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Reducing computing time of energy system models by a myopic approach

A case study based on the PERSEUS-NET model


In this paper, the performance of the existing energy system model PERSEUS-NET is improved in terms of computing time. Therefore, the possibility of switching from a perfect foresight to a myopic approach has been implemented. PERSEUS-NET is a linear optimization model generating scenarios of the future German electricity generation system until 2030, whilst considering exogenous regional characteristics such as electricity demand and existing power plants as well as electricity transmission network restrictions. Up to now, the model has been based on a perfect foresight approach, optimizing all variables over the whole time frame in a single run, thus determining the global optimum. However, this approach results in long computing times due to the high complexity of the problem. The new myopic approach splits the optimization into multiple, individually smaller, optimization problems each representing a 5 year period. The change within the generation system in each period is determined by optimizing the subproblem, whilst taking into account only the restrictions of that particular period. It was found that the optimization over the whole time frame with the myopic approach takes less than one tenth of the computing time of the perfect foresight approach. Therefore, we analyse in this paper the advantages and draw-backs of a change in the foresight as a way of reducing the complexity of energy system models. For PERSEUS-NET it is found that the myopic approach with stable input parameters is as suitable as the perfect foresight approach to generate consistent scenarios, with the advantage of significantly less computing time.

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Demand processes


Energy carriers and materials \(( {{ ec}\in { EC}})\)

EC\(_\mathrm{seas}\), EC\(_\mathrm{non-seas}\) :

Seasonal and non-seasonal energy carriers


Electricity as energy carrier


Sinks of the graph structure \(( {{ exp} \in { EXP}})\)


Generation processes


Sources of the graph structure \(( {{ imp}\in { IMP}})\)


CO\(_{2}\) emission allowances \(( {{ kyp}\in { KYO}})\)


Processes \(( {{ proc}\in { PROC}})\)


Producers \(( {{ prod}\in { PROD}})\)


Time slots \(( {{ seas}\in { SEAS}})\)


Year, period \(( {t\in T})\)


Units \(( {{ unit}\in { UNIT}})\)

Avai\(_\mathrm{unit,t}\) :

Availability factor for the generation unit unit in period \(t\)

\(\alpha _\mathrm{t }\) :

Discount factor

\(\lambda _\mathrm{proc,ec }\) :

Share of energy carrier ec related to total input/output of the process proc

\(\eta _\mathrm{prod,prod^{^{\prime }},ec,t }\) :

Flow efficiency of energy carrier ec between producers prod and prod’

\(\eta _\mathrm{proc,t }\) :

Efficiency of process proc in period \(t\)

CapRes\(_\mathrm{unit,t}\) :

Installed capacity of unit unit at the beginning of period \(t\)

Cfix\(_\mathrm{unit,t }\) :

Fixed annual operation costs of the generation unit unit in period \(t\)

Cfuel\(_\mathrm{imp,prod^{\prime },ec }\) :

Fuel costs for the delivery of the energy carrier ec to producer prod’ in period \(t\)

Cinv\(_\mathrm{unit,t}\) :

Specific investment for commissioning the generation unit unit in period \(t\)

Ckyo\(_\mathrm{kyo,t}\) :

Costs for the acquisition of CO\(_{2}\) allowances from the contingent kyo in period \(t\)

Cload\(_\mathrm{unit,t}\) :

Load change costs for the generation unit unit in period \(t\)

Cvar\(_\mathrm{proc,t}\) :

Variable operating costs of the process proc in period \(t\)

D\(_\mathrm{t,seas}\) :

Demand for electricity in time slice seas in period \(t\)

h\(_\mathrm{seas}\) :

Number of hours in season seas

Cap\(_\mathrm{unit,t}\) :

Installed capacity of the generation unit unit in period \(t\)

Fl\(_\mathrm{imp,prod^{\prime },ec,t}\) :

Level of ec-flow from the source of the graph structure imp to producer prod’ per year

Fl\(_\mathrm{prod,prod^{\prime },ec,t}\) :

Level of ec-flow from producer prod’ to producer prod per year

Fl\(_\mathrm{prod,exp,ec,t}\) :

Level of ec-flow from producer prod to the sink of the graph structure exp per year

FS\(_\mathrm{prod,prod^{\prime },ec,t,seas}\) :

Level of ec-flow from producer prod’ to producer prod per time slot

FS\(_\mathrm{prod,exp,ec,t,seas}\) :

Level of ec-flow from producer prod to the sink of the graph structure exp per year

KyoCert\(_\mathrm{kyo,t}\) :

Procurement of CO\(_{2}\) allowances kyo in period \(t\)

LVchange\(_\mathrm{unit,seas-1,seas,t }\) :

Load change of generation unit unit between time slices seas-1 and seas in \(t\)

NewCap\(_\mathrm{unit,t}\) :

Newly installed capacity of generation unit unit in a period \(t\)

PL\(_\mathrm{proc,t}\) :

Activity level of process proc per year in period \(t\)

PS\(_\mathrm{proc,t,seas}\) :

Activity level of process proc in time slot seas in period \(t\)


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Correspondence to Sonja Babrowski.

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Babrowski, S., Heffels, T., Jochem, P. et al. Reducing computing time of energy system models by a myopic approach. Energy Syst 5, 65–83 (2014).

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  • Myopic
  • Perfect foresight
  • Energy system modelling