DOI: https://doi.org/10.14710/ijred.2023.49165

Demand response based microgrid's economic dispatch

1College of Electrical Engineering and New Energy (CEENE), China Three Gorges University (CTGU), Yichang, China

2College of Electrical Engineering and Information, Southwest Petroleum University (SWPU), Chengdu, China

3Department of Electrical Engineering, Bayeh Institute, Amchit, Lebanon

Received: 24 Sep 2022; Revised: 16 Apr 2023; Accepted: 20 Jun 2023; Available online: 30 Jun 2023; Published: 15 Jul 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

The development of energy management tools for next-generation Distributed Energy Resources (DER) based power plants, such as photovoltaic, energy storage units, and wind, helps power systems be more flexible. Microgrids are entities that coordinate DERs in a persistently more decentralized fashion, hence decreasing the operational burden on the main grid and permitting them to give their full benefits. A new power framework has emerged due to the integration of DERs-based microgrids into the conventional power system. With the rapid advancement of microgrid technology, more emphasis has been placed on maintaining the microgrids' long-term economic feasibility while ensuring security and stability. The objective of this research is to provide a multi-objective economic operation technique for microgrids containing air-conditioning clusters (ACC) taking demand response into account. A dynamic price mechanism is proposed, accurately reflecting the system's actual operational status. For economic dispatch, flexible loads and air conditioners are considered demand response resources. Then, a consumer-profit model and an AC operating cost model are developed, with a set of pragmatic constraints of consumer comfort. The generation model is then designed to reduce the generation cost. Finally, a microgrid simulation platform is developed in MATLAB/Simulink, and a case is designed to evaluate the proposed method's performance. The findings show that consumer profit increases by 69.2% while ACC operational costs decrease by 18.2%. Moreover, generation costs are reduced without sacrificing customer satisfaction.

Fulltext View|Download
Keywords: Air-Conditioning Cluster (ACC); Demand Response (DR); Distributed Generation (DG);Economic Operation; Micro- gas turbine (MGT); Microgrid (MG)
Funding: No Funding Resources associated with this work.

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Prospects of low carbon development for Pakistan’s energy and power sector in the post Covid scenario Synthesis of Al-Y doped-lithium lanthanum zirconate and the effect of cold isostatic pressure to its electrical properties Investigating the potential of avocado seeds for bioethanol production: A study on boiled water delignification pretreatment A comprehensive review on the use of biodiesel for diesel engines More recent articles
Most cited articles
Solmap: Project In India's Solar Resource Assessment Wind Energy Potential at Badin and Pasni Costal Line of Pakistan Determination of Sliced Pineapple Drying Characteristics in A Closed Loop Heat Pump Assisted Drying System Renewable Energy in Eastern North Africa in Terms of Patterns of Coupling to Czisch European HVDC Super Grid Performance investigation of a gasifier and gas engine system operated on municipal solid waste briquettes More cited articles
  1. Ali, S., Ullah, K., Hafeez, G., Khan, I., Albogamy, F.R., Haider, S.I. (2022). Solving day-ahead scheduling problem with multi-objective energy optimization for demand side management in smart grid, Engineering Science and Technology: an International Journal, 36, 101135; https://doi.org/10.1016/j.jestch.2022.101135
  2. Attaviriyanupap, P., Kita, H., Tanaka, E., Hasegawa, J. (2002). A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function. IEEE Transactions on Power System, 17, 411–6. https://doi.org/10.1109/TPWRS.2002.1007911
  3. Basu, M. (2008). Dynamic economic emission dispatch using nondominated sorting genetic algorithm-II. International Journal of Electric Power and Energy Systems, 30, 140–9. https://doi.org/10.1016/j.ijepes.2007.06.009
  4. Battula, A.R., Vuddanti, S., Salkuti, S.R. (2021). Review of Energy Management System Approaches in Microgrids. Energies, 14, 5459. https://doi.org/10.3390/en14175459
  5. Borenstein, S. (2005). The long-run efficiency of real-time electricity pricing. Energy Journal, 26 (3). https://doi.org/10.5547/issn0195-6574-ej-vol26-no3-5
  6. Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn., 3(1), 1–122. https://doi.org/10.3390/en14175459
  7. Calderaro, V,. Conio, G,. Galdi, V., Massa, G., & Piccolo, A. (2014). Active management of renewable energy sources for maximizing power production. International Journal of Electric Power and Energy System, 57, 64–72. https://doi.org/10.1016/j.ijepes.2013.11.040
  8. Chen, D.J., Gong, Q.W., & Zhang, M.L. (2011). Multi-objective optimal dispatch in wind power integrated system incorporating energy-environmental efficiency. Proc. CSEE, 31(13), 10-17
  9. Dashtdar, M., Flah, A., Hosseinimoghadam, S.M.S., El-Fergany A. (2022). Frequency control of the islanded microgrid including energy storage using soft computing. Sci Rep, 12, 20409. https://doi.org/10.1038/s41598-022-24758-6
  10. David, P.C., Jakob, S., Panajotis, A., & Nedjib, D. (2015). A new thermostat for real-time price demand response: Cost, comfort and energy impacts of discrete-time control without deadband. Applied Energy, 155, 816-825, https://doi.org/10.1016/j.apenergy.2015.06.048
  11. Dutta G. & Mitra K. (2017). A literature review on dynamic pricing of electricity. Journal of Operational Research Society, 68 (10), 1131-1145. https://doi.org/10.1057/s41274-016-0149-4
  12. Han, X.S., Gooi, H.B., & Kirschen, D.S. (2001). Dynamic economic dispatch: feasible and optimal solutions. IEEE Transactions on Power System, 16, 22–8. https://doi.org/10.1109/59.910777
  13. Hao, H., Huang, B., & Ji, P. (2021). Optimal Configuration of An Island Microgrid Considering Demand Response Strategy. 2021 36th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 300-304, https://doi.org/10.1109/YAC53711.2021.9486661
  14. Hsiao, C.-H., Huang, W.-T., Chen, L.-R., Lin, W.-C., & Li, L.-C. (2021). Economic Dispatch of Microgrids Using Particle Swarm optimization and Binning Method. 2021 IEEE International Future Energy Electronics Conference (IFEEC), 1-5, https://doi.org/10.1109/IFEEC53238.2021.9661738
  15. Imtiaz, B., Cui, Y., & Zafar, I. (2021). Economic Dispatch of Microgrid Incorporating Demand Response Using Dragonfly Algorithm. 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), 59-68; https://doi.org/10.1109/AEECA52519.2021.9574430
  16. Jindal, A., Singh, M., & Kumar, N. (2018). Consumption-Aware Data Analytical Demand Response Scheme for Peak Load Reduction in Smart Grid. IEEE Transactions on Industrial Electronics, 65, 8993-9004; https://doi.org/10.1109/TIE.2018.2813990
  17. John, H., Yu. D.C., & Bhattarai, K. (2008). An Economic Dispatch Model Incorporating Wind Power. IEEE Transactions on Energy Conversion, 23(2), 603-611. https://doi.org/10.1109/TEC.2007.914171
  18. Julia, F., Victor, v-L. (2022). Dynamic electricity tariffs: Designing reasonable pricing schemes for private households. Energy Economics, 112, 106146, https://doi.org/10.1016/j.eneco.2022.106146
  19. Kumar, D., Verma, Y.P. & Khanna, R. (2019). Demand response-based dynamic dispatch of microgrid system in hybrid electricity market. International Journal of Energy Sector Management, 13(2), 318-340. https://doi.org/10.1108/IJESM-12-2017-0008
  20. Liu, X., Ning, N., Wang, G., Liu, D., Chen, K., & Yuan, J. (2021). Distributed Optimal Dispatch Method for Smart Community Demand Response Based on Machine Learning. 2021 4th International Conference on Energy, Electrical and Power Engineering (CEEPE), 2021, 1040-1046; https://doi.org/10.1109/CEEPE51765.2021.9475821
  21. Ma, R., Li, K., Li, X., & Qin, Z. (2015). Economic and low-carbon day-ahead Pareto-optimal scheduling for wind farm integrated power systems with demand response. Journal of Modern Power Systems and Clean Energy, 3(3), 393-401; https://doi.org/10.1007/s40565-014-0094-7
  22. Maximilian, J.B. (2022). Dynamic pricing of electricity: Enabling demand response in domestic households. Energy Policy, 164, 112878, https://doi.org/10.1016/j.enpol.2022.112878
  23. Mhankale, S.E. & Thorat, A.R. (2018). Droop Control Strategies of DC Microgrid: A Review. 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), 372-376; https://doi.org/10.1109/ICCTCT.2018.8550854
  24. Murty, V.V.V.S.N. & Kumar, A. (2020). Optimal Energy Management and Techno-economic Analysis in Microgrid with Hybrid Renewable Energy Sources. Journal of Modern Power Systems and Clean Energy, 8(5), 929-940; https://doi.org/10.35833/MPCE.2020.000273
  25. Nguyen, D.H., Narikiyo, T., & Kawanishi, M. (2018). Optimal Demand Response and Real-Time Pricing by a Sequential Distributed Consensus-Based ADMM Approach. IEEE Transactions on Smart Grid, 9(5), 4964-4974; https://doi.org/10.1109/TSG.2017.2676179
  26. Pothireddy, K.M.R., Vuddanti, S., Salkuti, S.R. (2022). Impact of Demand Response on Optimal Sizing of Distributed Generation and Customer Tariff. Energies, 15, 190. https://doi.org/10.3390/en1501019
  27. Pourbabak, H., Luo, J., Chen, T., & Su, W. (2018). A novel consensus-based distributed algorithm for economic dispatch based on local estimation of power mismatch. IEEE Transactions on Smart Grid, 9(6), 5930-5942. https://doi.org/10.1109/TSG.2017.2699084
  28. Rajkumar, R.K., Ramachandaramurthy, V.K., & Yong, B. (2011). Techno-economical optimization of hybrid PV/wind/battery system using Neuro-Fuzzy. Energy, 36(8), 5148-5153. https://doi.org/10.1016/j.energy.2011.06.017
  29. Recalde, A.A., Manuel, S., & Alvarado, A. (2020). Design optimization for reliability improvement in microgrids with wind– tidal– photovoltaic generation. Electric Power Systems Research, 188, Article No. 106540; https://doi.org/10.1016/j.epsr.2020.106540
  30. Ross, D.W. & Sungkook, K. (1980). Dynamic economic dispatch of generation. IEEE Transactions on Power Apparatus and System. PAS-99(6), 2060–2068. https://doi.org/10.1109/TPAS.1980.319847
  31. Saeed, M.H., Fangzong, W., Kalwar, B.A., & Iqbal, S. (2021a). A Review on Microgrids’ Challenges & Perspectives. IEEE Access, 9, 166502-166517; https://doi.org/10.1109/ACCESS.2021.3135083
  32. Saeed, M.H., Fangzong, W., Salem, S., Khan, Y.A., Kalwar, B.A., & Fars, A. (2021b). Two-stage intelligent planning with improved artificial bee colony algorithm for a microgrid by considering the uncertainty of renewable sources. Energy Reports, 7, 8912-8928; https://doi.org/10.1016/j.egyr.2021.10.123
  33. Saeed, M. H., Fangzong, W., & Kalwar, B. A. (2022). Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application. International Journal of Renewable Energy Development, 11(2), 533-546: https://doi.org/10.14710/ijred.2022.41952
  34. Saeed, M.H., Iqbal, S., & Kalwar, B. A. (2022). Electricity market management through optimum installation of distributed generation sources and optimum placement based on LMP and ISC. Advances in Engineering and Intelligence Systems, 001(01). https://doi.org/10.22034/aeis.2022.147534
  35. Salkuti, S.R., Abhyankar, A. R., Bijwe, P. R. (2015). Co-optimization of Energy and Demand-Side Reserves in Day-Ahead Electricity Markets. International Journal of Emerging Electric Power Systems, 16(2), 195-206. https://doi.org/10.1515/ijeeps-2014-0020
  36. Salkuti, S.R., Bijwe, P.R. & Abhyankar, A.R. (2016). Optimal dynamic emergency reserve activation using spinning, hydro and demand-side reserves. Frontiers in Energy. 10, 409–423. https://doi.org/10.1007/s11708-016-0431-9
  37. Salkuti, S.R. (2017). Optimizing energy and demand response programs using multi-objective optimization. Electrical Engineering, 99, 397–406. https://doi.org/10.1007/s00202-016-0438-6
  38. Salkuti, S.R. (2018). Emergency reserve activation considering demand-side resources and battery storage in a hybrid power system. Electrical Engineering, 100, 1589–1599. https://doi.org/10.1007/s00202-017-0637-9
  39. Salkuti, S.R. (2022). Emerging and Advanced Green Energy Technologies for Sustainable and Resilient Future Grid. Energies, 15, 6667. https://doi.org/10.3390/en15186667
  40. Ulbig, A., Borsche, T.S., & Andersson, G. (2014). Impact of low rotational inertia on power system stability and operation. IFAC Proceedings, 47 (3), 7290-7297, https://doi.org/10.3182/20140824-6-ZA-1003.02615
  41. Vardakas, J. S., Zorba, N., & Verikoukis, C. V. (2015). A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Commun. Surveys Tuts., 17(1), 152-178. https://doi.org/10.1109/COMST.2014.2341586
  42. Wang, J., Cary, N.B., Hu, Z., Tan, Z. (2010). Demand response in China. Energy, 35(4), 1592-1597. https://doi.org/10.1016/j.energy.2009.06.020
  43. Wang, Y., Han, S., & Liu, S. (2020). Distributed consensus-based algorithm for dynamic economic dispatch with wind turbine and energy storage. 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), 618-625, https://doi.org/10.1109/ICCSS52145.2020.9336855
  44. Waseem, M., Lin, Z., Ding, Y., Wen, F., Liu, S., & Palu, I. (2021). Technologies and Practical Implementations of Air-conditioner Based Demand Response. Journal of Modern Power Systems and Clean Energy, 9(6), 1395-1413, https://doi.org/10.35833/MPCE.2019.000449
  45. Wood, A.J., Wollenberg, B.F., & Sheblé, G.B. (2013). Power Generation, Operation, and Control, 3rd ed. New York, NY, USA: Wiley
  46. Wu, H., Liu, X., & Ding, M. (2014). Dynamic economic dispatch of a microgrid: Mathematical models and solution algorithm. International Journal of Electrical Power & Energy Systems, 63, 336-346; https://doi.org/10.1016/j.ijepes.2014.06.002
  47. Xu, H., Meng, Z., & Wang, Y. (2020). Economic dispatching of microgrid considering renewable energy uncertainty and demand sid response. Energy Reports, 6(9), 2020, 196-204, https://doi,org/10.1016/j.egyr.2020.11.261
  48. Zachar, M. & Daoutidis, P. (2016). Economic dispatch for microgrids with constrained external power exchange. IFAC-PapersOnLine, 49(7), 833-838; https://doi.org/10.1016/j.ifacol.2016.07.293

Last update:

  1. Energy optimization management of microgrid using improved soft actor-critic algorithm

    Zhiwen Yu, Wenjie Zheng, Kaiwen Zeng, Ruifeng Zhao, Yanxu Zhang, Mengdi Zeng. International Journal of Renewable Energy Development, 13 (2), 2024. doi: 10.61435/ijred.2024.59988

Last update: 2024-11-20 18:48:22

No citation recorded.