CN106952132A - A kind of optimum price quotation method of cogeneration cooling heating system - Google Patents

Abstract

The invention discloses a kind of optimum price quotation method of cogeneration cooling heating system：The service data of S01, the Bi-level Programming Models for setting up cogeneration cooling heating system, input cogeneration cooling heating system and Utilities Electric Co., and set constraint to adjust；S02, initialization；S03, the parameter for updating particle swarm optimization algorithm；S04, internal layer particle swarm optimization algorithm is carried out to k-th particle；S05, judge whether k-th of particle meets constraints, meet and then enter step S06；S06, outer layer particle swarm optimization algorithm is carried out to k-th particle；Whether S07, k-th of particle of judgement meet constraints, meet and then enter step S08；S08, the optimal particle speed for being met inside and outside layer particle swarm optimization algorithm and position, and optimal location is chosen as optimal energy prices.By formulating rational electric power and Gas Prices, efficiency of energy utilization is improved, the economic benefit of cogeneration cooling heating system and Utilities Electric Co. has been ensured.

Description

A kind of optimum price quotation method of cogeneration cooling heating system

Technical field

The present invention relates to a kind of optimum price quotation method of cogeneration cooling heating system.

Background technology

In recent years, because operational efficiency is excellent, energy utilization rate is high, good in economic efficiency, cogeneration of heat and power and hot and cold, Electricity Federation production System is rapidly developed.In addition, hot and cold, cogeneration system can also flexibly tackle the fluctuation of energy cost, and in very great Cheng The energy loss produced in transmission of electricity, process of distributing electricity is avoided on degree.Existing research focuses on hot and cold, cogeneration system mostly In technological innovation and economic benefit, but the income to Utilities Electric Co. is not analyzed.By the electric power selling price for optimizing system Lattice and natural gas buying price ratio (referred to hereinafter as energy prices), can preferably improve Utilities Electric Co. and cogeneration cooling heating system (Combined Cooling Heating and Power：CCHP common benefit), therefore, for cogeneration cooling heating system report The research of valency strategy becomes more and more important.

The content of the invention

Regarding to the issue above, the present invention provides a kind of optimum price quotation method of cogeneration cooling heating system, reasonable by formulating Electric power and Gas Prices, improve efficiency of energy utilization, ensured the economic benefit of cogeneration cooling heating system and Utilities Electric Co..

To realize above-mentioned technical purpose, above-mentioned technique effect is reached, the present invention is achieved through the following technical solutions：

A kind of optimum price quotation method of cogeneration cooling heating system, comprises the following steps：

The fortune of S01, the Bi-level Programming Models for setting up cogeneration cooling heating system, input cogeneration cooling heating system and Utilities Electric Co. Row data, and the power-balance constraint and electric power of Bi-level Programming Models, the quotation constraint of natural gas are set；

S02, the inside and outside layer population of initialization：The scale, the speed of each particle and position of inside and outside layer population are set；

S03, the parameter for updating particle swarm optimization algorithm；

S04, internal layer particle swarm optimization algorithm is carried out to k-th particle, obtains internal layer optimal partial individual values and global value, Update the speed and position of k-th of particle of internal layer；

S05, judge whether k-th of particle meets constraints, meet and then enter step S06；

S06, outer layer particle swarm optimization algorithm is carried out to k-th particle, obtain outer layer optimum individual value and global value, update The speed of k-th of particle of outer layer and position；

Whether S07, k-th of particle of judgement meet constraints, meet and then enter step S08；

S08, the optimal particle speed for being met inside and outside layer particle swarm optimization algorithm and position, and choose optimal location It is used as optimal energy prices.

It is preferred that, in step S05, if k-th of particle is unsatisfactory for constraints,：

Make k numerical value plus 1, judge whether current k values are more than total population, if so, then making k numerical value plus 1 and entering step Rapid S03；Otherwise, it is directly entered step S03.

It is preferred that, in step S07, if k-th of particle is unsatisfactory for constraints,：

Make k numerical value plus 1, judge whether current k values are more than total population, if so, then making k numerical value plus 1 and entering step Rapid S03；Otherwise, it is directly entered step S03.

It is preferred that, in step S01, the outer layer plan model of Bi-level Programming Models is：

In formula,Represent the totle drilling cost of cogeneration cooling heating system；Respectively prime mover, absorption system The cost of investment of cold；The respectively operating cost of prime mover, donkey boiler；For at Utilities Electric Co. Purchases strategies；For load electric cost；For power selling income.

It is preferred that, in step S01, the internal layer plan model of Bi-level Programming Models is：

In formula,For internal layer object function；Respectively Utilities Electric Co. sells electric power, the receipts of natural gas Enter；Respectively Utilities Electric Co. bought power from energy market, the cost of natural gas；It is public for electric power Take charge of from cogeneration cooling heating system purchases strategies；Loss_totFor the power attenuation of power distribution network, w₀It is network loss weighted factor.

It is preferred that, in step S04, the speed of k-th of particle of internal layer, location updating formula are as follows：

v_k(t+1)=ψ [ω₀×v_k(t)+C₁×rand₁(p_k(t)-X_k(t))

+C₂×rand₂×(p_g(t)-X_k(t))]

X_k(t+1)=X_k(t)+v_k(t+1)

In formula, t represents iterations, v_k(t)、X_k(t) speed of the particle k in the t times iteration, position are represented respectively； rand₂、rand₁It is the random number between 0-1；p_k(t)、p_g(t) part and the overall situation of the particle in the t times iteration are represented respectively Optimal location；ω₀It is inertia weight coefficient, ψ represents constriction coefficient.

It is preferred that, in step S03, the parameter of renewal includes inertia weight coefficient ω₀, constriction coefficient ψ and Studying factors C₁、C₂, Wherein：

ω₀More new formula be：

Wherein, ω₁And ω₂It is the initial and end value of inertia weight coefficient respectively；t^maxIt is maximum iteration；

C₁And C₂More new formula be：

In formula, c_1f、c_2f、c_1iAnd c_2iIt is C respectively₁Final value, C₂Final value, C₁Initial value and C₂Initial value；

ψ more new formula is：

Φ=C₁+C₂

In formula, Φ is the Hybrid Learning factor.

The beneficial effects of the invention are as follows：

Firstth, the present invention can ensure cogeneration cooling heating system and Utilities Electric Co. simultaneously by setting up Bi-level Programming Models Economic benefit.

Secondth, the present invention can quickly calculate optimum price quotation by the excellent method to one's profit of population, shorten the optimization time.

3rd, the present invention realizes the Multi-class propagation of the energy, substantially increases efficiency of energy utilization, improves cold and hot Electricity Federation The stability of production system.

Brief description of the drawings

Fig. 1 is the operating structure schematic diagram of cogeneration cooling heating system of the present invention；

Fig. 2 is a kind of flow chart of the optimum price quotation method of cogeneration cooling heating system of the invention；

Fig. 3 is the Bi-level Programming Models figure of cogeneration cooling heating system of the present invention；

Fig. 4 is the IEEE-37 bus test system schematic diagrames of modification；

Fig. 5 is the load prediction schematic diagram of power industry user；

Fig. 6 is the optimal energy quotation result schematic diagram of electric power, natural gas.

Embodiment

Technical solution of the present invention is described in further detail with specific embodiment below in conjunction with the accompanying drawings, so that ability The technical staff in domain can be better understood from the present invention and can be practiced, but illustrated embodiment is not as the limit to the present invention It is fixed.

Cogeneration cooling heating system operating structure is as shown in figure 1, system is main by prime mover, donkey boiler, absorption refrigeration Machine is constituted, and with power distribution network Collaboration, the waste heat from prime mover will be absorbed by steam generator, pass through Absorption Refrigerator Refrigeration duty is driven, this will greatly reduce the system power consumption of load boom period.In the present invention, it is assumed that power distribution network is used to transmit energy Source, Utilities Electric Co. is as the operator of power distribution network, and it is reduced by being bought power from CCHP (i.e. cogeneration cooling heating system) With the electricity transaction amount of energy market, when electric power price and natural gas purchase valency be in rational proportion when, system has very High energy utilization rate, while can also produce good economic well-being of workers and staff.

As shown in Fig. 2 a kind of optimum price quotation method of cogeneration cooling heating system, comprises the following steps：

Step 1, the Bi-level Programming Models for setting up cogeneration cooling heating system, input cogeneration cooling heating system and Utilities Electric Co. Service data, and the power-balance constraint and electric power of Bi-level Programming Models, the quotation constraint of natural gas are set.

Bi-level Programming Models include inside and outside layer plan model, introduce in detail below：

Outer layer plan model will realize minimum using the total annual cost of cogeneration cooling heating system as outer layer object function：

In formula,Represent the totle drilling cost of cogeneration cooling heating system；Respectively prime mover, absorption system The cost of investment of cold；The respectively operating cost of prime mover, donkey boiler；For at Utilities Electric Co. Purchases strategies；For load electric cost；For power selling income.

In the present invention, extract it is annual in representative 7 day carry out system operating analysis, outer layer object function into This and the volume of receipts are represented with net present value (NPV) (net present value, NPV), are expressed as：

Wherein, i_rProfit margin is represented, n represents system operation time (year).

Internal layer plan model by Utilities Electric Co. year total income and year total network loss of power distribution network constitute, be：

In formula,For internal layer object function；Respectively Utilities Electric Co. sells electric power, the receipts of natural gas Enter；Respectively Utilities Electric Co. bought power from energy market, the cost of natural gas；It is public for electric power Take charge of from cogeneration cooling heating system purchases strategies；Loss_totFor the power attenuation of power distribution network, w₀It is network loss weighted factor.

The clearing of electric power are according to the trading volume and transaction value per period electric power：

Wherein, under daily d is per period h, the power that Utilities Electric Co. powers to load is P_D,d,h, price isEnergy city The electricity sales amount of field is P_market,d,h, market clearing price isCogeneration cooling heating system electricity sales amount isFormulate Electricity price is λ_elec,d,h。

The clearing of natural gas are according to the trading volume and transaction value per period natural gas：

Wherein, under daily d is per period h, the quantity of heat production of donkey boiler isPrime mover power output isIt is auxiliary The natural gas conversion ratio for helping boiler, prime mover is respectivelyHV is heating value of natural gas；Point It is not Utilities Electric Co., the Gas Prices of energy market.

The power attenuation of power distribution network is：

Wherein, P_L,d,hFor total network loss under daily d per periods h.

Energy prices are constrained to：

Wherein, λ_elec,d,hThe Power Pricing of cogeneration cooling heating system is represented,Represent minimum, most respectively Big price, only when cogeneration cooling heating system Power Pricing is reasonable, Utilities Electric Co. just can at cogeneration cooling heating system power purchase, Therefore λ_elec,d,hOutline is less thanχ_gas,d,hRepresent that cogeneration cooling heating system buys the negotiated prices of natural gas,Minimum, maximum negotiated prices are represented respectively, to promote hot and cold, cogeneration system development, χ_gas,d,h Outline is less than market clearing price

Due to having complementary relation between electric power, the price of natural gas and demand, when the increase of distribution network load amount When, the electric power and Gas Prices that cogeneration cooling heating system is quoted can also increase, and be shown below：

Wherein, ratio_elec、ratio_gasElectric power, the rate of price rises of natural gas, χ are represented respectively_gas,d,hRepresent cool and thermal power Co-generation system buys the negotiated prices of natural gas.

Under unit time period, active power needs to maintain balance：

Wherein, P_Di,d,hLoad power for every node per the period.

Economic relation between Utilities Electric Co. and cogeneration cooling heating system is as shown in Figure 3.Based on Bi-level Programming Models, wherein Outside optimization correspondence CCHP minimum total annual cost, interior optimization will then realize the maximum return and power distribution network of Utilities Electric Co. simultaneously Minimum network loss.

Step 2, the inside and outside layer population of initialization：Scale, the speed of each particle including setting inside and outside layer population And position.

Step 3, the parameter for updating particle swarm optimization algorithm：Including inertia weight coefficient ω₀, constriction coefficient ψ and learn because Sub- C₁、C₂, wherein：

ω₀More new formula be：

Wherein, ω₁And ω₂It is the initial and end value of inertia weight coefficient respectively；t^maxIt is maximum iteration.Such as, ω can be chosen₁=0.9, ω₂=0.4, ω in whole iterative process₀Span is (ω₂,ω₁)。

C₁And C₂More new formula be：

In formula, c_1f、c_2f、c_1iAnd c_2iIt is C respectively₁Final value, C₂Final value, C₁Initial value and C₂Initial value, the present invention in, It is preferred that, parameter C₁0.5, C is changed to from 2.5₂2.5 are changed to from 0.5.

ψ more new formula is：

Φ=C₁+C₂

In formula, Φ is the Hybrid Learning factor.

Step 4, internal layer particle swarm optimization algorithm is carried out to k-th particle, obtain internal layer optimal partial individual values and the overall situation Value, updates the speed and position of k-th of particle of internal layer, the speed of k-th of particle of internal layer, location updating formula are as follows：

v_k(t+1)=ψ [ω₀×v_k(t)+C₁×rand₁(p_k(t)-X_k(t))

+C₂×rand₂×(p_g(t)-X_k(t))]

X_k(t+1)=X_k(t)+v_k(t+1)

In formula, t represents iterations, v_k(t)、X_k(t) speed of the particle k in the t times iteration, position are represented respectively； rand₂、rand₁It is the random number between 0-1；p_k(t)、p_g(t) part and the overall situation of the particle in the t times iteration are represented respectively Optimal location；ω₀It is inertia weight coefficient, for adjusting the balance between part and global search, and according to both search Ability determines value, and ψ represents constriction coefficient.In the present invention, the comparison that inertia weight coefficient initial value is set is high, to improve the overall situation In search capability, the present invention, inertia weight coefficient value scope is (0.4,0.9).

Because population diversity is not enough, particle swarm optimization algorithm is rapid in the convergence of search phase early stage.In order to avoid being absorbed in Local Minimum simultaneously improves convergence rate, can introduce mutation operator U：When the more new position of particle is more than limiting chi^max, then set and update Position is X^max·U；When the renewal speed of particle is more than limit V^max, then it is V to set renewal speed^max·U。

Step 5, judge whether k-th of particle meets constraints, meet and then enter step 6, otherwise into step 7.

Step 6, outer layer particle swarm optimization algorithm is carried out to k-th particle, obtain outer layer optimum individual value and global value, more In the speed of new k-th of particle of outer layer and position, this step, the more new formula of particle rapidity and position synchronously rapid 4.

Step 7, the numerical value for making k plus 1 (internal layer for entering next particle optimizes), judge whether current k values are more than always Population, if so, then making k numerical value plus 1 (directly skipping the particle) and entering step 3；Otherwise, it is directly entered step 3.

Whether step 8, k-th of particle of judgement meet constraints, meet and then enter step 9, otherwise into step 10.

Step 9, algorithm terminate, and are met optimal particle speed and the position of inside and outside layer particle swarm optimization algorithm, and select Optimal location is taken as optimal energy prices.Optimal location (two dimension) just represents best price (electric power, natural gas).

Step 10, the numerical value for making k plus 1 (outer layer for entering next particle optimizes), judge whether current k values are more than always Population, if so, then making k numerical value plus 1 (directly skipping the particle) and entering step 3；Otherwise, it is directly entered step 3.

In order to verify the availability and stability of the inventive method, and it can be applied to formulate the cogeneration cooling heating system energy Among quotation, the present embodiment is with following data instance explanation.It is proposed by the invention based on dual layer resist particle swarm optimization algorithm By the IEEE-37 bus test systems applied to modification, as shown in Figure 4.Node 33 as cogeneration cooling heating system access point, its Load (8.48MW) is more than industrial load (peak value 3.33MW).It is assumed that prime mover has the service life of 20 years, investment income rate 6%. In order to ensure Utilities Electric Co. can obtain power selling income at cogeneration cooling heating system, current invention assumes that sale of electricity price is tied less than market Calculate price.To weighted factor w₀Value is tested from 0.1 to 0.9, and Utilities Electric Co. can obtain the receipts of maximum when finding to take 0.1 Enter.

In emulation, the per period electric load and amount of natural gas of power distribution network are all based on domestic energy market data.This The representative energy demand situation for representing whole season for one day is chosen in invention from each season, as shown in figure 5, then Constituted representative one week with this four days situation, to represent industrial user in 1 year to the 4 of electric power and Natural Gas Demand Individual typical scene：First day (spring, the May 4) of one week, second day one week to the 5th day (summer, July 13), one week Six days (autumn, November 2) and weekend (winter, January 12).

Fig. 6 provides the electric power and the annual optimal period price of natural gas of cogeneration cooling heating system under 4 typical scenes. As shown in Figure 6, in peak absences, lowest price appears in and at 3 points at 4 points, and the maximum price of peak period appears in the time 17th, 19 and 21 points.It is observed that peak valley and non-peak valley the best electric price difference are in the spring, the summer, autumn and winter be 0.142 yuan respectively/ KWh, 0.163 yuan/kWh, 0.125 yuan/kWh and 0.161 yuan/kWh.Corresponding minimum and maximum optimal Gas Prices difference is other It is 0.043 yuan/m³, 0.052 yuan/m³, 0.045 yuan/m³With 0.056 yuan/m³.Understood with reference to Fig. 5, unstable electric load is production The basic reason of raw minimum, maximum electricity price；Compare the best price of each season electric power and natural gas, it is known that, work as power distribution network Electric load increase when, Gas Prices increase therewith, and vice versa, and this shows, electric power and natural gas are deposited per period price In sizable relevance.

When reaching the optimal period price of electric power quarterly and natural gas, Utilities Electric Co. and cogeneration cooling heating system Income can be effectively ensured.Carry out understanding after economy calculating according to the model set up, prime mover and absorption refrigeration The optimum capacity of machine is respectively 8.271MW and 4.884MW, and the cogeneration cooling heating system investment payback time is, Utilities Electric Co. in 2.73 Income net present value (NPV) be 31.97 × 10⁷Member/year, the total revenue net present value (NPV) of cogeneration cooling heating system is 2.80 × 10⁷Member/year.Phase Than in the 27.06 × 10 of cool and thermal power piece-rate system⁷Member/annual earnings, can bring more using cogeneration cooling heating system to Utilities Electric Co. High net present value (NPV) income.In addition, compared with the fully automatic operation of cogeneration cooling heating system, if cogeneration cooling heating system and power distribution network Network is run parallel can make the net present value (NPV) revenue growth about 18.14% of Utilities Electric Co..Above-mentioned analysis result has been summarised in table 1.

The optimum results of the cogeneration cooling heating system of table 1

When particle swarm optimization algorithm of the present invention use based on Bi-level Programming Models has made cogeneration cooling heating system per The optimal energy prices of section.Based on according to domestic energy market price, the cool and thermal power using natural gas as prime mover can be calculated Optimum capacity, price strategy, operating cost and the system economy of co-generation system, while income can also be carried out to Utilities Electric Co. Analysis.Simulation Example is carried out on IEEE-37 bus test systems, is as a result shown, optimal time-of-use tariffs and Gas Prices tool There is certain relevance, cogeneration cooling heating system carries out optimal electrical power price according to this relation, can ensure Utilities Electric Co. and cold The income of co-generation unit.

In Optimized model, outer layer object function is the minimum operating cost of system, including capital investment and supplying power for outside into This, and the influence that is configured to system optimization of weight analysis energy prices；Internal layer studies Utilities Electric Co. and cogeneration cooling heating system, electricity The electricity transaction situation in power market.In addition, also will account for some constraintss of cogeneration cooling heating system, such as correlated variables Upper and lower limit constraint, power-balance constraint.It is analysis cogeneration cooling heating system to the dependences of energy prices, Simulation Example is from every Respectively extracted in season representative one day and carry out calculating analysis.To employing hospital and the public affairs that cogeneration cooling heating system is powered Residence carry out emulation testing, as a result show, when payback period be less than 2.8 years, the internal rate of return (IRR) be higher than 47% when, CCHP System has good economic benefit.In addition, the excellent algorithm of dual layer resist population used based on the present invention, CCHP system System can also be while the deep analysis of the income progress to Utilities Electric Co., by regulating system capacity and configuration, have ensured each side Economic benefit.

The beneficial effects of the invention are as follows：

Firstth, the present invention can ensure cogeneration cooling heating system and Utilities Electric Co. simultaneously by setting up Bi-level Programming Models Economic benefit.

Secondth, the present invention can quickly calculate optimum price quotation by the excellent method to one's profit of population, shorten the optimization time.

3rd, the present invention realizes the Multi-class propagation of the energy, substantially increases efficiency of energy utilization, improves cold and hot Electricity Federation The stability of production system.

The preferred embodiments of the present invention are these are only, are not intended to limit the scope of the invention, it is every to utilize this hair The equivalent structure that bright specification and accompanying drawing content are made either equivalent flow conversion or to be directly or indirectly used in other related Technical field, be included within the scope of the present invention.

Claims (10)

1. a kind of optimum price quotation method of cogeneration cooling heating system, it is characterised in that comprise the following steps：

The operation number of S01, the Bi-level Programming Models for setting up cogeneration cooling heating system, input cogeneration cooling heating system and Utilities Electric Co. According to, and the power-balance constraint and electric power of Bi-level Programming Models, the quotation constraint of natural gas are set；

S02, the inside and outside layer population of initialization：The scale, the speed of each particle and position of inside and outside layer population are set；

S03, the parameter for updating particle swarm optimization algorithm；

S04, internal layer particle swarm optimization algorithm is carried out to k-th particle, obtain internal layer optimal partial individual values and global value, update The speed of k-th of particle of internal layer and position；

S05, judge whether k-th of particle meets constraints, meet and then enter step S06；

S06, outer layer particle swarm optimization algorithm is carried out to k-th particle, obtain outer layer optimum individual value and global value, update outer layer The speed of k-th particle and position；

Whether S07, k-th of particle of judgement meet constraints, meet and then enter step S08；

S08, the optimal particle speed for being met inside and outside layer particle swarm optimization algorithm and position, and choose optimal location conduct Optimal energy prices.

2. a kind of optimum price quotation method of cogeneration cooling heating system according to claim 1, it is characterised in that step S05 In, if k-th of particle is unsatisfactory for constraints,：

Make k numerical value plus 1, judge whether current k values are more than total population, if so, then making k numerical value plus 1 and entering step S03；Otherwise, it is directly entered step S03.

3. a kind of optimum price quotation method of cogeneration cooling heating system according to claim 1, it is characterised in that step S07 In, if k-th of particle is unsatisfactory for constraints,：

Make k numerical value plus 1, judge whether current k values are more than total population, if so, then making k numerical value plus 1 and entering step S03；Otherwise, it is directly entered step S03.

4. a kind of optimum price quotation method of cogeneration cooling heating system according to claim 1, it is characterised in that step S01 In, the outer layer plan model of Bi-level Programming Models is：

$\begin{array}{c}\min C_{tot}^{CCHP}=C_{invest}^{PM}+C_{invest}^{AC}+C_{run}^{PM}+C_{run}^{boiler}\\ +C_{purch}^{DN}+C_{dem}^{DN}-{\mathrm{In}}_{sale}^{DN}\end{array}$

In formula,Represent the totle drilling cost of cogeneration cooling heating system；Respectively prime mover, Absorption Refrigerator Cost of investment；The respectively operating cost of prime mover, donkey boiler；For the power purchase at Utilities Electric Co. Cost；For load electric cost；For power selling income.

5. a kind of optimum price quotation method of cogeneration cooling heating system according to claim 4, it is characterised in that step S01 In, the internal layer plan model of Bi-level Programming Models is：

$\begin{array}{c}{\mathrm{OF}}_{tot}^{DU}=({\mathrm{In}}_D^{elec}-C_{market}^{elec}-C_{purch}^{CCHP})\\ -w_0\×{\mathrm{Loss}}_{tot}+({\mathrm{In}}_D^{gas}-C_{market}^{gas})\end{array}$

In formula,For internal layer object function；Respectively Utilities Electric Co. sells electric power, the income of natural gas；Respectively Utilities Electric Co. bought power from energy market, the cost of natural gas；For Utilities Electric Co. from Cogeneration cooling heating system purchases strategies；Loss_totFor the power attenuation of power distribution network, w₀It is network loss weighted factor.

6. a kind of optimum price quotation method of cogeneration cooling heating system according to claim 1, it is characterised in that step S04 In, the speed of k-th of particle of internal layer, location updating formula are as follows：

v_k(t+1)=ψ [ω₀×v_k(t)+C₁×rand₁(p_k(t)-X_k(t))

+C₂×rand₂×(p_g(t)-X_k(t))]

X_k(t+1)=X_k(t)+v_k(t+1)

In formula, t represents iterations, v_k(t)、X_k(t) represent respectively speed of the particle k in the t times iteration,

Position；rand₂、rand₁It is the random number between 0-1；p_k(t)、p_g(t) office of the particle in the t times iteration is represented respectively Portion and global optimum position；ω₀It is inertia weight coefficient, ψ represents constriction coefficient.

7. a kind of optimum price quotation method of cogeneration cooling heating system according to claim 6, it is characterised in that step S03 In, the parameter of renewal includes inertia weight coefficient ω₀, constriction coefficient ψ and Studying factors C₁、C₂, wherein：

ω₀More new formula be：

${\mathrm{\ω}}_0=({\mathrm{\ω}}_1-{\mathrm{\ω}}_2)\×\left(\frac{t^{max}-t}{t^{max}}\right)+{\mathrm{\ω}}_2$

Wherein, ω₁And ω₂It is the initial and end value of inertia weight coefficient respectively；t^maxIt is maximum iteration；

C₁And C₂More new formula be：

$C_1=(c_{1f}-c_{1i})\frac{t}{t^{max}}+c_{1i}$

$C_2=(c_{2f}-c_{2i})\frac{t}{t^{max}}+c_{2i}$

In formula, c_1f、c_2f、c_1iAnd c_2iIt is C respectively₁Final value, C₂Final value, C₁Initial value and C₂Initial value；

ψ more new formula is：

$\mathrm{\ψ}=\frac{2}{|2-\mathrm{\Φ}-\sqrt{{\mathrm{\Φ}}^2-4\mathrm{\Φ}}|}$

Φ=C₁+C₂

In formula, Φ is the Hybrid Learning factor.

8. the optimum price quotation method of a kind of cogeneration cooling heating system according to claim 7, it is characterised in that variation is set Operator U：When the more new position of particle is more than limiting chi^max, then it is X to set more new position^max·U；

When the renewal speed of particle is more than limit V^max, then it is V to set renewal speed^max·U。

9. a kind of optimum price quotation method of cogeneration cooling heating system according to claim 5, it is characterised in that outer layer target The cost and the volume of receipts of function are expressed as with net present value (NPV) NPV：

$NPV=\frac{{(1+i_r)}^n-1}{i_r{(1+i_r)}^n}$

Wherein, i_rRepresent profit margin,_nRepresent system operation time；

The clearing of electric power are according to the trading volume and transaction value per period electric power：

${\mathrm{In}}_D^{elec}=\underset{d=1}{\overset{7}{\Σ}}\underset{h=1}{\overset{24}{\Σ}}{P}_{D,d,h}\×{\mathrm{\τ}}_{d,h}^{elec}\×NPV\×\frac{365}{7}$

$C_{market}^{elec}=\underset{d=1}{\overset{7}{\Σ}}\underset{h=1}{\overset{24}{\Σ}}{P}_{market,d,h}\×{\mathrm{MCP}}_{market,d,h}^{elec}\×NPV\×\frac{365}{7}$

$C_{purch}^{CCHP}=\underset{d=1}{\overset{7}{\Σ}}\underset{h=1}{\overset{24}{\Σ}}{P}_{sale,d,h}^{PM}\×{\mathrm{\λ}}_{elec,d,h}\×NPV\×\frac{365}{7}$

Wherein, under daily d is per period h, the power that Utilities Electric Co. powers to load is P_D,d,h, price isEnergy market Electricity sales amount is P_market,d,h, market clearing price isCogeneration cooling heating system electricity sales amount isFormulate electricity price For λ_elec,d,h；

The clearing of natural gas are according to the trading volume and transaction value per period natural gas：

${\mathrm{In}}_D^{gas}=\underset{d=1}{\overset{7}{\Σ}}\underset{h=1}{\overset{24}{\Σ}}(\frac{H_{d,h}^{boiler}}{{\mathrm{\η}}_{d,h}^{boiler}HV}+\frac{P_{d,h}^{PM}}{{\mathrm{\η}}_{d,h}^{PM}HV})\×{\mathrm{\τ}}_{d,h}^{gas}\×NPV\×\frac{365}{7}$

$C_{market}^{gas}=\underset{d=1}{\overset{7}{\Σ}}\underset{h=1}{\overset{24}{\Σ}}(\frac{H_{d,h}^{boiler}}{{\mathrm{\η}}_{d,h}^{boiler}HV}+\frac{P_{d,h}^{PM}}{{\mathrm{\η}}_{d,h}^{PM}HV})\×{\mathrm{MCP}}_{market,d,h}^{gas}\×NPV\×\frac{365}{7}$

Wherein, under daily d is per period h, the quantity of heat production of donkey boiler isPrime mover power output isAid in pot Stove, the natural gas conversion ratio of prime mover are respectivelyHV is heating value of natural gas；It is respectively Utilities Electric Co., the Gas Prices of energy market；

The power attenuation of power distribution network is：

${\mathrm{Loss}}_{tot}=\underset{d=1}{\overset{7}{\Σ}}\underset{h=1}{\overset{24}{\Σ}}{P}_{L,d,h}\×\frac{365}{7}$

Wherein, P_L,d,hFor total network loss under daily d per periods h.

10. the optimum price quotation method of a kind of cogeneration cooling heating system according to claim 9, it is characterised in that work as distribution During net load increase, the electric power and Gas Prices that cogeneration cooling heating system is quoted also increase, and：

${\mathrm{ratio}}_{elec}=\frac{P_{D,d,h}-P_{D,d,(h-1)}}{{\mathrm{\λ}}_{elec,d,h}-{\mathrm{\λ}}_{elec,d,(h-1)}}>0$

${\mathrm{ratio}}_{gas}=\frac{P_{D,d,h}-P_{D,d,(h-1)}}{{\mathrm{\χ}}_{gas,d,h}-{\mathrm{\χ}}_{gas,d,(h-1)}}>0$

Wherein, ratio_elec、ratio_gasElectric power, the rate of price rises of natural gas, χ are represented respectively_gas,d,hRepresent CCHP The negotiated prices of systems buying natural gas.