The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Demand Side Management in Smart Grid Using Heuristic Optimization
Open accessAbstract
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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“All these works neglect grid constraints, and therefore there is no strict control of congestion events and voltage issues along the feeders.”
From Congestion mitigation in unbalanced residential networks with OPF-based demand management (2022) · cites this paper as [13]
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Cites
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Research works printed but not linked: 2
- 10.1109/59.667401 · DOI
- agrawal, 2006, Overview of DOE microgrid activities, Proc Symp Microgrid
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A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms
Vardakas, J. S., Zorba, N., & Verikoukis, C. V. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2341586 · Cited by 983
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Gelažanskas, L., & Gamage, K. A. A. (2013) · Sustainable Cities and Society · doi:10.1016/j.scs.2013.11.001 · Cited by 624
Bio-inspired computation: Where we stand and what's next
Ser, J. D., Osaba, E., Molina, D., Yang, X., Salcedo‐Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello, C. A. C., & Herrera, F. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.008 · Cited by 610
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Jordehi, A. R. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.12.054 · Cited by 500
Load forecasting, dynamic pricing and DSM in smart grid: A review
Khan, A. R., Mahmood, A., Safdar, A., Khan, Z. A., & Khan, N. A. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.10.117 · Cited by 468
Metadata
- Authors
- Logenthiran, T., Srinivasan, D., & Shun, T. Z.
- Affiliations
- National University of Singapore (SG)
- Venue
- IEEE Transactions on Smart Grid
- Published
- 2012-09
- Volume
- 3
- Issue
- 3
- Pages
- 1244-1252
- ISSN
- 1949-3053 (ISSN-L)
- 1949-3061
- 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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