ESO (Electricity Systems Optimisation Framework)

The Electricity Systems Optimisation (ESO) framework contains a suite of power system capacity expansion and unit commitment models at different levels of spatial and temporal resolution and modelling complexity.




Initial contribute: 2019-10-18


Imperial College London
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Quoted from: Heuberger, C.F., Rubin, E.S., Staffell, I., Shah, N. and Mac Dowell, N., 2017. Power capacity expansion planning considering endogenous technology cost learning. Applied Energy, 204, pp.831-845.

the ESO-XEL model is a mixed-integer linear program for cost-optimal electricity generation and storage capacity planning including endogenous treatment of technology cost learning. It builds on the model formulation presented in Heuberger et al. [55] and is extended to perform optimal capacity expansion on a national-scale while considering international electricity interconnectors.

The ESO-XEL model does not aim at being put on the same level with large-scale energy system models (a subset listed in Table 2), which build on a rich modelling history often dating back to the 1990s and which are mostly developed and maintained by multi-institutional and international research groups. The advantage of such models is their ability to cover multiple energy vectors (e.g., transport, industry, residential) from supply to end-use. However, the management of such model structures and large corresponding data sets can lead to difficulties in recognising the complex underlying effects, which for example promote the increased deployment of a power technology and evoke cost reductions.

The strength of the ESO-XEL model is to provide a transparent and flexible framework which enables us to scientifically observe interdependencies and determine the origin of whole-system effects caused by and leading to technology cost reduction. The distinguishing mark of the ESO-XEL model is its technical detail in the operational power plant behaviour, and high granularity in the representation of time. It simultaneously performs optimal capacity expansion in 5-yearly increments and unit commitment of the power plants on an hourly scale with a rigorous energy balance over time rather than “time slicing”. A mathematically rigorous data clustering approach reduces the full hourly data sets from 8760 to 504 h per year with a maximum deviation in results of 6.5%. Section 4 will further expand on this point. The high time-wise granularity is essential in systems with a high penetration of power generation from intermittent renewable sources (iRES). System reliability and operability issues are decisive in system planning and can often not be addressed accurately if only a small subset of hours is considered.

We present the model formulation building on our previous work [55][56] in Sections 3.2 Power system design and expansion3.7 Piecewise linear formulation of the learning curve model while reviewing existing capacity expansion models without the representation of technology learning. In the relevant literature this concept is also referred to as generation expansion planning (GEP). Existing GEP models have been reviewed from both a conceptual [57] and a detailed modelling perspective [58][59][60]. The studies highlight two main categories of GEP models, the centralised/monopolistic and the decentralised/deregulated market view. Large centralised national-scale GEPs have been developed for example for the Greek [61] and Polish [60] power system, with a minimum time granularity of years or days. Murphy and Zou contribute modelling frameworks and case studies for GEP in imperfect markets [62][63].



Clara F. Heuberger (2019). ESO (Electricity Systems Optimisation Framework) , Model Item, OpenGMS,


Initial contribute : 2019-10-18



Imperial College London
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