An energy demand-side management and net metering decision framework

Abstract

Demand side management (DSM) and net metering (NM) are two important strategies that can be used in smart energy management systems to help utilities reduce peak loads and increase the penetration of renewable energy, while also enabling customers to save energy and reduce energy bills. In order to effectively implement DSM and NM programs, it is important to identify the customers who are most likely to benefit from these programs and to quantify the economic impact and benefit for both utilities and end-users. This paper proposes a decision framework that utilizes smart meter data and clustering analysis to identify representative load patterns, and a five-stage method for segmenting load patterns to create a consumption intensity matrix. A fuzzy ensemble based multi-criteria decision making framework is then applied to identify the most potential customer groups and quantify the economic benefit and impact. This paper presents a novel approach for identifying potential customers for DSM and NM programs and developing personalized services for energy demand management. The proposed framework is evaluated through a case study, which demonstrates its robustness in decision making and identifies 13 typical load patterns, seven for weekdays and six for weekends, representing diverse consumption behaviors. The weekday load patterns perform better than the weekend load patterns in terms of the five-stage method.