The long-term implications for renewable energy


11.2% of power Generation capacity in 2020 will be intermittent wind and solar power. These power sources are known to have a low capacity factor and an even lower capacity credit. This means that wind and solar are not yet effective at replacing base load fossil fuel or nuclear capacity on a one for one basis in terms of megawatts. For renewable sources such as wind and solar to start to form a sizable percentage of our base load plants, the installed capacity has to outnumber the fossil fuel plants they are replacing. A the same time, fossil fuel, or other high-capacity-factor power plants must be maintained, or built to act as back-up sources of energy to mitigate capacity problems.

In the 2011 IEA report on the cost of generating electricity by different technologies, a base load factor of 84% was used for gas, coal and nuclear plants. Country-specific load factors were applied for renewables because they are largely site-specific but the medians were 24% for wind and 14% for solar. Capacity credits are complex to calculate and are wholly country-specific and can only be calculated by utilities with access to operational data.

However, as wind and solar increase in scale, the effect that individual plants intermittencies have on the grid are mitigated by other plants in the system. Denmark, for example, has one of the worlds largest penetrations of wind energy, and it is able to utilise much of it for base load power production because the farms are spread out geographically and inclement weather in one location does not necessarily mean that another power plant isn’t generating electricity. As such, Denmark has become one of the worlds largest wind-energy exporters.

A 100% reliance on renewable energy will be extremely difficult to achieve, but as more and more countries are developing their renewable energy portfolios to critical mass, the shares of fossil fuel power will continue to decline.


The Top 4 BioMass Power Plants in the World


Biomass energy is a catch-all term for energy which is generated by burning organic matter (mostly wood). While this technology is still polluting, it is not releasing gasses into the atmosphere which cannot be reabsorbed in a short time span. When you are burning wood, you are releasing the compounds found in the tree, which can be re-grown in a relatively short period of time.

Wood is not the only source of biomass fuel, though it is the most commonly used source. There are many different plant species and sources for the wood that is used to generate biomass power. Not only can trees be used, but other plants can be used such as bamboo, switchgrass, hemp, but even by-products such as oils and saps from the trees and plants.

Because some food crops can also be used as a fuel source, there is a direct competition for resources when it comes to production. Generally, fuel can be sold at higher prices than food, so more land is dedicated to the fuel production rather than food production. Using the waste products of food production as a biofuel is a good compromise and is seen more frequently today.

Other forms of biofuel include methane and ethane which can be produced in landfills from waste, but also as agricultural waste. Even algae have energy producing qualities, and might prove to be a valuable source of biofuels in the future.

Geography and a country’s economy greatly influence the use of biofuels, and in the list below you will see that the largest individual plants, with the exception of one plant, are located in Europe. Europe is a leading region in climate policy and environmentally conscious energy production, and it offers the resources and development to easily deploy these sources.

The Top Capacity BioMass Power Plants in the World:

Alholmens Kraft, Finland

Maasvlakte 3, Netherlands

Połaniec, Poland

Atikokan Generating Station, Canada


Changing-landscpae_Energy.jpgTo maintain the integrity of the grid, the individual components must work in unison. The grid is designed with some leeway to be able to absorb disruption, though it is thanks to a set of highly technical components and increasing automation that the grid is able to function to the level of reliability and at the scale at which we have become accustomed.

At the consumer level, installing a solar panel, or other means of generating electricity for a single property isn’t disruptive to the grid. It is when these systems become part of a larger whole or penetrate the market sufficiently that the grid will have to be adapted to take this into account. Starting at the household level, the smart meter can form the link between the micro generator and the distribution grid.

As the grid expands, the generation landscape has to be considered and the systems need to be in place for the shift in power generation sources that we are seeing, beyond a household smart meter. DSOs and TSOs are slowly taking the steps to ensure the future proofing of the grids, however, progress is slow. The technology requires significant upfront investment and is very capital intensive. The bulk of many systems operators networks are reaching the end of their useful lifespans and the focus is on renewal. Renewing a grid takes time, and a lack of investment in the early 2000s has left grid operators facing dilemmas of having to maintain reliability with the aging infrastructure while catching up with investments.

20-20-20 and the Smart Grid



The EU’s 20-20-20 target in the 2007 Energy Policy and the 2009 Third Energy Package has spurred development of the smart grid. To achieve the goal the EU has included the smart grid as one of the region’s seven priority energy technologies in its Strategic Energy Technology Plan (SET Plan). To implement the plan the EU set up two initiatives: the European Electricity Initiative (EEI) and European Electricity Grid Initiative (EEGI).

Major EU projects include the ADDRESS (Active Distribution networks with full integration of Demand and distributed energy Resources) to demonstrate active distribution networks and SUSPLAN, which is ‘investigating the development of regional and Pan-European guidelines for more efficient integration of renewable energy into future infrastructures’. Through the European Energy Programme for Recovery (EEPR) (a programme to provide financial support for projects in the energy area which would aid in economic recovery, lower carbon emissions and help to provide a secure energy supply) a total of €2,365 million is available for electricity and gas infrastructure interconnections. Some of which may be used for ‘smart’ interconnection technologies.

A mandate has been introduced requiring 80% coverage with smart meters by 2020 and an entire roll out by 2022. To drive the market member states must conduct an economic assessment of smart metering by 2012.The Third Package of the 2009 Energy Directive on gas and electricity required the implementation of intelligent metering system to assist consumers on the market. In addition  the third Energy Performance of Buildings Directive (EPBD) stipulates that Member States ‘shall encourage the introduction of intelligent metering systems whenever a building is constructed or undergoes major renovation’.

To date the majority of smart grid projects have focused on smart meter deployment. Finland, Sweden and Italy have been early adopters in the smart meter market, followed by France, Spain, the UK and Ireland. Government policies, especially smart meter mandates, are driving growth in the smart meter market. The only country with full scale deployment is Italy, with Enel creating its own business case for early deployment of the technology. In order to ensure consistency a mandate for standardisation of components has been introduced.

Calculations to determine reserve values

perform-quick-calculations-google-searches-fly.1280x600.jpgThere are two primary ways to determine reserve quantities: the volumetric method and decline curve analysis.

The volumetric method

This calculation is derived from the formulae –

Gas-in-Place = 43,560 * A * H * f * Sg * Bg

Reserves = (Recovery Factor) * (Gas-in-Place)

This method may appear to be fairly ironclad until we consider the uncertainties that are inherent in each of the measurements required as inputs to the equations. It relies on the accuracy of the maps made by the geologist and geophysicist, which are derived from interpretations of complex data that, for the most part, are derived from physical parameters beneath the surface. These parameters are the inputs for the areal extent of the hydrocarbon accumulation (A), the thickness of the pay zone (H), the porosity of the rock (f), the gas or oil saturation (Sg or So) and fluid composition (Bg). Interestingly, but also a mathematical reality, is that if each variable is off by only 10%, then the gas-in-place will be off by approximately 40% and the reserves could be off by approximately 50%.

Decline curve analysis

Decline curve analysis is widely accepted as the more accurate method for reserve determination, presuming ample production history is available to make the predictions. This is a little bit like trying to determine what a child will look like once he grows to maturity. When he is an infant, we might look at the size of his feet or hands to estimate his mature weight or height, or by analogy consider the height and weight of his mother and father. As he reaches the teenage years, we can be more exact in our prediction, but ultimately we only really know once he is fully grown.

An example using a tight gas sand producing well may help to understand the hazards in this method:

Stage 1

Based on decline curve analysis, after the first 100 days of actual production with output measured in thousand cubic feet per day (Mcfpd), this well would only show an expected ultimate recovery of about 230 million cubic feet (MMcf).

Stage 2

As time goes by, more production data is obtained, and decline curve analysis would predict that the well is capable of recovering more gas.

Stage 3

After about five years, a much clearer picture appears of what the well will actually produce.

This example helps explain the uncertainties in extrapolating decline curves to a well’s ultimate recovery or reserve quantity. In this case, the reserve prediction continued to increase with time. It is not uncommon, however, to have the opposite occur in instances such as a well prematurely ‘watering out,’ having mechanical failures resulting in early abandonment or any number of unexpected problems.


Solar and Wind Integration into the Grid


Solar and Wind energy have seen significant growth in the last decade and are forecast to grow even more in the years to come. As governments have set themselves targets of reducing greenhouse gas emissions, they have fostered the development of these technologies through subsidies and other incentives.

However, as the scale of manufacturing increases and as technology has developed, the costs of solar and wind power has come down significantly, making it more feasible for many projects. With this growing production of electricity through wind and solar, there have been a number of interesting parallel developments in the grid and other infrastructure.

Without sufficient scale, wind and solar wouldn’t be able to flourish as they have. Interconnecting a national grid with that of a country’s neighbours and creating a larger grid allows for more assets to be utilized as best as possible. This in turn can reduce the need for spinning reserve, vital to smaller-scale networks.

Effective means of energy storage are also needed to allow for the further integration of renewables into the grid. It is the automotive industry that appears to be picking up the reins on these developments, by integrating their electric vehicle and battery storage technologies into the grid. Companies such as Tesla, BMW, and Renault-Nissan are all developing storage solutions, either at the consumer level, grid level, or both.

With the increased integration of these technologies, so too arises the need for control systems and monitoring. Smart meters, and other smart devices are being rolled out at increasing rates to allow for TSOs and DSOs to better manage the grid. This also leads to a more efficient use of resources and prevents waste of energy through load balancing and more effective demand response during times of high renewable energy production.

Data Privacy issues in the Smart Grid

pic-smart-grid.jpgData privacy in the Smart Grid refers to a range of potential problems from the improper use of the information. It is technically possible that an employee at a utility could use information from a smart meter to determine when customers are out of their house or have purchased new electrical items, and thus when to steal the owners possessions or stalk them.

Utilities or other companies could use the information for marketing purposes or use consumption behaviour data to introduce non-competitive pricing. By introducing very low pricing targeted towards the individual consumer to drive competitors off the market.

Not to mention utilities need to store all of this data and also source sufficient storage facilities that has both the capacity needed and is very secure. It is not unfeasible that utilities may need store exabytes (million terabytes) of data, which will be costly. However, every year the cost of storage halves and the storage of this information may cost US $4,000 in 2025.

It is also possible that applications will be developed whereby real-time energy usage is uploaded onto a twitter page or facebook account using a special application. Consumers may inadvertently give this information to hackers or so called ‘friends’ that use this information to stalk the consumer or burgle their house.

There needs to be regulation in place to ensure that similar incidents don’t take place with data generated from the smart grid. While data privacy laws are in place in most of the major smart grid markets, nothing specifically refers to the smart grid. In September 2010 a law with new privacy protections for consumers’ energy use data was signed in California. This legislation includes specific information on information disclosure, data security/protection, liability, and continued use. Since then, policies have been implemented and there are frameworks in place to ensure consumer privacy as these systems have been rolled out.