CapacityExpansion: A capacity expansion modeling framework in Julia
Journal of Open Source Software
CapacityExpansion is a Julia (Bezanson, Edelman, Karpinski, & Shah, 2017) implementation of a scale-independent capacity expansion modelling framework. It provides an extensible, multi-carrier, simple-to-use generation and transmission capacity expansion model that allows users to address a diverse set of research questions in the area of energy systems planning and can be used to plan and validate energy systems at scales ranging from districts to entire global regions. CapacityExpansion
... cityExpansion provides simple integration of (clustered) time-series, geographical, cost, and technology input data. The software features a modular model setup and an investment and dispatch optimization that uses the JUMP modelling language (Dunning, Huchette, & Lubin, 2017). The software further provides an interface between the optimization result and further analysis. Infrastructure planning in the energy sector Energy systems convert different energy resources to meet desired demands like electric and thermal energy demands. Political, economic, and technological changes require expansions of the infrastructure of energy systems. Expanding the infrastructure has to balance multiple political, environmental, and economic objectives. Capacity expansion planning can be an essential tool during the planning process (Gacitua et al., 2018) . Capacity expansion planning is used to compute cost-optimal energy system designs under given sets of constraints from the perspective of a central planner. The resulting cost-optimal energy system design can be used to inform policy decisions that incentivize the industry to invest in this design (Johnston, Mileva, Nelson, & Kammen, 2013) . Similarly, cost-optimal energy system designs can be used by companies for their investment strategies. Aspects of the energy system design that capacity expansion planning aims to answer are what the optimal technology mix is in regards to location, time, and installed generation, conversion, storage, and transmission capacities. The design optimization is done while using an integrated dispatch formulation to ensure that supply can equal demand at all nodes and time steps. The model determines the costs, emissions, power generation, energy storage, and power flows based on the installed capacities. Capacity expansion planning is formulated as a mathematical optimization problem. Like any optimization problem, capacity expansion planning has certain degrees of freedom, consisting of constraints and an objective function that is minimized: Typical degrees of freedom (also called decision variables) are installed capacities, power generation, energy storage, and power flows. Some constraints ensure that the model is physically consistent in itself and following the rules of thermodynamics, e.g. energy balances ensure energy conservation over time. Other constraints restrict the solution space to external conditions like costs, demands, Kuepper et al., (2020). CapacityExpansion: A capacity expansion modeling framework in Julia.