Comparative analysis of dynamic pricing schemes in distributed energy management of residential users in smart grid

Increasing power demand, greenhouse gas emissions, and the old infrastructure are serious concerns in the existing power system. With the advent of the smart grid, demand response (DR) has emerged as an effective approach to handle these issues. The selection of an appropriate DR program is vital to acquire the maximum benefits for the utility and the consumers. In this context, a distributed energy management scheme for residential consumers is presented and analyzed to observe the impact of different pricing schemes. The three dynamic pricing schemes considered in this work are based on linear function, the logarithmic function, and the penalty-based linear function of aggregated load. A non-cooperative game is used to formulate the energy management problem of the consumers. The Nash equilibrium of the game is obtained using the proximal decomposition algorithm. The results are obtained for different cases based on the presence of a storage device, a dispatchable generation unit, and two different modes of operation of an electric vehicle. The best pricing scheme is chosen based on the minimum cost, the peak-to-average ratio of the system load profile, and consumer comfort.

Keywords:

Demand response, Dynamic price comparison, Nash equilibrium Proximal decomposition algorithm, Energy management,

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[1] Internet Web-Site: https://www.iea.org/reports/india-energy-outlook-2021, India Energy Outlook 2021, 19 July 2021.
[2] Jordehi, AR. Optimisation of demand response in electric power systems, a review. Renew Sustain Energy Rev. 2019; 103:308-319. DOI:10.1016/j.rser.2018.12.054.
[3] Internet Web-Site: https://pv-magazine-usa.com/2017/08/03/falling-lithium-ion-battery-prices-to-drive-rapid-storage-uptake/, Clover I. pv magazine - Photovoltaics Markets and Technology. 2017, 19 July 2021.
[4] Shakeri, M, Shayestegan, M, Abunima, H, Salim Reza, SM, Akhtaruzzaman, M, Alamoud, ARM, Sopian, K, Amin, N. An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy Build. 2017; 138: 154-164. DOI:10.1016/j.enbuild.2016.12.026.
[5] Albadi, MH, El-Saadany, EF. A summary of demand response in electricity markets. Electr Power Syst Res. 2008; 78(11):1989-1996. DOI:10.1016/j.epsr.2008.04.002.
[6] Mohsenian-Rad, AH, Wong, VWS, Jatskevich, J, Schober, R, Leon-Garcia, A. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid. 2010; 1(3):320-331. DOI:10.1109/TSG.2010.2089069.
[7] Kou, X, Li, F, Dong, J, Starke, M, Munk, J, Xue, Y, Olama, M, Zandi, H. A Scalable and Distributed Algorithm for Managing Residential Demand Response Programs using Alternating Direction Method of Multipliers (ADMM). IEEE Trans Smart Grid. 2020; 11(6):4871-4882. DOI:10.1109/TSG.2020.2995923.
[8] Anjos, MF, Lodi, A, Tanneau, M. A decentralized framework for the optimal coordination of distributed energy resources. IEEE Trans Power Syst. 2019; 34(1):349-359. DOI:10.1109/TPWRS.2018.2867476.
[9] Fudenberg D, Tirole J. Game Theory. London, UK: MIT Press, 1991
[10] Rajasekhar, B, Pindoriya, N, Tushar, W, Yuen, C. Collaborative Energy Management for a Residential Community: A Non-Cooperative and Evolutionary Approach. IEEE Trans Emerg Top Comput Intell. 2019; 3(3):177-192. DOI:10.1109/TETCI.2018.2865223.
[11] Fadlullah, ZM, Quan, DM, Kato, N, Stojmenovic I. GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid. IEEE Syst J. 2014; 8(2):588-597. DOI:10.1109/JSYST.2013.2260934.
[12] Nguyen, HK, Song, JB, Han, Z. Demand side management to reduce Peak-to-Average Ratio using game theory in smart grid. In: IEEE INFOCOM Workshops; 25-30 March 2012: IEEE, pp. 91-96. DOI:10.1109/INFCOMW.2012.6193526.
[13] Wang, X, Mao, X, Khodaei, H. A multi-objective home energy management system based on internet of things and optimization algorithms. J Build Eng. 2021; 33: 101603. DOI:10.1016/j.jobe.2020.101603.
[14] Wang, F, Zhou, L, Ren, H, Liu, X, Talari, S, a Shafie-khah, M, Joao P.S. Catal˜ao. Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response. IEEE Trans Ind Appl. 2018; 54(2):1017-1028. DOI:10.1109/TIA.2017.2781639.
[15] Haseeb, M, Kazmi, SAA, Malik, MM, Ali, S, Bukhari, SBA, Shin, DR. Multi Objective Based Framework for Energy Management of Smart Micro-Grid. IEEE Access. 2020; 8: 220302-220319. DOI:10.1109/ACCESS.2020.3041473.
[16] Chamandoust, H, Derakhshan, G, Hakimi, SM, Bahramara, S. Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources. J Energy Storage 2020; 27:101112. DOI:10.1016/j.est.2019.101112.
[17] Liu, N, Cheng, M, Yu, X, Zhong, J, Lei, J. Energy-Sharing Provider for PV Prosumer Clusters: A Hybrid Approach Using Stochastic Programming and Stackelberg Game. IEEE Trans Ind Electron. 2018; 65(8): 6740-6750. DOI:10.1109/TIE.2018.2793181.
[18] Dinh, HT, Yun, J, Kim, DM, Lee, KH, Kim, D. A Home Energy Management System with Renewable Energy and Energy Storage Utilizing Main Grid and Electricity Selling. IEEE Access. 2020; 8: 49436-49450. DOI:10.1109/ACCESS.2020.2979189.
[19] Hou, X, Wang, J, Huang, T, Wang, T, Wang, P. Smart Home Energy Management Optimization Method Considering Energy Storage and Electric Vehicle. IEEE Access. 2019; 7: 144010-144020. DOI:10.1109/ACCESS.2019.2944878.
[20] Dinh, HT, Kim, D. An Optimal Energy-Saving Home Energy Management Supporting User Comfort and Electricity Selling with Different Prices. IEEE Access. 2021; 9: 9235-9249. DOI:10.1109/ACCESS.2021.3050757.
[21] Liu, G, Jiang, T, Ollis, TB, Zhang X, Tomsovic K. Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics. Appl Energy. 2019; 239: 83-95. DOI:10.1016/j.apenergy.2019.01.210.
[22] Das, S, Acharjee P, Bhattacharya, A. Charging Scheduling of Electric Vehicle incorporating Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technology considering in Smart-Grid. IEEE Trans Ind Appl. 2020; 57(2):1688-1702. DOI:10.1109/TIA.2020.3041808.
[23] Zhou, L, Zhang, Y, Lin, X, Li, C, Cai, Z, Yang, P. Optimal sizing of PV and bess for a smart household considering different price mechanisms. IEEE Access. 2018; 6: 41050-41059. DOI:10.1109/ACCESS.2018.2845900.
[24] Mediwaththe, CP, Stephens, ER, Smith, DB, Mahanti, A. Competitive Energy Trading Framework for Demand-Side Management in Neighborhood Area Networks. IEEE Trans Smart Grid. 2018; 9(5): 4313-4322. DOI:10.1109/TSG.2017.2654517.
[25] Liu, X, Gao, B, Wu, C, Tang, Y. Demand-side management with household plug-in electric vehicles: A Bayesian game-theoretic approach. IEEE Syst J. 2018; 12(3):2894-2904. DOI:10.1109/JSYST.2017.2741719.
[26] Wang, K, Li, H, Maharjan, S, Zhang, Y, Guo, S. Green Energy Scheduling for Demand Side Management in the Smart Grid. IEEE Trans Green Commun Netw. 2018; 2(2): 596-611. DOI:10.1109/TGCN.2018.2797533.
[27] Ghorbanian, M, Dolatabadi, SH, Siano, P. Game Theory-Based Energy-Management Method Considering Autonomous Demand Response and Distributed Generation Interactions in Smart Distribution Systems. IEEE Syst J. 2020; 15(1): 905-914. DOI:10.1109/jsyst.2020.2984730.
[28] Ye, F, Qian, Y, Hu, RQ. A Real-Time Information Based Demand-Side Management System in Smart Grid. IEEE Trans Parallel Distrib Syst. 2016; 27(2): 329-339. DOI:10.1109/TPDS.2015.2403833.
[29] Maharjan, S, Zhu, Q, Zhang, Y, Gjessing, S, Başsar, T. Dependable demand response management in the smart grid: A stackelberg game approach. IEEE Trans Smart Grid. 2013; 4(1): 120-132. DOI:10.1109/TSG.2012.2223766.
[30] Shinde, P, Swarup, KS. Stackelberg game-based demand response in multiple utility environments for electric vehicle charging. IET Electr Syst Transp. 2018; 8(3): 167-174. DOI:10.1049/iet-est.2017.0046.
[31] Guo, F, Wen, C, Li, Z. Distributed optimal energy scheduling based on a novel PD pricing strategy in smart grid. IET Gener Transm Distrib. 2017; 11(8): 2075-2084. DOI:10.1049/iet-gtd.2016.1722.
[32] Mishra, MK, Parida, SK. A Game Theoretic Approach for Demand-Side Management Using Real-Time Variable Peak Pricing Considering Distributed Energy Resources. IEEE Syst J. 2020: 1-11. DOI:10.1109/JSYST.2020.3033128.
[33] Kakran, S, Chanana, S. Energy Scheduling of Smart Appliances at Home under the Effect of Dynamic Pricing Schemes and Small Renewable Energy Source. Int J Emerg Electr Power Syst. 2018; 19(2): 1-12. DOI:10.1515/ijeeps-2017-0187.
[34] Monfared, HJ, Ghasemi, A, Loni, A, Marzband, M. A hybrid price-based demand response program for the residential micro-grid. Energy 2019; 185:274-285. DOI:10.1016/j.energy.2019.07.045.
[35] Asgher, U, Rasheed, MB, Awais, M. Demand Response Benefits for Load Management Through Heuristic Algorithm in Smart Grid. In: RAEE 2018 Int Symp Recent Adv Electr Eng; 17-18 Oct. 2018: IEEE, pp. 1-6. DOI:10.1109/RAEE.2018.8706886.
[36] Singh, BP, Gore, MM. Pricing scheme to ease energy poverty of low-income population in smart grid. Int Trans Electr Energy Syst. 2020; 30(11): 1-22. DOI:10.1002/2050-7038.12615.
[37] Azzam, SM, Salah, M, Elshabrawy, T, Ashour, M. A Decentralized Optimization Algorithm for Residential Demand Side Management in Smart Grids. In: IOTSMS 2019 Sixth International Conference on Internet of Things: Systems, Management and Security; 22-25 Oct. 2019: IEEE, pp. 307-313. DOI: 10.1109/IOTSMS48152.2019.8939169.
[38] Mishra, MK, Parida, SK. A Comparative Analysis of Real Time and Time of Use Pricing Schemes in Demand Side Management Considering Distributed Energy Resources. In: IEEE PES Innov Smart Grid Technol Eur ISGT-Europe; 29 Sept.-2 Oct. 2019: IEEE. DOI:10.1109/ISGTEurope.2019.8905770.
[39] Shi, W, Li, N, Xie, X, Chu, CC, Gadh, R. Optimal residential demand response in distribution networks. IEEE J Sel Areas Commun. 2014; 32(7): 1441-1450. DOI:10.1109/JSAC.2014.2332131.
[40] Atzeni, I, Ordonez, LG, Scutari, G, Palomar, DP, Fonollosa, JR. Demand-Side Management via Distributed Energy Generation and Storage Optimization. IEEE Trans Smart Grid. 2013; 4(2): 866-876. DOI:10.1109/TSG.2012.2206060.
[41] Nguyen, HK, Song, J, Bin, Han, Z. Distributed Demand Side Management with Energy Storage in Smart Grid. IEEE Trans Parallel Distrib Syst. 2015; 26(12): 3346-3357. DOI:10.1109/TPDS.2014.2372781.
[42] Internet Web-Site: https://www.iexindia.com/marketdata/areaprice.aspx, Indian Energy Exchange, 19 July 2021.