Minimization of Energy Loss using Integrated Evolutionary Approaches

Transcription

1 Minimization of Energy Loss using Integrated Evolutionary Approaches Attia A. El-Fergany, Member, IEEE, Mahdi El-Arini, Senior Member, IEEE Paper Number:

2 Presentation's Outline Aim of this work, Introduction, Methodology & Evolutionary Algorithms, Test Scenarios & Results, Conclusions, Future work.

3 Aim of this Proposed work In this work, a hybridization of Meta-heuristic s algorithms that attempts to minimize the real energy losses including line security have been developed and proposed. The control variables used in this problem are: AVR operating values of generators (continuous variables), Transformer s LTC tap positions (discrete variables), Schedules of power generation output in MW s (Continuous variables). Effects of changing number of control variables were discussed and demonstrated

4 Major Factors of Power Loss Power Plant Losses of Transmission Line Substation Losses of Transformer Losses of Distribution Line Consumers (Domestic,Industrial,Commercial etc.)

5 Electric System Loss & Energy loss Total electric energy losses in the electric system consists of transmission, transformer, and distribution losses between the supply and receiving points. The difference between energy input and output as a result of transfer of energy between two points. (IEEE Dec. 2000) P loss = P P source load

6 The advantages of loss reduction Savings in fuel costs & emissions, Prevention of line overloads on system equipment, Improved voltage profiles over the system, Reduce the overall cost of power transmission.

7 Simple Genetic Algorithm (GA) { } initialize population; evaluate population; while Termination Criteria Not Satisfied { select parents for reproduction; perform recombination and mutation; evaluate population; }

8 Issues for GA GA has some disadvantages. The population size, the choice of the important parameters such as the rate of mutation and crossover, and the selection criteria of new population should carefully be carried out. Any inappropriate choice will make it difficult for the algorithm to converge, or it simply produces meaningless results and the results different from two successive executions.

9 Simulated Annealing (SA) High temperature Slow cooling(annealing) Elements move freely System crystallizes into a state of minimal energy

10 Research Phases Data Collections & entry, Modeling of Objective function & Penalties and Constraints, Simulations & Results.

11 Objective Function Where, Minimize N g i= 1 L i= 1 C P Gi is real power generation at bus i, P Di is real power demand at bus i, N g is number of generators, N L is number of loads, C e is energy cost in $/kwh, and T is period for energy loss. P Gi N P Di e T

12 Network Power balance P Q Gi Gi P Di Q Di = = V i V N [ Y ( + )] ij V j cosθij δ j δi i j= 1 [ N Y ( + )] ij V j sinθij δ j δi j= 1, i, i = 1,...,N 1 = 1,...,N 1 Where, Q Gi is reactive power generation at bus i, Q Di is reactive power demand at bus i, Y ij is admittance magnitude between bus iand bus j, and θ ij is admittance angle between bus iand bus j. The equality constraints power balances can be solved using full NR to generate a solution of the load flow (LF) problem.

13 Inequality Constraints Voltage limits; V min i V i V max i, i N: Set of Buses Real and Reactive power generation limits, P P P,i = 1...N min Gi min Gi Gi Gi max Gi Q Q Q,i = 1...N max Gi g g Transformer tap setting; t min k t k t max k, k N T :Set of Transformers Overload in lines are checked by Rated Sli Sli, i = 1, 2,...,nbr

14 Tool Used Modeling & Simulations The program code was developed using MATLAB R2011a and executed on a LAPTOP with Processor Intel Core i5 CPU 2.40 GHz with a 4.0 GB of RAM with 32-bit Windows 7 operating system. The power flow equations were solved using full N-R LF method with a tolerance of 10-4.

15 Overall main steps of proposed integrated approach

16 Merits of this proposed integration The proposed hybrid approach requires only few parameters to be tuned for SA, which makes it attractive from an implementation point of viewpoint. It is worth to state that metaheuristic algorithms are stochastic in nature; each run will usually produce slightly different results. With this proposed hybridization, with each run, the obtained results are same.

17 Demerits of this approach The most time-consuming parts in this method are the repeated power flow calculations, the computational time of this proposed algorithm is being relatively high.

18 Simulation Scenarios Normal operating conditions, Different overload patterns, Single line outages / contingency with different loading conditions.

19 One line diagram - IEEE-30 Bus System

20 SUMMARIES AND COMPARISONS AFTER APPLYING PROPOSED APPROACH OF 6 AND 16 CONTROL VARIABLES WITH N-R LF (IEEE- 30 BUS SYSTEM) LOADING CONDITIONS N-R LF Run load patterns - 6 Pg s control variables Optimization load patterns - 16 control variables Optimization Loading 100% 120% 135% 100% 120% 135% 100% 120% 135% Total losses (MW) Fuel Cost ($/h) Emission (Ton/h) Computational time (Sec.) Overloaded lines Reduction after applying Proposed approach referred to N-R LF None None None None None None None None None / / / 47.35% 30.4% 17.41% 56.63% 38.32% 25.72%

21 WITH 120% LOADING AND SINGLE LINE OUTAGE SCENARIOS (IEEE-30 BUS SYSTEM) Line s Outage Total losses (MW) N-R LF Overloaded lines None 2-6 None Optimization with 6 Variables Total losses (MW) Computational time (Sec.) Overloaded lines None None None None None None Reductions from N-R LF run% 49.40% 36.06% 29.62% 31.00% 31.58% 28.66% Optimization with 16 Variables Total losses (MW) Computational time (Sec.) Overloaded lines None None None None None None Reductions from case of 6-contol variables% 11.52% 9.58% 10.63% 10.68% 9.52% 12.19%

22 Conclusions The proposed approach was tested with single line outage s and being able to satisfy all constraints including overloading condition of lines that improves the system performance. Can be adapted easily to any given power network. Requires only few parameters to be tuned, which makes it attractive from an implementation point of viewpoint. Better results obtained with increasing no. of control variable. However, the CPU increases.

23 Future Work Extend single Objective to multi-objectives to include security margin enhancements, fuel cost minimization, emission minimization, etc Introduce new control variables like FACTS device, Reactive power compensation, etc

24 Thank you!?