Comprehensive Optimal Management System of Distributed Resources Using Dynamic Neural Network in Modeling of Electricity Consumption Uncertainty for Grid-Connected Microgrids
Subject Areas : electrical and computer engineeringMohammad Veysi 1 , محمدرضا سلطانپور 2 * , jafar Khalilpour 3 , hadi niaei 4
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Keywords: Microgridupstream gridneighborhood systemreliabilitydynamic neural network,
Abstract :
In this paper, to enhance the optimal planning for power management of micrigrids, a strategy is proposed using power sharing through coordination between microgrids and the neighborhood system, which has no additional costs for generating units. The uncertainty values of electrical consumers are modeled by dynamic neural network, considering the implementation process and high accuracy of forecasting. In another view, to supply the electrical energy of microgrid, diesel generator, renewable energies such as solar energy and wind energy and so, battery energy storage are used, in addition to the upstream grid connection. As well as, using of the reliability factors, along with a detailed assessment of current costs will improve the performance of microgrid. Hence, the loss of power supply probability (LPSP) and loss of load expectations (LOLE) are expressed as factors for assessing the accuracy of current costs. The proposed model is implemented in GAMS and MATLAB environment and the simulation results clearly demonstrate the desired performance of the proposed algorithm, and leads to gaining revenue for the under-study system.
[1] W. P. Lee, J. Y. Choi, and D. J. Won, "Coordination strategy for optimal scheduling of multiple microgrids based on hierarchical system," Energies, vol. 10, no. 9, p. 1336, Sept. 2017.
[2] M. Yazdani-Damavandi, N. Neyestani, M. Shafie-Khah, J. Contreras, and J. P. Catalao, "Strategic behavior of multi-energy players in electricity markets as aggregators of demand side resources using a bi-level approach," IEEE Trans. on Power Systems, vol. 33, no. 1, pp. 397-411, Mar. 2017.
[3] A. Hussain, V. H. Bui, and H. M. Kim, "A resilient and privacy-preserving energy management strategy for networked microgrids," IEEE Trans. on Smart Grid, vol. 9, no. 3, pp. 2127-2139, Sept. 2016.
[4] F. Brahman, M. Honarmand, and S. Jadid, "Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system," Energy and Buildings, vol. 90, pp. 65-75, Mar. 2015.
[5] S. Pazouki, M. R. Haghifam, and A. Moser, "Uncertainty modeling in optimal operation of energy hub in presence of wind, storage and demand response," International J. of Electrical Power & Energy Systems, vol. 61, pp. 335-345, Jul. 2014.
[6] M. Tasdighi, H. Ghasemi, and A. Rahimi-Kian, "Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling," IEEE Trans. on Smart Grid, vol. 5, no. 1, pp. 349-357, Jul. 2013.
[7] ع. مهدی‌زاده و ن. تقی‌زادگان کلانتری، "برنامه‌ریزی بهینه اقتصادی یک ریزشبکه در حالت جزیره‌ای با در نظر گرفتن منابع تجدیدپذیر بادی و خورشیدی، باتری و سیستم ذخیره‌ساز هیدروژنی در حضور برنامه پاسخگویی بار،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، الف- مهندسی برق، سال 15، شماره 1، صص. 11-1، بهار 1396.
[8] J. Yang, J. Liu, Z. Fang, and W. Liu, "Electricity scheduling strategy for home energy management system with renewable energy and battery storage: a case study," IET Renewable Power Generation, vol. 12, no. 6, pp. 639-648, Apr. 2017.
[9] M. Marzband, H. Alavi, S. S. Ghazimirsaeid, H. Uppal, and T. Fernando, "Optimal energy management system based on stochastic approach for a home microgrid with integrated responsive load demand and energy storage," Sustainable Cities and Society, vol. 28, no. 1, pp. 256-264, Jan. 2017.
[10] N. Zhang, Y. Yan, S. Xu, and W. Su, "Game-theory-based electricity market clearing mechanisms for an open and transactive distribution grid," IEEE Power & Energy Society General Meeting, 5 pp., Denver, CO, USA, 26-30 Jul. 2015.
[11] Y. Yang, S. Bremner, C. Menictas, and M. Kay, "Battery energy storage system size determination in renewable energy systems: a review," Renewable and Sustainable Energy Reviews, vol. 91, pp. 109-125, Aug. 2018.
[12] V. Mohan, R. Suresh, J. G. Singh, W. Ongsakul, and N. Madhu, "Microgrid energy management combining sensitivities, interval and probabilistic uncertainties of renewable generation and loads," IEEE J. on Emerging and Selected Topics in Circuits and Systems, vol. 7, no. 2, pp. 262-270, Mar. 2017.
[13] F. Alismail, P. Xiong, and C. Singh, "Optimal wind farm allocation in multi-area power systems using distributionally robust optimization approach," IEEE Trans. on Power Systems, vol. 33, no. 1, pp. 536-544, Mar. 2017.
[14] Y. Liu, J. Yang, X. Zhu, Y. Wang, B. He, J. Zhu, et al., "Bi-level planning model for optimal allocation of WT-PV-ESS in distribution networks," The J. of Engineering, vol. 13, no. pp. 1696-1701, Apr. 2017.
[15] X. Yan, C. Gu, F. Li, and Z. Wang, "LMP-based pricing for energy storage in local market to facilitate PV penetration," IEEE Trans. on Power Systems, vol. 33, no. 3, pp. 3373-3382, Dec. 2017.
[16] K. Mahmud, M. S. H. Nizami, J. Ravishankar, M. J. Hossain, and P. Siano, "Multiple home-to-home energy transactions for peak load shaving," IEEE Trans. on Industry Applications, vol. 56, no. 2, pp. 1074-1085, Jan. 2020.
[17] M. I. Azim, W. Tushar, and T. K. Saha, "Investigating the impact of P2P trading on power losses in grid-connected networks with prosumers," Applied Energy, vol. 263, p. 114687, Apr. 2020.
[18] A. Paudel, L. Sampath, J. Yang, and H. B. Gooi, "Peer-to-peer energy trading in smart grid considering power losses and network fees," IEEE Trans. on Smart Grid, vol. 11, no. 6, pp. 4727-4737, May 2020.
[19] K. Zhang, S. Troitzsch, S. Hanif, and T. Hamacher, "Coordinated market design for peer-to-peer energy trade and ancillary services in distribution grids," IEEE Trans. on Smart Grid, vol. 11, no. 4, pp. 2929-2941, Jan. 2020.
[20] Z. Liang, Q. Alsafasfeh, T. Jin, H. Pourbabak, and W. Su, "Risk-constrained optimal energy management for virtual power plants considering correlated demand response," IEEE Trans. on Smart Grid, vol. 10, no.2, pp. 1577-1587, Nov. 2017.
[21] J. C. do Prado and W. Qiao, "A stochastic decision-making model for an electricity retailer with intermittent renewable energy and short-term demand response," IEEE Trans. on Smart Grid, vol. 10, no. 3, pp. 2581-2592, Feb. 2018.
[22] F. Jabari, A. Masoumi, and B. Mohammadi-Ivatloo, "Long-term solar irradiance forecasting using feed-forward back-propagation neural network," in Proc. 3rd Int. Conf. of IEA, 6 pp., Tehran, Iran, 28 Feb.-1 Mar. 2017.
[23] H. Dongmei, H. Shiqing, H. Xuhui, and Z. Xue, "Prediction of wind loads on high-rise building using a BP neural network combined with POD," J. of Wind Engineering and Industrial Aerodynamics, vol. 170, no. 1, pp. 1-17, Nov. 2017.
[24] A. Masoumi, S. Ghassem-Zadeh, S. H. Hosseini, and B. Z. Ghavidel, "Application of neural network and weighted improved PSO for uncertainty modeling and optimal allocating of renewable energies along with battery energy storage," Applied Soft Computing, vol. 88, p. 105979, Mar. 2020.
[25] M. J. H. Moghaddam, et al., "Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm," Renewable Energy, vol. 135, pp. 1412-1434, May 2019.
[26] ر. غفارپور، س. زمانیان، ع. خان احمدی و م. وحید پاکدل، " طراحی بهینه و اجرای سیستم تأمین انرژی پایگاه مرزی با در نظر گرفتن عدم قطعیت،" نشریه علوم و فناوري پدافند نوين، سال 10، شماره 3، صص. 250-243، پاييز 1398.
[27] A. Masoumi, F. Jabari, and B. Mohammadi-Ivatloo, "Wind speed forecasting using back propagation artificial neural networks in north of Iran," J. of Energy Management and Technology, vol. 1, no. 3, pp. 21-27, Dec. 2017.
[28] F. Hamzeh Aghdam, J. Salehi, and S. Ghaemi, "Assessment of power flow constraints impact on the energy management system of multi-microgrid based distribution network," J. of Energy Management and Technology, vol. 2, no. 3, pp. 31-41, Sept. 2018.
[29] A. Luth, J. M. Zepter, P. C. del Granado, and R. Egging, "Local electricity market designs for peer-to-peer trading: the role of battery flexibility," Applied Energy, vol. 229, pp. 1233-1243, Nov. 2018.
[30] M. Marzband, R. R. Ardeshiri, M. Moafi, and H. Uppal, "Distributed generation for economic benefit maximization through coalition formation-based game theory concept," International Trans. on Electrical Energy Systems, vol. 27, no. 6, p. 2313, Jun. 2017.
[31] M. Sedghi, S. K. Hannani, and M. Boroushaki, "Estimation of weibull parameters for wind energy application in Iran's cities," Wind and Structures, vol. 21, no. 2, pp. 203-221, Aug. 2015.
[32] M. Majidi, S. Nojavan, and K. Zare, "Optimal stochastic short-term thermal and electrical operation of fuel cell/photovoltaic/battery/grid hybrid energy system in the presence of demand response program," Energy Conversion and Management, vol. 144, pp. 132-142, Jul. 2017.
[33] T. Khalili, A. Jafari, M. Abapour, and B. Mohammadi-Ivatloo, "Optimal battery technology selection and incentive-based demand response program utilization for reliability improvement of an insular microgrid," Energy, vol. 169, pp. 92-104, Feb. 2019.
[34] M. Marzband, F. Azarinejadian, M. Savaghebi, E. Pouresmaeil, J. M. Guerrero, and G. Lightbody, "Smart transactive energy framework in grid-connected multiple home microgrids under independent and coalition operations," Renewable Energy, vol. 126, pp. 95-106, Oct. 2018.
[35] E. Sorin, L. Bobo, and P. Pinson, "Consensus-based approach to peer-to-peer electricity markets with product differentiation," IEEE Trans. on Power Systems, vol. 34, no. 2, pp. 994-1004, Oct. 2018.