CN114595961A - Scheduling method and device for biomass energy multi-energy utilization system - Google Patents
Scheduling method and device for biomass energy multi-energy utilization system Download PDFInfo
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Abstract
The invention belongs to the technical field of energy utilization, and particularly discloses a scheduling method and a device of a biomass energy multi-energy utilization system, wherein the method comprises the following steps: constructing a biomass energy multi-energy utilization system mathematical model, including a biomass natural gas combined supply system model and an energy storage device model; constructing an optimized scheduling model of the biomass energy multi-energy utilization system, taking the lowest daily running total cost of the biomass energy multi-energy utilization system as a target function, and taking the self condition of a mathematical model of the biomass energy multi-energy utilization system as a constraint condition; the optimized scheduling model is solved and calculated using an improved mayflies algorithm. The scheme can effectively realize the optimized scheduling of the biomass energy multi-energy utilization system, reduces the total daily running cost and has wide application prospect.
Description
Technical Field
The invention belongs to the technical field of energy utilization, and particularly relates to a scheduling method and device of a biomass energy multi-energy utilization system.
Background
Rural economy develops the situation well, and rural infrastructure condition takes place fundamental change. However, the energy utilization efficiency in rural areas is low, and the problems of how to effectively treat and realize the recycling of biomass waste and the like are still outstanding. The rural areas in China have sufficient illumination and rich wind energy resources, and how to realize the comprehensive coupling utilization of biomass waste and distributed wind energy and photovoltaic, the utilization efficiency of energy is improved, the energy utilization reliability of the rural areas is ensured, the transformation of a power distribution network in the rural areas is delayed, the environment of the rural areas is improved, and the rural modernization development is realized. At present, some research results on the comprehensive utilization of multiple power supplies exist, for example, in the 'sea island microgrid energy optimization scheduling method including ocean energy power generation' published in the electric power construction of 2021 by the authors of cattle farming and the like, an optimization scheduling strategy of the sea island microgrid is proposed, and the optimization scheduling strategy only researches multiple power supply energy sources without considering other types of energy forms such as cold and heat. The method aims at the problems that the prior art for comprehensive utilization of biomass multi-energy in rural areas is few, related models and algorithms are not complete, and deep research is needed.
Disclosure of Invention
The invention aims to provide a scheduling method and a device of a biomass energy multi-energy utilization system, which can solve the technical problems that biomass multi-energy comprehensive utilization in rural areas is difficult and related models and algorithms are still imperfect.
The invention provides a scheduling method of a biomass energy multi-energy utilization system, which comprises the following steps:
s1, constructing a biomass energy multi-energy utilization system mathematical model, including a biomass and natural gas combined supply system model and an energy storage device model;
s2, constructing an optimized scheduling model of the biomass energy multi-energy utilization system, taking the lowest daily running total cost of the biomass energy multi-energy utilization system as a target function, and taking the self condition of the mathematical model of the biomass energy multi-energy utilization system as a constraint condition;
s3, adopting the improved dayflies algorithm to solve and calculate the optimized scheduling model.
Preferably, the biomass energy multi-energy utilization system mathematical model comprises a distributed wind power and photovoltaic and biomass natural gas combined supply system, a biomass natural gas boiler, a heat storage tank, a storage battery and a biomass natural gas fermentation pool.
Preferably, the biomass-natural gas combined supply system mainly supplies electric energy and heat energy, and the mathematical model of the biomass-natural gas combined supply system is as follows:
in the formula, PH(t) is the heating power; pE(t) is the generated power; etaEThe power supply efficiency is improved; etaHThe heat supply efficiency is improved; etaRThe recovery rate of the waste heat of the flue gas is obtained;
the charge-discharge principle of the heat storage tank and the storage battery is consistent, and the unified mathematical model is as follows:
wherein E (t) is the total energy of the energy storage device; delta is the self-discharging efficiency of the energy storage device, and the numerical value is very small; pch(t) and Pdis(t) is the charging and discharging power of the energy storage device; etachAnd ηdisCharging and discharging efficiency of the energy storage device; Δ T is a unit period.
Preferably, the objective function in S2 is:
in the formula, CiMaintenance costs for photovoltaic, wind power, storage batteries, thermal energy storage tanks, biomass and natural gas combined supply systems and boilers; ce1The cost of electricity purchase from the distribution grid; pi(t) real-time output power of photovoltaic, wind power, storage batteries, heat energy storage tanks, biomass and natural gas combined supply systems and boilers; pgrid(t) is the tie line interaction power; eebuyPurchasing electric carbon emission for an external power distribution network; eHCarbon emission of a biomass and natural gas combined supply system and a boiler; pgd(t) carbon emission power for biomass and natural gas combined supply system and boiler; a is1、b1And c1Calculating a carbon emission parameter of the purchased electric quantity for the external power distribution network; a is2、b2And c2Calculating parameters for carbon emission of a biomass and natural gas combined supply system and a boiler; pgbh(t) outputting thermal power for the biomass natural gas boiler; ce2Which is a carbon emission cost.
Preferably, the constraint conditions in S2 are:
(1) constraint of electric heat balance
In the formula, Peload(t) is the electrical load; p ishload(t) is the thermal load; pPV(t) power of the photovoltaic; pWT(t) is the power of the wind power; pestore(t) is the charge and discharge power of the lithium battery; phstore(t) is the heat charging and discharging power of the heat energy storage tank;
(2) junctor interaction power constraints
Pgridmin≤Pgrid(t)≤Pgridmax (6)
In the formula, PgridminMinimum interaction power for the tie line; pgridmaxMaximum interaction power for the tie line;
(3) power constraints for various energy supply and storage devices
In the formula (I), the compound is shown in the specification,
the maximum power of the photovoltaic power, the wind power, the storage battery, the heat energy storage tank, the biomass natural gas combined supply system and the boiler is obtained;
(4) energy storage device restraint
0≤E(t)≤Emax (8)
In the formula, EmaxIs the maximum capacity of the energy storage device.
Preferably, said modified mayflies algorithm comprises in particular:
s31, providing an optimal dayflies helping strategy; the expression thereof is as follows,
in the formula, Xworst(t) are the worst positions of mayflies; xnworst(t) the positions of dayflies after helping to hold; t is the iteration number of the algorithm; gbestThe optimal positions experienced by all mayflies.
Preferably, the S31 is followed by S32: the adaptive weight factor is added as follows:
xij(male)(t+1)＝w×xij(male)(t)+vij(male)(t+1) (11)
yij(fmale)(t+1)＝w×yij(fmale)(t)+vij(fmale)(t+1) (12)
in the formula, vij(male)(t +1) is the velocity of the male parent; x is the number ofij(male)(t) and xij(male)(t +1) is the position of the male parent; w is the adaptive weight factor.
The invention also provides a system for scheduling the biomass energy multi-energy utilization system, which is used for realizing the steps of the scheduling method of the biomass energy multi-energy utilization system and comprises the following steps:
the model building module is used for building a biomass energy multi-energy utilization system mathematical model, and comprises a biomass and natural gas combined supply system model and an energy storage device model;
the model optimization scheduling module is used for constructing an optimization scheduling model of the biomass energy multi-energy utilization system, the lowest daily operation total cost of the biomass energy multi-energy utilization system is taken as a target function, and the self condition of the mathematical model of the biomass energy multi-energy utilization system is taken as a constraint condition;
and the algorithm solving module is used for solving and calculating the optimized scheduling model by adopting the improved dayflies algorithm.
The invention also provides electronic equipment which comprises a memory and a processor, wherein the processor is used for realizing the steps of the biomass energy multi-energy utilization system scheduling method when executing the computer management program stored in the memory.
The invention also provides a computer readable storage medium, on which a computer management program is stored, wherein the computer management program realizes the steps of the scheduling method of the biomass energy multi-energy utilization system when being executed by the processor.
Compared with the prior art, the biomass energy multi-energy utilization system scheduling method and device provided by the invention comprise the following steps: constructing a biomass energy multi-energy utilization system mathematical model, including a biomass and natural gas combined supply system model and an energy storage device model; constructing an optimized scheduling model of the biomass energy multi-energy utilization system, taking the lowest daily running total cost of the biomass energy multi-energy utilization system as a target function, and taking the self condition of a mathematical model of the biomass energy multi-energy utilization system as a constraint condition; the optimized scheduling model is solved and calculated using an improved mayflies algorithm. The scheme can effectively realize the optimized scheduling of the biomass energy multi-energy utilization system, reduces the total daily running cost and has wide application prospect.
Drawings
FIG. 1 is a flow chart of a scheduling method of a biomass energy multi-energy utilization system according to the present invention;
FIG. 2 is a typical winter daily electric energy scheduling curve diagram of the scheduling method of the biomass energy multi-energy utilization system according to the present invention;
FIG. 3 is a typical winter daily heat energy scheduling curve diagram of the scheduling method of the biomass energy multi-energy utilization system according to the present invention;
FIG. 4 is a schematic diagram of a hardware structure of a possible electronic device provided in the present invention;
fig. 5 is a schematic diagram of a hardware structure of a possible computer-readable storage medium according to the present invention.
Detailed Description
The following detailed description of the present invention is provided in conjunction with the accompanying drawings, but it should be understood that the scope of the present invention is not limited to the specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element or component but not the exclusion of any other element or component.
As shown in fig. 1, a biomass energy multi-energy utilization system scheduling method according to a preferred embodiment of the present invention includes the following steps:
s1, constructing a biomass energy multi-energy utilization system mathematical model, including a biomass and natural gas combined supply system model and an energy storage device model;
s2, constructing an optimized scheduling model of the biomass energy multi-energy utilization system, taking the lowest daily running total cost of the biomass energy multi-energy utilization system as a target function, and taking the self condition of the mathematical model of the biomass energy multi-energy utilization system as a constraint condition;
s3, adopting the improved dayflies algorithm to solve and calculate the optimized scheduling model.
In one particular implementation scenario:
1. mathematical model of biomass energy multi-energy utilization system
The biomass energy multi-energy utilization system mainly comprises a distributed wind power and photovoltaic and biomass natural gas combined supply system, a biomass natural gas boiler, a heat storage tank, a storage battery, a biomass natural gas fermentation pool and the like. The biomass natural gas combined supply system model comprises a biomass natural gas combined supply system model and an energy storage device model. As follows:
1.1 Biomass natural gas combined supply system model
The biomass natural gas combined supply system mainly supplies electric energy and heat energy, and the mathematical model of the biomass natural gas combined supply system is as follows:
in the formula, PH(t) is the heating power; pE(t) is the generated power; etaEThe power supply efficiency is improved; etaHThe heat supply efficiency is improved; etaRThe recovery rate of the waste heat of the flue gas is high.
1.2 energy storage device model
The charge and discharge principle of the heat storage tank and the storage battery is similar, and the unified mathematical model is as follows:
wherein E (t) is the total energy of the energy storage device in the t period; delta is the self-discharging efficiency of the energy storage device, and the numerical value is very small; p isch(t) and Pdis(t) charging and discharging power of the energy storage device in a t period; etachAnd ηdisCharging and discharging efficiency of the energy storage device; Δ T is a unit period.
2. Optimized scheduling model of biomass energy multi-energy utilization system
2.1 objective function
The biomass energy multi-energy utilization system objective function is the lowest total daily operation cost. The mathematical formula is as follows:
in the formula, CiMaintenance costs for photovoltaic, wind power, storage batteries, thermal energy storage tanks, biomass and natural gas combined supply systems and boilers; ce1The cost of electricity purchase from the distribution grid; pi(t) real-time output power of photovoltaic, wind power, storage batteries, heat energy storage tanks, biomass and natural gas combined supply systems and boilers; pgrid(t) is the tie line interaction power; eebuyPurchasing electric carbon emission for an external power distribution network; eHCarbon emission of a biomass and natural gas combined supply system and a boiler; pgd(t) carbon emission power for biomass and natural gas combined supply system and boiler; a is1、b1And c1Calculating a carbon emission parameter of the purchased electric quantity for the external power distribution network; a is2、b2And c2Calculating parameters for carbon emission of a biomass and natural gas combined supply system and a boiler; pgbh(t) outputting thermal power for the biomass natural gas boiler; ce2Which is a carbon emission cost.
2.2 constraint Condition
(1) Constraint of electrothermal balance
In the formula, Peload(t) is the electrical load; phload(t) is the thermal load; pPV(t) power of the photovoltaic; pWT(t) is the power of the wind power; p isestore(t) is the charge and discharge power of the lithium battery; phstoreAnd (t) is the heat charging and discharging power of the heat energy storage tank.
(2) Junctor interaction power constraints
Pgridmin≤Pgrid(t)≤Pgridmax (6)
In the formula, PgridminMinimum interaction power for the tie line; pgridmaxMaximum interaction power for the tie.
(3) Power constraints of respective energy supply and storage devices
In the formula (I), the compound is shown in the specification,
the power is the maximum power of photovoltaic, wind power, a storage battery, a heat energy storage tank, a biomass natural gas combined supply system and a boiler.
(4) Energy storage device restraint
0≤E(t)≤Emax (8)
In the formula, EmaxIs the maximum capacity of the energy storage device.
3. Optimized scheduling solving algorithm
The mayflies optimization algorithm is a swarm intelligence search algorithm proposed by Konstantinos Zervoudakis and Stelios Tsaarakis, which mainly simulates the social behavior of mayflies, including sports, puppetry, breeding. During evolution, the entire population is divided into three types: male, female and progeny. Wherein the position updating formula for male mayflies is:
xij(male)(t+1)＝xij(male)(t)+vij(male)(t+1) (10)
in the formula: v. ofij(male)(t) the speed of movement of the ith male mayflies in the jth dimension during the tth iteration; x is the number ofij(male)(t) is the position of the ith male mayfly in the j dimension during the t iteration; a is1And a2Positive attraction coefficient for social effect; p is a radical of formulabestThe optimal position for the current dayflies experienced; gbestThe optimal positions experienced by all mayflies; β is the visibility coefficient of mayflies; r ispIs the current position and pbestA Cartesian distance between locations; r isgIs the current position and gbestA cartesian distance therebetween. The cartesian distance is calculated as:
in the formula, XiThe position corresponding to the historically highest fitness value for the ith mayfly; xijThe position corresponding to the historical highest fitness value of the ith dayfly in the j-th dimension.
When the values of mayflies are high, the male mayflies will be recoupled thereto. Male dayflies perform a characteristic dance action during the idol process, in which case the following formula applies:
vij(male)(t+1)＝vij(male)(t)+dr (12)
wherein d is a dancing coefficient; r is a random number between [ -1,1 ].
Unlike male dayflies, the female dayflies do not cluster during their movement, but instead converge towards the positions of the male dayflies, the speed and position update formula for the female dayflies is:
yij(fmale)(t+1)＝yij(fmale)(t)+vij(fmale)(t+1) (14)
in the formula, vij(fmale)(t) is the speed of motion in the jth dimension for the ith female mayfly during the tth iteration; y isij(female)(t) is the position in the jth dimension of the ith female dayfly during the tth iteration; f is a random walk coefficient; r is a radical of hydrogenmfIs the Cartesian distance between male and female dayflies. As the fitness value of female dayflies is smaller than that of male dayflies, they will be dayflies to malesThe positions are closed; and when the fitness value of a female mayflies is greater than that of a male mayflies, the progression will be accelerated on the basis of the last exercise process.
Velocity v of offspringij(offspring)(t) and position xij(offspring)(t) is determined by inheriting a male parent and a female parent.
vij(offspring)(t)＝Cvij(male)(t)+(1-C)vij(fmale)(t) (15)
xij(offspring)(t)＝Cxij(male)(t)+(1-C)yij(fmale)(t) (16)
In the formula, vij(male)(t) and xij(male)(t) is the speed and position of the male parent; v. ofij(fmale)(t) and yij(fmale)(t) is the speed and position of the female parent; c is [0, 1]]A random number in between.
The flow of the mayflies algorithm is as follows:
(1) parameters in the mayfly population are initialized.
(2) Calculating the fitness value corresponding to each mayfly, selecting p therefrombestAnd gbest。
(3) The speed and position of each mayfly in the mayfly population are updated according to formulas (9) - (16).
(4) After a mayflies, the fitness value for each mayflies is recalculated, and p is updatedbestAnd gbest。
Compared with the algorithms such as particle swarm, the traditional mayflies have good convergence speed and precision, but the overall convergence speed and search precision of the mayflies still have room for further improvement, which is not very ideal, and the search of solutions has hysteresis, so that improvements on the traditional mayflies are needed. The improvement mode is as follows:
(1) the optimal mayflies helping strategy is proposed. In order to improve the overall searching capacity, speed and accuracy of a population, the mayflies in the optimal positions in the population assist the worst dayflies, enhancing the optimizing capacity of the worst dayflies, and further improving the optimizing capacity of the population as a whole, the expression is as follows:
in the formula, Xworst(t) are the worst positions of mayflies; xnworst(t) the positions of dayflies after helping to hold; and T is the iteration number of the algorithm.
In the early stages of the algorithm (i.e. in the early stages of the algorithm)
And (3) the random-0.3 random disturbance amplitude is large, so that the search range is large, and the global search is emphasized. At the later stage of the algorithm (i.e. at
) The random disturbance amplitude of 0.3+ rand multiplied by 0.3 is small, so that the search range is reduced, and local search is emphasized.
(2) An adaptive weight factor is added. The self-adaptive weight factor is added, so that the searching precision of the algorithm can be effectively improved, the convergence of the algorithm is accelerated, and the overall performance of the algorithm is improved. The adaptive weight factors mentioned herein are as follows:
xij(male)(t+1)＝w×xij(male)(t)+vij(male)(t+1) (19)
yij(fmale)(t+1)＝w×yij(fmale)(t)+vij(fmale)(t+1) (20)
4. example analysis
Taking a biomass energy multi-energy utilization system in northern China as an example, the system comprises a distributed wind power and photovoltaic and biomass natural gas combined supply system, a biomass natural gas boiler, a heat storage tank, a storage battery, a biomass natural gas fermentation tank and the like. The parameters of each energy supply device of the system are shown in table 1, the parameters of the energy storage device are shown in table 2, the time-of-use electricity purchasing price is shown in table 3, and the electricity selling price to the power distribution network is 0.65 yuan/kWh.
TABLE 1 System energy supply device parameters
TABLE 2 System energy storage device parameters
TABLE 3 time-of-use electricity purchase price
FIG. 2 is a graph of electric energy scheduling for a typical daylily typical in winter using the mayflies algorithm described herein, the biomass fermentation pool is self-built by operators of the biomass multi-energy utilization system, the biomass raw materials are sufficient, so the electric energy output of the biomass-natural gas co-generation system is stable, the photovoltaic and wind power work basically according to the predicted output, the storage battery is charged during the electricity price valley period and discharged during the electricity price peak period to perform the peak clipping and valley filling functions.
Fig. 3 is a heat energy scheduling curve of a typical daily biomass energy and multi-energy utilization system in winter obtained by adopting the modified dayflies algorithm provided by the present invention, the biomass fermentation pool is built by operators of the biomass energy and multi-energy utilization system, biomass raw materials are sufficient, so that the heat energy output of the biomass natural gas combined supply system and the biomass natural gas boiler is stable, the heat storage tank stores the redundant heat in the low valley period of the heat load, and the redundant heat is released in the peak period of the heat load, thereby meeting the requirements of the system.
The total daily operating costs of the biomass multi-energy utilizing system are calculated by using the improved mayflies algorithm, dayflies algorithm and particle swarm algorithm, and the total daily operating costs of the biomass multi-energy utilizing system obtained by using the improved mayflies algorithm is 2.91 × 108The daily operation total cost of the biomass energy multi-energy utilization system obtained by mayflies algorithm is 3.02 multiplied by 108The total daily running cost of the biomass energy multi-energy utilization system obtained by adopting the particle swarm optimization is 3.19 in a production108And (5) Yuan. The total daily operating cost of the biomass-energy multi-energy utilization system obtained by adopting the improved mayflies algorithm is reduced by 3.64% and 8.8% compared with the total daily operating cost of the system obtained by the mayflies algorithm and the particle swarm algorithm, which proves the effectiveness of the algorithm provided herein and realizes the optimized scheduling of the biomass-energy multi-energy utilization system.
The embodiment of the invention also provides a system for scheduling the biomass energy multi-energy utilization system, which is used for realizing the steps of the scheduling method of the biomass energy multi-energy utilization system and comprises the following steps:
the model building module is used for building a biomass energy multi-energy utilization system mathematical model, and comprises a biomass and natural gas combined supply system model and an energy storage device model;
the model optimization scheduling module is used for constructing an optimization scheduling model of the biomass energy multi-energy utilization system, the lowest daily operation total cost of the biomass energy multi-energy utilization system is taken as a target function, and the self condition of the mathematical model of the biomass energy multi-energy utilization system is taken as a constraint condition;
and the algorithm solving module is used for solving and calculating the optimized dispatching model by adopting the improved mayflies algorithm.
Fig. 4 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 4, an embodiment of the present invention provides an electronic device, which includes a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320, where the processor 1320 executes the computer program 1311 to implement the following steps: s1, constructing a biomass energy multi-energy utilization system mathematical model, including a biomass and natural gas combined supply system model and an energy storage device model;
s2, constructing an optimized scheduling model of the biomass energy multi-energy utilization system, taking the lowest daily running total cost of the biomass energy multi-energy utilization system as a target function, and taking the self condition of the mathematical model of the biomass energy multi-energy utilization system as a constraint condition;
s3, adopting the improved dayflies algorithm to solve and calculate the optimized scheduling model.
Please refer to fig. 5, which is a schematic diagram of an embodiment of a computer-readable storage medium according to the present invention. As shown in fig. 5, the present embodiment provides a computer-readable storage medium 1400, on which a computer program 1411 is stored, which computer program 1411, when executed by a processor, implements the steps of: s1, constructing a biomass energy multi-energy utilization system mathematical model, including a biomass and natural gas combined supply system model and an energy storage device model;
s2, constructing an optimized scheduling model of the biomass energy multi-energy utilization system, taking the lowest daily running total cost of the biomass energy multi-energy utilization system as a target function, and taking the self condition of the mathematical model of the biomass energy multi-energy utilization system as a constraint condition;
s3, adopting the improved dayflies algorithm to solve and calculate the optimized scheduling model.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain certain principles of the invention and its practical application to enable one skilled in the art to make and use various exemplary embodiments of the invention and various alternatives and modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (10)
1. A scheduling method of a biomass energy multi-energy utilization system is characterized by comprising the following steps:
s1, constructing a biomass energy multi-energy utilization system mathematical model, including a biomass and natural gas combined supply system model and an energy storage device model;
s2, constructing an optimized scheduling model of the biomass energy multi-energy utilization system, taking the lowest daily running total cost of the biomass energy multi-energy utilization system as a target function, and taking the self condition of the mathematical model of the biomass energy multi-energy utilization system as a constraint condition;
s3, adopting the improved dayflies algorithm to solve and calculate the optimized scheduling model.
2. The scheduling method of the biomass energy multi-energy utilization system according to claim 1, wherein the mathematical model of the biomass energy multi-energy utilization system comprises a distributed wind power and photovoltaic, biomass natural gas combined supply system, a biomass natural gas boiler, a heat storage tank, a storage battery and a biomass natural gas fermentation tank.
3. The scheduling method of biomass energy multi-energy utilization system according to claim 1, wherein the biomass natural gas combined supply system mainly supplies electric energy and heat energy, and the mathematical model is as follows:
in the formula, PH(t) is the heating power; pE(t) is the generated power; etaEThe power supply efficiency is improved; etaHThe heat supply efficiency is improved; etaRThe recovery rate of the waste heat of the flue gas is obtained;
the charge-discharge principle of the heat storage tank and the storage battery is consistent, and the unified mathematical model is as follows:
wherein E (t) is the total energy of the energy storage device; delta is the self-discharging efficiency of the energy storage device; pch(t) and Pdis(t) is the charging and discharging power of the energy storage device; etachAnd ηdisCharging and discharging efficiency of the energy storage device; Δ T is a unit period.
4. The scheduling method of a biomass energy multi-energy utilization system according to claim 3, wherein the objective function in S2 is:
in the formula, CiMaintenance costs for photovoltaic, wind power, storage batteries, thermal energy storage tanks, biomass and natural gas combined supply systems and boilers; ce1The cost of electricity purchase from the distribution grid; pi(t) real-time output power of photovoltaic, wind power, storage batteries, heat energy storage tanks, biomass and natural gas combined supply systems and boilers; pgrid(t) is the tie line interaction power; eebuyPurchasing electric carbon emission for an external power distribution network; eHCarbon emission of a biomass and natural gas combined supply system and a boiler; pgd(t) carbon emission power for biomass and natural gas combined supply system and boiler; a is1、b1And c1Calculating a carbon emission parameter of the purchased electric quantity for the external power distribution network; a is2、b2And c2Calculating parameters for carbon emission of a biomass and natural gas combined supply system and a boiler; pgbh(t) outputting thermal power for the biomass natural gas boiler; ce2Which is a carbon emission cost.
5. The scheduling method of the biomass energy multi-energy utilization system according to claim 4, wherein the constraint conditions in S2 are as follows:
(1) constraint of electric heat balance
In the formula, Peload(t) is the electrical load; phload(t) is the thermal load; pPV(t) power of the photovoltaic; pWT(t) is the power of the wind power; pestore(t) is the charge and discharge power of the lithium battery; p ishstore(t) is the heat charging and discharging power of the heat energy storage tank;
(2) junctor interaction power constraints
Pgridmin≤Pgrid(t)≤Pgridmax (6)
In the formula, PgridminMinimum interaction power for the tie line; pgridmaxMaximum interaction power for the tie line;
(3) power constraints for various energy supply and storage devices
In the formula (I), the compound is shown in the specification,
the maximum power of the photovoltaic power, the wind power, the storage battery, the heat energy storage tank, the biomass natural gas combined supply system and the boiler is obtained;
(4) energy storage device restraint
0≤E(t)≤Emax (8)
In the formula, EmaxIs the maximum capacity of the energy storage device.
6. The biomass energy multi-energy utilization system scheduling method according to claim 1, characterized in that said modified mayflies algorithm comprises in particular:
s31, providing an optimal dayflies helping strategy; the expression thereof is as follows,
in the formula, Xworst(t) are the worst positions of mayflies; xnworst(t) the positions of dayflies after helping to hold; t is the iteration number of the algorithm; gbestThe optimal positions experienced by all mayflies.
7. The scheduling method of a biomass energy multi-energy utilization system according to claim 6, wherein said S31 is followed by S32: the adaptive weight factor is added as follows:
xij(male)(t+1)＝w×xij(male)(t)+vij(male)(t+1) (11)
yij(fmale)(t+1)＝w×yij(fmale)(t)+vij(fmale)(t+1) (12)
in the formula, vij(male)(t +1) is the velocity of the male parent; x is the number ofij(male)(t) and xij(male)(t +1) is the position of the male parent; w is the adaptive weight factor.
8. A system for scheduling a biomass energy multi-energy utilization system, wherein the system is used for implementing the steps of the scheduling method of the biomass energy multi-energy utilization system according to any one of claims 1 to 7, and comprises:
the model building module is used for building a biomass energy multi-energy utilization system mathematical model, and comprises a biomass and natural gas combined supply system model and an energy storage device model;
the model optimization scheduling module is used for constructing an optimization scheduling model of the biomass energy multi-energy utilization system, the lowest daily operation total cost of the biomass energy multi-energy utilization system is taken as a target function, and the self condition of the mathematical model of the biomass energy multi-energy utilization system is taken as a constraint condition;
and the algorithm solving module is used for solving and calculating the optimized scheduling model by adopting the improved dayflies algorithm.
9. An electronic device, comprising a memory and a processor, wherein the processor is configured to implement the steps of the biomass energy multi-energy utilization system scheduling method according to any one of claims 1 to 7 when executing a computer management program stored in the memory.
10. A computer-readable storage medium, having a computer management-like program stored thereon, wherein the computer management-like program, when executed by a processor, implements the steps of the biomass energy multi-energy utilization system scheduling method according to any one of claims 1 to 7.
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