This post is a part of BioBiz’s Bio-CNG Perspectives.
BioBiz, a division of EAI, is a leading market intelligence & strategic consulting firm for the Indian bio-based sectors.
This blog post uses the terms bio-CNG and renewable natural gas (RNG) interchangeably.
Bio-CNG or bio-compressed natural gas, also known as sustainable natural gas or biomethane, is a biogas which has been upgraded to a quality similar to fossil natural gas and having a methane concentration of 90% or greater. As the gas is derived from natural and renewable sources, it is also termed renewable natural gas (RNG).
Economics plays a major role in determining viability of a project. For RNG, economics is critical as it is a new segment. Of the various segments which contribute to the economics of a project such as capital cost, operational cost, feedstock, logistics cost and more, logistics cost plays a major role as, for a RNG project, logistics involves transport of waste feedstock to the processing plant as well as supply of bio-CNG cylinders. Economics of logistics depends on several factors – type of vehicles used, distance travelled, transport framework and more. Thus for entrepreneurs who are looking for owned or outsourced logistics, it is critical to consider all these parameters and undertake a detailed study before deciding the business model for the project.
This blog post provides details on the economics of transport for a RNG project and the optimal business model for an early mover.
Economics of transport for a RNG project
Estimating cost of transport for RNG projects needs to consider the following:
- The type of transport framework – will it be direct transport from the source of waste to RNG refinery or will it be a hub-and-spoke model with two stages of transport?
- Types of business model used for asset ownership – will the transport vehicles be owned and operated by the RNG project owner, or will transport be outsourced to a 3rd party logistics company?
Within the above two aspects, we also need to consider other aspects:
- Types of vehicles used for transport
- Total distance covered by the transport
1. Type of transport framework
Transport of waste feedstock to the RNG facility could have one of the two frameworks:
- Direct transport: In this scenario, we consider waste to be picked up from the sources and directly transported to the RNG plant without any intermediate transfer point. Typically, this model works where the number of sources from where waste needs to be collected is few
- Hub and spoke model: In cases where there are many sources from where waste feedstock is collected (say, from dozens of restaurants), it might be more optimal to have a hub where waste from many sources is aggregated, and a larger vehicle transports it from the hub to the RNG facility.
2. Type of business model
- Fully owned – In this scenario, all the transport vehicles are owned and operated by the owner of the RNG plant. This is an asset-heavy model, but it has its own advantages as it gives a greater control over operations and economics
- Outsourced transport – In this scenario, the entire transport operations are outsourced to a third party logistics (3PL) provider and the RNG plant owner needs to pay only based on the amount of transport used. Such a payment could take different forms, but in many cases will be in the form of fee per ton per km travelled.
3. Types of vehicles used for transport
Depending on the amount of waste to be carried, the types of vehicles used can vary, all the way from a Tata Ace or an even smaller goods carrier to a large truck that can carry 40 tons. Typically, biogas facilities located not far from the city and using less than 10 tons per day of feedstock will be predominantly using small goods carriers (up to 5 tons load capacity). Biogas and RNG facilities using upwards of 50 tons a day could use trucks that can carry 10 tons or more.
4. Total distance covered by transport
The total distance covered by the transport for carrying the feedstock is a critical aspect that will determine the overall economics for the RNG business. While it is preferable to locate RNG production facilities as close as possible to the source of feedstock, very high land costs near cities or industrial zones will necessitate these facilities to be in locations that are quite far from the source of feedstock. The overall economics of the project is thus affected by the location of the RNG facility in two ways:
A lower land cost implies a lower upfront cost, and that is good for economics. However, a lower land cost most times implies much larger distances for the feedstock to be transported and thus a higher transport cost. The location of the project, and hence the average distance travelled by the feedstock, hence needs to take both these aspects into account.
With the above dimensions taken into account, an economic model is developed to determine total transport costs for the RNG facility. Analysis is done for both operational models – assets fully owned and transport fully outsourced.
The following are the results and highlights.
Economics of logistics
With the above inputs, we try to arrive at answers to the following questions, for specific capacities:
- What is the optimal distance at which the plant should be located?
- Which of the two logistics models – outsourced or own – is optimal?
Background and assumptions
Capacity of the plant (tons per day of feedstock): 5 TPD to 250 TPD
Average distance of the plant from source of waste (km): 10 kms to 200 kms (for shorter average distances, it will be a direct transport; for distances longer than 50 kms, it could be a hub and spoke model of transport)
Types of vehicles used (be it outsourced or asset ownership): vehicles of load capacity ranging from 1 ton to 40 tons (1, 3, 5, 8, 10, 20 & 40 tons capacity). Smaller vehicles are used for short distances or from source to hub; large vehicles (10 tons and above) are usually for long distances. We also assume that these vehicles will be used in such a way that their capacity utilization is maximized.
|Capacity of plant (tons per day)||Average distance transported for feedstock (km)||Direct or hub & spoke?||Average logistics cost per ton of waste||Average logistics cost per ton of RNG|
|Asset owned||Outsourced||Asset owned||Outsourced|
|25||50||Hub & Spoke||0.44||0.52||11.1||12.9|
|50||50||Hub & Spoke||0.33||0.42||8.4||10.4|
|100||50||Hub & Spoke||0.31||0.37||7.7||9.1|
|100||100||Hub & Spoke||0.5||0.57||12.5||14.25|
|250||50||Hub & Spoke||0.14||0.23||3.5||5.9|
|250||100||Hub & Spoke||0.22||0.44||5.5||11|
|250||200||Hub & Spoke||0.4||0.7||10.5||17.6|
Table 1: Economics of logistics
The chart below shows how the average logistics cost varies (in Rs/ton of RNG produced) for varying combinations of capacities and transport distance for the feedstock.
For any given plant location, as expected, the average unit logistics cost decreases with increase in the capacity of plant
Inversely, for any plant capacity, once again as expected, the average unit logistics cost increases with increase in the distance the waste is transported
However, the rate of above increases and decreases differs when one goes from one capacity to the next highest (for a specified plant location/distance), or when one goes from one distance to another for transport (for a specified capacity).
For any given capacity, locating the plant at a significant distance from source could increase the cost of transport, but it could also result in significant decrease in the cost of land. The optimal location thus will be the one that is at a reasonable distance as to have low land costs while at the same time ensuring that the cost of transport is not too high. Based on the chart and table, the following are the optimal distances, for various capacities of the plant:
- 10 TPD – 10-15 kms
- 25 TPD – 15-25 kms
- 50 TPD – 30-40 kms
- 100 TPD – 50-75 kms
- 250 TPD – 75-100 kms
Which of the two operational models – asset ownership or outsourced – is more economical?
Interestingly, for all the combinations, the economics seem to favor ownership of logistics assets and operations. The economic advantages of the “ownership” model provide cost reductions ranging from 10% to almost 70% in some cases. All these however assume that the project owners operate the vehicles in an efficient manner with high capacity utilizations.
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