Smart grids are complex systems that incorporate a number of technologies, consumer interactions and decision points. This complexity makes it difficult to define detailed development and deployment scenarios. Smart grid technologies are being developed worldwide, so much of the research, development and demonstration (RD&D) can be discussed in a global context. But deployment needs to be discussed at the regional level, where important factors such as the age of infrastructure, demand growth, generation make-up, and regulatory and market structures vary significantly.
Regional analysis and impacts for deployment
Motivated by economic, security or environmental factors, countries will choose their own priorities when adopting smart grid technologies. Where possible, the costs and benefits of different approaches must be quantified to assess the impacts of potential smart grid deployment. The following regional characteristics need to be taken into account in any regional assessment:
• Current and planned mix of supply, including fossil, nuclear and renewable generation.
• Current and future demand, and sectoral make-up of demand, such as manufacturing industry, residential load prevalence or the deployment of electric vehicles.
• Status of existing and planned new transmission and distribution networks.
• Ability to interconnect with neighbouring regions.
• Regulatory and market structure.
• Climatic conditions and resource availability.
Quantification of peak demand and the impact of smart grids
The incentives, or drivers, behind smart grid deployment and the interactions between such drivers need to be understood in the context of local or regional electrical systems. This roadmap has expanded upon the ETP 2010 scenarios to develop a more detailed regional electricity system for four regions: OECD North America, OECD Europe, OECD Pacific and China. Data in the analysis includes:
• Annual demand.
• Electric vehicle (EV) deployment and peak demand as a function of EV deployment.
• Demand response potential.
• Future potential electricity use in buildings.
• Deployment of advanced metering infrastructure.
The model focuses on the demand side of the electricity system; variable renewable deployment is considered in the discussion but not in the analysis itself. The scenarios modelled are shown in Figure 11. In the SGMAX scenario, there is strong regulatory and policy support for the development and deployment of smart grids, whereas the SGMIN scenario assumes little policy support. The amount of clean technology installed – such as heat pumps, variable renewable resources (varRE) and electric vehicles (EVs/PHEVs) – follows the deployment pathways developed by the ETP 2010 analysis in the Baseline and BLUE Map Scenarios.
Since smart grids are already being deployed, policy support is assumed to be at least at a minimum level; a scenario without smart grids will be shown only as reference case to demonstrate that where EVs/PHEVs are deployed with no consideration for electricity system operation, they can have a significant negative impact on peak demand. The key variables used, in addition to ETP 2010 analysis values, are the reduction of peak demand through demand response and electric vehicle connections: grid-to-vehicle (G2V), or battery charging, and vehicle-to-grid (V2G), in which electricity flows from batteries into the grid.
Impact of electric vehicles on peak demand
The deployment of EV/PHEV technology can have a significant positive or negative impact on peak demand. The demand cycle for EV/PHEV charging could be similar to the daily demand cycles of residential and service sector consumers – adding to existing peak demand. If charging is performed in a controlled fashion, simply through a scheduling process, or interactively with signals from utilities, the impact on peak demand could be significantly minimised. The electricity storage in EVs/PHEVs could also be used to reduce the impact of peak demand by providing electricity at or near end-user demand (V2G). Figure 12 shows both the positive and negative impact of EVs/PHEVs on peak demand for OECD North America with no demand response capability installed. The trend is similar in all regions.
Figure 12 shows the SG0 case with total peak demand under the BLUE Map Scenario with no demand response capability and deployment of EVs/PHEVs to 2050. In this case, peak demand increases faster than overall consumption – 29% over the 2010 value by 2050. When some level of scheduling spreads out the charging of EVs/ PHEVs over the course of the day, the increase in peak demand is reduced to 19% over the 2010 value. When both scheduled charging and V2G are deployed, peak demand increases by only 12% by 2050. With the addition of demand response, peak demand could be held steady at 2010 values.
Regional scenarios for deployment to 2050
This roadmap compares the impact of smart grids on system operation among four regions, combining the ETP BLUE Map Scenario with the SGMAX and SGMIN scenarios. In the SGMIN BLUE Map Scenario, deployments of clean energy technology such as VarRE and EVs/PHEVs are significant, but policy support for smart grids is modest. In the SGMAX BLUE Map Scenario, deployments of clean energy technology such as varRE and EVs/PHEVs are the same as in the SGMIN case, but the policy support for smart grids is strong. Tables 7 and 8 look at the increase in peak demand and overall electricity demand compared with 2010 values for the different regions
Table 7 shows that China will see more growth in electricity demand than the other regions will see in 40 years on a net and percentage basis. The other regions will only see growth in the range of 22% to 32% from 2010 to 2050, and no net growth in the near future because of low economic growth and the deployment of energy efficiency technologies. Some minor reductions in transmission and distribution line losses have been included in the analysis, but they have little impact on overall demand.
Table 8 shows that in all cases, the SGMAX scenario sees a significant decrease in peak demand, providing the opportunity to delay investments in and/or reduce stress on existing infrastructure, especially in the context of new loads such as EVs/ PHEVs. The most interesting case is North America, where a 22% increase in overall electricity demand can be seen, but only a 1% increase in peak demand by 2050 in the SGMAX case. China’s overall demand growth has a dramatic effect on the country’s peak demand over 2010 levels and is the dominant driver for this increase in the analysis. In other regions, peak demand is increased by deployment of EVs/PHEVs and greater use of electricity in buildings. All regions except China show that the deployment of smart grids, even to a minimum level, can decrease the rate of peak load demand to a level below overall demand growth.
Interpreting results and further analysis
The regional results provide guidance for the types of pathways that each region might follow as they develop smart grids. China has the opportunity to deploy smart grid technologies to better plan and design the new infrastructure that is being built, thereby reducing the negative impacts on peak demand from the deployment of EVs/PHEVs. OECD Europe and OECD Pacific demonstrate similar trends with respect to all drivers, but OECD Europe shows the highest peak demand of the OECD regions considered. OECD Europe also must manage deployment within an older infrastructure base and with higher deployments of variable generation technology. OECD North America can benefit significantly from the deployment of smart grids, given that it is the largest electricity market in the world and has an ageing infrastructure, especially at the transmission level. A North American smart grid pathway might therefore focus on the benefits of demand response and transmission system monitoring and management.
This roadmap provides some insights into the benefits and possible regional pathways for smart grids deployment, but more analysis is needed, particularly of the generation side, to provide a more complete picture of system performance. Additional regional examination is also needed to consider specific system attributes. Major characteristics of developing countries were not considered in this modelling, and should be added to provide insights into developing regions.
Smart grid CO2 emissions reduction estimates to 2050
Although electricity consumption only represents 17% of final energy use today, it leads to 40% of global CO2 emissions, largely because almost 70% of electricity is produced from fossil fuels (IEA, 2010). In the ETP BLUE Map Scenario, as a result of decarbonisation, electricity generation contributes only 21% of global CO2 emissions, representing an annual reduction of over 20 Gt of CO2 by 2050. Smart grid technologies will be needed to enable these emissions reductions. Direct reductions will occur through feedback on energy usage, lower line losses, accelerated deployment of energy efficiency programmes, continuous commissioning of service sector load, and energy savings due to peak load management. Indirect benefits arise from smart grid support for the wider introduction of electric vehicles and variable renewable generation.
Taking these direct and indirect emissions reductions into account, the ETP BLUE Map Scenario estimates that smart grids offer the potential to achieve net annual emissions reductions of 0.7 Gt to 2.1 Gt of CO2 by 2050 (Figure 13). North America shows the highest potential for CO2 emissions reduction in the OECD, while China has highest potential among non OECD member countries.
Estimating smart grid investment costs and operating savings
A high-level cost/benefit analysis is vital for the deployment of smart grids. Work carried out so far in the roadmap process is providing the foundation for such an analysis, but more effort is needed as additional data and modelling become available. The cost discussion needs to include the three main electricity stakeholders: utilities, consumers and society.
Utilities will experience both costs and savings in the deployment of smart grids, in the areas of operating and capital expenditure. The deployment of new generation (such as variable generation) and end-use technologies (such as electric vehicles) could increase the need for investment in infrastructure, therefore raising capital expenditures; but smart grids have the potential to reduce peak demand, better manage generation from both variable and dispatchable sources, and therefore reduce the potential increases in conventional infrastructure costs. Operating savings can come from decreased costs for maintenance, metering and billing, and fuel savings through increased efficiencies and other areas.
Electricity production costs fluctuate according to basic supply and demand conditions in the market, generation variability (such as unplanned outages), system congestion and the prices of commodities such as oil, gas, coal and nuclear fuel. In markets where consumers are billed using pricing schemes that do not vary based on real production costs (flat-rate based pricing), there is no real- or near real-time link to production costs and consumption. Smart grids can help consumers manage energy use – by taking advantage of lower off-peak prices, for example – so that even if the price of electricity is significantly higher during peak times, their monthly or annual bills would change little. Technology that can accomplish this varies in industrial, service and consumer sectors; some of it is mature and has been deployed for many years, especially in the industrial sectors. Further study is required of the costs and benefits and behavioural aspects of electricity usage in order to identify solutions that enable consumers to manage electricity better and minimise costs.
The environmental costs and security benefits to society of the electricity system are not completely taken into account in current regulatory frameworks for production, use and market arrangements. Companies typically invest large amounts of capital to build electricity system assets and receive regulated rates of return over a long time period – especially in the transmission and distribution sectors. In the current technologically mature market, this is a low-risk, low-reward model. Future grid regulation, however, will need to incorporate factors such as greenhouse gas emission reductions and system security into operating costs. For smart grid deployment to become a reality, all stakeholders must bear their fair share of benefits, costs and risks – especially end-users, who ultimately pay for the electricity service. This can only happen through clever market design and regulation, and sustained stakeholder engagement that will enable new technology demonstration or deployment at an acceptable level of risk, taking into account the existing status of the system as well as future needs. If this is accomplished, the costs and benefits can be rationalised and defended, ensuring the development of a clean, secure and economical electricity system.
Source: Technology Roadmap- Smart Grids
© OECD/IEA, 2011