Difference between revisions of "PyPSA-Africa"

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−
=PyPSA-Africa: Open Source Electricity Network Model=
+
= PyPSA-Africa: Open-Source Electricity Network Model =
−
==Introduction==
 
−
'''PyPSA-Africa''' is an open-source electricity system model designed to study Africa’s power system development and energy transition. Built on the [https://pypsa.org/ PyPSA framework] (Python for Power System Analysis), it enables researchers, policymakers, and practitioners to explore least-cost electrification pathways and integration of renewable energy across the continent.
 
  
−
This page is a more detailed case study of PyPSA-Africa, complementing the general [[Open Energy System Models]] overview.
+
{| class="wikitable"
−
==Key Features==
+
|+ Article Information
−
*'''Continental Coverage:''' Models electricity supply and demand across African countries.
+
|-
−
*'''Open Source:''' Code and data are freely available, promoting transparency and collaboration.
+
! Sector
−
*'''Data Integration:''' Uses geospatial data, renewable resource potentials, population distribution, and grid infrastructure.
+
| Energy Planning
−
*'''Scenario Modelling:''' Supports comparison of policy, technology, and investment options.
+
|-
−
*'''Capacity Building:''' Enables African universities and institutions to conduct advanced modelling.
+
! Sub-sector
−
==Relevance for Energy Access==
+
| Power System Modelling
−
PyPSA-Africa supports planning toward universal electricity access by:
+
|-
−
*Identifying least-cost electrification options (grid extension, mini-grids, off-grid).
+
! Geographic Scope
−
*Assessing renewable energy integration at scale, particularly solar and wind.
+
| Africa
−
*Enabling regional cooperation and analysis of cross-border power trade.
+
|-
−
*Helping policymakers evaluate national electrification strategies.
+
! Country Focus
−
==Challenges==
+
| Nigeria
−
*'''Data gaps''' and inconsistencies remain a barrier.
+
|-
−
*'''Technical expertise''' is still limited in some African institutions.
+
! Software
−
*'''Policy uptake''' of modelling insights is not guaranteed.
+
| PyPSA-Africa
−
==Future Outlook==
+
|-
−
Ongoing development aims to improve data quality, enhance usability, and expand the African research network around open modelling tools. Efforts are also underway to integrate PyPSA with other open tools such as [[OnSSET]] and [[OSeMOSYS]].
+
! Programming Language
−
==External Links==
+
| Python
−
*[https://pypsa-meets-africa.org/ PyPSA Meets Africa Project]
+
|-
−
*[https://github.com/pypsa-meets-africa/ GitHub Repository]
+
! Licence
−
==Attribution==
+
| Open Source
−
This summary is adapted from:  '''Brown, T. et al. (2021). "PyPSA meets Africa: Developing an open source electricity network model of the African continent." arXiv preprint.'''  Licensed under [https://creativecommons.org/licenses/by/4.0/ CC BY 4.0]. Original available at: [https://arxiv.org/abs/2110.10628 arXiv].
+
|-
 +
! Related SDGs
 +
| SDG 7 • SDG 9 • SDG 13
 +
|}
 +
 
 +
== Key Takeaways ==
 +
 
 +
* PyPSA-Africa is an open-source electricity system model developed for analysing and optimising Africa's power systems.
 +
* The framework supports least-cost planning by evaluating generation, transmission and storage investments simultaneously.
 +
* It enables governments, researchers and development partners to compare multiple future energy scenarios using transparent and reproducible methodologies.
 +
* PyPSA-Africa promotes collaboration through openly available datasets and source code, strengthening energy planning capacity across Africa.
 +
* The model can support Nigeria's long-term electricity planning, renewable energy integration and transmission expansion.
 +
 
 +
== Introduction ==
 +
 
 +
Planning modern electricity systems has become increasingly complex as countries pursue universal energy access, integrate renewable energy technologies, strengthen electricity reliability and reduce greenhouse gas emissions. Meeting these objectives requires planners to evaluate thousands of technical, economic and policy variables while balancing affordability, security of supply and environmental sustainability.
 +
 
 +
Power system models provide an evidence-based approach to these challenges by simulating how electricity systems perform under different policy and investment scenarios. Rather than relying solely on historical trends or engineering judgement, these models help governments, utilities and researchers identify cost-effective pathways for developing future electricity systems.
 +
 
 +
'''PyPSA-Africa''' is an open-source electricity system modelling framework specifically developed to analyse Africa's power systems. Built on the '''Python for Power System Analysis (PyPSA)''' framework, it combines openly available datasets with mathematical optimisation techniques to model electricity generation, transmission, storage and demand across the African continent.
 +
 
 +
Unlike many proprietary modelling tools, PyPSA-Africa promotes transparency and reproducibility by making both its source code and modelling datasets openly accessible. This enables governments, research institutions and development organisations to evaluate alternative energy futures using consistent methodologies while adapting the framework to national and regional planning needs.
 +
 
 +
The model has become increasingly relevant as African countries expand renewable energy deployment, strengthen regional electricity markets and pursue universal electricity access under continental initiatives such as the African Single Electricity Market (AfSEM) and regional power pools.
 +
 
 +
This page serves as a detailed case study of PyPSA-Africa and complements the general [[Open Energy System Models]] overview.
 +
 
 +
== Why Open-Source Energy Planning Matters ==
 +
 
 +
Energy infrastructure investments often remain in operation for several decades. Decisions made today regarding power generation, transmission networks and electricity access therefore have long-term economic, environmental and social implications.
 +
 
 +
Open-source modelling frameworks improve transparency by allowing assumptions, datasets and analytical methods to be reviewed, tested and improved by a broad community of users. This supports evidence-based policymaking while reducing dependence on proprietary software.
 +
 
 +
Compared with commercial modelling platforms, open-source tools offer several advantages:
 +
 
 +
* transparent methodologies;
 +
* publicly available source code;
 +
* reproducible analytical workflows;
 +
* lower software costs;
 +
* flexibility to adapt models for local contexts;
 +
* collaborative development by international research communities.
 +
 
 +
These characteristics have made PyPSA-Africa an increasingly valuable resource for governments, universities, utilities and development partners seeking robust analytical tools for long-term electricity planning.
 +
 
 +
== How PyPSA-Africa Works ==
 +
 
 +
PyPSA-Africa follows a structured workflow that transforms technical, economic and spatial datasets into evidence that can support electricity planning and investment decisions.
 +
 
 +
Rather than producing a single forecast, the framework enables users to compare multiple development pathways under different policy, technology and economic assumptions.
 +
 
 +
{| class="wikitable"
 +
|+ Typical PyPSA-Africa Modelling Workflow
 +
! Stage
 +
! Purpose
 +
|-
 +
| Data Collection
 +
| Compile electricity demand, renewable resource, transmission and technology datasets.
 +
|-
 +
| Network Creation
 +
| Build a digital representation of generators, substations, storage systems and transmission networks.
 +
|-
 +
| Scenario Development
 +
| Define assumptions relating to electricity demand, fuel prices, renewable energy targets and policy objectives.
 +
|-
 +
| System Optimisation
 +
| Calculate the least-cost combination of generation, storage and transmission investments capable of meeting future electricity demand.
 +
|-
 +
| Results Analysis
 +
| Evaluate investment requirements, electricity generation, transmission expansion, system costs and greenhouse gas emissions.
 +
|}
 +
 
 +
The optimisation process enables planners to assess how different investment strategies influence electricity costs, system reliability and renewable energy integration over time.
 +
 
 +
== Core Features of PyPSA-Africa ==
 +
 
 +
=== Continental Coverage ===
 +
 
 +
PyPSA-Africa models electricity systems across the African continent using harmonised datasets and consistent analytical methods. This continental perspective enables comparisons between countries while supporting regional planning initiatives and cross-border electricity trade.
 +
 
 +
=== Generation Expansion Planning ===
 +
 
 +
The framework identifies the least-cost combination of electricity generation technologies required to satisfy projected electricity demand under user-defined constraints.
 +
 
 +
Rather than assuming predetermined investment decisions, the model evaluates multiple technology options—including solar photovoltaic (PV), wind power, hydropower, battery storage and thermal generation—to determine optimal investment pathways.
 +
 
 +
=== Transmission Network Optimisation ===
 +
 
 +
Electricity generation must be supported by adequate transmission infrastructure. PyPSA-Africa therefore models transmission expansion alongside generation planning.
 +
 
 +
The framework can analyse:
 +
 
 +
* expansion of high-voltage transmission networks;
 +
* reinforcement of existing infrastructure;
 +
* transmission congestion;
 +
* electricity imports and exports;
 +
* regional interconnections;
 +
* network losses.
 +
 
 +
By evaluating generation and transmission simultaneously, planners can identify investments that improve both system reliability and economic efficiency.
 +
 
 +
=== Renewable Energy Integration ===
 +
 
 +
The framework incorporates high-resolution renewable resource datasets to estimate the technical potential of solar, wind and hydropower across Africa.
 +
 
 +
By modelling weather-dependent renewable generation together with storage technologies and transmission infrastructure, PyPSA-Africa enables users to evaluate different pathways towards low-carbon electricity systems while maintaining system reliability.
 +
 
 +
=== Scenario Modelling ===
 +
 
 +
Future electricity systems remain uncertain due to changing demand, technology costs, fuel prices and policy priorities.
 +
 
 +
PyPSA-Africa allows users to compare multiple scenarios, including:
 +
 
 +
* least-cost development pathways;
 +
* high renewable energy scenarios;
 +
* net-zero emissions pathways;
 +
* accelerated electricity demand growth;
 +
* battery storage expansion;
 +
* regional electricity market integration.
 +
 
 +
Scenario analysis enables policymakers to understand how different assumptions influence future investment requirements and electricity system performance.
 +
 
 +
== Input Data Requirements ==
 +
 
 +
The quality of any power system model depends on the quality of the data used to develop it. PyPSA-Africa integrates multiple open datasets describing electricity infrastructure, renewable energy resources, economic parameters and projected electricity demand.
 +
 
 +
The framework automatically processes and harmonises these datasets, enabling users to construct consistent electricity network models across African countries.
 +
 
 +
Typical input datasets include:
 +
 
 +
{| class="wikitable"
 +
|+ Common Inputs Used by PyPSA-Africa
 +
! Dataset
 +
! Purpose
 +
|-
 +
| Electricity demand
 +
| Forecast future electricity consumption across regions.
 +
|-
 +
| Existing generation assets
 +
| Represent installed generation capacity and operational power plants.
 +
|-
 +
| Renewable resource data
 +
| Estimate solar irradiation, wind speeds and hydrological resources.
 +
|-
 +
| Transmission network data
 +
| Model existing and planned electricity transmission infrastructure.
 +
|-
 +
| Technology costs
 +
| Evaluate investment, operation and maintenance costs.
 +
|-
 +
| Fuel prices
 +
| Estimate operational costs for thermal generation technologies.
 +
|-
 +
| Weather data
 +
| Simulate hourly renewable electricity generation profiles.
 +
|-
 +
| Policy assumptions
 +
| Incorporate emissions limits, renewable energy targets and planning constraints.
 +
|}
 +
 
 +
Because many of these datasets are openly available, the modelling process remains transparent and reproducible, allowing users to verify assumptions and update analyses as new information becomes available.
 +
 
 +
== Model Outputs ==
 +
 
 +
PyPSA-Africa produces a wide range of outputs that support long-term electricity planning and investment decision-making.
 +
 
 +
Typical outputs include:
 +
 
 +
* optimal generation capacity by technology;
 +
* transmission expansion requirements;
 +
* battery and energy storage deployment;
 +
* investment requirements;
 +
* operating costs;
 +
* electricity generation by source;
 +
* renewable energy penetration;
 +
* greenhouse gas emissions;
 +
* electricity flows between regions;
 +
* transmission utilisation;
 +
* system reliability indicators.
 +
 
 +
These outputs enable governments and planners to compare alternative development pathways and assess their economic, technical and environmental implications.
 +
 
 +
== Practical Applications ==
 +
 
 +
PyPSA-Africa has been applied to a broad range of electricity planning and policy questions across Africa. By integrating technical, economic and spatial information, the model supports strategic decision-making for governments, utilities, researchers and development organisations.
 +
 
 +
Common applications include:
 +
 
 +
=== Integrated Resource Planning ===
 +
 
 +
Governments can use PyPSA-Africa to evaluate long-term electricity generation strategies and identify cost-effective investment pathways under different demand and policy scenarios.
 +
 
 +
=== Renewable Energy Integration ===
 +
 
 +
The framework supports analysis of large-scale deployment of solar, wind and hydropower, helping planners understand how renewable energy can be integrated while maintaining system reliability.
 +
 
 +
=== Transmission Expansion Planning ===
 +
 
 +
The model identifies where additional transmission infrastructure may be required to accommodate future electricity demand and renewable energy generation.
 +
 
 +
=== Regional Power Trade ===
 +
 
 +
PyPSA-Africa can evaluate electricity exchanges between neighbouring countries, supporting planning for regional electricity markets and power pools.
 +
 
 +
=== Climate and Decarbonisation Studies ===
 +
 
 +
Researchers use the framework to assess pathways towards low-carbon electricity systems by comparing scenarios with different emissions limits, renewable energy targets and technology costs.
 +
 
 +
=== Investment Planning ===
 +
 
 +
Development partners and financial institutions can use model outputs to estimate investment requirements and identify infrastructure priorities for future electricity sector development.
 +
 
 +
== Comparing PyPSA-Africa with Other Open Energy System Models ==
 +
 
 +
Different energy models are designed to answer different planning questions. Rather than competing with one another, they are often used together to provide complementary insights.
 +
 
 +
{| class="wikitable"
 +
|+ Comparison of Selected Open Energy System Models
 +
! Model
 +
! Primary Purpose
 +
! Typical Applications
 +
|-
 +
| '''PyPSA-Africa'''
 +
| Power system optimisation
 +
| Generation planning, transmission expansion, renewable energy integration and storage analysis.
 +
|-
 +
| '''OnSSET'''
 +
| Electrification planning
 +
| Grid extension, mini-grids and standalone system planning.
 +
|-
 +
| '''OSeMOSYS'''
 +
| Integrated energy system optimisation
 +
| National energy strategies and long-term policy analysis.
 +
|-
 +
| TEMBA
 +
| Continental energy modelling
 +
| Regional investment planning and African energy transition studies.
 +
|-
 +
| PLEXOS
 +
| Commercial electricity market modelling
 +
| Utility planning, electricity market analysis and operational studies.
 +
|}
 +
 
 +
In practice, these models can be complementary. For example, OnSSET may identify the least-cost technology for electrifying unserved communities, while PyPSA-Africa can subsequently evaluate how those electrification decisions influence generation capacity, storage requirements and transmission investments at national or regional scales.
 +
 
 +
== Stakeholders and Potential Uses ==
 +
 
 +
PyPSA-Africa supports a diverse range of users involved in electricity planning and energy policy.
 +
 
 +
{| class="wikitable"
 +
|+ Typical Users of PyPSA-Africa
 +
! Stakeholder
 +
! Potential Application
 +
|-
 +
| National Governments
 +
| Long-term electricity planning, renewable energy policy and investment prioritisation.
 +
|-
 +
| Utilities
 +
| Transmission expansion planning and generation portfolio optimisation.
 +
|-
 +
| Regulators
 +
| Evaluation of policy options and electricity market reforms.
 +
|-
 +
| Development Partners
 +
| Assessment of investment needs and programme design.
 +
|-
 +
| Universities
 +
| Teaching, research and capacity development in energy systems modelling.
 +
|-
 +
| Research Institutions
 +
| Scenario analysis, policy evaluation and climate studies.
 +
|-
 +
| Private Sector
 +
| Renewable energy investment analysis and infrastructure planning.
 +
|}
 +
 
 +
The open-source nature of the framework allows users to modify model assumptions, incorporate local datasets and develop customised analyses tailored to specific planning objectives. This flexibility has contributed to its growing adoption within Africa's energy research and planning community.
 +
 
 +
== Benefits of PyPSA-Africa ==
 +
 
 +
PyPSA-Africa offers several advantages for electricity planning in Africa:
 +
 
 +
* promotes transparency through open-source software and publicly available datasets;
 +
* supports reproducible and evidence-based decision-making;
 +
* enables integrated analysis of generation, storage and transmission infrastructure;
 +
* facilitates collaboration between governments, researchers and development partners;
 +
* reduces barriers to advanced energy modelling by eliminating software licensing costs;
 +
* supports regional planning and cross-border electricity market analysis.
 +
 
 +
These characteristics make PyPSA-Africa a valuable tool for countries seeking to expand electricity access while accelerating the transition towards cleaner and more resilient power systems.
 +
 
 +
== Relevance for Nigeria ==
 +
 
 +
Nigeria continues to pursue universal electricity access while expanding renewable energy generation and strengthening its national transmission network. Achieving these objectives requires evidence-based planning tools capable of evaluating multiple investment pathways under varying technical, economic and policy conditions.
 +
 
 +
PyPSA-Africa provides a framework that can support national and sub-national electricity planning by analysing the interactions between electricity demand, generation, transmission and storage. The model enables planners to compare alternative investment strategies using transparent assumptions and openly available datasets.
 +
 
 +
Potential applications in Nigeria include:
 +
 
 +
* supporting long-term Integrated Resource Planning (IRP);
 +
* evaluating renewable energy deployment scenarios;
 +
* identifying priority transmission investments;
 +
* assessing battery energy storage requirements;
 +
* analysing renewable energy zones;
 +
* supporting least-cost electrification strategies alongside complementary tools such as '''OnSSET''';
 +
* evaluating regional electricity trade opportunities through the West African Power Pool (WAPP);
 +
* informing policy development and investment planning.
 +
 
 +
Although PyPSA-Africa is not intended to replace detailed engineering studies or operational planning tools, it provides valuable strategic insights that can support national decision-making and long-term infrastructure development.
 +
 
 +
== Capacity Building and Research ==
 +
 
 +
Beyond electricity planning, PyPSA-Africa has become an important platform for strengthening energy modelling capacity across Africa.
 +
 
 +
Because the software is open source and developed using Python, universities, research institutions, government agencies and independent researchers can use it without the licensing costs associated with proprietary modelling software.
 +
 
 +
Collaborative initiatives such as the '''PyPSA Meets Africa''' project have helped build a growing community of African energy modellers through training workshops, technical documentation, collaborative research and open knowledge sharing.
 +
 
 +
By encouraging reproducible research and open collaboration, the framework contributes to developing local expertise that can support evidence-based energy planning across the continent.
 +
 
 +
== Challenges and Limitations ==
 +
 
 +
While PyPSA-Africa provides a powerful analytical framework, its outputs depend on the quality of available data and the assumptions selected by users.
 +
 
 +
Important challenges include:
 +
 
 +
* incomplete or inconsistent datasets in some countries;
 +
* uncertainty associated with long-term demand and technology cost projections;
 +
* computational requirements for large-scale analyses;
 +
* the need for specialised technical expertise in optimisation modelling;
 +
* dependence on modelling assumptions that may influence results.
 +
 
 +
Like all planning models, PyPSA-Africa should therefore be viewed as a decision-support tool rather than a predictor of the future. Its results are most valuable when interpreted alongside engineering studies, economic analyses and stakeholder consultations.
 +
 
 +
== Future Outlook ==
 +
 
 +
As Africa's electricity sector evolves, open-source energy system models are expected to play an increasingly important role in supporting long-term planning and investment decisions.
 +
 
 +
Future development of PyPSA-Africa is expected to focus on:
 +
 
 +
* improving the quality and coverage of openly available datasets;
 +
* strengthening representation of battery storage and other flexibility technologies;
 +
* supporting sector coupling between electricity, transport and industry;
 +
* enhancing climate resilience analysis;
 +
* expanding modelling of regional electricity markets and power pools;
 +
* improving usability through automation and collaborative development.
 +
 
 +
Continued collaboration between governments, research institutions and development partners will help strengthen the framework while encouraging wider adoption across Africa.
 +
 
 +
== See Also ==
 +
 
 +
* [[Open Energy System Models]]
 +
* '''OnSSET'''
 +
* '''OSeMOSYS'''
 +
 
 +
== External Links ==
 +
 
 +
* [https://pypsa-meets-africa.org/ PyPSA Meets Africa]
 +
* [https://pypsa.org/ PyPSA Official Website]
 +
* [https://github.com/pypsa-meets-africa PyPSA Meets Africa GitHub Repository]
 +
* [https://github.com/PyPSA/PyPSA PyPSA GitHub Repository]
 +
 
 +
== References ==
 +
 
 +
<references/>
 +
 
 +
== Further Reading ==
 +
 
 +
* Brown, T. ''et al.'' (2021). ''PyPSA meets Africa: Developing an open-source electricity network model of the African continent.'' arXiv. Available at: https://arxiv.org/abs/2110.10628
 +
 
 +
* PyPSA Documentation. Available at: https://pypsa.readthedocs.io/
 +
 
 +
* PyPSA Meets Africa Project. Available at: https://pypsa-meets-africa.org/
 +
 
 +
== Attribution and Licence ==
 +
 
 +
This article synthesises publicly available information from the PyPSA-Africa project, the PyPSA open-source community, Brown ''et al.'' (2021), the PyPSA Meets Africa initiative and related publications on power system planning and energy modelling. It has been prepared as an educational resource for Energypedia and does not reproduce the original publications. Readers are encouraged to consult the official project documentation, GitHub repositories and cited literature for detailed technical guidance, methodologies and software updates.
 +
 
 +
== Categories ==
  
 
[[Category:Nigeria Off-Grid Solar Knowledge Hub]]
 
[[Category:Nigeria Off-Grid Solar Knowledge Hub]]

Latest revision as of 15:24, 4 August 2026

PyPSA-Africa: Open-Source Electricity Network Model

Article Information
Sector Energy Planning
Sub-sector Power System Modelling
Geographic Scope Africa
Country Focus Nigeria
Software PyPSA-Africa
Programming Language Python
Licence Open Source
Related SDGs SDG 7 • SDG 9 • SDG 13

Key Takeaways

  • PyPSA-Africa is an open-source electricity system model developed for analysing and optimising Africa's power systems.
  • The framework supports least-cost planning by evaluating generation, transmission and storage investments simultaneously.
  • It enables governments, researchers and development partners to compare multiple future energy scenarios using transparent and reproducible methodologies.
  • PyPSA-Africa promotes collaboration through openly available datasets and source code, strengthening energy planning capacity across Africa.
  • The model can support Nigeria's long-term electricity planning, renewable energy integration and transmission expansion.

Introduction

Planning modern electricity systems has become increasingly complex as countries pursue universal energy access, integrate renewable energy technologies, strengthen electricity reliability and reduce greenhouse gas emissions. Meeting these objectives requires planners to evaluate thousands of technical, economic and policy variables while balancing affordability, security of supply and environmental sustainability.

Power system models provide an evidence-based approach to these challenges by simulating how electricity systems perform under different policy and investment scenarios. Rather than relying solely on historical trends or engineering judgement, these models help governments, utilities and researchers identify cost-effective pathways for developing future electricity systems.

PyPSA-Africa is an open-source electricity system modelling framework specifically developed to analyse Africa's power systems. Built on the Python for Power System Analysis (PyPSA) framework, it combines openly available datasets with mathematical optimisation techniques to model electricity generation, transmission, storage and demand across the African continent.

Unlike many proprietary modelling tools, PyPSA-Africa promotes transparency and reproducibility by making both its source code and modelling datasets openly accessible. This enables governments, research institutions and development organisations to evaluate alternative energy futures using consistent methodologies while adapting the framework to national and regional planning needs.

The model has become increasingly relevant as African countries expand renewable energy deployment, strengthen regional electricity markets and pursue universal electricity access under continental initiatives such as the African Single Electricity Market (AfSEM) and regional power pools.

This page serves as a detailed case study of PyPSA-Africa and complements the general Open Energy System Models overview.

Why Open-Source Energy Planning Matters

Energy infrastructure investments often remain in operation for several decades. Decisions made today regarding power generation, transmission networks and electricity access therefore have long-term economic, environmental and social implications.

Open-source modelling frameworks improve transparency by allowing assumptions, datasets and analytical methods to be reviewed, tested and improved by a broad community of users. This supports evidence-based policymaking while reducing dependence on proprietary software.

Compared with commercial modelling platforms, open-source tools offer several advantages:

  • transparent methodologies;
  • publicly available source code;
  • reproducible analytical workflows;
  • lower software costs;
  • flexibility to adapt models for local contexts;
  • collaborative development by international research communities.

These characteristics have made PyPSA-Africa an increasingly valuable resource for governments, universities, utilities and development partners seeking robust analytical tools for long-term electricity planning.

How PyPSA-Africa Works

PyPSA-Africa follows a structured workflow that transforms technical, economic and spatial datasets into evidence that can support electricity planning and investment decisions.

Rather than producing a single forecast, the framework enables users to compare multiple development pathways under different policy, technology and economic assumptions.

Typical PyPSA-Africa Modelling Workflow
Stage Purpose
Data Collection Compile electricity demand, renewable resource, transmission and technology datasets.
Network Creation Build a digital representation of generators, substations, storage systems and transmission networks.
Scenario Development Define assumptions relating to electricity demand, fuel prices, renewable energy targets and policy objectives.
System Optimisation Calculate the least-cost combination of generation, storage and transmission investments capable of meeting future electricity demand.
Results Analysis Evaluate investment requirements, electricity generation, transmission expansion, system costs and greenhouse gas emissions.

The optimisation process enables planners to assess how different investment strategies influence electricity costs, system reliability and renewable energy integration over time.

Core Features of PyPSA-Africa

Continental Coverage

PyPSA-Africa models electricity systems across the African continent using harmonised datasets and consistent analytical methods. This continental perspective enables comparisons between countries while supporting regional planning initiatives and cross-border electricity trade.

Generation Expansion Planning

The framework identifies the least-cost combination of electricity generation technologies required to satisfy projected electricity demand under user-defined constraints.

Rather than assuming predetermined investment decisions, the model evaluates multiple technology options—including solar photovoltaic (PV), wind power, hydropower, battery storage and thermal generation—to determine optimal investment pathways.

Transmission Network Optimisation

Electricity generation must be supported by adequate transmission infrastructure. PyPSA-Africa therefore models transmission expansion alongside generation planning.

The framework can analyse:

  • expansion of high-voltage transmission networks;
  • reinforcement of existing infrastructure;
  • transmission congestion;
  • electricity imports and exports;
  • regional interconnections;
  • network losses.

By evaluating generation and transmission simultaneously, planners can identify investments that improve both system reliability and economic efficiency.

Renewable Energy Integration

The framework incorporates high-resolution renewable resource datasets to estimate the technical potential of solar, wind and hydropower across Africa.

By modelling weather-dependent renewable generation together with storage technologies and transmission infrastructure, PyPSA-Africa enables users to evaluate different pathways towards low-carbon electricity systems while maintaining system reliability.

Scenario Modelling

Future electricity systems remain uncertain due to changing demand, technology costs, fuel prices and policy priorities.

PyPSA-Africa allows users to compare multiple scenarios, including:

  • least-cost development pathways;
  • high renewable energy scenarios;
  • net-zero emissions pathways;
  • accelerated electricity demand growth;
  • battery storage expansion;
  • regional electricity market integration.

Scenario analysis enables policymakers to understand how different assumptions influence future investment requirements and electricity system performance.

Input Data Requirements

The quality of any power system model depends on the quality of the data used to develop it. PyPSA-Africa integrates multiple open datasets describing electricity infrastructure, renewable energy resources, economic parameters and projected electricity demand.

The framework automatically processes and harmonises these datasets, enabling users to construct consistent electricity network models across African countries.

Typical input datasets include:

Common Inputs Used by PyPSA-Africa
Dataset Purpose
Electricity demand Forecast future electricity consumption across regions.
Existing generation assets Represent installed generation capacity and operational power plants.
Renewable resource data Estimate solar irradiation, wind speeds and hydrological resources.
Transmission network data Model existing and planned electricity transmission infrastructure.
Technology costs Evaluate investment, operation and maintenance costs.
Fuel prices Estimate operational costs for thermal generation technologies.
Weather data Simulate hourly renewable electricity generation profiles.
Policy assumptions Incorporate emissions limits, renewable energy targets and planning constraints.

Because many of these datasets are openly available, the modelling process remains transparent and reproducible, allowing users to verify assumptions and update analyses as new information becomes available.

Model Outputs

PyPSA-Africa produces a wide range of outputs that support long-term electricity planning and investment decision-making.

Typical outputs include:

  • optimal generation capacity by technology;
  • transmission expansion requirements;
  • battery and energy storage deployment;
  • investment requirements;
  • operating costs;
  • electricity generation by source;
  • renewable energy penetration;
  • greenhouse gas emissions;
  • electricity flows between regions;
  • transmission utilisation;
  • system reliability indicators.

These outputs enable governments and planners to compare alternative development pathways and assess their economic, technical and environmental implications.

Practical Applications

PyPSA-Africa has been applied to a broad range of electricity planning and policy questions across Africa. By integrating technical, economic and spatial information, the model supports strategic decision-making for governments, utilities, researchers and development organisations.

Common applications include:

Integrated Resource Planning

Governments can use PyPSA-Africa to evaluate long-term electricity generation strategies and identify cost-effective investment pathways under different demand and policy scenarios.

Renewable Energy Integration

The framework supports analysis of large-scale deployment of solar, wind and hydropower, helping planners understand how renewable energy can be integrated while maintaining system reliability.

Transmission Expansion Planning

The model identifies where additional transmission infrastructure may be required to accommodate future electricity demand and renewable energy generation.

Regional Power Trade

PyPSA-Africa can evaluate electricity exchanges between neighbouring countries, supporting planning for regional electricity markets and power pools.

Climate and Decarbonisation Studies

Researchers use the framework to assess pathways towards low-carbon electricity systems by comparing scenarios with different emissions limits, renewable energy targets and technology costs.

Investment Planning

Development partners and financial institutions can use model outputs to estimate investment requirements and identify infrastructure priorities for future electricity sector development.

Comparing PyPSA-Africa with Other Open Energy System Models

Different energy models are designed to answer different planning questions. Rather than competing with one another, they are often used together to provide complementary insights.

Comparison of Selected Open Energy System Models
Model Primary Purpose Typical Applications
PyPSA-Africa Power system optimisation Generation planning, transmission expansion, renewable energy integration and storage analysis.
OnSSET Electrification planning Grid extension, mini-grids and standalone system planning.
OSeMOSYS Integrated energy system optimisation National energy strategies and long-term policy analysis.
TEMBA Continental energy modelling Regional investment planning and African energy transition studies.
PLEXOS Commercial electricity market modelling Utility planning, electricity market analysis and operational studies.

In practice, these models can be complementary. For example, OnSSET may identify the least-cost technology for electrifying unserved communities, while PyPSA-Africa can subsequently evaluate how those electrification decisions influence generation capacity, storage requirements and transmission investments at national or regional scales.

Stakeholders and Potential Uses

PyPSA-Africa supports a diverse range of users involved in electricity planning and energy policy.

Typical Users of PyPSA-Africa
Stakeholder Potential Application
National Governments Long-term electricity planning, renewable energy policy and investment prioritisation.
Utilities Transmission expansion planning and generation portfolio optimisation.
Regulators Evaluation of policy options and electricity market reforms.
Development Partners Assessment of investment needs and programme design.
Universities Teaching, research and capacity development in energy systems modelling.
Research Institutions Scenario analysis, policy evaluation and climate studies.
Private Sector Renewable energy investment analysis and infrastructure planning.

The open-source nature of the framework allows users to modify model assumptions, incorporate local datasets and develop customised analyses tailored to specific planning objectives. This flexibility has contributed to its growing adoption within Africa's energy research and planning community.

Benefits of PyPSA-Africa

PyPSA-Africa offers several advantages for electricity planning in Africa:

  • promotes transparency through open-source software and publicly available datasets;
  • supports reproducible and evidence-based decision-making;
  • enables integrated analysis of generation, storage and transmission infrastructure;
  • facilitates collaboration between governments, researchers and development partners;
  • reduces barriers to advanced energy modelling by eliminating software licensing costs;
  • supports regional planning and cross-border electricity market analysis.

These characteristics make PyPSA-Africa a valuable tool for countries seeking to expand electricity access while accelerating the transition towards cleaner and more resilient power systems.

Relevance for Nigeria

Nigeria continues to pursue universal electricity access while expanding renewable energy generation and strengthening its national transmission network. Achieving these objectives requires evidence-based planning tools capable of evaluating multiple investment pathways under varying technical, economic and policy conditions.

PyPSA-Africa provides a framework that can support national and sub-national electricity planning by analysing the interactions between electricity demand, generation, transmission and storage. The model enables planners to compare alternative investment strategies using transparent assumptions and openly available datasets.

Potential applications in Nigeria include:

  • supporting long-term Integrated Resource Planning (IRP);
  • evaluating renewable energy deployment scenarios;
  • identifying priority transmission investments;
  • assessing battery energy storage requirements;
  • analysing renewable energy zones;
  • supporting least-cost electrification strategies alongside complementary tools such as OnSSET;
  • evaluating regional electricity trade opportunities through the West African Power Pool (WAPP);
  • informing policy development and investment planning.

Although PyPSA-Africa is not intended to replace detailed engineering studies or operational planning tools, it provides valuable strategic insights that can support national decision-making and long-term infrastructure development.

Capacity Building and Research

Beyond electricity planning, PyPSA-Africa has become an important platform for strengthening energy modelling capacity across Africa.

Because the software is open source and developed using Python, universities, research institutions, government agencies and independent researchers can use it without the licensing costs associated with proprietary modelling software.

Collaborative initiatives such as the PyPSA Meets Africa project have helped build a growing community of African energy modellers through training workshops, technical documentation, collaborative research and open knowledge sharing.

By encouraging reproducible research and open collaboration, the framework contributes to developing local expertise that can support evidence-based energy planning across the continent.

Challenges and Limitations

While PyPSA-Africa provides a powerful analytical framework, its outputs depend on the quality of available data and the assumptions selected by users.

Important challenges include:

  • incomplete or inconsistent datasets in some countries;
  • uncertainty associated with long-term demand and technology cost projections;
  • computational requirements for large-scale analyses;
  • the need for specialised technical expertise in optimisation modelling;
  • dependence on modelling assumptions that may influence results.

Like all planning models, PyPSA-Africa should therefore be viewed as a decision-support tool rather than a predictor of the future. Its results are most valuable when interpreted alongside engineering studies, economic analyses and stakeholder consultations.

Future Outlook

As Africa's electricity sector evolves, open-source energy system models are expected to play an increasingly important role in supporting long-term planning and investment decisions.

Future development of PyPSA-Africa is expected to focus on:

  • improving the quality and coverage of openly available datasets;
  • strengthening representation of battery storage and other flexibility technologies;
  • supporting sector coupling between electricity, transport and industry;
  • enhancing climate resilience analysis;
  • expanding modelling of regional electricity markets and power pools;
  • improving usability through automation and collaborative development.

Continued collaboration between governments, research institutions and development partners will help strengthen the framework while encouraging wider adoption across Africa.

See Also

External Links

References


Further Reading

  • Brown, T. et al. (2021). PyPSA meets Africa: Developing an open-source electricity network model of the African continent. arXiv. Available at: https://arxiv.org/abs/2110.10628

Attribution and Licence

This article synthesises publicly available information from the PyPSA-Africa project, the PyPSA open-source community, Brown et al. (2021), the PyPSA Meets Africa initiative and related publications on power system planning and energy modelling. It has been prepared as an educational resource for Energypedia and does not reproduce the original publications. Readers are encouraged to consult the official project documentation, GitHub repositories and cited literature for detailed technical guidance, methodologies and software updates.

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