A comprehensive modelling framework for demand side flexibility in smart grids

The increasing share of renewable energy generation in the electricity system comes with significant challenges, such as the volatility of renewable energy sources. To tackle those challenges, demand side management is a frequently mentioned remedy. However, measures of demand side management need a high level of flexibility to be successful. Although extensive research exists that describes, models and optimises various processes with flexible electrical demands, there is no unified notation. Additionally, most descriptions are very process-specific and cannot be generalised. In this paper, we develop a comprehensive modelling framework to mathematically describe demand side flexibility in smart grids while integrating a majority of constraints from different existing models. We provide a universally applicable modelling framework for demand side flexibility and evaluate its practicality by looking at how well Mixed-Integer Linear Program solvers are able to optimise the resulting models, if applied to artificially generated instances. From the evaluation, we derive that our model improves the performance of previous models while integrating additional flexibility characteristics.

[1]  Frieder Borggrefe,et al.  The potential of demand-side management in energy-intensive industries for electricity markets in Germany , 2011 .

[2]  Marek Jawurek,et al.  Decentralized Intelligence in Energy Efficient Power Systems , 2012 .

[3]  Johann L. Hurink,et al.  Mathematical modelling of devices and flows in energy systems ∗ , 2014 .

[4]  Ning Lu,et al.  Appliance Commitment for Household Load Scheduling , 2011, IEEE Transactions on Smart Grid.

[5]  Anna Scaglione,et al.  Reduced-Order Load Models for Large Populations of Flexible Appliances , 2015, IEEE Transactions on Power Systems.

[6]  S. Ashok,et al.  Load-management applications for the industrial sector , 2000 .

[7]  Valentin Robu,et al.  Online mechanism design for scheduling non-preemptive jobs under uncertain supply and demand , 2014, AAMAS.

[8]  J. Christopher Beck Principles and Practice of Constraint Programming , 2017, Lecture Notes in Computer Science.

[9]  Ruggero Schleicher-Tappeser,et al.  How renewables will change electricity markets in the next five years , 2012 .

[10]  Kai Hufendiek,et al.  Enabling demand side integration - assessment of appropriate information and communication technology infrastructures, their costs and possible impacts on the electricity system , 2015 .

[11]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[12]  Wolf Fichtner,et al.  On the economic potential for electric load management in the German residential heating sector : An optimising energy system model approach , 2014 .

[13]  Zhonghui Luo,et al.  An milp formulation for load-side demand control , 1998 .

[14]  Matthias Templ,et al.  Analysis of commercial and free and open source solvers for linear optimization problems 1 , 2012 .

[15]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[16]  Jiangfeng Zhang,et al.  Optimal scheduling of household appliances for demand response , 2014 .

[17]  Sebastian Lehnhoff,et al.  Energy Informatics , 2015, Lecture Notes in Computer Science.

[18]  Jan Dimon Bendtsen,et al.  Heuristic Optimization for the Discrete Virtual Power Plant Dispatch Problem , 2014, IEEE Transactions on Smart Grid.

[19]  J. Moon,et al.  Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage , 2014 .

[20]  Paul Denholm,et al.  Role of Energy Storage with Renewable Electricity Generation (Report Summary) (Presentation) , 2010 .

[21]  Colin N. Jones,et al.  Model Predictive Control for Market-Based Demand Response Participation , 2014 .

[22]  Jan Dimon Bendtsen,et al.  A taxonomy for modeling flexibility and a computationally efficient algorithm for dispatch in Smart Grids , 2013, 2013 American Control Conference.

[23]  Christof Weinhardt,et al.  Load Shifting, Interrupting or Both? Customer Portfolio Composition in Demand Side Management , 2016 .

[24]  Rüdiger Zarnekow,et al.  Energy Informatics , 2013, Business & Information Systems Engineering.

[25]  Hartmut Schmeck,et al.  Electrical Load Management in Smart Homes Using Evolutionary Algorithms , 2012, EvoCOP.

[26]  A. Oudalov,et al.  Sizing and Optimal Operation of Battery Energy Storage System for Peak Shaving Application , 2007, 2007 IEEE Lausanne Power Tech.

[27]  Pascal Van Hentenryck,et al.  Residential Demand Response under Uncertainty , 2013, CP.

[28]  Hartmut Schmeck,et al.  Modeling and Valuation of Residential Demand Flexibility for Renewable Energy Integration , 2017, IEEE Transactions on Smart Grid.

[29]  Paul Denholm,et al.  Role of Energy Storage with Renewable Electricity Generation , 2010 .

[30]  Ignacio E. Grossmann,et al.  Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes , 2012, Comput. Chem. Eng..

[31]  Pedro M. Castro,et al.  Dynamic modelling and scheduling of an industrial batch system , 2002 .

[32]  C. H. Antunes,et al.  Categorization of residential electricity consumption as a basis for the assessment of the impacts of demand response actions , 2014 .

[33]  Bodil Merethe Larsen,et al.  The flexibility of household electricity demand over time , 2001 .

[34]  Johannes Gärttner,et al.  Group Formation in Smart Grids : Designing Demand Response Portfolios , 2016 .

[35]  Nico Keyaerts,et al.  How to Engage Consumers in Demand Response: A Contract Perspective , 2013 .

[36]  S. Ashok,et al.  Peak-load management in steel plants , 2006 .

[37]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[38]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[39]  C. Pantelides,et al.  A simple continuous-time process scheduling formulation and a novel solution algorithm , 1996 .