Datensets für Demand-Side-Management - Literatur-Review-Basierte Analyse und Forschungsagenda

Demand-Side-Management (DSM) beschreibt die Bestrebung, das Stromnetz bzw. die Lastverteilungen ausbalanciert zu gestalten. Zur Realisierung von DSM existieren verschiedene Ansätzen, die bislang nur unzureichend evaluiert und verglichen wurden. Als Voraussetzung sowohl für das Evaluieren als auch das Vergleichen dieser Ansätze sind jedoch geeignete Inputdaten, sog. Datensets, nötig. Um die Eignung bestehender Datensets zu prüfen, werden zunächst Anforderungen an DSM-Datensets aus existierenden Ansätzen abgeleitet und konzeptualisiert. Die Klassifizierung der Anforderungen stellt die Basis für das anschließende Literatur Review, bei dem insgesamt 17 relevante Datensets identifiziert wurden. Die Analyse der Datensets zeigt, dass Restriktionen hinsichtlich der Nutzerpräferenzen in keinem Datenset berücksichtigt werden. Im Rahmen der Forschungsagenda werden hierfür Lösungsansätze diskutiert.

[1]  Toru Yamamoto,et al.  Distributed Demand-side management with load uncertainty , 2014, Proceedings of the 2014 ITU kaleidoscope academic conference: Living in a converged world - Impossible without standards?.

[2]  Rim. Missaoui,et al.  Energy fluxes optimization for PV integrated building , 2011, 2011 IEEE Trondheim PowerTech.

[3]  E. Caamaño-Martín,et al.  A semi-distributed electric demand-side management system with PV generation for self-consumption enhancement , 2011 .

[4]  Lang Tong,et al.  Multi-scale stochastic optimization for Home Energy Management , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[5]  Weicong Kong,et al.  A rule based domestic load profile generator for future smart grid , 2014, 2014 Australasian Universities Power Engineering Conference (AUPEC).

[6]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[7]  Honggang Bu,et al.  Adaptive Scheduling of Smart Home Appliances Using Fuzzy Goal Programming , 2014 .

[8]  Yuguang Fang,et al.  Optimal Threshold Policy for In-Home Smart Grid with Renewable Generation Integration , 2015, IEEE Transactions on Parallel and Distributed Systems.

[9]  Wen Zhang,et al.  An optimal day-ahead dispatch strategy for deferrable loads , 2014, 2014 International Conference on Power System Technology.

[10]  S. Q. Ali,et al.  Comparison of pursuit and ε-Greedy algorithm for load scheduling under real time pricing , 2012, 2012 IEEE International Conference on Power and Energy (PECon).

[11]  Giuseppe Tommaso Costanzo,et al.  An overview of demand side management control schemes for buildings in smart grids , 2013, 2013 IEEE International Conference on Smart Energy Grid Engineering (SEGE).

[12]  Anthony Rowe,et al.  BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research , 2012 .

[13]  M. Fathi,et al.  Localized demand-side management in electric power systems , 2012, Iranian Conference on Smart Grids.

[14]  N. Malik,et al.  Load scheduling with maximum demand and time of use pricing for microgrids , 2013, 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS).

[15]  Jack Kelly,et al.  Metadata for Energy Disaggregation , 2014, 2014 IEEE 38th International Computer Software and Applications Conference Workshops.

[16]  Takashi Matsuyama,et al.  A distributed coordination framework for on-line scheduling and power demand balancing of households communities , 2014, 2014 European Control Conference (ECC).

[17]  Guo Chen,et al.  A Genetic Evolutionary Task Scheduling Method for Energy Efficiency in Smart Homes , 2012 .

[18]  Georgios B. Giannakis,et al.  Efficient and scalable demand response for the smart power grid , 2011, 2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

[20]  Richard T. Watson,et al.  Analyzing the Past to Prepare for the Future: Writing a Literature Review , 2002, MIS Q..

[21]  Sun Yingyun,et al.  Optimization of economic dispatch problem integrated with stochastic demand side response , 2014, 2014 IEEE International Conference on Intelligent Energy and Power Systems (IEPS).

[22]  Weiliang Zhao,et al.  Cost-Driven Residential Energy Management for Adaption of Smart Grid and Local Power Generation , 2014 .

[23]  Mani B. Srivastava,et al.  It's Different: Insights into home energy consumption in India , 2013, BuildSys@SenSys.

[24]  Ralf Knackstedt,et al.  Demand Side Management in Residential Contexts - A Literature Review , 2015, GI-Jahrestagung.

[25]  Francesco Piazza,et al.  Optimal Task and Energy Scheduling in Dynamic Residential Scenarios , 2012, ISNN.

[26]  Gyung-Leen Park,et al.  Power Load Distribution for Wireless Sensor and Actuator Networks in Smart Grid Buildings , 2013, Int. J. Distributed Sens. Networks.

[27]  Vinny Cahill,et al.  Multi-agent residential demand response based on load forecasting , 2013, 2013 1st IEEE Conference on Technologies for Sustainability (SusTech).

[28]  Clark W. Gellings,et al.  THE CONCEPT OF DEMAND-SIDE MANAGEMENT , 1988 .

[29]  Alberto Leon-Garcia,et al.  Game-Theoretic Demand-Side Management With Storage Devices for the Future Smart Grid , 2014, IEEE Transactions on Smart Grid.

[30]  S. Q. Ali,et al.  Pursuit Algorithm for optimized load scheduling , 2012, 2012 IEEE International Power Engineering and Optimization Conference Melaka, Malaysia.

[31]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[32]  Shuai Lu,et al.  Robust scheduling of smart appliances with uncertain electricity prices in a heterogeneous population , 2014 .

[33]  Hye-Jin Kim,et al.  Multithreaded Power Consumption Scheduler Based on a Genetic Algorithm , 2011, FGIT-FGCN.

[34]  Naoyuki Morimoto,et al.  A Power Allocation Management System Using an Algorithm for the Knapsack Problem , 2014, 2014 IEEE 38th International Computer Software and Applications Conference Workshops.

[35]  Björn Niehaves,et al.  Reconstructing the giant: On the importance of rigour in documenting the literature search process , 2009, ECIS.

[36]  C.W. Gellings,et al.  The concept of demand-side management for electric utilities , 1985, Proceedings of the IEEE.

[37]  Fred Popowich,et al.  AMPds: A public dataset for load disaggregation and eco-feedback research , 2013, 2013 IEEE Electrical Power & Energy Conference.

[38]  Jeannie R. Albrecht,et al.  Smart * : An Open Data Set and Tools for Enabling Research in Sustainable Homes , 2012 .

[39]  Yingsong Huang,et al.  Smooth electric power scheduling in power distribution networks , 2012, 2012 IEEE Globecom Workshops.

[40]  Paul McNamara,et al.  Hierarchical Demand Response using Dantzig-Wolfe decomposition , 2013, IEEE PES ISGT Europe 2013.

[41]  Phani Chavali,et al.  A Distributed Algorithm of Appliance Scheduling for Home Energy Management System , 2014, IEEE Transactions on Smart Grid.

[42]  Omar Abou Khaled,et al.  Appliance consumption signature database and recognition test protocols , 2013, 2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA).

[43]  Andrea Monacchi,et al.  GREEND: An energy consumption dataset of households in Italy and Austria , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[44]  J. Jardini,et al.  Daily load profiles for residential, commercial and industrial low voltage consumers , 2000 .