Exploiting flexibility in smart grids at scale

Large parts of the worldwide energy system are undergoing drastic changes at the moment. Two of these changes are the increasing share of intermittent generation technologies and the advent of the smart grid. A possible application of smart grids is demand response, i.e., the ability to influence and control power demand to match it with fluctuating generation. We present a heuristic approach to coordinate large amounts of time-flexible loads in a smart grid with the aim of peak shaving with a focus on algorithmic efficiency. A practical evaluation shows that our approach scales to large instances and produces results that come close to optimality.

[1]  Brendan Mumey,et al.  Peak demand scheduling in the Smart Grid , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

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

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

[4]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[5]  Boon Loong Ng,et al.  Automated Residential Demand Response: Algorithmic Implications of Pricing Models , 2012, IEEE Trans. Smart Grid.

[6]  Ying Li,et al.  Automated Residential Demand Response: Algorithmic Implications of Pricing Models , 2012, IEEE Transactions on Smart Grid.

[7]  Philip M. Wolfe,et al.  Multiproject Scheduling with Limited Resources: A Zero-One Programming Approach , 1969 .

[8]  Johann Hurink,et al.  Time-constrained project scheduling , 2008, J. Sched..

[9]  Thomas Erlebach,et al.  Scheduling with Release Times and Deadlines on a Minimum Number of Machines , 2004, IFIP TCS.

[10]  Hartmut Schmeck,et al.  Customizable Energy Management in Smart Buildings Using Evolutionary Algorithms , 2014, EvoApplications.

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

[12]  Sudipto Guha,et al.  Machine minimization for scheduling jobs with interval constraints , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[13]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[14]  Edo Macan,et al.  Measuring the Capacity Impacts of Demand Response , 2009 .

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

[16]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[17]  Audrey Zibelman,et al.  Deployment of Demand Response as a Real-Time Resource in Organized Markets , 2008 .

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

[19]  Richard F. Deckro,et al.  Resource constrained project crashing , 1989 .