EEG recently ran a three-part webinar series on the impact of energy planning and modelling on policymaking in Sub-Saharan Africa and South Asia. Head of Research and Engagement Benjamin Klooss highlights some of the key messages from the webinars, and discusses the EEG research projects that are covering this topic.

An essential input for effective policy and investment decision-making, strategic energy planning involves identifying a country’s or region’s future energy needs, and shaping broad pathways for meeting them in ways that satisfy goals for energy access, energy security, climate action and environmental protection. The use of effective planning models and tools allows decision-makers to assess scenarios and policy choices in a comprehensive manner. Clear, evidence-based planning can align incentives and actions of key stakeholders. Linking them to wider economic and social priorities as well as political realities can enhance the chance of implementation and impact. The EEG programme aims to enhance the evidence base for better decision-making, and our webinar series on modelling sought to share insights and approaches in support of improved energy planning.

Models make visible the interdependencies and trade-offs within the complex energy system. This has significant potential to improve and define national electrification strategies (or provide a basis where they don’t yet exist), and to determine the location, sizing and timing of generation and transmission infrastructure development. With a wide variety of model types available, modelling can be used to address a range of other issues, from planning and operational challenges to optimising the dispatch of variable renewable energy sources in a grid system and power trading. Models can also assess the environmental, health, social, gender and economic aspects associated with these choices.

However, in Sub-Saharan Africa and South Asia, policy makers, regulators and other government officials often face a conundrum. Planning well-designed and cost-effective electricity systems requires knowledge, data, analytical models and tools that they frequently lack. Yet even if they are available, such tools and models need to be adapted to the specific context and the particular questions that they are supposed to resolve. But limited institutional capacity constrains the ability to frame such questions, design scenarios, tailor existing models and use them effectively. All too often, there is also an underlying lack of reliable data for informing policy analysis in low-income countries.

Energy modelling for better policymaking webinar series

Our recent three-part webinar series aimed to explore how the use of modelling and decision-support tools can directly contribute towards enhanced electricity sector policymaking in Sub-Saharan Africa and South Asia. Each session brought together a leading panel of experts and a live audience who helped to shape the discussion by asking questions and sharing their views and experience.

We examined the current utilisation of energy modelling and decision-support tools in different decision-making areas, as well as practical solutions to optimise their use. Among many other things, we addressed key questions on how national ownership, coherence and inclusivity, capacity, robustness, transparency and accessibility of models can be improved; the different types of models available (and which are the most effective for different types of decision-making); the impact of modelling on gender, equality and social inclusion outcomes; and gaps in the modelling skills base and training capacity. These were addressed through three lenses: 1) opportunities and hurdles for models to enhance policymaking; 2) the strengths and limitations of different approaches to resolve particular energy sector challenges; and 3) the frontiers in energy modelling and their relevance to resolving emerging challenges in Sub-Saharan Africa and South Asia.

Key insights from the EEG modelling webinar series:

1. Models and tools can make a real difference for planning and decision-making – but this requires clarity of purpose. They must be designed up-front to tackle specific challenges and be locally relevant. For example, resolving challenges around electricity dispatch requires different types of models than long-range scenario planning in support of the energy transition. Similarly, enhancing policy-making at municipal, national or international level requires different granularity of model specification. Equally, is the purpose to enhance decisions and policy on a facet of the energy sector, the whole energy system or also to assess the nexus with wider factors such as pollution, land, water, minerals, jobs, economic growth and equity? These and other questions will determine the modelling approach and choice of tools

2. Data access and data quality matter for model design as well as accuracy. Models and tools need to be underpinned by data that is commensurate to the purpose. Dispatch optimisation and power trade models require high-frequency data, potentially at sub-national level. Coarser data may suffice for long-range scenario planning to assess plausible ranges of outcomes. ‘Bottom-up’ least-cost optimisation models require detailed technology input data, while some ‘top-down’ models may be based on statistical analysis. Recognition of underlying data quality is essential in determining any degree of accuracy. There are trade-offs: greater granularity may enhance understanding of levers and drivers, but too much detail may reduce degrees of freedom and compound forecasting errors. Models thus need to be fit for purpose, defined through clarity of purpose and data quality.

3. The tools exist – skill is needed to select and tailor them. Many modelling platforms are readily available both on an open source and proprietary basis, but all need to be tailored to meet the specific purpose and data availability. Models may be modular, allowing e.g. the combination of detailed electricity dispatch and full energy system analysis, or linking e.g. energy and economic models. Comprehensive computable general equilibrium (CGE) and machine learning tools are alternatives within the long menu of choices. There also is a choice between deterministic point-forecast or probabilistic modelling. Each approach has its unique strengths and drawbacks. Selection of the ‘right’ model(s) and tailoring requires skills, which may not exist to sufficient degree yet in all countries of Sub-Saharan Africa and Asia. External expertise may be needed for model construction and initial operation. Programmes such as EEG, the academic community more widely, and international agencies as well as donors can make positive contributions to this.

4. Novel modelling approaches enhance the realm of the possible. Models represent a stylised version of reality, designed to capture critical dimensions while being solvable within an analytical framework. Recent advances in research as well as computing power are broadening the toolbox for speed of analysis, comprehensiveness and realism. The latest approaches focus on spatial data, nexus issues, transition risks, scale, heterogeneity of actors and the existence of multiple equilibria. They comprise integrated, agent-based, non-linear and dynamic models among others. Artificial intelligence and machine learning remove boundaries of quantity and type of data, allowing the input of any quantitative or non-numerical as well as visual (e.g. satellite imagery) data. Yet all modelling approaches return to the question of how what you expect to happen compares to what you see happening, i.e. the forecasted trajectory versus the reality on the ground. Here also modelling is advancing, with a suite of models that directly include political and social factors. For now, these so-called ‘soft factors’ require manual assessment, but innovation does not stand still. Balancing complexity and breadth with analytical focus on key drivers will be essential for the policy- and decision-makers that models are supposed to serve. This increases the importance of defining at the outset the purpose and limits of the selected energy modelling approach for better decision-making.

5. National or end-user ownership of models is key for longevity. Where external experts helped build and operate models, it is essential that end-users are trained to maintain and run them. Such capacity building is the most sustainable way to ensuring that models remain in use, data is kept up-to-date and long-term impact can be secured. While licencing approaches are an alternative, whereby users pay an ongoing fee for access to external experts, they are likely to be more costly. Moreover, capacity building alongside model construction offers a chance for citizens of Sub-Saharan and African countries to acquire new transferrable skills of wider economic benefit. It also ensures models directly capture local knowledge of energy sector challenges. All EEG-funded research projects have local capacity building at their heart. International agencies such as the IEA and IRENA also promote it directly.

6. Transparency secures impact – and there are many ways to achieve it. Model insights are more likely to be acted upon if outputs are understood and trusted. Otherwise, models risk being ‘black boxes’ that deliver outputs with little end-user understanding of how they are derived. This can be avoided through open access to and review of model inputs (data, model design and assumptions) as well as accessibility of outputs to key stakeholders. There is a particular challenge for artificial intelligence approaches, where measures to allow user interference in algorithms and tracing of interim outputs tend to degrade overall model robustness, because they stop machine learning algorithms from running fully. A key solution here as well as for all other modelling approaches is direct participation of decision-makers or their staff in the modelling process. Transparency and trust can be achieved by building a shared understanding of the data and undertaking joint analysis of findings. Machine learning tools in particular offer the possibility to move towards live adaptive modelling, where information is continuously updated to assess and characterise uncertainty, rather than develop single (‘one shot’) forecasts that can be outdated quickly. For all modelling, the process is at least as valuable as the end-product, because it is through the process that trade-offs and leavers are identified, tested and quantified.

All three webinars can be watched here.

EEG research on energy planning

The webinar series complements EEG’s research into energy planning in Sub-Saharan Africa and South Asia. A range of researchers from EEG-funded projects with modelling elements spoke on the three panels. They are listed below with links to their related projects, for further information:

• Dr Ranjit Deshmukh, Associate Professor, University of California Santa Barbara (UCSB), and Eugenia Masvikeni, Energy and Environmental Finance Specialist, SADC Centre for Renewable Energy and Energy Efficiency (SACREEE): ‘Accelerating large-scale renewable energy deployment in Southern Africa by bridging analysis and application through decision-support tools’; and ‘Renewable energy decision support tools and optimal energy pathways for Southern Africa’.

• Dr Jay Taneja, Assistant Professor, University of Massachusetts – Amherst (UMass): ‘Electricity demand forecasting in agriculture’ in Ethiopia.

• Dr Gabrial Anandarajah, Associate Professor, University College London (UCL), and William Usher, Assistant Professor, KTH Royal Institute of Technology: ‘Energy system development pathways for Ethiopia’.

• Dr Debabrata Chattopadhyay, Senior Energy Specialist at the World Bank Energy Sector Management Assistance Program (ESMAP): ‘Planning for dispatch efficiency’ in Pakistan and Nigeria.

• Dr Jyoti K Parikh, Executive Director of Integrated Research and Action for Development (IRADe): ‘Declining renewable energy costs and regional power trade in South Asia’ (see also this interview).

• Stephen Lee, Researcher, Massachusetts Institute of Technology (MIT): Probabilistic forecasts of electricity demand (forthcoming EEG paper).

EEG’s work on energy planning also includes acting as the Secretariat of the Roundtable Initiative on Strategic Energy Planning, which works with major development partners and technical institutions to improve the support they provide for energy planning in developing countries. The key principles include: a) national ownership; b) coherence and inclusivity; c) capacity; d) robustness; and e) transparency and accessibility. Key signatories to the principles include leading organisations represented at our webinar series.

We would also like to express our gratitude to the following presenters at the EEG webinar series:

• Dr Asami Miketa, Senior Programme Officer, Power Sector Investment Planning, International Renewable Energy Agency (IRENA)

• Marco Baroni, Independent Energy Expert and Lecturer, Sciences Po, and formerly International Energy Agency (IEA)

• Dr Rachel Freeman, Research Fellow in Energy Transitions, University College London (UCL)

• Dr Steve Pye, Associate Professor, University College London (UCL)

• S K Soonee, Advisor and former & founder CEO of the Power System Operation Corporation (POSOCO) of India

• Dr Matteo Rocco, Assistant Professor at the Department of Energy, Politecnico di Milano

Further information on the EEG programme and recordings of all three webinars can be seen here.