Representation of Balancing Options for Variable Renewables in Long-Term Energy System Models: An Application to OSeMOSYS

Abstract

The growing complexity and the many challenges related to fast-changing and highly de-carbonised electricity systems require reliable and robust open source energy modelling frameworks. Their reliability may be tested on a series of well-posed benchmarks that can be used and shared by the modelling community. This paper describes and integrates stand-alone, independent modules to compute the costs and benefits of flexible generation options in the open source energy investment modelling framework OSeMOSYS. The modules are applied to a case study that may work as a benchmark. The whole documentation of the modules and the test case study are retrievable, reproducible, reusable, interoperable, and auditable. They create a case to help establish a FAIR-compliant, user-friendly, and low-threshold model and data standards in modelling practices. As is well known, one of the options for balancing high shares of variable renewables is flexible power generation by dispatchable units. The associated costs need to be considered for short-term operational analyses and for long-term investment plans. The added modules contribute to extending the modelling capacity by introducing (a) costs of ramping, (b) non-linear decrease of efficiency at partial load operation, and (c) refurbishment of existing units in the cost minimisation objective function of OSeMOSYS. From application to the test case study, two main insights are drawn: costs of ramping and decreased partial load efficiency may influence the competitiveness of generation technologies in the provision of reserve capacity; and refurbishment of existing units may represent attractive investment options for increasing flexibility. Both effects are also seen in the long-term and may impact infrastructure investment decisions to meet decarbonisation targets. These effects would not be captured without the introduction of the modules.

Long-term Energy System Modelling for a Clean Energy Transition in Egypt's Energy Sector

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Supporting Decarbonization Strategies of Local Energy Systems by De-Risking Investments in Renewables: A Case Study on Pantelleria Island

“…The objective function to be minimized is the net present cost of the energy system to meet the given energy and energy service demands over the simulation period. In other words, it is the sum over all regions (r) and years (y) of the discounted costs of all technologies (t) and storage systems (s) [40]:…”

“…The framework makes use of three technology cost parameters: CapitalCostr,t,y (CC), that is, the capital investment cost of a technology per unit of capacity; VariableCostr,t,m,y (VC), that The objective function to be minimized is the net present cost of the energy system to meet the given energy and energy service demands over the simulation period. In other words, it is the sum over all regions (r) and years (y) of the discounted costs of all technologies (t) and storage systems (s) [40]:…”

Modelling the electricity mix for Togo by 2050 using OSeMOSYS

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