A stochastic optimisation framework for analysing economic returns and risk distribution in the LNG business

Abstract

Purpose -The liquefied natural gas (LNG) business comprises a number of economic activities with inherent risks. The purpose of this paper is to propose an integrated modelling approach, as part of the investment decision-making process, for optimising economic returns from LNG whilst taking into account uncertainty in various key input parameters. Design/methodology/approach -Inter-linked cash flow and pricing models of the LNG chain were constructed. Net present value was maximised based on selection of netback pricing variables and level of investment shareholding. Constraints were placed on the minimum acceptable returns. The risk affinity of the decision maker was captured in the form of a chance-constrained optimisation problem. A genetic algorithm was applied for numerical optimisation, in combination with Monte Carlo simulations to account for the stochastic nature of the problem. Findings -Based on the results of a case study, the deterministic solution, having no consideration to uncertainty, was found to be both sub-optimal and provided an unsatisfactory risk outcome. The stochastic approach yielded an optimal solution with due consideration to risk. Various scenarios show that the choice of the decision variables significantly impacts the trade-off between risk and returns along the LNG chain to government and investor.Research limitations/implications -The suitability of the methodology to the operational phase of the LNG business which incorporates different elements of risk, such as market dynamics and logistics, is as yet untested. Originality/value -This framework may be useful in the formulation of optimal commercial structure of firms, investment portfolio and gas/LNG pricing arrangements for host governments involved in the LNG business.

Supply chain strategies as drivers of financial performance in liquefied natural gas networks

“…In addition, investment is a dominant higher-order theme in the final empirical template because of its influence on other higher-order SCSs, including IT and automation, sourcing, capacity development and sustainability. Investment decisions in LNG networks involve a substantial trade-off between risk and returns by investors (Furlonge, 2011). LNG project feasibility, including market conditions, long-term supply contracts and access to feedstock is a determinant for investment into LNG networks (Nikhalat-Jahromi et al , 2017).…”

“…Investment decisions in LNG networks involve a substantial trade-off between risk and returns by investors (Furlonge, 2011). LNG project feasibility, including market conditions, long-term supply contracts and access to feedstock is a determinant for investment into LNG networks (Nikhalat-Jahromi, et al, 2017).…”

Simulating a Commercial Power Aggregator at Scale Design and Lessons Learned

“…Although simulation approaches using stochastic optimization with constrained chance (Li, 2015) provides a potential solution to managing risk, the bucket approach presented below will allow us to represent risk as in a way that is familiar to many risk professionals in trading companies and banks. In addition, run times for stochastic optimization algorithms can be of the order of 30 minutes to just over one hour (Furlonge, 2011) and may prove to be impractical in the context of real time electricity market clearing.…”