Reliability Evaluation of Smart Distribution Systems Considering Load Rebound Characteristics

Load rebound characteristics (LRC), which means that restoration of distribution systems with large thermostatically controlled load (TCL) after an outage may create significantly higher load demand than normal (namely the demand if this outage does not occur), will make the reliability index—expected energy not supplied (EENS) of distribution systems obtained by traditional methods pessimistic and inaccurate. Since EENS is one of the base points of investment planning for distribution system operator, it is necessary to consider the LRC in the reliability evaluation and provide a more accurate evaluation on EENS. This paper proposed a new reliability evaluation method of smart distribution systems based on a nonnegative k-singular value decomposition (NN-K-SVD) algorithm and a sequential Monte Carlo (SMC) simulation. The NN-K-SVD algorithm is used to identify the TCL in total load. A reliability model considering the LRC is established based on an electric water heater model. The SMC simulation is used to mimic the operation of distribution systems based on the established reliability evaluation model, and calculate reliability indices. Furthermore, the feasibility of the proposed method is validated by extensive cases studies.

[1]  Jose Ignacio Moreno,et al.  Paving the road toward Smart Grids through large-scale advanced metering infrastructures , 2015 .

[2]  Yu-Hsiu Lin,et al.  Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation , 2012 .

[3]  Eric C. Larson,et al.  Disaggregated End-Use Energy Sensing for the Smart Grid , 2011, IEEE Pervasive Computing.

[4]  Peng Zhang,et al.  Reliability Evaluation of Active Distribution Systems Including Microgrids , 2013, IEEE Transactions on Power Systems.

[5]  Zhaohong Bie,et al.  Reliability evaluation of integrated energy systems based on smart agent communication , 2016 .

[6]  Fangxing Li,et al.  Hardware Design of Smart Home Energy Management System With Dynamic Price Response , 2013, IEEE Transactions on Smart Grid.

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

[8]  David Fischer,et al.  A stochastic bottom-up model for space heating and domestic hot water load profiles for German households , 2016 .

[9]  Chee-Yee Chong,et al.  Statistical Synthesis of Physically Based Load Models with Applications to Cold Load Pickup , 1984 .

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

[11]  A.C. Liew,et al.  Neural-network-based signature recognition for harmonic source identification , 2006, IEEE Transactions on Power Delivery.

[12]  Pierluigi Siano,et al.  Modeling the reliability of multi-carrier energy systems considering dynamic behavior of thermal loads , 2015 .

[13]  Chongqing Kang,et al.  Residential smart meter data compression and pattern extraction via non-negative K-SVD , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[14]  Di Wu,et al.  Fast Assessment of Frequency Response of Cold Load Pickup in Power System Restoration , 2016, IEEE Transactions on Power Systems.

[15]  Wenyuan Li,et al.  Reliability Assessment of Electric Power Systems Using Monte Carlo Methods , 1994 .

[16]  Michael Elad,et al.  K-SVD and its non-negative variant for dictionary design , 2005, SPIE Optics + Photonics.

[17]  Brandon J. Murrill,et al.  Smart Meter Data: Privacy and Cybersecurity , 2012 .

[18]  Max D. Anderson,et al.  An Analytical Method for Quantifying the Electrical Space Heating Component of a Cold Load Pick Up , 1982, IEEE Transactions on Power Apparatus and Systems.

[19]  Roland P. Malhamé,et al.  A physically-based computer model of aggregate electric water heating loads , 1994 .

[20]  Fred Schweppe,et al.  Physically Based Modeling of Cold Load Pickup , 1981, IEEE Transactions on Power Apparatus and Systems.

[21]  Zhaohong Bie,et al.  Customer satisfaction based reliability evaluation of active distribution networks , 2016 .

[22]  Hairong Qi,et al.  Non-Intrusive Energy Disaggregation Using Non-Negative Matrix Factorization With Sum-to-k Constraint , 2017, IEEE Transactions on Power Systems.

[23]  James McDonald,et al.  Cold Load Pickup , 1979, IEEE Transactions on Power Apparatus and Systems.

[24]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[25]  Kaamran Raahemifar,et al.  A survey on Advanced Metering Infrastructure , 2014 .

[26]  Kevin P. Schneider,et al.  Evaluating the magnitude and duration of cold load pick-up on residential distribution using multi-state load models , 2016, IEEE Transactions on Power Systems.

[27]  Roy Billinton,et al.  A reliability test system for educational purposes-basic distribution system data and results , 1991 .

[28]  A. Pahwa,et al.  A voltage drop-based approach to include cold load pickup in design of distribution systems , 2004, IEEE Transactions on Power Systems.

[29]  M. M. Adibi Distribution System Design Optimization for Cold Load Pickup , 2000 .