What is the Purpose of this Package?ο
This package was created by researchers from the WE3 Lab at Stanford University to assist in their energy-water nexus research. Our goal is to address the complexity of computing the electricity bill of an industrial consumer. We originally developed these methods while evaluating the cost-minimizing energy management strategies for water systems, such as wastewater treatment plants [Musabendesu π½. πΆππππ. ππππ. (2021); Bolorinos πΈππ£ππππ. πππ. πππβπππ. (2023); Chapin πΈππ£ππππ. πππ. πππβπππ. (2025)], desalination facilities [Rao π΄πΆπ ππ’π π‘πππ. πΆβππ. πΈππ. (2024)], and water distribution [Rao πππ‘. πππ‘ππ (2024)].
Unlike wholesale market pricing mechanisms, such as locational marginal prices (LMPs), which are straightforward to calculate directly as a timeseries in units of β$/MWhβ, retail electricity prices are broken down into a set of discrete charges. In particular, the demand charge is the maximum consumption during a specified time period, so computing a demand charge is a nonlinear operation (What Makes Electricity Tariffs so Complex?).
In addition to the mathematical complexity behind computing the electricity bill and optimizing bill savings, the wide variation in tariff structure is a challenge. Some regions offer flat prices, whereas others have up to four different time-of-use (TOU) pricing periods (i.e., βsuper-off-peakβ, βoff-peakβ, βmid-peakβ, and βon-peakβ periods). We designed a machine-readable format for encoding all relevant tariff data, including TOU periods, tiered charges, daily demand charges, and unit conversions [Chapin πππ‘. πππ. π·ππ‘π (2024)].
Our research focuses on investigating the benefits of flexible operation of water systems, but we hope that this package will be useful to the wider community for seamlessly optimizing industrial consumersβ electricity-related costs and emissions!