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Recent Advances in Electrical & Electronic Engineering

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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Optimization of Residential Electricity Consumption based on Multienergy Coordinated Control of Generation, Load, and Storage

Author(s): Jun Tao, Minfu A., Zaixin Yang and Sheng Xiang*

Volume 16, Issue 5, 2023

Published on: 22 February, 2023

Page: [541 - 550] Pages: 10

DOI: 10.2174/2352096516666230201102524

Price: $65

Abstract

Introduction: The power system is translating to the “generation, network, load, and storage multiple coordination control” model, which helps to increase the proportion of renewable energy consumption and reduce the operation cost.

Materials and Methods: The residential electrical system is an important component in the power system, which includes distributed photovoltaic systems, flexible loads, electric vehicles, and battery storage systems, which have the potential to realize multiple coordination control and minimize the net cost of the electricity consumption in a residential electrical system by the coordinated cooperation of generation, flexible loads, and the battery storage system. The comfort level of the residents is also maintained during control. Therefore, a nonlinear economic model is introduced to represent coordinated cooperation of generation, flexible loads, and the battery storage system, and a nonlinear economic predictive controller is proposed to solve the problems of this model.

Results: Based on the forecasted generation/load for a tumbling prediction window, the impact of different control trajectories is estimated, then the minimum cost trajectory for flexible loads and battery is selected, and only the first control action is implemented. Then, the prediction window is moved to the next time interval, and the same process is repeated. The case study shows that the proposed method reduces about 24.36% of the net cost of the residential electrical system.

Conclusion: Meanwhile, the hot-water temperature and indoor temperature of the household are maintained without affecting the end-user comfort, and the state-of-charge (SOC) for the electric vehicle and the battery system is always kept under constraints without affecting usage. The proposed method has a good performance for the coordinated cooperation of generation, flexible loads, and battery storage system in the residential electrical system. The proposed method also increases the consumption of PV, with the implementation in more residential buildings that will benefit in achieving carbon neutrality by 2060 in China.

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[1]
J. Freire, and P. Pereira, "Home EMS controlled by neural networks", IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Oct 13-16, 2021, Toronto, ON, Canada, pp. 1-6, 2021.
[http://dx.doi.org/10.1109/IECON48115.2021.9589482]
[2]
X. Xu, J. Li, Y. Xu, Z. Xu, and C.S. Lai, "A two-stage gametheoretic method for residential PV panels planning considering energy sharing mechanism", IEEE Trans. Power Syst., vol. 35, no. 5, pp. 3562-3573, 2020.
[http://dx.doi.org/10.1109/TPWRS.2020.2985765]
[3]
M.A.Z. Alvarez, K. Agbossou, A. Cardenas, S. Kelouwani, and L. Boulon, "Demand response strategy applied to residential electric wáter heaters using dynamic programming and K-means clustering", IEEE Trans. Sustain. Energy, vol. 11, no. 1, pp. 524-533, 2020.
[http://dx.doi.org/10.1109/TSTE.2019.2897288]
[4]
R. Han, Q. Hu, Z. Guo, X. Quan, Z. Wu, and R. Hu, "Optimal allocation method of residential ir-conditioners: Trade-off solutions between economic costs and aggregation reliability", IEEE Open Access J. Power Energy, vol. 9, pp. 131-142, 2022.
[http://dx.doi.org/10.1109/OAJPE.2022.3151493]
[5]
F. Rassaei, W.S. Soh, and K.C. Chua, "Demand response for residential electric vehicles with random usage patterns in Smart grids", IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1367-1376, 2015.
[http://dx.doi.org/10.1109/TSTE.2015.2438037]
[6]
K. Worthmann, C.M. Kellett, P. Braun, L. Grüne, and S.R. Weller, "Distributed and decentralized control of residential energy systems incorporating battery storage", IEEE Trans. Smart Grid, vol. 6, no. 4, pp. 1914-1923, 2015.
[http://dx.doi.org/10.1109/TSG.2015.2392081]
[7]
S. Xiang, L. Chang, B. Cao, Y. He, and C. Zhang, "A novel domestic electric water heater control method", IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3246-3256, 2020.
[http://dx.doi.org/10.1109/TSG.2019.2961214]
[8]
X. Kou, Y. Du, F. Li, H. Pulgar-Painemal, H. Zandi, J. Dong, and M.M. Olama, "Model-based and data-driven HVAC control strategies for residential demand response", IEEE Open Access J. Power Energy, vol. 8, pp. 186-197, 2021.
[http://dx.doi.org/10.1109/OAJPE.2021.3075426]
[9]
F. Elghitani, and E. El-Saadany, "Smoothing net load demand variations using residential demand management", IEEE Trans. Industr. Inform., vol. 15, no. 1, pp. 390-398, 2019.
[http://dx.doi.org/10.1109/TII.2018.2852482]
[10]
M. Sun, T. Zhang, Y. Wang, G. Strbac, and C. Kang, "Using Bayesian deep learning to capture uncertainty for residential net load forecasting", IEEE Trans. Power Syst., vol. 35, no. 1, pp. 188-201, 2020.
[http://dx.doi.org/10.1109/TPWRS.2019.2924294]
[11]
H.K. Hwang, A.Y. Yoon, H.K. Kang, and S-I. Moon, "Retail electricity pricing strategy via an artificial neural network-based demand response model of an energy storage system", IEEE Access, vol. 9, pp. 13440-13450, 2021.
[http://dx.doi.org/10.1109/ACCESS.2020.3048048]
[12]
F. Ruelens, B.J. Claessens, S. Quaiyum, B. De Schutter, R. Babuska, and R. Belmans, "Reinforcement learning applied to an electric water heater: From theory to practice", IEEE Trans. Smart Grid, vol. 9, no. 4, pp. 3792-3800, 2018.
[http://dx.doi.org/10.1109/TSG.2016.2640184]
[13]
K. Zhao, “Research on economic scheduling model of distributed photovoltaic power systems,” Yanshan University, Master Dissertation, Hebei, China, 2019. [https://kreader.cnki.net/Kreader/CatalogViewPage.aspx?dbCode=CMFD&filename=1020614350.nh&tablename=CMFD202001&compose=&first=1&uid=WEEvREcwSlJHSldSdmVpbisvR0MzVFdCR2FDeHZwZzl2VjN0VkFxRytVZz0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKens W4IQMovwHtwkF4VYPoHbKxJw!!
[14]
P. Paudyal, P. Munankarmi, Z. Ni, and T.M. Hansen, "A hierarchical control framework with a novel bidding scheme for residential community energy optimization", IEEE Trans. Smart Grid, vol. 11, no. 1, pp. 710-719, 2020.
[http://dx.doi.org/10.1109/TSG.2019.2927928]
[15]
J. Liu, Y. Zhou, H. Yang, and H. Wu, "Net-zero energy management and optimization of commercial building sectors with hybrid renewable energy systems integrated with energy storage of pumped hydro and hydrogen taxis", Appl. Energy, vol. 321, no. 119312, p. 119312, 2022.
[http://dx.doi.org/10.1016/j.apenergy.2022.119312]
[16]
Y. Zhou, "Transition towards carbon-neutral districts based on storage techniques and spatiotemporal energy sharing with electrification and hydrogenation", Renew. Sustain. Energy Rev., vol. 162, no. 112444, p. 112444, 2022.
[http://dx.doi.org/10.1016/j.rser.2022.112444]
[17]
Y. Zhou, "Energy sharing and trading on a novel spatiotemporal energy network in Guangdong-Hong Kong-Macao Greater Bay Area", Appl. Energy, vol. 318, no. 119131, p. 119131, 2022.
[http://dx.doi.org/10.1016/j.apenergy.2022.119131]
[18]
M. Ostadijafari, A. Dubey, and N. Yu, "Linearized Priceresponsive HVAC controller for optimal scheduling of smart building loads", IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3131-3145, 2020.
[http://dx.doi.org/10.1109/TSG.2020.2965559]
[19]
H. K. Khalil, and J. Grizzle, Nonlinear Systems, vol. 3, Upper saddle river, NJ, USA: Prentice-Hall, 2002. https://vdocuments.net/nonlinear-systems-khalil-3rd-edition.html.
[20]
C. Ye, Y. Ding, P. Wang, and Z. Lin, "A data-driven bottom-up approach for spatial and temporal electric load forecasting", IEEE Trans. Power Syst., vol. 34, no. 3, pp. 1966-1979, 2019.
[http://dx.doi.org/10.1109/TPWRS.2018.2889995]
[21]
W. Liu, C. Ren, and Y. Xu, "PV generation forecasting with missing input data: A super-resolution perception approach", IEEE Trans. Sustain. Energy, vol. 12, no. 2, pp. 1493-1496, 2021.
[http://dx.doi.org/10.1109/TSTE.2020.3029731]
[22]
Y. Zhou, and S. Zheng, "Machine-learning based hybrid demandside controller for high-rise office buildings with high energy flexibilities", Appl. Energy, vol. 262, no. 114416, p. 114416, 2020.
[http://dx.doi.org/10.1016/j.apenergy.2019.114416]
[23]
E. McKenna, and M. Thomson, "High-resolution stochastic integrated thermal–electrical domestic demand model", Appl. Energy, vol. 165, no. 1, pp. 445-461, 2016.
[http://dx.doi.org/10.1016/j.apenergy.2015.12.089]

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