Régulation et Environnement

Du 13/06/2022 de 12:00 à 13:15

Salle R2.01, Campus Jourdan, 75014 Paris

WOLAK Frank (Stanford University)

Nonlinear Price Schedules for Urban Water Utilities to Balance Revenue and Conservation Goals

This paper formulates and estimates a household-level, billing-cycle water demand model under increasing block prices that accounts for the impact of monthly weather variation, the amount of vegetation on the household’s property, and customer-level heterogeneity in demand due to household demographics. The model utilizes US Census data on the distribution of household demographics in the utility’s service territory to recover the impact of these factors on water demand. An index of the amount of vegetation on the household’s property is obtained from NASA satellite data. The household-level demand models are used to compute the distribution of utility-level water demand and revenues for any possible price schedule. It can be used to design nonlinear pricing plans that achieve competing revenue or water conservation goals, which is crucial for water utilities to manage increasingly uncertain water availability yet still remain financially viable. Knowledge of how these demands differ across customers based on observable household characteristics can allow the utility to reduce the utility-wide revenue or sales risk it faces for any pricing plan. Knowledge of how the structure of demand varies across customers can be used to design personalized (based on observable household demographic characteristics) increasing block price schedules to further reduce the risk the utility faces on a system-wide basis. For the utilities considered, knowledge of the customer-level demographics that predict demand differences across households reduces the uncertainty in the utility’s system-wide revenues from 22 to 84 percent. Further reductions in the uncertainty in the utility’s system-wide revenues, in the range of 10 to 73 percent, are possible by re-designing the utility’s nonlinear price schedules to minimize the revenue risk it faces given the distribution of household-level demand in its service territory.