Access Economics Pty Limited. 2009. The Economic Benefits of Intelligent Technologies. April.
"This report reviews the potential economic benefits that may flow from the adoption of smart technologies and systems in different parts of the economy...The amount of all forms of data that are regularly collected throughout the community rapidly expand on a daily basis. Intelligent systems have the capacity of using these data to improve decision-making and co-ordination throughout society, thereby lifting economic efficiency and living standards" (i).
"Overall, the adoption of smart technologies and systems in the five areas identified are conservatively estimated to result in:
• an increase in the net present value (NPV) of Gross Domestic Product...of between $35 billion to $80 billion over the first ten years, with precise estimates depending on how much spare capacity is in the economy;
• in the case where the economy is operating at full employment, an increase of labour productivity of around 0.5% as the deployment of the technologies becomes widespread; and
• in the case where the economy is operating at less than full employment the impact on jobs is more pronounced as the technologies become widespread. In 2014 alone this results in more than 70,000 jobs being added to the economy" (ii).
This report explores different areas in which smart technology can have profound effects, specifically electricity, transport, health, water and high-speed broadband (HSBB).
"The rollout of high speed broadband both enables the more effective implementation of smart technologies in other areas of the economy-including electricity, water, transport and health-as well as providing other direct and substantive benefits to individuals, businesses and the environment through the availability to deliver a range of commercial, government and information services more efficiently" (1).
The report reviews the literature on the topic, and then explores smart-technologies in an equilibrium model. Two scenarios are then explored: an economy at full employment and an economy with less than full employment.
Wednesday, August 19, 2009
Tuesday, August 4, 2009
Transforming America's Power Industry
The Brattle Group. Transforming America's Power Industry: The Investment Challenge 2010-2030.
The AEO makes projections out to 2030 of US electricity consumption by type of production. The Brattle Group, through the use of the Regional Capacity Model (RECAP), then took these projections, makes its own projections out to 2030 and develops scenarios around uncertainty.
In the Brattle Group's Reference Scenario, one important initial assumption that was changed was the overall price of construction for different types of energy. These revised numbers came from an updated EPRI report that highlighted the increasing cost of construction above inflation, which was the assumption of the AEO 2008 report.
The Brattle Group also used higher Fuel Prices than AEO in their forecast.
The Reference Scenario is similar to the AEO figure, but incorporates higher fuel and construction costs (outlined in more detail in the report). "The Reference Scenario should not be viewed as our 'base' or 'most likely' scenario, but rather a starting point for our analysis" (11).
In addition to the Reference Scenario, the Brattle Group produced scenarios that focused on energy savings provided by EE/DR (Energy Efficiency/Demand Response) measures. These scenarios were broken down two-fold: firstly, there was assumed to be "realistically achievable potential" (RAP). Secondly, there was assumed to be "maximum achievable potential" (MAP). "...our RAP Efficiency Base Case Scenario includes EE/DR savings as our best estimate of projected demand for electricity prior to the full modeling of price response or a national carbon policy" (16).
"One of the two components of the EPRI forecast is energy efficiency (EE). The EE forecasts consider an extensive set of technologies and measures for the residential, commercial, and industrial sectors. These EE technologies and measures affect different end uses. Programs, products, and services that encourage customers to adopt EE technologies and measures come in several forms, including rebates and subsidies. Following are some of the various technologies and measures considered in teh EPRI study and the end uses they affect" (16).
1. Residential High-Efficiency Equipment: HVAC, lighting, etc.
2. Commercial High-Efficiency Equipment: Same as above for commercial buildings.
3. Industrial High-Efficiency Equipment: Further along the same lines as above, but for industry.
Demand Response, on the other hand, is focused heavily on reducing peak demand for energy. Three types of demand response were modeled:
1. Direct Load Control (DLC): utility companies can shut off an end-user's A/C, for example.
2. Interruptible Service: utility companies can ask industry or commercial groups to reduce energy at certain times.
3. Dynamic Pricing: This requires AMI infrastructure.
"A major cost that is likely to be capitalized in the EE/DR forecast is investment in AMI, the equipment that enables dynamic pricing (as well as a wide range of operational benefits and reliability improvements). Harvesting potential gains from DR programs will require a substantial capital investment in AMI, as well as consumption patterns away from peak periods in response to price signals. To estimate the capital cost of DR initiatives, we separately projected the investment in AMI that likely would be necessary to support these forecasts. Our projection of AMI investment costs is driven primarily by three factors:" (22-3).
1. Final AMI Penetration Rate: "For the MAP Efficiency Scenario, we have assumed that 30 percent of residential customers and 50 percent of C&I customers would be equipped with AMI. These participation rates were reduced by roughly 60 percent to produce the RAP Efficiency Base Case Scenario" (23).
2. AMI Deployment Rate Over Time: "We assume that AMI deployment will begin in 2010 for C&I customers and in 2015 for residential customers. Full deployment will be reached in 2030 for the RAP Efficiency Base Case Scenario. Deployment is accelerated under the MAP Efficiency Scenario, reaching full deployment in 2020" (23).
3. Cost of AMI per Customer: "Based on a review of California shareholder filings for AMI budget approval, we have estimated the full cost per residential customer to be $300. The cost per C&I customer is estimated at $1,500" (23).
The fourth scenario developed explored the implications of the "Prism Analysis" conducted by EPRI. This focuses on the different feasible areas in which the electricity sector can reduce carbon emissions. The Prism Analysis is comprised of seven events:
1. Energy Efficiency Reduction in Load Growth
2. Doubling the level of Renewable Generation Capacity Over AEO 2008 Forecast
3. Tripling Nuclear Capacity over AEO 2008.
4. Improve Efficiency of Coal Generation
5. CCS
6. PHEVs
7. Increased Distributed Energy Resources (DER)
Four major technologies comprise the core of these seven events:
1. Energy Effiency: The assumptions in the Prism RAP Scenario for EE/DR were the same as in the RAP Efficiency Base Case Scenario
2. CCS: All factories after 2020 were assumed to have CCS with 90% efficacy
3. Renewables: These increased assuming more and expanding RPS requirements
4. Nuclear
The AEO makes projections out to 2030 of US electricity consumption by type of production. The Brattle Group, through the use of the Regional Capacity Model (RECAP), then took these projections, makes its own projections out to 2030 and develops scenarios around uncertainty.
In the Brattle Group's Reference Scenario, one important initial assumption that was changed was the overall price of construction for different types of energy. These revised numbers came from an updated EPRI report that highlighted the increasing cost of construction above inflation, which was the assumption of the AEO 2008 report.
The Brattle Group also used higher Fuel Prices than AEO in their forecast.
The Reference Scenario is similar to the AEO figure, but incorporates higher fuel and construction costs (outlined in more detail in the report). "The Reference Scenario should not be viewed as our 'base' or 'most likely' scenario, but rather a starting point for our analysis" (11).
In addition to the Reference Scenario, the Brattle Group produced scenarios that focused on energy savings provided by EE/DR (Energy Efficiency/Demand Response) measures. These scenarios were broken down two-fold: firstly, there was assumed to be "realistically achievable potential" (RAP). Secondly, there was assumed to be "maximum achievable potential" (MAP). "...our RAP Efficiency Base Case Scenario includes EE/DR savings as our best estimate of projected demand for electricity prior to the full modeling of price response or a national carbon policy" (16).
"One of the two components of the EPRI forecast is energy efficiency (EE). The EE forecasts consider an extensive set of technologies and measures for the residential, commercial, and industrial sectors. These EE technologies and measures affect different end uses. Programs, products, and services that encourage customers to adopt EE technologies and measures come in several forms, including rebates and subsidies. Following are some of the various technologies and measures considered in teh EPRI study and the end uses they affect" (16).
1. Residential High-Efficiency Equipment: HVAC, lighting, etc.
2. Commercial High-Efficiency Equipment: Same as above for commercial buildings.
3. Industrial High-Efficiency Equipment: Further along the same lines as above, but for industry.
Demand Response, on the other hand, is focused heavily on reducing peak demand for energy. Three types of demand response were modeled:
1. Direct Load Control (DLC): utility companies can shut off an end-user's A/C, for example.
2. Interruptible Service: utility companies can ask industry or commercial groups to reduce energy at certain times.
3. Dynamic Pricing: This requires AMI infrastructure.
"A major cost that is likely to be capitalized in the EE/DR forecast is investment in AMI, the equipment that enables dynamic pricing (as well as a wide range of operational benefits and reliability improvements). Harvesting potential gains from DR programs will require a substantial capital investment in AMI, as well as consumption patterns away from peak periods in response to price signals. To estimate the capital cost of DR initiatives, we separately projected the investment in AMI that likely would be necessary to support these forecasts. Our projection of AMI investment costs is driven primarily by three factors:" (22-3).
1. Final AMI Penetration Rate: "For the MAP Efficiency Scenario, we have assumed that 30 percent of residential customers and 50 percent of C&I customers would be equipped with AMI. These participation rates were reduced by roughly 60 percent to produce the RAP Efficiency Base Case Scenario" (23).
2. AMI Deployment Rate Over Time: "We assume that AMI deployment will begin in 2010 for C&I customers and in 2015 for residential customers. Full deployment will be reached in 2030 for the RAP Efficiency Base Case Scenario. Deployment is accelerated under the MAP Efficiency Scenario, reaching full deployment in 2020" (23).
3. Cost of AMI per Customer: "Based on a review of California shareholder filings for AMI budget approval, we have estimated the full cost per residential customer to be $300. The cost per C&I customer is estimated at $1,500" (23).
The fourth scenario developed explored the implications of the "Prism Analysis" conducted by EPRI. This focuses on the different feasible areas in which the electricity sector can reduce carbon emissions. The Prism Analysis is comprised of seven events:
1. Energy Efficiency Reduction in Load Growth
2. Doubling the level of Renewable Generation Capacity Over AEO 2008 Forecast
3. Tripling Nuclear Capacity over AEO 2008.
4. Improve Efficiency of Coal Generation
5. CCS
6. PHEVs
7. Increased Distributed Energy Resources (DER)
Four major technologies comprise the core of these seven events:
1. Energy Effiency: The assumptions in the Prism RAP Scenario for EE/DR were the same as in the RAP Efficiency Base Case Scenario
2. CCS: All factories after 2020 were assumed to have CCS with 90% efficacy
3. Renewables: These increased assuming more and expanding RPS requirements
4. Nuclear
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