Feedlot Energy Efficiency and Cost Reduction

Summary

A long list of energy efficiency technologies suited to Australian feedlots was generated based upon previous works, published data, site visits, discussions, and published MLA projects.

The following opportunities to reduce energy costs at Australian feedlots were analysed:

• Low power factors (PF): Low PF results in either a very high kVA charge being paid or oversized generator rating and fuel consumption for a given kW load.Over-spec voltage: Feedlots located in rural areas near or at the fringe of the distribution grid are likely receiving three phase power at more than 415 V, as grid operators tend to step power higher to ensure supply over long lines. Over-spec voltage reduces power use efficiency and reduces motor lifespan.
• Low levels of automation and reliance on manual operation: Tends to lead to very large load spikes from manually starting all motors at once (also see "Poor motor starter choice" below) and difficulty in implementing energy management practices such as load shedding and demand management.
• Motor type options: Direct-on-line (DOL) motor starters draw up to ten times the normal running current during starting, spiking the site kVA. The peak kVA observed in any instant in a month is charged for that entire month, so feedlots should aim to keep the peak as low as possible. This can be slightly mitigated by star, delta, or star-delta starters, however more efficient options exist.
• High thermal fuel costs: Natural gas, liquid petroleum gas (LPG) and LNG (liquid natural gas) are not the lowest cost fuels available in Australia. The lowest cost fuels and heat sources were found to be heat recovery, solar thermal (for raising up to 80 to 95 oC hot water) and biomass. Lagging and use of higher temperature wetting water in conjunction with higher quality steam provide efficiency gains.

The above issues make a significant contribution in the estimated 4.1 petajoules of electrical energy Australian feedlots consume per annum (or 1,139,000 MWh), which in Q1 2019 had an estimated cost of $317 million.