Cited by 1,229Smart Grid Energy ManagementElectric Power System OptimizationSmart Grid and Power Systems

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Demand Side Management in Smart Grid Using Heuristic Optimization

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Logenthiran, Thillainathan, Srinivasan, Dipti, Shun, Tan Zong·2012·IEEE Transactions on Smart Grid

DOI 10.1109/tsg.2012.2195686

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Abstract

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

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Handbook of Evolutionary Computation
(1997) · Institute of Physics eBooks · doi:10.1887/0750308958 · Cited by 2,999
Market Operations in Electric Power Systems
Shahidehpour, M., Yamin, H. Y., & Li, Z. (2002) · Wiley eBooks · doi:10.1002/047122412x · Cited by 1,617
Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system
Logenthiran, T., Srinivasan, D., & Khambadkone, A. M. (2010) · Electric Power Systems Research · doi:10.1016/j.epsr.2010.07.019 · Cited by 324
Self-citation
Demand-Side Management: Concepts and Methods
Gellings, C. W., & Chamberlin, J. H. (1988) · Medical Entomology and Zoology · Cited by 277
Modern Power System Analysis
Ten, C., & Hou, Y. (2024) · CRC Press eBooks · doi:10.1201/9781003129769 · Cited by 224
Direct load control-A profit-based load management using linear programming
Ng, K.-H., & Sheblé, G. B. (1998) · IEEE Transactions on Power Systems · doi:10.1109/59.667401 · Cited by 221
Dispatch of direct load control using dynamic programming
Hsu, Y., & Su, C.-C. (1991) · IEEE Transactions on Power Systems · doi:10.1109/59.119246 · Cited by 201
An optimization method for load management scheduling
Cohen, A. I., & wang, C. (1988) · IEEE Transactions on Power Systems · doi:10.1109/59.192913 · Cited by 165
A linear programming model for reducing system peak through customer load control programs
Kurucz, C. N., Brandt, D., & Sim, S. R. (1996) · IEEE Transactions on Power Systems · doi:10.1109/59.544648 · Cited by 145
Short term generation scheduling of a Microgrid
Logenthiran, T., & Srinivasan, D. (2009) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon.2009.5396184 · Cited by 136
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10.1109/59.667401 · DOI
agrawal, 2006, Overview of DOE microgrid activities, Proc Symp Microgrid

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Authors: Logenthiran, T., Srinivasan, D., & Shun, T. Z.
Affiliations: National University of Singapore (SG)
ORCID: Logenthiran, Thillainathan
Srinivasan, Dipti
Venue: IEEE Transactions on Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2012-09
Volume: 3
Issue: 3
Pages: 1244-1252
ISSN: 1949-3053 (ISSN-L)
1949-3061
DOI: 10.1109/tsg.2012.2195686
OpenAlex: W2055134499
Cited by: 1,229
References: 21
Citations by year: 2026: 35 · 2025: 40 · 2024: 49 · 2023: 75 · 2022: 103 · 2021: 110 · 2020: 106 · 2019: 129 · 2018: 146 · 2017: 142 · 2016: 107 · 2015: 89 · 2014: 59 · 2013: 35 · 2012: 4
FWCI: 38.24
Type: journal article
Language: en
Open access: green
doi.org
Locations: IEEE Transactions on Smart Grid (publishedVersion)
National University of Singapore (submittedVersion)
Newcastle University ePrints (Newcastle Univesity)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
Keywords: demand-side management, smart grid, load shifting, day-ahead scheduling, peak shaving, flexible loads, evolutionary algorithms, heuristic optimization, energy cost reduction

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Demand Side Management in Smart Grid Using Heuristic Optimization

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