A modeler's guide to handle complexity in energy systems optimization

Kotzur, Leander; Nolting, Lars; Hoffmann, Maximilian; Groß, Theresa; Smolenko, Andreas; Priesmann, Jan; Büsing, Henrik; Beer, Robin; Kullmann, Felix; Singh, Bhanwar; Praktiknjo, Aaron; Stolten, Detlef; Robinius, Martin

doi:10.1016/j.adapen.2021.100063

Cited by 94 publications

(39 citation statements)

References 194 publications

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“…Oftentimes, this means formulating a deterministic problem and simplifying meteorological data across space or time, e.g. by ignoring climate-related uncertainty, modelling a few days per year, assuming perfect foresight, or aggregating sites or time periods [56,57]. As deterministic models, many energy system models return the optimal planning decision or operational strategy for a given set of meteorological and other inputs.…”

Section: The Disconnect Between Energy System and Climate Modelling C...mentioning

confidence: 99%

Overcoming the disconnect between energy system and climate modeling

Craig

Wohland

Stoop

et al. 2022

Joule

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Section: The Disconnect Between Energy System and Climate Modelling C...mentioning

confidence: 99%

Overcoming the disconnect between energy system and climate modeling

Craig

Wohland

Stoop

et al. 2022

Joule

Self Cite

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“…However, the litera-ture largely approximates this system behavior via MILP models where binary variables are used within the piecewise linear approximations of nonlinear system dynamics [35]. Besides the approximation through formulating a MILP for a nonlinear system, there are further complexity reduction methods, such as time series aggregation, that can be applied to MILP models for energy system optimization [36].…”

Section: Energy System Optimization 121 Multi-objective Mixed Integer...mentioning

confidence: 99%

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

et al. 2022

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A major barrier to investments in clean and future-proof energy technologies of local multi-energy systems (L-MESs) is the lack of knowledge about their impacts on profitability and carbon footprints due to their complex techno-economic interactions. To reduce this problem, decision support tools should integrate various forms of decarbonization measures. This paper proposes the Demand Response Analysis Framework (DRAF), a new open-source Python decision support tool that integrally optimizes the design and operation of energy technologies considering demand-side flexibility, electrification, and renewable energy sources. It quantifies decarbonization and cost reduction potential using multi-objective mixed-integer linear programming and provides decision-makers of L-MESs with optimal scenarios regarding costs, emissions, or Pareto efficiency. DRAF supports all steps of the energy system optimization process from time series analysis to interactive plotting and data export. It comes with several component templates that allow a quick start without limiting the modeling possibilities thanks to a generic model generator. Other key features are the access and preparation of time series, such as dynamic carbon emission factors or wholesale electricity prices; and the generation, handling, and parallel computing of scenarios. We demonstrate DRAF’s capabilities through three case studies on (1) the DR of industrial production processes, (2) the design optimization of battery and photovoltaic systems, and (3) the design optimization and DR of distributed thermal energy resources.

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“…The term fast-charging generally encompasses AC charging at capacities higher than 22 kW and DC charging, which is also often referred to as quick or rapid charging at high power levels [32]. These chargers are found within the public charging infrastructure [33]. The expansion and densification of the public charging infrastructure have been used important incentives-along with bonus payments and tax benefits for BEV drivers-to increase the adoption of electric vehicles in many countries [4,34].…”

Section: Importance Of Fast-charging Infrastructurementioning

confidence: 99%

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

2022

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Given the ongoing transformation of the transport sector toward electrification, expansion of the current charging infrastructure is essential to meet future charging demands. The lack of fast-charging infrastructure along highways and motorways is a particular obstacle for long-distance travel with battery electric vehicles (BEVs). In this context, we propose a charging infrastructure allocation model that allocates and sizes fast-charging stations along high-level road networks while minimizing the costs for infrastructure investment. The modeling framework is applied to the Austrian highway and motorway network, and the needed expansion of the current fast-charging infrastructure in place is modeled under different future scenarios for 2030. Within these, the share of BEVs in the car fleet, developments in BEV technology and road traffic load changing in the face of future modal shift effects are altered. In particular, we analyze the change in the requirements for fast-charging infrastructure in response to enhanced driving range and growing BEV fleets. The results indicate that improvements in the driving range of BEVs will have limited impact and hardly affect future costs of the expansion of the fast-charging infrastructure. On the contrary, the improvements in the charging power of BEVs have the potential to reduce future infrastructure costs.

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A modeler's guide to handle complexity in energy systems optimization

Cited by 94 publications

References 194 publications

Overcoming the disconnect between energy system and climate modeling

Overcoming the disconnect between energy system and climate modeling

Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems

Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network

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