Study of a Distribution Line Overload Control Strategy Considering the Demand Response

Abstract—Relieving distribution-line overload is an important measure for boosting the reliability of electric power systems. Increasing capacity and load shifting are the current commonly used methods for relieving distribution-line overload. The present study proposes the use of a new method for relieving distribution-line overload considering the demand response. First, based on the demand elasticity matrix, a control strategy for the electricity price-load-overload degree is formed. This strategy revises the load-level curve and effectively reduces the power-system overload risk. Under electricity price responses, situations of overload are still present, but by using a response strategy based on stimulus demand, this strategy regards the minimum economic loss from overload as the target function for interruptible load cutoff. The influence of different load-interrupt schemes on the degree of relief is discussed. Finally, the study employs data from a specific domestic demonstration area to verify the feasibility of the proposed method. The results reveal that by maximally reducing the line overload risk via demand-response means, the safe operation of distribution lines is ensured.

[1]  T. V. Garcez,et al.  Multidimensional Risk Assessment of Manhole Events as a Decision Tool for Ranking the Vaults of an Underground Electricity Distribution System , 2014, IEEE Transactions on Power Delivery.

[2]  Yan Zhang,et al.  Flexible load shedding strategy considering real-time dynamic thermal line rating , 2013 .

[3]  Yong Tae Yoon,et al.  The Design of an Optimal Demand Response Controller Under Real Time Electricity Pricing , 2013 .

[4]  Xiaochuan Luo,et al.  Corrective Line Switching With Security Constraints for the Base and Contingency Cases , 2013, IEEE Transactions on Power Systems.

[5]  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).

[6]  H. Johal,et al.  Demand response as a strategy to support grid operation in different time scales , 2012, 2012 IEEE Energy Conversion Congress and Exposition (ECCE).

[7]  Ronnie Belmans,et al.  Automated residential demand response based on dynamic pricing , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[8]  A. Haddad,et al.  Proposal for Probabilistic Risk Assessment in Grounding Systems and Its Application to Transmission Substations , 2012, IEEE Transactions on Power Delivery.

[9]  J. Mutale Management and control of residential energy through implementation of real time pricing and demand response , 2012, 2012 IEEE Power and Energy Society General Meeting.

[10]  Alireza Soroudi,et al.  Simultanous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts , 2012 .

[11]  C. Monteiro,et al.  Optimum residential load management strategy for real time pricing (RTP) demand response programs , 2012 .

[12]  Javier Contreras,et al.  Reactive Control for Transmission Overload Relief Based on Sensitivity Analysis and Cooperative Game Theory , 2012, IEEE Transactions on Power Systems.

[13]  Michael Negnevitsky,et al.  Pool-Based Demand Response Exchange—Concept and Modeling , 2011, IEEE Transactions on Power Systems.

[14]  Wang Jianxue A Decision Model of Direct Load Control in Electricity Markets , 2010 .

[15]  M. P. Moghaddam,et al.  Demand response modeling considering Interruptible/Curtailable loads and capacity market programs , 2010 .

[16]  Wuang Chun-yang Characteristics Analysis of Power Load Based on Fuzzy Clustering , 2010 .

[17]  Yao Jun-yu Combinational Recognition Model for Demand Side Load Profile in Shanghai Power Grid , 2010 .

[18]  J.D. McCalley,et al.  Power System Risk Assessment and Control in a Multiobjective Framework , 2009, IEEE Transactions on Power Systems.

[19]  Yao Junyu Calculation of Characteristic Attributes of Consumer Aggregations Based on Multi-objective Clustering , 2009 .

[20]  Liu Qiang,et al.  Study of attributes of interruptible load consumers in electricity markets , 2007 .

[21]  Hu Jian-yu Classification of substation load characteristics based on gray relevancy clustering , 2007 .

[22]  Na Yu,et al.  Optimal TOU Decision Considering Demand Response Model , 2006, 2006 International Conference on Power System Technology.

[23]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.

[24]  Zhong Qing Customer response analysis of interruptible load management , 2005 .

[25]  Ding Ming COORDINATION TO POWER SYSTEM OPERATIONAL RELIABILITY IN POWER MARKET BASED ON PRICE ELASTICITY , 2005 .

[26]  Tang Wai-wen THE CHARACTERISTICS CLASSIFICATION AND SYNTHESIS OF POWER LOAD BASED ON FUZZY CLUSTERING , 2005 .

[27]  V. Vittal,et al.  Online Risk-Based Security Assessment , 2002, IEEE Power Engineering Review.

[28]  V. Vittal,et al.  Risk Assessment for Transformer Loading , 2001, IEEE Power Engineering Review.

[29]  Chuin-Shan Chen,et al.  Response of large industrial customers to electricity pricing by voluntary time-of-use in Taiwan , 1995 .