A Stochastic Optimization Framework for Realizing Combined Value Streams From Customer-Side Resources

Abstract

Due to numerous supporting policies aimed at decarbonizing electricity infrastructures in different regions of the world, customer-side resources are becoming increasingly valuable. Consequently, load serving entities (LSEs) which typically have access to these customer-side resources can use them for multiple services simultaneously. In this paper, we discuss a stochastic optimization framework for using clusters of residential HVACs, electric water heaters (EWH) and behind-the-meter (BTM) batteries, spread around the LSE’s distribution network, for energy arbitrage, peak shaving and market based frequency regulation simultaneously. Our framework captures the effects of controlling the consumption of the customer-side resources on the voltages in the LSE’s distribution network. We also discuss two real-time dispatch algorithms capable of eliciting fast response from the resources to derive regulation signals from the market operator with minimal voltage violations. We evaluate the optimization models and dispatch algorithms using a HELICS-based co-simulation platform and real-world data from New York Independent System Operator (NYISO).