Abstract
Almost nothing in today’s world runs without power. Right from the air conditioner and water heaters to phone charging and electricity. Energy has its own role to play in everything that happens. But with increasing number of homes, the power consumption increases. The number of electricity sources does not follow the same rate of increase. So it becomes very difficult to supply all sections of a place with power simultaneously. Some areas will have to face blackout, whereas other places will have proper supply. And the electricity department needs revenue every month. This paper provides an optimal solution to provide electricity to a city divided into different sections or areas, when only a limited amount of energy units are available or generated. Assuming that each grid has its own power consumption and as per that and the revenue, the approach used in the proposed work is 0/1 knapsack problem to provide energy in a smart grid such that almost all of the power is used and maximum revenue is generated. This can be done through various methods like dynamic programming, greedy approach, brute force, backtracking, etc. We also find out that which approach will give the best solution with least time complexity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gyamera, L., Atripatri, I.: Energy efficiency of smart cities: an analysis of the literature (2017)
Choi, S., Park, S., Kang, D.-J., Han, S.-J., Kim, H.-M.: A microgrid energy management system for inducing optimal demand response. In: IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 19–24. Brussels, Belgium (2011)
Cecati, C., Citro, C., Siano, P.: Combined operations of renewable energy systems and responsive demand in a smart grid. IEEE Trans. Sustain. Energy 2(4), 468–476 (2011)
Pourmousavi, S., Nehrir, M., Colson, C., Wang, C.: Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization. IEEE Trans. Sustain. Energy 1(3), 193–201 (2010)
Siano, P., Cecati, C., Yu, H., Kolbusz, J.: Real time operation of smart grids via FCN networks and optimal power flow. IEEE Trans. Ind. Informat. 8(4), 944–952 (2012)
Gavriluta, C., Candela, J.I., Citro, C., Rocabert, J., Luna, A., Rodri guez, P.: Decentralized primary control of MTDC networks with energy storage and distributed generation. IEEE Trans. Ind. Appl. 50(6), 4122–4131 (2014)
Al-Awami, A.T., Sortomme, E.: Coordinating vehicle-to-grid services with energy trading. IEEE Trans. Smart Grid 3(1), 453–462 (2012)
Johansson, B.: On Distributed Optimization in Networked Systems. Ph.D Thesis, Royal Institute of Technology (KTH) (2008)
Nedic, A., Ozdaglar, A., Parrilo, P.A.: Constrained consensus and optimization in multi-agent networks. IEEE Trans. Autom. Control 55(4), 922–938 (2010)
Basu, S., Kannayaram, G., Ramasubbareddy, S., Venkatasubbaiah, C.: Improved genetic algorithm for monitoring of virtual machines in cloud environment. In: Smart Intelligent Computing and Applications, pp. 319–326. Springer, Singapore
Somula, R., Sasikala, R.: Round robin with load degree: An algorithm for optimal cloudlet discovery in mobile cloud computing. Scalable Comput.: Pract. Exp. 19(1), 39–52 (2018)
Somula, R., Anilkumar, C., Venkatesh, B., Karrothu, A., Kumar, C.P., Sasikala, R.: Cloudlet services for healthcare applications in mobile cloud computing. In: Proceedings of the 2nd International Conference on Data Engineering and Communication Technology, pp. 535–543. Springer, Singapore (2019)
Somula, R.S., Sasikala, R.: A survey on mobile cloud computing: mobile computing + cloud computing (MCC = MC + CC). Scalable Comput.: Pract. Exp. 19(4), 309–337 (2018)
Somula, R., Sasikala, R.: A load and distance aware cloudlet selection strategy in multi-cloudlet environment. Int. J. Grid High Perform. Comput. (IJGHPC) 11(2), 85–102 (2019)
Somula, R., Sasikala, R.: A honey bee inspired cloudlet selection for resource allocation. In: Smart Intelligent Computing and Applications, pp. 335–343. Springer, Singapore (2019)
Nalluri, S., Ramasubbareddy, S., Kannayaram, G.: Weather prediction using clustering strategies in machine learning. J. Comput. Theor. Nanosci. 16(5–6), 1977–1981 (2019)
Sahoo, K.S., Tiwary, M., Mishra, P., Reddy, S.R.S., Balusamy, B., Gandomi, A.H.: Improving end-users utility in software-defined wide area network systems. IEEE Trans. Netw. Serv. Manag. (2019)
Sahoo, K.S., Tiwary, M., Sahoo, B., Mishra, B.K., RamaSubbaReddy, S., Luhach, A.K.: RTSM: response time optimisation during switch migration in software-defined wide area network. IET Wirel. Sens. Syst. IET Wirel. Sens. Syst. (2019)
Somula, R., Kumar, K.D., Aravindharamanan, S., Govinda, K.: Twitter sentiment analysis based on US presidential election 2016. In: Smart Intelligent Computing and Applications, pp. 363–373. Springer, Singapore (2016)
Sai, K.B.K., Subbareddy, S.R., Luhach, A.K.: IOT based air quality monitoring system using MQ135 and MQ7 with machine learning analysis. Scalable Comput.: Pract. Exp. 20(4), 599–606 (2019)
Somula, R., Narayana, Y., Nalluri, S., Chunduru, A., Sree, K.V.: POUPR: properly utilizing user-provided recourses for energy saving in mobile cloud computing. In: Proceedings of the 2nd International Conference on Data Engineering and Communication Technology, pp. 585–595. Springer, Singapore (2019)
Vaishali, R., Sasikala, R., Ramasubbareddy, S., Remya, S., Nalluri, S.: Genetic algorithm based feature selection and MOE fuzzy classification algorithm on Pima Indians diabetes dataset. In: 2017 International Conference on Computing Networking and Informatics (ICCNI), pp. 1–5. IEEE (2017)
Somula, R., Sasikala, R.: A research review on energy consumption of different frameworks in mobile cloud computing. In: Innovations in Computer Science and Engineering, pp. 129–142. Springer, Singapore (2019)
Saraswathi, R.V., Nalluri, S., Ramasubbareddy, S., Govinda, K., Swetha, E.: Brilliant corp yield prediction utilizing internet of things. In: Data Engineering and Communication Technology, pp. 893–902. Springer, Singapore (2020)
Kumar, I.P., Sambangi, S., Somukoa, R., Nalluri, S., Govinda, K.: Server security in cloud computing using block-chaining technique. In: Data Engineering and Communication Technology, pp. 913–920. Springer, Singapore (2020)
Kumar, I.P., Gopal, V.H., Ramasubbareddy, S., Nalluri, S., Govinda, K.: Dominant color palette extraction by k-means clustering algorithm and reconstruction of image. In: Data Engineering and Communication Technology, pp. 921–929. Springer, Singapore (2020)
Nalluri, S., Saraswathi, R. V., Ramasubbareddy, S., Govinda, K., Swetha, E.: Chronic heart disease prediction using data mining techniques. In: Data Engineering and Communication Technology, pp. 903–912. Springer, Singapore (2020)
Krishna, A.V., Ramasubbareddy, S., Govinda, K.: Task scheduling based on hybrid algorithm for cloud computing. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 415–421. Springer, Singapore (2020)
Srinivas, T.A.S., Ramasubbareddy, S., Govinda, K., Manivannan, S.S.: Web image authentication using embedding invisible watermarking. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 207–218. Springer, Singapore (2020)
Krishna, A.V., Ramasubbareddy, S., Govinda, K.: A unified platform for crisis mapping using web enabled crowd sourcing powered by knowledge management. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 195–205. Springer, Singapore (2020)
Kalyani, D., Ramasubbareddy, S., Govinda, K., Kumar, V.: Location-based proactive handoff mechanism in mobile ad hoc network. In: International Conference on Intelligent Computing and Smart Communication 2019, pp. 85–94. Springer, Singapore (2020)
Bhukya, K.A., Ramasubbareddy, S., Govinda, K., Srinivas, T.A.S.: Adaptive mechanism for smart street lighting system. In: Smart Intelligent Computing and Applications, pp. 69–76. Springer, Singapore (2020)
Srinivas, T.A.S., Somula, R., Govinda, K.: Privacy and security in aadhaar. In: Smart Intelligent Computing and Applications, pp. 405–410. Springer, Singapore (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Aditya Sai Srinivas, T., Ramasubbareddy, S., Sharma, A., Govinda, K. (2021). Optimal Energy Distribution in Smart Grid. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_36
Download citation
DOI: https://doi.org/10.1007/978-981-15-5679-1_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5678-4
Online ISBN: 978-981-15-5679-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)