Customer targeting optimization system for price-based demand response programs

Abstract

Summary In a demand‐side management (DSM) program, a set of activities are designed to influence electricity consumption in a way that may produce desired changes in the utility's load shape. Detailed analysis when planning DSM programs is crucial because the utility intends to achieve technical and economic benefits but does not wish to lose revenue unnecessarily. To assist utilities in planning for price‐based demand response (DR) programs (an approach to DSM), this paper proposes an optimization system focused on the targeting of residential customers to enroll in a new time‐of‐use tariff. The developed system uses optimal power flow to estimate the reduction needed to achieve the proposed objectives of the DR program in each bus, followed by the selection of the customers that will provide this reduction via binary particle swarm optimization. The optimization system has been tested using real data acquired from a Brazilian utility including customers' load curves, radial distribution feeder data, a time‐of‐use tariff, and estimated elasticity matrices from the literature. The main results show that the system can be of great value supporting power utilities' implementation of price‐based DR programs.

Optimal time period clustering of time‐of‐use schemes based on elastic loads' responsiveness

“…The elasticities are also obtained from Aalami et al 25 as an aggregate value for all customers forming the utility's load pattern. Figure 7 shows the obtained clusters for summer and winter seasons, which indicates the values of cluster type in the proposed model (4). As it is shown, the only selected peak periods are hours 21 to 24 in summer scenario while, the peak periods for winter scenario are hours 16 to 22.…”

“…Demand response (DR) is a critical effective measure that can interact as a key role to foster a better efficiency and operation in electrical energy systems . DR programs are generally divided into two main categories, including price‐based DR (PBDR) and incentive‐based DR (IBDR) schemes . PBDR programs are dependent on time by which the electricity price varies in different periods of day (ie, time‐of‐use [TOU], real‐time pricing [RTP], and critical peak pricing [CPP]), while in IBDR programs, the enrolled customers are subject to incentives in exchange to load curtailments .…”

“…1,2 DR programs are generally divided into two main categories, including price-based DR (PBDR) and incentive-based DR (IBDR) schemes. 3,4 PBDR programs are dependent on time by which the electricity price varies in different periods of day (ie, time-of-use [TOU], real-time pricing [RTP], and critical peak pricing [CPP]), while in IBDR programs, the enrolled customers are subject to incentives in exchange to load curtailments. 5,6 Among different PBDR schemes, the TOU tariff rate has been widely implemented for different types of customers (ie, residential, commercial, and industrial).…”

An Agent-Based Approach for the Planning of Distribution Grids as a Socio-Technical System

“…The case study presented in this paper uses feeder data adapted from a real feeder of a Brazilian distribution company, as previously described in References [29] and [30]. While feeder data were processed to obtain a suitable scenario for the system tests, they represented conditions consistent with Brazilian distributions grids: Several customers connected to the same transformer.…”

A Novel Approach for Residential Neighborhoods’ Electricity Demand in Iraq Distribution Power Grids

“…Electrical design engineers of Iraq DPGs aim to meet two objectives [2][3][4]: studying the current residential dwelling load profile characteristics [5][6][7][8][9][10][11] and model the electricity kW demand [12][13][14][15][16][17][18]. The common factor among residential dwellings, called the diversity factor ( ), is the backbone of electricity demand modeling.…”