Abstract
Nowadays, many countries are concerned about the environmental problems which are mainly caused due to insensible and careless use of energy. The ignorance of the people towards energy consumption is greatly affecting the environment. The contribution of electric sector in polluting the environment is the highest among all other sectors that work on energy. High demands of electricity during peak hours result in increased production of electricity using fossil fuel based plants which increases the level of CO2 in the atmosphere. However improved performance of the grid system in reducing the peak loads and availability of electricity can reduce the green house gas emission which is considered as the main reason of climate change. The work in this chapter is mainly focused on optimal load scheduling for energy cost minimization and peak load reduction. The proposed model uses Time of Use (TOU) pricing tariff in the optimization process. The optimization problem has been solved with multiple optimization techniques including Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and a hybrid algorithm formed by combining GA and PSO. The proposed scheme has many applications like peak load reduction and energy cost minimization which can benefit consumers and utilities.
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Kothari DP, Nagrath IJ(2006) Modern power systems analysis, 3rd edn. McGraw-Hill
Gellings CW, Chamberlin JH (1993) Demand side management: concepts and methods, 2nd edn. PennWell Books, Tulsa
Masters GM (2004) Renewable and efficient electric power systems. Wiley, Hoboken
Samadi P, Mohsenian-Rad H, Schober R, Wong WS (2012) Advanced demand side management for the future smart grid using mechanism design. IEEE Trans Smart Grid 3:1170–1180
Faruqui A, George S (2005) Quantifying customer response to dynamic pricing. Electr J 18:53–63
Kumar S, Sodha NS, Wadhwa K(2013) Dynamic tariff structures for demand side management and demand response. An approach paper from India
Demand response: an introduction (2006) South West Energy Efficiency Project. Rockey Mountain Institute, Colorado
Albadi MH, El-Saadany EF (2007) Demand response in electricity markets: an overview. In: IEEE conference
Mohsenian-Rad AH, Wong VWS, 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 1:320–331
Farhadi P, Taheri B (2017) Smart meter tariff pricing using load demand response model. In: 5th international Istanbul smart grid and cities congress and fair (ICSG)
Sianaki OA, Hussain O, Dillon T, Tabesh AR(2010) Intelligent decision support system for including consumers’ preferences in residential energy consumption in smart grid. In: IEE second international conference on computational intelligence, modelling and simulation
Lujano-Rojasa JM, Monteiro C, Dufo-Lopez R, Bernal-Agustin J (2012) Optimum residential load management strategy for real time pricing (RTP) demand response programs. Energy Policy 45:671–679
Federal Energy Regulatory Commission (FERC) (2003), White paper: wholesale power market platform
AEMC (Australian Energy Market Commission) (2015) National electricity market. http://www.aemc.gov.au/Australias-Energy-Market/Markets-Overview/National-electricity-market
Smart demand response market by end user—residential, commercial, and industrial and global industry analysis, size, share, growth, trends and forecast 2014–2025 Industry Report (2016)
Smart demand response market analysis by application (Residential, commercial, industrial) and segment forecasts to 2022, 2016 Base Year for Estimate, Report ID: 978-1-68038-694-3 (2015)
Global automated demand response management systems market for the forecast period 2018–2023, segmented by geography—growth, trends and forecast (2018)
Demand response system market: global industry analysis and forecast 2016–2024, report, industry: IT and Telecommunication (2018)
Lund H (2015) Renewable energy systems: a smart energy systems approach to the choice and modeling of 100% renewable solutions. Academic Press
Stevens L, Wilcox M, Leopold A, Taylor C, Waters L(2016) Poor people’s energy outlook 2016, 1st edn. Practical Action Publishing Ltd., Rugby, Warwickshire
Schnitzer D, Lounsbury DS, Carvallo JP, Deshmukh R, Apt J, Kammen DM (2014) Microgrids for rural electrification: a critical review of best practices based on seven case studies, Technical report. University California Berkeley & Carnegie Mellon University, Berkeley
Jung J, Villaran M (2017) Optimal planning and design of hybrid renewable energy systems for microgrids. Renew Sustain Energy Rev 75:180–191
Bigerna S, Bollino CA, Micheli S (2016) Socio-economic acceptability for smart grid development: a comprehensive review. J Clean Prod 131(9):399–409
Hsu YY, Su CC (1991) Dispatch of direct load control using dynamic programming. IEEE Trans Power Syst 6:1056–1061
Kurucz CN, Brandt D, Sim S (1996) A linear programming model for reducing system peak through customer load control programs. IEEE Trans Power Syst 11:1817–1824
Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z(2011) An integer linear programming and game theory based optimization for demand-side management in smart grid. In: IEEE international workshop on smart grid communications and networks
Zhu Z, Tang J, Lambotharan S, Chin WH, Fan Z (2012) An integer linear programming based optimization for home demand-side management in smart grid. IEEE PES innovative smart grid technologies (ISGT)
Nguyen HK, Song JB, Han Z (2012) Demand side management to reduce peak-to-average ratio using game theory in smart grid. IEEE INFOCOM workshop on communications and control for sustainable energy systems: green networking and smart grids, pp 91–96
Bu S, Yu FR (2013) A game-theoretical scheme in the smart grid with demand-side management: towards a smart cyber-physical power infrastructure. IEEE Trans 1:22–32
Yi Liu, Yuen C, Huang S, Hassan NU, Wang X, Xie S (2014) Peak-to-average ratio constrained demand-side management with consumer’s preference in residential smart grid. IEEE J Sel Top Sig Process 8:1084–1097
Fadlullah ZM, Quan DM, Kato N, Stojmenovic I (2014) GTES: an optimized game -theoretic demand -side management scheme for smart grid. IEEE Syst J
Soliman HM, Leon-Garcia A (2014) Game-theoretic demand-side management with storage devices for the future smart grid. IEEE Trans Smart Grid 5:1475–1485
Chen H, Li Y, Louie RHY, Vucetic B (2014) Autonomous demand-side management based on energy consumption scheduling and instantaneous load billing: an aggregative game approach. IEEE Trans Smart Grid 5:1744–1754
Raj CA, Aravind E, Sundaram BR, Vasudevan SK (2015) Smart meter based on real time pricing. Smart Grid Technol 21:120–124
Zhao C, Dong S, Li F, Song Y (2015) Optimal home energy management system with mixed types of loads. CSEE J Power Energy Syst 1:29–36
Barbatoa A, Caponea A, Chenb L, Martignonb F, Paris S (2015) A distributed demand-side management framework for the smart grid. Elsevier Comput Commun 57:13–24
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 81:138–148
Huang Y, Wang L, Wu Q (2014) A hybrid PSO-DE algorithm for smart home energy management. ICSI 2014, Part II, LNCS 8795:292–300
Venayagamoorthy GK (2010) A dynamic optimization method for a smart grid. In: Conference: power and energy society general meeting. IEEE
Labeeuw W, Deconinck G (2013) Residential electrical load model based on mixture model clustering and Markov Models. IEEE Trans Industr Inf 9:1561–1568
Prinsloo G, Mammoli A, Dobson R (2017) Customer domain supply and load coordination: a case for smart villages and transactive control in rural off-grid microgrids. Elsevier Energy 135(1):1–12
Goutam D, Krishnendranath M (2017) A literature review on dynamic pricing of electricity. J Oper Res Soc 68:1131–1145
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Das, N.R., Rai, S.C., Nayak, A.K. (2020). Scheduling Operations of Smart Appliances Using Demand Response. In: Patnaik, S., Sen, S., Mahmoud, M. (eds) Smart Village Technology. Modeling and Optimization in Science and Technologies, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-37794-6_19
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DOI: https://doi.org/10.1007/978-3-030-37794-6_19
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