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Copy of Planning and operation of distributed generation units in distribution network for system loss minimizationOPTIMAL PENET

Planning and operation of distributed generation units in distribution network for system loss minimization OPTIMAL PENE
by

Vin B

on 13 May 2011

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Transcript of Copy of Planning and operation of distributed generation units in distribution network for system loss minimizationOPTIMAL PENET

PLANNING AND OPERATION OF DISTRIBUTION NETWORK FOR INCORPORATING DISTRIBUTED GENERATION UNITS TO MINIMISE SYSTEM LOSS Analysis of impact of penetration level on system annual hourly losses Optimal Sizing and siting of DG units have been determined for different penetration levels Impact of DG penetration level on annual Distribution reactive power loss penetration levels w.r.t. hourly distribution power losses for different seasons, as follows Optimal penetration of DG at optimal sites for different load levels Annual hourly optimal DG power injection at optimal sites Renewable energy mix Guided by:
Shri. Anil Swarnkar
Asst. Professor
MNIT Jaipur Project by:
Ch. Krishna Bharadwaj
Radha Rajesh
Vinay Bale Methodology: Global Optimum Local Optimum Variable Generation Model wrt Load Optimal Renewable Mix Reconfiguration Hybrid Approach Biomass
Wind
Solar Shortest path: Dijkstras Algorithm Minimum span tree: Prim's Algorithm Impact of penetration level on annual distribution energy losses Fitness Function:
J=∑_(j=1)^96▒〖P_loss ×90〗 GA options:
Total population : 100
Selection : Roulette
No. Of generations : 50
Crossover : Heuristic
Crossover Fraction : 0.8
Mutation : Adaptive Feasible DG penetration
v/s
Annual Distribution Real Energy loss DG penetration
v/s
Annual Distribution Reactive Energy loss Winter Summer Spring Fall For different load demands, different values of DG penetration are going to yield minimum power losses.
Minimum power losses are achieved by varying the DG input according to the load level. A Real time Generation model approach for system loss minimization based on consumer demand Curves Approximated by
'Shape-Preserving Spline Method' The optimal power injection at different DG nodes as a function of % load levels Total DG generation v/s load demand with the optimal power injections obtained from our continuous generation model Comparison of Annual hourly power loss (MW) for different optimization models Graphical Representation of Installed Capacities Genetic algorithm based system reconfiguration with
Distributed Generation for system loss minimization Distribution loss comparison with network reconfiguration Reconfiguration strategy for minimising distribution power loss A Hybrid GA employing minimum spanning tree and shortest path algorithms shortest paths between DG locations and load bus Final radial network obtained using minimum spanning tree algorithm from the above network The graphical representation of the generation load model as obtained by using hybrid ga. Power Loss Comparison Annual hourly optimal DG power injection at optimal sites II Comparision of annual energy loss under different optimisation methodologies Annual power generated by different renewable sources Conclusion Object To develop a methodology for optimal sizing and siting of DG units for system loss minimisation
To optimally incorporate intermittent renewable energy resources in the distribution network Decoding a chromosome into a tree Crossover Example System Voltage profile future scope Economic Analysis for incorporating renewable resources
Demand Side Management
To enhance the proposed hybrid GA algorithm to accomodate different DG penetration levels Variable Generation model
Optimal renewable resource mix
Hybrid GA combining optimal DG placement and system reconfiguration (Novelty) Optimal Solution of Hybrid GA Annual hourly optimal DG power injection at optimal sites II Annual hourly optimal DG power injection at optimal sites I
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