The WADE Economic Model – which calculates the impact of decentralized energy – has now been applied to various countries and regions around the world. Sytze Dijkstra compares the results and examines how these depend on the country concerned, its existing generation mix and projected electricity growth rates.

The World Alliance for Decentralized Energy (WADE) Economic Model calculates economic and environmental impacts of new decentralized energy plant. It was designed in 2002 by Marty Collins and Tom Casten of Primary Energy, a WADE member in the US, who found that no existing computer model compared conventional centralized generation (CG) directly with a decentralized energy (DE) system. Furthermore, many existing electricity models separated the supply and demand sides, and did not analyse the system as a whole. For instance, transmission and distribution requirements were often neglected despite the significant implications of these on the costs and efficiency of the system as a whole. The model envisaged by Collins and Casten would provide a realistic simulation of the whole electricity system by including these elements. Its results would be especially useful in convincing policymakers of the economic advantages of DE, which are often questioned but need to be accepted before it can be taken seriously as a viable alternative to centralized electricity generation.

The WADE Economic Model is a computer model that compares decentralized energy with conventional centralized generation for different countries worldwide
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Collins and Casten initially created the model for application to the US electricity system and therefore designed it for the American situation. They subsequently realized that the model had a relevance and validity that reached far wider than the US and the idea of running the model for other countries around the world emerged.

During its use following the US application, WADE has developed and improved the model further to:

  • reflect different circumstances in other countries
  • fine-tune the model to varying local circumstances.

As a result, the model’s possibilities have been extended, new functions have been added and user-friendliness has been improved.

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However, the fundamental assumptions and functioning of the model have not changed; they have withstood extensive independent examination and scrutiny for each new application. This shows their relevance and validity in many different circumstances and has created confidence in the reliability of the WADE Economic Model.

Previous model applications

The WADE Economic Model has been applied to a range of different countries and areas (Table 1). Its use in a variety of circumstances has contributed to its improvement. For example, the areas of previous applications vary greatly in terms of:

  • geographic area (e.g. US and Ireland)
  • energy sources (e.g. Nigeria and Ontario)
  • technologies (e.g. UK and Brazil)
  • future demand scenarios (the EU and China).

This emphasizes the wide applicability of the model and its strong basic principles, which have proved relevant in all cases.

A number of reputable organizations and governments have used the WADE Economic Model. This suggests growing interest and trust in the model and its uniqueness in directly comparing DE and CG.

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The results of the WADE Economic Model have been used for various purposes, ranging from promotion only to directly informing policy development. The use of the model outputs has become more substantial and influential over time – WADE expects this to continue. Government involvement is crucial in ensuring that model results are translated into actions through strategy decisions and policy measures. The model applications are therefore not merely a theoretic or academic exercise, but of practical interest to inform energy choices.

Results of previous model applications

The model is currently being used in a number of places around the world (Table 2). Table 3 gives an overview of the results from previous WADE Economic Model applications for seven parameters:

  • capital costs
  • retail costs (delivered electricity costs)
  • carbon dioxide (CO2) emissions
  • fossil fuel use
  • nitrogen oxide (NOx) emissions
  • sulphur dioxide (SO2) emissions
  • particulate matter ≤ 10 µm (PM10) emissions.
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The data represent the percentage saving for a 100% DE scenario compared with a 100% CG scenario. Figures 1-4 depict these results graphically.

Two main conclusions can be draw from the cost results for the previous applications shown in Figures 1 and 2.

Figure 1. Retail cost results for previous WADE Economic Model applications
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Figure 2. Capital cost results for previous WADE Economic Model applications
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First, the cost results are surprisingly similar as, in all but one case, DE was cheaper than CG. The DE savings differ somewhat between countries being dependent on local circumstances. For instance, building transmission and distribution (T&D) in a small and relatively dense country like Ireland is clearly easier than in a much larger one; hence, the advantage of DE requiring less T&D is less important. The German scenario was different because it focussed on reducing CO2 emissions through the use of renewables, while cost considerations were secondary. Even so, the delivered electricity costs are still lower.

Secondly, capital costs and retail costs results are generally similar in each application, with the capital cost saving generally slightly higher than the retail cost saving due to the higher fuel, operation and maintenance costs for DE.

The model applications not only vary in terms or area, but also over time (the applications were made in different years). This affects the results as market conditions change over time. For instance, fuel prices have been rising sharply over the past two years, making the impact of fuel costs much larger in later applications. In the 2002 US application, the oil price was set at $4.50/MMBtu ($4.27/GJ) and the gas price at $4.30/MMBtu ($4.08/GJ) with no increase over 20 years. But in the UK baseline scenario, the chosen oil price was £3.92 (€5.67) per GJ, increasing at 3% a year, and the gas price was £3.19 (€4.60) per GJ, rising 5% every year. This clearly influences the cost results from the model.

Figure 3. CO2 emissions results for previous WADE Economic Model applications
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Figure 4. Fossil fuel use results for previous WADE Economic Model applications
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The following can be drawn from Figures 3 and 4.

  • CO2 emissions and fuel use results show positive DE savings in all cases apart from the Brazil application. This is because large hydroelectric plants are the main CG technology in Brazil. Decentralizing the energy system therefore means replacing renewable energy with gas-fired sources.
  • The CO2 emissions and fuel savings results show the strength of the German application. DE is very effective at cutting CO2 emissions, though at a slightly higher capital cost.
  • The potential for CO2 and pollutant emission savings depends heavily on the future fuel mix. For instance, fossil fuel use and emissions savings are lower for the EU than for China. This is because much of electricity generation in the EU is relatively clean and efficient gas-fired, while China’s energy supply is still heavily coal-based. For DE scenarios, gas-fired CHP is one of the major options, Decentralization in China thus has the additional benefit of fuel-switching, leading to lower fuel use and emissions.

Scenario modelling

Previous applications of the WADE Economic Model included a wide range of scenarios to analyse the sensitivity of the results to changes in inputs so as to compare various possible future developments. Table 4 gives an overview of these scenarios.

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Many of these scenarios are relevant to every application in any area (e.g. fuel price sensitivity), while others are very specific (e.g. those analysing particular generation portfolios). Clearly the list of possible scenarios is not exhausted and it is likely that, in the course of future applications, new scenarios will be developed to analyse different parameters such as the impact of different carbon mitigation strategies or fuel supply issues.

The scenario modelling in previous applications has provided interesting insights into the impact of different parameters on the overall results.

Figure 5. Impact of demand growth on capital costs
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The demand growth trend has proved the single most important factor determining the costs, emissions and fuel use of the system. This is particularly clear from the German results, where projected demand growth was necessary. In the UK application, reducing annual demand growth by 0.5% increased the CO2 emissions savings from 17% to 68%. For China, capital costs at a demand growth of 8% would be more than 2.5 times higher than at the baseline 4.8%. Figure 5 shows the impact of demand growth trend on capital costs for the China and EU applications, while Figure 6 shows the effect on CO2 emissions for the UK and Ireland.

Figure 6. Impact of demand growth on CO2 emissions
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Fuel price trends have become increasingly relevant since the model was first designed; oil and gas have reached record high prices, and fuel imports and security are now a major political issue.

Later applications have therefore often included analysis to evaluate the impacts of rising fuel prices. Changing the projections for fuel price trends affects the delivered electricity costs, while other results are unchanged. The impacts of high fuel prices are significant, and both CG and DE are affected. However, price trends differ for different fossil fuels; the fuel mix for the CG and DE scenarios thus determines how they change relative to each other, and the resulting DE saving. In China, for instance, CG is mostly coal-fired and DE is likely to be based around gas-fired CHP; if gas prices rise more rapidly than coal prices, the DE saving would fall.

The impact of different fuel mixes is a result of the generation mixes that are analysed. The technologies chosen to meet demand in the CG and DE scenarios have different characteristics and so affect the results differently. For instance, the installation costs of gas engines are different to those for an equivalent of wind energy. Furthermore, the efficiencies of coal-fired steam turbines and micro-CHP units are not the same.

Various future generation mixes are therefore analysed in all applications of the WADE Economic Model. In general, these reflect the issues relevant to that country at the time. For instance, the Australian model run looked at the possibility of using carbon capture and storage due to the need to reduce greenhouse gas emissions from coal-fired power plants. The UK application focused on the replacement of nuclear energy because that was an important question in the Energy Review at the time. The precise impact of different fuel mixes is very location-specific and it is difficult to draw any general conclusions.

New applications

WADE is always identifying new opportunities to:

  • apply the model to new regions
  • work with organizations and governments all over the world to strengthen the model further.

Previous applications have mostly been at a national level but, in the future, applications are likely to be regional or even at city level. Electricity supply and energy policy are increasingly influenced by sub-national initiatives, so applications of the WADE Economic model on this level can be valuable in comparing different strategies. For example, the model could be used to analyse the impact of state-level carbon emission trading regimes.

Cities represent a completely different energy system. Generally there is a spatial separation of generation and demand, and strong interconnection with adjacent areas. Furthermore, transmission and distribution issues have to be considered separately. This poses new challenges for the WADE Economic Model.

Further development of the WADE Economic Model

There is scope for improving and developing the Economic Model further. WADE undertakes this work both as part of specific applications in co-operation with other organizations and by itself in response to new findings or developments in the energy sector. Recent updates have improved the detail of the outputs for fuel use and added the option for users to incorporate CO2 emission costs in the results.

Future development of the model is likely to include changes to improve its applicability to smaller areas such as cities, and to extend the freedom for users to specify separate transmission and distribution characteristics of the system to be analysed. Other possible additions are new technologies such as carbon capture and storage, and the internalization of externalities other than carbon emissions.

Sytze Dijkstra is a Research Executive at WADE, Edinburgh, Scotland, UK. Fax: 44 131 625 3334 E-mail:

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