Developing an Algorithm to Consider Mutliple Demand Response Objectives

Authors

  • D. Behrens Department of Information Systems and Enterprise Modelling, University of Hildesheim, Hildesheim, Germany
  • T. Schoormann Department of Information Systems and Enterprise Modelling, University of Hildesheim, Hildesheim, Germany
  • R. Knackstedt Department of Information Systems and Enterprise Modelling, University of Hildesheim, Hildesheim, Germany
Volume: 8 | Issue: 1 | Pages: 2621-2626 | February 2018 | https://doi.org/10.48084/etasr.1819

Abstract

Due to technological improvement and changing environment, energy grids face various challenges, which, for example, deal with integrating new appliances such as electric vehicles and photovoltaic. Managing such grids has become increasingly important for research and practice, since, for example, grid reliability and cost benefits are endangered. Demand response (DR) is one possibility to contribute to this crucial task by shifting and managing energy loads in particular. Realizing DR thereby can address multiple objectives (such as cost savings, peak load reduction and flattening the load profile) to obtain various goals. However, current research lacks algorithms that address multiple DR objectives sufficiently. This paper aims to design a multi-objective DR optimization algorithm and to purpose a solution strategy. We therefore first investigate the research field and existing solutions, and then design an algorithm suitable for taking multiple objectives into account. The algorithm has a predictable runtime and guarantees termination.

Keywords:

optimization, demand response, demand side management, algorithm engineering, greedy heuristic

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References

T. M. Lawrence, R. T. Watson, M. -C. Boudreau, J. Mohammadpour, “Data Flow Requirements for Integrating Smart Buildings and a Smart Grid through Model Predictive Control”, Procedia Engineering, vol. 180, pp. 1402–1412, 2017 DOI: https://doi.org/10.1016/j.proeng.2017.04.303

S. Seidel, J. C. Recker, J. vom Brocke, “Sensemaking and Sustainable Practicing: Functional Affordances of Information Systems in Green Transformations”, Management Information Systems Quarterly, Vol. 37, No. 4, pp. 1275–1299,. 2013 DOI: https://doi.org/10.25300/MISQ/2013/37.4.13

Q. Hu, F. Li, “Hardware Design of Smart Home Energy Management System With Dynamic Price Response”, IEEE Transactions on Smart Grid, Vol. 4, No. 4, pp. 1878–1887, 2013 DOI: https://doi.org/10.1109/TSG.2013.2258181

A. M. Kosek, G. T. Costanzo, H. W. Bindner, O. Gehrke, “An Overview of Demand Side Management Control Schemes for Buildings in Smart Grids”, 2013 IEEE International Conference on Smart Energy Grid Engineering, 2013 DOI: https://doi.org/10.1109/SEGE.2013.6707934

L. Merkert, I. Harjunkoski, A. Isaksson, S. Saynevirta, A. Saarela, and G. Sand, “Scheduling and energy – Industrial challenges and opportunities”, Computers & Chemical Engineering, Vol. 72, , pp. 183–198, 2015 DOI: https://doi.org/10.1016/j.compchemeng.2014.05.024

P. Siano, “Demand response and smart grids—A survey”, Renewable and Sustainable Energy Reviews, Vol. 30, pp. 461–478, 2014 DOI: https://doi.org/10.1016/j.rser.2013.10.022

D. Steen, L. A. Tuan, L. Bertling, “Price-Based Demand-Side Management For Reducing Peak Demand In Electrical Distribution Systems – With Examples From Gothenburg”, available at: http://publications.lib.chalmers.se/records/fulltext/163330/local_163330.pdf, 2012

L. Hillemacher, Lastmanagement mittels dynamischer Strompreissignale bei Haushaltskunden (Load management through dynamic price signals), PhD Thesis, Karlsruher Instituts fur Technologie, 2014

A. R. S. Vidal, L. A. A. Jacobs, L. S. Batista, “An evolutionary approach for the demand side management optimization in smart grid”, IEEE Symposium on Computational Intelligence Applications in Smart Grid, pp. 1–7, 2014 DOI: https://doi.org/10.1109/CIASG.2014.7011561

S. Salinas, M. Li, P. Li, “Multi-Objective Optimal Energy Consumption Scheduling in Smart Grids”, IEEE Transactions on Smart Grid, Vol. 4, No. 1, pp. 341–348, 2013 DOI: https://doi.org/10.1109/TSG.2012.2214068

N. Bassamzadeh, R. Ghanem, S. Lu, S. J. Kazemitabar, “Robust scheduling of smart appliances with uncertain electricity prices in a heterogeneous population”, Energy and Buildings, Vol. 84, pp. 537–547, 2014

L. Song, Y. Xiao, M. van der Schaar, “Non-stationary demand side management method for smart grids”, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7759–7763, 2014

R. Batchu, N. M. Pindoriya, “Residential Demand Response Algorithms: State-of-the-Art, Key Issues and Challenges”, International Conference on Wireless and Satellite Systems, pp. 18–32, 2015 DOI: https://doi.org/10.1007/978-3-319-25479-1_2

Berkeley Lab, “Distributed Energy Resources Customer Adoption Model (DER-CAM) | Building Microgrid”, available at: https://building-microgrid.lbl.gov/projects/der-cam

A. Saad Al-Sumaiti, M. H. Ahmed, M. M. A. Salama, “Smart Home Activities: A Literature Review”, Electric Power Components and Systems, Vol. 42, No. 3–4, pp. 294–305, 2014 DOI: https://doi.org/10.1080/15325008.2013.832439

V. S. K. Murthy Balijepalli, V. Pradhan, S. A. Khaparde, R. M. Shereef, “Review of demand response under smart grid paradigm”, IEEE PES Innovative Smart Grid Technologies – India, pp. 236–243, 2011 DOI: https://doi.org/10.1109/ISET-India.2011.6145388

C. Gerwig, D. Behrens, H. Lessing, R. Knackstedt, “Demand Side Management in Residential Contexts - A Literature Review”, INFORMATIK 2015. Bonn: Gesellschaft für Informatik, pp. 93–107, 2015

W. Ketter, J. Collins, P. P. Reddy, C. M. Flath, The Power Trading Agent Competition, ERIM, 2011

S. Q. Ali, S. D. Maqbool, T. P. I. Ahamed, N. H. Malik, “Pursuit Algorithm for optimized load scheduling”, IEEE International Power Engineering and Optimization Conference, pp. 193–198, 2012 DOI: https://doi.org/10.1109/PEOCO.2012.6230859

C. Keerthisinghe, G. Verbic, A. C. Chapman, “Evaluation of a multi-stage stochastic optimisation framework for energy management of residential PV-storage systems”, Australasian Universities Power Engineering Conference, pp. 1–6, 2014 DOI: https://doi.org/10.1109/AUPEC.2014.6966552

W. Zhao, P. Cooper, P. Perez, L. Ding, “Cost-Driven Residential Energy Management for Adaption of Smart Grid and Local Power Generation”, International Symposium for Next Generation Infrastructure, 2013 DOI: https://doi.org/10.14453/isngi2013.proc.53

Y. Huang, S. Mao, R. M. Nelms, “Smooth electric power scheduling in power distribution networks”, IEEE Globecom Workshops, pp. 1469–1473, 2012 DOI: https://doi.org/10.1109/GLOCOMW.2012.6477802

P. McNamara, S. McLoone, “Hierarchical Demand Response for Peak Minimization Using Dantzig #x2013;Wolfe Decomposition”, IEEE Transactions on Smart Grid, Vol. 6, No. 6, pp. 2807–2815, 2015 DOI: https://doi.org/10.1109/TSG.2015.2467213

R. Verschae, H. Kawashima, T. Kato, T. Matsuyama, “A distributed coordination framework for on-line scheduling and power demand balancing of households communities”, European Control Conference, pp. 1655–1662, 2014 DOI: https://doi.org/10.1109/ECC.2014.6862394

F. De Angelis, M. Boaro, D. Fuselli, S. Squartini, F. Piazza, Q. WeiDing Wang, “Optimal Task and Energy Scheduling in Dynamic Residential Scenarios”, in Advances in Neural Networks, pp. 650–658, 2012 DOI: https://doi.org/10.1007/978-3-642-31346-2_73

N. Bassamzadeh, R. Ghanem, S. Lu, S. J. Kazemitabar, “Robust scheduling of smart appliances with uncertain electricity prices in a heterogeneous population”, Energy and Buildings, Vol. 84, pp. 537–547, 2014 DOI: https://doi.org/10.1016/j.enbuild.2014.08.035

S. -J. Kim, G. B. Giannakis, “Efficient and scalable demand response for the smart power grid”, 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, pp. 109–112, 2011

L. Song, Y. Xiao, M. van der Schaar, “Non-stationary demand side management method for smart grids”, IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7759–7763, 2014 DOI: https://doi.org/10.1109/ICASSP.2014.6855110

T. Cortes-Arcos, J. L. Bernal-Agustín, R. Dufo-Lopez, J. M. Lujano-Rojas, J. Contreras, “Multi-objective demand response to real-time prices (RTP) using a task scheduling methodology”, Energy, Vol. 138, pp. 19–31, 2017 DOI: https://doi.org/10.1016/j.energy.2017.07.056

D. Behrens, T. Schoormann, R. Knackstedt, “Towards a Taxonomy of Constraints in Demand-Side-Management-Methods for a Residential Context”, 20th International Conference on Business Information Systems, pp. 283-295, 2017 DOI: https://doi.org/10.1007/978-3-319-59336-4_20

D. Behrens, C. Ruether, T. Schoormann, K. Ambrosi, R. Knackstedt, “Effects of Constraints in Residential Demand-Side-Management Algorithms - A Simulation-based Study”, International Conference on Operation Research, 2017 DOI: https://doi.org/10.1007/978-3-319-89920-6_35

A. R. Hevner, S. T. March, J. Park, S. Ram, “Design Science in Information Systems Research”, Management Information Systems Quarterly, Vol. 28, No. 1, pp. 75–105, 2004 DOI: https://doi.org/10.2307/25148625

P. Sanders, “Algorithm Engineering – An Attempt at a Definition”, in: Efficient Algorithms, Springer, pp. 321–340, 2009 DOI: https://doi.org/10.1007/978-3-642-03456-5_22

W. Ketter, M. Peters, J. Collins, A. Gupta, “Competitive Benchmarking: An IS Research Approach to Address Wicked Problems with Big Data and Analytics”, Management Information Systems Quarterly, Vol. 40, No. 4, pp. 1057–1080, 2016 DOI: https://doi.org/10.25300/MISQ/2016/40.4.12

K. Fuller, R. Ramanath, M. Bohm, H. Krcmar, “Decision Support for the Selection of Appropriate Customer Integration Methods”, 12th International Conference on Wirtschaftsinformatik, pp. 1360-1374, 2015

A. -H. Mohsenian-Rad, V. W. Wong, J. Jatskevich, R. Schober, A. Leon-Garcia, “Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid”, IEEE Transactions on Smart Grid, Vol. 1, No. 3, pp. 320–331, 2010 DOI: https://doi.org/10.1109/TSG.2010.2089069

J. A. Nelder, R. Mead, “A Simplex Method for Function Minimization”, The Computer Journal, Vol. 7, No. 4, pp. 308–313, 1965 DOI: https://doi.org/10.1093/comjnl/7.4.308

A. H. Land, A. G. Doig, “An Automatic Method of Solving Discrete Programming Problems”, Econometrica, Vol. 28, No. 3, pp. 497–520, 1960 DOI: https://doi.org/10.2307/1910129

A. Monacchi, D. Egarter, W. Elmenreich, S. D’ Alessandro, A. M. Tonello, “GREEND: An Energy Consumption Dataset of Households in Italy and Austria”, IEEE International Conference on Smart Grid Communications, pp. 511-516, 2014 DOI: https://doi.org/10.1109/SmartGridComm.2014.7007698

D. Behrens, T. Schoormann, R. Knackstedt, “Datensets für Demand-Side-Management–Literatur-Review-Basierte Analyse und Forschungsagenda” (Datasets for demand side management – Literature review based analysis and research agenda), in Lecture Notes in Informatics, 2016

H. A. Cao, C. Beckel, T. Staake, “Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns”, 39th Annual Conference of the IEEE Industrial Electronics Society, pp. 4733–4738, 2013 DOI: https://doi.org/10.1109/IECON.2013.6699900

G. Hoogsteen, A. Molderink, J. L. Hurink, G. J. M. Smit, “Generation of flexible domestic load profiles to evaluate Demand Side Management approaches”, IEEE International Energy Conference, pp. 1–6, 2016 DOI: https://doi.org/10.1109/ENERGYCON.2016.7513873

N. Pflugradt, Load Profile Generator, available at: www.

loadprofilegenerator.de

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How to Cite

[1]
D. Behrens, T. Schoormann, and R. Knackstedt, “Developing an Algorithm to Consider Mutliple Demand Response Objectives”, Eng. Technol. Appl. Sci. Res., vol. 8, no. 1, pp. 2621–2626, Feb. 2018.

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