Research ArticleOpen AccessGoogle Scholar indexed
Intelligent Building Load Scheduling Based on Multi-Objective Multi-Verse Algorithm
College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
Lianhu Town Central Health Center, Chongqing, China
Willfar Information Technologies Co. Ltd., Changsha, China
- 1 College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
- 2 College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
- 3 College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
- 4 College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
- 5 College of Information Engineering & Hunan Province Engineering Research Center for Multi-Energy Collaborative Control Technology, Xiangtan University, Xiangtan, China
- 6 Lianhu Town Central Health Center, Chongqing, China
- 7 Willfar Information Technologies Co. Ltd., Changsha, China
Energy and Power Engineering·Volume 13 (2021)·Pages 19–29·Published 8 April 2021·DOI10.4236/epe.2021.134B003
Copy link · social · email
Abstract
In the multi-objective of intelligent building load scheduling, aiming at the problem of how to select Pareto frontier scheme for multi-objective optimization algorithm, the current optimal scheme mechanism combined with multi-objective multi-verse algorithm is used to optimize the intelligent building load scheduling. The update mechanism is changed in updating the position of the universe, and the process of correction coding is omitted in the iterative process of the algorithm, which reduces the com-putational complexity. The feasibility and effectiveness of the proposed method are verified by the optimal scheduling experiments of residential loads.
KeywordsIntelligent BuildingLoad SchedulingMulti-Objective OptimizationMulti-Objective Multi-Verse Algorithm
- Setlhaolo, D., Sichilalu, S. and Zhang, J. (2017) Residential Load Management in an Energy Hub with Heat Pump Water Heater. Appl. Energy, 208, 551-560. https://doi.org/10.1016/j.apenergy.2017.09.099
- Pallonetto, F., Oxizidis, S., Milano, F. and Finn, D. (2016) The Effect of Time-of-Use Tariffs on the Demand Response Flexibility of an All-Electric Smart-Grid-Ready Dwelling. Energy Build, 128, 56-67. https://doi.org/10.1016/j.enbuild.2016.06.041
- Cui, H. and Zhou, K. (2018) Industrial Power Load Scheduling Considering Demand Response. J. Clean. Prod., 204, 447-460. https://doi.org/10.1016/j.jclepro.2018.08.270
- Priya, V., Sathiya Kumar, C. and Kannan, R. (2019) Resource Scheduling Algorithm with Load Balancing for Cloud Service Provisioning. Appl. Soft Comput, J., 76, 416-424. https://doi.org/10.1016/j.asoc.2018.12.021
- Ahmad, A. and Khan, J.Y. (2020) Real-Time Load Scheduling, Energy Storage Control and Comfort Management for Grid-Connected Solar Integrated Smart Buildings. Appl. Energy, 259, Article ID: 114208. https://doi.org/10.1016/j.apenergy.2019.114208
- Zhang, J., Zhu, X., Chen, T., Yu, Y. And Xue, W. (2020) Improved MOEA/D Approach to Many-Objective Day-Ahead Scheduling with Consideration of Adjustable Outputs of Renewable Units and Load Reduction in Active Distribution Networks. Energy, 210, Article ID: 118524. https://doi.org/10.1016/j.energy.2020.118524
- Patnam, B.S.K. and Pindoriya, N.M. (2020) Demand Response in Consumer-Centric Electricity Market: Mathematical Models and Optimization Problems. Electr. Power Syst. Res., 106923. https://doi.org/10.1016/j.epsr.2020.106923
- Haider, H.T., See, O.H. and Elmenreich, W. (2016) Residential Demand Response Scheme Based on Adaptive Consumption Level Pricing. Energy, 113, 301-308. https://doi.org/10.1016/j.energy.2016.07.052
- Zhang, N., Hu, Z., Springer, C., Li, Y. and Shen, B. (2016) A Bi-Level Integrated Generation-Transmission Planning Model Incorporating the Impacts of Demand Response by Operation Simulation. Energy Convers. Manag., 123, 84-94. https://doi.org/10.1016/j.enconman.2016.06.020
- Feuerriegel, S. and Neumann, D. (2016) Integration Scenarios of Demand Response into Electricity Markets: Load Shifting, Financial Savings and Policy Implications. Energy Policy, 96, 231-240. https://doi.org/10.1016/j.enpol.2016.05.050
- Wang, Y., Lin, H., Liu, Y., Sun, Q. and Wennersten, R. (2018) Management of Household Electricity Consumption under Price-Based Demand Response Scheme. J. Clean. Prod., 204, 926-938. https://doi.org/10.1016/j.jclepro.2018.09.019