2019Unpublished venueRequires access

Comparison of Peak Demand Shaving Potential of Demand Response and Distributed Energy Storage in Residential Buildings

Akintonde Abbas, Sumedh Halbe, Badrul Chowdhury

Open publisher page 12 citations

Abstract

Due to a growing power demand and cyclic energy consumption patterns, thermostatically controlled loads (TCLs) in residential buildings have become major candidates for residential demand response (DR) programs. However, in recent times, there has been an increased interest in the usage of distributed energy storage, such as batteries, for grid support and energy management applications including peak shaving and frequency regulation, which overlaps with typical objectives of TCL-centric residential DR programs. In this paper, we present a comparison between peak shaving using TCLs and distributed battery energy storage. A detailed model of 100 residential homes, developed using GridLAB-D, is used to simulate demand response strategies for the TCLs as well as battery charging and discharging algorithms to achieve peak load reduction while maintaining predefined customer comfort levels. Different customer enrolment scenarios are also considered for more realistic results. An economic analysis of both solutions is also presented. The results obtained show that while TCLs alone may not be able to realistically eliminate the most expensive peaking plants, they can still provide cheaper peak load reductions when compared with distributed battery energy storage units, with the reductions increasing as more customers are enrolled.

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What this paper is about

Due to a growing power demand and cyclic energy consumption patterns, thermostatically controlled loads (TCLs) in residential buildings have become major candidates for residential demand response (DR) programs. However, in recent times, there has been an increased interest in the usage of distributed energy storage, such as batteries, for grid support and energy management applications including peak shaving and frequency regulation, which overlaps with typical objectives of TCL-centric residential DR programs. In this paper, we present a comparison between peak shaving using TCLs and distributed battery energy storage. A detailed model of 100 residential homes, developed using GridLAB-D, is used to simulate demand response strategies for the TCLs as well as battery charging and discharging algorithms to achieve peak load reduction while maintaining predefined customer comfort levels. Different customer enrolment scenarios are also considered for more realistic results. An economic analysis of both solutions is also presented. The results obtained show that while TCLs alone may not be able to realistically eliminate the most expensive peaking plants, they can still provide cheaper peak load reductions when compared with distributed battery energy storage units, with the reductions increasing as more customers are enrolled.

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Available abstract

Due to a growing power demand and cyclic energy consumption patterns, thermostatically controlled loads (TCLs) in residential buildings have become major candidates for residential demand response (DR) programs. However, in recent times, there has been an increased interest in the usage of distributed energy storage, such as batteries, for grid support and energy management applications including peak shaving and frequency regulation, which overlaps with typical objectives of TCL-centric residential DR programs. In this paper, we present a comparison between peak shaving using TCLs and distributed battery energy storage. A detailed model of 100 residential homes, developed using GridLAB-D, is used to simulate demand response strategies for the TCLs as well as battery charging and discharging algorithms to achieve peak load reduction while maintaining predefined customer comfort levels. Different customer enrolment scenarios are also considered for more realistic results. An economic analysis of both solutions is also presented. The results obtained show that while TCLs alone may not be able to realistically eliminate the most expensive peaking plants, they can still provide cheaper peak load reductions when compared with distributed battery energy storage units, with the reductions increasing as more customers are enrolled.

Key concepts: Peaking power plant, Demand response, Peak demand, Load management, Energy storage, Distributed generation, Peak load, Computer science

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