Rise in energy prices and energy security concerns have become more crucial for the industrial sector to opt for an alternative fuel. Biogas could be used as fuel for electricity generation, boilers or upgraded and compressed as bio compressed natural gas (BioCNG) that can be utilised in various applications, such as transportation, fuel for industry and injected into a gas grid (as biomethane). Biogas, produced by anaerobic digestion (AD) of palm oil mill effluent (POME) can be used as a fuel substitute for natural gas in industry while reducing CO2 emission. Virtual distribution of BioCNG, which is from remotely located natural gas pipeline, has become economically attractive to the industry. However, this concept poses some issues concerning logistic due to the scattered spatial distribution of palm oil mills which discharge large quantities of POME for biogas recovery. Addressing these aspect require an integrated spatial planning and optimisation to synthesise location and allocation network of BioCNG virtual transportation to the respective industry. This paper presents the development of integrated spatial planning and optimisation of BioCNG supply and distribution network through virtual pipelines to meet the on-site energy demand of specific industry, while minimising the total operational cost. The data from network analysis of ArcGIS will be coded into the Generalised Algebraic Modelling System (GAMS) model to generate a supply cost curve for multiple sources of energy carrier, i.e., LNG, NG through pipeline network, BioCNG through virtual pipelines with and without upgrading. Two case studies were evaluated to determine the optimal energy supply allocation for demand location. This model is beneficial for BioCNG network management while reducing carbon emissions. Results showed the optimised combination of energy supply to meet energy demand of selected industries, which was 198.9 MW of BioCNG production and 401.1 MW of LNG import. The percentage cost reduction of the optimised energy supply is 36.3%. (C) 2019 Elsevier Ltd. All rights reserved.