Joint energy and reserve dispatch in a multi-area competitive market using time-varying differential evolution

In a deregulated electricity industry, competitive bidding for energy and reserve is increasingly gaining importance and playing a decisive role in maintaining system reliability. The transmission capacity limits have a significant impact on market cost, therefore these limits should also be considered while optimizing the joint dispatch. In this paper, a detailed comparison of classical differential evolution (DE) mutation strategies is carried out to study the role of tuning parameters. For avoiding the time and labor lost in tuning DE parameters, an improved DE algorithm with time varying chaotic mutation and crossover is proposed for solving the multi-product, multi-area market dispatch problem with physical and operational constraints. The proposed approach effectively handles the complex constraints like reserve requirements, zonal power balance constraints, area spinning reserve constraints, tie-line constraints and reserve and capacity coupling constraints. The efficiency and accuracy of the proposed algorithm is tested on two different test systems and is found to be better than classical DE in terms of convergence behavior and solution quality.

[1]  B. Venkatesh,et al.  A probabilistic reserve market incorporating interruptible load , 2006, IEEE Transactions on Power Systems.

[2]  M. Rashidi-Nejad,et al.  Pricing and Allocation of Spinning Reserve and Energy in Restructured Power Systems via Memetic Algorithm , 2007, 2007 Large Engineering Systems Conference on Power Engineering.

[3]  Seyed Hamid Hosseini,et al.  Transmission constrained energy and reserve dispatch by harmony search algorithm , 2009, 2009 IEEE Power & Energy Society General Meeting.

[4]  M. O'Malley,et al.  Reliability and Reserve in Competitive Electricity Market Scheduling , 2001, IEEE Power Engineering Review.

[5]  Javidi Dasht Bayaz,et al.  contingency reserve pricing via a joint energy and reserve dispatching approach , 2001 .

[6]  Seyed Hossein Hosseinian,et al.  Generation and reserve dispatch in a competitive market using constrained particle swarm optimization , 2010 .

[7]  Fushuan Wen,et al.  Coordination of bidding strategies in day-ahead energy and spinning reserve markets , 2002 .

[8]  Manjaree Pandit,et al.  Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch , 2009 .

[9]  J.-P. Chiou,et al.  A variable scaling hybrid differential evolution for solving large-scale power dispatch problems , 2009 .

[10]  Elasticity coefficient of climatic conditions for electricity consumption analysis , 2010, 2010 International Conference on Power System Technology.

[11]  N. Amjady,et al.  Stochastic Multiobjective Market Clearing of Joint Energy and Reserves Auctions Ensuring Power System Security , 2009, IEEE Transactions on Power Systems.

[12]  Anastasios G. Bakirtzis Joint energy and reserve dispatch in a competitive pool using Lagrangian relaxation , 1998 .

[13]  Bing Li,et al.  Optimizing Complex Functions by Chaos Search , 1998, Cybern. Syst..

[14]  F. Bouffard,et al.  An electricity market with a probabilistic spinning reserve criterion , 2004, IEEE Transactions on Power Systems.

[15]  Leandro dos Santos Coelho,et al.  Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems , 2009, Math. Comput. Simul..

[16]  Xingwang Ma,et al.  Energy and ancillary service dispatch in a competitive pool , 1998 .

[17]  Manisha Sharma,et al.  Reserve Constrained Multi-Area Economic Dispatch Employing Evolutionary Approach , 2010, Int. J. Appl. Evol. Comput..

[18]  Chun-Lung Chen Optimal generation and reserve dispatch in a multi-area competitive market using a hybrid direct search method , 2005 .

[19]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[20]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[21]  Manisha Sharma,et al.  Multi-area economic dispatch with tie-line constraints employing evolutionary approach , 2010 .

[22]  L. Coelho,et al.  Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect , 2006, IEEE Transactions on Power Systems.

[23]  S. Chowdhury,et al.  Management of emergency reserves dispatching in electricity networks , 2010, 2010 International Conference on Power System Technology.

[24]  P. Shamsollahi,et al.  Functional requirements of energy and ancillary service dispatch for the interim ISO New England electricity market , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[25]  Luigi Fortuna,et al.  Chaotic sequences to improve the performance of evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[26]  Xingwang Ma,et al.  Energy and Reserve Dispatch In a Multi-zone Electricity Market , 1999 .

[27]  Kit Po Wong,et al.  Optimal dispatch of spinning reserve in a competitive electricity market using genetic algorithm , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[28]  M. Shahidehpour,et al.  Restructured Electrical Power Systems: Operation: Trading, and Volatility , 2001 .