ECONOMIC PRODUCTION WITH CONTROL POLLUTION IN THERMAL PLANTS USING SIMULATED ANNEALING OPTIMIZATION

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1 ECONOMIC PRODUCTION WITH CONTROL POLLUTION IN THERMAL PLANTS USING SIMULATED ANNEALING OPTIMIZATION Ismal ZIANE Fard BENHAMIDA IRECOM laboratory, Department of Electrotechncs, Unversty of Sd Bel Abbes Sd Bel Abbes, 000, Algera Abstract: The generaton of electrc energy from the thermal plants needs the combustble fuel to move the turbnes. These power plants exst for more than 100 years. As a result, the combustble fuel emtes some gas lke NO x, SO, and CO to the atmosphere. These gas emssons make an mpact on envronment (ar polluton and water polluton). Ths paper presents a smulated annealng optmzaton for fndng economc producton wth control polluton n the thermal plants. The paper formulates a mult objectve dspatch problem wth valve pont effect consdered n both fuel cost and emsson functons. The mult-objectve problem of the generaton and emsson dspatch s solved to fnd the generaton levels that best compromse the generaton cost and the emsson level whle satsfyng the power balance constrant. The smulated annealng algorthm from s tested on a 10 generator test system wth valve pont effect. The results of the proposed method are compared wth other technques reported n recent lterature. Key words: thermal plant; economc dspatch; polluton; valve effect; mult-objectve optmzaton. 1. Introducton The techncal-economc characterstcs of power plants are determnng for ther explotaton. Three types of characterstcs have an nfluence for the explotaton of one short-term power plant: ts producton cost; ts techncal constrants and ts relablty. The most mportant of these three characterstcs s the varable cost of producton. For thermal power plants, t reflects manly the cost of the used fuel and the other costs of explotaton and mantenance of the power plant. The cost of the fuel s estmated by usng values of specfc consumpton of heat (a quantty of necessary heat energy to produce some electrcty) of the power plant and the prce of the fuel [1]. The economc dspatchng s a problem of statc optmzaton whch conssts n dstrbutng the producton of the actve power of the varous power plants of the network, so as to explot the latter n a most economcal possble way. Ths dstrbuton obvously has to respect the producton lmts of power plants. The varable to be optmzed s thus the producton cost []. On the other hand, the amendments of the world law of 1990 (clean Ar Act Amendments) forced the producers of energy and the engneers n charge of the electrcal energy management to modfy ther algorthms of standard optmzaton by takng n consderaton the polluton (to reduce the emssons to the atmosphere of greenhouse gas by the thermal power plants whch are toxc gases (NO x, SO, and CO ) [3] [4].. Thermal plants mpact on envronment On the ste of a thermal power plant, the emsson of dusts and harmful gases pollute drectly the ar. Secondly, these dusts and most of the toxc gases are rejected n the atmosphere fall agan on the ground wth atmospherc precptaton (ran, snow) or n the form of dry partcles, what causes the water and grounds polluton and at damage the flora and fauna [5]. In fact, the thermal power plant needs water for condensaton purposes. Ths water, when heated, s generally rejected to short dstance from the source pont. If the plant produce electrc power and heat (hgheffcency power plant), the amount of water released s lower. Dependng on local condtons, the calores dscharged may cause a thermal polluton of surface waters; as a result t ncreases the water temperature [5]. Fg. 1. Gas emsson from thermal plant [6]. In early 1970s, an mportant ncrease of concentraton of some greenhouse gases, n evdent lnk wth the anthropologcal actvty, lead to predct the eventualty of a clmate change by due to greenhouse effect. So that, the average temperature of the globe 1

2 would become establshed near 18 C nstead of 15 C [7]. 3. Mechansm of thermal plants The steam power plants exst for more than 100 years. The man mprovement s the replacement n the Watt machnes of pston motors by turbnes. The maxmum temperature of the steam, even under hgh pressure does not exceed 500 C, so that the steel blades of turbnes resst well and can be thnned and profled [8]. The valve-pont effects are taken nto consderaton n the economc and emsson dspatch problem (EED) by supermposng the basc quadratc gas emsson and characterstcs wth the rectfed exponental component as follow [11]: ( ) ex p () E P P P P where, and are the emsson co-effcent of unt. The transmsson losses P L can be found usng B- coeffcents P n n B P P L j 1 j 1 Fg.. Gas turbne electrc power generaton n thermal plant [10]. The thermal power plants have several valves of steam admsson, whch are used for the generated power control. Every tme, a valve of admsson s opened, we regster a sudden ncrease of losses and t results undulatons n the curve of fuel cost. Wth a gradual openng of valves, these losses decrease gradually untl the valve s completely opened [9]. 4. Economc and emsson dspatch Problem formulaton Wth Valve Effect The valve-pont effects are taken nto consderaton n the economc dspatch problem (ED) by supermposng the basc quadratc fuel-cost and characterstcs wth the rectfed snusodal component as follows [10]: ( ) sn (1), m n F P a P b P c e f P P where a, b, c,,e and f are the cost co-effcent of unt. Fg. 3. Valve-pont effect. where B j s the transmsson lne coeffcents. The total supply must be equal to power demand where P D s the load demand. P P P (4) D L The above mentoned mult- objectve optmzaton problem can be converted to a sngle objectve optmzaton problem by ntroducng prce penalty factors as follows: F ( P ) w F ( P ) w E ( P ) (5) T 1 where w 1 and w s weght factors of the mult-objectve optmzaton of the economc and emsson dspatch. 5. Smulated Annealng for economc and emsson dspatch problem Smulated Annealng s heurstc method whch ntroduced n the feld of optmzaton n 1980 by Krkpatrck and ndependently by Cerny [1]. Smulated annealng s a robust, general-purpose combnatoral optmzaton algorthm based on probablstc methods whch has been appled to gve the optmal for many areas such as VLSI crcut desgn, neuralnetworks, mage processng, code desgn, capactor placement n power systems, and economc load dspatch. The name smulated annealng comes from an analogy between combnatoral optmzaton and the physcal process of annealng. In physcal annealng a sold s cooled very slowly, startng from a hgh temperature, n order to acheve a state of mnmum nternal energy. It s cooled slowly so that thermal equlbrum s acheved at each temperature. Thermal equlbrum can be characterzed by the Boltzmann dstrbuton

3 T E ( X k BT ) / ( k BT ) (6) P X x e e where X s a random varable ndcatng the current state, E X s the energy of state x, k B s Boltzmann s constant, and T s temperature. The evoluton of the state of a sold n a heat bath toward thermal equlbrum can be effcently smulated by a smple algorthm based on Monte Carlo technques whch was proposed by Metropols [13] n Intalsaton Choose an ntal S X ; S* S; C 0; (Global teraton count) T T 0 ; (T 0 Intal system temperature) Iteratve Processes Nbter 0 ; Whle (Nbter < nb_ter) C C+1; Nbter Nbter+1; ' Generate randomly a S N S ; ΔF F(S)-F(S); f (ΔF <0) then S S ; Otherwse Prob (ΔF, T) exp (-ΔF/T); Generate q unformly n the nterval: [0,1]; If (q< prob (ΔF, T)) then S S ; If F(S) < F(S*) then S* S; T= α T; (0 < α <1 coolng coeffcent) Fg. 4. Smulated Annealng Algorthm. 6. Smulatons And Results The proposed Smulated Annealng algorthm has been tested n a 10 generator system wth valve effects. The software was mplemented by the MATLAB language. The generator cost coeffcents, emsson coeffcents, generaton lmts, and loss coeffcents of 10 unts system are taken from [11]. The mult-objectve EELD for the 10- unt system s solved usng SA when system demand s 000 MW. For conductng the test, the ntal temperature s fxed at 0 C, alpha s fxed at 0.99 and max tres s The fnal temperature s 1e-10 C. Table 1 and table present the economc and the envronmental respectvely. E Table 1 Best fuel cost. ABC_PSO [11] DE [14] SA P P P P P P P P P P Losses (MW) / F ($/hr) E(lb/hr) Table Best gas emsson. ABC_PSO [11] DE [14] SA P P P P P P P P P P Losses (MW) / F ($/hr) E(lb/hr) From tables 1 and, we can see that the economc gves the best fuel cost ( $) and the envronmental gves the mnmum gas emsson ( lb). Table 3 Non-domnant s for cost and emsson objectves. Soluton Number Weght Objectve w 1 w Fuel cost($/hr) Emsson (lb/hr) Table 3 presents the economc and emsson obtaned by the varaton of weght factors (w 1 and w ). In Smulated Annealng, the best compromse can gve $ and lb when w1= w=

4 Power output (MW) Power output (MW) Emsson (lb/hr) Emsson output (lb/hr) Fuel cost (($/hr) Journal of Electrcal Engneerng Best compromse X = ,513; Y = 4495, emsson wth w1=0, fuel cost wth w=0, Fuel cost($/hr) weght factors Fg. 5. Pareto optmal front for cost and gas emsson objectves. Fgure 5 can show the pareto optmal front for fuel cost and gas emsson objectves from the economc to the envronmental. It s seen that for mult-objectve economc emsson dspatch, the results of power generaton are seen n Table IV. Fg. 6. Fuel cost and gas emsson by weght factors varaton. We can see from fgure 6 the results of fuel cost and gas emsson usng the varaton of weght factors w 1 and w. Table 4 Power generaton dspatch and losses. Soluton Power Generaton Dspatch Losses Number P 1 P P 3 P 4 P 5 P 6 P 7 P 8 P 9 P 10 (MW) economc the best compromse envronmental P1 P P3 P4 P5 P6 P7 P8 P9 P10 Losses Soluton Number 0 P1 P P3 P4 P5 P6 P7 P8 P9 P10 losses Generator number Fg. 7. Power Generaton Dspatch by weght factors varaton. Fg. 8. Power output for each generator wth w 1 =0.5 and w =0.5. 4

5 The fgure 7 presents the power generaton dspatch wth losses n 10-unt system by weght factors varaton. It s seen that the three vertcal lnes present the power output for each generator for economc, best compromse and envronmental s. The fgure 8 shows the power output for the all 10 generators when w1=w=0.5. We can see also that the losses n ths system represent 4.16 % from the total power generaton. Concluson In ths paper, we try to fnd the optmal for mnmum fuel cost and mnmum gas emsson, and gve a compromse between both the economc and the envronmental s. SA algorthm s used n ths work for determnng the mult-objectve economc and emsson power dspatch problem. The problem s defned as a dual objectve optmzaton problem, to reduce the producton cost and emsson rate. One system s tested (10 generator system wth valve-pont effects and transmsson losses). The proposed method s compared wth other methods (ABC_PSO and DE). Results reveal the effectveness and usefulness of the smulated annealng method n both aspect of economc and emsson dspatch n power systems. References 1. Zane, Contrbuton à l étude de dspatchng dynamque sous contrantes de sécurté par la programmaton quadratque, Magster thess, Sd-Bel-abbes unversty, Algera, S. Anjum., Mult Objectve Economc Emsson Dspatch UsngModfed Mult Objectve Partcle Swarm Optmzaton, Master thess, Thapar Unversty, Punjab, Inda, B. Mahdad, K. Srar, B. Tarek, and A. Bensalem, Dspatchng Economque du Réseau Electrque Algéren en Présence des dspostfs Shunt FACTS avec Consdératon de la Polluton, conférence nternatonale sur les réseaux électrques ICEN 10, Sd bel-abbes, Algera, U. Güvenç., Combned Economc and Emsson Dspatch usng Genetc Algorthm based on smlarty crossover, Scentfc Research and Essays, Vol.5, no. 17, pp , B. Seguna, J. Soussanab, émssons de gaz à effet de serre et changement clmatque: causes et conséquences observées pour l agrculture et l élevage, Courrer de l envronnement de l INRA, no.55, pp , htm 9. S. Mouassa, Optmsaton de l écoulement de pussance par une méthode métaheurstque (technque des abelles) en présence d une source renouvelable (éolenne) et des dspostfs FACTS, Magster thess, Setf unversty, Algera, K.M. abo-al-ez, A. Fathy, Comparatve Study for Combned Economc and Emsson Dspatch Problem Consderng Valve Pont Effect, Journal of Electrc Engneerng (JEE), Vol 1, pp 1-13, E.D. Manteaw, N.A. Odero., Combned Economc and Emsson Dspatch usng ABC_PSO Hybrd algorthm wth valve pont loadng effect, Internatonal Journal of Scentfc and Research Publcatons, vol., Issue 1, pp 1-9, December, S. Krkpatrck and C. D. Gelatt and M. P. Vecch,Optmzaton by Smulated Annealng, Scence, Number 4598, Vol.0, pp , 983, N. Metropols, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller., Equaton of State Calculatons by Fast Computng Machnes, Journal of Chemcal Physcs, Vol. 1, pp , M. Basu, Economc Envronmental Dspatch Usng mult-objectve dfferental Evoluton, Elsever Appled Soft Computng 11, pp ,