Abstract
In the anticipated perspective, electric vehicles (EVs) will take over the transportation exchange. The Multi-objective Mayfly Optimization Algorithm (MMOA) is projected to solve the dynamic economic emission dispatch (DEED) issue of a 10 thermal units (TUs) system with EVs considering several physical constraints, such as valve point loading effect (VPE), ramp rate limits (RRL), prohibited operating zone (POZ), level of charge (LOC) storage for a day with the hourly change of load profile. The effect of both grid-to-vehicle (G2V) load and vehicle-to-grid (V2G) support on DEED is studied. The multi-objective DEED optimization is dealt with the Fuzzy Decision Making (FDM) technique to decipher the considered issue. Furthermore, the management of demand for 30,000 EVs in crest shaving and valley filling (CSVF) zones are analyzed. This paper studied the coordinated use of V2G power and demand management of G2V to cut down emission and total expenses of electric grids with consideration of all four physical constraints.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
References
Al-Bahrani LT, Patra JC (2015) Orthogonal PSO algorithm for economic dispatch of power under power grid constraints. In 2015 IEEE Intl Conf Systs, Man, and Cybernetics, IEEE, pp 14–19. https://doi.org/10.1109/smc.2015.16
Al-Bahrani LT, Horan B, Seyed mahmoudian M, Stojcevski A (2020) Dynamic economic emission dispatch with load demand management for the load demand of electric vehicles during crest shaving and valley filling in smart cities environment. Energy 195:116946–116959. https://doi.org/10.1016/j.energy.2020.116946
Alham MH, Elshahed M, Ibrahim DK, El Zahab EEDA (2016) A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renew Energy 96:800–811. https://doi.org/10.1016/j.renene.2016.05.012
Arul R, Velusami S, Ravi G (2015) A new algorithm for combined dynamic economic emission dispatch with security constraints. Energy 79:496–511. https://doi.org/10.1016/j.energy.2014.11.037
Bai X, Qiao W (2015) Robust optimization for bidirectional dispatch coordination of large-scale V2G. IEEE Trans Smart Grid 6(4):1944–1954. https://doi.org/10.1109/tsg.2015.2396065
Behera S, Behera S, Barisal AK (2018) Solution of dispatch problem with dynamic and static load using improved CFBPSO. IEEE Int Conf Appl Electro Signal Process Commun 1:1–4. https://doi.org/10.1109/AESPC44649.2018.9033210
Behera S, Behera S, Barisal AK (2021) Dynamic combined economic emission dispatch integrating plug-in electric vehicles and renewable energy sources. Int J Ambient Energy. https://doi.org/10.1080/01430750.2021.1918243
Biswas PP, Suganthan PN, Qu BY, Amaratunga GAJ (2018) Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power. Energy 150:1039–1057. https://doi.org/10.1016/j.energy.2018.03.002
Boulanger A (2011) Vehicle electrification: status and issues. Proc IEEE 99(6):1116–1138. https://doi.org/10.1109/JPROC.2011.2112750
Božic D, Pantoš M (2015) Impact of electric-drive vehicles on power system reliability. Energy 83:511–520. https://doi.org/10.1016/j.energy.2015.02.055
Debnath UK, Ahmad I, Habibi D, Saber AY (2015) Energy storage model with gridable vehicles for economic load dispatch in the smart grid. Int J Elect Power Energy Syst 64:1017–1024. https://doi.org/10.1016/j.rser.2012.11.042
Esmaeeli M, Golshannavaz S, Siano P (2020) Determination of optimal reserve contribution of thermal units to afford the wind power uncertainty. J Ambient Intell Humaniz Comput 11(4):1565–1576. https://doi.org/10.1007/s12652-019-01231-3
Fathollahi-Fard AM, Hajiaghaei-Keshteli M, Tavakkoli Moghaddam R (2020) Red deer algorithm (RDA): a new nature-inspired meta-heuristic. Soft Comput 24:1–29. https://doi.org/10.1007/s00500-020-04812-z
Gholami A, Ansari J, Jamei M, Kazemi A (2014) Environmental/economic dispatch incorporating renewable energy sources and plugin vehicles. IET Gener Trans Distrib 8(12):2183–2198. https://doi.org/10.1049/iet-gtd.2014.0235
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley
Goudarzi A, Ahmadi A, Swanson AG, Van Coller J (2016) Non-convex optimisation of combined environmental economic dispatch through cultural algorithm with the consideration of the physical constraints of generating units and price penalty factors. SAIEE Afr Res J 107(3):146–166. https://doi.org/10.23919/SAIEE.2016.8532239
Gough R, Dickerson C, Rowley P, Walsh C (2017) Vehicle-to-grid feasibility: a techno-economic analysis of EV-based energy storage. Appl Energy 192:12–23. https://doi.org/10.1016/j.apenergy.2017.01.102
Guille C, Gross G (2009) A conceptual framework for the vehicle-to-grid VG implementation. Energy Policy 37(11):4379–4390. https://doi.org/10.1016/j.enpol.2009.05.053
Haghrah A, Nekoui MA, Nazari-Heris M, Mohammadi-ivatloo B (2020) An improved real-coded genetic algorithm with random walk-based mutation for solving combined heat and power economic dispatch. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02589-5
Han Y, Young P, Zimmerle D (2013) Microgrid generation units optimum dispatch for fuel consumption minimization. J Ambient Intell Humaniz Comput 4(6):685–701. https://doi.org/10.1007/s12652-012-0158-3
Hashemi-Dezaki H, Hamzeh M, Askarian-Abyaneh H, Haeri-Khiavi H (2015) Risk management of smart grids based on managed charging of PHEVs and vehicle-to-grid strategy using Monte Carlo simulation. Energy Convers Manag 100:262–276. https://doi.org/10.1016/j.enconman.2015.05.015
Hoehne CG, Chester M (2016) Optimizing plug-in electric vehicle and vehicle-to-grid charge scheduling to minimize carbon emissions. Energy 115:646–657. https://doi.org/10.1016/j.energy.2016.09.057
Hota PK, Barisal AK, Chakrabarti R (2010) Economic Emission load dispatch through fuzzy based bacterial foraging algorithm. Int J Electr Power Energy Syst 32(7):794–803. https://doi.org/10.1016/j.ijepes.2010.01.016
Hou YH, Lu LJ, Xiong XY, Wu YW (2005) Economic dispatch of power systems based on the modified particle swarm optimization algorithm. Trans Distri Conf Exhibition: Asia and Pacific 2005, IEEE/PES, pp 1–6. https://doi.org/10.1109/tdc.2005.1546751
Hu F, Hughes KJ, Ingham DB, Ma L, Pourkashanian M (2019) Dynamic economic and emission dispatch model considering wind power under energy market reform: a case study. Int J Electr Power Energy Syst 110:184–196. https://doi.org/10.1016/j.ijepes.2019.03.004
Jafari V, Rezvani MH (2021) Joint optimization of energy consumption and time delay in IoT-fog-cloud computing environments using NSGA-II metaheuristic algorithm. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-021-03388-2
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf Neur Net 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968
Kheshti M, Kang X, Li J, Regulski P, Terzija V (2017) Lightning flash algorithm for solving nonconvex combined emission economic dispatch with generator constraints. IET Gener Trans Distrib 12(1):104–116. https://doi.org/10.1049/iet-gtd.2017.0257
Kothari DP, Dhillon JS (2011) Power system optimization, 2nd edn. PHI Learning Private Ltd
Liang H, Liu Y, Li F, Shen Y (2018) A multiobjective hybrid bat algorithm for combined economic/emission dispatch. Int J Electr Power Energy Syst 101:103–115. https://doi.org/10.1016/j.ijepes.2018.03.019
Liang H, Liu Y, Li F, Shen Y (2019) Dynamic economic/emission dispatch including PEVS for peak shaving and valley filling. IEEE Trans Indust Electron 66(4):2880–2890. https://doi.org/10.1109/tie.2018.2850030
Lokeshgupta B, Sivasubramani S (2018) Multi-objective dynamic economic and emission dispatch with demand side management. Int J Electr Power Energy Syst 97:334–343. https://doi.org/10.1016/j.ijepes.2017.11.020
Lopes JAP, Soares FJ, Almeida PMR (2012) Integration of electric vehicles in the electric power system. Proc IEEE 99(1):168–183. https://doi.org/10.1016/j.rser.2012.11.042
Meng A, Hu H, Yin H, Peng X, Guo Z (2015) Crisscross optimization algorithm for large-scale dynamic economic dispatch problem with valve-point effects. Energy 93(2):2175–2190. https://doi.org/10.1016/j.energy.2015.10.112
Muhammad B (2019) Energy consumption, CO2 emissions and economic growth in developed, emerging and Middle East and North Africa countries. Energy 179:232–245. https://doi.org/10.1016/j.ijepes.2019.03.004
Nagarajan K, Rajagopalan A, Angalaeswari S, Natrayan L, Mammo WD (2022) Combined economic emission dispatch of microgrid with the incorporation of renewable energy sources using improved mayfly optimization algorithm. Comput Intell Neurosci. https://doi.org/10.1155/2022/6461690
Nanaki EA, Koroneos CJ (2016) Climate change mitigation and deployment of electric vehicles in urban areas. Renew Energy 99:1153–1160. https://doi.org/10.1016/j.renene.2016.08.006
Qu B, Qiao B, Zhu Y, Jiao Y, Xiao J, Wang X (2019) Using Multi-objective evolutionary algorithm to solve dynamic environment and economic dispatch with EVs. Int Conf on Swarm Intelligence Springer, Cham. 31–39. 10.1007/ 978–3–319–61833–3_4.
Richardson DB (2012) Electric Vehicles and the electric grid: A review of modeling approaches, impacts and renewable energy integration. Renew Sust Energ Rev Elsevier 19:247–254. https://doi.org/10.1016/j.rser.2012.11.042
Saber AY, Venayagamoorthy G (2011) Plug-in vehicles and renewable energy sources for cost and emission reductions. IEEE Transns Indust Electr 58(4):1229–1238. https://doi.org/10.1109/tie.2010.2047828
Saldanha JJA, Dos Santos EM, De Mello APC, Bernardon DP (2016) Control strategies for smart charging and discharging of plug-in electric vehicles. Smart Cities Technol 1:121–141
Silva M, Coelho L, Lebensztajn L (2012) Multiobjective biogeography-based optimization based on predator-prey approach. IEEE Trans Magn 48(2):951–954. https://doi.org/10.1109/tmag.2011.2174205
Strbac G (2008) Demand side management: benefits and challenges. Energy Policy 36(12):4419–4426. https://doi.org/10.1016/j.enpol.2008.09.030
Tan KM, Ramachandaramurthy VK, Yong JY (2016) Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. Renew Sustain Energy Rev 53:720–732. https://doi.org/10.1016/j.rser.2015.09.012
Vo DN, Schegner P, Ongsakul W (2013) Cuckoo search algorithm for non-convex economic dispatch. IET Gener Transm Distrib 6(7):645–654. https://doi.org/10.1049/iet-gtd.2012.0142
Yang XS (2008) Nature-inspired meta heuristic algorithms. Luniver Press
Yang Z, Li K, Niu Q, Xue Y, Foley A (2014) A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads. J Mod Power Syst Clean Energy 2(4):298–307. https://doi.org/10.1007/s40565-014-0087-6
Yilmaz S, Chambers J, Patel M (2019) Comparison of clustering approaches for domestic electricity load profile characterization-Implications for demand side management. Energy 180:665–677. https://doi.org/10.1016/j.energy.2019.05.124
Yuan D, Lu Z, Zhang J, Li X (2019) A hybrid prediction-based micro grid energy management strategy considering demand-side response and data interruption. Int J Electr Power Energy Syst 113:139–153. https://doi.org/10.1016/j.ijepes.2019.05.045
Zervoudakis K, Tsafarakis S (2020) A mayfly optimization algorithm. Comput Indust Eng 145:106559–106637. https://doi.org/10.1016/j.cie.2020.106559
Zhao J, Wen F, Dong Z, Xue Y, Wong KP (2012) Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization. IEEE Trans Indust Inform 8(4):889–899. https://doi.org/10.1109/tii.2012.2205398
Zou D, Li S, Kong X, Ouyang H, Li Z (2019) Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy. Appl Energy 237:646–670. https://doi.org/10.1016/j.apenergy.2019.01.056
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Behera, S., Behera, S. & Barisal, A.K. Dynamic economic emission dispatch including electric vehicles’ demand management and vehicle to grid support considering physical constraints. J Ambient Intell Human Comput 14, 2739–2757 (2023). https://doi.org/10.1007/s12652-023-04518-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-023-04518-8