Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

Hilpert, Simon; Günther, Stephan; Kaldemeyer, Cord; Krien, Uwe; Pleßmann, Guido; Wiese, Frauke; Wingenbach, Clemens

doi:10.20944/preprints201702.0055.v1

Cited by 17 publications

(11 citation statements)

References 68 publications

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“…Optimisation problems are formulated using the Pythonbased optimization modeling language Pyomo [53], [54], which is solver agnostic and intuitive to extend. The use of Pyomo also allows inter-operability with other energy system frameworks that use Pyomo, such as calliope [11], oemof [14] and urbs [20]. In PyPSA lower-level functions in Pyomo have been exploited to improve computational performance.…”

Section: H Planned New Featuresmentioning

confidence: 99%

“…PyPSA differs from more general energy system models such as calliope [11], oemof [14], OSeMOSYS [15] and urbs [20] by offering more detailed modelling of power networks, in particular the physics of power flow according to the impedances in the network. PyPSA can model a more general energy network using link components (see Section II-D), but cannot, for example, yet do the multiyear dynamic investment that OSeMOSYS does.…”

Section: Comparison To Other Power System Toolsmentioning

confidence: 99%

“…The consideration of multiple time periods is important on the operational side for unit commitment of conventional generators and the optimisation of storage and demand side management, and on the investment side for optimising infrastructure capacities over representative load and weather situations. Several tools have subsets of these capabilities, such as calliope [11], minpower [12], MOST [13], oemof [14], OSeMOSYS [15], PLEXOS [16], PowerGAMA [17], PRIMES [18], TIMES [19] and urbs [20], but their representations of electrical grids are often simplified.…”

Section: Introductionmentioning

confidence: 99%

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PyPSA: Python for Power System Analysis

Brown¹,

Hörsch²,

Schlachtberger

2018

JORS

320

233

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Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit).

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Section: H Planned New Featuresmentioning

confidence: 99%

Section: Comparison To Other Power System Toolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PyPSA: Python for Power System Analysis

Brown¹,

Hörsch²,

Schlachtberger

2018

JORS

320

233

View full text Add to dashboard Cite

show abstract

“…Last but not least, the energy transition displays a fundamental societal effort which may be better addressed in a transparent way to raise public acceptance for scientifically reasonable changes. Recently these aspects have been addressed more often in energy research gaining greater relevance (e.g., [11][12][13][14][15]).…”

Section: Introductionmentioning

confidence: 99%

Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids

et al. 2019

Self Cite

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The energy transition towards renewable and more distributed power production triggers the need for grid and storage expansion on all voltage levels. Today’s power system planning focuses on certain voltage levels or spatial resolutions. In this work we present an open source software tool eGo which is able to optimize grid and storage expansion throughout all voltage levels in a developed top-down approach. Operation and investment costs are minimized by applying a multi-period linear optimal power flow considering the grid infrastructure of the extra-high and high-voltage (380 to 110 kV) level. Hence, the common differentiation of transmission and distribution grid is partly dissolved, integrating the high-voltage level into the optimization problem. Consecutively, optimized curtailment and storage units are allocated in the medium voltage grid in order to lower medium and low voltage grid expansion needs, that are consequently determined. Here, heuristic optimization methods using the non-linear power flow were developed. Applying the tool on future scenarios we derived cost-efficient grid and storage expansion for all voltage levels in Germany. Due to the integrated approach, storage expansion and curtailment can significantly lower grid expansion costs in medium and low voltage grids and at the same time serve the optimal functioning of the overall system. Nevertheless, the cost-reducing effect for the whole of Germany was marginal. Instead, the consideration of realistic, spatially differentiated time series led to substantial overall savings.

show abstract

“…Recently these aspects have been addressed more often in energy research gaining greater relevance (e.g. [11][12][13][14][15]).…”

Section: Introductionmentioning

confidence: 99%

Integrated techno-economic power system planning of transmission and distribution grids

Müller¹,

Schachler²,

Scharf³

et al. 2019

Preprint

Self Cite

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The energy transition towards renewable and more decentral power production triggers the need for grid and storage expansion on all voltage levels. Today's power system planning focuses on certain voltage levels or spatial resolutions. In this work we present an open source software tool eGo which is able to optimize grid and storage expansion throughout all voltage levels in a developed top-down approach. System costs are minimized by applying a linear optimal power flow considering the grid infrastructure of the extra-high and high-voltage (380 to 110 kV) level. Hence, the common differentiation of transmission and distribution grid is partly dissolved, integrating the high-voltage level into the optimization problem. Consecutively, optimized curtailment and storage units are allocated in the medium voltage grid in order to lower medium and low voltage grid expansion needs, that are consequently determined. Here, heuristic optimization methods using the non-linear power flow were developed. Applying the tool on future scenarios we derived cost-efficient grid and storage expansion for all voltage levels in Germany. Due to the integrated approach storage expansion and curtailment can significantly lower grid expansion costs in medium and low voltage grids and at the same time serve the optimal functioning of the overall system. Nevertheless, the cost-reducing effect for the whole of Germany was marginal. Instead, the consideration of realistic, spatially differentiated time series lead to substantial overall savings.

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Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

Cited by 17 publications

References 68 publications

PyPSA: Python for Power System Analysis

PyPSA: Python for Power System Analysis

Integrated Techno-Economic Power System Planning of Transmission and Distribution Grids

Integrated techno-economic power system planning of transmission and distribution grids

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