A smart meter data-driven distribution utility rate model for networks with prosumers. (June 2021)

• Record Type: Journal Article
• Authors: Venkatraman, Athindra; Thatte, Anupam A.; Xie, Le
• Is Part Of: Utilities policy. Volume 70(2021)
• Publication Date: 2021-06
• Subjects: Rate design -- Distributed energy resources -- Solar PV -- Behind-the-meter storage -- Utility cost recovery; Public utilities -- United States -- Periodicals; Public utilities -- Government policy -- United States -- Periodicals; 363.6
• DOI: 10.1016/j.jup.2021.101212
• Languages: English
• ISSNs: 0957-1787

Abstract

Abstract: Distribution grids across the world are undergoing profound changes due to advances in energy technologies. Electrification of the transportation sector and the integration of Distributed Energy Resources (DERs), such as photo-voltaic panels and energy storage devices, have gained substantial momentum, especially at the grid edge. Transformation in the technological aspects of the grid could directly conflict with existing distribution utility retail tariff structures. We propose a smart meter data-driven rate model to recover distribution network-related charges, where the implementation of these grid-edge technologies is aligned with the interest of the various stakeholders in the electricity ecosystem. The model envisions a shift from charging end-users based on their KWh volumetric consumption, towards charging them a grid access fee that approximates the impact of end-users' time-varying demand on their local distribution network. The proposed rate incorporates two cost metrics affecting distribution utilities (DUs), namely magnitude and variability of customer demand. The proposed rate can be applied to prosumers and conventional consumers without DERs. Highlights: Cost-causation based Distribution Utility Rate Model for networks with prosumers. Shift from volumetric ($ per kWh) price to access fee based on customer grid impact. Suitable for utilities with increasing Distributed Energy Resources at Grid-Edge. Aligns with Utility Revenue Stability and Cost-Causation. Ex-Post calculation of residential customer bills with real smart meter data.