Dynamic Multiobjective Optimal Dispatch of Distribution Network Considering Time of Use–Based Demand Response
Publication: Journal of Energy Engineering
Volume 148, Issue 2
Abstract
A dynamic dispatch program schedules the controllable generation units during the entire dispatch period to minimize the economic cost and increase other benefits. On the other hand, demand response (DR) improves energy utilization and increases power quality. The coordination of dynamic dispatch with DR is potentially important to increase the benefits to distribution network areas. In this paper, a dynamic multiobjective optimal dispatch (DMOD) problem is developed to effectively integrate with the time of use (TOU)-based DR for optimal distribution network dispatch. DR is concerned with the optimal pricing during peak, off-peak, and valley periods such that the load curve of the dispatch is optimally shaped. In a combined dispatch procedure, dynamic multistage generators, energy storage operations, and power exchanged between upstream grid dispatches are simultaneously optimized to decrease the system operational cost and the active power loss along with the responsive load based on a DR scheme. In addition, a hybrid teaching-learning multiobjective particle swarm optimization algorithm (HTL-MOPSO) is proposed for solving the DMOD incorporation with the DR model. The proposed method is validated in a simulation study using an IEEE 33 bus system. The comprehensive numerical results reveal not only the efficiency of the proposed combined DMOD with the DR model but also the practical DR benefits to customers and distribution network operators.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author on reasonable request.
Acknowledgments
This work was financially supported by the Natural Science Foundation of Anhui Province (No. 2008085QE239); Visiting Scholarship of State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University) (No. 2007DA10512718405), the Doctoral Special Research Fund of Hefei University of Technology (No. JZ2019HGBZ0146), and China Postdoctoral Science Foundation (2020M682645).
References
Aalami, H. A., M. P. Moghaddam, and G. R. Yousefi. 2010. “Demand response modeling considering interruptible/curtailable loads and capacity market programs.” Appl. Energy 87 (1): 243–250. https://doi.org/10.1016/j.apenergy.2009.05.041.
Abdi, H., E. Dehnavi, and F. Mohammadi. 2016. “Dynamic economic dispatch problem integrated with demand response (DEDDR) considering non-linear responsive load models.” IEEE Trans. Smart Grid 7 (6): 2586–2595. https://doi.org/10.1109/TSG.2015.2508779.
Arasteh, H. R., M. P. Moghaddam, M. K. Sheikh-El-Eslami, and A. Abdollahi. 2013. “Integrating commercial demand response resources with unit commitment.” Int. J. Electr. Power Energy Syst. 51 (Oct): 153–161. https://doi.org/10.1016/j.ijepes.2013.02.015.
Baran, M. E., and F. F. Wu. 1989. “Network reconfiguration in distribution systems for loss reduction and load balancing.” IEEE Power Eng. Rev. 9 (4): 101–102. https://doi.org/10.1109/MPER.1989.4310642.
Carpinelli, G., G. Celli, S. Mocci, F. Mottola, F. Pilo, and D. Proto. 2013. “Optimal integration of distributed energy storage devices in smart grids.” IEEE Trans. Smart Grid 4 (2): 985–995. https://doi.org/10.1109/TSG.2012.2231100.
Chen, F., J. Zhou, C. Wang, C. Li, and P. Lu. 2017. “A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching.” Energy 121 (Feb): 276–291. https://doi.org/10.1016/j.energy.2017.01.010.
Cheng, T., M. Chen, P. J. Fleming, Z. Yang, and S. Gan. 2017. “A novel hybrid teaching learning based multi-objective particle swarm optimization.” Neurocomputing 222 (Jan): 11–25. https://doi.org/10.1016/j.neucom.2016.10.001.
Cheng, T., M. Chen, Y. Wang, B. Li, M. A. S. Hassan, T. Chen, and R. Xu. 2018. “Adaptive robust method for dynamic economic emission dispatch incorporating renewable energy and energy storage.” Complexity 2018 (Jan): 1–13. https://doi.org/10.1155/2018/2517987.
Dehnavi, E., and H. Abdi. 2016. “Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem.” Energy 109 (Aug): 1086–1094. https://doi.org/10.1016/j.energy.2016.05.024.
Fan, J.-Y., and L. Zhang. 1998. “Real-time economic dispatch with line flow and emission constraints using quadratic programming.” IEEE Trans. Power Syst. 13 (2): 320–325. https://doi.org/10.1109/59.667345.
Gabash, A., and P. Li. 2012a. “Active-Reactive optimal power flow in distribution networks with embedded generation and battery storage.” IEEE Trans. Power Syst. 27 (4): 2026–2035. https://doi.org/10.1109/TPWRS.2012.2187315.
Gabash, A., and P. Li. 2012b. “Flexible optimal operation of battery storage systems for energy supply networks.” IEEE Trans. Power Syst. 28 (3): 2788–2797. https://doi.org/10.1109/TPWRS.2012.2230277.
Gaing, Z.-L. 2004. “Particle swarm optimization to solving the economic dispatch considering the generator constraints.” IEEE Trans. Power Syst. 18 (3): 1187–1195. https://doi.org/10.1109/TPWRS.2003.814889.
Hajibandeh, N., M. Ehsan, S. Soleymani, M. Shafie-Khah, and J. P. Catalão. 2017. “The mutual impact of demand response programs and renewable energies: A survey.” Energies 10 (9): 1353. https://doi.org/10.3390/en10091353.
Hajibandeh, N., M. Shafie-Khah, G. J. Osório, J. Aghaei, and J. P. Catalão. 2018. “A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators.” Appl. Energy 212 (Feb): 721–732. https://doi.org/10.1016/j.apenergy.2017.12.076.
Heydarian-Forushani, E., M. P. Moghaddam, M. K. Sheikh-El-Eslami, M. Shafie-Khah, and J. P. S. Catalão. 2014. “A stochastic framework for the grid integration of wind power using flexible load approach.” Energy Convers. Manage. 88 (Dec): 985–998. https://doi.org/10.1016/j.enconman.2014.09.048.
Holttinen, H., A. Tuohy, M. Milligan, E. Lannoye, and V. Silva. 2013. “The flexibility workout: Managing variable resources and assessing the need for power system modification.” IEEE Power Energy Mag. 11 (6): 53–62. https://doi.org/10.1109/MPE.2013.2278000.
Li, B., Z. Ma, P. Hidalgo-Gonzalez, A. Lathem, N. Fedorova, G. He, H. Zhong, M. Chen, and D. M. Kammen. 2021. “Modeling the impact of EVs in the Chinese power system: Pathways for implementing emissions reduction commitments in the power and transportation sectors.” Energy Policy 149 (Feb): 111962. https://doi.org/10.1016/j.enpol.2020.111962.
Li, S., D. Zhang, A. B. Roget, and Z. O’Neill. 2014. “Integrating home energy simulation and dynamic electricity price for demand response study.” IEEE Trans. Smart Grid 5 (2): 779–788. https://doi.org/10.1109/TSG.2013.2279110.
Liang, J. J., A. K. Qin, P. N. Suganthan, and S. Baskar. 2006. “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions.” IEEE Trans. Evol. Comput. 10 (3): 281–295. https://doi.org/10.1109/TEVC.2005.857610.
Liang, Y., F. Liu, C. Wang, and S. Mei. 2017. “Distributed demand-side energy management scheme in residential smart grids: An ordinal state-based potential game approach.” Appl. Energy 206 (Nov): 991–1008. https://doi.org/10.1016/j.apenergy.2017.08.123.
Lokeshgupta, B., and S. Sivasubramani. 2018. “Multi-objective dynamic economic and emission dispatch with demand side management.” Int. J. Electr. Power Energy Syst. 97 (Apr): 334–343. https://doi.org/10.1016/j.ijepes.2017.11.020.
Mokarram, M. J., M. Gitizadeh, T. Niknam, and S. Niknam. 2019. “Robust and effective parallel process to coordinate multi-area economic dispatch (MAED) problems in the presence of uncertainty.” IET Gener. Transm. Distrib. 13 (18): 4197–4205. https://doi.org/10.1049/iet-gtd.2019.0319.
Niknam, T., and H. Doagou-Mojarrad. 2012. “Multiobjective economic/emission dispatch by multiobjective -particle swarm optimization.” IET Gener. Transm. Distrib. 6 (5): 363–377. https://doi.org/10.1049/iet-gtd.2011.0698.
Nikzad, M., and B. Mozafari. 2014. “Reliability assessment of incentive and priced-based demand response programs in restructured power systems.” Int. J. Electr. Power Energy Syst. 56 (Mar): 83–96. https://doi.org/10.1016/j.ijepes.2013.10.007.
Tripathi, P. K., S. Bandyopadhyay, and S. K. Pal. 2007. “Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients.” Inf. Sci. 177 (22): 5033–5049. https://doi.org/10.1016/j.ins.2007.06.018.
Wang, J., H. Zhong, Z. Ma, Q. Xia, and C. Kang. 2017. “Review and prospect of integrated demand response in the multi-energy system.” Appl. Energy 202 (Sep): 770–782. https://doi.org/10.1016/j.apenergy.2017.05.150.
Wang, J., H. Zhong, C. Wu, E. Du, Q. Xia, and C. Kang. 2019. “Incentivizing distributed energy resource aggregation in energy and capacity markets: An energy sharing scheme and mechanism design.” Appl. Energy 252 (Oct): 113471. https://doi.org/10.1016/j.apenergy.2019.113471.
Wu, L. H., Y. N. Wang, X. F. Yuan, and S. W. Zhou. 2010. “Environmental/economic power dispatch problem using multi-objective differential evolution algorithm.” Electr. Power Syst. Res. 80 (9): 1171–1181. https://doi.org/10.1016/j.epsr.2010.03.010.
Yang, Q., and X. Fang. 2017. “Demand response under real-time pricing for domestic households with renewable DGs and storage.” IET Gener. Transm. Distrib. 11 (8): 1910–1918. https://doi.org/10.1049/iet-gtd.2016.1066.
Yang, Y., and W. Wu. 2019. “A distributionally robust optimization model for real-time power dispatch in distribution networks.” IEEE Trans. Smart Grid 10 (4): 3743–3752. https://doi.org/10.1109/TSG.2018.2834564.
Younes, M., F. Khodja, and R. L. Kherfane. 2014. “Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration.” Energy 67 (Apr): 595–606. https://doi.org/10.1016/j.energy.2013.12.043.
Zou, F., L. Wang, X. Hei, D. Chen, and B. Wang. 2013. “Multi-objective optimization using teaching-learning-based optimization algorithm.” Eng. Appl. Artif. Intell. 26 (4): 1291–1300. https://doi.org/10.1016/j.engappai.2012.11.006.
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History
Received: Sep 24, 2020
Accepted: Aug 30, 2021
Published online: Dec 24, 2021
Published in print: Apr 1, 2022
Discussion open until: May 24, 2022
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