CN111144633A - CCHP micro-grid operation optimization method - Google Patents
CCHP micro-grid operation optimization method Download PDFInfo
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Abstract
The invention relates to a CCHP micro-grid operation optimization method, which comprises the following steps: s1: constructing a micro-grid system model; s2: constructing a micro-grid operation data set; s3: establishing an objective function of a microgrid economic operation optimization model; s4: establishing a constraint condition of a microgrid economic operation optimization model; s5: and solving the optimal solution of the CCHP microgrid economic optimization problem based on a Q learning stress application quantum particle swarm optimization algorithm. The invention optimizes the population initialization by using the chaotic optimization algorithm, can increase the diversity of particles and the capability of searching global optimum, and is not easy to fall into the global optimum. Meanwhile, the searching capacity of each particle for the optimal solution is further improved by adopting a criss-cross algorithm, a better optimization effect can be obtained when the cold-heat-electricity combined supply micro-grid is subjected to economic optimization, the problem that local optimization is easy to fall into in the economic optimization problem is effectively solved, and a better economic optimization result is obtained.
Description
Technical Field
The invention relates to the field of microgrid economy, in particular to a CCHP microgrid operation optimization method.
Background
With the rapid development of economy, fossil fuels and other non-renewable natural resources are exhausted, the global ecological environment is continuously deteriorated, and the problem between the supply and the demand of energy in China is increasingly prominent. As the world's largest energy consumption country and carbon dioxide emission country, energy-saving power generation scheduling is developed, and a safe, efficient and clean power system is urgently established, which maximally utilizes renewable energy and other clean energy to reduce the consumption of non-renewable energy and the emission of pollutants. Among them, the combined cooling, heating and power micro-grid system is concerned by its efficient, green and economic energy utilization mode. On the other hand, it can improve the consumption rate of some renewable energy sources to a certain extent, including light energy and wind energy. The power generation capacity of renewable energy sources in China is rapidly increased, particularly the annual increase of wind power generation and solar photovoltaic power generation is kept fast, and the renewable energy sources continuously lead the world for many years.
The combined cooling heating and power system is a main form of a distributed energy technology, and is a combined production and power system which takes natural gas as primary energy, generates cold, heat and electric energy in accordance with specifications through energy gradient utilization, aims to improve energy utilization efficiency, reduces emission of carbide and harmful gas, and conforms to the general trend of coordinated development of current energy and environment. The system can make the comprehensive utilization rate of energy reach the maximum of the system under the condition of meeting the requirements of cold, heat and electricity at the same time, and is characterized in that the energy with different qualities is utilized in a gradient way, the available heat energy with high temperature and high grade is used for generating electricity, and the rest heat energy with low temperature and low grade is used for supplying heat and cooling. At present, the power supply of China mainly depends on a coal-fired large-scale thermal power plant, the power generation efficiency is about 35 percent, the power consumption and the line loss rate of the power plant are deducted, and the utilization efficiency of a terminal can only reach 30 percent; the efficiency of a common coal-fired heating boiler is about 70 percent, and the coal-fired heating boiler has increasingly serious pollution problem to the environment and causes great harm and loss to human society. And under the condition that the combined cooling, heating and power system meets the requirements of cooling, heating and power at the same time, the comprehensive utilization rate of energy can reach 90 percent. Meanwhile, the combined cooling heating and power system is used as a multi-target solution which can simultaneously solve the problems of high-efficiency utilization of energy, reduction of pollutant emission and improvement of the economical efficiency of an energy system, can effectively relieve the outstanding contradiction between economic development and energy and environment, and has wide market application prospect. Therefore, the research on the characteristics of the combined cooling heating and power system has important theoretical and practical significance.
However, the economic optimization of the operation of the CCHP microgrid is still less than ideal at present.
Disclosure of Invention
The invention provides a CCHP microgrid operation optimization method for overcoming the defect that the economy optimization of CCHP microgrid operation in the prior art is not ideal enough.
The invention utilizes a cold-heat-electricity combined supply type micro-grid system considering renewable energy consumption, which consists of micro-sources such as a wind turbine generator, a photovoltaic cell, a gas turbine, a gas boiler, a waste heat boiler, an absorption refrigeration device, an electric refrigerator, an energy storage battery and the like and cold-heat-electricity loads; has high practicability and rationality.
The invention aims to solve the technical problem of providing a CCHP (combination cooling-heating and power cogeneration system) microgrid operation optimization method. The traditional particle swarm optimization algorithm is widely applied due to the simple method and the high convergence speed, but the particle swarm optimization algorithm is not a global optimization algorithm, so the optimization performance of the particle swarm optimization algorithm is improved from the following four points. Firstly, initialization processing is carried out on a population by adopting the ergodicity of a chaotic algorithm, and the diversity of particles is increased. Second, a traditional quantum-behaved particle swarm optimization is optimized using an improved Q-learning algorithm. Thirdly, a method combining a crisscross algorithm with an improved quantum particle swarm optimization algorithm is provided. Fourthly, the improved quantum particle swarm optimization algorithm is applied to the field of micro-grid economic operation.
To solve the above technical problem, the method of the present invention comprises the steps of:
s1: constructing a micro-grid system model;
s2: constructing a micro-grid operation data set;
s3: establishing an objective function of a microgrid economic operation optimization model;
s4: establishing a constraint condition of a microgrid economic operation optimization model;
s5: and solving the optimal solution of the CCHP microgrid economic optimization problem based on a Q learning stress application quantum particle swarm optimization algorithm.
Preferably, the microgrid system model constructed in S1 includes a photovoltaic cell pack output power model, a wind power generator output power model, a gas generator output power model, an energy storage battery output power model, a gas boiler output power model, an electric refrigerator output power model, an absorption refrigerator output power model, and a heat recovery system output power model.
Preferably, the microgrid operation data set in S2 includes collecting data of all devices in the microgrid to form a microgrid-related data set, where the data includes illumination intensity and wind speed subject to weibull distribution generated by monte carlo algorithm, historical microgrid cold load, heat load and electrical load data, wind turbine generator, photovoltaic cell, gas generator, gas boiler, absorption refrigeration equipment, electrical refrigerator, storage battery, and operation and equipment parameters of the large power grid, as well as various cost parameters and emission standard coefficients;
the shape parameter and the scale parameter of the wind speed calculation formula are fitted through historical wind speed, and the two parameters of the solar radiation intensity calculation formula are fitted through historical illumination intensity; and finally, simulating the output power of the photovoltaic cell and the output power of the wind driven generator according to the relation between the wind speed, the illumination intensity and the output power.
Preferably, the objective function of the microgrid economic operation optimization model in S3 is as follows:
min f＝F1+F2
wherein f represents the total operating cost of the microgrid for one month; m represents the number of days of a month, n represents the time period of a day, consisting of morning, noon, afternoon and evening; t is1,n(n ═ 1, 2, 3, 4) represents the length of time that the wind turbine is in use during time period n; f. of1,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the wind turbine over time period n; t is2,n(n ═ 1, 2, 3, 4) represents the length of time that the photovoltaic cell is in use during period n; f. of2,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the photovoltaic cell over time period n; t is3,n(n ═ 1, 2, 3, 4) represents the length of time that the gas generator is in use during period n; f. of3,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the gas generator over time period n; t is4,n(n-1, 2, 3, 4) represents the length of time that the energy storage battery is in use during period n; f. of4,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the energy storage battery over time period n; t is5,n(n ═ 1, 2, 3, 4) represents the length of time that the gas boiler has been in use during period n; f. of5,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the gas boiler over time period n; t is6,n(n ═ 1, 2, 3, 4) represents the length of time that the electric refrigerator is in use during period n; f. of6,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the electric refrigerator over time period n; t is7,n(n ═ 1, 2, 3, 4) represents the length of time that the absorption chiller is in use during period n; f. of7,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the absorption chiller over time period n; f. of8,n(n ═ 1, 2, 3, 4) represents the interaction cost of the period n microgrid with the large power grid; f. of9,n(n is 1, 2, 3, 4) represents the total pollution discharge cost of each unit in the time period n; cfuelIs the fuel price; n ispgu,tNumber of gas turbines connected to the microgrid for time t, αpgu,i,tThe operating state of the gas turbine i at the time t is represented by a value range of {0, 1}, wherein the value of 0 represents the stop working state, the value of 1 represents the normal working state, and F represents the normal working stateboi,m,tThe output power of the mth gas boiler in the time period t.
Preferably, the constraints of the microgrid economic operation optimization model established in S4 include: the system comprises a micro-source capacity constraint, a micro-source output power constraint, a storage battery charge and discharge constraint, an electric refrigerator constraint, an absorption refrigerator constraint, a service time constraint, an exhaust gas amount constraint and a micro-grid energy balance constraint.
Preferably, the first and second electrodes are formed of a metal,
(1) micro-source capacity constraint:
in the formula:
respectively the lower limit of the installation capacity of the photovoltaic, the fan, the PGU and the boiler,
respectively the upper limit of the installation capacity of the photovoltaic, the fan, the PGU and the boiler; n is a radical ofPVTotal number of photovoltaic units, NWTIs the total number of wind turbine generators, NpguIs the total number of gas turbines, NboiThe total number of the boilers; pPV,jFor photovoltaic installation of capacity, PWT,kFor fan installation capacity, Ppgu,iFor PGU installation capacity, Pboi,mCapacity is installed for the boiler;
(2) and (3) micro-source output power constraint:
in the formula:
respectively the lower limits of the output power of the photovoltaic generator, the fan, the PGU and the boiler;
respectively the upper limits of the output power of the photovoltaic generator, the fan, the PGU and the boiler;
(3) and (3) charge and discharge restraint of the storage battery:
in the formula:
represents the lower limit of the state of charge of the l-th group of batteries,
represents the upper limit of the state of charge of the first group of storage batteries; lambda [ alpha ]1For charging the accumulator by a factor of lambda2The discharge coefficient of the storage battery is set as,
the rated capacity of the first group of storage batteries,
respectively the charging and discharging power of the first group of storage batteries in the t period;
(4) the electric refrigerator restrains:
in the formula (I), the compound is shown in the specification,
the maximum refrigerating power of the electric refrigerator;
(5) restraint of the absorption refrigerator:
in the formula (I), the compound is shown in the specification,
the maximum refrigeration power of the absorption refrigerator;
(6) using a time constraint:
wherein, T1minThe shortest operation time of the wind turbine generator in one day is set; t is1maxThe maximum operation time of the wind turbine generator in one day is set; t is2minThe shortest operation time of the photovoltaic cell in one day; t is2maxThe maximum operation time of the photovoltaic cell in one day; t is3minThe shortest operation time of the electric refrigerator in one day; t is3maxThe maximum operation time of the electric refrigerator in one day; t is4minThe shortest operation time of the energy storage battery in one day is set; t is4maxThe maximum operation time of the energy storage battery in one day is set; t is5minThe shortest operating time of the gas turbine in one day; t is5maxThe maximum operating duration of the gas turbine per day; t is6minThe shortest operation time in one day of the gas boiler; t is6maxThe maximum operation time of the gas-fired boiler in one day; t is7minThe shortest operation time in one day of the gas boiler; t is7maxThe maximum operation time of the gas-fired boiler in one day;
(7) restriction of the amount of exhaust gas discharged:
Ggb≤Ggb.max
Gbl≤Gbl.max
wherein G isgbTotal amount of waste emissions for a gas turbine a day; ggb.maxThe maximum total waste emission of the gas turbine is one day; gblThe total amount of the waste emission of the gas-fired boiler in one day; gbl.maxThe maximum total waste discharge amount of the gas-fired boiler in one day;
(8) energy balance constraint of the microgrid:
the operation strategy of the microgrid of the invention is as follows: the electric load in the microgrid is completely provided by a micro source in the system, namely the gas turbine, the photovoltaic cell, the wind driven generator and the energy storage battery are jointly born, the electric energy of the system is firstly supplied by the photovoltaic cell and the wind driven generator, if the electric energy is insufficient, the energy storage battery is firstly used for supplementing, and when the electric quantity of the energy storage battery is exhausted, the gas turbine is started to supplement the insufficient electric energy for the system; if the electric energy generated by the photovoltaic cell and the wind driven generator is insufficient, purchasing the electric energy from a large power grid; the heat load of the system is mainly provided by a gas turbine and a gas boiler, and when the heat energy generated by the micro source is far greater than the heat energy required by the system, the multi-waste heat energy is recovered by a heat recovery system or the heat energy is converted into cold energy for storage by using an absorption refrigerator; the cold load of the system is mainly provided by a gas generator, an electric refrigerator and an absorption refrigerator, the gas turbine is used for supplying firstly, if the gas turbine is short of supply, the cold energy stored in the system is firstly used for providing the cold energy for the system, if the stored cold energy is short of supply, the electric refrigerator is started to provide the cold energy for the system, and if the cold energy generated by the system is surplus, the surplus cold energy is stored.
(a) The electric energy balance constraint is as follows:
wherein n ispv,t、nWT,t、npgu,tα total number of photovoltaic cells, number of fans and number of gas turbines which are connected into the microgrid at the moment t respectivelypv,m,t、αWT,k,t、αpgu,i,tThe operating states of the photovoltaic cell m, the fan k and the gas turbine i at the moment t are respectively in a value range of {0, 1}, wherein the value of 0 represents the stop working state, and the value of 1 represents the normal working state; pgrid,tElectric energy interacted with a large power grid in a time period t; n is a radical ofBarIs the total number of battery packs, Pgrid,tIs the interaction power with the large power grid at the moment t, Pload,tPower demand for the load at time t;
Ppv,m,tthe expression is the output power of a unit photovoltaic cell:
wherein r is the actual solar illumination intensity; a and b are shape parameters of Beta distribution; r ism,tRepresenting the actual output power of the mth photovoltaic cell in the t period and the actual illumination intensity of the geographic position of the photovoltaic cell; r ismaxRepresents the maximum illumination intensity; ppv,N＝rmaxS- η represents the photovoltaic power generation output when the intensity of sunlight is maximum, wherein S represents the area of the photovoltaic cell array, and η represents the photoelectric conversion efficiency;
PWT,k,tfor the actual output power of the kth fan in the period t, the expression is as follows:
in the formula: v. ofk,tThe actual wind speed of the geographical position of the kth fan in the time period t is obtained;
rated wind speed, cut-in wind speed and cut-out wind speed of a kth fan are set;
Ppgu,i,toutputting electric power for the ith gas generator in a t period;
the charging and discharging power of the l group of storage batteries in the t period is respectively represented by the following expressions:
in the formula: sbat,l,t、Sbat,l,t-1The charge states of the first group of storage batteries in t and t-1 periods, deltabat,lIs the self-discharge rate of the l-th group of storage batteries,
respectively the charge-discharge efficiency of the first group of storage batteries;
(b) thermal energy balance constraint:
wherein n isboi,tTotal number of gas boiler operations at time t, αpgu,m,tTotal number of gas turbine operations for time t, αboi,m,tThe operation state of the mth gas boiler at the moment tth;
Qrec,i,tthe waste heat recovery quantity of the ith gas generator in the t period is as follows:
Fpgu,i,t＝Ppgu,i,t/ηpgu,i
Qrec,i,t＝Ppgu,i,t(1-ηpgu,i)ηrec,i/ηpgu,i
in the formula ηpgu,i、ηrec,iThe power generation efficiency and the heat recovery efficiency of the ith gas generator are respectively; ppgu,i,tOutputting electric power for the ith gas generator in a t period; fpgu,i,tFuel consumption of the ith gas generator in a t period;
Qboi,m,tfor the heat production quantity of the mth gas boiler in the t period, the expression is as follows:
Fboi,m,t＝Qboi,m,t/ηboi,m
in the formula, Fboi,m,tη for the fuel consumption of the mth gas boiler during the period tboi,mThe thermal efficiency of the mth gas boiler;
QHrs,tfor the thermal power provided by the heat recovery system in the period t, the expression is as follows:
in the formula, QHrs,tThermal power for the heat recovery system during time t ηHrs,tIs the heat recovery efficiency of the heat recovery system in the time period t; n is a radical ofGtThe number of operating gas turbines at time t;
(c) cold energy balance constraint:
wherein Q isAc,tFor the cold power provided by the absorption refrigerator in the period t, the expression is as follows:
in the formula (I), the compound is shown in the specification,
cold power provided to the heat recovery system during time t;
providing cold power for the gas boiler in a time period t; cOPAc,tThe energy efficiency ratio of the electric refrigerator in the time period t;
QEc,tfor the cold power provided by the electric refrigerator in the period t, the expression is as follows:
QEc,t＝PEc,tCOPEc,t
in the formula, PEc,tPower for the electric refrigerator to refrigerate during a time period t; cOPEc,tThe energy efficiency ratio of the electric refrigerator in the time period t;
the demand of the system cooling load is a period t.
Preferably, S5 includes the steps of:
s5.1, initializing parameter setting, including expansion and contraction coefficient β value range, maximum iteration times of an algorithm, a discount factor gamma, the step number m of forward looking required for calculating a Q value, randomly generated initial solutions x (i), setting of local optimal solutions, and calculating of a global optimal solution;
s5.2: calculating local attractors: introducing quantum state particle behaviors, and obtaining the positions of particles by using a Monte Carlo method according to a probability density equation and a distribution function;
s5.3: calculating an average optimal solution;
s5.4: updating the new position of each particle in the particle swarm;
s5.5, selecting an optimal parameter strategy β according to a Q learning method;
s5.6: updating the position value and generating a new group;
s5.7: returning to the step S5.5 for circulation until the iteration is finished; and the output optimal solution is the optimal solution in the economic optimization problem, the optimal solution is substituted into an objective function of economic optimization, and the minimum cost in the optimal solution is calculated.
Preferably, S5.1 comprises:
s5.1.1 randomly generates an initial solution.
And carrying out chaotic treatment on the initial particle population. Secondly, reducing the particles to a solving range through a mapping formula to form an improved particle swarm initialization matrix, and expressing the improved particle swarm initialization matrix by a matrix B:
wherein xijA j-dimension value representing the i-th particle, i 1.. n, j 1.. d;
s5.1.2 calculating population fitness value, and recording local optimum particle piAnd a globally optimal particle pg。
Preferably, S5.5 comprises:
s5.5.1 for each new particle, generating n new offspring particles using a given n parameter selection strategy, and setting t to 1;
s5.5.2 when t<m, each descendant uses the formula Li,j(t)＝2β|mj(t)-Xi,j(t) | generating n new offspring and calculating their fitness values; wherein m is the number of steps needed to look ahead for calculating the Q value; m isjThe average optimum position of the jth particle, β is the expansion and contraction coefficient, Xi,j(t) is the j dimension value of the ith particle in the t step;
s5.5.3 performing criss-cross operation on n individuals and calculating the fitness value of the individuals;
wherein, the criss-cross operation comprises a transverse criss-cross operation and a longitudinal criss-cross operation; calculating the fitness values of the n individuals after the longitudinal and transverse interlacing operation;
s5.5.4: selecting an individual corresponding to the minimum fitness value from the fitness values of the individuals directly subjected to Q learning and the fitness values subjected to Q learning and then to cross operation, and reserving the individual as a new individual; let t + +;
s5.5.5, calculating Q value corresponding to each parameter selection strategy, selecting β value corresponding to the parameter selection strategy which maximizes Q as current β value, and discarding other n-1 β values.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the invention optimizes the population initialization by using the chaotic optimization algorithm, can increase the diversity of particles and the capability of searching global optimum, and is not easy to fall into the global optimum. Meanwhile, the searching capacity of each particle for the optimal solution is further improved by adopting a criss-cross algorithm, a better optimization effect can be obtained when the cold-heat-electricity combined supply micro-grid is subjected to economic optimization, the problem that local optimization is easy to fall into in the economic optimization problem is effectively solved, and a better economic optimization result is obtained.
Drawings
Fig. 1 is a flowchart of a CCHP microgrid operation optimization method of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a CCHP microgrid operation optimization method, which comprises the following steps:
s1: constructing a micro-grid system model;
s2: constructing a micro-grid operation data set;
s3: establishing an objective function of a microgrid economic operation optimization model;
s4: establishing a constraint condition of a microgrid economic operation optimization model;
s5: and solving the optimal solution of the CCHP microgrid economic optimization problem based on a Q learning stress application quantum particle swarm optimization algorithm.
The method of this embodiment is specifically described below with reference to fig. 1:
s1 in fig. 1 depicts constructing a microgrid system model. The micro-grid system model comprises a photovoltaic battery pack output power model, a wind driven generator output power model, a gas generator output power model, an energy storage battery output power model, a gas boiler output power model, an electric refrigerator output power model, an absorption refrigerator output power model and a heat recovery system output power model.
Step 2 in fig. 1 describes the construction of a microgrid operating dataset. The microgrid operation data set comprises data of all devices in a microgrid, and related data sets of the microgrid are formed, wherein the data comprise illumination intensity generated by a Monte Carlo algorithm and wind speed conforming to Weibull distribution, historical microgrid cold load, heat load and electric load data, operation and device parameters of a wind turbine generator, a photovoltaic cell, a gas generator, a gas boiler, absorption refrigeration equipment, an electric refrigerator, a storage battery and a large power grid, and various cost parameters and emission standard coefficients. The shape parameter and the scale parameter of the wind speed calculation formula are fitted through historical wind speed, and the two parameters of the solar radiation intensity calculation formula are fitted through historical illumination intensity; and finally, simulating the output power of the photovoltaic cell and the output power of the wind driven generator according to the relation between the wind speed, the illumination intensity and the output power.
S3 in fig. 1 describes an objective function for establishing optimization of operation of the microgrid. The objective function of the microgrid economic operation optimization model is as follows:
min f＝F1+F2
wherein f represents the total operating cost of the microgrid for one month; m represents the number of days of a month, n represents the time period of a day consisting of morning, noon, afternoon and eveningComposition is carried out; t is1,n(n ═ 1, 2, 3, 4) represents the length of time that the wind turbine is in use during time period n; f. of1,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the wind turbine over time period n; t is2,n(n ═ 1, 2, 3, 4) represents the length of time that the photovoltaic cell is in use during period n; f. of2,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the photovoltaic cell over time period n; t is3,n(n ═ 1, 2, 3, 4) represents the length of time that the gas generator is in use during period n; f. of3,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the gas generator over time period n; t is4,n(n-1, 2, 3, 4) represents the length of time that the energy storage battery is in use during period n; f. of4,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the energy storage battery over time period n; t is5,n(n ═ 1, 2, 3, 4) represents the length of time that the gas boiler has been in use during period n; f. of5,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the gas boiler over time period n; t is6,n(n ═ 1, 2, 3, 4) represents the length of time that the electric refrigerator is in use during period n; f. of6,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the electric refrigerator over time period n; t is7,n(n ═ 1, 2, 3, 4) represents the length of time that the absorption chiller is in use during period n; f. of7,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the absorption chiller over time period n; f. of8,n(n ═ 1, 2, 3, 4) represents the interaction cost of the period n microgrid with the large power grid; f. of9,n(n is 1, 2, 3, 4) represents the total pollution discharge cost of each unit in the time period n; cfuelIs the fuel price; n ispgu,tNumber of gas turbines connected to the microgrid for time t, αpgu,i,tThe operating state of the gas turbine i at the time t is represented by a value range of {0, 1}, wherein the value of 0 represents the stop working state, the value of 1 represents the normal working state, and F represents the normal working stateboi,m,tThe output power of the mth gas boiler in the time period t.
S4 in fig. 1 describes the constraints for building the microgrid economic operation optimization model. The following constraints are included:
(1) micro-source capacity constraint:
in the formula:
lower and upper limits of photovoltaic, fan, PGU and boiler installation capacities, NPVTotal number of photovoltaic units, NWTIs the total number of wind turbine generators, NpguIs the total number of gas turbines, NboiIs the total number of boilers.
(2) And (3) micro-source output power constraint:
in the formula:
the lower limit and the upper limit of the output power of the photovoltaic power generator, the fan power generator, the PGU and the boiler are respectively.
(3) And (3) charge and discharge restraint of the storage battery:
in the formula:
respectively representing the lower and upper limits, lambda, of the state of charge of the group I storage batteries1For charging the accumulator by a factor of lambda2The discharge coefficient of the storage battery.
(4) The electric refrigerator restrains:
in the formula (I), the compound is shown in the specification,
the maximum refrigerating power of the electric refrigerator;
(5) restraint of the absorption refrigerator:
in the formula (I), the compound is shown in the specification,
the maximum refrigeration power of the absorption refrigerator;
(6) using a time constraint:
wherein, T1minThe shortest operation time of the wind turbine generator in one day is set; t is1maxThe maximum operation time of the wind turbine generator in one day is set; t is2minThe shortest operation time of the photovoltaic cell in one day; t is2maxThe maximum operation time of the photovoltaic cell in one day; t is3minFor an electric refrigeratorThe medium and shortest running time; t is3maxThe maximum operation time of the electric refrigerator in one day; t is4minThe shortest operation time of the energy storage battery in one day is set; t is4maxThe maximum operation time of the energy storage battery in one day is set; t is5minThe shortest operating time of the gas turbine in one day; t is5maxThe maximum operating duration of the gas turbine per day; t is6minThe shortest operation time in one day of the gas boiler; t is6maxThe maximum operation time of the gas-fired boiler in one day; t is7minThe shortest operation time in one day of the gas boiler; t is7maxThe maximum operation time of the gas boiler in one day, and the meanings of the other variables are the same as those of the gas boiler.
(7) Restriction of the amount of exhaust gas discharged:
Ggb≤Ggb.max
Gbl≤Gbl.max
wherein G isgbTotal amount of waste emissions for a gas turbine a day; ggb.maxThe maximum total waste emission of the gas turbine is one day; gblThe total amount of the waste emission of the gas-fired boiler in one day; gbl.maxThe maximum total waste discharge amount of the gas boiler in one day.
(8) And (5) energy balance constraint of the microgrid.
The operation strategy of the microgrid of the invention is as follows: the electric load in the microgrid is completely provided by a micro source in the system, namely the gas turbine, the photovoltaic cell, the wind driven generator and the energy storage battery are jointly born, the electric energy of the system is firstly supplied by the photovoltaic cell and the wind driven generator, if the electric energy is insufficient, the energy storage battery is firstly used for supplementing, and when the electric quantity of the energy storage battery is exhausted, the gas turbine is started to supplement the insufficient electric energy for the system; if the electric energy generated by the photovoltaic cell and the wind driven generator is insufficient, purchasing the electric energy from a large power grid; the heat load of the system is mainly provided by a gas turbine and a gas boiler, and when the heat energy generated by the micro source is far greater than the heat energy required by the system, the multi-waste heat energy is recovered by a heat recovery system or the heat energy is converted into cold energy for storage by using an absorption refrigerator; the cold load of the system is mainly provided by a gas generator, an electric refrigerator and an absorption refrigerator, the gas turbine is used for supplying firstly, if the gas turbine is short of supply, the cold energy stored in the system is firstly used for providing the cold energy for the system, if the stored cold energy is short of supply, the electric refrigerator is started to provide the cold energy for the system, and if the cold energy generated by the system is surplus, the surplus cold energy is stored.
(a) The electric energy balance constraint is as follows:
wherein N isBarIs the total number of battery packs, Pgrid,tIs the interaction power with the large power grid at the moment t, Pload,tPower demand for the load at time t;
Ppv,m,tthe expression is the output power of a unit photovoltaic cell:
wherein r is the actual solar illumination intensity; a and b are shape parameters of Beta distribution; r ism,tRepresenting the actual output power of the mth photovoltaic cell in the t period and the actual illumination intensity of the geographic position of the photovoltaic cell; r ismaxRepresents the maximum illumination intensity; ppv,N＝rmaxS- η represents the photovoltaic power generation output when the intensity of sunlight is maximum, wherein S represents the area of the photovoltaic cell array, and η represents the photoelectric conversion efficiency;
PWT,k,tfor the actual output power of the kth fan in the period t, the expression is as follows:
in the formula: v. ofk,tThe actual wind speed of the geographical position of the kth fan in the time period t is obtained;
rated wind speed, cut-in wind speed and cut-out wind speed of a kth fan are set;
Ppgu,i,toutputting electric power for the ith gas generator in a t period;
the charging and discharging power of the l group of storage batteries in the t period is respectively represented by the following expressions:
in the formula: sbat,l,t、Sbat,l,t-1The charge states of the first group of storage batteries in t and t-1 periods, deltabat,lIs the self-discharge rate of the l-th group of storage batteries,
the charge-discharge efficiency of the first group of storage batteries,
rated capacity of the first group of storage batteries;
npv,t、nWT,t、npgu,tα total number of photovoltaic cells, number of fans and number of gas turbines which are connected into the microgrid at the moment t respectivelypv,m,t、αWT,k,t、αpgu,i,tThe operating states of the photovoltaic cell m, the fan k and the gas turbine i at the moment t are respectively in a value range of {0, 1}, wherein the value of 0 represents the stop working state, and the value of 1 represents the normal working state; pgrid,tThe electric energy is interacted with a large power grid in a time period t.
(b) Thermal energy balance constraint:
wherein n isboi,tTotal number of gas boiler operations at time t, αpgu,m,tTotal number of gas turbine operations for time t, αboi,m,tThe operation state of the mth gas boiler at the moment tth;
Qrec,i,tthe waste heat recovery quantity of the ith gas generator in the t period is as follows:
Fpgu,i,t＝Ppgu,i,t/ηpgu,i
Qrec,i,t＝Ppgu,i,t(1-ηpgu,i)ηrec,i/ηpgu,i
in the formula ηpgu,i、ηrec,iThe power generation efficiency and the heat recovery efficiency of the ith gas generator are respectively; ppgu,i,tOutputting electric power for the ith gas generator in a t period; fpgu,i,tFuel consumption of the ith gas generator in a t period;
Qboi,m,tfor the heat production quantity of the mth gas boiler in the t period, the expression is as follows:
Fboi,m,t＝Qboi,m,t/ηboi,m
in the formula, Fboi,m,tη for the fuel consumption of the mth gas boiler during the period tboi,mThe thermal efficiency of the mth gas boiler;
QHrs,tfor the thermal power provided by the heat recovery system in the period t, the expression is as follows:
in the formula, QHrs,tThermal power for the heat recovery system during time t ηHrs,tIs the heat recovery efficiency of the heat recovery system in the time period t; n is a radical ofGtThe number of operating gas turbines at time t;
αboi,m,t、αpgu,i,trepresents the mth gas in the time period tThe operating states of the boiler and the ith gas turbine are in a value range of {0, 1},
the demand for the system thermal load is a time period t.
(c) Cold energy balance constraint:
wherein Q isAc,tFor the cold power provided by the absorption refrigerator in the period t, the expression is as follows:
in the formula (I), the compound is shown in the specification,
cold power provided to the heat recovery system during time t;
providing cold power for the gas boiler in a time period t; cOPAc,tThe energy efficiency ratio of the electric refrigerator in the time period t;
QEc,tfor the cold power provided by the electric refrigerator in the period t, the expression is as follows:
QEc,t＝PEc,tCOPEc,t
in the formula, PEc,tPower for the electric refrigerator to refrigerate during a time period t; cOPEc,tThe energy efficiency ratio of the electric refrigerator in the time period t;
the demand of the system cooling load is a period t.
S5 in fig. 1 describes solving the optimal solution of the CCHP microgrid economic optimization problem based on the Q learning boost quantum particle swarm optimization algorithm.
S5.1: the input model parameters initialize the parameter set sum.
The parameters of the model comprise operating parameters including operating cost parameters of a micro-grid system wind turbine generator, a photovoltaic cell, an electric refrigerator, an absorption refrigerator, an energy storage battery, a gas turbine and a gas boiler, interactive operating cost and waste gas emission parameters of a large power grid, daily use time of each unit and fuel price, initialization parameter setting including expansion and contraction coefficient β value range, maximum iteration times of an algorithm, a discount factor gamma, step number m needed by calculating a Q value and needed by looking forward, randomly generated initial solutions x (i), setting of local optimal solutions and calculation of global optimal solutions.
In this embodiment, the expansion and contraction coefficient β is set to be {0.6-1.2}, the maximum iteration number of the algorithm is 5000, the discount factor γ is 0.8, and the number of steps m needed to look ahead to calculate the Q value is 10.
During initialization, the running cost f of the wind turbine generator in the microgrid model1,nOperating costs f of the photovoltaic cells2,nOperating cost f of gas generator3,nOperating cost f of energy storage battery4,nOperating cost f of gas boiler5,nRunning cost f of electric refrigerator6,nRunning cost f of absorption refrigerator7,nInteraction cost f of micro-grid and large power grid8,nAnd total sewage disposal cost f9,nCollectively as an individual, the length of the particle is 9, the total number of the individual is set to be 200, and a null matrix B of 200 × 9 is set, wherein the matrix B is:
(a) an initial solution is randomly generated. And carrying out chaotic treatment on the initial particle population. Secondly, reducing the particles to a solving range through a mapping formula to form an improved particle swarm initialization matrix, and expressing the improved particle swarm initialization matrix by a matrix C:
wherein xijThe j-th dimension value of the i-th particle is represented, i 1.. n, j 1.. d. Performing chaos processing on the matrix B by adopting a Logistic equation pair, wherein the Logistic equation pair isThe process is as follows:
xn+1＝μxn(1-xn)
where μ is the control variable, xnIn (0, 1), the fixed point which can not be a chaotic variable is 0.25, 0.5, 0.75;
secondly, the matrix B after the chaos processing is restored to a solution space through the following formula,
xi＝zi(ximax-ximin)+ximin、
finally, the matrix B is an initialization matrix for improving the particle swarm.
(b) Calculating population fitness value and recording local optimal particles piAnd a globally optimal particle pg. And calculating the population fitness value to obtain an objective function of the microgrid model.
S5.2: and calculating local attractors. The calculation formula for obtaining the local attractor by simplifying the classical particle swarm optimization algorithm is as follows:
in the formula, pi,jIs a local optimal solution; p is a radical ofg,jIs a global optimal solution.
(a) Introducing quantum state particle behavior. And (4) according to the probability density equation and the distribution function, obtaining the position of the particle by using a Monte Carlo method.
Wherein, probability density equation Q and distribution function F, the equation is defined as:
Li,j(t)＝2β|mj(t)-Xi,j(t)|
obtaining the particle position through a Monte Carlo simulation algorithm;
wherein, the formula is:
s5.3: and calculating an average optimal solution. Wherein, the formula for calculating the average optimal solution is as follows:
where M is the size of the algorithm population, PiIs a locally optimal solution for particle i.
S5.4: the new positions of the individual particles in the population are updated.
The formula for updating the new position of the particle swarm is as follows:
Xi,j(t+1)＝pi,j±β|mj(t)-Xi,j(t)|·ln(1/u)
s5.5, selecting an optimal parameter strategy β according to the Q learning method.
S5.5.1: for each new particle, generating n new offspring particles using a given n parameter selection strategy, and setting t to 1;
the calculation formula of the parameter selection strategy is as follows:
α＝(α0-α1)×(t/MaxIter)+α1
s5.5.2: when t is<m Each offspring using the formula Li,j(t)＝2β|mj(t)-Xi,j(t) | generating n new offspring and calculating their fitness values;
wherein m is the number of steps needed to look ahead for calculating the Q value; m isjThe average optimum position of the jth particle, β is the expansion and contraction coefficient, Xi,j(t) is in the t-th stepThe value of the j dimension of the ith particle of (1).
S5.5.3: the n individuals are subjected to a crisscross operation, and fitness values thereof are calculated. Wherein, the criss-cross operation comprises a transverse criss-cross operation and a longitudinal criss-cross operation.
And (3) transverse crossing by adopting a formula:
Mhc(i,d)＝r1X(i,d)+(1-r1)X(j,d)+c1(X(i,d)-X(j,d))
Mhc(j,d)＝r2X(j,d)+(1-r2)X(i,d)+c2(X(j,d)-X(i,d))
in the formula, c1,c2Is [ -1,1 [ ]]A random number of (c); r is1,r2Is [ -1,1 [ ]]A random number of (c); x (i), X (j) are dimensions d of parent particles X (i), X (j), respectively; mhc(i,d),MhcAnd (j, d) are respectively X (i, d), and the d-dimension filial generation generated by the transverse intersection of X (j, d).
Longitudinal crossing, adopting a formula:
Mvc(i,d1)＝rX(i,d1)+(1-r)X(i,d2)
r is [0,1 ]]A random number of (c); mvc (i, d)1) D being particles i1And d2Dimension is the daughter particle produced by longitudinal crossing.
S5.5.4: selecting an individual corresponding to the minimum fitness value from the fitness values of the individuals directly subjected to Q learning and the fitness values subjected to Q learning and then to cross operation, and reserving the individual as a new individual; let t + +;
s5.5.5, calculating the Q value corresponding to each parameter selection strategy, selecting the β value corresponding to the parameter selection strategy which maximizes the Q as the current β value, and discarding the other n-1 β values, wherein the Q value is calculated as follows:
Q(a)＝r(a)+γQ(a(1))+γ2·Q(a(2))+Λγm·Q(a(m))
wherein m represents the number of steps taken to look forward; a, a(i)Belongs to A, and i is more than or equal to 1 and less than or equal to m.
S5.6: the location value is updated and a new population is generated.
S5.7: and returning to the step S5.5 for circulation until the iteration is finished, and outputting the optimal solution.
And solving the optimal solution of the economic optimization problem according to the improved quantum particle swarm optimization algorithm model, wherein the output optimal solution is the optimal solution in the economic optimization problem, substituting the optimal solution into an objective function of economic optimization, and calculating the minimum cost of the optimal solution.
The terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. A CCHP microgrid operation optimization method is characterized by comprising the following steps:
s1: constructing a micro-grid system model;
s2: constructing a micro-grid operation data set;
s3: establishing an objective function of a microgrid economic operation optimization model;
s4: establishing a constraint condition of a microgrid economic operation optimization model;
s5: and solving the optimal solution of the CCHP microgrid economic optimization problem based on a Q learning stress application quantum particle swarm optimization algorithm.
2. The CCHP microgrid operation optimization method according to claim 1, wherein the microgrid system model constructed in S1 comprises a photovoltaic cell pack output power model, a wind driven generator output power model, a gas generator output power model, an energy storage battery output power model, a gas boiler output power model, an electric refrigerator output power model, an absorption refrigerator output power model and a heat recovery system output power model.
3. The CCHP microgrid operation optimization method of claim 2, wherein the microgrid operation data set in S2 includes collecting data of all devices in the microgrid, forming a microgrid-related data set, the data including illumination intensity and wind speed subject to weibull distribution generated by monte carlo algorithm, obtaining historical microgrid cooling load, thermal load and electrical load data, operation and device parameters of wind turbines, photovoltaic cells, gas generators, gas boilers, absorption refrigeration equipment, electric refrigerators, storage batteries and the large power grid, and various cost parameters and emission standard coefficients;
the shape parameter and the scale parameter of the wind speed calculation formula are fitted through historical wind speed, and the two parameters of the solar radiation intensity calculation formula are fitted through historical illumination intensity; and finally, simulating the output power of the photovoltaic cell and the output power of the wind driven generator according to the relation between the wind speed, the illumination intensity and the output power.
4. The CCHP microgrid operation optimization method according to claim 3, wherein an objective function of the microgrid economic operation optimization model S3 is as follows:
min f＝F1+F2
wherein f represents the total operating cost of the microgrid for one month; m represents the number of days of a month, n represents the time period of a day, and is selected from the group consisting of morning, noon, afternoon, and eveningForming; t is1,n(n ═ 1, 2, 3, 4) represents the length of time that the wind turbine is in use during time period n; f. of1,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the wind turbine over time period n; t is2,n(n ═ 1, 2, 3, 4) represents the length of time that the photovoltaic cell is in use during period n; f. of2,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the photovoltaic cell over time period n; t is3,n(n ═ 1, 2, 3, 4) represents the length of time that the gas generator is in use during period n; f. of3,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the gas generator over time period n; t is4,n(n-1, 2, 3, 4) represents the length of time that the energy storage battery is in use during period n; f. of4,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the energy storage battery over time period n; t is5,n(n ═ 1, 2, 3, 4) represents the length of time that the gas boiler has been in use during period n; f. of5,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the gas boiler over time period n; t is6,n(n ═ 1, 2, 3, 4) represents the length of time that the electric refrigerator is in use during period n; f. of6,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the electric refrigerator over time period n; t is7,n(n ═ 1, 2, 3, 4) represents the length of time that the absorption chiller is in use during period n; f. of7,n(n ═ 1, 2, 3, 4) represents the unit operating cost of the absorption chiller over time period n; f. of8,n(n ═ 1, 2, 3, 4) represents the interaction cost of the period n microgrid with the large power grid; f. of9,n(n is 1, 2, 3, 4) represents the total pollution discharge cost of each unit in the time period n; cfuelIs the fuel price; n ispgu,tNumber of gas turbines connected to the microgrid for time t, αpgu,i,tThe operating state of the gas turbine i at the time t is represented by a value range of {0, 1}, wherein the value of 0 represents the stop working state, the value of 1 represents the normal working state, and F represents the normal working stateboi,m,tThe output power of the mth gas boiler in the time period t.
5. The CCHP microgrid operation optimization method according to claim 4, wherein the constraint conditions of the microgrid economic operation optimization model established in the S4 include: the system comprises a micro-source capacity constraint, a micro-source output power constraint, a storage battery charge and discharge constraint, an electric refrigerator constraint, an absorption refrigerator constraint, a service time constraint, an exhaust gas amount constraint and a micro-grid energy balance constraint.
6. The CCHP microgrid operation optimization method of claim 5,
(1) micro-source capacity constraint:
in the formula:
respectively the lower limit of the installation capacity of the photovoltaic, the fan, the PGU and the boiler,
respectively the upper limit of the installation capacity of the photovoltaic, the fan, the PGU and the boiler; n is a radical ofPVTotal number of photovoltaic units, NWTIs the total number of wind turbine generators, NpguIs the total number of gas turbines, NboiThe total number of the boilers; pPV,jFor photovoltaic installation of capacity, PWT,kFor fan installation capacity, Ppgu,iFor PGU installation capacity, Pboi,mCapacity is installed for the boiler;
(2) and (3) micro-source output power constraint:
in the formula:
respectively the lower limits of the output power of the photovoltaic generator, the fan, the PGU and the boiler;
respectively the upper limits of the output power of the photovoltaic generator, the fan, the PGU and the boiler;
(3) and (3) charge and discharge restraint of the storage battery:
in the formula:
represents the lower limit of the state of charge of the l-th group of batteries,
represents the upper limit of the state of charge of the first group of storage batteries; lambda [ alpha ]1For charging the accumulator by a factor of lambda2The discharge coefficient of the storage battery is set as,
the rated capacity of the first group of storage batteries,
respectively the charging and discharging power of the first group of storage batteries in the t period;
(4) the electric refrigerator restrains:
in the formula (I), the compound is shown in the specification,
the maximum refrigerating power of the electric refrigerator;
(5) restraint of the absorption refrigerator:
in the formula (I), the compound is shown in the specification,
the maximum refrigeration power of the absorption refrigerator;
(6) using a time constraint:
wherein, T1minThe shortest operation time of the wind turbine generator in one day is set; t is1maxThe maximum operation time of the wind turbine generator in one day is set; t is2minThe shortest operation time of the photovoltaic cell in one day; t is2maxThe maximum operation time of the photovoltaic cell in one day; t is3minThe shortest operation time of the electric refrigerator in one day; t is3maxThe maximum operation time of the electric refrigerator in one day; t is4minThe shortest operation time of the energy storage battery in one day is set; t is4maxThe maximum operation time of the energy storage battery in one day is set; t is5minThe shortest operating time of the gas turbine in one day; t is5maxThe maximum operating duration of the gas turbine per day; t is6minThe shortest operation time in one day of the gas boiler; t is6maxThe maximum operation time of the gas-fired boiler in one day; t is7minThe shortest operation time in one day of the gas boiler; t is7maxThe maximum operation time of the gas-fired boiler in one day;
(7) restriction of the amount of exhaust gas discharged:
Ggb≤Ggb.max
Gbl≤Gbl.max
wherein G isgbTotal amount of waste emissions for a gas turbine a day; ggb.maxThe maximum total waste emission of the gas turbine is one day; gblThe total amount of the waste emission of the gas-fired boiler in one day; gbl.maxThe maximum total waste discharge amount of the gas-fired boiler in one day;
(8) energy balance constraint of the microgrid:
(a) the electric energy balance constraint is as follows:
wherein n ispv,t、nWT,t、npgu,tα total number of photovoltaic cells, number of fans and number of gas turbines which are connected into the microgrid at the moment t respectivelypv,m,t、αWT,k,t、αpgu,i,tThe operating states of the photovoltaic cell m, the fan k and the gas turbine i at the moment t are respectively in a value range of {0, 1}, wherein the value of 0 represents the stop working state, and the value of 1 represents the normal working state; pgrid,tElectric energy interacted with a large power grid in a time period t; n is a radical ofBarIs the total number of battery packs, Pgrid,tIs the interaction power with the large power grid at the moment t, Pload,tPower demand for the load at time t;
Ppv,m,tthe expression is the output power of a unit photovoltaic cell:
wherein r is the actual solar illumination intensity; r ism,tRepresenting the actual output power of the mth photovoltaic cell in the t period and the actual illumination intensity of the geographic position of the photovoltaic cell; r ismaxRepresents the maximum illumination intensity; ppv,N＝rmaxS- η represents the photovoltaic power generation output when the intensity of sunlight is maximum, wherein S represents the area of the photovoltaic cell array, and η represents the photoelectric conversion efficiency;
PWT,k,tfor the actual output power of the kth fan in the period t, the expression is as follows:
in the formula: v. ofk,tThe actual wind speed of the geographical position of the kth fan in the time period t is obtained;
rated wind speed, cut-in wind speed and cut-out wind speed of a kth fan are set;
Ppgu,i,toutputting electric power for the ith gas generator in a t period;
the charging and discharging power of the l group of storage batteries in the t period is respectively represented by the following expressions:
in the formula: sbat,l,t、Sbat,l,t-1The charge states of the first group of storage batteries in t and t-1 periods, deltabat,lFor self-discharge of the first group of accumulatorsThe ratio of the total weight of the particles,
respectively the charge-discharge efficiency of the first group of storage batteries;
(b) thermal energy balance constraint:
wherein n isboi,tTotal number of gas boiler operations at time t, αpgu,m,tTotal number of gas turbine operations for time t, αboi,m,tThe operation state of the mth gas boiler at the moment tth;
Qrec,i,tthe waste heat recovery quantity of the ith gas generator in the t period is as follows:
Fpgu,i,t＝Ppgu,i,t/ηpgu,i
Qrec,i,t＝Ppgu,i,t(1-ηpgu,i)ηrec,i/ηpgu,i
in the formula ηpgu,i、ηrec,iThe power generation efficiency and the heat recovery efficiency of the ith gas generator are respectively; ppgu,i,tOutputting electric power for the ith gas generator in a t period; fpgu,i,tFuel consumption of the ith gas generator in a t period;
Qboi,m,tfor the heat production quantity of the mth gas boiler in the t period, the expression is as follows:
Fboi,m,t＝Qboi,m,t/ηboi,m
in the formula, Fboi,m,tη for the fuel consumption of the mth gas boiler during the period tboi,mThe thermal efficiency of the mth gas boiler;
QHrs,tfor the thermal power provided by the heat recovery system in the period t, the expression is as follows:
in the formula, QHrs,tIs heatRecovering thermal power provided by the system during time t ηHrs,tIs the heat recovery efficiency of the heat recovery system in the time period t; n is a radical ofGtThe number of operating gas turbines at time t;
(c) cold energy balance constraint:
wherein Q isAc,tFor the cold power provided by the absorption refrigerator in the period t, the expression is as follows:
in the formula (I), the compound is shown in the specification,
cold power provided to the heat recovery system during time t;
providing cold power for the gas boiler in a time period t; cOPAc,tThe energy efficiency ratio of the electric refrigerator in the time period t;
QEc,tfor the cold power provided by the electric refrigerator in the period t, the expression is as follows:
QEc,t＝PEc,tCOPEc,t
in the formula, PEc,tPower for the electric refrigerator to refrigerate during a time period t; cOPEc,tThe energy efficiency ratio of the electric refrigerator in the time period t;
the demand of the system cooling load is a period t.
7. The CCHP microgrid operation optimization method according to claim 6, characterized in that S5 includes the following steps:
s5.1, initializing parameter setting, including expansion and contraction coefficient β value range, maximum iteration times of an algorithm, a discount factor gamma, the step number m of forward looking required for calculating a Q value, randomly generated initial solutions x (i), setting of local optimal solutions, and calculating of a global optimal solution;
s5.2: calculating local attractors: introducing quantum state particle behaviors, and obtaining the positions of particles by using a Monte Carlo method according to a probability density equation and a distribution function;
s5.3: calculating an average optimal solution;
s5.4: updating the new position of each particle in the particle swarm;
s5.5, selecting an optimal parameter strategy β according to a Q learning method;
s5.6: updating the position value and generating a new group;
s5.7: returning to the step S5.5 for circulation until the iteration is finished; and the output optimal solution is the optimal solution in the economic optimization problem, the optimal solution is substituted into an objective function of economic optimization, and the minimum cost in the optimal solution is calculated.
8. The CCHP microgrid operation optimization method of claim 7, wherein S5.1 comprises:
s5.1.1 randomly generates an initial solution.
And carrying out chaotic treatment on the initial particle population. Secondly, reducing the particles to a solving range through a mapping formula to form an improved particle swarm initialization matrix, and expressing the improved particle swarm initialization matrix by a matrix B:
wherein xijA j-dimension value representing the i-th particle, i 1.. n, j 1.. d;
s5.1.2 calculating population fitness value, and recording local optimum particle piAnd a globally optimal particle pg。
9. The CCHP microgrid operation optimization method of claim 8, wherein S5.5 comprises:
s5.5.1 for each new particle, generating n new offspring particles using a given n parameter selection strategy, and setting t to 1;
s5.5.2 when t<m, each descendant uses the formula Li,j(t)＝2β|mj(t)-Xi,j(t) | generating n new offspring and calculating their fitness values; wherein m is the number of steps needed to look ahead for calculating the Q value; m isjThe average optimum position of the jth particle, β is the expansion and contraction coefficient, Xi,j(t) is the j dimension value of the ith particle in the t step;
s5.5.3 performing criss-cross operation on n individuals and calculating the fitness value of the individuals;
wherein, the criss-cross operation comprises a transverse criss-cross operation and a longitudinal criss-cross operation; calculating the fitness values of the n individuals after the longitudinal and transverse interlacing operation;
s5.5.4: selecting an individual corresponding to the minimum fitness value from the fitness values of the individuals directly subjected to Q learning and the fitness values subjected to Q learning and then to cross operation, and reserving the individual as a new individual; let t + +;
s5.5.5, calculating Q value corresponding to each parameter selection strategy, selecting β value corresponding to the parameter selection strategy which maximizes Q as current β value, and discarding other n-1 β values.
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