The results of the WADE Economic Model – which predicts the cost and environmental impact of decentralized energy – depend heavily on the characteristics of the alternative, central generation scenarios. Marc Godin, Mark Tinkler, Bert Dreyer and Anouk Kendall take a look at results for the Canadian city of Calgary and the province of Ontario.

When compared to conventional centralized power generation (CG), decentralized energy (DE) production, particularly with combined heat and power (CHP), can reduce air emissions and support energy efficiency strategies. However, many energy professionals are somewhat sceptical about how DE can address future energy needs. Most importantly, they question the economic rationale of DE compared to conventional central generation. In this article we apply an economic model (see box on page 88) to assess the economic and greenhouse gas emissions impact of introducing DE into the power supply mix of two Canadian jurisdictions: the City of Calgary and the Province of Ontario.

CALGARY

Rising electricity demand
Located in Alberta near the Foothills of the Rockies, the City of Calgary is one of Canada’s fastest-growing population centres. Driven by a booming economy and high prices for oil and gas, the city is growing at a rapid pace. In 2005, electricity demand was 8286 GWh with an annual growth rate of 2.3% while the winter power peak stood at 1480 MW with an annual growth rate of 2.6%.


A recently commissioned 848 kW cogeneration system installed at the Ontario Police College in Aylmer. The WADE Economic Model shows the benefits of potential decentralized energy in Calgary and Ontario (GE Jenbacher)
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The City of Calgary has minimal electrical generation inside city limits other than stand-by generation. The vast majority of electrical power is supplied by coal-fired power plants further north in the Edmonton region. Another component of supply is provided by wind power from southern Alberta.

Future supply of electricity from centralized generation is expected to come predominantly from new coal-fired plants, but combined-cycle and cogeneration power plants fuelled with natural gas are also expected to continue to be part of the mix.

Overall transmission losses are estimated at 5.0% in Alberta. ENMAX, the local electricity distribution company in Calgary, estimates the overall losses of its distribution system at approximately 3%. In order to satisfy growth needs until 2015 through CG, a new CAN$390 million (US$340 million) 500 kV bulk transmission line between Edmonton and Calgary must be built and is currently in the planning stage. A new transmission line is also being proposed to connect Alberta to Montana. The Montana-Alberta Tie Line (MTAL) is expected to cost CAN$120 million (US$100 million) and have a capacity of 300 MW, for a unit cost of CAN$400/kW (US$350/kW).

Decentralized energy growth is expected to come mainly from two elements:

  • 100 kW to 10 MW CHP gas turbines and internal combustion engines in local manufacturing plants and industrial locations providing baseload electrical and thermal energy.
  • Microturbine and small CHP systems in the 30-100 kW range serving institutions such as hospitals, apartment buildings and grocery stores with baseload electrical and thermal energy; and 1 kW to 30 kW units serving residential buildings.
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Some renewable technologies are present today in Alberta and were modelled as part of the future supply mix. On the CG side, Alberta has the largest installed wind power capacity in Canada, but it is only 2% of the mix, a level similar to biomass plants. For DE, the current amount is very small and small amounts of solar and landfill gas generation were added as part of the future urban supply mix.

Results from the WADE model

The model study covered the 20-year period from 2005 to 2025. Over this period, electricity demand in the city of Calgary is modelled to increase at an annual rate of 2.3% from the present level to 12,973 GWh in 2025. When using 100% CG to satisfy incremental demand, 1392 MW of new generation capacity needs to be added. By contrast, if demand is met with 100% DE, only 1302 MW of new capacity is needed. Less DE capacity is required to meet the same demand because DE avoids transmission and distribution losses.


Figure 2. Capital costs for incremental demand in 2025 in Calgary
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For the City of Calgary, the study determined that DE has the potential to reduce capital costs by CAN$1.1 billion (US$0.95 billion) in terms of present day dollars, or 22%, between 2005 and 2025. While DE has higher unit capital costs than CG because it does not benefit from economies of scale and because technologies are still at a relatively early stage of development, DE results in lower total capital costs because it requires slightly less generation capacity and no transmission infrastructure.


Figure 3. Cost of delivered electricity for incremental demand in 2025 in Calgary
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The total capital cost advantage of DE is partially offset by higher fuel costs. In Alberta, natural gas costs significantly more than coal. Therefore, with DE, net 2025 incremental power costs decrease by CAN$0.009/kWh (US$0.008/kWh) or 8%, when compared to total reliance on CG. Details are shown on Table 1, and Figures 2 and 3.

In this study, unit distribution costs were assumed to be identical for CG and DE, which is the most conservative assumption. However, assuming that DE would require 10% of distribution costs, the cost of delivered electricity would have been reduced by CAN$0.033/kWh (US$0.029/kWh) or 28% as compared to 100% CG.

The use of DE would reduce incremental CO2 (carbon dioxide) emissions by 2,010,000 tonnes per year, or 57%. Substitution of coal by natural gas contributes to the improved environmental impact. The avoidance of transmission and distribution losses improves the energy efficiency of the system as a whole. The reduced consumption of energy and fuel directly results in reduced emissions. Combined incremental emissions of NOx (nitrogen oxides), SOx (sulphur dioxide) and PM10 (particulate matter) are also reduced, by 3390 tonnes per year, or 64%.

ONTARIO

Generation gap looms
Ontario, the largest Canadian province, is at a critical juncture for its future electricity needs. Existing coal-fired plants are being phased out in an effort to improve air quality while, at the same time, a significant portion of its nuclear facilities need refurbishing. In December 2005, the Ontario Power Authority (OPA) issued its ‘Supply Mix Advice Report’ outlining its assessment of and approach to new energy supply for Ontario. The Report states:

‘The nature of the problem is clear: a lack of investment to expand electricity capacity in Ontario in the past decade. With supply already tight as a result of this under-investment, the sector faces the loss of a major part of its current supply mix as most units of its nuclear fleet reach the end of their design life over the next several years. The loss of nuclear generation would come immediately on the heels of replacement of coal-fired stations, scheduled for completion by 2009. Together, the combination of demand growth and generation retirements would create a gap of roughly 24,000 MW by 2025, equivalent to about 80% of Ontario’s current capacity.’

The OPA’s efforts to address this issue were substantial. A detailed assessment was undertaken of different supply technologies and resources, and associated risks, based on recent publications and sources of information from industry organizations, research studies, public agencies and developers. The assessment included identification of capital and operating costs, performance characteristics, technology life-cycle, environmental impact and other relevant characteristics.

In performing its analysis, the OPA assembled a number of different potential electricity supply portfolios including new natural gas generation, nuclear (new and refurbished) and renewable energy options based on Ontario’s available wind, biomass and hydroelectric resources. The OPA’s work also placed considerable emphasis on Conservation and Demand Management (CDM), assuming a potential for 1500-2000 MW of equivalent supply due to CDM initiatives such as smart metering, energy efficiency and demand response programmes.

For the most part, however, the OPA report’s perspective is primarily central generation in nature. The potential for significant DE and small-scale CHP contribution, and the associated costs and emissions impacts, were not seriously considered. It also should be noted that the OPA study took little or no account of the additional costs of new transmission and distribution (T&D) infrastructure to deliver greater amounts of power from centralized generation to load centres.

Adding DE to the supply mix

The present study offered a prime opportunity to test the performance of the WADE Model against the thorough CG analysis undertaken by the OPA – essentially to ‘calibrate’ the WADE model against the detailed modelling work done specifically for the Ontario situation. Subsequently, the WADE Model could then be utilized to extend the OPA analysis to consider, in greater detail, the potential benefits of an energy supply scenario with significant amounts of DE.

Accordingly, the following steps were undertaken to populate the WADE Model for Ontario:

  • Utilizing 2005 as the base year, input the current energy supply mix in Ontario in terms of capacity (MW), energy (TWh) and associated load factors.
  • Select an appropriate energy supply portfolio from the OPA study and adjust CG growth characteristics (input data for the Model) until the generation supply mix for 2025 (output data) resembles that of the selected portfolio.
  • Wherever possible, incorporate all relevant assumptions of the OPA study including, for example, annual growth in average and peak demand, expected load factors for various types of generation, capacity retirements, capital and operating costs for new generation, financing assumptions, fuel costs, and generation emissions characteristics.
  • Obtain from appropriate sources, Ontario-based where possible, other required information including: typical T&D costs, average and peak T&D losses, and small-scale DE generation capital/operating costs and emissions characteristics.
  • Run the Model and compare the resulting CG output characteristics (such as capital costs and emissions) against similar supply mix results for the portfolio selected. Fine tune if needed.
  • Develop a ‘DE future’ scenario that envisions a reasonable mix of distributed generation supply. Adjust the Model’s DE growth characteristics (input data for the Model) until the generation supply mix for 2025 (output data) resembles that of the proposed DE scenario. The Model will then provide a detailed comparison of capital costs (capacity and T&D), electricity retail cost (cents/kWh), and emissions characteristics for various levels of CG vs DE to 2025.

The future CG scenario modelled in this study was Portfolio 1A of the OPA Supply Mix Advice Report in which all expected procurements, new renewable and conservation resources, and out-of-province purchases materialize. Most of Ontario’s nuclear units are refurbished or replaced as they reach the end of their estimated service lives between 2013 and 2025. The entirety of Ontario’s coal-fired fleet is removed from service and replaced by 2009. The WADE Model was able to closely model this scenario. It should be noted, however, that the OPA study took little or no account of the additional costs of new transmission and distribution infrastructure to deliver greater amounts of power from centralized generation to load centres.

The future DE energy supply options considered in this study are as follows:

  • gas micro CHP (residential and small commercial microturbines, Stirling engines, residential fuel cells in the 5-75 kW range)
  • gas CHP (industrial cogeneration at small and medium industrial sites)
  • biomass CHP (farm wastes, sewage treatment and municipal solid waste management)
  • landfill gas
  • local hydro (low-head, run-of-river)
  • solar (residential/commercial – generally small installations)
  • fuel cells
  • gas engine combined heat and emergency power (CHeP) using gas-fired internal combustion engine back-up generators to provide mid-merit power
  • substation peakers.

In 2005, Ontario had 30,631 MW of generation capacity and consumed a total of 157 TWh. The Ontario Power Authority (OPA) estimates that electricity consumption in the province will grow at an annual rate of 0.9%, with peak growth rate at 1.3%. After accounting for conservation and demand management (CDM) programmes, net demand is forecasted to be 179 TWh in 2025.

Results from the WADE model

After taking into account the continuing operation of about 13,300 MW of existing generation in Ontario (after all coal and nuclear planned retirements), the model calculates that a 100% CG option would require about 41,300 MW of capacity producing 179 TWh. If all newly added generation were DE, total capacity would be reduced to 38,500 MW and generation to 170 TWh. As more DE is added, for which T&D losses are minimal, the actual energy requirement is reduced.

The overall capital costs associated with the 100% CG case is CAN$119.8 billion, comprised of $58.0 billion for new generation, $14.8 billion for new transmission and $47.0 billion for new distribution. As shown in Figure 4, as DE penetration increases, all components of cost decrease. In the extreme of 100% DE, total savings are calculated as $44.7 billion. For the more realistic scenario of 10% DE, total savings are estimated at $4.75 billion (4.0%).


Figure 4. Total capital costs for new additions in Ontario, to 2025
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With DE, there is a general overall reduction in the cost of delivered electricity. The greatest cost benefit of DE is attributable to reduced capital costs for generation, transmission and distribution. (For the case of generation, CG options of nuclear and renewables are relatively expensive so DE is fairly cost-competitive). However, these reductions are substantially offset by higher fuel costs. For 100% DE, the delivered cost would be reduced by about 1.0 cents/kWh (7.9%). For the 10% DE case, the model calculates a marginal 0.13 cents/kWh reduction (1%). Details for all cases are provided in Figure 5.

As previously noted, Portfolio 1A of the OPA was adopted as the CG scenario. This portfolio depends primarily on new or refurbished nuclear power and renewable energy alternatives. Consequently, the air emissions characteristics of this CG portfolio are relatively good. While the DE generation mix is also relatively clean, it is more highly dependent on natural gas as a primary fuel source. As a result, in contrast with the results obtained for Calgary, emissions are actually found to increase with increasing DE utilization. For the case of 50% DE, CO2 emissions increase from 11.6 million tonnes to 18.9 million tonnes. For the 10% DE case, the increase is about 1.6 million tonnes (14%). These results should be qualified by the statement that the model did not account for any potential net reductions in CO2 equivalence by, for example, the conversion of landfill gas methane emissions, which have 22 times the greenhouse gas value of CO2, to useful electricity and CO2.


Figure 5. Cost of delivered electricity for incremental demand in 2025 in Ontario
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A discussion on the merits of nuclear energy is beyond the scope of this study. However, it is instructional to note that Portfolio 1B of the OPA Study assumes a scenario in which nuclear retirements are not replaced by new or refurbished nuclear, but rather by new gas-fired generation. Therefore, CO2 emissions increase substantially over time, approaching 20 million tonnes by 2025. Compared to this CG alternative, the DE alternative would result in comparable amounts of CO2 emissions.

CONCLUSION

The City of Calgary could save up to $1.1 billion (22%) in capital investment by meeting electricity demand growth to 2025 with DE. Delivered electricity costs could be 8% lower with DE. For the Province of Ontario, the scenario of meeting incremental electricity demand growth to 2025 with 100% DE could avoid as much as $44.7 billion (37%) in capital costs by 2025. A more realistic 10% DE penetration would reduce capital costs by $4.75 billion (4%) by 2025. Delivered electricity costs would be reduced by 1.0 cents/kWh or 7.9% for 100% DE and by $0.13 cents/kWh or 1% for 10% DE.

Lower T&D costs are the key difference between CG and DE. DE requires significantly less T&D investment than CG to meet the same level of demand. This significant advantage is partially offset by higher unit capital costs for DE generation in Alberta and by higher fuel costs in Ontario.

In an Alberta context, DE provides a cost-effective solution for lowering CO2 emissions. When compared with predominantly coal-fired CG, DE – even when heavily tilted toward natural-gas-fuelled cogeneration – will result in reductions of CO2 emissions. However, in situations where CG is dominated by nuclear and hydro power, such as in one of the leading scenarios for Ontario, DE fuelled primarily with natural gas will result in increased CO2 emissions.

The findings of this study are generally consistent with the findings of similar WADE model studies done elsewhere, for example in the United States and in China, where it was shown that retail costs could be reduced by 35% and 28% respectively, and capital costs by 34% and 38% respectively, in addition to resulting in emissions reductions.

The WADE model does not fully account for greenhouse gas reductions stemming from, for example, the conversion of landfill gas or biogas to electricity, as well as greenhouse gas reductions associated from the improved efficiency of the thermal side of cogeneration technologies. If these aspects were to be taken into account, the net greenhouse gas impact of DE technologies would likely lead to further improvements.

In addition to the cost reductions and environmental emissions reductions identified above as benefits of DE, there are a number of other key considerations not quantified by the WADE Model, but which are important enough to be mentioned. These include items such as increased reliability, increased system voltage support, and increased total energy efficiency.

This work highlights the need for governments, regulators and key industry stakeholders to adopt strategies that encourage the development of cleaner and more sustainable energy solutions, which optimize heat and power efficiencies and reduce emissions.

Marc Godin is with Portfire Associates, Calgary, Alberta, Canada. Mark Tinkler is with Emerging Energy Options, Bert Dreyer with EMF Technical Services and Anouk Kendall with NewERA and IRIS Environmental Systems.
e-mail: marc.godin@portfire.com


The WADE economic model

The World Alliance for Decentralized Energy (WADE) has developed a robust economic model to evaluate the economic and environmental value of DE as a part of the future energy supply mix. An emphasis on transmission and distribution (T&D) network capital and energy requirements differentiates the WADE Model’s approach from other energy economic analyses. New T&D systems are critically important because they represent a key difference between DE and CG. DE is located close to the load and there is no need for new transmission lines and less need for distribution infrastructure. In addition, electricity is lost during transmission and distribution. Losses increase with the square of the circuit loading and can be significant during peak periods. By reducing transmission and distribution losses, DE improves energy efficiency and contributes to reductions in greenhouse gas emissions.

By tailoring input assumptions, based on an understanding of specific regional conditions, the WADE Model can be adapted to any country, region or city in the world. It has already been used in a number of jurisdictions to evaluate the value of DE as a part of the future energy supply mix. Studies have been done in the UK, Ireland, Portugal, the European Union, China, Nigeria, Australia and the United States.

Starting with generation capacity for year 0 and estimates of capacity retirement and expected load growth, the model builds user-specified capacity to meet future growth over a 20-year period. The inputs and outputs of the WADE Model are illustrated on Figure 1.

The Model’s data input requirements are detailed and extensive, requiring comprehensive information on a range of factors including:

  • existing capacity and generation by technology type
  • current and future pollutant emissions by technology type
  • future and current heat rates and fuel consumption by technology type
  • future and current capital and investment costs by technology type and for transmission and distribution (T&D)
  • future and current average operation and maintenance (O&M) costs and fuel expenses by technology type
  • system growth properties for the chosen system
  • estimates of existing yearly capacity retirement by technology type, to be entered in five-year blocks.
  • estimates of future growth in capacity by technology type, to be entered in five-year blocks.

The Model outputs are:

  • total capital costs for investment (generation capacity and T&D) over 20 years
  • retail (delivered electricity) costs in year 20 (T&D amortization + generation plant amortization + O&M + fuel costs) for new generation capacity
  • fossil fuel use by the new capacity in year 20, both in total and by type
  • CO2 and other pollutant (SOx, NOx, PM10) emissions from new generation capacity in year 20
  • generation by source in year 20.

Figure 1. The WADE Economic Model: inputs and outputs
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The WADE Model builds cases for new capacity to meet incremental demand over 20 years, ranging from scenarios with 0% DE/100% CG to 100% DE/0% CG. The model also builds cases between these extremes. In addition, the model enables users to run any number of scenarios that, for example, favour certain technologies, change fuel prices or meet specific environmental goals.

The WADE Model takes into account many real but little-understood features of electricity system operation – such as the significant impact of peak-time network losses on the amount of generation required to meet new demand.

Further information regarding the WADE Model can be found at: www.localpower.org/resources/wademodel.htm