Ứng dụng thuật toán particle swarm optimization cải tiến tính toán điều độ kinh tế hỗn hợp nhiệt điện

I H C QU H IH PH M NG C HI U NG D NG THU OPTIMIZATION C I TI KINH T H N H P NHI : THI T B , M 60.52.51 LU TP H I N N I -HCM IH b ng d n khoa h c : cv ch m nh cv ch m nh cv Lu cb ov t nH cv c aH ih m: ng ch m b o v lu n c a Ch t ch H ng Khoa qu c s a ch a (n CH T CH H NG NG KHOA N NT C I H C QU C GIA TP.HCM I CH T NAM c l p - T - H IH NHI M V LU SHV: 12214298 27/11/1988 H U GIANG : 60.52.51 I NG D NG THU C I TI KINH T H N H P NHI T II NHI M V I DUNG: kinh t h n h p nhi d ng thu hi u qu c a thu III N ic M V : 20/01/2014 IV M V : 20/06/2014 V NG D N:TS HU 20 NG D N CH NHI M B (H (H NG KHOA (H N NT 14 O L IC c HCM Xin g i l i tri l , n gia b n t t c nh ng h ct p i c u Ph m Ng c Hi u ng T LU Lu n xu t gi i thu kinh t h n h p nhi thu n (CHPED) Thu Particle Swarm Optimization ( h s co nh it M n t di chuy c th nd a pseudo- mt nghi m ng n s h i t IPSO th ng (Lagrangian relaxation, the genetic algorithm, the improved ant colony search algorithm, ) K t qu th nghi m cho th xu t so v i ng c bi kinh t h n h p nhi t n cho h th ng l n N a lu n - s i thi u chung Gi i thi u v n h th n, m n h p nhi t m quan tr ng c a vi gi - t i u c a lu T ng quan ng quan v m m ts - cc kinh t h n h p nhi - n n gi i quy thu - IPSO Gi i thi u v kinh t h n h p nhi t t qu p d ng n t qu Improved Particle Swam Optimization i h th ng thu c vi t b ng Matlab cho: qua t n th c gi i v i nhi u ph t i h th qua t n th i h th qua t n th c gi i v i nhi u ph t i h th qua t n th c gi i v i nhi u ph t - ng k b u IPSO, t xu t lu nv k t qu u ti p theo c ABSTRACT This thesis proposes an Improved Particle Swarm Optimization (IPSO) for the Combined Heat and Power Economic Dispatch problem (CHPED) The IPSO here is the Particle Swarm Optimization (PSO) with constriction factor enhanced by the pseudo-gradient for speeding up its convergence process The purpose of the pseudo-gradient is to guide the movement of particles in positive direction sothat they can quickly move to the optimization The proposed IPSO has been tested on various systems and compared to Lagrangian relaxation, the genetic algorithm, the improved ant colony search algorithm, Test results indicate that the proposed IPSO is better than the other methods due to a lower total cost and faster computational time, especially for large-scale combined heat and power economic dispatch problems The main contents of the thesis will be presented as follows: - Chapter 1: Introduction Introduction to the problem that combined heat and power economic dispatch, the goal of the research, the importance of finding a solution to this problem and the scope of the research - Chapter 2: Overview An overview to the aim of the CHPED problem and presenting some typical methods for the CHPED problem - Chapter 3: Addressing problem Set an objective function and the constraints of the combined heat and power economic dispatch - Chapter 4: Problem- solving methodology Presenting the IPSO algorithm and applying it to combined heat and power economic dispatch problem - Chapter 5: Results of calculation Submitting all the results of CHPED problem by Improved Particle Swam Optimization algorithm written in Matlab for: + Problem with generator-system ignoring losses and other constraints The problems are solved with different loads + Problem with generator-system ignoring losses and other constraints + Problem with generator-system ignoring losses and other constraints The problems are solved with different loads + Problem with generator-system ignoring losses and other constraints The problems are solved with different loads - Chapter 6: Summary and research directions Submitting the conclusion and results obtained by means of IPSO, which suggest further research directions L uc ab , t qu c, li ng c c thu th c trong b t k n g c n c s d n lu TP HCM lu n Ph m Ng c Hi u 14 M CL C DANH M C T VI T T T i DANH M U ii DANH M NH iii DANH M C B NG iv : GI I THI U CHUNG .1 1.1 T M QUAN TR NG 1.2 M .1 1.3 PH M VI U .2 1.4 N U : T NG QUAN C .4 cv t cv ng nhi t c v gi i h n N GI I QUY 4.1 GI I THI U THU 4.2 M PSEUDO-GRADIENT 4.3 THU 4.4 10 : K T QU 14 KINH T CHO H TH N, T) B QUA T N TH T 14 ng h p PD=200MW, HD=115MWth: 15 ng h p PD=175MW, HD=110MWth: 17 ng h p PD=225MW, HD=125MWth: 19 KINH T CHO H TH N, QUA T N TH T : 21 KINH T CHO H TH N, T) B QUA T N TH T : 23 ng h p PD=300MW, HD=150MWth: 25 ng h p PD=250MW, HD=175MWth: 26 ng h p PD=160MW, HD=220MWth: 28 5.4 KINH T CHO H TH NG N, T) B QUA T N TH T : 29 5.4 ng h p PD=500MW, HD=300MWth: 32 5.4 ng h p PD=450MW, HD=400MWth: 35 ng h p PD=250MW, HD=300MWth: 38 : T NG K 6.1 T NG K N U .41 41 U .41 DANH M C T VI T T T CHP: Combined Heat and Power ED: Economic Dispatch CHPED: Combined Heat and Power Economic Dispatch PSO: Particle Swarm Optimization IPSO: Improved Particle Swarm Optimization ALHN: Augmented Lagrange Hopfield Network CS: Cuckoo Search EP: Evolutionary Programming GA: Genetic Algorithm HSA: Harmony Search Algorithm IACSA:Improved Ant Colony Search Algorithm IGA-MU: Improved Genetic Algorithm with Multiplier Updating IPSO: Improved Particle Swarm Optimization LR: Lagrangian Relaxation LRSS-CSS: Lagrangian Relaxation with the Surrogate Subgradient using the Constant Step Size MADS-DACE: Mesh Adaptive Direct Search-Design and Analysis of Computer Experiments MADS-LHS:Mesh Adaptive Direct Search- Latin Hypercube Sampling MADS-PSO:Mesh Adaptive Direct Search-Particle Swarm Optimization i - t: v i Gi i h 13 thi nhi t- nc 14 thi nhi t- nc 30 15 thi nhi t- nc a thi nhi t- nc 31 5.4 ng h p PD=500MW, HD=300MWth: 5.17 K t qu t t nh t ch y 50 l n b ng IPSO cho h th ng B ng 5.15 K t qu sau 50 l n ch y b ng IPSO cho h th ng l ch nh nh t n l n nh t ($/h) ($/h) IPSO 22,009.4520 Th i gian x ($/h) 22,087.9222 22,023.0968 15.1936 0.369 32 5.18 K t qu t t nh t ch y 50 l n b ng PSO cho h th ng B ng 5.16 K t qu sau 50 l n ch y b ng PSO cho h th ng l ch nh nh t n l n nh t ($/h) ($/h) PSO 22,021.6215 Th i gian x ($/h) 22,543.5634 22,072.4644 74.2953 0.374 33 B ng 5.17 u ch y b ng IPSO cho h th ng IPSO PSO P1 (MW) 135 134.7929 P2 (MW) 60.6937 62.7080 P3(MW) 13.9857 12.6837 P4(MW) 80.6036 80.5714 P5(MW) 209.7170 209.2440 H2(MWth) 92.8638 94.6026 H3(MWth) 41.7081 40.7436 H4(MWth) 40.6606 40.7143 H5(MWth) 64.7675 64.2064 H6(MWth) 59.9999 59.7331 22,009.4520 22,021.6215 Cost($/h) Nh - K t qu IPSO g i t sau 120 - K t qu PSO g i t sau p ng h i t nhanh quanh mt - IPSO cho k t qu t t 34 ng h p PD=450MW, HD=400MWth: 5.19 K t qu t t nh t ch y 50 l n b ng IPSO cho h th ng B ng 5.18 K t qu sau 50 l n ch y b ng IPSO cho h th ng nh nh t l n nh t ($/h) ($/h) IPSO 22,429.7939 l ch Th i gian chu n x ($/h) 22,818.3825 22,573.9688 125.8314 0.374 35 20 K t qu t t nh t ch y 50 l n b ng PSO cho h th ng B ng 5.19 K t qu sau 50 l n ch y b ng PSO cho h th ng l ch nh nh t n l n nh t ($/h) ($/h) PSO 22,436.2550 Th i gian x ($/h) 24,878.3396 22,754.1111 463.5679 0.383 36 B ng 5.20 u ch y b ng IPSO cho h th ng IPSO PSO P1 (MW) 128.2980 127.6362 P2 (MW) 94.9615 92.6253 P3(MW) 10 10 P4(MW) 70.4844 65.8128 P5(MW) 146.2633 153.9257 H2(MWth) 122.4454 120.4286 H3(MWth) 40 40 H4(MWth) 36.1293 34.0058 H5(MWth) 141.4254 145.6486 H6(MWth) 60 59.9169 22,429.7939 22,436.2550 Cost($/h) Nh - K t qu IPSO g i t sau p, ng h i t nhanh quanh i t sau p, ng h i t nhanh quanh mt - K t qu PSO g mt - IPSO cho k t qu t t l n v n cao 37 5.4.3 ng h p PD=250MW, HD=300MWth: 21 K t qu t t nh t ch y 50 l n b ng IPSO cho h th ng B ng 5.21 K t qu sau 50 l n ch y b ng IPSO cho h th ng l ch nh nh t n l n nh t ($/h) ($/h) IPSO 15,855.5490 Th i gian x ($/h) 15,859.5626 15,859.1313 0.2699 0.384 38 22 K t qu t t nh t ch y 50 l n b ng PSO cho h th ng B ng 5.22 K t qu sau 50 l n ch y b ng PSO cho h th ng l ch nh nh t n l n nh t ($/h) ($/h) PSO 15,858.8301 Th i gian x ($/h) 16,014.4231 15,872.7249 34.2973 0.367 39 B ng 5.23 u ch y b ng IPSO cho h th ng IPSO PSO P1 (MW) 83.6436 83.6436 P2 (MW) 40 40 P3(MW) 10 10 P4(MW) 35 35 P5(MW) 81.3564 81.3564 H2(MWth) 75 75 H3(MWth) 40 40 H4(MWth) 20 20 H5(MWth) 105 105 H6(MWth) 60 60 15,858.8300 15,858.8301 Cost($/h) Nh - K t qu IPSO g i t sau p, ng h i t nhanh quanh i t sau p, ng h i t nhanh quanh mt - K t qu PSO g mt - IPSO cho k t qu gi nhanh K t qu c th c hi n n Matlab R2008a v i PC core i3-4330, ddram III 4G bus 1333MHz 40 T NG K U 6.1 T NG K Lu nhi IPSO n h th gi i quy kinh t h n h p c ki kinh t v i t- n xu t nhi t b qua t n IPSO cho th m l i gi i t kinh t h n h p nhi n ch C n c i thi n thu t qu ng h l n t qu t xu t IPSO, s l pc n S ph n t Nd, s l n i Iter Qua th nghi m th y r ng s id Nd, Iter d l n Do v y i mc il n k t qu k t qu i y th U V i nh ng k t qu c vi c gi m r - 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ih n (2014): H ng ih 44 ... thesis proposes an Improved Particle Swarm Optimization (IPSO) for the Combined Heat and Power Economic Dispatch problem (CHPED) The IPSO here is the Particle Swarm Optimization (PSO) with constriction... Economic Dispatch CHPED: Combined Heat and Power Economic Dispatch PSO: Particle Swarm Optimization IPSO: Improved Particle Swarm Optimization ALHN: Augmented Lagrange Hopfield Network CS: Cuckoo Search... n t t c nh ng h ct p i c u Ph m Ng c Hi u ng T LU Lu n xu t gi i thu kinh t h n h p nhi thu n (CHPED) Thu Particle Swarm Optimization ( h s co nh it M n t di chuy c th nd a pseudo- mt nghi m