Solar Energy Economics 101: Gentle Introduction to LCOE

The sun has been the source of most of our energy (except geothermal). Wind & wave are secondary/tertiary effects of solar energy. Even coal, oil, gas and other fossil fuels are merely stored (i.e. time-shifted) form of solar energy locked in hydrocarbons. The figure below shows the incident solar energy potential

             

Solar energy conversion refers to a number of mechanisms: solar thermal (conversion to heat for solar cooking, or solar water heating, HVAC via solar chillers, or very high temperatures for electricity conversion via heat engines) and solar photovoltaic (PV, including concentrated or thermo-photovotaics) are the two important categories. Another important aspect is that solar energy conversion is modular, i.e. can be done cheaply at at the distributed retail (home/businesses), or community levels in addition to being provisioned as large centralized solar farms, i.e. utility scale.

A Solar Photovoltaic (PV) system comprises the module, wiring, and inverter system (that subsumes controls and grid-tie in etc). Solar PV modules have no moving parts and last 30+ years (and are warrantied for 25 years to produce 80% of original rated capacity at that time); and inverters have 10-25 year warranties. Solar PV module costs have dropped rapidly, as have balance of system (BOS) and installation costs. This is depicted by the "learning" curve. As more forms / types and business models for solar PV penetration happen the total number of units installed and learnings from them will correspondingly grow. The measure used here is capital cost per Watt-peak (eg: $/Wp) and is applied to either a component cost (eg: module cost, inverter cost) or to the full installed system. Today the installed costs for a solar PV system (including inverters etc) has dropped to US$1.5-3.5/ Wp (note: sometime this does not include costs for leasing the land/area for the system) in western economies, and US$0.8-1.5/Wp in India. The lower end of installed costs are either in emerging markets like India or in utility installations at scale. The higher end are for distributed/retail installations. Interestingly, even high labor cost economies like Germany and Australia have installed costs of $2/Wp or less. For example a 4kWp system would cost under US$8000 installed. India has possibly the lowest installed costs of 76-80c/Wp at utility scale (as of 2015 end, as surveyed by CERC). Solar module costs at utility scale as of 2015 are appoximately 25-30% of total costs in USA, but 50-65% of costs in India. 

                   

Wayne Gretzky, the legendary (ice) hockey player once summed up his secret-of-success: "I skate to where the puck is going to be, not where it has been" ...Project out the trends of costs from the above diagram, an imagine where the "puck" (i.e. costs) are going... Think about the economic implications! We have to prepare for that world we will be living in.

Coming back to solar.... Once the system is installed (i.e. CAPEX incurred or costs are "sunk"), it has fairly low operational costs (eg: periodic cleaning to reduce dust/soiling accumulation). What is variable though is the (daily) "Energy Yield", i.e. the normalized energy in kilo-watt-hours (or "units") per day produced per kWp (kilo-watt-peak), i.e. kWh/d/kWp generated by the system. Note that in the McKinsey graph later, they use annual energy yield, i.e. kWh/year/kWp as the measure (multiplying the daily energy yield by 365 will give the annual energy yield). The energy yield is a function of the solar irradiance characteristics (function of latitude, weather conditions (eg: cloudiness), dust / soiling / birds, and any temporary or persistent partial shadowing), the operating temperature, the solar PV technology (c-Si, poly-Si, CdTe etc), and how the solar panels are wired w/ inverters (string vs microinverters) etc. [Note: A grid tie system will generally not produce energy for safety (islanding) reasons when there is a power cut, unless advanced inverters are used to switchover and charge a battery system.This is a factor for geographies with highly intermittent power supplies like India ]. The energy yield in Bangalore, India is around 5-5.5 kWh/d/kWp (or about 4.5 kWh/d/kWp if you adjust for power cuts, uncleaned dust, aerosols etc) year round while in Melbourne, Australia or London UK or New York, USA can vary between 3.5 - 5 kWh/d/kWp, or an average of about 4 kWh/d/kWp. Here are examples of annualized energy yield numbers: 3 kWh/d/kWp = 1095 kWh/y/kWp; 4 kWh/d/kWp = 1460 kWh/y/kWp; 5 kWh/d/kWp = 1825 kWh/y/kWp; and 6 kWh/d/kWp = 2190 kWh/y/kWp.

Some solar calculators (eg:  http://www.energymatters.com.au/climate-data/) gives average solar irradiance in kWh/m^2/day (the web site seems to have a typo - should be kWh and not kW), plotted on a monthly basis. Once you have the solar irradiance, and the solar module/system efficiency (eg: 15%), you can work out that a 1kWp system needs 6.67 m^2/kWp; therefore dividing the data by 6.67 gives a energy production yield (i.e. kWh/day/kWp) on a month-by-month basis. NREL has a solar resource map / data for international sites (eg: India: http://www.nrel.gov/international/ra_india.html and http://rredc.nrel.gov/solar/new_data/India/ ). The India solar resource map (with solar irradiance in kWh/m^2/day) is reproduced below, as are maps for Australia (1 MJ = 0.278 kWh. So 24 MJ/m^2/day = 6.67 kWh/m^2/day for instance). The colors are not directly comparable across the maps (use caution!).

If you divide the (daily) Energy Yield by 24 (i.e. hours in a day), you get a measure called "Capacity Factor" (Cf), which estimates what fraction of rated capacity you are actually getting out of the system. For instance in Bangalore, the Cf is about 21-22%, and for Melbourne it is 15-16%. The capacity factor is used to compare renewables with each other or with traditional energy generation systems. For example Nuclear and Hydro tend to have high capacity factors (80-90%+), and wind energy ranges from 30-45% (function of location, turbine height, scale etc). Solar at 15-25% capacity factor therefore is quite low. As an example of the economic impact of this, consider if you are building a utility solar plant and plan a transmission line to match the rated peak of the solar production, the transmission capacity will be unused 75-85% on average (while the cost of capital has to be borne)! This is why transmission lines in utility scale solar are sometimes undersized, and if there is excess solar production on the short term (that the line or grid cannot absorb), it is curtailed (and wasted unless it can be stored temporarily and time-shifted). What is remarkable, is that despite the low capacity factor of solar, the impact of reducing costs, and modularity (that allows both distributed, community-scale and utility-scale deployments), its growth rate is tremendous and inexorable. The figure below shows that the installed power normalized by the capacity factor is still growing to overtake all other forms of renewable energy before 2020, and continue tremendous growth beyond! This is why we should pay attention to Solar since it, combined with wind (that is also growing rapidly) will change the landscape of our renewable energy mix.

      

Before we get too excited, we should reflect that all renewables still form a fairly small fraction of total energy capacity installed, and energy produced on average, and has a large geographic variation. This also points to a large upside in growth potential with declining costs and ability to harness, package and manage the energy generated by renewable options.

                      

                   

 

In conclusion, the final measure I will introduce is Levelized Cost of Energy (LCOE), i.e. cost/kWh, and the concept of grid parity. The LCOE is a "cost" measures that "levelizes" or "flattens" the capital / operating costs, and normalizes it over the energy yield i.e. kWh estimated to be produced by the system, assuming a discount factor. We saw that a 1 kilo-Watt-peak (kWp) solar PV system can offer an average Energy Yield of about 4 - 5 kWh/day/kWp in many parts of the world. We annualize the actual energy yield & discount it  by the cost of capital or discount rate for the denominator. Note that the discounting is done over N periods (i.e. a fixed time horizon). Similarly the fixed and variable costs of the solar system are discounted and put in the numerator. This gives a "levelized" cost, i.e. cost / kWh or cost / unit that can be compared against other forms of energy (eg: coal-fired electricity, diesel-generated electricity, natural gas, hydro, wind etc).

                   

The LCOE is a measure of "cost". Lets turn to "revenue" or "value" yield i.e. how much the energy yielded converts into dollars of value. A unit (kWh) of energy generated by the system can have an economic value determined by the local utility price, or diesel cost or policies (eg: feed-in tarriff or net metering). For example, in (averaged) net metering, with a tiered tarriff structure (eg: California) where a heavy residential user pays over 20 US cents/kWh at the margin, offsets or saves that amount via solar. This is the "monetized" value of the solar energy yield, i.e. by multiplying 5 kWh / d / kWp   x  20 cents / kWh = 1 USD / d / kWp. In finance terminology, this is the "cash flow" from solar on a day-by-day basis. Since solar PV produces energy over years, the future "cash flows" has to be discounted by the "cost of capital" to get a discounted cash flow (DCF). If we compare the actual solar discounted revenue yield to LCOE, we can ask the question which is greater. If the revenue yield is greater, we have achieved or exceeded economic parity / break-even.

One specific simplification is called "grid parity", where you can compare the LCOE with the marginal cost of electricity offset from the grid. For example if the marginal cost of (prior) tier 4 pricing in California is 34 c / kWh (also see McKinsey graph below, please note it is a little dated already) and LCOE is 13 c / kWh, we say that solar generation is cheaper at the margin, and therefore has achieved grid parity in that location or region. Companies like SolarCity, SunRun, Vivint offer a third-party owned service where a homeowner can get solar for their home with zero up front costs, as long as they can sign a 20 year PPA around 13-15 c/kWh in 2015. Financial analysis are also done for payback periods, NPV, IRR and other metrics. These financial metrics essentially compare revenue and costs, and ask how quickly we recover costs (payback period), or the rate of return (IRR) or net value (discounted revenue minus discounted costs, for NPV).

                 

    
McKinsey has a nice graph (a little dated now) that plots installed cost of solar ($/Watt-peak), solar (annual) energy yield kWh/kWp and the retail power price. It shows how different countries (size) and pricing structures indicate whether they have achieved grid parity or not. For example Germany has low energy yield (left of the graph), but due to a high retail price (or equivalently feed-in tarriff), it has achieved grid parity. India has much better solar resource, and is a large market, but its retail prices are lower. Notably, this graph shows that China, India (at their base rates) are still away from grid parity due to a combination of cost of capital and low retail price, and therefore, either the solar cost has to drop (to $1/Wp installed) or there needs to be policy/subsidy support to stimulate the market. But recent market information suggests the $1/Wp installed cost point has been reached at utility scale. The graph also shows that at current rate of cost declines in which year one can expect the "cross-over" i.e. grid parity in different markets. The graph also shows approximate grid parity price of 15 c / kWh today (the transition between light blue and dark blue). The grid parity price in markets like India is now 9-10c / kWh at the retail level (i.e. Rs.5.8 - 6.5), and around 5 c/kWh at wholesale (Rs.3.5-4/kWh). Recently two companies (SunEdison and SB Energy) have bid Rs. 4.63/kWh (or 7 c/kWh) in India to win several 100 MW of solar in reverse auctions, i.e. we are very close to grid parity in India today at both the wholesale and retail levels.

To summarize in layman's terms, as the capital cost of solar rapidly declines, the Io in LCOE numerator declines rapidly. The annual costs Ai tends to be low for solar. The LCOE formula is also a function of N (the amortization period). Energy yield is variable function of the efficiency of the installation, local irradiance that falls on the system, and any non-linear effects due to shadowing, soiling etc. It also assumes that solar production is not curtailed or wasted due to inverter, grid / load availability constraints. In some cases, there may not be a feed-in tarriff policy in some regions, i.e. any solar production that doesnt offset local demand is donated to the grid for free. 

Subject to these subtle and operational issues, we can see that innovations to reduce capital costs (Io) or increase energy yield (Et) are the key to bringing solar to grid parity without subsidies. Also note the important role played by the "cost of capital" or discount rate i (or r). Financial engineering innovations have been a big part of solar companies work to make solar affordable. For instance in the US, financial innovations have allowed the cost of capital to drop from 20% in 2008 to 4-8% today. This has a huge impact on afforability of solar. We now need to make similar innovations at the technical and financial levels to bring solar affordably to the emerging markets and the poor.

Back to the Future: Lessons from the Telephony-to-Internet Saga for the Energy Economy

We have seen this movie before. Its called "Back to the Future". In the mid-80s, the second edition of the movie series predicted flat screen TVs, fingerprint scanners, video conferencing and flying cars in 2015.

 

However, the first edition of the movie was the most memorable one going back to the past from 1985 to 1955.... But now that we are in 2015, lets take a dive back to the mid-80s when  the movie was released... Incidentally, its also the time when there was another movie playing in real life in technology: called the Telephony-to-Internet saga.

A worldwide sprawling synchronous infrastructure called the telephone network sized for peak demand, i.e. capacity sized to serve the largest amount of simultaneous demand (with very high probability). When a phone call is made, instantaneously an end to end "circuit" and its associated capacity is reserved to allow the call to be made. A shift from analog to digitization of the telephony infrastructure: time-division multiplexing (TDM), optical transport of large bundles of time-synchronized digital information (SONET). The emergence of a few new applications (fax, email) that rode on top of the digitized infrastructure as an overlay network. The emergence of a small amount of buffering or storage capacity to temporarily store "packets" of demand at the overlay nodes (both at the source and intermediate "routers"), supporting the idea of "packet switching" which admitted asynchrony and allowed the capacity of routers to be sized above average demand, but well below peak demand.  The emergence of end to end decentralized control algorithms (carrier sense multiple access & randomized controls in Ethernet, decentralized flow control in TCP) allowed demand to be responsive and shaped dynamically to match the actual capacity on the paths (instead of reserving peak capacity as in telephony circuits).  Application services like email which appeared to be worse than the current highly reliable telephone voice service, but much better and quicker than snail mail and memos. And finally the emergence of HTTP, web browsers and the WWW. The rest, as they say, is history. Recently Whatsapp allows users to make calls via mobile devices to other mobile device users, tied to their phone numbers, symbolizing how voice has transitioned to another application over the Internet. 

If you look back at the abridged history, the three pivotal technologies that underpinned the Telephony-to-Internet transformation were:

(a) digitization of infrastructure that allowed overlay applications over a synchronous infrastructure sized for peak demand

(b) the introduction of buffering (i.e. storage), and the notion of asynchronously switched "packets" instead of time-synchronized  switching of bits (or bit-bundles)

(c) the emergence of decentralized controls (embedded in Ethernet, TCP/IP, and inter-domain routing policies) that allowed demand to dynamically respond to capacity available

Peak Energy Techno-Economics

While the analogy is not perfect, the electricity grid is displaying a number of similarities so that we can selectively learn the right lessons from history. For instance, to understand the equivalent analog of (a) we need to appreciate the economic implications of a synchronous infrastructure designed for peak demand and what degrees of freedom allow us to "overlay" flexible supply/demand sources on it. When we look at graphs as the picture below which show rapid growth of clean energy or renewable options (eg: solar / wind) in the future, it is important to understand the nature of these sources (solar, wind) are fundamentally different, amenable to IT-driven management and some lessons from history could be valuable as analogs.

     

The electricity grid is sized for (an estimate of) peak demand, and operates synchronously, i.e. when you turn on a switch for your lamp, a signal via grid frequency is instantly conveyed to remote electricity generators which spin up or down slightly to supply your lamp's needs in real-time. In countries like India, where even in urban centres, often there is not enough supply available (or economically contracted by the utility) to meet demand, customers have to bear a power cut. This power outage is often unscheduled, and the situation is worse in rural settings, where either the grid does not even reach them or even if it does, power supply is available only for a few hours of the day. The assets deployed both on the grid side and consumer side are essentially idle during power cuts, a huge opportunity cost. A peak-demand sized infrastructure is quite expensive, compared to an infrastructure that could be sized somewhere between peak and average demand (and the difference is managed smartly). This "peaking" effect is also reflected in wholesale spot prices of electricity in such markets, where peak spot prices tend to be 5-10X costlier than at other times (which is why it is economized on by utilities). Utilities also maintain spinning generation reserves based upon natural gas or diesel to handle peaks and they are very expensive, and used only for a few hours in the day. 

The need  for peaking capacity has only intensified over the decades and is being further intensified due to the uptake of renewables. The illustration below shows how the demand in ISO-NE (New England in USA) has evolved. Think of it as a frequency histogram, and it is saying that the top 500 hours (top percentile of load) occurs with very small frequency, i.e. a few hours or days for the entire year, but spinning reserve generation capacity, transmission and distribution capacity (poles, wires, power systems) has to be peak provisioned 24 x 365 to ensure reliability. This is why the tarriff structure in many regions (eg: California) is tiered to discourage peak demand (or large levels of demand, which correlates with users who contribute to peak demand). This is also why enterprises (commercial/industrial energy users) pay "demand charges" by KW-peak, i.e. peak-power they consume (to reflect the grid sunk investment costs they drive), even if they cross that level only for a few minutes in a month.

Overlay Technologies for the Energy Infrastructure:

If we can introduce "overlay" technology that is cheaper, but allows the offsetting of this peak demand, its economic value would be the capex/opex saved by offsetting peak capacity required otherwise. We could achieve this either by (i) generating energy spatio-temporally matched to when/where peak demand occurs, or (ii) storing energy (in thermal, chemical forms), or (iii) time-shifting ("when") / space-shifting ("where") demand or supply  to arrange the "when-where" matching of demand / supply (i.e. virtual storage),  It is worth re-emphasizing that the economically relevant comparable at the margin is not average energy price, but the peak (or tiered) energy price applicable to that marginal unit of energy used.

Consider method (i) where we overlay renewable generation matched to consumption. Solar energy generated at homes in hot locations (eg: Arizona, Middle East, India etc), tends to roughly coincide with peak demands for energy without any further intervention. In contrast wind energy that blows faster at night, and is remote (i.e. it has to compete at wholesale, not retail prices) may be less valuable economically purely from a timing/coincidence of demand perspective. This relationship of economic value to coincidence of supply/demand means that it is more valuable to have a low capacity-factor generator like Solar (i.e. producing on average less than its peak rating) than a higher capacity factor resource like Wind which may not be in production coincident with peak demand. However it is important to temper this point noting that as Solar penetration increases, the peak net demand will shift to the evening time (also called the "duck curve" effect), and the value of an overlay technology like wind or storage may be higher in that context.

The second pattern (ii) is  "overlay" technology such as battery storage (or other forms of energy storage like thermal storage). Tesla Energy recently announced a 10 kWh PowerWall battery for homes priced at $3500 for 10kWh, or $350/kWh (wholesale price prior to installation/inverter costs). Given its 10 year warranty, 365 days/year, or approx 3500 cycles, which implies a simple levelized cost of 350/3500 = 10 cents / kWh LCOE (ignoring other costs and discounting cash flows). If the user has sunk costs in solar at home, then at the margin, time-shifting the solar energy to offset peak electricity prices would be attractive if this arbitrage was worth at least 10 c/kWh. The ability to provide other services (eg: backup power during outages) is not factored.

The third pattern (iii) is where demand and supply do not coincide, and beyond batteries, a set of predictive analytics and control can be used to match them spatially ("where") and temporally ("when"). IBM Research in partnership with clients  has pioneered a number of cloud-based insights capabilities for demand/supply management for utilities via the Smarter Energy Research Institute (SERI). On the decentralized demand response front (i.e. the ability to make appliance energy demands flexible), IBM Researchers developed an innovation called nplug motivated by power-cuts in India which allows at a plug level the ability to shift demand automatically to match capacity (as implicitly inferred by analyzing grid voltage) without need for any price signal, communication or coordination protocol with centralized entities. This was also inspired by the randomized distributed control methods used in Ethernet, but adapted to the grid.  As we can see in all these examples of "overlays", information technology (analytics, optimization, controls) needs to be interwoven with energy technology (solar, wind, grid, battery), and with an awareness of the economic, policy, technical context, the entire "overlay package" should perform the "when-where" matching of demand and supply.

Energy Storage: Ability to Absorb and Manage Uncertainty

Lets move on to (b), the impact of energy storage beyond the "peak-demand" driven economics of synchronous infrastructure. To understand this, we need to come back to the shift from circuit-to-packet switching. Lets ask the question: "What is a telephone "circuit" really?" A circuit uses signaling at call set up and locked down (i.e. reserved) capacity end to end. From a queueing perspective (see diagram below) this set up a D/D/1 queue time synchronously, i.e. deterministic input feeding a deterministic output. Elementary D/D/1 queueing analysis states that if capacity is matched to (peak) demand, the amount of buffers is just 1 unit (i.e. effectively zero).  If there was no capacity available at the time of a call, a circuit could not be established in signaling, and a call would be rejected (similar to power cuts in India or rolling blackouts when there is no capacity; and huge spikes in wholesale market price for capacity 10X greater than normal during such demand spikes). The telephony "switch" uses time-synchronization to avoid the need for buffering, and literally switches bits from its input to its output port instantaneously. Packet switching's fundamental abstraction - a set of bits, with a header allowing it to be self sufficient - allowed those set of bits to be buffered at "routers" or "caches" or "storage" more generally.  

                           

Here is the key insight: Buffers or energy storage imply that the queueing disciplines could admit random arrivals and random departures; and packet switched networks allowed flexible networks of such queues M/M/1 being the simplest. However if the randomness of demand/supply could be "shaped" (eg: by shaping the statistics, truncating tail behavior) or managed/controlled end-to-end (or even locally) to match demand/supply , the residual externality (or mismatch) can be minimized. It is important to realize that with good matching, the "peak" not does not just shift around, but it is attenuated through clever spatio-temporal matching and smoothing. The combination of storage and smart controls/optimization driven by predictive analytics will allow energy storage to penetrate faster and have a greater transformative effect on the grid. The degrees of freedom for such uncertainty management, shaping and "when-where" matching of demand/supply are numerous and specific to the nature of individual technology options (renewables, storage, grids (DC/AC), power electronics) which allows a myriad set of formulations and contexts. But the demand-supply management and matching problem under uncertainty, and with energy storage is fundamentally an opportunity for information technology.

Decentralized Control / Management: Towards an Internet-of-Energy-Networks

The third important lesson from the Telephony-to-Internet saga is the importance of de-centralized controls/management. At a basic level, the decentralized controls can help locally shape the nature of randomness of demand / supply and optimize the amount of storage or external grid or policy support needed to accomplish the matching. We have seen this earlier when we mentioned the nplug technology for distributed demand response inspired by Ethernet-style controls. But beyond this, decentralized controls along with modular technology at lower costs (eg: as is happening with distributed solar, battery, demand-management systems), fundamentally empower the end-user or customer to take control of their choices (in this case energy choices). Remember the AT&T monopoly of the telephone network that got transformed into an interacting network of autonomous systems (with many market participants)? We are seeing this happen in the Energy ecosystem both at the home level (with the explosive growth of distributed solar), and at the commercial / industrial level (eg: Apple's recent $850M investment to procure 130 MW solar power from FirstSolar, or Walmart's announcement to cover all its roofs to generate over 100MW). Note that in the context of enterprises with distributed operations, we could imagine a cloud-based service to manage the energy resources across sites of say, Wal-Mart.

Beyond these "self-service" models where a consumer does-it-all by themselves, we are also starting to see the rapid emergence alternative energy service companies like SolarCity, SunEdison etc who package installation and management of multiple energy resources (solar, battery, demand response (eg: NEST)) & outage tolerance possibly in microgrid configurations, with a package of financing and policy incentives as well. The notion of microgrid is important because it allows the emergence of "autonomous systems" that can interconnect. Microgrid "domains" will serve to take control of energy choices (storage, renewables, demand management, DC/AC grid choices) within the domain, but will be interconnected to other microgrids and to utility grids. This is akin to the emergence of "Internet Service Providers" (ISPs) in the 1990s like AOL, Excite@Home and others who overlaid their internet services or access services via modems atop the existing telephony infrastructure, and offering email etc. Once this trend gets established and the new service providers grow, they will necessarily need to interconnect with the existing service providers and with each other. This tends to drive the market structure from a single switched network to a "network" of networks model, which was the genesis of the term "Internet". This interaction needs to be governed so that one provider takes responsibility for the choices they make and the externalities they impose on other providers: this is managed through inter-domain routing protocols on the Internet. When you examine some of the higher penetration solar regions like Hawaii, we are starting to see externalities and instabilities being imposed by solar on the grid operator (HECO), and the need for urgent solutions to manage this via a combination of energy storage and distributed controls. It is useful to emphasize here that the notion of users defecting en mass from utility grids is a short sighted view (as much as local area networks and enterprises defecting from wide area networks in the Internet): the internet transformation has taught us that interconnection by itself has greater value.

Information technology has a huge role in defining and managing this set of uncertainties and governing the economic spillover effects, again closely integrated with the technology options at the energy level (renewables, grid, energy storage) and financial/policy levels. It is important to note that "decentralized" means that no single entity has overriding control/bargaining power, but the granularity of decentralization could evolve as a function of technological and economic constraints. For instance, managing a large number of commercial sites could be done via a cloud-based service, but under the autonomous management of an entity like Walmart with their own selection of vendors. Similarly, data centres and cloud providers could choose to go towards renewable-powered DC grids locally; or the emergence of micro-grids that are DC-based typically in a community- or corporate-ecosystem setting (ranging from Rural Electrification plays to managed EV-fleets such as e-Bike/e-Scooter share programs, e-Taxi fleets, or e-Logistics fleets etc). A large range of integrated energy products that embed IT / financial innovation (eg: solar lights with e-financing/payment via mobile phones in Africa to a distributed fleet of e-transport options that personalise public transportation, with the ability to space-time shift renewable energy).

Analog of Internet's Fiber-optic Transformation in Clean Energy: 

Finally, one phenomena we saw in the Internet era was that the new ISPs built their own long distance networks, driven by falling costs in optical networking. When renewables can be firmed up with storage, and the costs of solar/wind/storage continue to drop, while land costs start to become important, and mismatches in time and space matter, it  may become economical to locate renewable farms in places like deserts in other countries (eg: Australia, Middle East, Africa), and have "cheap" long distance transmission networks "wheeling" the firmed renewable power to population centres. A few of these concepts (eg: DesertTec, and ours) are illustrated below: the basic idea is to be agnostic to the specific combination of renewables (subject to economic / technical feasibility) and build large interconnection links that span time zones - especially in the east-west directions so that time-of-day differentials will create a market (eg: India's morning power supplied by Australian sun, and evening power supplied by a combination of Australian wind and Middle Eastern sun. The new utilities like SunEdison, NRG etc will also start investing in their own transmission networks just like in the Internet era. We will first see this kind of transmission networks within countries; multi-country grids are being deployed in Europe currently as well.

        

                                   

In summary, the lens of the Telephony-to-Internet saga offers some useful analogies for the Energy infrastructure transformation. Every analogy is imperfect, and interpretations must be made with great care, and focus on the actual context (technology, policy, financial/economic) of the new infrastructure. Information technology, along with other enablers (new grid technology, financial/policy innovations and the core renewables/energy storage/electrified transport technologies) will be deeply integrated together in a vast array of end product and services in this new emerging Internet of Energy-Networks.