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Electrical Engineering

18.04.2024 | Original Paper

A comprehensive review of demand-side management in smart grid operation with electric vehicles

verfasst von: Satish Jagannath Ghorpade, Rajesh B. Sharma

Erschienen in: Electrical Engineering

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Abstract

Demand-side management of smart grid with electric vehicles (EVs) is overviewed in this review paper. The major objective of the work is to reduce the hourly peak load to obtain a steady load schedule, maximize user satisfaction and reduce cost. This review allows for the probability of leveling the everyday energy load arc and unstable demand response to hourly prices from one time period to another. To obtain a balanced everyday load schedule, increase user satisfaction, and cut costs, the main aim is to reduce peak hourly load. A management system for an EV connected to the national grid for a future household with controllable electric loads. The approach that has been presented enables the integration of EVs and renewable resources while also optimizing the demand and generation in hourly distribution. The agents are taken into account for managing load, storage, and generation; specifically, they are EV aggregators. The vehicle-to-grid (V2G) combination of electric vehicles is a key aspect of this study; with this capability, EVs may offer power grid-specific services like load shifting and congestion management. By maximizing the hourly distribution of demand as well as generation, accounting for technical limitations, and enabling the addition of EVs and RES.

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Literatur
1.
Stalon CG (1992) Restructuring the electric industry. Resour Energy 14(1–2):55–76CrossRef
2.
Mohsenian-Rad AH, Wong VW, Jatskevich J, Schober R, Leon-Garcia A (2010) Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1(3):320–331CrossRef
3.
Chai B, Chen J, Yang Z, Zhang Y (2014) Demand response management with multiple utility companies: a two-level game approach. IEEE Trans Smart Grid 5(2):722–731CrossRef
4.
Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Industr Inf 7(3):381–388CrossRef
5.
Ibars C, Navarro M, Giupponi L (2010) Distributed demand management in smart grid with a congestion game. In: 2010 first IEEE international conference on smart grid communications, IEEE, pp 495–500
6.
Chen C, Kishore S, Snyder LV (2011) An innovative RTP-based residential power scheduling scheme for smart grids. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 5956–5959
7.
Erkoc M, Al-Ahmadi E, Celik N, Saad W (2015) A game theoretic approach for load-shifting in the smart grid. In: 2015 IEEE international conference on smart grid communications (SmartGridComm), IEEE, pp 187–192
8.
Yaagoubi N, Mouftah HT (2013) A comfort based game theoretic approach for load management in the smart grid. In: 2013 IEEE green technologies conference (GreenTech), IEEE, pp 35–41
9.
Koonamparampath J, Sawant M, Atharva K, Sheikh A (2019) A Stackelberg game theoretic approach for optimal electricity pricing dynamics employing time-of-use algorithm. In: 2019 6th international conference on control, decision and information technologies (CoDIT), IEEE, pp 1628–1633
10.
Alshehri K, Liu J, Chen X, Başar T (2015) A Stackelberg game for multi-period demand response management in the smart grid. In: 2015 54th IEEE conference on decision and control (CDC), IEEE, pp 5889–5894
11.
Popov I, Krylatov A, Zakharov V, Ivanov D (2017) Competitive energy consumption under transmission constraints in a multi-supplier power grid system. Int J Syst Sci 48(5):994–1001MathSciNetCrossRef
12.
Chaudhary P. Demand Response for Energy-Efficient and Optimal Integration of Renewable Energy Sources in a Smart Grid 5–1
13.
Gelazanskas L, Gamage KA (2014) Demand side management in smart grid: a review and proposals for future direction. Sustain Cities Soc 11:22–30CrossRef
14.
Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3(3):1244–1252CrossRef
15.
Sarker E, Halder P, Seyedmahmoudian M, Jamei E, Horan B, Mekhilef S, Stojcevski A (2021) Progress on the demand side management in smart grid and optimization approaches. Int J Energy Res 45(1):36–64CrossRef
16.
Rajesh P, Shajin F (2020) A multi-objective hybrid algorithm for planning electrical distribution system. Eur J Electr Eng 22(4–5):224–509CrossRef
17.
Afzal M, Huang Q, Amin W, Umer K, Raza A, Naeem M (2020) Blockchain enabled distributed demand side management in community energy system with smart homes. IEEE Access 8:37428–37439CrossRef
18.
Javaid N, Hafeez G, Iqbal S, Alrajeh N, Alabed MS, Guizani M (2018) Energy efficient integration of renewable energy sources in the smart grid for demand side management. IEEE Access 6:77–96CrossRef
19.
Jang Y, Byon E, Jahani E, Cetin K (2020) On the long-term density prediction of peak electricity load with demand side management in buildings. Energy Build 228:110450CrossRef
20.
Jo J, Park J (2020) Demand-side management with shared energy storage system in smart grid. IEEE Trans Smart Grid 11(5):4466–4476CrossRef
21.
López KL, Gagné C, Gardner MA (2018) Demand-side management using deep learning for smart charging of electric vehicles. IEEE Trans Smart Grid 10(3):2683–2691CrossRef
22.
Lyden A, Pepper R, Tuohy PG (2018) A modelling tool selection process for planning of community scale energy systems including storage and demand side management. Sustain Cities Soc 39:674–688CrossRef
23.
Noor S, Yang W, Guo M, van Dam KH, Wang X (2018) Energy demand side management within micro-grid networks enhanced by blockchain. Appl Energy 228:1385–1398CrossRef
24.
Satheesh Kumar S, Ashok Kumar B, Senthilrani S (2023) Review of electric vehicle (EV) charging using renewable solar photovoltaic (PV) nano grid. Energy Environ 35(2):1089–1117CrossRef
25.
Yang X, Zhang Y, He H, Ren S, Weng G (2018) Real-time demand side management for a microgrid considering uncertainties. IEEE Trans Smart Grid 10(3):3401–3414CrossRef
26.
Saffre F, Gedge R (2010) Demand-side management for the smart grid. In: 2010 IEEE/IFIP network operations and management symposium workshops, IEEE, pp 300–303
27.
Sharda S, Singh M, Sharma K (2021) Demand side management through load shifting in IoT based HEMS: overview, challenges and opportunities. Sustain Cities Soc 65:102517CrossRef
28.
Tronchin L, Manfren M, Nastasi B (2018) Energy efficiency, demand side management and energy storage technologies–a critical analysis of possible paths of integration in the built environment. Renew Sustain Energy Rev 95:341–353CrossRef
29.
Wang K, Li H, Maharjan S, Zhang Y, Guo S (2018) Green energy scheduling for demand side management in the smart grid. IEEE Trans Green Commun Netw 2(2):596–611CrossRef
30.
Islam MM, Zhong X, Sun Z, Xiong H, Hu W (2019) Real-time frequency regulation using aggregated electric vehicles in smart grid. Comput Ind Eng 134:11–26CrossRef
31.
Triviño-Cabrera A, Aguado JA, de la Torre S (2019) Joint routing and scheduling for electric vehicles in smart grids with V2G. Energy 175:113–122CrossRef
32.
López MA, De La Torre S, Martín S, Aguado JA (2015) Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support. Int J Electr Power Energy Syst 64:689–698CrossRef
33.
Puttamadappa C, Parameshachari BD (2019) Demand side management of small scale loads in a smart grid using glow-worm swarm optimization technique. Microprocess Microsyst 71:102886CrossRef
34.
Sachan S, Deb S, Singh SN (2020) Different charging infrastructures along with smart charging strategies for electric vehicles. Sustain Cities Soc 60:102238CrossRef
35.
Babar M, Tariq MU, Jan MA (2020) Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid. Sustain Cities Soc 62:102370CrossRef
36.
Sami I, Ullah Z, Salman K, Hussain I, Ali SM, Khan B, Mehmood CA, Farid U (2019) A bidirectional interactive electric vehicles operation modes: Vehicle-to-grid (V2G) and grid-to-vehicle (G2V) variations within smart grid. In: 2019 international conference on engineering and emerging technologies (ICEET), IEEE, pp 1–6
37.
Faddel S, Mohammed OA (2018) Automated distributed electric vehicle controller for residential demand side management. IEEE Trans Ind Appl 55(1):16–25CrossRef
38.
Rajesh P, Kannan R, Vishnupriyan J, Rajani B (2022) Optimally detecting and classifying the transmission line fault in power system using hybrid technique. ISA Trans 130:253–264CrossRef
39.
Jarvis R, Moses P (2019) Smart grid congestion caused by plug-in electric vehicle charging. In: 2019 IEEE Texas Power and Energy Conference (TPEC), IEEE, pp 1–5
40.
Shakerighadi B, Anvari-Moghaddam A, Ebrahimzadeh E, Blaabjerg F, Bak CL (2018) A hierarchical game theoretical approach for energy management of electric vehicles and charging stations in smart grids. IEEE Access 6:67223–67234CrossRef
41.
Acharya S, Dvorkin Y, Pandžić H, Karri R (2020) Cybersecurity of smart electric vehicle charging: a power grid perspective. IEEE Access 8:214434–214453CrossRef
42.
Amamra SA, Marco J (2019) Vehicle-to-grid aggregator to support power grid and reduce electric vehicle charging cost. IEEE Access 7:178528–178538CrossRef
43.
Asrari A, Ansari M, Khazaei J, Fajri P (2019) A market framework for decentralized congestion management in smart distribution grids considering collaboration among electric vehicle aggregators. IEEE Trans Smart Grid 11(2):1147–1158CrossRef
44.
Khemakhem S, Rekik M, Krichen L (2019) Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid. Energy 167:312–324CrossRef
45.
Kaur K, Kumar N, Singh M (2018) Coordinated power control of electric vehicles for grid frequency support: MILP-based hierarchical control design. IEEE Trans Smart Grid 10(3):3364–3373CrossRef
46.
Metke AR, Ekl RL (2010) Security technology for smart grid networks. IEEE Trans Smart Grid 1(1):99–107CrossRef
47.
Kakran S, Chanana S (2018) Smart operations of smart grids integrated with distributed generation: a review. Renew Sustain Energy Rev 81:524–535CrossRef
48.
Fang X, Misra S, Xue G, Yang D (2011) Smart grid—the new and improved power grid: a survey. IEEE Commun Surv Tutor 14(4):944–980CrossRef
49.
Kabalci Y (2016) A survey on smart metering and smart grid communication. Renew Sustain Energy Rev 57:302–318CrossRef
50.
Baharlouei Z, Hashemi M (2013) Demand side management challenges in smart grid: a review. In: 2013 smart grid conference (SGC), IEEE, pp 96–101
51.
McDaniel P, McLaughlin S (2009) Security and privacy challenges in the smart grid. IEEE Secur Priv 7(3):75–77CrossRef
52.
Dawoud B, Amer EH, Gross DM (2007) Experimental investigation of an adsorptive thermal energy storage. Int J Energy Res 31(2):135–147CrossRef
53.
Parikh PP, Kanabar MG, Sidhu TS (2010) Opportunities and challenges of wireless communication technologies for smart grid applications. In: IEEE PES general meeting, IEEE, pp 1–7
54.
Fan Z, Kulkarni P, Gormus S, Efthymiou C, Kalogridis G, Sooriyabandara M, Zhu Z, Lambotharan S, Chin WH (2012) Smart grid communications: overview of research challenges, solutions, and standardization activities. IEEE Commun Surv Tutor 15(1):21–38CrossRef
55.
Cleveland (2006) IEC TC57 security standards for the power system’s information infrastructure-beyond simple encryption. In: 2005/2006 IEEE/PES transmission and distribution conference and exhibition, IEEE, pp 1079–1087
56.
Das S, Acharjee P, Bhattacharya A (2020) Charging scheduling of electric vehicle incorporating grid-to-vehicle and vehicle-to-grid technology considering in smart grid. IEEE Trans Ind Appl 57(2):1688–1702CrossRef
57.
Di Santo KG, Di Santo SG, Monaro RM, Saidel MA (2018) Active demand side management for households in smart grids using optimization and artificial intelligence. Measurement 115:152–161CrossRef
58.
Khan A, Memon S, Sattar TP (2018) Analyzing integrated renewable energy and smart-grid systems to improve voltage quality and harmonic distortion losses at electric-vehicle charging stations. IEEE Access 6:26404–26415CrossRef
59.
Liu RS, Hsu YF (2018) A scalable and robust approach to demand side management for smart grids with uncertain renewable power generation and bi-directional energy trading. Int J Electr Power Energy Syst 97:396–407CrossRef
60.
Melhem FY, Grunder O, Hammoudan Z, Moubayed N (2018) Energy management in electrical smart grid environment using robust optimization algorithm. IEEE Trans Ind Appl 54(3):2714–2726CrossRef
61.
Guelpa E, Marincioni L, Deputato S, Capone M, Amelio S, Pochettino E, Verda V (2019) Demand side management in district heating networks: a real application. Energy 182:433–442CrossRef
62.
Tang R, Wang S, Li H (2019) Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids. Appl Energy 250:118–130CrossRef
63.
Khan A, Javaid N, Ahmad A, Akbar M, Khan ZA, Ilahi M (2019) A priority-induced demand side management system to mitigate rebound peaks using multiple knapsack. J Ambient Intell Humaniz Comput 10:1655–1678CrossRef
64.
Su H, Zio E, Zhang J, Chi L, Li X, Zhang Z (2019) A systematic data-driven demand side management method for smart natural gas supply systems. Energy Convers Manag 185:368–383CrossRef
65.
Kumar KP, Saravanan B (2019) Day ahead scheduling of generation and storage in a microgrid considering demand side management. J Energy Storage 21:78–86CrossRef
66.
Yilmaz S, Chambers J, Patel MK (2019) Comparison of clustering approaches for domestic electricity load profile characterisation-Implications for demand side management. Energy 180:665–677CrossRef
67.
Walzberg J, Dandres T, Merveille N, Cheriet M, Samson R (2019) Accounting for fluctuating demand in the life cycle assessments of residential electricity consumption and demand-side management strategies. J Clean Prod 240:118251CrossRef
68.
Luo XJ, Fong KF (2019) Development of integrated demand and supply side management strategy of multi-energy system for residential building application. Appl Energy 242:570–587CrossRef
69.
Peltokorpi A, Talmar M, Castren K, Holmström J (2019) Designing an organizational system for economically sustainable demand-side management in district heating and cooling. J Clean Prod 219:433–442CrossRef
70.
Wu J, Zhang B, Jiang Y, Bie P, Li H (2019) Chance-constrained stochastic congestion management of power systems considering uncertainty of wind power and demand side response. Int J Electr Power Energy Syst 107:703–714CrossRef
71.
Chatterjee S, Dawn S, Hore S (2020) Artificial cell swarm optimization. Frontier Applications of Nature Inspired Computation, pp 196–214
72.
Latifi M, Khalili A, Rastegarnia A, Bazzi WM, Sanei S (2020) Demand-side management for smart grid via diffusion adaptation. IET Smart Grid 3(1):69–82CrossRef
73.
Qin H, Wu Z, Wang M (2020) Demand-side management for smart grid networks using stochastic linear programming game. Neural Comput Appl 32:139–149CrossRef
74.
Reka SS, Venugopal P, Alhelou HH, Siano P, Golshan ME (2021) Real time demand response modeling for residential consumers in smart grid considering renewable energy with deep learning approach. IEEE Access 9:56551–56562CrossRef
75.
Sobhani SO, Sheykhha S, Madlener R (2020) An integrated two-level demand-side management game applied to smart energy hubs with storage. Energy 206:118017CrossRef
76.
Gong L, Cao W, Liu K, Zhao J (2020) Optimal charging strategy for electric vehicles in residential charging station under dynamic spike pricing policy. Sustain Cities Soc 63:102474CrossRef
77.
Xiong Y, Gan J, An B, Miao C, Bazzan AL (2017) Optimal electric vehicle fast charging station placement based on game theoretical framework. IEEE Trans Intell Transp Syst 19(8):2493–2504CrossRef
78.
Xiao D, An S, Cai H, Wang J, Cai H (2020) An optimization model for electric vehicle charging infrastructure planning considering queuing behavior with finite queue length. J Energy Storage 29:101317CrossRef
79.
Covic N, Lacevic B (2020) Wingsuit flying search—a novel global optimization algorithm. IEEE Access 8:53883–53900CrossRef
80.
Talatahari S, Azizi M (2021) Chaos game optimization: a novel metaheuristic algorithm. Artif Intell Rev 54:917–1004CrossRef
81.
Wang H, Huang J (2016) Incentivizing energy trading for interconnected microgrids. IEEE Trans Smart Grid 9(4):2647–2657CrossRef
82.
Wang J, Zhong H, Qin J, Tang W, Rajagopal R, Xia Q, Kang C (2019) Incentive mechanism for sharing distributed energy resources. J Mod Power Syst Clean Energy 7(4):837–850CrossRef
83.
Fan S, Ai Q, Piao L (2018) Bargaining-based cooperative energy trading for distribution company and demand response. Appl Energy 226:469–482CrossRef
84.
Papadopoulos P, Skarvelis-Kazakos S, Grau I, Cipcigan LM, Jenkins N (2012) Electric vehicles’ impact on British distribution networks. IET Electr Syst Transp 2(3):91–102CrossRef
85.
Sarabi S, Davigny A, Courtecuisse V, Riffonneau Y, Robyns B (2016) Potential of vehicle-to-grid ancillary services considering the uncertainties in plug-in electric vehicle availability and service/localization limitations in distribution grids. Appl Energy 171:523–540CrossRef
86.
Dharmakeerthi CH, Mithulananthan N, Saha TK (2014) Impact of electric vehicle fast charging on power system voltage stability. Int J Electr Power Energy Syst 57:241–249CrossRef
87.
Tabari M, Yazdani A (2014) Stability of a dc distribution system for power system integration of plug-in hybrid electric vehicles. IEEE Trans Smart Grid 5(5):2564–2573CrossRef
88.
Manríquez F, Sauma E, Aguado J, de la Torre S, Contreras J (2020) The impact of electric vehicle charging schemes in power system expansion planning. Appl Energy 262:114527CrossRef
89.
Shirvani M, Memaripour A, Eghtedari M, Fayazi H (2014) Small signal stability analysis of power system following different outages. International Journal of Academic Research. 6(2)
90.
Foust T, Jones R, Graves E, McCoskey J, Yoon HS (2016) Effect of an electric vehicle mode in a plug-in hybrid electric vehicle with a post-transmission electric motor. Int J Electr Hybrid Veh 8(4):302–320CrossRef
91.
Paidi ER, Nechifor A, Albu MM, Yu J, Terzija V (2019) Development and validation of a new oscillatory component load model for real-time estimation of dynamic load model parameters. IEEE Trans Power Delivery 35(2):618–629CrossRef
92.
Meyer FJ, Lee KY (1982) Improved dynamic load model for power system stability studies. IEEE Trans Power Appar Syst 9:3303–3309CrossRef
93.
Kundur P, Paserba J, Ajjarapu V, Andersson G, Bose A, Canizares C, Hatziargyriou N, Hill D, Stankovic A, Taylor C, Van Cutsem T (2004) Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions. IEEE Trans Power Syst 19(3):1387–1401CrossRef
94.
Botterud A, Zhou Z, Wang J, Sumaili J, Keko H, Mendes J, Bessa RJ, Miranda V (2012) Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of Illinois. IEEE Trans Sustain Energy 4(1):250–261CrossRef
95.
Tavakoli A, Negnevitsky M, Nguyen DT, Muttaqi KM (2015) Energy exchange between electric vehicle load and wind generating utilities. IEEE Trans Power Syst 31(2):1248–1258CrossRef
96.
Sortomme E, El-Sharkawi MA (2010) Optimal charging strategies for unidirectional vehicle-to-grid. IEEE Trans Smart Grid 2(1):131–138CrossRef
97.
Tomić J, Kempton W (2007) Using fleets of electric-drive vehicles for grid support. J Power Sour 168(2):459–468CrossRef
98.
Khodayar ME, Wu L, Li Z (2013) Electric vehicle mobility in transmission-constrained hourly power generation scheduling. IEEE Trans Smart Grid 4(2):779–788CrossRef
99.
Talebizadeh E, Rashidinejad M, Abdollahi A (2014) Evaluation of plug-in electric vehicles impact on cost-based unit commitment. J Power Sources 248:545–552CrossRef
100.
Liu C, Wang J, Botterud A, Zhou Y, Vyas A (2012) Assessment of impacts of PHEV charging patterns on wind-thermal scheduling by stochastic unit commitment. IEEE Trans Smart Grid 3(2):675–683CrossRef
101.
Göransson L, Karlsson S, Johnsson F (2010) Integration of plug-in hybrid electric vehicles in a regional wind-thermal power system. Energy Policy 38(10):5482–5492CrossRef
102.
Khodayar ME, Wu L, Shahidehpour M (2012) Hourly coordination of electric vehicle operation and volatile wind power generation in SCUC. IEEE Trans Smart Grid 3(3):1271–1279CrossRef
103.
Al-Awami AT, Sortomme E (2011) Coordinating vehicle-to-grid services with energy trading. IEEE Trans Smart Grid 3(1):453–462CrossRef
104.
Arseneau R, Heydt GT, Kempker MJ (1997) Application of IEEE standard 519–1992 harmonic limits for revenue billing meters. IEEE Trans Power Delivery 12(1):346–353CrossRef
105.
Biroon RA, Abdollahi Z, Hadidi R (2019) Fast and regular electric vehicle charging impacts on the distribution feeders. In: 2019 IEEE industry applications society annual meeting, IEEE, pp 1–7
106.
Zhang L, Li Y (2013) Optimal charging strategy for EV charging stations by two-stage approximate dynamic programming. IFAC Proc Vol 46(5):423–430CrossRef
107.
Mullan J, Harries D, Bräunl T, Whitely S (2011) Modelling the impacts of electric vehicle recharging on the Western Australian electricity supply system. Energy Policy 39(7):4349–4359CrossRef
108.
Weiller C (2011) Plug-in hybrid electric vehicle impacts on hourly electricity demand in the United States. Energy Policy 39(6):3766–3778CrossRef
109.
He Y, Venkatesh B, Guan L (2012) Optimal scheduling for charging and discharging of electric vehicles. IEEE Trans Smart Grid 3(3):1095–1105CrossRef
110.
Lunz B, Yan Z, Gerschler JB, Sauer DU (2012) Influence of plug-in hybrid electric vehicle charging strategies on charging and battery degradation costs. Energy Policy 46:511–519CrossRef
111.
Nagata T (2018) A multi-agent based micro-grid operation method considering charging and discharging strategies of electric vehicles. IEEJ Trans Power Energy 138(7):598–604CrossRef
112.
Fairley P (2010) Speed bumps ahead for electric-vehicle charging. IEEE Spectr 47(1):13–14CrossRef
113.
Habib S, Kamran M, Rashid U (2015) Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks–a review. J Power Sources 277:205–214CrossRef
114.
Xu Y, Pan F (2012) Scheduling for charging plug-in hybrid electric vehicles. In: 2012 IEEE 51st IEEE conference on decision and control (CDC), IEEE, pp 2495–2501
115.
Iwafune Y, Ogimoto K, Azuma H (2019) Integration of electric vehicles into the electric power system based on results of road traffic census. Energies 12(10):1849CrossRef
116.
Shaaban MF, Eajal AA, El-Saadany EF (2015) Coordinated charging of plug-in hybrid electric vehicles in smart hybrid AC/DC distribution systems. Renew Energy 82:92–99CrossRef
117.
Thomas P, Chacko FM (2014) Electric vehicle integration to distribution grid ensuring quality power exchange. In: 2014 international conference on power signals control and computations (EPSCICON), IEEE, pp 1–6
118.
Qian K, Zhou C, Allan M, Yuan Y (2010) Modeling of load demand due to EV battery charging in distribution systems. IEEE Trans Power Syst 26(2):802–810CrossRef
119.
Saber AY, Venayagamoorthy GK (2010) Intelligent unit commitment with vehicle-to-grid—a cost-emission optimization. J Power Sources 195(3):898–911CrossRef
120.
Peterson SB, Whitacre JF, Apt J (2010) The economics of using plug-in hybrid electric vehicle battery packs for grid storage. J Power Sources 195(8):2377–2384CrossRef
121.
Pang C, Dutta P, Kezunovic M (2011) BEVs/PHEVs as dispersed energy storage for V2B uses in the smart grid. IEEE Trans Smart Grid 3(1):473–482CrossRef
122.
Su W, Eichi H, Zeng W, Chow MY (2011) A survey on the electrification of transportation in a smart grid environment. IEEE Trans Industr Inf 8(1):1CrossRef
123.
Nodushan MM, Ghadimi AA, Salami A (2013) Voltage sag improvement in radial distribution networks using reconfiguration simultaneous with DG placement. Indian J Sci Technol 6(7):4682–4689CrossRef
124.
Wang J, Liu C, Ton D, Zhou Y, Kim J, Vyas A (2011) Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power. Energy Policy 39(7):4016–4021CrossRef
125.
Turton H, Moura F (2008) Vehicle-to-grid systems for sustainable development: an integrated energy analysis. Technol Forecast Soc Chang 75(8):1091–1108CrossRef
126.
Ahmet NU. An overview of battery electric vehicles and plug-in hybrid electric vehicles
127.
Duoba M, Lohse-Busch H, Rask E (2012) Evaluating plug-in vehicles (plug-in hybrid and battery electric vehicles) using standard dynamometer protocols. World Electr Veh J 5(1):196–209CrossRef
128.
Hajimiragha A, Canizares CA, Fowler MW, Elkamel A (2009) Optimal transition to plug-in hybrid electric vehicles in Ontario, Canada, considering the electricity-grid limitations. IEEE Trans Industr Electron 57(2):690–701CrossRef
129.
Hajimiragha AH, Canizares CA, Fowler MW, Moazeni S, Elkamel A (2011) A robust optimization approach for planning the transition to plug-in hybrid electric vehicles. IEEE Trans Power Syst 26(4):2264–2274CrossRef
130.
Hadley SW, Tsvetkova AA (2009) Potential impacts of plug-in hybrid electric vehicles on regional power generation. Electr J 22(10):56–68CrossRef
131.
Green RC II, Wang L, Alam M (2011) The impact of plug-in hybrid electric vehicles on distribution networks: a review and outlook. Renew Sustain Energy Rev 15(1):544–553CrossRef
132.
Shahnia F, Ghosh A, Ledwich G, Zare F (2013) Predicting voltage unbalance impacts of plug-in electric vehicles penetration in residential low-voltage distribution networks. Electr Power Compon Syst 41(16):1594–1616CrossRef
133.
Priya Esther B, Shivarama Krishna K, Sathish Kumar K, Ravi K (2016) Demand side management using bacterial foraging optimization algorithm. In: Information systems design and intelligent applications: proceedings of third international conference INDIA 2016, Springer India, pp 657–666
134.
Barolli L, Miwa H, (Eds.) (2022) Advances in Intelligent Networking and Collaborative Systems. In: The 14th international conference on intelligent networking and collaborative systems (INCoS-2022), Springer Nature
135.
Zafar A, Shah S, Khalid R, Hussain SM, Rahim H, Javaid N (2017) A meta-heuristic home energy management system. In: 2017 31st international conference on advanced information networking and applications workshops (WAINA), IEEE, pp 244–250
136.
Awais M, Javaid N, Shaheen N, Iqbal Z, Rehman G, Muhammad K, Ahmad I (2015) An efficient genetic algorithm based demand side management scheme for smart grid. In: 2015 18th international conference on network-based information systems, IEEE, pp 351–356
137.
Arabali A, Ghofrani M, Etezadi-Amoli M, Fadali MS, Baghzouz Y (2012) Genetic-algorithm-based optimization approach for energy management. IEEE Trans Power Delivery 28(1):162–170CrossRef
138.
Zhou Y, Chen Y, Xu G, Zhang Q, Krundel L (2014) Home energy management with PSO in smart grid. In: 2014 IEEE 23rd international symposium on industrial electronics (ISIE), IEEE, pp 1666–1670
139.
Rasheed MB, Javaid N, Ahmad A, Khan ZA, Qasim U, Alrajeh N (2015) An efficient power scheduling scheme for residential load management in smart homes. Appl Sci 5(4):1134–1163CrossRef
140.
Wu B, Ma H, Pan Z, Wang J, Qu W, Wang B (2014) Drying and quality characteristics and models of carrot slices under catalytic infrared heating. Int Agric Eng J 23(2):70–79
141.
Wang L, Wang Z, Yang R (2012) Intelligent multiagent control system for energy and comfort management in smart and sustainable buildings. IEEE Trans Smart Grid 3(2):605–617CrossRef
142.
Ru N, Jianhua Y (2008) A GA and particle swarm optimization based hybrid algorithm. In: 2008 IEEE congress on evolutionary computation (IEEE World Congress on Computational Intelligence), IEEE, pp 1047–1050
143.
Javaid N, Javaid S, Abdul W, Ahmed I, Almogren A, Alamri A, Niaz IA (2017) A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies 10(3):319CrossRef
144.
Ahmad A, Khan A, Javaid N, Hussain HM, Abdul W, Almogren A, Alamri A, Azim Niaz I (2017) An optimized home energy management system with integrated renewable energy and storage resources. Energies 10(4):549CrossRef
145.
Yang HT, Yang CT, Tsai CC, Chen GJ, Chen SY (2015) Improved PSO based home energy management systems integrated with demand response in a smart grid. In: 2015 IEEE congress on evolutionary computation (CEC), IEEE, pp 275–282
146.
Manzoor A, Javaid N, Ullah I, Abdul W, Almogren A, Alamri A (2017) An intelligent hybrid heuristic scheme for smart metering based demand side management in smart homes. Energies 10(9):1258CrossRef
147.
Zhang J, Wu Y, Guo Y, Wang B, Wang H, Liu H (2016) A hybrid harmony search algorithm with differential evolution for dayahead scheduling problem of a microgrid with consideration of power flow constraints. Appl Energy 183:791–804CrossRef
148.
Pamir, Javaid N, Mohsin SM, Iqbal A, Yasmeen A, Ali I (2019) A hybrid bat-crow search algorithm based home energy management in smart grid. In: Complex, intelligent, and software intensive systems: proceedings of the 12th international conference on complex, intelligent, and software intensive systems (CISIS-2018), Springer International Publishing, pp 75–88
149.
Man KF, Tang KS, Kwong S (1996) Genetic algorithms: concepts and applications [in engineering design]. IEEE Trans Industr Electron 43(5):519–534CrossRef
150.
Bozorg-Haddad O, Solgi M, Loáiciga HA (2017) Meta-heuristic and evolutionary algorithms for engineering optimization. WileyCrossRef
151.
Back T (1994) Selective pressure in evolutionary algorithms: a characterization of selection mechanisms. In: Proceedings of the first IEEE conference on evolutionary computation, IEEE World Congress on Computational Intelligence, IEEE, pp 57–62
152.
Balci HH, Valenzuela JF (2004) Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method. Int J Appl Math Comput Sci 14(3):411–421MathSciNet
153.
Saadatpour M, Afshar A (2013) Multi objective simulation-optimization approach in pollution spill response management model in reservoirs. Water Resour Manag 27:1851–1865CrossRef
154.
Afshar A, Massoumi F, Afshar A, Mariño MA (2015) State of the art review of ant colony optimization applications in water resource management. Water Resour Manag 29:3891–3904CrossRef
155.
Logenthiran T, Srinivasan D, Khambadkone AM (2011) Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electr Power Syst Res 81(1):138–148CrossRef
156.
Bharathi C, Rekha D, Vijayakumar V (2017) Genetic algorithm based demand side management for smart grid. Wireless Pers Commun 93:481–502CrossRef
157.
Vose MD, Liepins GE (1991) Punctuated equilibria in genetic search. Complex Syst 5(1):31–44MathSciNet
158.
Seyedmahmoudian M, Horan B, Soon TK, Rahmani R, Oo AM, Mekhilef S, Stojcevski A (2016) State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems–a review. Renew Sustain Energy Rev 64:435–455CrossRef
159.
Gendreau M, Potvin JY (eds) (2010) Handbook of metaheuristics. Springer, New York
160.
Meire PM, Ervynck A (1986) Are oystercatchers (Haematopus ostralegus) selecting the most profitable mussels (Mytilus edulis)? Anim Behav 34(5):1427–1435CrossRef
161.
Lobo JL, Del Ser J, Bifet A, Kasabov N (2020) Spiking neural networks and online learning: an overview and perspectives. Neural Netw 121:88–100CrossRef
162.
Ma Y, Houghton T, Cruden A, Infield D (2012) Modeling the benefits of vehicle-to-grid technology to a power system. IEEE Trans Power Syst 27(2):1012–1020CrossRef
163.
Sundstrom O, Binding C (2011) Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Trans Smart Grid 3(1):26–37CrossRef
164.
Lin W, Wu Z, Lin L, Wen A, Li J (2017) An ensemble random forest algorithm for insurance big data analysis. IEEE Access 5:16568–16575CrossRef
Metadaten
Titel
A comprehensive review of demand-side management in smart grid operation with electric vehicles
verfasst von
Satish Jagannath Ghorpade
Rajesh B. Sharma
Publikationsdatum
18.04.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Electrical Engineering
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI
https://doi.org/10.1007/s00202-024-02330-x