components as well as linearization schemes relevant for optimization of design, operation, and control of sector-coupled energy systems

Model compendium, data, and optimization benchmarks for sector-coupled energy systems

Decarbonization and defossilization of energy supply as well as increasing decentralization of energy generation necessitate the development of efficient strategies for design and operation of sector-coupled energy systems. Today, design and operation of process and energy systems rely on powerful numerical methods, in particular, optimization methods. The development of such methods benefits from reproducible benchmarks including transparent model equations and complete input data sets. However, to the authors’ best knowledge and with respect to design and optimal control of sector-coupled energy systems, there is a lack of available benchmarks. Hence, this article provides a model compendium, exemplary realistic data sets, as well as two case studies (i.e., optimization benchmarks) for an industrial/research campus in an open-source description. The compendium includes stationary, quasi-stationary, and dynamic models for typical components as well as linearization schemes relevant for optimization of design, operation, and control of sector-coupled energy systems

Sustainable Design of Industrial Energy Supply Systems - Development of a model-based decision support framework

Energy and media supply systems and related infrastructure at industrial sites have grown historically and is largely dependent on the use of fossil fuels. High fuel prices and the emission reduction targets of companies challenge existing supply concepts. Supply concepts usually remain in place for decades due to the long-lived nature of generation technologies and distribution systems. Today's investment decisions are therefore confronted with a changing environment in which the share of volatile renewables from solar and wind is continuously increasing. The long planning horizons make design decisions very complex. Optimization-based design approaches automatically derive cost- or carbon-optimal selections of generation technologies and procurement tariffs. Thus, they enable faster and more accurate planning decisions in techno-economic feasibility studies. In this work, a novel optimization model for techno-economic feasibility studies in industrial sites is developed. The optimization model uses a generic technology formulation with base classes, which takes into account the large variety of technologies and procurement tariffs at industrial sites. The optimization model also includes two reserve concepts: an operating reserve concept for short-term disruptions and a redundancy concept for long-term plant failures. The two concepts ensure security of supply for production-related energy requirements and thereby contributes to avoidance of costly production outages. The optimization model is integrated into an optimization framework to effectively calculate decarbonization strategies. The framework uses time series aggregation and heuristic decomposition techniques. Time series aggregation is performed by an integer program and results in a robust selection of representative days. The selection of representative days is used in a multi-year planning model to derive transformation roadmaps. Transformation roadmaps analyze the evolution of energy supply systems to long-term trends and consider adaptive investment decisions. A transformation strategy with myopic foresight (MYOP) solves the multi-year planning problem sequentially and is solved up to 98 % faster than a transformation approach with perfect foresight (PERF). The high uncertainties in early planning phases and the resulting need for detailed sensitivity analysis make this approach the preferred choice for many feasibility studies. The newly developed optimization framework is used in numerous research and consulting projects for urban districts, microgrids and factories. In this work, the capabilities of the framework are demonstrated for three use cases (automotive, pharmaceutical, dairy) of factory sites in southern Germany. In the use cases, decarbonization strategies for electricity, steam, heating and cooling supply are analyzed. Simulation evaluations identify changing operating patterns of combined heat and power (CHP) plants along the 15-year planning horizon. In addition, electrification of heating demand leads to a significant increase of total electricity demands. The results derived with the framework provide decision makers in industrial companies a clear view of the long-term impact of their investment decisions on decarbonization strategies