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Chapter 7. Realizing the Potential of Renewable and Distributed GenerationWilliam Lilley, Jennifer Hayward and Luke ReedmanChapter OutlineIntroduction 161Modeling Approach 165Modeling Framework 165Scenario Definition 169Results and Discussion 172Modeling Results 172Value of Potential Benefits 180Conclusions 181References 182Smart grids provide a mechanism to help unlock the economic, social, and environmental benefits that might be realized through more efficient use of large centralized renewable generation and the deployment of renewable and non-renewable resources located near the point of use. Economic analysis suggests that the savings from allowing greater use of intermittent resources are large and may well cover much of the costs in developing smart grid technologies. These benefits are additional to a number of other potential benefits already identified for smart grids, including increased system security, enhanced consumer interaction, and improved power quality.Renewable energy resources, climate change, scenariosIntroductionIn a traditional network such as the one shown inFigure 7.1, electricity is produced by large centralized plant located remote to the user. These plant typically convert energy contained in a fuel (e.g., coal, gas, or nuclear material) into electricity via some form of spinning machine, typically a turbine. The output from these prime movers is fed to a generator, which develops electricity at low voltage. This electricity is then converted to a high voltage for efficient transport through the use of a step-up transformer. The electricity travels through the transmission network toward the end-user at high voltage to reduce losses. When the electricity nears major load centers (e.g., a town), it enters the more widely spread distribution network for transport to numerous end-users. When entering the distribution network the voltage is brought to a lower voltage level by a step-down transformer. This step might typically occur a number of times before reaching the final consumer.

Figure 7.1A simplified view of electricity generation and transfer.Source: CSIRO

Typically the amount of power produced by a given plant is determined by a central control authority or market operator. In Australia's eastern states, for example, this is the Australian Energy Market Operator (AEMO). In the United States, market operators are called independent system operators (ISOs) or regional transmission organizations (RTOs).1These organizations control the dispatch of power to meet system-wide demand. Dispatch takes into account issues such as scheduled outages, power flows including losses, the price offered by each generator for supplying electricity, and a prediction of aggregated demand. The system is then balanced through small changes to dispatch and ancillary services, which control frequency and voltage.1Chapter 6describes CAISO, andChapter 17describes PJM.Because these large centralized plant are being fed a consistent source of fuel, their output is readily controlled and predictable. In response to concerns about climate change as well as fuel diversity, energy security, and a host of other reasons, there is a movement toward bringing large renewable generators into the supply system. These systems are typically connected where there is a good natural resource and where there is access to the high voltage transmission system or higher voltage sections of the distribution network. A number of these renewable generators operate by capturing a source of energy, which is variable by nature, for instance the wind or sun. As a consequence their output is less controllable and less predictable; hence these plants are referred to as intermittent renewable generators. Because their output can vary, their use can be problematic for the finely tuned electricity system, which must balance supply and demand within quite stringent limits.The rise in electricity prices in many developed countries has been driven by expenditure on distribution networks to meet growing demand from large consumer devices such as air conditioners. The use of this equipment can lead to large demands on supply at certain times of the year, in this case on very hot days. The network must be rated to meet this large demand that typically occurs on only a small number of days per year. In response to rising prices to deal with this demand, there has been a trend toward the introduction of measures to better understand and control demand and to provide local supply to avoid transmission and distribution losses. This local generation is referred to as distributed generation (DG), also often referred to as embedded generation.The introduction of DG into a distribution network poses potential problems to a system essentially designed to cater to one-way flow from large centralized plant located in remote locations to the end-user far away. These new two-way flows need to be measurable and controllable to ensure that issues around safety and performance are not unduly affected by the use of DG.Adapting the way in which energy is used and supplied is a major challenge facing the world's economies as they attempt to reduce emissions in response to climate change and to reduce large expenditures in the supply and transfer of energy. Ensuring that new sources of energy supply and management can be integrated with existing technical and economic frameworks is a challenge being addressed through the emergence of smart grid infrastructure and control techniques, including intermediary steps such as minigrid architecture, further described inChapter 8. In this chapter the results of a modeling analysis are provided that considers the value that smart grids may provide by enabling the increased use of intermittent renewable and distributed generation.As smart grids represent a new and evolving way in which energy is generated and delivered, the cost and benefits are yet to be well characterized. Programs such as Smart Grid, Smart City in New South Wales, Australia (http://www.ret.gov.au/energy/energy_programs/smartgrid/Pages/default.aspx), and SmartGridCity in Boulder, Colorado (http://smartgridcity.xcelenergy.com/), have been developed to explore these issues and report outcomes to industry and the wider community. In other studies such as a recent report by EPRI[1], efforts have been made to quantify at a high level the potential cost and benefits associated with smart grids.In the case of EPRI's report, the major benefits considered are: Allowing direct participation by consumers; Accommodating all generation and storage options; Enabling new products, services, and markets; Providing power quality for the digital economy; Optimizing asset utilization and operation efficiently; Anticipation and response to system disturbances; Resilience to attack and natural disaster.However, as can be seen inFigure 7.2, the benefits associated with changes to the development and use of centralized and distributed generation were outside the scope of their study.

Figure 7.2Modeling scope for EPRI cost/benefit analysis of smart grid in the United States; the dashed line represents the components of the energy sector in the scope of the EPRI study[1].Source: EPRI[1]

Potential changes that may be required by the smart grid to deal with intermittency include: Better forecasting techniques for grid-connected wind and solar generation (e.g., the Australian Wind Energy Forecast System [AWEFS] in Australia and AEMO[2]) to allow more accurate dispatch of supply to match demand; Better control of the output of intermittent renewable generators to constrain plant ramp rates, that is, the rate in which output varies (e.g., the semi-scheduled rules within the Australian National Energy Market; AEMO[3]); The use of storage including electric vehicles (seeChapter 5,Chapter 18andChapter 19) to increase revenue earned by renewable generators; The adoption of new architectures such as mini grids (seeChapter 8) that can provide local areas with high penetration of intermittent generation through a combination of sophisticated control of generation devices and demand.This chapter attempts to put a value on the benefits of a smart grid on a global scale. The analysis posits that greater amounts of renewable and distributed generation can be facilitated by a smart grid. Previous studies such as EPRI[1]do not estimate the benefits of a smart grid on the integration of renewable and distributed generation. This chapter presents modeling of the global electricity sector to examine the impact of intermittency constraints on renewable generation. Varying this constraint in the model is a means to estimate the potential benefits of a smart grid in facilitating greater deployment of renewable generation.Section Modeling Approach presents the methodology of the economic modeling. Section Results and Discussion presents results and discussion of the modeling. Finally, section Conclusions provides conclusions resulting from the analysis.Modeling ApproachThe modeling in this chapter complements the approach used by EPRI[1]in their U.S. study by examiningusing simple assumptionsthe economic benefits derived from increasing levels of intermittent distributed and renewable generation in the grid over a long time frame. In the context of increasing global electricity demand, there are numerous supply-side options that may become economically feasible over time. The model assumes that smart grids will allow increasing levels of intermittent renewable and distributed generation into electricity networks. Prior to discussion of the scenarios, a brief overview of the modeling framework is presented below.Modeling FrameworkThis chapter provides an estimate of the potential economic benefits from the integration of smart grids through the application of CSIRO's Global and Local Learning Model (GALLM).2GALLM is an international and regional electricity sector model that features endogenous technology learning via the use of experience curves. GALLM projects the global uptake of electricity generation technologies under business as usual and alternative policy environments. Currently the model is separated into three zones; developed countries,3less developed countries,4and Australia[4]. The model estimates the least-cost mix of different electricity generation technologies to meet electricity demand in each region over time, factoring in different initial generation capacities[5], different resources[6],[7],[8]and[9]and different electricity demand growth rates[8].2The Commonwealth Scientific and Industrial Research Organisation (CSIRO;http://www.csiro.au) is Australia's national science agency and one of the largest and most diverse research agencies in the world.3Developed countries in the model include Austria, Belgium, Bulgaria, Canada, Croatia, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Latvia, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Russia, Slovenia, Spain, Sweden, Switzerland, United Kingdom, Ukraine, and United States.4Less developed countries are countries not included as developed countries.In the model, the uptake of a limited set of DG technologies has been considered by modeling generic combined heat and power (CHP), generic fuel cells, and rooftop PV. Large-scale intermittent technologies include wind, solar thermal, large-scale PV, wave, and ocean/tidal current. Non-intermittent large-scale technologies in the model are black and brown coal, pf (pulverized fuel); black and brown coal combined cycle; black and brown coal with carbon capture and storage (CCS); gas open and combined cycle; gas with CCS; nuclear; biomass; hot fractured rocks; conventional geothermal; and hydroelectric.Table 7.1lists the main technology cost and performance assumptions of GALLM.Table 7.1Technology Cost and Performance Assumptions of GALLM

Source:CSIRO.

Notes: NA: not applicable; HHV: Higher heating value; IGCC: Integrated gasification combined cycle; O&M: operating and maintenance; BOP: Balance of plant; DEV: Developed countries; AUS: Australia; LDC: Less developed countries; inst: installations.

TechnologyCapital Cost ($/kW Sent Out)Efficiency HHV (%)Capacity Factor (%)O&M ($/MWh)Learning Rate (%)

Black coal, pf194835.1805.48NA

Black coal, IGCC300441.0807.782.0

Black coal with CCS434825.2809.835.0

Brown coal, pf289528.0807.34NA

Brown coal, IGCC332041.0808.402.0

Brown coal with CCS738017.08010.555.0

Gas open cycle44920.02019.97NA

Gas combined cycle89249.0807.702.0

Gas with CCS290040.08025.692.2

Nuclear397134.0806.993.0

Hydro3246NA2021.98NA

Biomass292426.04515.545.0

Solar thermal5898NA2524.2314.6

Hot fractured rocks4633NA8011.998.0 (BOP)

Conventional geothermal2878NA8011.998.0 (BOP)

Wave7000NA5032.139.0

Ocean current5200NA3538.619.0

Wind1518 (DEV); 1742 (AUS); 1389 (LDC)NA2915.334.3 (turbine) 11.3 (AUS inst) 19.8 (global inst)

PV rooftop10529 (DEV); 9960 (AUS); 11858 (LDC)NA202.1420.0 (module) 17.0 (BOS all regions)

PV large scale6969 (DEV); 6615 (AUS); 7867 (LDC)NA2012.83As above

CHP160042.1416.53NA

Fuel cells1250050.03055.520.0

To encourage the development of CHP, heat credits5have been added into the objective function of the model. The CHP heat credit was initially 31.08 AU$/MWh, and this is reduced by 0.025% per year to reflect the fact that there may be an oversupply of heat.6European feed-in-tariffs have also been included for rooftop PV, assuming 0.19 AU$/kWh and reduced by 8% per year over 20 years.Table 7.2provides a list of the fuel cost and CO2emission rate assumptions used in the modeling.5This is an allowance for heat production[10].6At the time of writing the Australian dollar (AU$) is roughly at parity with US$.Table 7.2Fuel Cost and Emissions Assumptions of GALLM

Source:CSIRO.

Fuel TypeCost of Fuel (AU$/GJ)Emissions (kgCO2/GJ)

Brown coal0.593.6

Black coal1.095.29

Natural gas0.930.062.9

Biomass fuel0.610.00

Uranium0.730.00

In GALLM, most technological development occurs as a result of global technology deployment such that all countries benefit from the spill-over effects of other countries investing in new technologies. Wind and PV are exceptions. Both wind and PV were assigned two experience curves: a global curve for the prime mover; and a local curve for installation and balance of system (BOS), where BOS only applies to PV costs. The twin experience curves for wind turbines and their installation in developed countries are shown inFigure 7.3.

Figure 7.3Experience curves for wind turbines and their installation in developed countries.Source: CSIRO, Note: International turbine wind turbine data with the experience curve and international installation data with the experience curve. Each data point represents a year where the first data point for turbines is from 1998 and the first data point for installation is from 2000.

Another feature of GALLM is that capital cost reductions can differ from that expected by the learning curve. For example, before the global economic crisis in 2008, the capital cost of energy technologies was extremely high. In regard to wind energy, the price rise was due to high demand and the resultant increased profit margins and higher materials prices that this allowed[11]. These market forces have been included in GALLM as a penalty constraint; if demand for one technology exceeds one third of total required new installed capacity, then a premium is placed on the price of that technology based on historical data for wind turbines[8]. One effect of implementing the penalty constraint in the model is that it creates a disincentive for too rapid an uptake of any single energy technology[4].Scenario DefinitionThe purpose of this chapter is to estimate the potential benefits of greater renewable and distributed generation that may be facilitated by the roll out of smart grids on a broad scale. To capture this, a new constraint is implemented in GALLM. It constrains the uptake of technology by imposing a variable constraint or cap on generation from intermittent renewable technologies including wind, solar thermal, PV, wave, and ocean current. Four scenarios that vary this constraint were considered in the current modeling: Case A: a base case with a maximum 20% cap on generation of intermittent renewable technologies by region; Case B: a maximum 30% cap in 2030 on generation of intermittent renewable technologies by region that increases linearly from 20% in 2020; Case C: a maximum 40% cap in 2030 on generation of intermittent renewable technologies by region that increases linearly from 20% in 2020; Case D: a maximum 50% cap in 2030 on generation of intermittent renewable technologies by region that increases linearly from 20% in 2020.In Case A, for example, the model does not force the deployment of intermittent renewable technologies in each region to 20%. Rather, the 20% is a maximum share of electricity demand in each region that can be met by intermittent renewable technologies if it is economic to do so. This 20% cap, while quite arbitrary, represents a value similar to limits often quoted in industry discussion (e.g., UKERC[12]and Myers et al.[13]). The 20% cap also represents an aspirational renewable target in some countries (e.g., Australia ORER[14]and the EU-25 ECCA[15]) and some U.S. states[16], to be reached by the year 2020.Figure 7.4shows the current technology mix of world power generation. It shows that intermittent renewables, labeled as other renewable inFigure 7.4, represent a small proportion of current global power production.

Figure 7.4Percentage of world power generation by fuel type.Source: IEA[17]

While some regions may have higher or lower targets, it is worth noting that such targets are generally set at a high level that simply assumes the intermittent sources can be accommodated without problems or constraints. Ultimately the actual amount that can be accommodated will be highly dependent on the type and size of generation technology, the location of the source of generation, the topology of the network, and market trading structures. In the analysis presented here, it is assumed that the same limit applies everywhere. The modeling then considers a future where the percentage of intermittent generation is able to increase on the presumption that smart grid technologies and techniques discussed in this book enable an increase in their use.For results displayed in this chapter, GALLM was operated under a business-as-usual (BAU) case and two different carbon price scenarios in which the price varied within regions in GALLM as shown inFigure 7.5. The first case, the 550 ppm, represents a lower price path consistent with a target of 550 parts per million (ppm) CO2equivalent (CO2-e) atmospheric concentration corresponding to 2.83.2C average global temperature increase by 2050, consistent with other studies by the International Energy Agency (IEA) and the UN's Intergovernmental Panel on Climate Change or IPCC[18]and[19]. The second case, the 450 ppm, represents a higher price path with a target of 450 ppm CO2-e corresponding to 2.02.4C temperature increase. As a point of comparison, the target ranges for the United States, the European Union, and Japan all correspond to entitlements for a global agreement between 450 ppm and 550 ppm atmospheric concentration of CO2-e[20].

Figure 7.5Carbon price trajectories used in the modeling for the 450 ppm and 550 ppm cases.Source: Commonwealth of Australia[21]

The corresponding carbon prices associated with the 450 ppm and 550 ppm targets are shown inFigure 7.5, taken from the Commonwealth of Australia[21]report examining the impacts of carbon pricing on the Australian economy. This report showed that higher carbon prices facilitated greater uptake of intermittent renewable technologies. For the 550 ppm scenario the prices start at AU$20 per ton of CO2-e and increase by 4% each year. For the 450 ppm case the starting price is AU$43 per ton of CO2-e rising at 4% per year. For modeling purposes it is assumed that a global emissions trading scheme (ETS), similar in nature to the one adopted in the EU, is adopted by all developed countries on commencement in 2013 and that developing countries begin trading in 2025.It should be noted that there is currently no national or global commitment to implement any of the modeled schemes. Hence these two carbon price trajectories are only a guide to possible future carbon prices were such schemes to be agreed and implemented some time in the future.Other groups, such as the International Energy Agency, in their modeling use an emissions reduction target scenario, the BLUE Map, consistent with reaching 450 ppm of atmospheric concentration of CO2-e, rather than assigning a carbon price[5]and[19]. This chapter uses the carbon prices inFigure 7.5, rather than an emissions reductions target used by the IEA. This is because GALLM is a partial equilibrium model and thus only covers electricity generation, not other emitting sectors such as transport and agriculture.Figure 7.6shows the estimated emissions reduction under the 550 ppm and 450 ppm scenarios and the IEA BLUE Map scenario.

Figure 7.6Projected global greenhouse gas emissions under 550 ppm, 450 ppm, and IEA BLUE Map scenarios. The 550 ppm and 450 ppm scenarios include emission reductions from forestry and agriculture, whereas the IEA BLUE Map only includes energy-related emission reductions.Source: Commonwealth of Australia[21]; IEA[19]

Results and DiscussionModeling ResultsInFigure 7.7Aa plot is provided for the BAU case with a 20% cap under the Case A scenario previously described. InFigure 7.7Bthe BAU case for a 50% cap under the Case D scenario is displayed for comparison. Both figures clearly show that global electricity demand is expected to grow significantly, increasing two-and-a-half times by 2050. In these figures coal represents all brown and black coal sources with and without carbon capture and sequestration (CCS). Gas represents both open cycle and combined cycle turbines with and without CCS. Non-intermittent renewable generation, NI RG, includes biomass, hot fractured rocks, conventional geothermal, and hydroelectric. RG represents intermittent renewable generation sources including large-scale PV and solar thermal, wind, wave, and ocean/tidal current. Small-scale rooftop PV is not included here as it is captured within DG, which represents small-scale technologies such as PV, CHP, and fuel cells.

Figure 7.7ACase A: Global technology mix assuming business as usual.

Figure 7.7BCase D: Global technology mix assuming business as usual.Source: CSIRO

Figure 7.7AandFigure 7.7Bshow that the absence of a carbon price has not prevented the uptake of solar-based renewable technologies and CHP. All of these technologies receive some form of government support in the model, such as feed-in tariffs or heat credit. Solar thermal has the additional advantage of providing some peaking power. These technologies, with the exception of CHP, have high learning rates as described inTable 7.1; therefore by increasing their cumulative capacity their capital costs fall over time. Black coal pulverized fuel plant continues generating beyond 2050, and black coal combined cycle, with its higher efficiency, absorbs the growing demand for electricity. Under this scenario very little CCS technology is developed. This is also the case for ocean energies, biomass, and geothermal plant.The modeling results indicate that the total amount of intermittent generation does not necessarily reach the cap in all three regions in the model, but it does reach the cap in at least one. This level of detail is not captured in these figures, which simply present the total worldwide generation. However, this means that even under a BAU case there are financial benefits to be gained by using smart grids, assuming they increase the amount of intermittent generation that can integrated into the grid.For comparative purposes, results from the IEA[19]projections for the year 2050 for uptake of various electricity generation technologies under their baseline scenario are also presented. The results are shown inTable 7.3. It is not apparent if any intermittent constraints have been made by the IEA in their modeling, and therefore they are compared with the range of outputs in the modeling presented here.Table 7.3BAU Cases A, Assuming a 20% Maximum Intermittency and D, Assuming a 50% Maximum Intermittency, and IEA Baseline Scenario Projected Share of Electricity Generation Technologies in the Year 2050

Source:CSIRO.

BAU Case A (% Share)BAU Case D (% Share)IEA Baseline (% Share)

Coal555441

Gas131214

Nuclear1110

Non-intermittent renewable7715

Intermittent renewable126

Distributed generation232414

Major differences between the modeling presented here and the IEA results lie in the share of coal to nuclear and the share of renewable generation, both intermittent and non-intermittent, and DG. The differences between coal and nuclear arise because of two issues: The first is that this modeling uses a flat price for coal and a cost curve for uranium, so that the more uranium is used the more expensive it becomes. Second, this modeling has a higher capital cost for nuclear than the IEA[19], which makes nuclear a less attractive option in GALLM. The IEA nuclear costs are primarily U.S. based while the figures in this analysis are global, including European costs informed by recent data from manufacturers. Furthermore, these baseline cases place no cost on greenhouse gas emissions; therefore there is no need to build zero-emission technologies unless they are economic.The IEA results show a bigger share of generation from non-intermittent renewable generation, most likely due to constraints in GALLM, particularly for hydroelectric generation. The IEA's biomass component includes waste, which is modeled here as DG since this can encompass bagasse and other locally produced biomass waste that is used as fuel close to where it has been harvested. GALLM predicts more DG mainly because of rooftop PV, which has a high learning rate compared to other technologies, and its capital cost becomes quite low in time, reaching 1400 $AU/kW by 2050. In this modeling there are no limits on the amount of rooftop PV that can be constructed, aside from the cap on intermittent sources. It is worth noting, however, that an earlier study[22]in Australia showed that PV installation was economically constrained rather than physically constrained by available roof space, assuming slightly less than one quarter of available rooftop space was used.InFigure 7.8Aa plot is provided for the 450 ppm case with a 20% cap (Case A). InFigure 7.8Bthe 450 ppm case for a 50% cap (Case D) is displayed for contrast. These results show that for a grid constrained by 20% intermittent resources, large centralized low-emission plant such as coal with CCS, shown as Coal, and nuclear play a strong role, while gas turbines provide a large amount of the generation mix because of its cheap price and its use in peaking operation. When the constraints on intermittency are relaxed, the strong price of carbon results in large amounts of rooftop PV, in the DG set, and solar thermal and wind, in the RG set, being economically deployed over the period to 2050. In this case nuclear also plays a strong role, while gas and coal with CCS, shown as Coal, are deployed at much lower rates than in the more constrained intermittency case.

Figure 7.8AGlobal technology mix under a 450 ppm case with a 20% cap on intermittent sources.

Figure 7.8BGlobal technology mix under a 450 ppm case with a 50% cap on intermittent sources.

For comparison,Table 7.4shows the projected share of electricity generation technologies in the year 2050 under the 450 ppm case compared with the IEA[19]Blue Map scenario, which has also been designed to achieve 450 ppm. In Case A where intermittent sources are limited to a cap of 20%, the modeling shows a 21% share in DG. In this case little large-scale intermittent renewable generation is predicted. When the intermittency constraint is relaxed in Case D, the DG and large-scale intermittent renewable technologies increase to a 54% share. In contrast, the IEA in their predictions shows a 34% total for intermittent renewables. The technologies that have increased the most due to the relaxing of the intermittent constraint are solar PV, solar thermal, and wind. In the IEA's case, the renewable technologies that have increased their share the most compared to the BAU case are wind and solar. Geothermal triples its share but it is starting from a lower BAU generation capacity.Table 7.4450 ppm Cases A and D and IEA BLUE Map Scenario Projected Share of Electricity Generation Technologies in the Year 2050

Source:CSIRO.

450 ppm Case A (% Share)450 ppm Case D (% Share)IEA BLUE Map (% Share)

Coal371612

Gas1667

Nuclear151524

Non-intermittent renewable10823

Intermittent renewable13419

Distributed generation212015

The differences between Case A and the IEA BLUE Map are again in the shares of nuclear vs. coal and of renewable generation, both intermittent and non-intermittent, vs. DG. However, there are fewer differences between Case D and the BLUE Map scenario. Case D has more intermittent renewable generation than non-intermittent, whereas the IEA has more non-intermittent generation. The GALLM model also predicts more than double the amount of intermittent renewable generation compared to nuclear, whereas the IEA has more nuclear than intermittent renewable generation.These differences reflect the generous 50% intermittent constraint, which is allowing more large-scale intermittent renewable technologies with high learning rates, such as solar thermal, wave, and wind, into the market. The more these technologies are deployed, the lower their capital costs become due to assumptions on learning rates. The IEA does not state the limits used in their modeling but the results suggest they may use a cap lower than 50% on intermittent technologies, yet not as low as 20% since both intermittent renewable generation and DG contain intermittent technologies and the total of these is 34%.It is worth reiterating that the modeling simply examines the savings that might be achieved by allowing an increase in intermittent generationmostly attributed to the presence of smart-grid-enabling infrastructure. It does not specify how this may be achieved, only the savings that might be realized through their use. There are many possible ways in which intermittent generation may be better integrated into electricity networks including the use of storage, better management of demand through increased consumer awareness and appliance automation, and better forecasting of demand and supply for instance as discussed below and covered in more detail in other chapters of this book.The modeling displayed above shows that in later years, traditional peaking plant such as gas turbines become less prevalent and slower reacting plant such as nuclear begin to dominate large centralized facilities. In simple terms in current electrical networks the balance between supply and demand is provided by these large plant, which receive appropriate signals from a central control to ramp their supply up or down as required. Ongoing developments in transmission and centralized dispatch fit within the wider smart grid paradigm, and it will need to continue to evolve to accommodate the technical performance characteristics of the generation mix as it changes in time.One example of a smart grid response to intermittency is the semi-scheduled rules for large intermittent plant in Australia[2]. In this case the market operator (AEMO) established rules for the operation of large intermittent plant that constrains the divergence of output of these plants from their nominated dispatch levels. By constraining large-scale fluctuations, the market operator is able to maintain system balance more readily than it could if these sources were left uncontrolled. In a related activity AEMO also commissioned a wind forecasting system to improve efficiency of overall dispatch and pricing, and to permit better network stability and security management[2]. It is expected that solar forecasting will be built in the near future. If non-renewable large-scale centralized plant begin to be dominated by slow reacting facilities such as nuclear, then mechanisms such as those used by AEMO may become even more important to maintain system balance.Much of the context for current smart grid discussion relates to distribution networks. A major reason for this is that historically they have acted passively, with system balance provided by large centralized plant matching aggregated demand to supply delivered through the transmission network as noted above. These large-scale operations can be considered smarter than their distribution counterparts as they already contain aspects of measurement and control. In many ways the emergence of smart grid technologies in distribution networks is driven by a need to bring these systems into line with the automated measurement and control of the large high-voltage networks.One emerging response in distribution networks is demand management through automated control of individual devicessuch as air conditioners, pool pumps, refrigerator compressors, and so onand consumer response to varying tariff structures, such as dynamic pricing.7In the future this is likely to play a significant role in controlling local demand as covered inChapter 2andChapter 9; demand management may make it more easy to balance supply, particularly if it is supplied by more variable sources.7Chapter 8andChapter 12describe these options in more detail.The role of storage in buffering the impacts of intermittency is an area of growing interest in both transmission and distribution systems, as further described inChapter 5. There are many different types of storage devices, such as batteries, thermal, compressed air, flywheels, ultra-capacitors, and superconducting magnetic devices. These devices can store and release energy at significantly different timescales and efficiencies. This variation in performance affects their potential application and arbitrage opportunities in the energy market. In contrast to these statically located devices, the emergence of electric vehicles, covered inChapter 18andChapter 19, provides both a temporally and spatially varying storage device that may contribute both to the use and supply of energy, with its effects most pronounced in distribution networks.A critical aspect of the modeling presented here is how the energy mix changes over time under different scenarios. The results show the emergence and dominance of fundamentally different types of generation technology, which vary in time and by end-use sector. In an earlier analysis[22]of the Australian energy market and the potential impact of DG, the modeling showed there is a large potential for gas-fired co- and tri generation to economically reduce emissions in the near future. In time, this potential is taken over by renewable technologies as their price becomes more favorable from increased learning and higher carbon prices. These findings are repeated here in the global modeling for rooftop PV and to a lesser extent CHP.In this case there are important implications for distribution network planning due to the manner in which the generation devices are connected into the grid. Taking the Australian case as an example, the increased use of large gas-fired co- and trigeneration plant is more ideally suited in the commercial and industrial sectors where waste heat can be well utilized and it is predicted to be more economically viable in the near term. PV on the other hand is predicted to be predominantly installed in the residential sector in later years after it becomes more economically attractive.Gas-fired CHP units are typically connected to the grid through a generator as a synchronous machine. These machines can produce and sustain large fault currents. A fault current is an abnormal current in a circuit due to a fault (usually a shortcircuit). The maximum (or making) fault current occurs in the first 20 ms while the steady-state fault current follows after approximately 4060 ms. To protect a circuit, the fault current must be high enough to operate a protective device as quickly as possible, and the protective device must be able to withstand the fault current. A calculation of fault currents in a system determines the maximum current at a particular location, and this value determines the appropriate rating of breakers and fuses. Changes to fault currents from this type of installation are already posing challenges to some network operators particularly in central business districts (CBDs). Considerable effort will be required to manage their potential impact by appropriately locating control equipment such as superconducting fault current limiters, one example of an emerging smart grid technology.Intermittent renewable generators on the other hand are connected to the grid via inverters that change DC output from the generating device (e.g., solar panel) to an AC waveform for export to the grid. These devices do not sustain large fault currents but can add harmonic distortions to the network, change voltage profiles on feeders, and be disconnected if voltage levels exceed preset bounds. In this case the installation of an integrated voltage control system including software and hardware will be required as part of a smart grid solution to ensure the successful integration of DG. Potential equipment includes automatic load tap changers, switched capacitors, medium voltage sensors, customer meters, and inverters able to operate at lagging or leading power factor.Detecting and isolating faults, restoring operation, controlling voltage, and controlling real and reactive power flows will be some of the significant issues for smart grids to address if these generators are to reach their full economic and environmental potential. Since the type of technology and location in which it is installed will vary in time, the development of the smart grid will need to be well planned and flexible accommodate this complex evolution in supply and demand. This has implications both for technological development and associated policy and regulation. In countries such as Australia where the once vertically integrated system has been separated for economic efficiency reasons, this coordination may be potentially more difficult. Furthermore the disaggregation may make it harder to measure and attribute the value of individual actions to the system as a whole, which could inhibit the uptake of the most efficient solutions[22].As noted above, the present analysis does not attempt to specify how the smart grid should evolve to meet the potential change in energy supply. Instead the focus is on the savings that might be achieved by allowing intermittent renewable technologies to reduce greenhouse gas emissions. Below the discussion considers how the savings may compare to costs of smart grids already identified by organizations such as EPRI.Value of Potential BenefitsWhile the previous figures show how the mix in generation technologies could vary in time,Table 7.5provides a summary of the undiscounted savings that may be achieved by increasing the amount of intermittent generation in the grid. This analysis has not specified how the intermittency may be accommodated as many of these technological issues are examined elsewhere within this book. Instead it focuses on the relative savings that could be realized from changes to capital expenditure, operation and maintenance, fuel and carbon prices, and revenues generated from feed-in-tariffs and heat credits for centralized and decentralized generation. From the table it is obvious that by 2050 very significant savingspotentially up to AU$20 trillioncould be achieved worldwide if the amount of intermittent supply can be increased in response to the world's reduction of greenhouse gas emissions.Table 7.5Undiscounted Cumulative Savings AU$ Billion from Increasing Intermittency Relative to the 20% Base Case (Case A) by 2050

Source:CSIRO.

Case B (Max 30%)Case C (Max 40%)Case D (Max 50%)

BAU268026782695

550 ppm9577868810522

450 ppm120241899720763

The value of AU$20 trillion does not include the costs actually spent on upgrading the network to a smart grid to enable intermittent sources to reach their potential. To estimate this cost, estimates from EPRI[1]for the United States have been extrapolated to a global scale. EPRI[1]estimates that upgrading the U.S. network to a fully functioning smart grid will cost between US$338 billion and 476 billion by 2030. Assuming that this level of expenditure allows the modeled levels of intermittency to be achieved, and extrapolating the upper end cost ($476 billion) using IEA data on U.S. and world demand, by 2050 roughly AU$6.4 trillion will be needed to upgrade the global electricity networks to a smart grid, all else being equal. When this is combined with the savings in generation from utilizing more distributed generation and large-scale intermittent technologies, it means that the total global cumulative savings from installing a smart grid, assuming it allows 50% intermittent generation onto networks, could be as high as AU$14 trillion by 2050 if the world acts to reduce atmospheric concentration of CO2e to 450 ppm.It is important to note that the modeling indicates most of these savings come in later years, from 2040 onwards, after current long-lived stock with sunk costs have retired and large amounts of new generation come online and are able to become profitable; that is, run for sufficient time to recover initial investment. It is also important to note that the result is formed through comparison to a base case where there is an assumed intermittency limit of 20%. This limit is quite arbitrary for the purpose of this high-level study and in reality will vary by location due to the types and size of generation assets, installation location of generation within the grid, topologies of the electrical network, and market mechanisms.While this examination is a simple high-level assessment, the outcomes show that it is an important consideration as the full benefits of smart grids will only be captured by examining the long-term changes that can occur through advanced operation of the electricity network, which takes into account all costs and benefits. The study also highlights the complexity in designing a smart grid that takes into account all potential changes in an evolving electricity system as consumers react to rising prices and the challenge of reducing emissions.This study has shown that substantial savings could be obtained by allowing an increase in the use of centralized and distributed intermittent renewable generation. These savings are above and beyond those typically noted in cost-benefit assessments of smart grids in which network-specific aspects such as increased reliability, customer engagement, and asset performance are considered. While these factors are exceptionally important, the high-level analysis presented here shows that even further benefit can be captured by taking into account the role of smart grids in providing an increase in the amount of intermittent renewable generation able to participate in electricity markets.ConclusionsThis chapter presents results focused on the potential net benefits that may occur by allowing a greater proportion of global energy supply to be met by intermittent renewable and DG resources out to 2050. It is assumed that the introduction of smart grids will enable a larger proportion of generation to be provided by intermittent and local generation devices. These savings come from reductions in capital expenditure, fuel costs, operation and maintenance costs, and carbon costs, and revenue from feed-in-tariffs and heat credits for DG.Modeling presented here clearly shows that savings from allowing an increased proportion of intermittent renewable and distributed generation can be very significant when considering how the world may meet the dual challenge of reducing emissions of greenhouse gases while accommodating the ongoing growth in demand. These savings are only realized by considering the long-term change to energy supply because of the lifetimes of the assets involved. This has important implications for smart grid use, planning, and development, which will be needed to ensure these renewable technologies reach their full potential.When coupled with more traditionally noted benefits such as increased reliability, security, and consumer awareness, the development of a smart grid appears to be a very favorable mechanism to help the world reduce its greenhouse gas emissions while maintaining current levels of supply enjoyed in many of the world's developed countries.References[1]EPRI,Estimating the Costs and Benefits of the Smart Grid. A Preliminary Estimate of the Investment Requirements and the Resultant Benefits of a Fully Functioning Smart Grid. 1022519 Final Report, March 2011.[2]AEMO,Australian Wind Energy Forecasting System (AWEFS).http://www.aemo.com.au/electricityops/awefs.html, 2011 (accessed 30.03.11).[3]AEMO,Semi-Dispatch of Significant Intermittent Generation: Proposed Market Arrangements.http://www.aemo.com.au/electricityops/140-0091.html, 2010 (accessed 20.03.11).[4]J.A. Hayward, P.W. Graham, P.K. Campbell,Projections of the Future Costs of Electricity Generation Technologies: An Application of CSIRO's Global and Local Learning Model (GALLM). CSIRO Report EP104982,http://www.csiro.au/resources/GALLM-report.html, 2011.[5]IEA,Energy Technology Perspectives 2008. (2008)IEA,Paris.[6]Jacobson, M.Z.,Review of solutions to global warming, air pollution, and energy security,Energy Environ. Sci.2(2009)148173.[7]Carnegie Wave Energy,Wave Energy as a Global Resource.http://www.carnegiecorp.com.au, 2010 (accessed 07.01.11).[8]IEA,IEA Wind Energy, Annual Report 2007. (2008)IEA,Paris;http://www.ieawind.org.[9]Fridleifsson, I.B.; Bertani, R.; Heunges, E.; Lund, J.W.; Ragnarsson, A.; Rybach, L.,The possible role and contribution of geothermal energy to the mitigation of climate change,In: (Editors: Hohmeyer, O.; Trittin, T.)IPCC Scoping meeting on Renewable Energy Sources(2008)Lbeck,Germany.[10]IEA,Projected Costs of Generating Electricity: 2010 edition. (2010)IEA,Paris.[11]Milborrow, D.,Dissecting wind turbine costs,WindStats Newsletter21(2008)2.[12]UKERC,The Costs and Impacts of Intermittency: An Assessment of the Evidence on the Costs and Impacts of Intermittent Generation on the British Electricity Network, Imperial College London, 2010, ISBN 1 90314 404 3.[13]Myers, K.; Klein, S.; Reindl, D.,Assessment of high penetration of solar photovoltaics in Wisconsin,Energy Policy38(11) (2010)73387345.[14]ORER, LRET/SRES The Basics.http://www.orer.gov.au/publications/lret-sres-basics.html, 2011 (accessed 15.05.11).[15]ECCA,The EU Climate and Energy Package.http://ec.europa.eu/clima/policies/package/index_en.htm, 2011 (accessed 30.03.11).[16]Jansson, P.M.; Michelfelder, R.A.,Integrating renewables into the U.S. grid: is it sustainable?Electricity J.21(6) (2008)921.[17]IEA,World Energy Outlook 2010. (2010)IEA,Paris.[18]IPCC,Climate Change 2007:Mitigation of Climate Change,In: (Editors: Metz, B.; Davidson, O.R.; Bosch, P.R.; Dave, R.; Meyer, L.A.)Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change(2007)Cambridge University Press,Cambridge.[19]IEA,Energy Technology Perspectives 2010. (2010)IEA,Paris.[20]R. Garnaut,The Garnaut Review 2011: Australia in the Global Response to Climate Change.http://www.garnautreview.org.au/update-2011/garnaut-review-2011.html, 2011.[21]Commonwealth of Australia,Australia's Low Pollution Future: The Economics of Climate Change Mitigation.http://www.treasury.gov.au/lowpollutionfuture/report/default.asp, 2008.[22]CSIRO,Intelligent Grid A Value Proposition for Distributed Energy in Australia. CSIRO Report ET/IR 1152,http://www.csiro.au/resources/IG-report.html, 2009.