Multi-time slots real-time pricing for smart grid with time-coupled constraints

ZHANG Li, GAO Yan, LIU Songtao, ZHU Hongbo

Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (10) : 2599-2609.

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Systems Engineering - Theory & Practice ›› 2019, Vol. 39 ›› Issue (10) : 2599-2609. DOI: 10.12011/1000-6788-2018-2489-11

Multi-time slots real-time pricing for smart grid with time-coupled constraints

  • ZHANG Li1,2, GAO Yan1, LIU Songtao3, ZHU Hongbo2
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Abstract

The real-time pricing mechanism is an ideal method of the smart grid in the future. Based on the social welfare maximization model, the real-time pricing strategy of smart grid is studied in this paper. According to the working characteristics of appliances, appliances are divided into three categories, which are must-run appliances, elastic appliances and semi-elastic appliances. For the coupling property about time of power consumption of elastic and semi-elastic appliance, a multi-time slots model is established. The multi-time slots model is decomposed into a set of single-time slot optimization problems by the relaxation method. Based on the theory of duality, a distributed algorithm is proposed. The real-time electricity price is obtained. In this algorithm, users do not need to disclose their specific power consumption information to the energy suppliers and other users, which protects users' personal privacy. Numerical simulation verifies the rationality of the model and the effectiveness of the algorithm.

Key words

smart grid / demand side management / real-time pricing / dual optimization

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ZHANG Li , GAO Yan , LIU Songtao , ZHU Hongbo. Multi-time slots real-time pricing for smart grid with time-coupled constraints. Systems Engineering - Theory & Practice, 2019, 39(10): 2599-2609 https://doi.org/10.12011/1000-6788-2018-2489-11

References

[1] Hu M, Xiao J, Cui S, et al. Distributed real-time demand response for energy management scheduling in smart grid[J]. International Journal of Electrical Power and Energy Systems, 2018, 99:233-245.
[2] Deng R, Yang Z, Chow M Y, et al. A survey on demand response in smart grids:Mathematical models and approaches[J]. IEEE Transactions on Industrial Informatics, 2015, 11(3):570-582.
[3] Wang Y, Wu L, Wang S. A fully-decentralized consensus-based ADMM approach for DC-OPF with demand response[J]. IEEE Transactions on Smart Grid, 2017, 8(6):2637-2647.
[4] Lu R, Hong S H, Zhang X. A dynamic pricing demand response algorithm for smart grid:Reinforcement learning approach[J]. Applied Energy, 2018, 220:220-230.
[5] Arora S, Taylor J W. Forecasting electricity smart meter data using conditional kernel density estimation[J]. Omega, 2016, 59:47-59.
[6] 朱红波, 高岩, 后勇, 等. 马尔可夫过程下多类用户智能电网实时电价[J]. 系统工程理论与实践, 2018, 38(3):807-816.Zhu H B, Gao Y, Hou Y, et al. Real-time pricing strategy for smart grid based on Markov decision processes[J]. Systems Engineering-Theory & Practice, 2018, 38(3):807-816.
[7] 赵定涛, 邓雅静, 范进. 个人碳交易理论视域下的阶梯电价定价模型[J]. 系统工程, 2013(10):15-20.Zhao D T, Deng Y J, Fan J. The multistep electricity pricing model based on the theory of personal carbon trading[J]. Systems Engineering, 2013(10):15-20.
[8] 张昕竹, 刘自敏. 分时与阶梯混合定价下的居民电力需求——基于DCC模型的分析[J]. 经济研究, 2015(3):146-158.Zhang X Z, Liu Z M. Residential electricity demand under time-of-use pricing and increasing block pricing:An analysis based on DCC model[J]. Economic Research Journal, 2015(3):146-158.
[9] 代业明, 高岩. 具有多类资源多类用户智能电网实时定价决策[J]. 系统工程理论与实践, 2015, 35(9):2315-2323.Dai Y M, Gao Y. Real-time pricing decision-making in smart grid with multi-type users and multi-type power sources[J]. Systems Engineering-Theory & Practice, 2015, 35(9):2315-2323.
[10] Nguyen D H, Narikiyo T, Kawanishi M. Optimal demand response and real-time pricing by a sequential distributed consensus-based ADMM approach[J]. IEEE Transactions on Smart Grid, 2018, 9(5):4964-4974.
[11] 高岩. 带有边际效用非递减用户的智能电网实时电价定价方法[J]. 工业工程与管理, 2018, 23(1):10-13.Gao Y. Real-time pricing strategy for smart grid with users of non-decreasing marginal benefit[J]. Industrial Engineering and Management, 2018, 23(1):10-13.
[12] Samadi P, Mohsenian-Rad A H, Schober R, et al. Optimal real-time pricing algorithm based on utility maximization for smart grid[C]//IEEE International Conference on Smart Grid Communications. IEEE, 2010:415-420.
[13] Tarasak P. Optimal real-time pricing under load uncertainty based on utility maximization for smart grid[C]//IEEE International Conference on Smart Grid Communications. IEEE, 2011:321-326.
[14] Williamson O E. Peak-load pricing and optimal capacity under indivisibility constraints[J]. American Economic Review, 1966, 56(4):810-827.
[15] Wang Y, Mao S, Nelms R M. Online algorithm for optimal real-time energy distribution in the smart grid[J]. IEEE Internet of Things Journal, 2013, 1(1):10-21.
[16] Zhang W, Li J, Chen G, et al. A comprehensive model with fast solver for optimal energy scheduling in RTP environment[J]. IEEE Transactions on Smart Grid, 2017, 8(5):2314-2323.
[17] Mohsenian-Rad A H, Leon-Garcia A. Optimal residential load control with price prediction in real-time electricity pricing environments[J]. IEEE Transactions on Smart Grid, 2010, 1(2):120-133.
[18] Samadi P, Mohsenian-Rad H, Schober R, et al. Advanced demand side management for the future smart grid using mechanism design[J]. IEEE Transactions on Smart Grid, 2012, 3(3):1170-1180.
[19] Zhu H, Gao Y, Hou Y, et al. Multi-time slots real-time pricing strategy with power fluctuation caused by operating continuity of smart home appliances[J]. Engineering Applications of Artificial Intelligence, 2018, 71:166-174.
[20] Qian L P, Zhang Y J A, Huang J, et al. Demand response management via real-time electricity price control in smart grids[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(7):1268-1280.
[21] Tang Q, Yang K, Zhou D, et al. A real-time dynamic pricing algorithm for smart grid with unstable energy providers and malicious users[J]. IEEE Internet of Things Journal, 2016, 3(4):554-562.
[22] Maharjan S, Zhu Q Y, Zhang Y, et al. Demand response management in the smart grid in a large population regime[J]. IEEE Transactions on Smart Grid, 2016, 7(1):189-199.
[23] 王宜举, 修乃华. 非线性最优化理论与方法[M]. 北京:科学出版社, 2015. Wang Y J, Xiu N H. Nonlinear optimization theory and method[M]. Beijing:Science Press, 2015.
[24] Boyd S, Vandenberghe L. 凸优化[M]. 北京:清华大学出版社, 2013. Boyd S, Vandenberghe L. Convex optimization[M]. Beijing:Tsinghua University Press, 2013.
[25] Deng R, Yang Z, Hou F, et al. Distributed real-time demand response in multiseller-multibuyer smart distribution grid[J]. IEEE Transactions on Power Systems, 2015, 30(5):2364-2374.
[26] 高岩. 非光滑优化[M]. 北京:科学出版社, 2018. Gao Y. Nonsmooth optimization[M]. Beijing:Science Press, 2018.

Funding

National Natural Science Foundation of China (11171221); American IBM Shared University Project (Optimization Methods on Smart Grid)
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