Journal of Energy Management and Technology

Journal of Energy Management and Technology

Day-Ahead Demand Response in Microgrid Operation Considering Renewable Uncertainty and Network Reconfiguration

Document Type : Original Article

Authors
1 Department of Electrical Engineering, Faculty of Engineering, Shahid Beheshti University, Tehran, Iran
2 Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
10.22109/jemt.2025.505550.1540
Abstract
Optimal energy management in microgrids will increase their economic and technical efficiency, and it is usually carried out as an optimization problem for day-ahead operation strategies. Although many perspectives have been proposed for optimal operation, with increasing energy demand and imbalance between production and consumption, the lack of coherent planning to reduce costs and environmental consequences is still felt. Therefore, creating a new framework to consider uncertainties and operation issues simultaneously is necessary to increase reliability. For this purpose, in this paper, a two-level energy management system is presented at the first level determines the role of the load response program, the unit Synchronization, the generator unit production rate, and the storage charge and discharge rate, then the network reconfiguration in the presence of renewable energy sources is considered with the aim of maximizing the network operating profit and minimizing environmental pollutants at the second level. Also, the output power of renewable energy sources, including solar and wind, with uncertainty and scenario generation, has been considered. The presented model has been tested as a combination of two software, MATLAB and DigiSilent, on a 33-bus IEEE network to make it more realistic and increase the accuracy of the simulation. The results show that the load response program, along with the network feeder rearrangement and the problem of bringing the units into orbit simultaneously, in addition to reducing losses by 20% and increasing reliability by 10%, results in profitability for the microgrid operator.
Keywords

Subjects


[1]     G. Ferruzzi, G. Cervone, L. Delle Monache, G. Graditi, and F. Jacobone, “Optimal bidding in a day-ahead energy market for micro grid under uncertainty in renewable energy production”, Energy, vol. 106, pp. 194–202, 2016.
[2]     M. Di Somma, G. Graditi, E. Heydarian-Forushani, Shafie-khah, and P. Siano. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects." Renewable energy, vol. 116, pp. 272-287, 2018.
[3]     S. Aghajan-Eshkevari, M. T. Ameli, and S. Azad. “Electric Vehicle Services to Support the Power Grid.” Electric Vehicle Integration via Smart Charging: Technology, Standards, Implementation, and Applications. Cham: Springer International Publishing, pp. 129-148, 2022.
[4]     Ch. Huang, K. Li, and N. Zhang. “Strategic joint bidding and pricing of load aggregators in day-ahead demand response market.” Applied Energy vol. 377, pp. 124552, 2025.
[5]     M. Nojavan, and P. Maghouli. “A Novel Dynamic Pricing Time‐Based Demand Response Program for Net‐Load Flexibility of Microgrids.” International Journal of Energy Research vol.1, pp. 2104716, 2024.
[6]     D. Gutiérrez-Oliva, A. Colmenar-Santos, and E. Rosales-Asensio. “A review of the state of the art of industrial microgrids based on renewable energy.” Electronics vol.11, pp. 1002, 2022.
[7]     I. Ben Hamida, S.B. Salah, F. Msahli, and M.F. Mimouni, “Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs” Renew. Energy, vol. 121, pp. 66–80, 2018.
[8]     K. Jalilpoor, M. T. Ameli, S. Azad, and Z. Sayadi. “Resilient energy management incorporating energy storage system and network reconfiguration: A framework of cyber‐physical system.” IET Generation, Transmission & Distribution, vol.17, pp. 1734-1749, 2023.
[9]     M. Choobdari, M. Samiei Moghaddam, R. Davarzani, A. Azarfar, and H. Hoseinpour. “Robust distribution networks reconfiguration considering the improvement of network resilience considering renewable energy resources.” Scientific Reports, vol. 14, pp. 23041, 2024.
[10]  A. Meydani, H. Shahinzadeh, H. Nafisi, and G. B. Gharehpetian. “Optimizing Microgrid Energy Management: Metaheuristic versus Conventional Techniques.” 2024 11th Iranian Conference on Renewable Energy and Distribution Generation (ICREDG). Vol. 11. IEEE, 2024.
[11]  M. Raheel Khan, Z. Maqsood Haider, F. H. Malik, F. M. Almasoudi, Kh. S. S. Alatawi, and M. Sh. Bhutta. "A comprehensive review of microgrid energy management strategies considering electric vehicles, energy storage systems, and AI techniques." Processes, vol. 12, pp. 270, 2024.
[12]  S. Nojavan, B. Mohammadi-Ivatloo, and K. Zare. “Optimal bidding strategy of electricity retailers using robust optimization approach considering time-of-use rate demand response programs under market price uncertainties”, IET Gener. Transm. Distrib, vol.9, pp. 328–338, 2015.
[13]  Sh. Ge, T. Li, and H. Liu. “Research on low carbon electricity under the probabilistic reliability evaluation”, IET Gener. Transm. Distrib., vol. 9, pp. 2374–2381, 2015.
[14]  P. Harsh, and D. Das. “Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources.” Sustainable Energy Technologies and Assessments, vol. 46, pp. 101225, 2021.
[15]  H. Qi, X. Yan, Y.Kang, Z. Yang, S. Ma, and Y. Mi. “Multi-objective optimization strategy for the distribution network with distributed photovoltaic and energy storage.” Frontiers in Energy Research, vol. 12, pp. 1418893, 2024.
[16]  T. T. Nguyen. “A novel metaheuristic method based on artificial ecosystem-based optimization for optimization of network reconfiguration to reduce power loss.” Soft Computing, vol. 25, pp. 14729-14740, 2021.
[17]  M. K. Kiptoo, O. B. Adewuyi, H. O. R. Howlader, A. Nakadomari, and T. Senjyu. “Optimal capacity and operational planning for renewable energy-based microgrid considering different demand-side management strategies.” Energies, vol. 16, pp. 4147, 2023.
[18]  M. R. Behbahani, A. Jalilian, A. Bahmanyar, and  D. Ernst. “Comprehensive Review on Static and Dynamic Distribution Network Reconfiguration Methodologies.” IEEE Access 2024.
[19]  J. Moosanezhad, A. Basem, F. khalafian, A. G. Alkhayer, A. H. Al-Rubaye, M. Khosravi, and H. Azarinfar. “Day-ahead resilience-economic energy management and feeder reconfiguration of a CCHP-based microgrids, considering flexibility of supply.” Heliyon 2024.
[20]  S. Pirouzi, M. Zadehbagheri, and S. Behzadpoor. “Optimal placement of distributed generation and distributed automation in the distribution grid based on operation, reliability, and economic objective of distribution system operator.” Electrical Engineering, pp. 1-14, 2024.
[21]  R. Parsibenehkohal, M. Jamil, and A. Ali Khan. “A multi-stage framework for coordinated scheduling of networked microgrids in active distribution systems with hydrogen refueling and charging stations.” International Journal of Hydrogen Energy, vol. 71, pp. 1442-1455, 2024.
[22]  A. Dogan. “Analyzing flexibility options for microgrid management from economical operational and environmental perspectives.” International Journal of Electrical Power & Energy Systems, vol. 158, pp. 109914, 2024.
[23]  M. Choobdari, M. Samiei Moghaddam, R. Davarzani, A. Azarfar, and H. Hoseinpour. “Robust distribution networks reconfiguration considering the improvement of network resilience considering renewable energy resources.” Scientific Reports, vol. 14, pp. 23041. 2024.
[24]  E. Akbari, A. Faraji Naghibi, M. Veisi, A. Shahparnia, and S. Pirouzi. “Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index.” Scientific Reports, vol. 14, pp. 19136, 2024.
[25]  J. Y. Li, J. J. Chen, Y. X. Wang, and W. G. Chen. “Combining multi-step reconfiguration with many-objective reduction as iterative bi-level scheduling for stochastic distribution network.” Energy, vol. 290, pp. 130198, 2024.
[26]  C. Álvarez-Arroyo, S. Vergine, A. Sánchez de la Nieta, L. Alvarado-Barrios, and G. D’Amico. “Optimising microgrid energy management: Leveraging flexible storage systems and full integration of renewable energy sources.” Renewable Energy, pp. 120701, 2024.
[27]  A. Niknami, M. Tolou Askari, M. Amir Ahmadi, M. Babaei Nik, and M. Samiei Moghaddam . “Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables.” Cleaner Engineering and Technology, vol. 20, pp. 100763, 2024.
[28]  A. Ali, A. S. Saand, Sh. Ali, R. A. Siddiqui, M. A. Koondhar, L. Albasha, and F. Alsaif. “A Novel Hybrid Multi-Operator Evolutionary Algorithm for Dynamic Distributed Generation Optimization and Optimal Feeder Reconfiguration.” 2024.
[29]  Sh. S. Parihar, and N. Malik. “Network reconfiguration in the presence of optimally integrated multiple distributed generation units in a radial distribution network.” Engineering Optimization, vol. 56, pp. 679-699, 2024.
[30]  M. Ahmadi Kamarposhti, I. Colak, H. Shokouhandeh, C. Iwendi, S. Padmanaban, and Sh. S. Band. “Optimum operation management of microgrids with cost and environment pollution reduction approach considering uncertainty using multi‐objective NSGAII algorithm.” IET Renewable Power Generation, vol. 19, pp. e12579, 2025.
[31]  S. Habib, M. Ahmadi Kamarposhti, H. Shokouhandeh, I. Colak, and E. M. Barhoumi. “Economic dispatch optimization considering operation cost and environmental constraints using the HBMO method.” Energy Reports, vol. 10, pp. 1718-1725, 2023.
[32]  M. Marzband, M. Javadi, J. L. Domínguez-García, and M. Mirhosseini Moghaddam “Non‐cooperative game theory-based energy management systems for energy district in the retail market considering DER uncertainties.” IET Generation, Transmission & Distribution, vol. 10, pp. 2999-3009, 2016.
[33]  M. S. Jonban, L. Romeral, M. Marzband, and A. Abusorrah. “Intelligent fault tolerant energy management system using first-price sealed-bid algorithm for microgrids.” Sustainable Energy, Grids and Networks, vol. 38, pp. 101309, 2024.
[34]  M. S. Jonban, L. Romeral, M. Marzband, and A. Abusorrah. “A reinforcement learning approach using Markov decision processes for battery energy storage control within a smart contract framework.” Journal of Energy Storage, vol. 86, pp. 111342, 2024.
[35]  J. Hossain, N. Saeed, R. Manojkumar, M. Marzband, Kh. Sedraoui, and Y. Al-Turki. “Optimal peak-shaving for dynamic demand response in smart Malaysian commercial buildings utilizing an efficient PV-BES system.” Sustainable Cities and Society, vol. 101, pp. 105107, 2024.
[36]  M. A. Mirzaei, K. Zare, B. Mohammadi-Ivatloo, M. Marzband, and A. Anvari-Moghaddam “Techno‐economic, environmental and risk analysis of coordinated electricity distribution and district heating networks with flexible energy resources.” IET Renewable Power Generation, vol. 17, pp. 2935-2949, 2023.
[37]  B. Ahmadisourenabadi, M. Marzband, S. Hosseini-Hemati, S. M. B. Sadati, and A. Rastgou. “Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a Bayesian network risk assessment.” Energy, vol. 308, pp. 133036, 2024.
[38]  M. Marzband, M. Javadi, S. A. Pourmousavi, and G. Lightbody. “An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory.” Electric Power Systems Research, vol. 157, pp. 187-199, 2018.
[39]  S. M. Mohseni‐Bonab, Seyed, and A. Rabiee. “Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi‐objective model at the presence of uncertain wind power generation.” IET Generation, Transmission & Distribution, vol. 11, pp. 815-829, 2017.
[40]  N. Gupta, A. Swarnkar, and K. R. Niazi. “Distribution network reconfiguration for power quality and reliability improvement using Genetic Algorithms.” International Journal of Electrical Power & Energy Systems, vol.54, pp. 664-671, 2014.
[41]  M. S. Khomami, K. Jalilpoor, M. Tourandaz Kenari, and M. S. Sepasian. “Bi‐level network reconfiguration model to improve the resilience of distribution systems against extreme weather events.” IET Generation, Transmission & Distribution, vol.13, pp. 3302-3310, 2019.
[42]  B. Yousefi-khangah, S. Ghassemzadeh, S. H. Hosseini, and B. Mohammadi-Ivatloo. “Short‐term scheduling problem in smart grid considering reliability improvement in bad weather conditions.” IET Generation, Transmission & Distribution, vol. 11, pp. 2521-2533, 2017.
[43]  Y. Xing, Ch. Meng, W. Song, H. Zhao, Q. Li and E. Hu “Evaluating the contribution of demand response to renewable energy exploitation in smart distribution grids considering multi-dimensional behavior-driven uncertainties.” Science and Technology for Energy Transition, vol. 79, pp. 77, 2024.
[44]  O. T. Amusan, N. I. Nwulu, and S. L. Gbadamosi. “Optimal Solar-Biomass-Diesel-Generator Hybrid Energy for Water Pumping System Considering Demand Response.” Engineering Reports, pp. e13055, 2024.
[45] M. J. H. Hadidian, A. Kalam, S. A. Nowdeh, A. Ahmadi, M. Babanezhad and S. Saha. “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, 2019.
Volume 10, Issue 1
Winter 2026
Pages 23-34

  • Receive Date 13 February 2025
  • Revise Date 08 May 2025
  • Accept Date 06 June 2025