A new simultaneous placement of distributed generation and demand response resources to determine virtual power plant

Summary Virtual power plant (VPP) is combining different types of generations and interruptible loads to be able to contribute to the market as a power plant with a significant output. In other words, different power generations are combined in a complementary manner to form a defined generation and a demand profile. Because the most important elements of these operators are distributed generation (DG) units and demand response loads (DRLs), the determination of optimal location, capacity, type, as well as the installation time of DGs, DRLs participation size, and place in the distribution system are the most important challenges of operators. In this paper, the formulation of optimal placement of DG resources and DRLs is presented and solved simultaneously in a distribution system to determine the optimal VPP. For this purpose, the concepts of commercial virtual power plant and technical virtual power plant are introduced first, and then the optimal VPP, which can send its energy bids as a short-term and long-term power scheduling to the power market, is determined. Here, commercial virtual power plant and technical virtual power plant will act jointly as commercial–technical virtual power plant to extract the results of the proposed optimization procedure. In this paper, the binary particle swarm optimization algorithm is used to solve the optimization problem in the distribution system. The proposed method is applied to the IEEE 33-bus distribution test network, and the results confirm the effectiveness of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.

[1]  Tuba Gozel,et al.  An analytical method for the sizing and siting of distributed generators in radial systems , 2009 .

[2]  Weerakorn Ongsakul,et al.  Optimal multiple distributed generation placement in microgrid system by improved reinitialized social structures particle swarm optimization , 2011 .

[3]  S. M. Moghaddas-Tafreshi,et al.  Bidding Strategy of Virtual Power Plant for Participating in Energy and Spinning Reserve Markets—Part I: Problem Formulation , 2011, IEEE Transactions on Power Systems.

[4]  Javad Olamaei,et al.  Optimal placement and sizing of DG (distributed generation) units in distribution networks by novel hybrid evolutionary algorithm , 2013 .

[5]  Antonio J. Conejo,et al.  Offering model for a virtual power plant based on stochastic programming , 2013 .

[6]  Nadarajah Mithulananthan,et al.  Multiple Distributed Generator Placement in Primary Distribution Networks for Loss Reduction , 2013, IEEE Transactions on Industrial Electronics.

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

[8]  Sandeep Kaur,et al.  A MINLP technique for optimal placement of multiple DG units in distribution systems , 2014 .

[9]  Nadarajah Mithulananthan,et al.  Analytical Expressions for DG Allocation in Primary Distribution Networks , 2010, IEEE Transactions on Energy Conversion.

[10]  Nadarajah Mithulananthan,et al.  Optimal DG placement in deregulated electricity market , 2007 .

[11]  M. M. Aman,et al.  A new approach for optimum DG placement and sizing based on voltage stability maximization and minimization of power losses , 2013 .

[12]  S. Kannan,et al.  Application and comparison of metaheuristic techniques to generation expansion planning problem , 2005, IEEE Transactions on Power Systems.

[13]  Nadarajah Mithulananthan,et al.  AN ANALYTICAL APPROACH FOR DG ALLOCATION IN PRIMARY DISTRIBUTION NETWORK , 2006 .

[14]  M. Tajeddini,et al.  Risk averse optimal operation of a virtual power plant using two stage stochastic programming , 2014 .

[15]  Mohammad Reza Mohammadi,et al.  Optimal placement of multitypes DG as independent private sector under pool/hybrid power market using GA-based Tabu Search method , 2013 .

[16]  S. E. Widergren,et al.  Demand or request: Will load behave? , 2009, 2009 IEEE Power & Energy Society General Meeting.

[17]  M. Parsa Moghaddam,et al.  Modeling and prioritizing demand response programs in power markets , 2010 .

[18]  M. H. Moradi,et al.  Optimal load shedding approach in distribution systems for improved voltage stability , 2010, 2010 4th International Power Engineering and Optimization Conference (PEOCO).

[19]  Mehrdad Tarafdar Hagh,et al.  A multi-stage MINLP-based model for sub-transmission system expansion planning considering the placement of DG units , 2014 .

[20]  Hadi Safari Farmad,et al.  Integration of demand side management, distributed generation, renewable energy sources and energy storages , 2012 .

[21]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[22]  M. M. Aman,et al.  A new approach for optimum simultaneous multi-DG distributed generation Units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm , 2014 .

[23]  L.F.A. Pereira,et al.  Multistage Model for Distribution Expansion Planning with Distributed Generation—Part II: Numerical Results , 2008, IEEE Transactions on Power Delivery.

[24]  Nadarajah Mithulananthan,et al.  Optimal placement of dispatchable and nondispatchable renewable DG units in distribution networks for minimizing energy loss , 2014 .

[25]  N. R. Friedman.  Distributed Energy Resources Interconnection Systems: Technology Review and Research Needs , 2002 .

[26]  Shahrin Md. Ayob,et al.  Novel hybrid fuzzy-Bees algorithm for optimal feeder multi-objective reconfiguration by considering multiple-distributed generation , 2014 .

[27]  Chandrasekhar Yammani,et al.  Multiobjective Optimization for Optimal Placement and Size of DG using Shuffled Frog Leaping Algorithm , 2012 .