Presenting a Multi-objective Mathematical Model for Smart Grids Considering Load Response Programs

Document Type : Research Paper

Authors

1 Ph.D., Department of Industrial Management, Faculty of Industrial and Technology Management, College of Management, University of Tehran, Tehran, Iran.

2 Prof., Department of Operations Management and Decision Sciences, Faculty of Industrial and Technology Management, College of Management, University of Tehran, Tehran, Iran.

3 Associate Prof., Department of Industrial Engineering, Faculty of Industrial and Systems Engineering, College of Engineering, University of Tehran, Tehran.

Abstract

Objective
The availability of energy is a vital aspect of a nation’s economic and social development, with energy consumption serving as a telling metric of the level of prosperity that can be achieved. However, the conventional systems of electricity production that rely on large, centralized power plants have become inadequate in recent years due to the high expenses of production, air pollution, and poor energy quality. In response to these challenges, smart grids have emerged and offer several advantages. Effective management of electricity demand is critical in the context of smart grids, and the implementation of demand-response techniques plays an instrumental role in achieving this objective. These programs enhance energy consumption patterns during peak load times, resulting in appropriate pricing and grid reliability. There are two distinct categories of load response programs: price-oriented and incentive-oriented. In the scope of this research, we focused on the former, which relies on real-time pricing. Our objective was to develop a multi-objective mathematical model that considers load response programs for smart energy grids.
 
Methods
The study employed a scenario-based approach and classified the parameters into two distinct categories: deterministic and non-deterministic. Wind speed, solar radiation, energy demand, and local electricity prices were marked as non-deterministic due to their nature. As each non-deterministic parameter adheres to a specific probability distribution, a scenario was created for each parameter based on its corresponding distribution. Subsequently, a mathematical multi-objective model was developed that aimed to minimize operating costs, reduce pollution emissions, and minimize peak load, along with the related constraints. After collecting the required data, the model was run using the GAMS programming language. In addition, the study evaluated the impact of load response programs on enhancing objective functions.
 
Results
The study findings demonstrate that the implementation of smart grids, accompanied by active consumer participation in load response programs, can result in a significant reduction in operating costs, pollution emissions, and peak load. Additionally, the study indicates that a higher level of consumer participation in load response programs can enhance the overall effectiveness of the programs. Specifically, the study shows that a 20% increase in consumer participation resulted in a 15%, 17%, and 13% improvement in operating costs, pollution emissions, and peak load reduction, respectively.
 
Conclusion
Smart grids represent a modern digital solution that streamlines the transfer of electricity between suppliers and consumers in the realm of energy transmission. This advanced system enables the regulation of home appliances, promoting energy conservation and cost-effectiveness, while simultaneously enhancing the reliability of the energy transmission network. Governments may opt to implement smart grids as a strategic solution to address complex issues such as energy independence, global warming, and pollution emissions.

Keywords

Main Subjects

References

 
Abdulnasser, G., Ali, A., Shaaban, M. F. & Mohamed, E. E. M. (2022). Stochastic multi-objectives optimal scheduling of energy hubs with responsive demands in smart microgrids. Journal of Energy Storage, 55, 105536.
Aghajani, G. R., Shayanfar, H. A. & Shayeghi, H. (2015). Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Conversion and Management, 106, 308–321.
Aghajani, G., Shayanfar, H., Shayeghi, H (2017). Demand side management in a smart micro-grid in the presence of renewable generation and demand response, Energy, 126, 622-637.
Awan, A., Abbasi, K. R., Rej, S., Bandyopadhyay, A. & Lv, K. (2022). The impact of renewable energy, internet use and foreign direct investment on carbon dioxide emissions: A method of moments quantile analysis. Renewable Energy, 189, 454–466.
Bahmani, R., Karimi, H. & Jadid, S. (2021). Cooperative energy management of multi-energy hub systems considering demand response programs and ice storage. International Journal of Electrical Power & Energy Systems, 130, 106904.
Cao, Y., Wang, Q., Du, J., Nojavan, S., Jermsittiparsert, K., Ghadimi, N. (2019).Optimal operation of CCHP and renewable generation-based energy hub considering environmental perspective: An epsilon constraint and fuzzy methods, Sustainable Energy, Grids and Networks, 20, 100274.
Chen, C., Zhao, H., Qiu, T., Hu, M., Han, H. & Ren, Z. (2017). An efficient power saving polling scheme in the internet of energy. Journal of Network and Computer Applications, 89, 48–61.
Cheng, Y., Peng, J., Liu, K., Jiang, F., Wu, Y. & Huang, Z. (2022). A distributed energy management scheme with the extended optimization horizon for Energy Internet. Sustainable Energy, Grids and Networks, 31, 1–15.
Cheng, Y., Peng, J., Liu, K., Jiang, F., Wu, Y., Huang, Z (2022). A distributed energy management scheme with the extended optimization horizon for Energy Internet, Sustainable Energy, Grids and Networks, 31, 100698.
Eghbali, N., Hakimi, S. M., Hasankhani, A., Derakhshan, G. & Abdi, B. (2022). Stochastic energy management for a renewable energy based microgrid considering battery, hydrogen storage, and demand response. Sustainable Energy, Grids and Networks, 30, 100652.
Eshraghi, A., Salehi, G., Heibati, S., Lari., K. (2019). An enhanced operation model for energy storage system of a typical combined cool, heat and power based on demand response program: The application of mixed integer linear programming. Building Services Engineering Research and Technology, 40, 47-74.
Fatemi, S., Ketabi, A., Mansouri, SA. (2023). A multi-level multi-objective strategy for eco-environmental management of electricity market among micro-grids under high penetration of smart homes, plug-in electric vehicles and energy storage devices, Journal of Energy Storage, 67, 107632.
Hong, Z., Feng, Y., Li, Z., Wang, Y., Zheng, H., Li, Z. & Tan, J. (2019). An integrated approach for multi-objective optimisation and MCDM of energy internet under uncertainty. Future Generation Computer Systems, 97, 90–104.
Hosseini, S., Ahmarinejad, A. (2021). Stochastic framework for day-ahead scheduling of coordinated electricity and natural gas networks considering multiple downward energy hubs. Journal of Energy Storage, 33, 102066.
Jalilian, F., Mirzaei, M., Zare, K., Mohammadi-Ivatloo, B., Marzband, M., Amjad Anvari-Moghaddam, A (2022). Multi-energy microgrids: An optimal despatch model for water-energy nexus, Sustainable Cities and Society, 77, 103573.
Jani, A., Karimi, H., Jadid, SH. (2022). Multi-time scale energy management of multi-microgrid systems considering energy storage systems: A multi-objective two-stage optimization framework, Journal of Energy Storage, 51, 10454.
Jordehi, A., Javadi, M., Catalão, J (2021). Day-ahead scheduling of energy hubs with parking lots for electric vehicles considering uncertainties, Energy, 229, 120709.
Karimi, H; Jadid, Sh; Hasanzadeh, S. (2023). Optimal-sustainable multi-energy management of microgrid systems considering integration of renewable energy resources: A multi-layer four-objective optimization. Sustainable Production And consumption, 36, 126–138.
Karimi, H., Jadid, S. & Makui, A. (2021). Stochastic energy scheduling of multi-microgrid systems considering independence performance index and energy storage systems. Journal of Energy Storage, 33, 1-12.
Karimi, H., Jadid, S. & Saboori, H. (2019). Multi-objective bi-level optimisation to design real-time pricing for demand response programs in retail markets. IET Generation, Transmission & Distribution, 13, 1287-1296.
Khan, A. R., Mahmood, A., Safdar, A., Khan, Z. A. & Khan, N. A. (2016). Load forecasting, dynamic pricing and DSM in smart grid: A review. Renewable and Sustainable Energy Reviews, 54, 1311–1322.
Li, P., Cai, G., Z. (2020). Multi-objective optimal allocation strategy for the energy internet in Huangpu District, Guangzhou, China. Frontiers in Energy, 14, 241–253.
Lin, C. C., Wu, Y. F. & Liu, W. Y. (2021). Optimal sharing energy of a complex of houses through energy trading in the Internet of energy. Energy, 220, 1-12.
Liu, H., Zhao, Y., Ge, Sh., Zhang, P., Liu, W., Xiaoguang, H. (2021). Reliability evaluation of regional energy Internet considering electricity–gas coupling and coordination between energy stations. IET Energy Systems Integration, 3, 238–249.
Long, H., Fu, X., Kong, W., Chen, H., Zhou, Y. & Yang, F. (2022). Key technologies and applications of rural energy internet in China. Information Processing in Agriculture, https://doi.org/10.1016/J.INPA.2022.03.001
Lu, X., Li, H., Zhou, K. & Yang, S. (2023). Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response. Energy, 262, 125564.
Mansouri, S.A., Ahmarinejad, A., Nematbakhsh, E., Javadi, M.S., Jordehi, A.R., Catalão, J.P. (2021). Energy management in microgrids including smart homes: A multi-objective approach, Sustainable Cities and Society, 69, 1-16.
Mehregan, M., Jafarnejad, A., Mohammadi, M. (2018). Proposing a Multi-objective Model for Ground Transportation of Hazardous Materials in the Hub Network (Case Study: National Iranian Oil Products Distribution Company). Industrial Management Journal, 10(2), 201-220. (in Persian)
Mohammadii, Y., Shakouri, G., H., Kazemi, A. (2022). A Multi-Objective Fuzzy Optimization Model for Electricity Generation and Consumption Management in a Micro Smart Grid. Sustainable Cities and Society, 86, 104119.
Monemi Bidgoli, M., Karimi, H., Jadid, Sh., Anvari-Moghaddam, A. (2021). Stochastic electrical and thermal energy management of energy hubs integrated with demand response programs and renewable energy: A prioritized multi-objective framework. Electric Power Systems Research, 196, 1-12.
Mokaramian, E., Shayeghi, H., Sedaghati, F. & Safari, A. (2021). Four-Objective Optimal Scheduling of Energy Hub Using a Novel Energy Storage, Considering Reliability and Risk Indices. Journal of Energy Storage, 40, 102731.
Miao, P., Yue, Z., Niu, T., Alizadeh, A. & Jermsittiparsert, K. (2021). Optimal emission management of photovoltaic and wind generation based energy hub system using compromise programming. Journal of Cleaner Production, 281, 124333.
Niazvand, F., Kharrati, S., Khosravi, F., Rastgou, A. (2021). Scenario-based assessment for optimal planning of multi-carrier hub-energy system under dual uncertainties and various scheduling by considering CCUS technology, Sustainable Energy Technologies and Assessments,46, 101300.
Parsian, F. & Rezaee, B. (2019). Determining the Energy Sources of the Electric Vehicles Charging Station According to Economic Factors. Industrial Management Journal, 11(2), 233-254. (in Persian)
PVWatts Calculator. Available online: https://pvwatts.nrel.gov (accessed on April 2022).
Qiu, C., Cui, S., Yao, H., Xu, F., Yu, F. R. & Zhao, C. (2019). A novel QoS-enabled load scheduling algorithm based on reinforcement learning in software-defined energy internet. Future Generation Computer Systems, 92, 43–51.
Radsar, M., Kazemi, A., Mehregan, M. & Razavi Hajiagha, S.H. (2021). Designing an algorithm based on network data envelopment analysis with desirable and undesirable indicators for the evaluation of the Iranian power industry. Industrial Management Journal, 13(1), 1-26. (in Persian)
Rakipour, D; Barati, H. (2019). Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response. Energy, 173, 384–399.
Shen, Y., Hu, W., Liu, M., Yang, F. & Kong, X. (2022). Energy storage optimization method for microgrid considering multi-energy coupling demand response. Journal of Energy Storage, 45, 1–9.
Soltani Nejad Farsangi, A., Hadayeghparast, S., Mehdinejad, M. & Shayanfar, H. (2018). A novel stochastic energy management of a microgrid with various types of distributed energy resources in presence of demand response programs. Energy, 160, 257–274.
 
Thirunavukkarasu, G., Seyedmahmoudian, M., Jamei, E., Horan, B., Mekhilef, S., Stojcevski, A. (2022). Role of optimization techniques in microgrid energy management systems—A review, Energy Strategy Reviews, 43, 100899.
Wang, K., Liang, Y., Jia, R., Wu, X., Wang, X., Dang, P.(2023). Two-stage stochastic optimal scheduling for multi-microgrid networks with natural gas blending with hydrogen and low carbon incentive under uncertain envinronments, Journal of Energy Storage, 72, 108319.
Wang, R., Xu, T., Xu, X., Gao, G., Zhang, Y., Zhu, K. (2023). Robust multi-objective load dispatch in microgrid involving unstable renewable generation, International Journal of Electrical Power & Energy Systems, 148, 108991.
Wang, Y., Huang, Y., Wang, Y., Zeng, M., Li, F., Wang, Y. & Zhang, Y. (2018). Energy management of smart micro-grid with response loads and distributed generation considering demand response. Journal of Cleaner Production, 197, 1069–1083
Ying, W Wu, Y., Guerrero, J. M. & Vasquez, J. C. (2021). A comprehensive overview of framework for developing sustainable energy internet: From things-based energy network to services-based management system. Renewable and Sustainable Energy Reviews, 150, 1-11.
Yunna, W, Zhang, T. & Yi, L. (2021). Regional energy internet project investment decision making framework through interval type-2 fuzzy number based Choquet integral fuzzy synthetic model. Applied Soft Computing, 111, 1-12.
Yang, S. X., Nie, T. qi & Li, C. C. (2022). Research on the contribution of regional Energy Internet emission reduction considering time-of-use tariff. Energy, 239, 1-12.
Yang, S. X., Zhu, C. X., Qiao, L. & Chi, Y. Y. (2020). Dynamic assessment of Energy Internet’s emission reduction effect - a case study of Yanqing, Beijing. Journal of Cleaner Production, 272, 1-12.
Yang, X., Su, X., Ran, Q. et al. (2021). Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China. Environmental Science and Pollution Research, 29, 23436–23460.
Yang, S. X., Nie, T. qi & Li, C. C. (2022). Research on the contribution of regional Energy Internet emission reduction considering time-of-use tariff. Energy, 239, 122170.
Zhou, K., Yang, S. & Shao, Z. (2016). Energy Internet: The business perspective. Applied Energy, 178, 212–222.
Zhao, J., Wang, X., Tian, H (2022). Optimization strategy and capacity planning for coordinated operation of regional energy internet system based on sparrow search algorithm. International Journal of Green Energy, 19, 1-17.
Industrial Management Journal
Volume 16, Issue 2
2024
Pages 215-249

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