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IRPopt (Integrated Resource Planning and Optimization)

The techno-economic mathematical optimization framework IRPopt (Integrated Resource Planning and Optimization) supports decision makers of municipal energy utilities as well as the public administration regarding future portfolio management

Brief description

The IRPopt (Integrated Resource Planning and optimization) modelling framework, primarily developed by Scheller (Fabian Scheller, 2018) at the Institute for Infrastructure and Resources Management at the University of Leipzig, is a mixed-integer linear programming modelling framework for economic dispatch with profit maximization as the primary objective. IRPopt is implemented on the modelling infrastructure platform IRPsim (Reichelt, Kühne, Scheller, Abitz, & Johanning, 2021) and both are open source licensed under GPLv3 (Fabian; Scheller & Reichelt, 2022). 

IRPopt is a dynamic, deterministic, and discrete municipal energy system model with adjustable temporal granularity and rolling optimization horizon. Its mathematical model is written in GAMS and it uses the IBM CPLEX solver.
With this framework, energy system models can be built out of a large portfolio of consumer-, storage-, producer- and distribution technology components and energy carriers like electricity, heat, hydrogen and multiple fossil fuels. Besides the energy flow between components, the monetary flow between agents like different suppliers, distributors, consumers or regulators can be modelled. The objective function maximizes profit. One major constraint is that demand (e.g., electricity) must be covered in each time step if no consumer side load shifting is allowed. With consumer side load shifting allowed, demand must be covered over adjustable temporal load shifting periods. Load shifting settings allow to shift 0 – 100% of load within the specified load shifting period and are adjustable.
IRPopt was already applied in the past to answer a wider range of research questions, for example, the potential of residential demand response through variable electricity tariffs (Fabian Scheller, Krone, Kühne, & Bruckner, 2018) or competition between simultaneous demand-side flexibility options in the case of community electricity storage systems (Fabian Scheller, Burkhardt, Schwarzeit, McKenna, & Bruckner, 2020), see also the following chapter with IRPopt use cases. 

The main advantages of IRPopt compared to many other models are modularity, temporal granularity and rolling optimization horizon. The modularity allows to build models efficiently out of a large portfolio of technology components over whole value chains. The temporal granularity can be freely adjusted, for example to ¼ hourly resolution. The optimization horizon of one year including an adjustable rolling horizon covers seasonal effects while keeping perfect foresight restricted. A more detailed model description can be found in supplementary material of this article and in (Fabian Scheller, 2018).

Exemplary model embedment
Figure 1: Exemplary model embedment for the H2-Flex use case

Figure 1 conceptually visualizes the model embedment and the input/output data stream of IRPopt including the input data stream of another model MICOES-Europe. The input data specification over different scenarios and over the sensitivity analysis is introduced in a following section. MICOES-Europe uses the main input parameters country specific electricity demand, power plant fleet, fuel- and CO2 prices and renewable electricity production to model day- ahead electricity spot prices and CO2 emission intensities. These are fed into IRPopt. Further data comes from a created techno-economic database which includes empirical and literature-based data, for example of the chlorine demand profile or electrolyzer specification of the CAE. The input data is fed through a web-based frontend to the backend where data is pre-processed before it is sent further to the GAMS model which utilizes the IBM CPLEX solver. The resulting raw output data in GDX format contains more than 100 variables and more than 1000 parameters, most of them distributed over sets dependent on the temporal granularity in this work either hourly or quarter-hourly (8760 or 35040 steps). Over the front- and backend an upgraded data export tool is used for exporting the relevant output data elements from the raw GDX files. The relevant output data is further evaluated, for example, by calculating relative differences between scenarios or the result sensitivity. The key performance indicators in this work are electricity costs and CO2 emissions, which are compared over different scenarios or sensitivity cases and result in potential savings through load shifting.

Identification of opportunities; potential users, added value provided by SPARCS

The modelling process of the various portfolios is accelerated in IRPopt by a pull-functionality with regard to basic configurations of technology options. Thus, the strategy unit gains efficient access to technical specifications for existing or planned generation facilities of the utility frequently updated by the engineering unit. Regarding an exemplary use case, the gross margin of selling heat and electricity to the customer serves as one of the key performance indicators. Accordingly, the portfolios are plotted against the economic and environmental impact. Thus, the decision maker is able to find a trade-off between both factors. As an additional dimension, the capital expenditure of the portfolios could be evaluated. Regarding the high complexity of strategic decision making the use case shows that IRPopt enables the quantitative evaluation of a wide range of uncertainty combined with a meaningful representation of results.

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