Agent based demand flexibility management for wind power forecasting error mitigation using the SG-BEMS framework

The integration process of renewable energy sources (RES) and distributed energy resources (DER) into the power system, is characterized by concerns that originate from their stochastic and uncontrollable nature. This means that system operators require reliable forecasting tools, in order to ensure efficient and reliable operation. Accordingly, this paper proposes the use of demand flexibility, to counteract the RES forecasting errors. For this purpose, distributed and decentralized intelligence is used, via the SG-BEMS framework, to invoke demand flexibility in a timely and effective fashion, while taking into account the negative effects on the building occupants comfort. Lastly, numerical results from a simulated case of study are presented, which confirm that demand flexibility can be used to mitigate the magnitude of forecast errors.

[1]  Kaushik Das,et al.  Smart grid congestion management through demand response , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[2]  Lingfeng Wang,et al.  Development of multi-agent system for building energy and comfort management based on occupant behaviors , 2013 .

[3]  Birgitte Bak-Jensen,et al.  Demand flexibility from residential heat pump , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[4]  Peter Cappers,et al.  Demand Response for Ancillary Services , 2013, IEEE Transactions on Smart Grid.

[5]  D. Kirschen.  Demand-side view of electricity markets , 2003 .

[6]  Wil L. Kling,et al.  Smart grid and smart building inter-operation using agent-based particle swarm optimization , 2015 .

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

[8]  Wil L. Kling,et al.  Comfort-constrained demand flexibility management for building aggregations using a decentralized approach , 2015, 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS).

[9]  Anastasios I. Dounis,et al.  Advanced control systems engineering for energy and comfort management in a building environment--A review , 2009 .

[10]  Chongqing Kang,et al.  Reducing curtailment of wind electricity in China by employing electric boilers for heat and pumped hydro for energy storage , 2016 .

[11]  D. S. Kirschen,et al.  Flexibility from the demand side , 2012, 2012 IEEE Power and Energy Society General Meeting.

[12]  Ross Baldick,et al.  Harnessing demand flexibility to match renewable production using localized policies , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[13]  A. Llombart,et al.  Statistical Analysis of Wind Power Forecast Error , 2008, IEEE Transactions on Power Systems.

[14]  Rafael Wisniewski,et al.  DiSC: A simulation framework for distribution system voltage control , 2015, 2015 European Control Conference (ECC).

[15]  Kecheng Liu,et al.  A Multi-Agent System for Building Control , 2006, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[16]  Lingfeng Wang,et al.  Multi-zone building energy management using intelligent control and optimization , 2013 .

[17]  Xiao Wei,et al.  Wind curtailment of China׳s wind power operation: Evolution, causes and solutions , 2016 .

[18]  Peng Zhao,et al.  An Energy Management System for Building Structures Using a Multi-Agent Decision-Making Control Methodology , 2010, 2010 IEEE Industry Applications Society Annual Meeting.

[19]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[20]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[21]  Yoonkee Kim,et al.  Building Energy Management System based on Smart Grid , 2011, 2011 IEEE 33rd International Telecommunications Energy Conference (INTELEC).

[22]  Paulo F. Ribeiro,et al.  A Game Theory Strategy to Integrate Distributed Agent-Based Functions in Smart Grids , 2013, IEEE Transactions on Smart Grid.

[23]  G. Andersson,et al.  On operational flexibility in power systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[24]  P. H. Nguyen,et al.  Agent-based control for building energy management in the smart grid framework , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[25]  Jose Medina,et al.  Demand Response and Distribution Grid Operations: Opportunities and Challenges , 2010, IEEE Transactions on Smart Grid.