Managing time-substitutable electricity usage using dynamic controls

摘要

A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

主权项

1. A method for regulating and modifying the intra-day electrical load, the method comprising the steps of:
monitoring electrical load demand in an electrical power distribution system and controlling the distribution of electrical power from an electrical power generator to a plurality of customers through an electrical grid; dividing a day into a plurality of time periods; expressing an electrical load during each one of the plurality of time periods as a function of a customer utility valuation of electricity consumption during the plurality of time periods and a time-substitutability of customer demand among the plurality of time periods, the customer utility valuation comprising a function of at least one incentive signal capable of being adjusted to affect a modification in electricity consumption by customers during each one of the plurality of time periods; and calculating a fraction of total daily electrical usage for each time period by dividing the electrical load for that time period by a sum of all electrical loads for all time periods and by shifting customer demand and electrical load among the plurality of time periods.