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Demand Side Management in Smart Grid Using Heuristic Optimization
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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
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-citationDemand-Side Management: Concepts and Methods
Gellings, C. W., & Chamberlin, J. H. (1988) · Medical Entomology and Zoology · Cited by 277
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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Consumer Preference Electricity Usage Plan for Demand Side Management in the Smart Grid
Longe, O. M., Ouahada, K., Ferreira, H. C., & Rimer, S. (2017) · SAIEE Africa Research Journal · doi:10.23919/saiee.2017.8531930 · Cited by 6
IoT-based energy management in smart homes: A literature review
Bindushree, G. T. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.16.3.1296 · Cited by 5
Shaker, M. A., Diaaeldin, I. M., Attia, M. A., Khamees, A. K., Omar, O. A. M., Alruwaili, M., Elrashidi, A. M., & Hamed, N. M. (2025) · Energies · doi:10.3390/en18164280 · Cited by 5
Supervised Learning based Demand Response Simulator with RTP and PTR in Context of Smart Grid
Sharma, A. K., Saxena, A., Soni, B. P., & Palwaliya, D. K. (2022) · IEEE International Conference on Power Electronics, Smart Grid, and Renewable Energy (PESGRE) · doi:10.1109/pesgre52268.2022.9715741 · Cited by 5
An algorithm for micro-load shedding in generation constrained electricity distribution network
Azasoo, J. Q., Kuada, E., Boateng, K. O., & Agyeman, M. O. (2017) · IEEE PES PowerAfrica · doi:10.1109/powerafrica.2017.7991257 · Cited by 5
Electricity Market Structure with Individual Pricing Mechanism
Mnatsakanyan, A., Almarzooqi, A. H., Bilbao, E., & Banda, P. (2018) · IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG) · doi:10.1109/pedg.2018.8447757 · Cited by 5
Shaving Peaks by Augmenting the Dependency Graph
Barth, L., & Wagner, D. (2019) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3307772.3328298 · Cited by 5
Design of a MATLAB GUI for Day Ahead Forecasting of PV Panel Power
Bektaş, S. Ç., Çakmak, R., & Altaș, İ. H. (2019) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu48272.2019.8946349 · Cited by 5
Priority-Based Energy Sharing and Management Among Prosumers in Smart Grids
Mussadiq, U., Ahmed, S., Gul, N., Kim, J., & Kim, S. M. (2022) · IEEE Access · doi:10.1109/access.2022.3141263 · Cited by 5
Flexibility based Coordination Framework For Three-Level Energy Management System
Hussain, S., El‐Bayeh, C. Z., Menon, R. P., Lai, C., & Eicker, U. (2022) · International Bhurban Conference on Applied Sciences and Technology (IBCAST) · doi:10.1109/ibcast54850.2022.9990113 · Cited by 5
Power Scheduling with Max User Comfort in Smart Home: Performance Analysis and Tradeoffs
Irfan, M., Hassan, C. A. U., Althobiani, F., Ayub, N., Khan, R. J. U. H., Ghandourah, E. I., Almas, M. A., Ghonaim, S. M., Shamji, V. R., & Rahman, S. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.035141 · Cited by 5
Liu, F., Chen, Y., Zhu, J., & Zhai, G. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.105161 · Cited by 5
Yadav, R. K., Bhadoria, V. S., & Hrisheekesha, P. N. (2024) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-234281 · Cited by 5
Heuristic algorithm for coordinating smart houses in MicroGrid
Arikiez, M., Grasso, F., & Zito, M. (2015) · IEEE International Conference on Smart Grid Communications (SmartGridComm) · doi:10.1109/smartgridcomm.2015.7436275 · Cited by 5
Integrated Management of Energy Resources in the Residential Sector Using Evolutionary Computation
Soares, A., Gomes, Á., & Antunes, C. H. (2015) · Advances in environmental engineering and green technologies book series · doi:10.4018/978-1-4666-6631-3.ch013 · Cited by 5
Minimizing the electricity cost of coordinating houses on microgrids
Arikiez, M., Alotaibi, F. M., Rehan, S., & Rohouma, W. M. (2016) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2016.7856284 · Cited by 5
Bugade, V. (2017) · Smart Grid and Renewable Energy · doi:10.4236/sgre.2017.83006 · Cited by 5
A demand response optimization model for home appliances load scheduling
Veras, J. M., Pinheiro, P. R., Silva, I. R. S., & Rabêlo, R. A. L. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8123070 · Cited by 5
Demand-Side Resources for Electric Energy Management
Sisodiya, S., & Kumbhar, G. B. (2018) · International Conference on Electrical Energy Systems (ICEES) · doi:10.1109/icees.2018.8442335 · Cited by 5
Optimizing Electricity Load and Cost for Demand Side Management in Smart Grid
Afzaal, A., & Nazir, M. (2018) · Mehran University Research Journal of Engineering and Technology · doi:10.22581/10.22581/muet1982.1804.15 · Cited by 5
Day-ahead Management of Energy Sources and Storage in Hybrid Microgrid to reduce Uncertainty
Kumar, K. P., & Saravanan, B. (2019) · GAZI UNIVERSITY JOURNAL OF SCIENCE · doi:10.35378/gujs.512736 · Cited by 5
Advance Green Energy Scheduling In Smart Grid Using IOT
Hassnuddin, M., & Kumar, S. (2019) · International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) · doi:10.1109/icraecc43874.2019.8995043 · Cited by 5
Kumar, P., Brar, G. S., & Singh, L. (2019) · International Conference on Power Systems (ICPS) · doi:10.1109/icps48983.2019.9067675 · Cited by 5
Rout, S., Biswal, G. R., & Sahoo, R. M. (2020) · IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) · doi:10.1109/gucon48875.2020.9231121 · Cited by 5
Effects of ‘invisible’ energy storage on power system operations
Gandhi, O., Kumar, D. S., Gallegos, C. D. R., Zhang, W., Reindl, T., & Srinivasan, D. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103626 · Cited by 5
Self-citationEmergence of Energy Services Ecosystems: Scenario Method as a Policy Enabler
Almpanopoulou, A., Bergman, J.-P., Ahonen, T., Blomqvist, K., Ritala, P., Honkapuro, S., & Ahola, J. (2017) · Journal of Innovation Management · doi:10.24840/2183-0606_005.001_0006 · Cited by 5
A two-stage residential demand response framework for smart community with transformer aging
Liu, J., Xiao, J., Zhou, B., Wang, Z., Zhang, H., & Zeng, Y. (2017) · Asia-Pacific Power and Energy Engineering Conference (APPEEC) · doi:10.1109/appeec.2017.8308907 · Cited by 5
Smart Load Control Strategy for Residential Demand Side Management
Mehta, R., Verma, P. P., & Srinivasan, D. (2018) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2018.8467947 · Cited by 5
Self-citationDemand Response Management of Residential Loads with Integrated Temperature Dependent Appliances
Saxena, A., & De, M. (2018) · International Conference on Emerging Applications of Information Technology · doi:10.1109/eait.2018.8470421 · Cited by 5
Business models for flexibility of electric vehicles
Lezama, F., Soares, J., Faia, R., Vale, Z., Macedo, L. H., & Romero, R. A. (2019) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3319619.3326807 · Cited by 5
AlShafeey, M., & Csáki, C. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1304/1/012017 · Cited by 5
Silva, B. N., Lee, K., Yoon, Y., Han, J., Cao, Z., & Han, K. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3834 · Cited by 5
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Attia, A. M. (2020) · IEEE International Conference on Smart Energy Grid Engineering (SEGE) · doi:10.1109/sege49949.2020.9182010 · Cited by 5
Weak Control Approach to Consumer-Preferred Energy Management
Shibasaki, S., Inoue, M., Arahata, M., & Gupta, V. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.1616 · Cited by 5
Real Time Multi-Objective Energy Management of a Smart Home
Chatterjee, A., Paul, S., & Ganguly, B. (2020) · IEEE International Conference on Power Electronics, Drives and Energy Systems · doi:10.1109/pedes49360.2020.9379539 · Cited by 5
Conceptual Microgrid Management Framework Based on Adaptive and Autonomous Multi-Agent Systems
Lizondo, D. F., Jimenez, V. A., Araujo, P. B., & Will, A. (2022) · Journal of Computer Science and Technology · doi:10.24215/16666038.22.e01 · Cited by 5
Jayasekara, N., Masoum, M. A. S., & Wolfs, P. (2013) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesmg.2013.6672759 · Cited by 5
A Hybrid Algorithm for Energy Management in Smart Grid
Shaheen, N., Javaid, N., Iqbal, Z., Muhammad, K., Azad, K., & Chaudhry, F. A. (2015) · International Conference on Network-Based Information Systems · doi:10.1109/nbis.2015.13 · Cited by 5
Heuristic algorithm for minimizing the electricity cost of air conditioners on a smart grid
Arikiez, M., Grasso, F., Kowalski, D., & Zito, M. (2016) · IEEE International Energy Conference (ENERGYCON) · doi:10.1109/energycon.2016.7513915 · Cited by 5
Demand-Side Management Program Planning Using Stochastic Load Forecasting with Extreme Value Theory
Wi, Y.-M., Kong, S., Lee, J., & Joo, S. (2016) · Journal of Electrical Engineering and Technology · doi:10.5370/jeet.2016.11.5.1093 · Cited by 5
Fuzzy Energy Management Controller for Smart Homes
Khalid, R., Abid, S., Zafar, A., Yasmeen, A., Khan, Z. A., Qasim, U., & Javaid, N. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-61542-4_19 · Cited by 5
Awais, M., Javaid, N., Mateen, A., Khan, N., Mohiuddin, A., & Rehman, M. H. A. (2018) · International Conference on Advanced Information Networking and Applications (AINA) · doi:10.1109/aina.2018.00130 · Cited by 5
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Deng, R., Luo, F., Ranzi, G., Zhao, Z., & Xu, Y. H. (2020) · Asia Conference on Power and Electrical Engineering (ACPEE) · doi:10.1109/acpee48638.2020.9136343 · Cited by 5
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Sharma, I. (2016) · UWSpace (University of Waterloo) · Cited by 5
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Shaaban, M. F. (2014) · UWSpace (University of Waterloo) · Cited by 5
Optimal Residential Demand Response under Dynamic Pricing in a Multi-Agent Framework
Wang, Z. (2015) · doi:10.82465/6739 · Cited by 5
Survey on Simulation and Vulnerability Testing in Smart GridPreprint
Banik, S., & Banik, T. (2024) · Preprints.org · doi:10.20944/preprints202402.0047.v2 · Cited by 5
Asaleye, D. A., Murphy, D. J., Shine, P., & Murphy, M. D. (2024) · Sustainability · doi:10.3390/su16188120 · Cited by 4
Real-time pricing considering carbon constraints for smart grid with multiple energy sources
Cheng, Q., & Lv, J. (2024) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0226962 · Cited by 4
Energy Demand Forecasting for High Energy Consumers: A Case Study in Brazil
dos Santos, L. C., Tabora, J. M., Rocha, C. A. F., C., C. C. M. D. M., Soares, T. M., & Tostes, M. E. D. L. (2024) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas61960.2024.10732158 · Cited by 4
Energy management of smart grid considering responsive load
Ahmady, M., Fereidunian, A., & Lesani, H. (2013) · Smart Grid Conference · doi:10.1109/sgc.2013.6733799 · Cited by 4
Design of Demand Response System Based on OpenADR
Guo, Y. K., Qi, B., Chen, S., & Zhong, M. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.521.444 · Cited by 4
A Cloud-Fog Based Environment Using Beam Search Algorithm in Smart Grid
Tehreem, K., Javaid, N., Bano, H., Ansar, K., Waheed, M., & Butt, H. (2018) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-98530-5_57 · Cited by 4
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Rodrigues, C. F. A., Pinheiro, H., & de Sousa, M. L. (2022) · MDPI eBooks · doi:10.3390/books978-3-03897-777-3-3 · Cited by 4
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Repke, M., Lange, A.-K., & Eckert, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16579-5_23 · Cited by 4
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Parmar, A., & Darji, P. (2023) · Smart Science · doi:10.1080/23080477.2023.2208398 · Cited by 4
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Soumya, P., & Swarup, K. S. (2016) · National Power Systems Conference · doi:10.1109/npsc.2016.7858920 · Cited by 4
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Zhang, L., Xu, B., Cong, P., Guo, Y., Jiang, S., & Rui, H. (2019) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2019.8790294 · Cited by 4
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Koul, B., Singh, K., & Brar, Y. S. (2021) · International Journal of Emerging Electric Power Systems · doi:10.1515/ijeeps-2021-0241 · Cited by 4
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Sharma, A. K., Alshamrani, A. M., Alnowibet, K. A., Alrasheedi, A. F., Saxena, A., & Mohamed, A. W. (2022) · Axioms · doi:10.3390/axioms11100538 · Cited by 4
Cerna, F. V., Dantas, J. T., Naderi, E., & Contreras, J. (2024) · Energy · doi:10.1016/j.energy.2024.130835 · Cited by 4
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Awais, M., Javaid, N., Shaheen, N., Adnan, R., Khan, N. A., Khan, Z. A., & Qasim, U. (2015) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2015.89 · Cited by 4
Multi-agent system for managing distributed energy storage and electrical vehicles
Logenthiran, T., & Srinivasan, D. (2014) · Intelligent Decision Technologies · doi:10.3233/idt-140215 · Cited by 4
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Alharbi, W., & Kankar Bhattacharya, B. (2014) · IEEE Electrical Power and Energy Conference (EPEC) · doi:10.1109/epec.2014.11 · Cited by 4
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Elbaz, S., & Zaiţ, A. (2016) · Scientific Annals of Economics and Business · doi:10.1515/saeb-2016-0137 · Cited by 4
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Gang, J., & Xiqiao, L. (2018) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2018.8361337 · Cited by 4
Naz, A., Javaid, N., Qureshi, T. N., Imran, M., Ali, M., & Khan, Z. A. (2018) · International Conference on Advanced Information Networking and Applications Workshops (WAINA) · doi:10.1109/waina.2018.00090 · Cited by 4
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Demand Side Management in Microgrids
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Fioretto, F., Agostino Dovier, A., & Pontelli, E. (2019) · Intelligenza Artificiale · doi:10.3233/ia-180037 · Cited by 4
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Abedini, M., Ebrahimi, J., Rezaei, M. M., & Nasri, M. (2020) · Iranian Electric Industry Journal of Quality and Productivity · doi:10.29252/ieijqp.9.356 · Cited by 4
Vikram, A., Karna, D., Kumar, A., & Rizwan, M. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/946/1/012007 · Cited by 4
Powroźnik, P., Szulim, R., Miczulski, W., & Piotrowski, K. (2021) · Applied Sciences · doi:10.3390/app11041626 · Cited by 4
Zheng, D., Zhang, W., Alemu, S. N., Wang, P., Bitew, G. T., Wei, D., & Yue, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821189-2.00001-2 · Cited by 5
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Küfeoğlu, S. (2021) · Sustainable development goals series · doi:10.1007/978-3-030-75093-0_5 · Cited by 4
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Gao, Z. X., Li, H., & Chen, L. (2022) · IEEE Access · doi:10.1109/access.2022.3153111 · Cited by 4
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Hong, W. (2018) · MDPI eBooks · doi:10.3390/books978-3-03897-287-7 · Cited by 4
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Autonomous demand-side optimization with load uncertainty
Manasseh, E., Ohno, S., Yamamoto, T., & Mvuma, A. N. (2014) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/elinfocom.2014.6914355 · Cited by 4
Intelligent DC power monitoring system and sensor network based on ZigBee-equipped smart sockets
Tsai, Y.-S., Chu, C.-Y., Li, M.-C., Lin, Y., & Chen, P. (2016) · International Symposium on Next-Generation Electronics · doi:10.1109/isne.2016.7543404 · Cited by 4
Xu, Z., Yang, P., Sibo, S., Zhang, Y., Peng, J., & Zheng, Q. (2016) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2016.7796505 · Cited by 4
A Hybrid Genetic Based on Harmony Search Method to Schedule Electric Tasks in Smart Home
Ahmad, M., Khan, A., Nadeem, Z., Yasmeen, A., Fatima, I., Zahoor, S., & Javaid, N. (2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-69835-9_14 · Cited by 4
5.13 Smart Grid Energy Management
Sharma, K., & Saini, L. M. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809597-3.00527-7 · Cited by 4
Multiple user based residential energy management scheme for smart homes
Awais, M., Rasheed, M. B., Iqbal, M. Z., & Usman, M. (2018) · International Conference on Power, Energy and Smart Grid (ICPESG) · doi:10.1109/icpesg.2018.8384492 · Cited by 4
Teh, J., Cheng, Y., & Lai, C. (2018) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c.2018.00095 · Cited by 4
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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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