CN111275251A - CCHP system cooling, heating and power combined supply optimization method containing sewage source heat pump - Google Patents

CCHP system cooling, heating and power combined supply optimization method containing sewage source heat pump Download PDF

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CN111275251A
CN111275251A CN202010045449.5A CN202010045449A CN111275251A CN 111275251 A CN111275251 A CN 111275251A CN 202010045449 A CN202010045449 A CN 202010045449A CN 111275251 A CN111275251 A CN 111275251A
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张宏业
吴杰康
蔡锦健
刘国新
蔡志宏
王瑞东
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Abstract

Aiming at the problem that the existing CCHP system of the traditional algorithm does not take the satisfaction rate of user requirements and the system operation cost as an optimized objective function, the invention provides a combined cooling, heating and power optimization method of the CCHP system of a heat pump containing a sewage source, which is characterized by comprising the following steps: s1, inputting original data of a combined cooling heating and power system; s2, constructing a CCHP micro-grid model; s3, constructing a target function; s4, constructing constraint conditions; and S5, solving an algorithm to obtain an optimized result. The method constructs the start-stop functions of the gas turbine and the waste heat boiler, calculates the start-stop cost of the two devices better, considers the satisfaction rate of user requirements, takes the satisfaction rate of the user requirements and the system operation cost as optimization objective functions, and optimizes the operation scheduling of the cooling, heating and power triple supply type micro-grid system.

Description

CCHP system cooling, heating and power combined supply optimization method containing sewage source heat pump
Technical Field
The invention belongs to the field of operation and control of power systems, and particularly relates to a method for solving the micro-grid dispatching output of a combined cooling heating and power system with a sewage source heat pump by using an improved crisscross algorithm.
Background
The CCHP system is a mature energy comprehensive utilization technology in many countries and regions, and is widely concerned and favored by governments and business industries of various countries due to the characteristics of being close to users, gradient utilization, high primary energy utilization efficiency, environmental friendliness, safe and reliable energy supply and the like. A number of researchers have conducted a great deal of research into the CCHP system at home and abroad. The heat pump is used as a low-investment and high-yield heat production device and is suitable for being used as a heat source in a CCHP system. Many scholars have also studied the energy complementation and coordination, the economy, etc. of the CCHP system with heat pump.
The sewage source heat pump is one of water source heat pumps, utilizes lower electric energy to extract heat in urban sewage or industrial sewage to recycle resources, not only saves primary energy, but also has higher economical efficiency, and the temperature of general sewage is higher than the environmental temperature, so that the heat producible by the sewage source heat pump is larger, and the sewage source heat pump also has excellent heat production capacity as heat source equipment. At present, the research on the sewage source heat pump in a CCHP system at home and abroad is less. The research on the optimization method of combined cooling heating and power of the CCHP system containing the sewage source heat pump is not sufficient.
The main purpose of the establishment of the CCHP system is to better meet the demands of users on the three loads of cold, heat and electricity, but the existing CCHP system does not take the satisfaction rate of the demands of the users and the operation cost of the system as an optimized objective function, so that the economy of the CCHP system is affected.
Disclosure of Invention
Aiming at the problem that the existing CCHP system of the traditional algorithm does not take the satisfaction rate of user requirements and the system operation cost as an optimization objective function, the invention provides a combined cooling heating and power optimization method of the CCHP system of the heat pump containing the sewage source, which can effectively solve the problems pointed out in the background technology.
The optimization method for combined cooling, heating and power of the CCHP system with the sewage source heat pump is characterized by comprising the following steps of:
s1, inputting original data of a combined cooling heating and power system, wherein the input of predicted cooling load, heat load, electric load, fan and photovoltaic cell output is included;
s2, constructing a CCHP micro-grid model, which comprises a gas turbine output power and heat production model, a waste heat boiler model, a fan and photovoltaic cell output model, an energy storage cell model, a gas boiler model, a sewage source heat pump model, an absorption refrigerator model and an electric refrigerator model;
s3, constructing a target function;
s4, constructing constraint conditions;
s5, solving an algorithm to obtain an optimized result;
wherein, the step of S3 specifically includes:
s3.1, taking the satisfaction rate of user requirements as an optimization objective function:
Figure BDA0002369228970000021
Figure BDA0002369228970000022
wherein, F1.1(ii) a rate of satisfaction of electrical load demand; f1.2Heat load satisfaction rate; f1.3A cold load satisfaction rate; f1.4Overall satisfaction rates for three types of users; mu, sigma,
Figure BDA0002369228970000023
Is a weight coefficient;
Figure BDA0002369228970000024
wherein, PMT.tThe electric power provided by the micro gas turbine at the time t;
Figure BDA0002369228970000025
for wind turbine generator system at t momentAn inter-output force;
Figure BDA0002369228970000026
the actual output of the photovoltaic cell at the time t;
Figure BDA0002369228970000027
and
Figure BDA0002369228970000028
η, respectively charging and discharging power at time tchaAnd ηdisRespectively charge efficiency and discharge efficiency; pEXB.tPurchasing power from the power distribution network for the combined supply system at the time t; pEXS.tThe power selling power from the combined supply system to the power distribution network; pEC.tThe power consumption of the electric refrigerator in the time period t; pSE.tThe power consumption of the sewage source heat pump in the time period t is determined;
Figure BDA0002369228970000029
an electrical load for a period of t;
since electricity purchase and electricity sale are not carried out simultaneously, the following regulations are provided:
PEXB.tPEXS.t＝0
Figure BDA00023692289700000210
wherein HMT.tThe heat production of the micro gas turbine is in a period t; hGF.tThe heat production quantity of the gas boiler in the time period t; hSE.tThe heat extraction amount of the sewage source heat pump is t time period;
Figure BDA00023692289700000211
for time t, thermal load αtIs the heat distribution coefficient of the waste heat boiler at the moment t βtIs the heat distribution coefficient of the sewage source heat pump at the moment t ηWTThe recovery efficiency of the waste heat boiler is obtained;
Figure BDA00023692289700000212
wherein Q isAC.tAnd QEC.tRefrigerating capacities of the electric refrigerator and the absorption refrigerator at a time period t respectively;
Figure BDA00023692289700000213
the time period is t, and the cooling load is t;
s3.2, taking the running cost as an optimization objective function:
F2＝CGAS+CEX+CPR+COS
wherein, F2For operating costs; cGAS、CEX、CPRAnd COSRespectively the system fuel cost, the difference between the system electricity purchasing cost and the electricity selling profit, the system equipment operation cost and the equipment start-stop cost;
the fuel cost of the system:
Figure BDA00023692289700000214
wherein epsilontIs the natural gas price; gMT.tThe gas consumption of the gas turbine in the time period t; gGF.tThe gas consumption of the gas boiler in the time period t;
the difference between the electricity purchasing cost and the electricity selling income of the system is as follows:
Figure BDA0002369228970000031
wherein, tauB.tAnd τS.tThe prices of electricity purchasing and electricity selling from the combined cooling, heating and power system to the power distribution network are respectively;
structure of electric rate:
Figure BDA0002369228970000032
Figure BDA0002369228970000033
equipment operating cost:
Figure BDA0002369228970000034
Figure BDA0002369228970000035
wherein KMT、KGF、KWT、KPV、KEC、KSEThe unit power running costs of a gas turbine, a gas boiler, a wind turbine generator, a photovoltaic cell, an electric refrigerator, a sewage source heat pump and an absorption refrigerator are respectively set; qAC.tThe refrigeration power of the absorption refrigerator in the t period;
equipment start-stop cost:
Figure BDA0002369228970000036
Figure BDA0002369228970000037
Figure BDA0002369228970000038
Figure BDA0002369228970000039
Figure BDA00023692289700000310
wherein the content of the first and second substances,
Figure BDA00023692289700000311
the coefficient of cut for the gas generator;
Figure BDA00023692289700000312
the rated power of the gas generator; cOP.MTFor gas turbine startup costs; cST.MTCost for gas turbine shutdown; cOP.GFThe startup cost for the gas boiler; cST.GFThe shutdown cost of the gas boiler;SSMTO.t、SSMTS.t、SSGFO.t、SSGFS.tfor start-stop coefficient, SSMTO.tAt 1, the micro gas turbine is turned on during the period t, SSMTS.t1 represents the micro gas turbine is shut down during time t; SSGFO.tIs 1, the gas boiler is started in the time period t, SSGFS.t1 represents the micro gas turbine is shut down during time t;
s3.3, synthesizing a satisfaction rate objective function and an operation cost objective function of user requirements to obtain a total objective function:
Figure BDA0002369228970000041
wherein the content of the first and second substances,
Figure BDA0002369228970000042
and δ are weight coefficients, respectively.
The specific method of the step S4 is as follows:
s4.1 energy balance constraints
Electric balance constraint:
the electric energy is mainly provided by gas turbine, distribution network, photovoltaic cell, wind turbine generator system, and the energy storage battery plays the undulant effect of exerting oneself of stabilizing wind turbine generator system and photovoltaic cell here:
Figure BDA0002369228970000043
and (3) thermal balance constraint:
the heat energy is mainly provided by the extracted heat energy generated by the sewage source heat pump and the waste heat boiler and the heat energy of the gas boiler:
Figure BDA0002369228970000044
cold balance constraint:
the cooling load demand is mainly provided by absorption chillers and electric chillers:
Figure BDA0002369228970000045
wherein, the heat of absorption formula refrigerator is provided by exhaust-heat boiler and sewage source heat pump:
QEC.t＝(HMT.t(1-αt)ηWT+HSE.t(1-βt))ηEC
in the formula, ηECThe refrigeration efficiency of the absorption refrigerator;
s4.2, restraining the operation condition of each device:
and (3) power generation power constraint of the micro gas turbine:
Figure BDA0002369228970000046
because the micro gas generator loses the natural gas to generate electricity and has certain constraint on the gas consumption, the climbing constraint exists on the corresponding generated power:
|PMT.t-PMT.t-1|≤aPPMT.t
wherein, aPThe power generation power climbing capacity coefficient of the micro gas turbine;
and (3) output force restraint of the gas boiler:
Figure BDA0002369228970000047
wherein the content of the first and second substances,
Figure BDA0002369228970000048
rated power for the gas boiler;
output restraint of the wind turbine generator:
Figure BDA0002369228970000049
wherein the content of the first and second substances,
Figure BDA00023692289700000410
the minimum cut coefficient of the fan is set;
Figure BDA00023692289700000411
rated power for the fan;
photovoltaic cell output restraint:
Figure BDA0002369228970000051
wherein the content of the first and second substances,
Figure BDA0002369228970000052
is the rated power of the photovoltaic cell;
energy storage equipment restraint:
considering the service life problem of the energy storage battery, the lowest storage capacity of the energy storage battery is limited, the maximum charging capacity and the maximum discharging capacity of the energy storage battery are restricted in each time period, the charging and the discharging cannot be simultaneously carried out in each time period, and the energy storage device is restricted as follows:
SBAT.min<SBAT.t<SBAT.max
Figure BDA0002369228970000053
Figure BDA0002369228970000054
Figure BDA0002369228970000055
wherein S isBAT.tThe storage capacity of the energy storage battery at the moment t; sBAT.minThe lowest allowable amount of stored electricity for the energy storage device; sBAT.maxThe rated power of the energy storage device; y ischaAnd ydisRespectively the maximum discharge rate and the minimum charge rate of the energy storage battery;
absorption chiller output restraint:
Figure BDA0002369228970000056
wherein the content of the first and second substances,
Figure BDA0002369228970000057
is the rated power of the absorption chiller;
electric refrigerator output restraint:
Figure BDA0002369228970000058
wherein the content of the first and second substances,
Figure BDA0002369228970000059
is the rated power of the electrical refrigerator.
The specific method of the step S5 is as follows:
s5.1: setting parameters such as population scale, iteration times and the like; setting a target function and randomly generating particle positions; training a BP neural network according to original data;
s5.2: selecting a gas turbine to run in a mode of fixing power by heat or fixing power by cold according to the water quantity and the water temperature of a sewage source;
s5.3: constructing a CCHP system model, an objective function and constraint conditions;
s5.4: carrying out transverse crossing on two non-repeated random pairings of particles in a population;
s5.5: comparing the fitness between the mediocre solution obtained after transverse crossing and the parent particles, and selecting the particles with high fitness as members of a new population;
s5.6: longitudinally crossing pairwise non-repeated random pairing between variables in each particle to obtain progeny particles;
s5.7: comparing the fitness between parent particles and child particles, and keeping the particles with high fitness;
s5.8: the iteration loop reaches a specified number of times or the precision reaches a convergence condition.
The invention has the beneficial effects that: the waste gas of the gas turbine is utilized to utilize waste heat through the waste heat boiler, meanwhile, the heat of urban sewage, industrial sewage and the like is extracted through the sewage source heat pump, the heat is converted into heat energy with higher quality through consuming a small amount of electricity, the heat energy and the heat obtained by extracting the waste heat in the waste gas of the gas turbine through the waste heat boiler are combined, meanwhile, start-stop functions of the gas turbine and the waste heat boiler are built, the start-stop cost of the two devices is calculated better, the satisfaction rate of user demands is considered, the satisfaction rate of the user demands and the system operation cost are taken as optimization objective functions, and the operation scheduling of the combined cooling, heating and power supply micro-grid system is optimized.
Drawings
Fig. 1 is a flowchart of an optimal scheduling method in a CCHP micro-grid according to the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
1. And inputting the original data of the combined cooling heating and power system. Inputting the predicted cold load, heat load, electric load, fan and photovoltaic cell output, electricity rate structure and initial values of the operation of each device of the CCHP system.
And then constructing a CCHP microgrid model. The CCHP microgrid model comprises a gas turbine output power and heat production model, a waste heat boiler model, a fan and photovoltaic cell output model, an energy storage cell model, a gas boiler model, a sewage source heat pump model, an absorption refrigerator model and an electric refrigerator model.
2. Constructing an objective function:
201. taking the satisfaction rate of user requirements as an optimization objective function:
Figure BDA0002369228970000061
Figure BDA0002369228970000062
wherein, F1.1(ii) a rate of satisfaction of electrical load demand; f1.2Heat load satisfaction rate; f1.3A cold load satisfaction rate; f1.4Overall satisfaction rates for three types of users; mu, sigma,
Figure BDA0002369228970000063
Are weight coefficients.
Figure BDA0002369228970000064
Wherein, PMT.tThe electric power provided by the micro gas turbine at the time t;
Figure BDA0002369228970000065
the actual output of the wind turbine generator at the time t;
Figure BDA0002369228970000066
the actual output of the photovoltaic cell at the time t;
Figure BDA0002369228970000067
and
Figure BDA0002369228970000068
η, respectively charging and discharging power at time tchaAnd ηdisRespectively charge efficiency and discharge efficiency; pEXB.tPurchasing power from the power distribution network for the combined supply system at the time t; pEXS.tThe power selling power from the combined supply system to the power distribution network; pEC.tThe power consumption of the electric refrigerator in the time period t; pSE.tThe power consumption of the sewage source heat pump in the time period t is determined;
Figure BDA0002369228970000069
is the electrical load for the period t.
Since electricity purchase and electricity sale are not carried out simultaneously, the following regulations are provided:
PEXB.tPEXS.t＝0
Figure BDA00023692289700000610
wherein HMT.tThe heat production of the micro gas turbine is in a period t; hGF.tThe heat production quantity of the gas boiler in the time period t; hSE.tFor heat extraction of sewage source heat pump in t periodAn amount;
Figure BDA0002369228970000071
for time t, thermal load αtIs the heat distribution coefficient of the waste heat boiler at the moment t βtIs the heat distribution coefficient of the sewage source heat pump at the moment t ηWTThe recovery efficiency of the waste heat boiler is improved.
Figure BDA0002369228970000072
Wherein Q isAC.tAnd QEC.tRefrigerating capacities of the electric refrigerator and the absorption refrigerator at a time period t respectively;
Figure BDA0002369228970000073
is the cooling load for the period t.
202. Taking the running cost as an optimization objective function:
F2＝CGAS+CEX+CPR+COS
wherein, F2For operating costs; cGAS、CEX、CPRAnd COSRespectively the system fuel cost, the difference between the system electricity purchasing cost and the electricity selling profit, the system equipment operation cost and the equipment start-stop cost.
The fuel cost of the system:
Figure BDA0002369228970000074
wherein epsilontIs the natural gas price; gMT.tThe gas consumption of the gas turbine in the time period t; gGF.tIs the gas consumption of the gas boiler in the time period t.
The difference between the electricity purchasing cost and the electricity selling income of the system is as follows:
Figure BDA0002369228970000075
wherein, tauB.tAnd τS.tFor buying and selling electricity to distribution network by combined cold and heat power supply systemA price;
structure of electric rate:
Figure BDA0002369228970000076
Figure BDA0002369228970000077
equipment operating cost:
Figure BDA0002369228970000078
Figure BDA0002369228970000079
wherein KMT、KGF、KWT、KPV、KEC、KSEThe unit power running costs of a gas turbine, a gas boiler, a wind turbine generator, a photovoltaic cell, an electric refrigerator, a sewage source heat pump and an absorption refrigerator are respectively set; qAC.tThe refrigerating power of the absorption refrigerator in the t period is shown.
Equipment start-stop cost:
Figure BDA00023692289700000710
Figure BDA0002369228970000081
Figure BDA0002369228970000082
Figure BDA0002369228970000083
Figure BDA0002369228970000084
wherein，
Figure BDA0002369228970000085
The coefficient of cut for the gas generator;
Figure BDA0002369228970000086
the rated power of the gas generator; cOP.MTFor gas turbine startup costs; cST.MTCost for gas turbine shutdown; cOP.GFThe startup cost for the gas boiler; cST.GFThe shutdown cost of the gas boiler; SSMTO.t、SSMTS.t、SSGFO.t、SSGFS.tFor start-stop coefficient, SSMTO.tAt 1, the micro gas turbine is turned on during the period t, SSMTS.t1 represents the micro gas turbine is shut down during time t; SSGFO.tIs 1, the gas boiler is started in the time period t, SSGFS.tA time of 1 represents the micro gas turbine off during time t.
203. And synthesizing a satisfaction rate objective function and an operation cost objective function of the user requirements to obtain a total objective function:
Figure BDA0002369228970000087
wherein the content of the first and second substances,
Figure BDA0002369228970000088
and δ are weight coefficients, respectively.
3. And (3) constructing a constraint condition:
301. energy balance constraint
Electric balance constraint:
the electric energy is mainly provided by gas turbine, distribution network, photovoltaic cell, wind turbine generator system, and the energy storage battery plays the undulant effect of exerting oneself of stabilizing wind turbine generator system and photovoltaic cell here:
Figure BDA0002369228970000089
and (3) thermal balance constraint:
the heat energy is mainly provided by the extracted heat energy generated by the sewage source heat pump and the waste heat boiler and the heat energy of the gas boiler:
Figure BDA00023692289700000810
cold balance constraint:
the cooling load demand is mainly provided by absorption chillers and electric chillers:
Figure BDA00023692289700000811
wherein, the heat of absorption formula refrigerator is provided by exhaust-heat boiler and sewage source heat pump:
QEC.t＝(HMT.t(1-αt)ηWT+HSE.t(1-βt))ηEC
in the formula, ηECThe refrigeration efficiency of the absorption refrigerator.
302. And (3) restricting the operation condition of each device:
and (3) power generation power constraint of the micro gas turbine:
Figure BDA0002369228970000091
because the micro gas generator loses the natural gas to generate electricity and has certain constraint on the gas consumption, the climbing constraint exists on the corresponding generated power:
|PMT.t-PMT.t-1|≤aPPMT.t
wherein, aPThe power generation power climbing capacity coefficient of the micro gas turbine.
And (3) output force restraint of the gas boiler:
Figure BDA0002369228970000092
wherein the content of the first and second substances,
Figure BDA0002369228970000093
is the rated power of the gas boiler.
Output restraint of the wind turbine generator:
Figure BDA0002369228970000094
wherein the content of the first and second substances,
Figure BDA0002369228970000095
the minimum cut coefficient of the fan is set;
Figure BDA0002369228970000096
the rated power of the fan.
Photovoltaic cell output restraint:
Figure BDA0002369228970000097
wherein the content of the first and second substances,
Figure BDA0002369228970000098
is the rated power of the photovoltaic cell.
Energy storage equipment restraint:
considering the service life problem of the energy storage battery, the lowest storage capacity of the energy storage battery is limited, the maximum charging capacity and the maximum discharging capacity of the energy storage battery are restricted in each time period, the charging and the discharging cannot be simultaneously carried out in each time period, and the energy storage device is restricted as follows:
SBAT.min<SBAT.t<SBAT.max
Figure BDA0002369228970000099
Figure BDA00023692289700000910
Figure BDA00023692289700000911
wherein S isBAT.tThe storage capacity of the energy storage battery at the moment t; sBAT.minThe lowest allowable amount of stored electricity for the energy storage device; sBAT.maxThe rated power of the energy storage device; y ischaAnd ydisRespectively, the maximum discharge rate and the minimum charge rate of the energy storage battery.
Absorption chiller output restraint:
Figure BDA00023692289700000912
wherein the content of the first and second substances,
Figure BDA00023692289700000913
is the rated power of the absorption chiller.
Electric refrigerator output restraint:
Figure BDA00023692289700000914
wherein the content of the first and second substances,
Figure BDA0002369228970000101
is the rated power of the electrical refrigerator.
4. The solving method comprises the following steps:
the traditional group optimization algorithm has the advantages of high convergence rate and the like, so that the traditional group optimization algorithm is widely applied at present. But all have the phenomenon of "precocity" by using the traditional group optimization algorithm. The vertical and horizontal cross algorithm has the advantages of high convergence speed and the like of the traditional group optimization algorithm, and the group has the capability of jumping out of local optimum by utilizing the vertical cross, so that the premature phenomenon is avoided. Therefore, an improved criss-cross algorithm is used as a combined cooling heating and power optimization method for a CCHP system of a heat pump containing a sewage source, and a longitudinal cross position updating formula is as follows:
MSVC(i,d1)＝rX(i,d1)+(1-r)X(i,d2)
i∈N(1,M),d1,d2∈N(1,D)
wherein r is [0,1 ]]Random numbers uniformly distributed thereon; MS (Mass Spectrometry)VC(i，d1) Is a granuleSub i d1And d2Progeny resulting from the cross of dimensions.
The internal parts of the same particles are longitudinally crossed at a certain probability, so that the global search capability of the population can be improved, but the defect of low efficiency exists, and each crossing needs to be normalized and reproduced, so that the update speed of the population is influenced. On the basis, X (i, d) in the formula of longitudinal intersection is used2) The improved CSO algorithm finds out potential linear relation between the longitudinal crossing scalars through a BP neural network, so that the longitudinal crossing process is more purposeful, and replaces two steps of normalization and recurrence. The improvement method comprises the following steps:
a. firstly, obtaining the original data of the optimal operation state according to the operation experience.
b. And training N BP neural networks by using the obtained original data. Each neural network corresponds to two different variables of the same particle, and when the input of one neural network in the N BP neural networks is d1And X (i, d)2) When output is
Figure BDA0002369228970000102
Wherein Xavg(i,d1) Denotes the d-th1The average of the dimensional variables;
Figure BDA0002369228970000103
n is the number of variables.
c. Mixing X (i, d)2) Instead, it is changed into
Figure BDA0002369228970000104
The significance of the method is that the cross direction is close to the optimal running state, so that the method has better global searching capability.
The method comprises the following steps:
s5.1: and setting parameters such as population scale, iteration times and the like. And setting an objective function and randomly generating particle positions. And training the BP neural network according to the original data.
S5.2: and selecting the operation mode of the gas turbine by using heat to fix the power or using cold to fix the power according to the water quantity and the water temperature of the sewage source.
S5.3: and constructing a CCHP system model, an objective function and constraint conditions.
S5.4: and transversely crossing two non-repeated random pairings of the particles in the population.
S5.5: and comparing the fitness between the mediocre solution obtained after transverse crossing and the parent particles, and selecting the particles with high fitness as members of the new population.
S5.6: and longitudinally crossing pairwise non-repeated random pairing between variables in each particle to obtain progeny particles after crossing.
S5.7: and comparing the fitness between the parent particles and the child particles, and keeping the particles with high fitness.
S5.8: the iteration loop reaches a specified number of times or the precision reaches a convergence condition.
The invention constructs a CCHP system model containing a gas turbine output power and heat production model, a waste heat boiler model, a fan and photovoltaic cell output model, an energy storage cell model, a gas boiler model, a sewage source heat pump model, an absorption refrigerator model and an electric refrigerator model. The waste heat of the waste gas of the gas turbine is utilized by the waste heat boiler, meanwhile, the heat of urban sewage, industrial sewage and the like is extracted by the sewage source heat pump, the heat is converted into heat energy with higher quality by consuming a small amount of electricity, the heat energy is combined with the heat obtained by extracting the waste heat in the waste gas of the gas turbine by the waste heat boiler, when the water quantity of the sewage is large or the temperature is higher, the heat energy supplied by the heat pump is higher, the gas turbine adopts a mode of 'fixing heat by electricity', otherwise, when the water quantity of the sewage is lower or the temperature is lower, the gas turbine adopts a mode of 'fixing electricity by heat', and supplies the heat energy to the CCHP system. The method constructs the start-stop functions of the gas turbine and the waste heat boiler at the same time, calculates the start-stop cost of the two devices better, considers the satisfaction rate of user requirements, and optimizes the operation scheduling of the cooling, heating and power triple supply type micro-grid system by taking the satisfaction rate of the user requirements and the system operation cost as optimization objective functions.
Moreover, aiming at the problems that the traditional algorithm is easy to fall into local optimization, the global search capability is poor, the convergence speed is low and the like, the improved vertical and horizontal crossing algorithm which utilizes the BP neural network to replace independent variables in vertical crossing with potential relation operators is provided, and the capability of vertical crossing to help groups to jump out of local optimization is greatly improved.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily change or replace the present invention within the technical scope of the present invention. Therefore, the protection scope of the present invention is subject to the protection scope of the claims.

Claims (4)

1. A combined cooling heating and power optimization method for a CCHP system with a sewage source heat pump is characterized by comprising the following steps:
s1, inputting original data of a combined cooling heating and power system, wherein the input of predicted cooling load, heat load, electric load, fan and photovoltaic cell output is included;
s2, constructing a CCHP micro-grid model, which comprises a gas turbine output power and heat production model, a waste heat boiler model, a fan and photovoltaic cell output model, an energy storage cell model, a gas boiler model, a sewage source heat pump model, an absorption refrigerator model and an electric refrigerator model;
s3, constructing a target function;
s4, constructing constraint conditions;
s5, solving an algorithm to obtain an optimized result;
wherein, the step of S3 specifically includes:
s3.1, taking the satisfaction rate of user requirements as an optimization objective function:
Figure FDA0002369228960000011
Figure FDA0002369228960000012
wherein, F1.1(ii) a rate of satisfaction of electrical load demand; f1.2Heat load satisfaction rate; f1.3A cold load satisfaction rate; f1.4Is a three-class userThe overall satisfaction rate; mu, sigma,
Figure FDA0002369228960000013
Is a weight coefficient;
Figure FDA0002369228960000014
wherein, PMT.tThe electric power provided by the micro gas turbine at the time t;
Figure FDA0002369228960000015
the actual output of the wind turbine generator at the time t;
Figure FDA0002369228960000016
the actual output of the photovoltaic cell at the time t;
Figure FDA0002369228960000017
and
Figure FDA0002369228960000018
η, respectively charging and discharging power at time tchaAnd ηdisRespectively charge efficiency and discharge efficiency; pEXB.tPurchasing power from the power distribution network for the combined supply system at the time t; pEXS.tThe power selling power from the combined supply system to the power distribution network; pEC.tThe power consumption of the electric refrigerator in the time period t; pSE.tThe power consumption of the sewage source heat pump in the time period t is determined;
Figure FDA0002369228960000019
an electrical load for a period of t;
since electricity purchase and electricity sale are not carried out simultaneously, the following regulations are provided:
PEXB.tPEXS.t＝0
Figure FDA00023692289600000110
wherein，HMT.tThe heat production of the micro gas turbine is in a period t; hGF.tThe heat production quantity of the gas boiler in the time period t; hSE.tThe heat extraction amount of the sewage source heat pump is t time period;
Figure FDA0002369228960000021
for time t, thermal load αtIs the heat distribution coefficient of the waste heat boiler at the moment t βtIs the heat distribution coefficient of the sewage source heat pump at the moment t ηWTThe recovery efficiency of the waste heat boiler is obtained;
Figure FDA0002369228960000022
wherein Q isAC.tAnd QEC.tRefrigerating capacities of the electric refrigerator and the absorption refrigerator at a time period t respectively;
Figure FDA0002369228960000023
the time period is t, and the cooling load is t; s3.2, taking the running cost as an optimization objective function:
F2＝CGAS+CEX+CPR+COS
wherein, F2For operating costs; cGAS、CEX、CPRAnd COSRespectively the system fuel cost, the difference between the system electricity purchasing cost and the electricity selling profit, the system equipment operation cost and the equipment start-stop cost;
the fuel cost of the system:
Figure FDA0002369228960000024
wherein epsilontIs the natural gas price; gMT.tThe gas consumption of the gas turbine in the time period t; gGF.tThe gas consumption of the gas boiler in the time period t;
the difference between the electricity purchasing cost and the electricity selling income of the system is as follows:
Figure FDA0002369228960000025
wherein, tauB.tAnd τS.tThe prices of electricity purchasing and electricity selling from the combined cooling, heating and power system to the power distribution network are respectively;
structure of electric rate:
Figure FDA0002369228960000026
Figure FDA0002369228960000027
equipment operating cost:
Figure FDA0002369228960000031
wherein KMT、KGF、KWT、KPV、KEC、KSEThe unit power running costs of a gas turbine, a gas boiler, a wind turbine generator, a photovoltaic cell, an electric refrigerator, a sewage source heat pump and an absorption refrigerator are respectively set; qAC.tThe refrigeration power of the absorption refrigerator in the t period;
equipment start-stop cost:
Figure FDA0002369228960000032
Figure FDA0002369228960000033
Figure FDA0002369228960000034
Figure FDA0002369228960000035
Figure FDA0002369228960000036
wherein the content of the first and second substances,
Figure FDA0002369228960000037
the coefficient of cut for the gas generator;
Figure FDA0002369228960000038
the rated power of the gas generator; cOP.MTFor gas turbine startup costs; cST.MTCost for gas turbine shutdown; cOP.GFThe startup cost for the gas boiler; cST.GFThe shutdown cost of the gas boiler; SSMTO.t、SSMTS.t、SSGFO.t、SSGFS.tFor start-stop coefficient, SSMTO.tAt 1, the micro gas turbine is turned on during the period t, SSMTS.t1 represents the micro gas turbine is shut down during time t; SSGFO.tIs 1, the gas boiler is started in the time period t, SSGFS.t1 represents the micro gas turbine is shut down during time t;
s3.3, synthesizing a satisfaction rate objective function and an operation cost objective function of user requirements to obtain a total objective function:
Figure FDA0002369228960000039
wherein the content of the first and second substances,
Figure FDA00023692289600000310
and δ are weight coefficients, respectively.
2. The optimization method for combined cooling, heating and power of the CCHP system of the sewage-source-containing heat pump according to claim 1, wherein the specific method of the step S4 is as follows:
s4.1 energy balance constraints
Electric balance constraint:
the electric energy is mainly provided by gas turbine, distribution network, photovoltaic cell, wind turbine generator system, and the energy storage battery plays the undulant effect of exerting oneself of stabilizing wind turbine generator system and photovoltaic cell here:
Figure FDA0002369228960000041
and (3) thermal balance constraint:
the heat energy is mainly provided by the extracted heat energy generated by the sewage source heat pump and the waste heat boiler and the heat energy of the gas boiler:
Figure FDA0002369228960000042
cold balance constraint:
the cooling load demand is mainly provided by absorption chillers and electric chillers:
Figure FDA0002369228960000043
wherein, the heat of absorption formula refrigerator is provided by exhaust-heat boiler and sewage source heat pump:
QEC.t＝(HMT.t(1-αt)ηWT+HSE.t(1-βt))ηEC
in the formula, ηECThe refrigeration efficiency of the absorption refrigerator;
s4.2, restraining the operation condition of each device:
and (3) power generation power constraint of the micro gas turbine:
Figure FDA0002369228960000044
because the micro gas generator loses the natural gas to generate electricity and has certain constraint on the gas consumption, the climbing constraint exists on the corresponding generated power:
|PMT.t-PMT.t-1|≤aPPMT，t
wherein, aPThe power generation power climbing capacity coefficient of the micro gas turbine;
and (3) output force restraint of the gas boiler:
Figure FDA0002369228960000045
wherein the content of the first and second substances,
Figure FDA0002369228960000046
rated power for the gas boiler;
output restraint of the wind turbine generator:
Figure FDA0002369228960000051
wherein the content of the first and second substances,
Figure FDA0002369228960000052
the minimum cut coefficient of the fan is set;
Figure FDA0002369228960000053
rated power for the fan;
photovoltaic cell output restraint:
Figure FDA0002369228960000054
wherein the content of the first and second substances,
Figure FDA0002369228960000055
is the rated power of the photovoltaic cell;
energy storage equipment restraint:
considering the service life problem of the energy storage battery, the lowest storage capacity of the energy storage battery is limited, the maximum charging capacity and the maximum discharging capacity of the energy storage battery are restricted in each time period, the charging and the discharging cannot be simultaneously carried out in each time period, and the energy storage device is restricted as follows:
SBAT.min＜SBAT.t＜SBAT.max
Figure FDA0002369228960000056
Figure FDA0002369228960000057
Figure FDA0002369228960000058
wherein S isBAR.tThe storage capacity of the energy storage battery at the moment t; sBAT.minThe lowest allowable amount of stored electricity for the energy storage device; sBAT.maxThe rated power of the energy storage device; y ischaAnd ydisRespectively the maximum discharge rate and the minimum charge rate of the energy storage battery;
absorption chiller output restraint:
Figure FDA0002369228960000059
wherein the content of the first and second substances,
Figure FDA00023692289600000510
is the rated power of the absorption chiller;
electric refrigerator output restraint:
Figure FDA00023692289600000511
wherein the content of the first and second substances,
Figure FDA00023692289600000512
is the rated power of the electrical refrigerator.
3. The optimization method for combined cooling, heating and power of the CCHP system of the sewage-source-containing heat pump according to claim 1, wherein the specific method of the step S5 is as follows:
s5.1: setting parameters such as population scale, iteration times and the like; setting a target function and randomly generating particle positions; training a BP neural network according to original data;
s5.2: selecting a gas turbine to run in a mode of fixing power by heat or fixing power by cold according to the water quantity and the water temperature of a sewage source;
s5.3: constructing a CCHP system model, an objective function and constraint conditions;
s5.4: carrying out transverse crossing on two non-repeated random pairings of particles in a population;
s5.5: comparing the fitness between the mediocre solution obtained after transverse crossing and the parent particles, and selecting the particles with high fitness as members of a new population;
s5.6: longitudinally crossing pairwise non-repeated random pairing between variables in each particle to obtain progeny particles;
s5.7: comparing the fitness between parent particles and child particles, and keeping the particles with high fitness;
s5.8: the iteration loop reaches a specified number of times or the precision reaches a convergence condition.
4. The CCHP system combined cooling heating and power optimization method of the sewage source heat pump according to claim 3, wherein for the S5.6 step, the following steps are adopted to improve the global search capability:
a. firstly, obtaining original data of an optimal running state according to running experience;
b. training N BP neural networks by using the obtained original data; each neural network corresponds to two different variables of the same particle, and when the input of one neural network in the N BP neural networks is d1And X (i, d)2) When output is
Figure FDA0002369228960000061
Wherein Xavg(i，d1) Denotes the d-th1The average of the dimensional variables;
Figure FDA0002369228960000062
n is the number of variables;
c. mixing X (i, d)2) Instead, it is changed into
Figure FDA0002369228960000063
The significance is to approach the cross direction to the optimal operation state.
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