landfill gas modelling: combining scientific rigour and
LANDFILL GAS MODELLING: COMBINING SCIENTIFIC RIGOUR AND COMMERCIAL KEY PERFORMANCE INDICATORS FOR RELIABLE FORECASTING R.G. GREGORY* * Gregory Environmental Consulting Ltd, 4A Middle Lane, Nether Broughton, Leicestershire, UK, LE14 3HD
SUMMARY: This new approach to LFG resource assessment combines the rigour of the scientific community with the commercial views and needs of the business community. It uses a commercially proven bulk gas generation model such as GasSim or LandGEM, and then converts the volumetric bulk gas generation rate into electrical energy, as produced by a spark ignition gas engine. By converting volumetric flows into energy flows, the variable amounts of air ingress into a landfill site are excluded from any modelling calculations. The data are then processed in a spreadsheet, taking into account three sets of parameters: the first set of parameters impact on the rate of bulk gas generation of landfill gas within the site (the bulk gas generation curve), which include the total quantum of degradable waste landfilled; the composition of the waste (the split of putrescible waste, paper and card, and textiles); the rate of waste degradation; and the impact of external waste diversion factors (regulatory and economic). The second set of parameters impact on the recovery of gas. These comprise all those operational factors which are gas field related and which impact on gas collection efficiency. The third set of parameters, which are not normally considered in resource assessment, are those that impact on the available energy which can be exported to the grid. This includes operational factors such as excess gas flared, potentially due to grid connection constraints, the duration of scheduled and unscheduled downtime; and parasitic load considerations. As landfills age, these latter parameters take on a greater significance in energy recovery terms.
1. INTRODUCTION Landfill gas (LFG) modelling research peaked in the early 2000s in the UK, when waste was being landfilled without any EU biological municipal waste (BMW) diversion targets being applied to the waste arisings, and the EU Landfill Directive (Council Directive 99/31/EC) was only just beginning to make its impact in the UK. This research resulted in the release of much technical guidance to operators, including the release of the risk assessment software GasSim v1.0 in 2002. The development of GasSim has been reported upon in many Sardinia Conferences since 2003, the most recent being Gregory and Browell (2011), with the release of GasSim v2.5.
Proceedings Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium/ 2 - 6 October 2017 S. Margherita di Pula, Cagliari, Italy / © 2017 by CISA Publisher, Italy
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
However, GasSim was developed for environmental risk management purposes, rather than energy forecasting, and the tool has always been difficult to use as a method of forecasting actual recoverable energy from an individual landfill or a series of landfills in an LFG portfolio. The financial sector has also tightened up significantly on the accuracy of forecasting required: prior to the global financial crisis, landfill gas forecasts with error bars of ± 20% were acceptable, and indeed this uncertainty was not far off the level of knowledge at the time, whereas now, financiers will ask questions about forecasts which are not aligned with past performance to within ± 3%. Experiences accrued over the past 30 years of landfill gas modelling, and the past 15 years of due diligence modelling for the purposes of LFG portfolio valuation, acquisition and divestiture have led to the development of an auditing protocol which is both objective and transparent. For reasons of commercial confidentiality, no quantification of an individual portfolio or site can be made in this paper, but the approaches presented are valid for every landfill studied over the past 30 years, and over 150 landfills have been modelled in this way.
2. THE LEVEL OF DETAIL REQUIRED FOR ASSESSMENT The new approach to LFG resource assessment described in this paper combines the rigour of the scientific community with the commercial views and needs of the business community, who measure completely different parameters to scientists, but who nevertheless need the scientific information to help improve energy recovery at either a site or portfolio level. This new approach continues to use GasSim as the source term for subsequent modelling. GasSim is the default LFG modelling software for the UK and Ireland, and has been used on every landfill in the UK for regulatory risk assessment, so the source term for every landfill has already been well defined. However, GasSim is not the only software capable of delivering a source term bulk gas production curve, and it is quite reasonable to use the software of choice for the jurisdiction in which the landfill or portfolio of landfills is situated, so the use of LandGEM, or the IPCC model, for example, are equally suitable, provided they are using appropriate regional waste composition data. The relative inflexibility of GasSim, or indeed any of the simple first order empirical landfill gas generation models, at modelling the recoverable gas resource at the resolution now required by financiers and developers globally has been replaced by a spreadsheet approach which allows three sets of parameters (all variables) to be modelled: § Parameters that impact on the rate of bulk gas generation of landfill gas within the site (the
bulk gas generation curve). The key parameters which need to be considered here are the total quantum of degradable waste landfilled; the composition of the waste (the split of putrescible waste, paper and card, and textiles); the rate of waste degradation; and the impact of external waste diversion factors (regulatory and economic). § Parameters that impact on the recovery of gas (the recoverable gas resource curve). Factors which affect the recovery of the gas comprise all those operational factors which are gas field related and which impact on gas collection efficiency. This can include the areal extent of permanent and sacrificial gas collection infrastructure; permanent and temporary capping, and daily cover; and the extent of significant gas recovery loss factors which sterilise or reduce gas collection from areas of the landfill surface, and the condition of the gas field infrastructure overall.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
§ Finally, and most importantly for resource assessment, parameters that impact on the
available energy which can be exported to the grid. This includes operational factors such as excess gas flared, potentially due to grid connection constraints, the duration of scheduled and unscheduled downtime; and parasitic load considerations. A major scientific review undertaken by Gregory et al (2014) has improved the understanding of the first two of these sets of modelling parameters significantly. However, any LFG resource assessment has to consider the effect of all three groups of parameters. Many resource estimates only consider the first two sets of parameters, and some have only considered the bulk gas generation curve, with obvious detrimental results for the investors.
3. FACTORS THAT IMPACT BULK GAS GENERATION The key parameters that impact on the rate of bulk gas generation of landfill gas within the site (the bulk gas generation curve) are the total quantum of degradable waste landfilled; the composition of the waste (the split of putrescible waste, paper and card, and textiles); the rate of waste degradation; and the impact of external waste diversion factors (regulatory and economic). 3.1 Waste Dynamics Factors The total quantum of waste in a landfill site, the rate of filling of waste into a landfill, and the composition in terms of the amount of biodegradable municipal waste (BMW) and equivalent biodegradable waste in the commercial and industrial waste streams are key parameters for quantifying the bulk gas generation rate which can be achieved from a landfill. Waste dynamics factors are those which are external to the parameters built into the model, and are typically the total tonnage landfilled, the variation in the fill rate from year to year, and the compositions of the waste streams landfilled. They can reflect the recycling targets of the country, the impact of regulation and legislation on materials landfilled, and the effect of the economy on the amount of materials arising as wastes nationally. GasSim and other first order models usually include generic waste compositions for their intended markets, so the modeller only has to determine the tonnage and split of municipal solid waste, commercial, industrial, inert, and other wastes accepted at the site. These compositions serve most sites well, although it is true to say that industrial waste landfills, or landfills which appear to generate hydrogen throughout their operational lives are not ideally suited to modelling using existing cellulose degradation models for gas generation. 3.2 Waste Characterisation Considerations Under the EU Landfill Directive (Council Directive 1999/31/EC), the quantities of Biodegradable Municipal Waste (BMW) permitted to be disposed of to landfill are gradually being limited across Europe as follows:
§ By 2006 to reduce the BMW landfilled to 75 % of that produced in 1995; § By 2009 to reduce the BMW landfilled to 50 % of that produced in 1995; and § By 2014 to reduce the BMW landfilled to 35 % of that produced in 1995.
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The UK, and other countries heavily reliant on landfilling, have been allowed to claim derogation to delay meeting these targets; such that the revised dates for the UK and NI are as follows: § By 2010 to reduce the BMW landfilled to 75 % of that produced in 1995; § By 2013 to reduce the BMW landfilled to 50 % of that produced in 1995; and § By 2020 to reduce the BMW landfilled to 35 % of that produced in 1995.
In order to bring about these reductions, a range of alternative processing, treatment and disposal options have or are being developed; such that increasing quantities of organic waste are being recycled, treated and/or combusted, to comply with the Landfill Directive requirements, rather than being sent to landfill. The waste diversion targets are represented in GasSim as different waste composition files, to be used in different model years, and which are supplied as defaults in GasSim. While these waste diversion factors may actually be incorrect at an individual site level, calibration of the model against power exported removes any inaccuracy in these data. The diversion targets do not apply to commercial or industrial wastes. It is these diversion targets that are responsible for the steepening downward slope of individual sites landfill gas generation profiles, post 2010, when the first set of diversion targets were met, even though sites may remain open and are filling. This is because the quantum of degradable material being landfilled is typically less, following the diversion factor being applied, than the quantum of material being degraded in the landfill in that model year. Defra has indicated that the 2020 target has already been reached in the UK nationally, although what is actually achieved at an individual site could of course vary from the national average, but calibration of the sites and the portfolio against kWe outputs can remove this potential for variance between model and reality. 3.3 The rate of Waste Degradation Because GasSim is an empirical model, the modelled waste degradation rate is the same as the bulk gas generation rate. Most empirical models only have a single, average waste degradation rate, but putrescible waste degrades much more quickly than paper and card waste, which in turn degrades much more quickly than textiles, and so GasSim uses three sets of waste degradation rates, for the rapidly degradable waste fraction, moderately degradable waste fraction, and slowly degradable waste fraction. Each of the three waste fractions has a waste degradation rate, k (units of y-1) which is based on first order kinetics. The relationship between the degradation rate, k, and the corresponding half-life for waste degradation is: Degradation rate, k (y-1) = Ln (2)/half-life (y) Different waste degradation half-lives can be used for sites which reflect primarily the effect of moisture content on degradability, but also incorporating into each of these sets of degradation rates a number of secondary or site-specific effects which modify waste degradability. Waste degradation can be influenced by many factors, and one of the most significant is the moisture content of the waste. This in turn is affected by the weather. Gregory et al (2014) produced for the UK Department of Environment, Food and Rural Affairs (Defra) a definitive
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science report on landfill methane emissions modelling which contained an empirical plot of which empirical waste degradation rates might apply across the UK and Ireland (Figure 1).
Figure 1. Approximation of the Effect of a Maritime Climate with a south-westerly prevailing storm track on typical UK waste degradation rates (Gregory et al 2014)
GasSim waste degradation rates are comparable to the range of published Intergovernmental Panel on Climate Change (IPCC) waste degradation rates between Temperate (Boreal) and Tropical (Wet) climates (IPCC, 2006). GasSim uses a series of fixed rates: § Super wet (or bioreactor). These rates are most suited to sites in Ireland and certain UK
§ § § §
west coast high rainfall sites. These are very fast waste degradation rates, and as such are conservative in terms of long term gas availability. They were first described in the literature by Gregory and Browell (2011). Wet. Wet waste degradation rates are the current UK default. Average. These waste degradation rates may apply in some landfills located in milder rainfall areas. Dry. These represent desert conditions not encountered in the UK. Saturated. If a site’s moisture content exceeds bioreactor conditions, the site may become flooded, and the effect of saturation is to rapidly slow the degradation of waste. Saturated waste degradation rates are the same as dry rates.
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Table 1 below summarises the variation in half-lives available in GasSim as defaults, and the equivalent values from the IPCC (2006). As can be seen in Table 1, the ranges used in GasSim as defaults are very similar to the IPCC parameters, which can be used in their stead in other regions of the world. Figure 2 below shows how straightforward it can be to choose the right waste degradation rates for a landfill site. It can be difficult to choose the most appropriate parameters for a specific landfill, but the overriding choice is determined by the slope of the historic energy exported, which by definition has to be beneath the bulk gas generation curve as the headroom between the two curves represents the modelled gas collection efficiency. Ideally a small gas colelction efficiency is the desired outcome, but it is more important to ensure that the bulk gas curve and the historic energy exported curve are sub-parallel.
Figure 2. A modelled site with historic energy exported data, a forecast energy exported curve, and two potential bulk gas generation curves, one with superwet waste degradation rates (the correct curve) and one with average waste degradation rates (the incorrect curve).
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
3.4 Using GasSim Results in Energy Modelling In risk assessment, the 95th percentile output from GasSim is used. This is an output from a probabilistic model that is unlikely to be exceeded more than 1 in 20 times, and this is the usual degree of conservatism employed in a risk assessment. In modelling the landfill gas resource, however, the 50th percentile in GasSim is used. This is equivalent to the median or most likely value output from GasSim. An investor is more likely to be interested in the P50, which is the 50th percentile, and the P90, which is the 10th percentile. More importantly, there is a mismatch between landfill operator’s records of volumetric measurement, volumetric measures of LFG production by LFG models, and the power data recorded by energy companies, who record kWh/h and MWh per period (month, quarter or year). All spreadsheet modelling should be undertaken in units of energy, to remove volumetric uncertainties and temperature and pressure effects. There are a number of steps required to achieve this transition from volume to energy. Firstly, LFG which is collected in a gas collection system comprises a mixture of two gas mixtures: LFG and entrained air. The landfill operator will measure or record volumetric flow which includes this entrained air, which has no contribution to the energy content of the LFG. This has to be normalised back to pure LFG to compare measured flows with model forecasts, and there have been and will no doubt continue to be countless discussions between modellers and site operators about the volumes collected by their gas plant, and the model’s accuracy and suitability, because of this necessary normalisation process. Gregory et al (2014) demonstrated from a huge dataset of over 53,000 individual measurements from a large fraction of the UK landfill gas plant operators that the average balance gas (N2 plus O2) concentration in abstraced LFG is 22%, with a standard deviation of 10%. Gregory et al (2014) also determined from this dataset that the normalised ratio of LFG is 57% CH4 and 43% CO2 (each with a standard deviation of 3%). This is a surprisingly consistent ratio, and can be used to determine the energy content of LFG in a site if insufficient data have been collected (three out of four parameters are required, and these are typically CH4, CO2 and O2. In some jurisdictions, CO2 is not regularly measured, resulting in insufficient data to perform normalisation. The next step in the conversion from volume to energy content uses the percentage of CH4 in the LFG and the lower heating value (LHV) of CH4 to calculate the energy content. The lower heating value (LHV) is determined by subtracting the heat of vaporisation of the water vapour from the higher heating value (HHV). This treats any H2O formed as a vapour. LHV calculations assume that the water component of a combustion process is in vapour state at the end of combustion, as opposed to the higher heating value (HHV) which assumes all of the water in a combustion process is in a liquid state after a combustion process. LHV is the correct parameter to use because LFG is a wet gas by definition, and use of the HHV will over-value the energy content of the LFG. The final step in the conversion of m3/h LFG to kWh/h, is the choice of electrical efficiency of a LFG generating set. While these are published for new LFG gas engines at up to 42% electrical efficiency, poorly maintained and ageing gas plant may actually be operating at below 30% electrical efficiency. A value for portfolio modelling is required that allows calibration midway through the maintenance cycle of any LFG engine, and a value of 38% is considered sufficiently mid-way to allow calibration around this value. The absolute value of this parameter is not important, when the forecast output is calibrated against actual historic output.
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4. FACTORS THAT IMPACT THE RECOVERY OF LANDFILL GAS Factors which affect the recovery of the gas comprise all those operational factors which are gas field related and which impact on the overall gas collection efficiency. This can include the areal extent of permanent and sacrificial gas collection infrastructure; permanent and temporary capping, and daily cover; and the extent of significant gas recovery loss factors which sterilise or reduce gas collection from areas of the landfill surface, and the condition of the gas field infrastructure overall. The original approach used in GasSim is a simple matrix of nine values (Golder Associates and the Environment Agency, 2005). This approach was deemed appropriate for regulation of emissions, but as GasSim has been subsequently used for gas resource assessment, the complexity of site operations is not incorporated in the simple matrix approach. The GasSim matrix parameters are shown in Table 2 below. Table 2 GasSim Gas Collection Efficiency Matrix Collection Efficiency (%) Daily Cover
Temporary Cap
Permanent Cap
No gas field
0
0
0
Temporary/sacrificial gas field Permanent/engineered gas field
30
50
65
60
85
95
The more advanced approach described here applies loss factors to specific areas of the site, as well as loss factors to practices which can sterilise or reduce gas collection from areas of the landfill surface, such as: § § § § §
stockpiles and over-tipped areas which can damage buried infrastructure; side slopes which do not have gas abstraction infrastructure; the effects of perched leachate and flooded sites; the effects of subsurface landfill fires or hot-spots; and the condition of the gas field infrastructure overall.
The factors modelled, and the empirical percentage of bulk gas lost due to the presence of these features, is given in Table 3. The areal extent of these factors determines the impact of each parameter, and not all parameters will be found on any one landfill. These values have a similar effect on the calculation of collection efficiency as the values in Table 2, but allow a more detailed approach to be taken to reflect real landfill opertations. The loss factors are empirical, determined by experience, but compare reasonably well with those in Table 2.
5. FACTORS THAT IMPACT ON ENERGY GENERATION These are parameters that impact on the available energy which can be exported to the grid. This includes operational factors such as excess gas flared, grid constraints, the duration of scheduled and unscheduled downtime; and parasitic load considerations. The factors described in this section below are rarely modelled in any LFG resource assessment.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Table 3. Factors applied to the Bulk Gas Generation forecast to achieve a Recoverable Gas Resource Area of Landfill
Value for Available Gas Yield Lost (%) Main Areas of Landfill Cover Operational area 100
Comments
Operational area with gas control
50
Collection efficiency is typically half that of permanently capped areas due to nature of well designs and the capping materials employed.
Temporary capped without gas control
100
No LFG is collected in these areas.
Temporary capped area with gas control
15
Gas collection efficiency is typically 85 % of a permanently capped area. This is the GasSim default value in Table 2.
Permanently capped area without gas control
100
Used when sites have permanently capped areas without gas extraction.
Permanently capped area with gas control
0
This is typically 5 % of the landfill area on large and 10 % on small landfills.
Gas is lost from this zone due to gas-well defects and other loss factors, so this factor may appear to have less than 100% gas collection efficiency even when the site is closed.
Other Loss Factors Overtipped areas
100
Where over-tipping is likely to be affecting gas collection.
Stockpiled areas
100
Where stockpiles may affect gas recovery.
Cell flanks without gas control
100
Where cell flanks do not have gas control.
Areas subject to hotspots
100
Where hotspots are known to be present and likely to affect gas generation and collection.
Areas subject to high leachate
75
Where high or perched leachate levels can affect gas recovery.
Other
100
Default is to assume 10 % for unknown and future losses.
100
Default is to assume 5% to 10% of all wells on a gas field will fail or not be operational, and a collection efficiency downgrade is attributed to all landfilled areas where a gas field is installed.
Percentage of failed wells in areas with gas control
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5.1 Parasitic Load Considerations Parasitic loads are simply loads which are used by the engine in its operation (losses from operation of fans, oil pumps and cooling). However, it is common to group parasitic load, auxiliary load and site load into one category loosely termed parasitic load. Auxiliary loads are ancillary electrical loads which reduce the electrical power delivered to the grid by a gas engine. Auxiliary loads are generally used to power other components of the landfill gas recovery operations, and are associated with gas delivery and conditioning. This includes, for example, gas boosters and any gas clean-up plant, for e.g. siloxanes, CFCs and HCFCs, and/or H2S. Site loads are the loads arising from on-site use not strictly defined by parasitic load or auxiliary load, e.g. a leachate treatment plant, or office heating and lighting. For a nominal 1MWe gas engine, a rule of thumb value for the combined parasitic and auxiliary load might be 30kW – 50kW, and for each additional 1MWe installed, another 20kW. It is not best practice to operate site loads from the power generated by the gas engine, as this is using energy which would typically attract a higher revenue if sold than the cost of purchasing grid electricity to operate on-site plant. Many electricity regulators will not allow auxiliary loads to be provided by external grid power, as such auxiliary loads are integral to the production and export of renewable energy. Parasitic and auxiliary loads combined can vary between 3.75 – >10% of the power production from the gas engine, depending on whether the gas engine is containerised or installed in a purpose built engine room, on the climate in which teh gas engine is operating, and theage of the gas engine. A typical average for a European portfolio might be 4.5 – 5%. When a site or portfolio is on the upside of the gas curve, parasitic and auxiliary loads are often ignored by operators, but they can become a significant component of the energy lost prior to export when a site is on the downcurve. 5.2 Maintenance Downtime, Engine Changes and Electrical Efficiency There is often a tradeoff between the duration of planned downtime, maintaining a gas engine in top condition, and the effect of lack of maintenance on power produced, seen as a deterioration in overall engine efficiency. A conservative default for a landfill site if no data are available is a combined value of 7% scheduled and unscheduled maintenance downtime. Some individual sites can exhibit downtimes as low as 3%, and there is no upper limit to this value for a site where the gas engine has put a piston through the engine casing, and there is no replacement available. At a portfolio level, where a constant electrical efficiency is modelled, the deterioration of a single engine is best modelled by an increase in site downtime. Equally, there are benefits to be seen when an old landfill gas engine is replaced, as the electrical efficiency of the new gas engine will show a measurable improvement. The power export curve in Figure 3 below shows such an effect. The capital cost of the replacement gas engine has to be offset against the net benefits of higher electrical efficiency, and better uptime performance. Figure 4 shows the benefit of examining data at high resolution (monthly or less). The downward spikes in the power generation curve reflect periods of maintenance.
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Engine replaced here
Figure 3. The effect of replacement of the landfill gas engine on power production
Optimal Energy Output Curve at 75% Collection Efficiency
Grid constraint period
Periods of scheduled maintenance
Figure 4. A power generation curve showing (1) the effect of a grid constraint, prior to achieving full grid capacity; and (2) downward spikes in energy generation reflecting periods of scheduled maintenance.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
5.3 Grid Connection Constraints and Excess Gas Flared
At some sites, there will be gas flared which is in excess of the typical amount of flaring which occurs on landfill sites with power generation, which is usually less than 10%. This value is typically reflective of the periods of scheduled and unscheduled maintenance/downtime. Where there is excess landfill gas flared, this is usually for one of two reasons. Either there is a grid connection constraint, or there is an odour management issue which requires additional effort to collect the gas, causing lower gas quality which may not be suitable for combustion in a gas engine. Where the gas quality is poor, this will be an operational decision to either flare or blend the gas with higher quality gas from a different part of the site, for example. Where there is a grid connection constraint, the cost of a new grid connection has to be assessed against the additional revenue which could be realised from power export. In many cases, while there is some potential energy which could be recovered, the cost of grid connection may be too high, and the benefit of additional gas utilisation is not cost-effective. Figure 4 above shows a site which was grid constrained at the start of the period of utilisation, but which had this constraint eased over the first year of operation. 5.4 Load Factor and Availability Load factor has often been used historically as a measure of the operating performance of the portfolio, and is defined as ‘the actual power exported to the grid as a proportion of the maximum electricity generation potential based on size of engine installed at a generation Site’. It has some merit for the vendor of an increasing landfill gas resource, as the load factor will be a high number, although you can have a high load factor and also spill a lot of excess gas to flare. The load factor includes planned and unplanned maintenance/downtime, auxiliary loads, parasitic losses and all other planned or unplanned losses e.g. partial disconnection of the gas field for capping, and from that perspective is a useful parameter. However, when the landfill gas resource is declining, and gas engines are derated, the load factor loses any of its limited value. Availability is defined as the ratio of time an engine is available to run compared to total time. Availability is often used by a vendor to demonstrate the efficiency of the gas utilisation plant, but this parameter is almost as useful as saying there is a landfill gas engine installed on site. It is possibly a surrogate for reporting values of downtime, but neither parameter, on its own, is of any value in defining the long-term value of a declining landfill gas resource.
6. UNDERSTANDING PORTFOLIO SITES NOT MODELLED IN DETAIL It has become routine for financiers or developers to focus on the top 80% of the energy content of any portfolio, and this is where the greatest amount of due diligence is likely to be applied, whether for purposes of valuation, acquisition or divestiture. By adopting the approach set out above, and investigating all three sets of parameters, it is easy to see how the uncertainty in the last 20% of the energy content of a portfolio can be more significant than simple extrapolation would suggest. Landfills closed for waste acceptance now make up a significant proportion of LFG to energy portfolios, and the uncertainty ranges in the second and third tier parameters can have a disproportionate significance compared to only ten years ago, when most LFG to energy
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portfolios had a preponderance of operational sites and the gas resource was always on the upcurve. This means that the potential impact on forecasts including the last 20% of the portfolio power generation value (the ‘balance’ sites) will be greater than previously thought. Historically in the UK, little modelling effort has been applied to the smaller sites that deliver the last 20% of the value of LFG portfolios. Sites that produce low volumes of gas, and which are at the end of their gassing life are supposedly difficult to forecast with any degree of accuracy. For example, some sites will continue to produce recoverable LFG at low gas production rates for many years, and such sites may not necessarily follow the same exponential decay curve in their latter years compared with the gas production rates observed at the start of the post-closure period of gas production. Historically, assessments might consider the difference between the top 80% of the energy produced and the total portfolio using a simple ratio, but this is likely to include an element of upside, as the gas resources in every site of the final 20% of the portfolio will most likely be declining, year on year, and some may be closing, while in the top 80%, there will be sites still operational and recieving waste. A worse case scenario will be achieved by curve-fitting the past five years performance of the balance sites. Curve fitting nearly always produces a worst case scenario as it is difficult to constrain the exponential curve. While the simple ratio approach will lead to an upside assessment, and the extrapolation approach will lead to an extreme downside assessment, these two approaches represent the two extremes of possibility. It is more realistic to return to first principles and model the exported power curve from each of the balance sites in the portfolio, using our knowledge of the half-lives which apply to the bulk gas curves of the sites which have had detailed assessment, to better quantify the shape of the forecast curve. In addition to this, exported power should be limited to the turndown ratio of the smallest gas engine in the fleet. This is commonly a Jenbacher 208, a nominal 330 kWe gas engine, which would yield 130 kWe at maximum turndown ratio. For small portfolios, e.g. less than 20 to 30 sites in all, it is recommended that every site is modelled in the same detail as the first, as that will eliminate any bias in the balance sites.
7. PRESENTING COLLECTION EFFICIENCY INFORMATION TO STAKEHOLDERS It has been said many times in the past that if you can model the bulk gas generation rate, why cannot you recover it and reap those energy benefits? As scientists, we understand why this is the case. But we are challenged when we put graphics together to demonstrate what landfill gas we are collecting, and what we are not, because we know that you cannot collect all the landfill gas generated, and sometimes, if there is a significant headroom between the bulk gas generation curve and the recoverable gas resource, it is difficult to explain why the difference cannot be exploited. An alternative upper bound is needed to demonstrate that there are opportunities to improve gas collection efficiency, but the benefits do not go as far as 100% of all the gas which is modelled or generated. Gregory et al (2014) sought information from the major UK operators and showed that a subset of 43 of the most modern UK landfills achieve what are believed to be very high gas collection efficiencies, giving in the region of 68% lifetime LFG collection efficiencies, compared to a lifetime average collection efficiency of 52% for all currently operational landfills in the UK. This observation is far from the regulators’ published target of 85% collection efficiency. Experience from due diligence on a significant proportion of the UK’s landfill gas resource is that the 85% collection efficiency value is only ever likely to be achieved post-closure on the best engineered landfills. If an operator is only ever likely to achieve an average of just over 50% lifetime collection
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
efficiency, we consider that a reasonable stretch target would be 75% collection efficiency in any given year. This is enough to focus on the actions necessary when managing a portfolio, and allows sites to be compared with each other and prioritized. This target has been defined here as the Optimal Energy Output curve. An illustration of this is shown in Figure 4. For some portfolio sites, landfill gas recovery will be above this Optimal Energy Output curve when modelled, but this is a suitable target for a portfolio, allowing the assessor to focus on the most challenging sites for gas collection.
8. CONCLUSIONS In summary, LFG recovery has become a well-established technique, using off the shelf technology. Waste compositions have changed due to Government policy and the BMW content has decreased. It is the approach of the LFG utilisation companies to maintenance regimes, maintaining high LFG collection efficiency, and tracking the gas curve with, for example, sacrificial systems in the early stages of landfilling which differentiates a good LFG to energy scheme from a great one. As gassing landfills age, the uncertainty ranges in the parameters which affect gas recovery can also have a disproportionate significance on the anticipated performance of a LFG utilisation scheme or portfolio, meaning more effort needs to be made in understanding the collection efficiency in ageing landfills. As all landfills in a portfolio age, and the portfolio gas curve is on a downward trajectory, operators need to refocus on what significant parameters should be measured as part of their operation’s key performance indicators (KPIs). It is common for the recoverable gas resource curve to follow a predictable downward trajectory, and at this point in a landfill’s operational life, the efficiency of engine maintenance operations becomes much more significant than when there was excess gas available during the filling phases, and before BMW waste diversion targets had taken effect. Bulk gas generation should continue to be modelled using established tools, as these are the tools which have gained acceptance globally: GasSim, the IPCC model, and LandGEM, for example. However, modelling operational gas loss factors is best undertaken in a spreadsheet environment to achieve the resolution required from todays investors. As landfills age, the factors which impact on subsequent energy generation, such as downtime, parasitic load, and grid constraints can become more significant, and these parameters need to be revisited to ensure best practice in energy recovery is achieved. When modelling large portfolios, the impact of the small, low production sites not modelled in detail (the 20% considered to be the balance sites, not meriting investigation) might become more significant with time, and these sites should ideally be modelled with the same level of critique as the largest sites in the portfolio. Finally, while high performing modern landfills might achieve in the region of 68% lifetime collection efficiencies, the average in the UK is closer to 52%. This observation is far from the regulators’ published target of 85% collection efficiency. Experience from due diligence on a significant proportion of the UK’s landfill gas resource is that the 85% collection efficiency value is only ever likely to be achieved post-closure on the best engineered landfills. A reasonable stretch target could be a 75% collection efficiency. This is enough to focus on the actions necessary when managing a portfolio, and allows sites to be compared with each other and prioritized. This target has been defined here as the Optimal Energy Output curve. For some portfolio sites, landfill gas recovery will be above this Optimal Energy Output curve when modelled, but this is a suitable target for a portfolio, allowing the assessor to focus on the most challenging sites for gas collection.
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REFERENCES Environment Agency (2004). Guidance on the Management of Landfill Gas. Report LFTGN03. Environment Agency and Golder Associates (2006). GasSim2 User Manual. Available from www.gassim.co.uk. Gregory R G and Browell, D W. (2011). Recent Developments in Understanding of Waste Degradation Rates and Modelling Landfill Gas Generation and Recovery. Proceedings Sardinia 2011, Thirteenth International Waste Management and Landfill Symposium. S. Margherita di Pula, Cagliari, Italy; 3 - 7 October 2011. Gregory R G, Stalleicken J, Lane R, Arnold S and Hall D H (2014). Review of Landfill Methane Emissions Modelling. Report to UK Department of the Environment, Food and Rural Affairs under contract WR1908. IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
SUMMARY: This new approach to LFG resource assessment combines the rigour of the scientific community with the commercial views and needs of the business community. It uses a commercially proven bulk gas generation model such as GasSim or LandGEM, and then converts the volumetric bulk gas generation rate into electrical energy, as produced by a spark ignition gas engine. By converting volumetric flows into energy flows, the variable amounts of air ingress into a landfill site are excluded from any modelling calculations. The data are then processed in a spreadsheet, taking into account three sets of parameters: the first set of parameters impact on the rate of bulk gas generation of landfill gas within the site (the bulk gas generation curve), which include the total quantum of degradable waste landfilled; the composition of the waste (the split of putrescible waste, paper and card, and textiles); the rate of waste degradation; and the impact of external waste diversion factors (regulatory and economic). The second set of parameters impact on the recovery of gas. These comprise all those operational factors which are gas field related and which impact on gas collection efficiency. The third set of parameters, which are not normally considered in resource assessment, are those that impact on the available energy which can be exported to the grid. This includes operational factors such as excess gas flared, potentially due to grid connection constraints, the duration of scheduled and unscheduled downtime; and parasitic load considerations. As landfills age, these latter parameters take on a greater significance in energy recovery terms.
1. INTRODUCTION Landfill gas (LFG) modelling research peaked in the early 2000s in the UK, when waste was being landfilled without any EU biological municipal waste (BMW) diversion targets being applied to the waste arisings, and the EU Landfill Directive (Council Directive 99/31/EC) was only just beginning to make its impact in the UK. This research resulted in the release of much technical guidance to operators, including the release of the risk assessment software GasSim v1.0 in 2002. The development of GasSim has been reported upon in many Sardinia Conferences since 2003, the most recent being Gregory and Browell (2011), with the release of GasSim v2.5.
Proceedings Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium/ 2 - 6 October 2017 S. Margherita di Pula, Cagliari, Italy / © 2017 by CISA Publisher, Italy
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
However, GasSim was developed for environmental risk management purposes, rather than energy forecasting, and the tool has always been difficult to use as a method of forecasting actual recoverable energy from an individual landfill or a series of landfills in an LFG portfolio. The financial sector has also tightened up significantly on the accuracy of forecasting required: prior to the global financial crisis, landfill gas forecasts with error bars of ± 20% were acceptable, and indeed this uncertainty was not far off the level of knowledge at the time, whereas now, financiers will ask questions about forecasts which are not aligned with past performance to within ± 3%. Experiences accrued over the past 30 years of landfill gas modelling, and the past 15 years of due diligence modelling for the purposes of LFG portfolio valuation, acquisition and divestiture have led to the development of an auditing protocol which is both objective and transparent. For reasons of commercial confidentiality, no quantification of an individual portfolio or site can be made in this paper, but the approaches presented are valid for every landfill studied over the past 30 years, and over 150 landfills have been modelled in this way.
2. THE LEVEL OF DETAIL REQUIRED FOR ASSESSMENT The new approach to LFG resource assessment described in this paper combines the rigour of the scientific community with the commercial views and needs of the business community, who measure completely different parameters to scientists, but who nevertheless need the scientific information to help improve energy recovery at either a site or portfolio level. This new approach continues to use GasSim as the source term for subsequent modelling. GasSim is the default LFG modelling software for the UK and Ireland, and has been used on every landfill in the UK for regulatory risk assessment, so the source term for every landfill has already been well defined. However, GasSim is not the only software capable of delivering a source term bulk gas production curve, and it is quite reasonable to use the software of choice for the jurisdiction in which the landfill or portfolio of landfills is situated, so the use of LandGEM, or the IPCC model, for example, are equally suitable, provided they are using appropriate regional waste composition data. The relative inflexibility of GasSim, or indeed any of the simple first order empirical landfill gas generation models, at modelling the recoverable gas resource at the resolution now required by financiers and developers globally has been replaced by a spreadsheet approach which allows three sets of parameters (all variables) to be modelled: § Parameters that impact on the rate of bulk gas generation of landfill gas within the site (the
bulk gas generation curve). The key parameters which need to be considered here are the total quantum of degradable waste landfilled; the composition of the waste (the split of putrescible waste, paper and card, and textiles); the rate of waste degradation; and the impact of external waste diversion factors (regulatory and economic). § Parameters that impact on the recovery of gas (the recoverable gas resource curve). Factors which affect the recovery of the gas comprise all those operational factors which are gas field related and which impact on gas collection efficiency. This can include the areal extent of permanent and sacrificial gas collection infrastructure; permanent and temporary capping, and daily cover; and the extent of significant gas recovery loss factors which sterilise or reduce gas collection from areas of the landfill surface, and the condition of the gas field infrastructure overall.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
§ Finally, and most importantly for resource assessment, parameters that impact on the
available energy which can be exported to the grid. This includes operational factors such as excess gas flared, potentially due to grid connection constraints, the duration of scheduled and unscheduled downtime; and parasitic load considerations. A major scientific review undertaken by Gregory et al (2014) has improved the understanding of the first two of these sets of modelling parameters significantly. However, any LFG resource assessment has to consider the effect of all three groups of parameters. Many resource estimates only consider the first two sets of parameters, and some have only considered the bulk gas generation curve, with obvious detrimental results for the investors.
3. FACTORS THAT IMPACT BULK GAS GENERATION The key parameters that impact on the rate of bulk gas generation of landfill gas within the site (the bulk gas generation curve) are the total quantum of degradable waste landfilled; the composition of the waste (the split of putrescible waste, paper and card, and textiles); the rate of waste degradation; and the impact of external waste diversion factors (regulatory and economic). 3.1 Waste Dynamics Factors The total quantum of waste in a landfill site, the rate of filling of waste into a landfill, and the composition in terms of the amount of biodegradable municipal waste (BMW) and equivalent biodegradable waste in the commercial and industrial waste streams are key parameters for quantifying the bulk gas generation rate which can be achieved from a landfill. Waste dynamics factors are those which are external to the parameters built into the model, and are typically the total tonnage landfilled, the variation in the fill rate from year to year, and the compositions of the waste streams landfilled. They can reflect the recycling targets of the country, the impact of regulation and legislation on materials landfilled, and the effect of the economy on the amount of materials arising as wastes nationally. GasSim and other first order models usually include generic waste compositions for their intended markets, so the modeller only has to determine the tonnage and split of municipal solid waste, commercial, industrial, inert, and other wastes accepted at the site. These compositions serve most sites well, although it is true to say that industrial waste landfills, or landfills which appear to generate hydrogen throughout their operational lives are not ideally suited to modelling using existing cellulose degradation models for gas generation. 3.2 Waste Characterisation Considerations Under the EU Landfill Directive (Council Directive 1999/31/EC), the quantities of Biodegradable Municipal Waste (BMW) permitted to be disposed of to landfill are gradually being limited across Europe as follows:
§ By 2006 to reduce the BMW landfilled to 75 % of that produced in 1995; § By 2009 to reduce the BMW landfilled to 50 % of that produced in 1995; and § By 2014 to reduce the BMW landfilled to 35 % of that produced in 1995.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
The UK, and other countries heavily reliant on landfilling, have been allowed to claim derogation to delay meeting these targets; such that the revised dates for the UK and NI are as follows: § By 2010 to reduce the BMW landfilled to 75 % of that produced in 1995; § By 2013 to reduce the BMW landfilled to 50 % of that produced in 1995; and § By 2020 to reduce the BMW landfilled to 35 % of that produced in 1995.
In order to bring about these reductions, a range of alternative processing, treatment and disposal options have or are being developed; such that increasing quantities of organic waste are being recycled, treated and/or combusted, to comply with the Landfill Directive requirements, rather than being sent to landfill. The waste diversion targets are represented in GasSim as different waste composition files, to be used in different model years, and which are supplied as defaults in GasSim. While these waste diversion factors may actually be incorrect at an individual site level, calibration of the model against power exported removes any inaccuracy in these data. The diversion targets do not apply to commercial or industrial wastes. It is these diversion targets that are responsible for the steepening downward slope of individual sites landfill gas generation profiles, post 2010, when the first set of diversion targets were met, even though sites may remain open and are filling. This is because the quantum of degradable material being landfilled is typically less, following the diversion factor being applied, than the quantum of material being degraded in the landfill in that model year. Defra has indicated that the 2020 target has already been reached in the UK nationally, although what is actually achieved at an individual site could of course vary from the national average, but calibration of the sites and the portfolio against kWe outputs can remove this potential for variance between model and reality. 3.3 The rate of Waste Degradation Because GasSim is an empirical model, the modelled waste degradation rate is the same as the bulk gas generation rate. Most empirical models only have a single, average waste degradation rate, but putrescible waste degrades much more quickly than paper and card waste, which in turn degrades much more quickly than textiles, and so GasSim uses three sets of waste degradation rates, for the rapidly degradable waste fraction, moderately degradable waste fraction, and slowly degradable waste fraction. Each of the three waste fractions has a waste degradation rate, k (units of y-1) which is based on first order kinetics. The relationship between the degradation rate, k, and the corresponding half-life for waste degradation is: Degradation rate, k (y-1) = Ln (2)/half-life (y) Different waste degradation half-lives can be used for sites which reflect primarily the effect of moisture content on degradability, but also incorporating into each of these sets of degradation rates a number of secondary or site-specific effects which modify waste degradability. Waste degradation can be influenced by many factors, and one of the most significant is the moisture content of the waste. This in turn is affected by the weather. Gregory et al (2014) produced for the UK Department of Environment, Food and Rural Affairs (Defra) a definitive
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
science report on landfill methane emissions modelling which contained an empirical plot of which empirical waste degradation rates might apply across the UK and Ireland (Figure 1).
Figure 1. Approximation of the Effect of a Maritime Climate with a south-westerly prevailing storm track on typical UK waste degradation rates (Gregory et al 2014)
GasSim waste degradation rates are comparable to the range of published Intergovernmental Panel on Climate Change (IPCC) waste degradation rates between Temperate (Boreal) and Tropical (Wet) climates (IPCC, 2006). GasSim uses a series of fixed rates: § Super wet (or bioreactor). These rates are most suited to sites in Ireland and certain UK
§ § § §
west coast high rainfall sites. These are very fast waste degradation rates, and as such are conservative in terms of long term gas availability. They were first described in the literature by Gregory and Browell (2011). Wet. Wet waste degradation rates are the current UK default. Average. These waste degradation rates may apply in some landfills located in milder rainfall areas. Dry. These represent desert conditions not encountered in the UK. Saturated. If a site’s moisture content exceeds bioreactor conditions, the site may become flooded, and the effect of saturation is to rapidly slow the degradation of waste. Saturated waste degradation rates are the same as dry rates.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Table 1 below summarises the variation in half-lives available in GasSim as defaults, and the equivalent values from the IPCC (2006). As can be seen in Table 1, the ranges used in GasSim as defaults are very similar to the IPCC parameters, which can be used in their stead in other regions of the world. Figure 2 below shows how straightforward it can be to choose the right waste degradation rates for a landfill site. It can be difficult to choose the most appropriate parameters for a specific landfill, but the overriding choice is determined by the slope of the historic energy exported, which by definition has to be beneath the bulk gas generation curve as the headroom between the two curves represents the modelled gas collection efficiency. Ideally a small gas colelction efficiency is the desired outcome, but it is more important to ensure that the bulk gas curve and the historic energy exported curve are sub-parallel.
Figure 2. A modelled site with historic energy exported data, a forecast energy exported curve, and two potential bulk gas generation curves, one with superwet waste degradation rates (the correct curve) and one with average waste degradation rates (the incorrect curve).
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
3.4 Using GasSim Results in Energy Modelling In risk assessment, the 95th percentile output from GasSim is used. This is an output from a probabilistic model that is unlikely to be exceeded more than 1 in 20 times, and this is the usual degree of conservatism employed in a risk assessment. In modelling the landfill gas resource, however, the 50th percentile in GasSim is used. This is equivalent to the median or most likely value output from GasSim. An investor is more likely to be interested in the P50, which is the 50th percentile, and the P90, which is the 10th percentile. More importantly, there is a mismatch between landfill operator’s records of volumetric measurement, volumetric measures of LFG production by LFG models, and the power data recorded by energy companies, who record kWh/h and MWh per period (month, quarter or year). All spreadsheet modelling should be undertaken in units of energy, to remove volumetric uncertainties and temperature and pressure effects. There are a number of steps required to achieve this transition from volume to energy. Firstly, LFG which is collected in a gas collection system comprises a mixture of two gas mixtures: LFG and entrained air. The landfill operator will measure or record volumetric flow which includes this entrained air, which has no contribution to the energy content of the LFG. This has to be normalised back to pure LFG to compare measured flows with model forecasts, and there have been and will no doubt continue to be countless discussions between modellers and site operators about the volumes collected by their gas plant, and the model’s accuracy and suitability, because of this necessary normalisation process. Gregory et al (2014) demonstrated from a huge dataset of over 53,000 individual measurements from a large fraction of the UK landfill gas plant operators that the average balance gas (N2 plus O2) concentration in abstraced LFG is 22%, with a standard deviation of 10%. Gregory et al (2014) also determined from this dataset that the normalised ratio of LFG is 57% CH4 and 43% CO2 (each with a standard deviation of 3%). This is a surprisingly consistent ratio, and can be used to determine the energy content of LFG in a site if insufficient data have been collected (three out of four parameters are required, and these are typically CH4, CO2 and O2. In some jurisdictions, CO2 is not regularly measured, resulting in insufficient data to perform normalisation. The next step in the conversion from volume to energy content uses the percentage of CH4 in the LFG and the lower heating value (LHV) of CH4 to calculate the energy content. The lower heating value (LHV) is determined by subtracting the heat of vaporisation of the water vapour from the higher heating value (HHV). This treats any H2O formed as a vapour. LHV calculations assume that the water component of a combustion process is in vapour state at the end of combustion, as opposed to the higher heating value (HHV) which assumes all of the water in a combustion process is in a liquid state after a combustion process. LHV is the correct parameter to use because LFG is a wet gas by definition, and use of the HHV will over-value the energy content of the LFG. The final step in the conversion of m3/h LFG to kWh/h, is the choice of electrical efficiency of a LFG generating set. While these are published for new LFG gas engines at up to 42% electrical efficiency, poorly maintained and ageing gas plant may actually be operating at below 30% electrical efficiency. A value for portfolio modelling is required that allows calibration midway through the maintenance cycle of any LFG engine, and a value of 38% is considered sufficiently mid-way to allow calibration around this value. The absolute value of this parameter is not important, when the forecast output is calibrated against actual historic output.
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4. FACTORS THAT IMPACT THE RECOVERY OF LANDFILL GAS Factors which affect the recovery of the gas comprise all those operational factors which are gas field related and which impact on the overall gas collection efficiency. This can include the areal extent of permanent and sacrificial gas collection infrastructure; permanent and temporary capping, and daily cover; and the extent of significant gas recovery loss factors which sterilise or reduce gas collection from areas of the landfill surface, and the condition of the gas field infrastructure overall. The original approach used in GasSim is a simple matrix of nine values (Golder Associates and the Environment Agency, 2005). This approach was deemed appropriate for regulation of emissions, but as GasSim has been subsequently used for gas resource assessment, the complexity of site operations is not incorporated in the simple matrix approach. The GasSim matrix parameters are shown in Table 2 below. Table 2 GasSim Gas Collection Efficiency Matrix Collection Efficiency (%) Daily Cover
Temporary Cap
Permanent Cap
No gas field
0
0
0
Temporary/sacrificial gas field Permanent/engineered gas field
30
50
65
60
85
95
The more advanced approach described here applies loss factors to specific areas of the site, as well as loss factors to practices which can sterilise or reduce gas collection from areas of the landfill surface, such as: § § § § §
stockpiles and over-tipped areas which can damage buried infrastructure; side slopes which do not have gas abstraction infrastructure; the effects of perched leachate and flooded sites; the effects of subsurface landfill fires or hot-spots; and the condition of the gas field infrastructure overall.
The factors modelled, and the empirical percentage of bulk gas lost due to the presence of these features, is given in Table 3. The areal extent of these factors determines the impact of each parameter, and not all parameters will be found on any one landfill. These values have a similar effect on the calculation of collection efficiency as the values in Table 2, but allow a more detailed approach to be taken to reflect real landfill opertations. The loss factors are empirical, determined by experience, but compare reasonably well with those in Table 2.
5. FACTORS THAT IMPACT ON ENERGY GENERATION These are parameters that impact on the available energy which can be exported to the grid. This includes operational factors such as excess gas flared, grid constraints, the duration of scheduled and unscheduled downtime; and parasitic load considerations. The factors described in this section below are rarely modelled in any LFG resource assessment.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Table 3. Factors applied to the Bulk Gas Generation forecast to achieve a Recoverable Gas Resource Area of Landfill
Value for Available Gas Yield Lost (%) Main Areas of Landfill Cover Operational area 100
Comments
Operational area with gas control
50
Collection efficiency is typically half that of permanently capped areas due to nature of well designs and the capping materials employed.
Temporary capped without gas control
100
No LFG is collected in these areas.
Temporary capped area with gas control
15
Gas collection efficiency is typically 85 % of a permanently capped area. This is the GasSim default value in Table 2.
Permanently capped area without gas control
100
Used when sites have permanently capped areas without gas extraction.
Permanently capped area with gas control
0
This is typically 5 % of the landfill area on large and 10 % on small landfills.
Gas is lost from this zone due to gas-well defects and other loss factors, so this factor may appear to have less than 100% gas collection efficiency even when the site is closed.
Other Loss Factors Overtipped areas
100
Where over-tipping is likely to be affecting gas collection.
Stockpiled areas
100
Where stockpiles may affect gas recovery.
Cell flanks without gas control
100
Where cell flanks do not have gas control.
Areas subject to hotspots
100
Where hotspots are known to be present and likely to affect gas generation and collection.
Areas subject to high leachate
75
Where high or perched leachate levels can affect gas recovery.
Other
100
Default is to assume 10 % for unknown and future losses.
100
Default is to assume 5% to 10% of all wells on a gas field will fail or not be operational, and a collection efficiency downgrade is attributed to all landfilled areas where a gas field is installed.
Percentage of failed wells in areas with gas control
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
5.1 Parasitic Load Considerations Parasitic loads are simply loads which are used by the engine in its operation (losses from operation of fans, oil pumps and cooling). However, it is common to group parasitic load, auxiliary load and site load into one category loosely termed parasitic load. Auxiliary loads are ancillary electrical loads which reduce the electrical power delivered to the grid by a gas engine. Auxiliary loads are generally used to power other components of the landfill gas recovery operations, and are associated with gas delivery and conditioning. This includes, for example, gas boosters and any gas clean-up plant, for e.g. siloxanes, CFCs and HCFCs, and/or H2S. Site loads are the loads arising from on-site use not strictly defined by parasitic load or auxiliary load, e.g. a leachate treatment plant, or office heating and lighting. For a nominal 1MWe gas engine, a rule of thumb value for the combined parasitic and auxiliary load might be 30kW – 50kW, and for each additional 1MWe installed, another 20kW. It is not best practice to operate site loads from the power generated by the gas engine, as this is using energy which would typically attract a higher revenue if sold than the cost of purchasing grid electricity to operate on-site plant. Many electricity regulators will not allow auxiliary loads to be provided by external grid power, as such auxiliary loads are integral to the production and export of renewable energy. Parasitic and auxiliary loads combined can vary between 3.75 – >10% of the power production from the gas engine, depending on whether the gas engine is containerised or installed in a purpose built engine room, on the climate in which teh gas engine is operating, and theage of the gas engine. A typical average for a European portfolio might be 4.5 – 5%. When a site or portfolio is on the upside of the gas curve, parasitic and auxiliary loads are often ignored by operators, but they can become a significant component of the energy lost prior to export when a site is on the downcurve. 5.2 Maintenance Downtime, Engine Changes and Electrical Efficiency There is often a tradeoff between the duration of planned downtime, maintaining a gas engine in top condition, and the effect of lack of maintenance on power produced, seen as a deterioration in overall engine efficiency. A conservative default for a landfill site if no data are available is a combined value of 7% scheduled and unscheduled maintenance downtime. Some individual sites can exhibit downtimes as low as 3%, and there is no upper limit to this value for a site where the gas engine has put a piston through the engine casing, and there is no replacement available. At a portfolio level, where a constant electrical efficiency is modelled, the deterioration of a single engine is best modelled by an increase in site downtime. Equally, there are benefits to be seen when an old landfill gas engine is replaced, as the electrical efficiency of the new gas engine will show a measurable improvement. The power export curve in Figure 3 below shows such an effect. The capital cost of the replacement gas engine has to be offset against the net benefits of higher electrical efficiency, and better uptime performance. Figure 4 shows the benefit of examining data at high resolution (monthly or less). The downward spikes in the power generation curve reflect periods of maintenance.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
Engine replaced here
Figure 3. The effect of replacement of the landfill gas engine on power production
Optimal Energy Output Curve at 75% Collection Efficiency
Grid constraint period
Periods of scheduled maintenance
Figure 4. A power generation curve showing (1) the effect of a grid constraint, prior to achieving full grid capacity; and (2) downward spikes in energy generation reflecting periods of scheduled maintenance.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
5.3 Grid Connection Constraints and Excess Gas Flared
At some sites, there will be gas flared which is in excess of the typical amount of flaring which occurs on landfill sites with power generation, which is usually less than 10%. This value is typically reflective of the periods of scheduled and unscheduled maintenance/downtime. Where there is excess landfill gas flared, this is usually for one of two reasons. Either there is a grid connection constraint, or there is an odour management issue which requires additional effort to collect the gas, causing lower gas quality which may not be suitable for combustion in a gas engine. Where the gas quality is poor, this will be an operational decision to either flare or blend the gas with higher quality gas from a different part of the site, for example. Where there is a grid connection constraint, the cost of a new grid connection has to be assessed against the additional revenue which could be realised from power export. In many cases, while there is some potential energy which could be recovered, the cost of grid connection may be too high, and the benefit of additional gas utilisation is not cost-effective. Figure 4 above shows a site which was grid constrained at the start of the period of utilisation, but which had this constraint eased over the first year of operation. 5.4 Load Factor and Availability Load factor has often been used historically as a measure of the operating performance of the portfolio, and is defined as ‘the actual power exported to the grid as a proportion of the maximum electricity generation potential based on size of engine installed at a generation Site’. It has some merit for the vendor of an increasing landfill gas resource, as the load factor will be a high number, although you can have a high load factor and also spill a lot of excess gas to flare. The load factor includes planned and unplanned maintenance/downtime, auxiliary loads, parasitic losses and all other planned or unplanned losses e.g. partial disconnection of the gas field for capping, and from that perspective is a useful parameter. However, when the landfill gas resource is declining, and gas engines are derated, the load factor loses any of its limited value. Availability is defined as the ratio of time an engine is available to run compared to total time. Availability is often used by a vendor to demonstrate the efficiency of the gas utilisation plant, but this parameter is almost as useful as saying there is a landfill gas engine installed on site. It is possibly a surrogate for reporting values of downtime, but neither parameter, on its own, is of any value in defining the long-term value of a declining landfill gas resource.
6. UNDERSTANDING PORTFOLIO SITES NOT MODELLED IN DETAIL It has become routine for financiers or developers to focus on the top 80% of the energy content of any portfolio, and this is where the greatest amount of due diligence is likely to be applied, whether for purposes of valuation, acquisition or divestiture. By adopting the approach set out above, and investigating all three sets of parameters, it is easy to see how the uncertainty in the last 20% of the energy content of a portfolio can be more significant than simple extrapolation would suggest. Landfills closed for waste acceptance now make up a significant proportion of LFG to energy portfolios, and the uncertainty ranges in the second and third tier parameters can have a disproportionate significance compared to only ten years ago, when most LFG to energy
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
portfolios had a preponderance of operational sites and the gas resource was always on the upcurve. This means that the potential impact on forecasts including the last 20% of the portfolio power generation value (the ‘balance’ sites) will be greater than previously thought. Historically in the UK, little modelling effort has been applied to the smaller sites that deliver the last 20% of the value of LFG portfolios. Sites that produce low volumes of gas, and which are at the end of their gassing life are supposedly difficult to forecast with any degree of accuracy. For example, some sites will continue to produce recoverable LFG at low gas production rates for many years, and such sites may not necessarily follow the same exponential decay curve in their latter years compared with the gas production rates observed at the start of the post-closure period of gas production. Historically, assessments might consider the difference between the top 80% of the energy produced and the total portfolio using a simple ratio, but this is likely to include an element of upside, as the gas resources in every site of the final 20% of the portfolio will most likely be declining, year on year, and some may be closing, while in the top 80%, there will be sites still operational and recieving waste. A worse case scenario will be achieved by curve-fitting the past five years performance of the balance sites. Curve fitting nearly always produces a worst case scenario as it is difficult to constrain the exponential curve. While the simple ratio approach will lead to an upside assessment, and the extrapolation approach will lead to an extreme downside assessment, these two approaches represent the two extremes of possibility. It is more realistic to return to first principles and model the exported power curve from each of the balance sites in the portfolio, using our knowledge of the half-lives which apply to the bulk gas curves of the sites which have had detailed assessment, to better quantify the shape of the forecast curve. In addition to this, exported power should be limited to the turndown ratio of the smallest gas engine in the fleet. This is commonly a Jenbacher 208, a nominal 330 kWe gas engine, which would yield 130 kWe at maximum turndown ratio. For small portfolios, e.g. less than 20 to 30 sites in all, it is recommended that every site is modelled in the same detail as the first, as that will eliminate any bias in the balance sites.
7. PRESENTING COLLECTION EFFICIENCY INFORMATION TO STAKEHOLDERS It has been said many times in the past that if you can model the bulk gas generation rate, why cannot you recover it and reap those energy benefits? As scientists, we understand why this is the case. But we are challenged when we put graphics together to demonstrate what landfill gas we are collecting, and what we are not, because we know that you cannot collect all the landfill gas generated, and sometimes, if there is a significant headroom between the bulk gas generation curve and the recoverable gas resource, it is difficult to explain why the difference cannot be exploited. An alternative upper bound is needed to demonstrate that there are opportunities to improve gas collection efficiency, but the benefits do not go as far as 100% of all the gas which is modelled or generated. Gregory et al (2014) sought information from the major UK operators and showed that a subset of 43 of the most modern UK landfills achieve what are believed to be very high gas collection efficiencies, giving in the region of 68% lifetime LFG collection efficiencies, compared to a lifetime average collection efficiency of 52% for all currently operational landfills in the UK. This observation is far from the regulators’ published target of 85% collection efficiency. Experience from due diligence on a significant proportion of the UK’s landfill gas resource is that the 85% collection efficiency value is only ever likely to be achieved post-closure on the best engineered landfills. If an operator is only ever likely to achieve an average of just over 50% lifetime collection
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
efficiency, we consider that a reasonable stretch target would be 75% collection efficiency in any given year. This is enough to focus on the actions necessary when managing a portfolio, and allows sites to be compared with each other and prioritized. This target has been defined here as the Optimal Energy Output curve. An illustration of this is shown in Figure 4. For some portfolio sites, landfill gas recovery will be above this Optimal Energy Output curve when modelled, but this is a suitable target for a portfolio, allowing the assessor to focus on the most challenging sites for gas collection.
8. CONCLUSIONS In summary, LFG recovery has become a well-established technique, using off the shelf technology. Waste compositions have changed due to Government policy and the BMW content has decreased. It is the approach of the LFG utilisation companies to maintenance regimes, maintaining high LFG collection efficiency, and tracking the gas curve with, for example, sacrificial systems in the early stages of landfilling which differentiates a good LFG to energy scheme from a great one. As gassing landfills age, the uncertainty ranges in the parameters which affect gas recovery can also have a disproportionate significance on the anticipated performance of a LFG utilisation scheme or portfolio, meaning more effort needs to be made in understanding the collection efficiency in ageing landfills. As all landfills in a portfolio age, and the portfolio gas curve is on a downward trajectory, operators need to refocus on what significant parameters should be measured as part of their operation’s key performance indicators (KPIs). It is common for the recoverable gas resource curve to follow a predictable downward trajectory, and at this point in a landfill’s operational life, the efficiency of engine maintenance operations becomes much more significant than when there was excess gas available during the filling phases, and before BMW waste diversion targets had taken effect. Bulk gas generation should continue to be modelled using established tools, as these are the tools which have gained acceptance globally: GasSim, the IPCC model, and LandGEM, for example. However, modelling operational gas loss factors is best undertaken in a spreadsheet environment to achieve the resolution required from todays investors. As landfills age, the factors which impact on subsequent energy generation, such as downtime, parasitic load, and grid constraints can become more significant, and these parameters need to be revisited to ensure best practice in energy recovery is achieved. When modelling large portfolios, the impact of the small, low production sites not modelled in detail (the 20% considered to be the balance sites, not meriting investigation) might become more significant with time, and these sites should ideally be modelled with the same level of critique as the largest sites in the portfolio. Finally, while high performing modern landfills might achieve in the region of 68% lifetime collection efficiencies, the average in the UK is closer to 52%. This observation is far from the regulators’ published target of 85% collection efficiency. Experience from due diligence on a significant proportion of the UK’s landfill gas resource is that the 85% collection efficiency value is only ever likely to be achieved post-closure on the best engineered landfills. A reasonable stretch target could be a 75% collection efficiency. This is enough to focus on the actions necessary when managing a portfolio, and allows sites to be compared with each other and prioritized. This target has been defined here as the Optimal Energy Output curve. For some portfolio sites, landfill gas recovery will be above this Optimal Energy Output curve when modelled, but this is a suitable target for a portfolio, allowing the assessor to focus on the most challenging sites for gas collection.
Sardinia 2017 / Sixteenth International Waste Management and Landfill Symposium / 2 - 6 October 2017
REFERENCES Environment Agency (2004). Guidance on the Management of Landfill Gas. Report LFTGN03. Environment Agency and Golder Associates (2006). GasSim2 User Manual. Available from www.gassim.co.uk. Gregory R G and Browell, D W. (2011). Recent Developments in Understanding of Waste Degradation Rates and Modelling Landfill Gas Generation and Recovery. Proceedings Sardinia 2011, Thirteenth International Waste Management and Landfill Symposium. S. Margherita di Pula, Cagliari, Italy; 3 - 7 October 2011. Gregory R G, Stalleicken J, Lane R, Arnold S and Hall D H (2014). Review of Landfill Methane Emissions Modelling. Report to UK Department of the Environment, Food and Rural Affairs under contract WR1908. IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
