CN109165788B - Optimization method of combined cooling heating and power system - Google Patents
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Abstract
The invention relates to an optimization method of a combined cooling heating and power system, which comprises the following steps: establishing a combined cooling heating and power system model, selecting a combined cooling and power microgrid as an optimization object, and performing mathematical modeling on equipment in a scene; determining decision variables and objective functions of optimization analysis of the combined cooling heating and power system; determining a constraint condition for optimizing the operation of the combined cooling heating and power system; selecting a whale optimization algorithm and introducing a chaotic cubic mapping and variation rule to optimize an initial population and a behavior rule; a mixed power-by-heat strategy is adopted as an operation strategy of a combined cooling heating and power system; and determining and evaluating the optimized evaluation index. The method adopts a mixed power-by-heat strategy and uses an improved whale algorithm for solving the combined cooling heating and power type microgrid model, chaotic initialization operation is carried out in an initial stage to improve searching capability, variation operation introduced in an enclosure stage can more quickly find out a global optimum value, and the purpose of quickly finding out an optimal solution for optimizing operation of a combined cooling heating and power system is achieved.
Description
Technical Field
The invention belongs to the technical field of new energy, and particularly relates to an optimization method of a combined cooling heating and power system.
Background
Energy problems are always hot issues concerned by various countries, such as development and utilization of new energy, construction of micro-grid, and the like, wherein Combined with Cooling and Heating and Power (CCHP) micro-grid is widely applied to solving the problem of electricity consumption in places such as hotels, buildings, supermarkets, and the like. The combined cooling heating and power supply type microgrid replaces a dispersed power supply mode of a traditional power grid, waste heat generated by power supply equipment is collected and utilized for cooling and heating loads, and therefore the energy utilization efficiency of the combined cooling and heating power supply type microgrid is improved to 88% from 59% compared with that of a traditional power supply mode. In a plurality of research fields related to combined cooling heating and power type micro-grids, a capacity optimization problem and a scheduling strategy are important for the construction and the use of the micro-grids. Currently common optimization methods include linear programming models and intelligent optimization algorithms. The most used operating strategies are "heat by electricity" and "heat by heat".
In the last two decades, the intelligent optimization algorithm has gained a rapid and drastic development, and compared with a linear programming model, the intelligent optimization algorithm has exhibited outstanding advantages in solving various complex problems. Various algorithms are emerging in succession, and representative algorithms such as particle swarm optimization, genetic algorithm, differential evolution algorithm, and the like are widely used in the industrial field. However, most of the intelligent algorithms are designed according to certain behavior rules of organisms in the nature, and the problems of low convergence speed, inaccurate result and the like of the algorithms are inevitable, so that the original algorithms need to be improved. The two basic scheduling strategies, namely 'electricity heat determination' and 'heat power determination', are different in implementation method and object which is preferentially met, and can meet the requirement of supply and demand balance, but the two strategies have different energy waste, which obviously violates the principle of maximizing energy utilization rate, so that the two strategies need to be perfected.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides an optimization method of a combined cooling heating and power system, solves the problems of high coupling degree and high complexity in capacity optimization of a microgrid system, improves an optimization algorithm to solve and improve the existing operation strategy, thereby realizing a quicker optimization process, shortening the time interval between hour scheduling and improving the utilization rate of energy.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
an optimization method of a combined cooling heating and power system comprises the following steps:
step 1, establishing a combined cooling heating and power system model, selecting a combined cooling and power microgrid as an optimization object, and performing mathematical modeling on equipment in a scene;
step 3, determining a constraint condition for optimizing operation of the combined cooling heating and power system;
and 6, determining and evaluating the optimized evaluation index.
Further, the combined cooling heating and power system model in step 1 is a combined cooling and power microgrid which uses a micro gas turbine as a prime mover and combines a photovoltaic power generation system and a gas boiler as a power generation unit.
Furthermore, the equipment in the scene comprises a micro gas turbine, a photovoltaic power generation system, a gas boiler, a storage battery pack and a heat storage tank; the individual devices were modeled as follows:
the mathematical model of the micro gas turbine is as follows:
HMT＝FMTηr(1-ηMT)
wherein, FMTIs the gas consumption of a micro gas turbine, HMTIs the waste heat of waste recovery, EMTIs the electric energy produced by a micro gas turbine etaMTAnd ηrIs the efficiency of the micro gas turbine and heat recovery system;
the mathematical model of the photovoltaic power generation system is as follows:
wherein E isPVIs the output power of the photovoltaic system, ESTCIs a Standard Test Condition (STC)) Nominal output, G and T are irradiance and temperature at the operating point, GSTCAnd TSTCIrradiance and temperature under STC, k is thermal power temperature coefficient;
the mathematical model of the gas boiler is as follows:
wherein, FgbIs the gas consumption of the gas boiler, HgbIs the supplementary heat quantity of the gas boiler etagbIs the efficiency of the gas boiler;
the mathematical model of the storage battery pack is as follows:
wherein E isb(t) and Eb(t-1) is the electric energy of the battery pack at t and t-1 hours, etabIs the self-discharge rate, etab,inAnd ηb,outIs the charge-discharge efficiency of the battery pack, Eb,inAnd Eb,outIs the power of charging and discharging of the storage battery pack;
the mathematical model of the heat storage tank is as follows:
wherein Htst(t) and Htst(t-1) is the heat energy of the heat storage tank at t and t-1 hours, etatstIs the self-heat-release rate, eta, of the heat storage tanktst,inAnd ηtst,outIs the heat absorption and release efficiency, Htst,inAnd Htst,outIs the heat absorption and release power.
Further, the decision variables in step 2 are:
X＝[NPV,NMT,Ngrid,Nbat,Ntst,Ngb,ratio]
wherein N isPV、NMT、Nbat、NtstAnd NgbRespectively the installation capacities of the photovoltaic system, the micro gas turbine, the storage battery, the heat storage tank and the gas boiler, NgridThe upper limit of interaction between the micro-grid and the grid, and the radio is the electric cooling load ratio;
the objective function in step 2 is the ratio of fuel consumption to cost, and the formula is as follows:
wherein C isiIs the cost of the ith equipment, NiIs the installation capacity of the ith equipment, FiIs fuel for the i-th plant, Evacancy(t) and Ewaste(t) is the deficit value of electric energy and the waste value of electric energy at time t, Hvacancy(t) and Hwaste(t) is the shortage and waste value of heat energy at time t; and lambda is a penalty coefficient.
Further, the constraint condition for the optimal operation of the combined cooling heating and power system in the step 3 is composed of an inequality constraint and an equality constraint, and includes capacity limitation, power load balance, heat load balance and cold load balance of each device, where the capacity limitation includes capacity constraint and climbing constraint.
Further, the chaotic initialization method in step 4 is as follows:
generating a random individual with D dimension in the range of [ -1, 1 ];
iterating SearchAgents _ no-1 time according to the following cubic mapping formula to obtain the remaining individuals;
y(n+1)＝4y(n)3-3y(n)；-1≤y(n)≤1；n＝0,1,2,...
mapping the chaotic variable to a solution space by using the following formula:
wherein L isd，UdFor searching upper and lower limits, x, of a d-th dimension variable of the spaceidCoordinate, y, of dimension d in search space for ith individual in WOA populationidIs the coordinate of the ith individual in the d-dimension in the chaotic space.
Further, the mutation operation method in step 4 is:
the method comprises the steps of obtaining two types of initialization populations according to uniform distribution and cubic chaotic mapping;
secondly, the population obtained through uniform distribution is used as a contrast population, and a Rand-to-best/1 cross strategy is adopted on the population obtained through cubic chaotic mapping to obtain a new population; the expression of the Rand-to-best/1 is as follows:
wherein the content of the first and second substances,
for the ith individual vector in the search space,
randomly selecting individual vectors in a search space, satisfying the condition that j is not equal to i,
obtaining the optimal vector so far;
selecting the individuals with smaller adaptive values in the two populations to form the population with the population number of Searchgents _ no, and performing subsequent hunting and searching processes, wherein the selected expression is as follows:
wherein
In order to use a population that is uniformly distributed,
for the population obtained by cubic chaos mapping, rand is [0, 1]]Random numbers in the range, p is the selection probability, and the fit () is the objective function.
Further, the step 5 adopts a hybrid heating setting strategy as follows: if the waste heat generated by the micro gas turbine running at rated electric power can not meet the heat energy requirements of the adsorption refrigerator and the heat exchanger, the heat energy stored in the heat storage tank is preferentially used, the heat energy requirement can not be met after the heat energy supplemented by the heat storage tank is obtained, and then the gas boiler is started to compensate the heat energy shortage; on the contrary, if the heat energy generated by the recovery micro gas turbine is larger than the heat energy requirement of the adsorption refrigerator and the heat exchanger, part of the heat energy is distributed to the recovery micro gas turbine under the constraint condition of the heat storage tank, and the redundant heat energy is discharged into the air.
Further, the evaluation index determined in step 6 includes:
first energy saving rate
PESR is defined as the rate of energy consumption saved by the CCHP system compared to the conventional separate supply system, PEC is the primary energy consumption of the SP system and the CCHP system;
cost of annual total cost savings
ATCSR is defined as the reduction ratio of the annual total investment cost of the CCHP system compared to the conventional separate supply system, ATC is the annual total cost of the SP system and the CCHP system;
power import saving rate
EISR is defined as the saving rate of the power inlet of the CCHP system compared with the traditional separate supply system, and EI is defined as the power purchase amount of the CCHP system and the SP system to the power grid;
fourth gas boiler fuel economy
BESR is defined as the fuel saving rate of the gas boiler in the CCHP system compared with the conventional separate supply system, and BEC is the fuel consumption rate of the gas boiler in the CCHP system and the SP system
The invention has the advantages and positive effects that:
1. the invention provides a mixed heat-based power-fixing strategy for introducing energy storage of two devices, namely a storage battery and a heat storage tank, on the basis of the traditional heat-based power fixing, and optimally configures the treatment of each device according to the aim of minimum fuel consumption, so that the load fluctuation phenomenon in the traditional strategy can be stabilized, and the flexibility of the system is improved.
2. In the invention, an improved whale algorithm is adopted in the selection of an optimization algorithm and is used for solving a combined cooling heating and power micro-grid model, and the improved whale algorithm is unusual in solving an optimization configuration model of a combined cooling heating and power system: the use of chaotic initialization improves the searching capability of the algorithm in the initial phase. The variation operation introduced in the enclosure stage leads the variation behaviors of other individuals by the optimal individual, increases the population diversity and can quickly find the global optimal value, thereby realizing the purpose of quickly finding the optimal solution for the optimal operation of the combined cooling heating and power system.
3. According to the method, an electric cooling load ratio, namely a proportionality coefficient between cooling energy generated by an electric refrigerator and cooling load is introduced into an improved strategy, and the coefficient is introduced into the solution of the model as a decision variable, so that the microgrid system can operate in a more flexible, more economic and more green mode.
4. The method takes the ratio between the fuel consumption and the cost economy as the objective function when solving the optimal operation problem of the combined cooling heating and power system, so that the relationship between the energy and the cost economy is conveniently balanced, and the maximization of the resource utilization rate is realized.
Drawings
FIG. 1 is a schematic flow chart of the optimization method of the present invention.
FIG. 2 is a flow chart of the improved whale algorithm program of the invention
FIG. 3 is a diagram of the electrical load balancing in the hybrid thermoelectric strategy of the present invention
FIG. 4 is a diagram illustrating the balance of cooling and heating loads in the hybrid heat-based power-on-demand strategy of the present invention
FIG. 5 is a graph of convergence of fitness values of the objective function in the present invention.
Detailed Description
The embodiments of the present invention will be described in detail with reference to the accompanying drawings.
An optimization method of a combined cooling heating and power system, as shown in fig. 1, includes the following steps:
step 1, establishing a combined cooling heating and power system model, selecting a combined cooling and power microgrid as an optimization object, and performing mathematical modeling on equipment in a scene.
In this step, the combined cooling heating and power system model is a combined cooling and power microgrid that uses a micro gas turbine as a prime mover and combines a photovoltaic power generation system and a gas boiler as a power generation unit.
The equipment in the scene comprises a micro gas turbine (MT), a photovoltaic power generation system (PV), a gas boiler, a storage battery pack and a heat storage tank.
The micro gas turbine (MT) employs a mathematical model as follows:
HMT＝FMTηr(1-ηMT)
wherein, FMTIs the gas consumption of a micro gas turbine, HMTIs the waste heat of waste recovery, EMTIs the electric energy produced by a micro gas turbine etaMTAnd ηrIs the efficiency of the micro gas turbine and heat recovery system.
The mathematical model expression of the photovoltaic power generation system (PV) is as follows:
wherein E isPVIs the output power of the photovoltaic system, ESTCIs the rated output (STC) under standard test conditions, G and T are the irradiance and temperature of the operating point, GSTCAnd TSTCIs the irradiance and temperature at STC, and k is the thermal power temperature coefficient.
The mathematical model expression of the gas boiler is as follows:
wherein, FgbIs the gas consumption of the gas boiler, HgbIs the supplementary heat quantity of the gas boiler etagbIs the efficiency of the gas boiler.
The mathematical model of the storage battery pack is as follows:
wherein E isb(t) and Eb(t-1) is the electric energy of the battery pack at t and t-1 hours, etabIs the self-discharge rate, etab,inAnd ηb,outIs the charge-discharge efficiency of the battery pack, Eb,inAnd Eb,outIs the power of charging and discharging the storage battery pack.
The mathematical model of the heat storage tank is as follows:
wherein Htst(t) and Htst(t-1) is the heat energy of the heat storage tank at t and t-1 hours, etatstIs the self-heat-release rate, eta, of the heat storage tanktst,inAnd ηtst,outIs the heat absorption and release efficiency, Htst,inAnd Htst,outIs the heat absorption and release power.
And 2, determining decision variables and objective functions of optimization analysis of the combined cooling heating and power system.
In this step, the objective function is determined as the ratio of fuel consumption to cost, and the specific formula is as follows:
wherein C isiIs the cost of the ith equipment, NiIs the installation capacity of the ith equipment, FiIs fuel for the i-th plant, Evacancy(t) and EwasteAnd (t) is the shortage value of the electric energy and the waste value of the electric energy at the moment t. Hvacancy(t) and Hwaste(t) is the shortage and waste value of heat energy at time t; and lambda is a penalty coefficient.
And 3, determining the constraint condition of the optimal operation of the combined cooling heating and power system.
In this step, the constraint condition for the optimal operation of the combined cooling, heating and power system is mainly composed of an inequality constraint and an equality constraint, and includes the capacity limit, the power load balance, the heat load balance and the cold load balance of each device.
(1) Capacity constraints
(2) Climbing restraint
Pi(t)-Pi(t-1)≤Pup
Wherein, Pi(t)，Pi(t-1) is the output of the i-th equipment at t moment and t-1 moment, PupIs the upper limit of the climbing power.
(3) Electric power balance
EPV(t)+EMT(t)+Egrid,out(t)+Eb,out(t)+Evacancy(t)＝Egrid,in(t)+Eb,in(t)+Ep(t)+Eec(t)+Ewaste(t)
Wherein E isPV(t)，EMT(t) represents the electrical energy generated by the photovoltaic power generation system (PV) and the micro-gas turbine (MT) at time t, respectively, Egrid,in(t)，Egrid,off(t) electric energy purchased and sold to the grid at time t, respectively, Eb,in(t)，Eb,out(t) represents the charging and discharging power of the secondary battery at time t, respectively, Evacancy(t)，Ewaste(t) respectively representing the power shortage and wasted power at time t, Ep(t) represents the electrical energy load demand at time t, Eec(t) represents the power consumed by the electric refrigerator at time t.
(4) Thermal load balancing
HMT(t)+Hgb(t)+Htst,out(t)+Hvacancy(t)＝Htst,in(t)+Hhe(t)+Hac(t)+Hwaste(t)
Wherein HMT(t)，Hgb(t) waste heat from the micro gas turbine (MT) and heat from the gas boiler at time t, Htst,in(t)，Htst,out(t) the heat energy absorbed by the heat storage tank at time t and the heat energy released, Hvacancy(t)，Hwaste(t) thermal energy deficit at respective times tAnd waste of heat energy, Hhe(t) the heat energy consumed by the heat exchanger at time t, HacAnd (t) represents the heat energy consumed by the adsorption refrigerator at time t.
(5) Cold load balancing
Qac(t)+Qec(t)＝Qc(t)
Wherein Q isac(t) is the cold energy, Q, generated by the adsorption refrigerator at time tec(t) is the cold energy, Q, generated by the electric refrigerator at time tc(t) is the cooling load demand at time t.
And 4, selecting an intelligent algorithm with good performance for optimization, selecting a Whale Optimization Algorithm (WOA) for improvement, introducing chaotic cube mapping and variation rules to optimize the initial population and the behavior rules, wherein the improved algorithm flow chart is shown in FIG. 2.
In this step, the intelligent algorithm selects WOA, the improved part is chaotic initialization and mutation operation, and the two parts are introduced as follows:
(1) chaos initialization
And initializing an initial population of the whale algorithm by adopting a cubic mapping model. The expression of the chaotic cube map is as follows:
y(n+1)＝4y(n)3-3y(n)；-1≤y(n)≤1；n＝0,1,2,...
specifically, the chaotic initialization comprises the following steps:
1) generating a random population of dimensions D in the range of [ -1, 1 ];
2) iterating SearchAgents _ no-1 time according to the cubic mapping formula to obtain the residual individuals;
3) the chaotic variables are mapped to a solution space using the following formula.
Wherein L isd，UdFor searching upper and lower limits, x, of a d-th dimension variable of the spaceidCoordinate, y, of dimension d in search space for ith individual in WOA populationidIs the coordinate of the ith individual in the d-dimension in the chaotic space.
(2) Mutation operation
In order to improve the performance of the WOA in the middle and later stages of iteration, a new population based on cross operation is merged into the original WOA population. And improving the expression of the Rand-to-best/1, wherein the modified expression of the Rand-to-best/1 is as follows:
wherein the content of the first and second substances,
for the ith individual vector in the search space,
randomly selecting individual vectors in a search space, satisfying the condition that j is not equal to i,
the optimal vector is obtained so far.
After the two parts are improved, the flow of the enclosing stage in the whale optimization algorithm is as follows:
1) obtaining two types of initialization populations according to uniform distribution and cubic chaotic mapping;
2) taking the population obtained by uniform distribution as a comparison population, and obtaining a new population by adopting a Rand-to-best/1 cross strategy on the population obtained by cubic chaotic mapping;
3) and selecting the individuals with smaller adaptive values from the two populations to form the population with the SearchAgents _ no according to a certain probability, and performing subsequent hunting and searching processes. The chosen expression is:
wherein
For seeds obtained by uniform distributionThe number of clusters is determined by the number of clusters,
for the population obtained by cubic chaos mapping, rand is [0, 1]]Random numbers in the range, p is the selection probability, and the fit () is the objective function.
And 5, improving the operation strategy of the existing combined cooling heating and power system, and providing an operation strategy based on load meeting requirements, namely a hybrid heat-utilization power-determination strategy.
In this step, the operation strategy of the existing combined cooling heating and power system is improved, and the proposed operation strategy based on the load demand is as follows: mixing a heat fixed electricity strategy. Under the strategy, the optimization algorithm optimally configures the output of each device according to the aim of minimum fuel consumption, and in this case, the micro gas turbine can work in a continuous working mode, namely, the micro gas turbine can run at rated power. At a certain moment, if the waste heat generated by the micro gas turbine running at rated electric power can not meet the heat energy requirements of the adsorption refrigerator and the heat exchanger, the heat energy stored in the heat storage tank is preferentially used (the gas boiler can consume natural gas), the heat energy requirement can not be met after the heat energy supplemented by the heat storage tank is obtained, and then the gas boiler is started to compensate the heat energy shortage; on the contrary, if the heat energy generated by the recovery micro gas turbine is larger than the heat energy requirement of the adsorption refrigerator and the heat exchanger, part of the heat energy is distributed to the recovery micro gas turbine under the constraint condition of the heat storage tank, and the redundant heat energy is discharged into the air. Under this strategy, there are fossil energy consuming devices such as micro gas turbines, gas boilers and power grids.
And 6, determining an evaluation index of the optimized analysis, and reasonably evaluating the analysis method.
In this step, the evaluation index for optimizing analysis includes:
1) primary energy saving rate
PESR is defined as the rate of energy consumption saved by the CCHP system compared to a conventional separation and Supply (SP) system, and PEC is the primary energy consumption of the SP system and the CCHP system.
2) Total annual cost savings
ATCSR is defined as the rate of reduction in the annual total investment cost of a CCHP system compared to an SP system. ATC is the annual total cost of the SP system and the CCHP system.
3) Power import savings ratio
EISR is defined as the power import savings of the CCHP system compared to the SP system. The EI is defined as the electricity purchased by the CCHP system and the SP system to the power grid.
4) Fuel saving rate of gas boiler
BESR is defined as the gas boiler fuel saving rate of the CCHP system compared to the SP system. BEC is the fuel consumption of the gas boiler in the CCHP system and the SP system.
And finally, inputting load original data on a computer, running a combined cooling heating and power system optimization strategy by using a combined cooling heating and power system optimization program compiled by MATLAB, and displaying the result on a display to achieve the purpose of optimization analysis.
In this embodiment, a cooling, heating and power load of a combined cooling, heating and power system in 24 hours a day is selected to complete the formulation of an optimized operation method, and multiple types of loads in the system are embodied in a load curve, fig. 3 is an electric load balance diagram in a hybrid heating and power-fixing strategy of the present invention, fig. 4 is a cooling and heating load balance diagram in the hybrid heating and power-fixing strategy of the present invention, and fig. 5 is a convergence diagram of an objective function fitness value of the present invention. Table 1 is a comparison of the indicators for different operating strategies.
TABLE 1
| Policy | ATCSR | BESR | EISR | PESR |
| Traditional method for determining heat with electricity (FEL) | -20.4927 | -0.5658 | 0.4367 | 0.2514 |
| Traditional electric power utilization by heat (FTL) | -23.3374 | 1 | 0.5681 | 0.3341 |
| Hybrid electric power with Heat (HFTL) | -23.3410 | 1 | 0.6207 | 0.3575 |
As shown in Table 1, the ATCSR of the FEL is substantially about-20, the ATCSR of the FTL and the HFTL is substantially about-23, and the FEL is 10% lower than the FTL and the HFTL. This is because the micro gas turbine needs to meet the requirement of cold and heat energy separately under the FTL and HFTL strategies, and the investment cost of the micro gas turbine is increased. The investment cost under the three strategies is obviously increased compared with that of an SP system, which shows that the economy of the combined cooling heating and power system is not as good as that of the SP system. The BESR of FEL is-0.5658, and the gas boiler is the main device for supplying cold and hot energy under this strategy, so the fuel consumption of the gas boiler is larger than that of the SP system. The BESR under the FTL and HFTL strategies is 1, because the waste heat generated by the micro gas turbine can meet the requirement of cold and hot energy without the participation of a gas boiler. In the aspect of EISR, the combined cooling heating and power system saves the electricity purchasing quantity of the power grid by more than 40% compared with an SP system under three strategies. The EISR of FTL and HFTL is larger than that of FEL, because the capacity of the micro gas turbine under the FEL strategy is smaller so as not to generate energy waste, and partial electric energy needs to be bought into the power grid. Among these, the EISR of HFTL is the largest, since micro gas turbines and batteries essentially assume the majority of the electrical energy supply tasks. The PESR is maintained above 25%, which shows that the fuel consumption of main equipment of the three strategies is reduced, and the pollution to the environment is reduced.
In summary, the indexes of the combined cooling, heating and power system under the HFTL strategy are superior to those of the traditional two strategies, so the HFTL strategy is worthy of recommendation.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.
Claims (8)
1. An optimization method of a combined cooling heating and power system is characterized in that: the method comprises the following steps:
step 1, establishing a combined cooling heating and power system model, selecting a combined cooling and power microgrid as an optimization object, and performing mathematical modeling on equipment in a scene;
step 2, determining decision variables and objective functions of optimization analysis of the combined cooling heating and power system;
step 3, determining a constraint condition for optimizing operation of the combined cooling heating and power system;
step 4, selecting a whale optimization algorithm and introducing a chaotic cubic mapping and variation rule to optimize an initial population and a behavior rule;
step 5, adopting a mixed power-by-heat strategy as an operation strategy of a combined cooling heating and power system;
step 6, determining and evaluating the optimized evaluation index;
the hybrid-thermal-setting strategy adopted in the step 5 is as follows: if the waste heat generated by the micro gas turbine running at rated electric power can not meet the heat energy requirements of the adsorption refrigerator and the heat exchanger, the heat energy stored in the heat storage tank is preferentially used, the heat energy requirement can not be met after the heat energy supplemented by the heat storage tank is obtained, and then the gas boiler is started to compensate the heat energy shortage; on the contrary, if the heat energy generated by the recovery micro gas turbine is larger than the heat energy requirement of the adsorption refrigerator and the heat exchanger, part of the heat energy is distributed to the recovery micro gas turbine under the constraint condition of the heat storage tank, and the redundant heat energy is discharged into the air.
2. The optimization method of a combined cooling, heating and power system according to claim 1, wherein the optimization method comprises the following steps: the combined cooling heating and power system model of the step 1 is a combined cooling and power type micro-grid which takes a micro gas turbine as a prime mover and combines a photovoltaic power generation system and a gas boiler as a power generation unit.
3. The optimization method of a combined cooling, heating and power system according to claim 1, wherein the optimization method comprises the following steps: the equipment in the scene comprises a micro gas turbine, a photovoltaic power generation system, a gas boiler, a storage battery pack and a heat storage tank; the individual devices were modeled as follows:
the mathematical model of the micro gas turbine is as follows:
HMT＝FMTηr(1-ηMT)
wherein, FMTIs the gas consumption of a micro gas turbine, HMTIs the waste heat of waste recovery, EMTIs the electric energy produced by a micro gas turbine etaMTAnd ηrIs the efficiency of the micro gas turbine and heat recovery system;
the mathematical model of the photovoltaic power generation system is as follows:
wherein E isPVIs the output power of the photovoltaic system, ESTCIs the rated output under standard test conditions, G and T are the irradiance and temperature of the operating point, GSTCAnd TSTCIrradiance and temperature under STC, k is thermal power temperature coefficient;
the mathematical model of the gas boiler is as follows:
wherein, FgbIs the gas consumption of the gas boiler, HgbIs the supplementary heat quantity of the gas boiler etagbIs the efficiency of the gas boiler;
the mathematical model of the storage battery pack is as follows:
wherein E isb(t) and Eb(t-1) is the electric energy of the battery pack at t and t-1 hours, etabIs the self-discharge rate, etab,inAnd ηb,outIs the charge-discharge efficiency of the battery pack, Eb,inAnd Eb,outIs the power of charging and discharging of the storage battery pack;
the mathematical model of the heat storage tank is as follows:
wherein Htst(t) and Htst(t-1) is the heat energy of the heat storage tank at t and t-1 hours, etatstIs the self-heat-release rate, eta, of the heat storage tanktst,inAnd ηtst,outIs the heat absorption and release efficiency, Htst,inAnd Htst,outIs the heat absorption and release power.
4. The optimization method of a combined cooling, heating and power system according to claim 1, wherein the optimization method comprises the following steps: the decision variables in step 2 are:
X＝[NPV,NMT,Ngrid,Nbat,Ntst,Ngb,ratio]
wherein N isPV、NMT、Nbat、NtstAnd NgbRespectively the installation capacities of the photovoltaic system, the micro gas turbine, the storage battery, the heat storage tank and the gas boiler, NgridThe upper limit of interaction between the micro-grid and the grid, and the radio is the electric cooling load ratio;
the objective function in step 2 is the ratio of fuel consumption to cost, and the formula is as follows:
wherein C isiIs the cost of the ith equipment, NiIs the installation capacity of the ith equipment, FiIs fuel for the i-th plant, Evacancy(t) and Ewaste(t) is the deficit value of electric energy and the waste value of electric energy at time t, Hvacancy(t) and Hwaste(t) is the shortage and waste value of heat energy at time t; and lambda is a penalty coefficient.
5. The optimization method of a combined cooling, heating and power system according to claim 1, wherein the optimization method comprises the following steps: and 3, the constraint condition of the optimal operation of the combined cooling heating and power system in the step 3 is composed of an inequality constraint part and an equality constraint part, and comprises the capacity limit, the power load balance, the heat load balance and the cold load balance of each device, wherein the capacity limit comprises the capacity constraint and the climbing constraint.
6. The optimization method of a combined cooling, heating and power system according to claim 1, wherein the optimization method comprises the following steps: the chaos initialization method in the step 4 comprises the following steps:
generating a random individual with D dimension in the range of [ -1, 1 ];
iterating SearchAgents _ no-1 time according to the following cubic mapping formula to obtain the remaining individuals;
y(n+1)＝4y(n)3-3y(n)；-1≤y(n)≤1；n＝0,1,2,...
mapping the chaotic variable to a solution space by using the following formula:
wherein L isd，UdFor searching upper and lower limits, x, of a d-th dimension variable of the spaceidCoordinate, y, of dimension d in search space for ith individual in WOA populationidIs the coordinate of the ith individual in the d-dimension in the chaotic space.
7. The optimization method of a combined cooling, heating and power system according to claim 1, wherein the optimization method comprises the following steps: the mutation operation method in the step 4 comprises the following steps:
the method comprises the steps of obtaining two types of initialization populations according to uniform distribution and cubic chaotic mapping;
secondly, the population obtained through uniform distribution is used as a contrast population, and a Rand-to-best/1 cross strategy is adopted on the population obtained through cubic chaotic mapping to obtain a new population; the expression of the Rand-to-best/1 is as follows:
wherein the content of the first and second substances,
for the ith individual vector in the search space,
randomly selecting individual vectors in a search space, satisfying the condition that j is not equal to i,
obtaining the optimal vector so far;
selecting the individuals with smaller adaptive values in the two populations to form the population with the population number of Searchgents _ no, and performing subsequent hunting and searching processes, wherein the selected expression is as follows:
wherein
In order to use a population that is uniformly distributed,
for the population obtained by cubic chaos mapping, rand is [0, 1]]Random numbers in the range, p is the selection probability, and the fit () is the objective function.
8. The optimization method of a combined cooling, heating and power system according to claim 1, wherein the optimization method comprises the following steps: the evaluation index determined in step 6 includes:
first energy saving rate
PESR is defined as the rate of energy consumption saved by the CCHP system compared to the conventional separate supply system, PEC is the primary energy consumption of the SP system and the CCHP system;
cost of annual total cost savings
ATCSR is defined as the reduction ratio of the annual total investment cost of the CCHP system compared to the conventional separate supply system, ATC is the annual total cost of the SP system and the CCHP system;
power import saving rate
EISR is defined as the saving rate of the power inlet of the CCHP system compared with the traditional separate supply system, and EI is defined as the power purchase amount of the CCHP system and the SP system to the power grid;
fourth gas boiler fuel economy
BESR is defined as the gas boiler fuel saving rate of the CCHP system compared to the conventional separate supply system, and BEC is the fuel consumption of the gas boilers in the CCHP system and the SP system.
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