ASSESSMENT OF CONSUMERS POWER CONSUMPTION OPTIMIZATION BASED ON DEMAND SIDE MANAGEMENT
To ensure the functioning of the energy system, coordination and increase the efficiency of its parts need new control mechanisms. Generation, transmission and consumption of electricity needed control mechanisms that include integration of self-organizing power and heat supply systems, built on multi-agent principle. Also they must correspond intellectual basis, monitoring and accumulation. This includes effectiveness assessment of the state and analysis of technical, technological and organizational management mechanisms. One of the main parts is interaction principles of energy systems in accordance with European Community policy at various levels at liberalized electricity market.
In most developed countries, demand management programs are widely used as a means of harmonizing the modes of generation and consumption in the power supply system. The main direct methods are set in the form of electricity tariffs. Indirect methods are set in the form of programs to manage electricity demand and the possibility of their application to manage electricity demand. Methods for estimating the unevenness of the daily schedule of electricity consumption and the factors influencing the technological environment are presented.
The work aims at scientific and applied problem – finding methods of estimation and features of managing the demand for electricity.
The use of the proposed estimation methods of electricity consumption influence non-uniformity on the level of power supplies system losses based on Frize QF power and optimization of consumers’ operation modes in the power supply system is considered.
Approaches and optimization mechanisms of the daily electricity consumption on the example of a residential complex with the possibility of energy accumulation are offered
Yousefi‐khangah, B., Ghassemzadeh, S., Hosseini, S. H., Mohammadi‐Ivatloo, B. (2017). Short‐term scheduling problem in smart grid considering reliability improvement in bad weather conditions. IET Generation, Transmission & Distribution, 11 (10), 2521–2533. doi: https://doi.org/10.1049/iet-gtd.2016.1261
Chiu, T.-C., Shih, Y.-Y., Pang, A.-C., Pai, C.-W. (2017). Optimized Day-Ahead Pricing With Renewable Energy Demand-Side Management for Smart Grids. IEEE Internet of Things Journal, 4 (2), 374–383. doi: https://doi.org/10.1109/jiot.2016.2556006
Cao, Z., Lin, J., Wan, C., Song, Y., Zhang, Y., Wang, X. (2017). Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Transactions on Smart Grid, 8 (4), 1943–1955. doi: https://doi.org/10.1109/tsg.2015.2512712
Liu, Y., Yuen, C., Huang, S., Hassan, N., Wang, X., Xie, S. (2014). Peak-to-average ratio constrained demand-side management with consumer's preference in residential smart grid. IEEE Journal of Selected Topics in Signal Processing, 8 (6), 1084–1097. doi: https://doi.org/10.1109/jstsp.2014.2332301
Liu, Y., Hassan, N. U., Huang, S., Yuen, C. (2013). Electricity cost minimization for a residential smart Grid with distributed generation and bidirectional power transactions. 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT). doi: https://doi.org/10.1109/isgt.2013.6497859
Srikanth Reddy, K., Panwar, L., Panigrahi, B. K., Kumar, R., Yu, H. (2017). Demand side management with consumer clusters in cyber‐physical smart distribution system considering price‐based and reward‐based scheduling programs. IET Cyber-Physical Systems: Theory & Applications, 2 (2), 75–83. doi: https://doi.org/10.1049/iet-cps.2017.0008
Chen, H., Li, Y., Louie, R. H. Y., Vucetic, B. (2014). Autonomous Demand Side Management Based on Energy Consumption Scheduling and Instantaneous Load Billing: An Aggregative Game Approach. IEEE Transactions on Smart Grid, 5 (4), 1744–1754. doi: https://doi.org/10.1109/tsg.2014.2311122
Costanzo, G. T., Zhu, G., Anjos, M. F., Savard, G. (2012). A System Architecture for Autonomous Demand Side Load Management in Smart Buildings. IEEE Transactions on Smart Grid, 3 (4), 2157–2165. doi: https://doi.org/10.1109/tsg.2012.2217358
Luo, T., Dolan, M. J., Davidson, E. M., Ault, G. W. (2015). Assessment of a New Constraint Satisfaction-Based Hybrid Distributed Control Technique for Power Flow Management in Distribution Networks with Generation and Demand Response. IEEE Transactions on Smart Grid, 6 (1), 271–278. doi: https://doi.org/10.1109/tsg.2014.2327482
Martirano, L., Habib, E., Parise, G., Greco, G., Manganelli, M., Massarella, F., Parise, L. (2017). Demand Side Management in Microgrids for Load Control in Nearly Zero Energy Buildings. IEEE Transactions on Industry Applications, 53 (3), 1769–1779. doi: https://doi.org/10.1109/tia.2017.2672918
Nguyen, H. K., Song, J. B., Han, Z. (2015). Distributed Demand Side Management with Energy Storage in Smart Grid. IEEE Transactions on Parallel and Distributed Systems, 26 (12), 3346–3357. doi: https://doi.org/10.1109/tpds.2014.2372781
Fadlullah, Z. M., Quan, D. M., Kato, N., Stojmenovic, I. (2014). GTES: An Optimized Game-Theoretic Demand-Side Management Scheme for Smart Grid. IEEE Systems Journal, 8 (2), 588–597. doi: https://doi.org/10.1109/jsyst.2013.2260934
Galvis, J. C., Costa, A. (2016). Demand Side Management Using Time of Use and Elasticity Price. IEEE Latin America Transactions, 14 (10), 4267–4274. doi: https://doi.org/10.1109/tla.2016.7786304
Qian, L. P., Zhang, Y. J. A., Huang, J., Wu, Y. (2013). Demand Response Management via Real-Time Electricity Price Control in Smart Grids. IEEE Journal on Selected Areas in Communications, 31 (7), 1268–1280. doi: https://doi.org/10.1109/jsac.2013.130710
Atzeni, I., Ordonez, L. G., Scutari, G., Palomar, D. P., Fonollosa, J. R. (2013). Demand-Side Management via Distributed Energy Generation and Storage Optimization. IEEE Transactions on Smart Grid, 4 (2), 866–876. doi: https://doi.org/10.1109/tsg.2012.2206060
Chai, B., Chen, J., Yang, Z., Zhang, Y. (2014). Demand Response Management With Multiple Utility Companies: A Two-Level Game Approach. IEEE Transactions on Smart Grid, 5 (2), 722–731. doi: https://doi.org/10.1109/tsg.2013.2295024
Gellings, C. W., Smith, W. M. (1989). Integrating demand-side management into utility planning. Proceedings of the IEEE, 77 (6), 908–918. doi: https://doi.org/10.1109/5.29331
Palensky, P., Dietrich, D. (2011). Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads. IEEE Transactions on Industrial Informatics, 7 (3), 381–388. doi: https://doi.org/10.1109/tii.2011.2158841
Eto, J. (1996). The Past, Present, and Future of U.S. Utility Demand-Side Management Programs. Ernest Orlando Lawrence Berkeley National Laboratory, University of California. Available at: https://escholarship.org/content/qt8g26151t/qt8g26151t.pdf
Kyrylenko, O. V., Strzelecki, R., Denysiuk, S. P., Derevianko, D. G. (2013). Main Features of the Stability and Reliability Enhancement of Electricity Grid with DG in Ukraine Based on IEEE Standards. Technical Electrodynamics, 6, 46–50. Available at: http://previous.techned.org.ua/2013_6/st10.pdf
Pang, C., Kezunovic, M., Ehsani, M. (2012). Demand side management by using electric vehicles as Distributed Energy Resources. 2012 IEEE International Electric Vehicle Conference. doi: https://doi.org/10.1109/ievc.2012.6183273
Hernando-Gil, I., Ilie, I.-S., Djokic, S. Z. (2012). Reliability performance of smart grids with demand-side management and distributed generation/storage technologies. 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). doi: https://doi.org/10.1109/isgteurope.2012.6465883
Huang, D., Billinton, R., Wangdee, W. (2010). Effects of demand side management on bulk system adequacy evaluation. 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems. doi: https://doi.org/10.1109/pmaps.2010.5529011
Huang, D., Billinton, R. (2012). Effects of Load Sector Demand Side Management Applications in Generating Capacity Adequacy Assessment. IEEE Transactions on Power Systems, 27 (1), 335–343. doi: https://doi.org/10.1109/tpwrs.2011.2164425
Zgurovets', O. V., Kostenko, G. P. (2007). Effektivnye metody upravleniya potrebleniem elektricheskoy energii. Problemy zahalnoi enerhetyky, 16, 75–80.
Benefits of demand response in electricity markets and recommendations for achieving them (2006). U.S. Department of Energy. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.730&rep=rep1&type=pdf
Opryshko, V. (2018). World practice of demand side management programs implementation mechanisms. Enerhetyka: ekonomika, tekhnolohiyi, ekolohiya, 3, 44–51. doi: https://doi.org/10.20535/1813-5420.3.2018.164340
Nakhodov, V. F., Zamulko, A. I., Al Sharari, M. I., Medintseva, D. O. (2016). Impact Assessment of Demand Change of Consumers for Electric Power for Unevenness of Daily Graphs of the Energy System Load. Research Bulletin of the National Technical University of Ukraine “Kyiv Politechnic Institute”, 1, 31–39. doi: https://doi.org/10.20535/1810-0546.2016.1.61736
Denysiuk, S., Opryshko, V. (2017). Evaluation of electric power in local electric power engineering systems consumption and generation unevenness. Praci Institutu elektrodinamiki Nacionalnoi akademii nauk Ukraini, 48, 43–51. doi: https://doi.org/10.15407/publishing2017.48.043
Attia, H. A. (2010). Mathematical Formulation of the Demand Side Management (DSM) Problem and its Optimal Solution. Proceedings of the 14th International Middle East Power Systems Conference (MEPCON’10). Cairo University, 953–959. Available at: https://www.researchgate.net/profile/Hussein-Attia-2/publication/266567458_Mathematical_Formulation_of_the_Demand_Side_Management_DSM_Problem_and_its_Optimal_Solution/links/56e9c0bd08aec8bc07812a32/Mathematical-Formulation-of-the-Demand-Side-Management-DSM-Problem-and-its-Optimal-Solution.pdf
Denysiuk, S., Opryshko, V. (2019). Analysis of the daily electricity consumption schedule optimization opportunities. Bulletin of the Kyiv National University of Technologies and Design. Technical Science Series, 6 (128), 20–28. doi: https://doi.org/10.30857/1813-6796.2018.6.2
Veremiichuk, Y., Prytyskach, I., Yarmoliuk, O., Opryshko, V. (2017). Energy Hub Function Optimization Models During Ukrainian Energy Resources Market Liberalization. Power and Electrical Engineering, 34, 49–52. doi: https://doi.org/10.7250/pee.2017.009
Oberst, C. A., Schmitz, H., Madlener, R. (2019). Are Prosumer Households That Much Different? Evidence From Stated Residential Energy Consumption in Germany. Ecological Economics, 158, 101–115. doi: https://doi.org/10.1016/j.ecolecon.2018.12.014
Tonkal', V. E., Novosel'tsev, A. V., Denisyuk, S. P., Zhuykov, V. Ya., Strelkov, V. T., Yatsenko, Yu. A. (1992). Balans energiy v elektricheskih tsepyah. Kyiv: Nauk. dumka, 312.
Kremer, N. Sh., Putko, B. A., Trishin, I. M., Fridman, M. N. (2016). Issledovanie operatsiy v ekonomike. Moscow: Izdatel'stvo Yurayt, 438.
Copyright (c) 2021 Serhii Denysiuk, Stefan Zaichenko , Vitalii Opryshko, Denys Derevianko
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.