Optimization of investments in natural gas distribution networks

This paper presents an optimization model and solution procedure for planning investments in gas distribution networks for residential customers. The situation can be considered a capital budgeting problem under uncertainty. There is uncertainty about whether a potential customer will convert to gas service if a distribution main is built, the revenue generated if the household does convert, and the cost of constructing the main. A fixed annual budget is allocated to a set of discrete, competing projects over time. The allocation is done by maximizing the expected net present value (NPV) given the decision-maker's risk preferences. The probability distribution of the NPV for each competing project is created from two statistical models. A binary probit model is used to estimate the probability of conversion for a potential customer. A random effects regression model is used to estimate the revenue generated should a particular potential customer switch to gas. A rollout value greedy heuristic was devised to solve the resulting optimization formulation. Two case studies based on data from a large gas company illustrate the analysis.