Precise and reliable monitoring of emissions from power plants is a challenge that is grappled by many. I will hopefully describe as simply as possible the different methods and why reliance on a single method can be risky, not practical or too costly for the outcome desired.
I will use a simple analogy relating to cars which hopefully will highlight the issues.
Following are the key performance indicators for an emissions monitoring system.
Precision (P1) related to how repeatable and close the results of a method, measurements and calculations are to the actual emissions for all operations. This is related to measurement uncertainty and level of simplifying assumptions used within the method.
Reliability (R) relates to how tolerant a method is to instrument failure and the reliability of the instruments used.
Time detail (T) relates to how often the measurement of emissions is available. (Yearly, monthly, daily and real-time)
Plant detail (P2) relates to the depth that the information is available (Station, Unit, Subsystem, Component)
Cost (C) related to the cost of measurements and calculations that are used for the method.
There are many ways to monitor the emissions from a power plant as detailed below:
|Generic Emissions||2||10||1||1||1||Very high level energy switching studies.
Typically used by the Australian Energy Market Operator (AEMO)
|xx.x l/100km for a petrol car
xx.x l/100km for a diesel car
xx.x l/100km for a hybrid car
– with a formula for converting litres of fuel to emissions.
|Curves for a particular plant||3||10||1||2||2||For comparison of different makes of a particular type of generator.||Using the manufacturers “Combined” xx.xl/100 km with a formula for converting litres of fuel to emissions.|
|Real-time curves for a particular plant||4||8||10||3||4||For comparison of different makes of a particular type of generator under different operating regimes and environments.||Using the manufacturers “Urban” and “Extra” xx.xl/100 km accumulated in real time reflecting actual usage of the car with a formula for converting litres of fuel to emissions.|
|Real-time actual emissions||4||6||10||2||9||Direct measurement of emissions. Very susceptible to instrument uncertainty and failure.
Not very accurate or robust due to the type of sensors used and the environment the sensors are being used in.
|Using exhaust flow and emissions sensors on exhaust to directly measure emissions. (Not actually used but explains the concept).|
|Real-time actual fuel consumption||5||7||10||2||6||Determination of the actual emissions over a short period (hours) based on fuel consumption.
Can identify when change occurs but no ability to identify where problems may be occurring.
Susceptible to single measurement uncertainty and failure.
|xx.x litres consumed from trip computer in car with a formula for converting litres of fuel to emissions.
xx.x l/100km from trip computer in car with a formula for converting l/100km to kgCO2 /100km.
|Fuel delivered less storage (Input/Output)||6||8||3||1||3||Determination of the actual emissions over a long period (months and years) based on fuel consumption and fuel quality.
No ability to identify where or when problems may be occurring.
Typically used for National Greenhouse and Energy Reporting (NGERs).
|Log of km travelled and fuel consumption from bowser less fuel in the fuel tank with a formula for converting litres of fuel to emissions.|
|Component Analysis||6||7||5||8||5||For finding the contributions to emissions caused by individual components within a generation system.||Mechanic hooking up diagnostic equipment to determine where performance is deviated from new.|
|System boundary deviation from curves
|7||8||5||4||4||Using curves and then adjusting the emissions based on actual deviations in energy loss at the system boundary.
Reliance on accuracy of OEM curves and single measurement uncertainty and failure.
|Measuring actual energy losses from the car such as radiator, exhaust and brakes and adjusting the manufacturers combined l/100km to reflect actual energy losses with a formula for converting litres of fuel to emissions.|
|Real-time mass and energy balance||9||9||10||10||6||Data reconciliation and validation.
Identification of problems and opportunities to improve.
Typically used for Energy Efficiency Opportunity (EEO).
Multiple measurements checked against other measurements in the process. Much less uncertainty and less reliance on a single measurement.
|During the development of a new car, the prototypes will be extensively instrumented and analysis conducted on the results to ensure the car is performing according to design and to identify areas where improvement could be made. Validation of the data to ensure the instruments are providing the correct information is an important part of the analysis as design and business decisions will be based on the data generated.|
A summary of the KPI for each method is provided in Fig 1
Fig 1 – Power plant emissions monitoring methods
Selecting a method
The method selected needs to reflect the importance of the desired KPI – with respect to the business outcome.
For example there is no point in investing in real-time monitoring and high plant detail, if the outcome desired is only an annual emissions figure for the national greenhouse gas inventory. On the other hand, if improved emissions was the target, then such a system would be near worthless.
In many cases there are multiple business outcomes desired from external compliance to internal process improvement. This leads to an unstructured use of multiple methods resulting in different results from each method.
The differing results are easily explained by the KPI for each method however less well understood by personnel within an organisation and the regulators of the industry. This becomes a source of confusion to many organisations due to the differing results.
If a structured use of multiple methods is used, the confusion disappears with the reliability and precision increased as detailed below.
Each method has a certain level of accuracy, reliability and complexity. The accuracy and reliability can be significantly enhanced by using multiple independent methods in a structured manner.
For example with your car the accuracy associated with real-time fuel consumption monitoring can be significantly improved if it reconciled with the log of km travelled and the fuel put into your car at the bowser.
If the two methods are used consistently then it provides a check and balance against each method and identifies instrument failure. Using two independent methods means you know there is a problem but it is difficult to identify which method is having the problem.
Using 3 or more methods means the accuracy is improved and it is possible to identify and quantify which method is failing.
Typically, at Synengco we use 3 to 4 methods and recently this has saved one of our customer significant expenses by identifying and quantifying a measurement error used to calculate fuel consumption and emissions generated. The benefit to our customer was in excess of $1 million.