CN109299829B - Polymorphic energy storage configuration method and system for photovoltaic greenhouse micro-energy network system - Google Patents
Polymorphic energy storage configuration method and system for photovoltaic greenhouse micro-energy network system Download PDFInfo
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Abstract
The embodiment of the invention provides a polymorphic energy storage configuration method and a polymorphic energy storage configuration system for a photovoltaic greenhouse micro-energy grid system, wherein the method comprises the following steps: establishing a double-layer planning model, wherein the double-layer planning model comprises an upper-layer planning model and a lower-layer planning model, the upper-layer planning model is used for optimally configuring the energy storage capacity of the polymorphic energy storage devices by taking the low investment and operation cost of a photovoltaic greenhouse micro-energy network system within one year as a target, and the lower-layer planning model is used for optimizing the time-shifting load working state quantity and the input and output power of the polymorphic energy storage devices within one day by taking the local consumption degree of a photovoltaic power supply as a target; acquiring the prediction data of a plurality of typical scene days and the occurrence days of the scene days of a photovoltaic greenhouse micro-energy network system; and solving the double-layer planning model according to the prediction data of the typical scene day to obtain the optimal energy storage capacity of each form of energy storage device of the photovoltaic greenhouse micro-energy network system. The embodiment of the invention realizes high-proportion on-site consumption of photovoltaic power generation under various meteorological scenes and greenhouse operation scenes all the year around.
Description
Technical Field
The embodiment of the invention relates to the technical field of energy, in particular to a polymorphic energy storage configuration method and a polymorphic energy storage configuration system for a photovoltaic greenhouse micro-energy grid system.
Background
As an extension of the energy Internet, the micro-energy network concept containing polymorphic energy provides a new idea for solving the problem of local consumption of distributed power generation. Photovoltaic power generation is widely applied to vast rural areas, wherein a photovoltaic greenhouse is a typical application combining photovoltaic power generation and facility agriculture, and a photovoltaic greenhouse micro-energy network system mainly based on modern planting industry is formed. The photovoltaic greenhouse micro-energy network system combines a multi-energy complementary control technology with a modern agricultural planting technology, fully exerts the advantages of solar energy, biomass energy and novel agricultural load, realizes the process of 'collecting on the spot, storing on the spot and using on the spot' of solar energy through optimized regulation and control, and finally achieves the purpose of promoting the growth of crops in the photovoltaic greenhouse. And the 'local storage' link plays a crucial role in energy space-time movement in the process of optimizing and regulating so as to achieve the purposes of storing the peak output of the photovoltaic power supply and providing energy for the load in the valley period of the output, so that how to configure the energy storage type and the capacity becomes a key premise for the successful implementation of the technology.
At present, most of researches on the micro-energy network are discussed in terms of concept and architecture, and researches on energy storage optimization configuration of the photovoltaic greenhouse micro-energy network system are not reported. In the prior art, a microgrid with a photovoltaic power supply usually adopts electric energy storage mainly comprising a storage battery, the energy storage form is single, and the investment and maintenance cost is increased along with the increase of the installation capacity of the storage battery, so that the configuration of the energy storage capacity in the microgrid is conservative, and the manufacturing cost and the maintenance cost are high; and the energy optimization scheduling strategy of the micro-grid with the economy as the target is realized by utilizing the energy space-time movement function of the stored energy at the end of the root, so that the frequent charging and discharging operation of the stored energy of the storage battery cannot be avoided, and the service life of the stored energy is greatly shortened under the influence of the frequent charging and discharging. Compared with the electricity energy storage configuration in the microgrid system which is widely researched at present, the photovoltaic greenhouse microgrid has loads of various energy forms such as electricity, heat, water and the like, so that the energy storage of various energy forms can be configured, and the traditional microgrid energy storage optimal configuration method with a single storage battery is not applicable to the polymorphic energy storage configuration of the photovoltaic greenhouse microgrid system.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a photovoltaic greenhouse micro-energy grid system polymorphic energy storage configuration method and system, which overcome the above problems or at least partially solve the above problems.
According to a first aspect of the embodiments of the present invention, there is provided a polymorphic energy storage configuration method for a photovoltaic greenhouse micro-energy grid system, the method including: establishing a double-layer planning model, wherein the double-layer planning model comprises an upper-layer planning model and a lower-layer planning model, the upper-layer planning model is used for optimally configuring the energy storage capacity of the polymorphic energy storage devices by taking the low investment and operation cost of a photovoltaic greenhouse micro-energy network system within one year as a target, and the lower-layer planning model is used for optimizing the time-shifting load working state quantity and the input and output power of the polymorphic energy storage devices within one day by taking the local consumption degree of a photovoltaic power supply as a target; the polymorphic energy storage comprises at least two of a reservoir, a methane tank, a phase change heat storage device and a storage battery; acquiring prediction data of a plurality of typical scene days and scene day occurrence days of a photovoltaic greenhouse micro-energy network system, wherein the prediction data comprises photovoltaic power generation prediction data and various types of load prediction data of production activities of a photovoltaic greenhouse; and solving the double-layer planning model according to the prediction data of the typical scene day to obtain the optimal energy storage capacity corresponding to each shape of energy storage device of the photovoltaic greenhouse micro-energy network system.
According to a second aspect of the embodiments of the present invention, there is provided a polymorphic energy storage configuration system of a photovoltaic greenhouse micro-energy grid system, the system including: the system comprises an establishing module, a judging module and a control module, wherein the establishing module is used for establishing a double-layer planning model, the double-layer planning model comprises an upper layer planning model and a lower layer planning model, the upper layer planning model is used for optimally configuring the energy storage capacity of the polymorphic energy storage devices by taking the low investment and operation cost of a photovoltaic greenhouse micro-energy network system within one year as a target, and the lower layer planning model is used for optimizing the working state quantity of each time-shiftable load and the input and output power of each morphological energy storage device within one day by taking the large local consumption degree of a photovoltaic; the polymorphic energy storage comprises at least two of a reservoir, a methane tank, a phase change heat storage device and a storage battery; the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring the prediction data of a plurality of typical scene days and the occurrence days of the scene days of a photovoltaic greenhouse micro-energy network system, and the prediction data comprises photovoltaic power generation prediction data and various types of load prediction data of production activities of the photovoltaic greenhouse; and the solving module is used for solving the double-layer planning model according to the prediction data of the typical scene day to obtain the optimal energy storage capacity corresponding to each form of energy storage device of the photovoltaic greenhouse micro-energy network system.
According to the polymorphic energy storage configuration method and system for the photovoltaic greenhouse micro-energy network system, the double-layer planning model is solved through the prediction data of a plurality of typical scene days of the photovoltaic greenhouse micro-energy network system, the optimal energy storage capacity corresponding to each morphological energy storage device of the photovoltaic greenhouse micro-energy network system is obtained, and therefore the energy storage capacities of the energy storage devices in various energy forms can be configured, the polymorphic load requirements can be met, the energy storage investment and operation maintenance cost can be reduced, and high-proportion on-site consumption of photovoltaic power generation in various meteorological scenes and greenhouse operation scenes all the year around is achieved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from these without inventive effort.
FIG. 1 is a schematic flow chart of a polymorphic energy storage configuration method of a photovoltaic greenhouse micro-energy grid system provided by an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a photovoltaic greenhouse micro-energy network system provided by an embodiment of the invention;
FIG. 3 is a schematic flow chart of a polymorphic energy storage configuration method of a photovoltaic greenhouse micro-energy grid system according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of a polymorphic energy storage configuration system of a photovoltaic greenhouse micro-energy grid system provided by an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at the problem that the requirement of multi-energy form configuration cannot be met by adopting a single energy form for energy storage in the prior art, the embodiment of the invention provides a multi-form energy storage configuration method for a photovoltaic greenhouse micro-energy network system.
Referring to fig. 1, the method includes:
101. the method comprises the steps of obtaining prediction data of a plurality of typical scene days and scene day occurrence days of a photovoltaic greenhouse micro-energy network system, wherein the prediction data comprises photovoltaic power generation prediction data and various types of load prediction data of production activities of the photovoltaic greenhouse.
Wherein, the polymorphic energy storage at least comprises the following forms: the storage tank, the methane tank, the phase change heat storage device and the storage battery are at least two types, and the embodiment of the invention is not limited to specific forms. The various types of loads include time-shiftable electrical loads, thermal loads, potential energy loads, and the like. The prediction data of the typical scene day are photovoltaic power generation prediction data and various types of load prediction data in one day.
102. Establishing a double-layer planning model, wherein the double-layer planning model comprises an upper-layer planning model and a lower-layer planning model, the upper-layer planning model is used for optimally configuring the energy storage capacity of the polymorphic energy storage devices by taking the low investment and operation cost of a photovoltaic greenhouse micro-energy network system within one year as a target, and the lower-layer planning model is used for optimizing the time-shifting load working state quantity and the input and output power of the polymorphic energy storage devices within one day by taking the local consumption degree of a photovoltaic power supply as a target; the polymorphic energy storage comprises at least two of a reservoir, a methane tank, a phase change heat storage device and a storage battery.
The embodiment of the invention does not limit the specific function expression of the upper-layer planning model and the lower-layer planning model, only the upper-layer planning model is needed to configure and obtain the optimal energy storage capacity of each form of energy storage device with the aim of realizing that the operating cost of the system is as low as possible within one year, and the lower-layer planning model optimally configures each time-shiftable load working state quantity (i.e. the time-shiftable load working plan or power) and the input and output power of each form of energy storage device within one day as large as possible with the aim of the local consumption degree of the photovoltaic power supply. In addition, the execution order of step 101 and step 102 may be interchanged.
103. And solving the double-layer planning model according to the prediction data of the typical scene day to obtain the optimal energy storage capacity corresponding to each shape of energy storage device of the photovoltaic greenhouse micro-energy network system.
Specifically, based on the established double-layer planning model, when the double-layer planning model is solved according to the prediction data of the typical scene day, because in a broad sense, the solution of the lower-layer planning model in the double-layer planning model is the calculation parameter of the upper-layer planning model, in the solving process, the working state quantity of each time-shiftable load and the input and output power of each form energy storage device in one day can be obtained by calculation based on the lower-layer planning model, and then the upper-layer planning model is solved, so that the optimal energy storage capacity is obtained finally.
According to the polymorphic energy storage configuration method for the photovoltaic greenhouse micro-energy network system, the double-layer planning model is solved through the prediction data of a plurality of typical scene days of the photovoltaic greenhouse micro-energy network system, and the optimal energy storage capacity corresponding to each morphological energy storage device of the photovoltaic greenhouse micro-energy network system is obtained, so that the energy storage capacities of the energy storage devices in various energy forms can be configured, the polymorphic load requirements can be met, the energy storage investment and the operation and maintenance cost can be reduced, and the high-proportion local consumption of photovoltaic power generation under various meteorological scenes and greenhouse operation scenes all the year around is realized.
Based on the content of the embodiment, as an optional embodiment, the objective function of the upper layer planning model is the sum of the annual investment cost corresponding to each energy storage device and the annual operation and maintenance cost of the photovoltaic greenhouse micro-energy network system; the constraint condition of the upper-layer planning model is the range of the energy storage capacity corresponding to each energy storage device.
Specifically, the specific expression and the constraint condition of the following function are only one possible implementation manner of the two-layer planning model, and the scope of the embodiment of the present invention is not limited thereto.
1. Upper-layer planning model with annual investment and operation cost as small as possible
The control variables of the upper-layer planning model are the construction capacities of energy storage devices in various forms such as a water storage tank, a methane tank, a phase change heat storage device, a storage battery and the like, and the planning aim of realizing the lowest annual investment and operation cost of the system is fulfilled.
1) Objective function
f1＝min(CXU+CZHAO+CRE+Cbt+C1) (1)
In the formula: cXUThe investment annual cost of the reservoir is saved; cZHAOThe investment annual cost of the methane tank; cREThe annual investment cost of the phase change heat storage device; cbtThe annual investment cost of the storage battery; c1The annual operation and maintenance cost of the photovoltaic greenhouse system is saved.
Wherein, the annual investment cost of the reservoir
In the formula: r is the discount rate; x is the service life of the reservoir; cXUDWInvestment cost for unit capacity of the reservoir; zXUAnd (5) building the volume of the reservoir.
Second annual investment cost of methane tank
In the formula: r is the discount rate; z is the life span of the methane tank; cXUDWInvestment cost of unit capacity of the methane tank; zZHAOAnd (5) putting the volume into the methane tank.
Annual investment cost of phase change heat storage
In the formula: r is the discount rate; e is the service life of the phase change heat storage device; cXUDWInvestment cost per unit capacity of the phase change heat storage device; zZHAOAnd (5) putting capacity into the phase change heat storage device.
Annual investment cost of storage battery
In the formula: r is the discount rate; w is the life span of the storage battery; cbtDWInvestment cost per unit capacity of the storage battery; zbtAnd (5) putting capacity into the storage battery.
Annual operation and maintenance cost of photovoltaic greenhouse system
In the formula: dsTotal days of occurrence of scene s in a year; n is the total number of scenes; pcx,iPower is purchased and sold for the micro-energy network system and the power distribution network at the ith time period, wherein the power is purchased positively and sold negatively; cp,iIs the actual time-of-use electricity price in the i period, Pcx,iWhen the time is less than 0, the electricity selling time-sharing price P of the time interval is takencx,iWhen the time is more than 0, the electricity purchasing time-of-use price of the time period is taken; pXU,iReservoir power for the ith time period; cXU_omThe maintenance cost for unit power of the reservoir; pZHAO,iThe power of the methane tank in the ith period; cZHAO_omThe production and maintenance cost of the methane tank per unit power is saved; pRE,iThe power of the phase change heat storage device is the ith time period; cRE_omThe maintenance cost for the unit power of the phase change heat storage device; pbt,iThe charging and discharging power of the storage battery in the ith time period is positive, and the charging is negative; cbt_omMaintenance cost for battery unit power; ppv,iPhotovoltaic power generation power for the ith time period; cpv_omThe maintenance cost of the unit power of the photovoltaic power generation is saved.
2) Constraint conditions
Maximum built-in capacity constraint of energy storage devices in various forms such as water storage pool, methane tank, phase change heat storage device, storage battery and the like
In the formula: zXU、ZZHAO、ZRE、ZbtThe construction capacities of the reservoir, the methane tank, the phase change heat storage device and the storage battery are respectively and are also control variables (solving variables) of the upper-layer planning model; zXUmax、ZZHAOmax、ZREmax、ZbtmaxThe maximum allowable building capacities of the water storage tank, the methane tank, the phase change heat storage device and the storage battery are respectively.
Based on the content of the above embodiment, as an optional embodiment, the objective function of the lower layer planning model is the sum of squares of the difference between the load energy and the photovoltaic power generation amount in one day; the constraint conditions of the lower-layer planning model comprise electric energy balance constraint, heat energy balance constraint, potential energy balance constraint, photovoltaic greenhouse micro-energy grid system and power distribution network power exchange constraint, energy storage device space constraint of each form, time-shifting load continuous working state constraint, time-shifting load non-working period constraint and input and output state quantity mutual exclusion constraint of the energy storage devices of each form.
Specifically, the control variables of the lower-layer planning model are energy storage input and output state quantities of various forms and time-shiftable load working state quantities in a day, and energy storage built-in capacity of various forms determined by the upper-layer planning model is used as a constraint boundary. The lower-layer planning model is equivalent to making a daily operation scheduling plan of the photovoltaic greenhouse micro-energy network system, so that the local consumption degree of the photovoltaic power supply is taken as an optimization target as much as possible, the objective function of the optimization scheduling problem is established to enable the energy consumption curve of the system in one day to be similar to the photovoltaic output curve to the maximum degree, and the objective function is described by adopting the square sum of the difference between the load energy consumption and the photovoltaic power generation amount in the scheduling period.
1) Objective function
In the formula: piThe unit is kW for the optimized load power in the period i; ppviThe predicted value of the power generation power of the photovoltaic power supply in the unit of kW is in the period i;
Pithe system consists of a non-time-shifting load, a time-shifting load and stored input and output energy in an i period:
Pi＝Pdxi+Pmovei+Pci (9)
in the formula, PdxiThe total energy of the non-timeshiftable load in the period i is kW; pmoveiIn the period i, the load power of electricity, heat and potential energy can be shifted, kW is shown as a formula (10); pciAnd storing the total input power, kW, for the period i, as shown in the formula (11).
In the formula, n is the number of time-shifting electric loads; m is the number of potential energy loads capable of time shifting; v is the number of time-shiftable thermal loads; pejThe power of the jth timeshiftable load is kW; ppjThe power of the jth potential energy load capable of time shifting, kW; pqjRespectively corresponding to the power, kW, of the jth timeshiftable thermal load; x is the number ofej,iFor the working state quantity of the jth type time-shiftable electric load in the ith scheduling period, 0 represents that the electric load is in a non-working state, and 1 represents that the electric load is in a working state; x is the number ofpj,iWorking state quantity of the jth type time-shiftable potential energy load in the i period; x is the number ofqj,iThe operating state quantity of the j-th type time-shiftable thermal load is a period i.
Pci＝PSIxSIi-PSOxSOi+PZIxZIi-PZOxZOi+PXIxXIi-PXOxXOi+PBIxBIi-PBOxBOi (11)
In the formula: x is the number ofSIi、xZIi、xXIiAnd xBIiRespectively representing input state quantities of the reservoir, the methane tank, the phase change heat storage device and the storage battery during the ith time period, wherein 0 represents that the reservoir, the methane tank, the phase change heat storage device and the storage battery are in an off state, and 1 represents that the reservoir, the methane tank, the phase change heat storage device and the storage battery are in an input state; x is the number ofSOi、xZOi、xXOiAnd xBOiRespectively representing the output state quantities of the reservoir, the methane tank, the phase change heat storage device and the storage battery during the period i, wherein 0 represents the non-working state, and 1 represents the output state; pSI、PZI、PXI、PBIRespectively representing the rated input power of the water storage pool, the methane tank, the phase change heat storage device and the storage battery; pSO、PZO、PXO、PBOiRespectively showing the rated output power of the water storage tank, the methane tank, the phase change heat storage device and the storage battery.
It is noted that the annual operation and maintenance cost C of the photovoltaic greenhouse system is calculated in the upper-layer planning model1While, the reservoir power P in the formula (6) thereofXU,iNamely, the reservoir input and output state quantity and the rated input and output power thereof in the formula (11) are obtained as follows:
similarly, the power P of the methane tank in the formula (6)ZHAO,iPhase change heat storage device power PRE,iAnd battery charge and discharge power Pbt,iThe corresponding energy storage input and output state quantity and the rated input and output power thereof in the formula (11) are obtained according to the similar formula (12), and are not described again here.
2) Constraint conditions
Electric energy balance constraint
In the formula: pcx,iPower is purchased and sold for the micro-energy network system and the power distribution network at the ith time period, wherein the power is purchased positively and sold negatively; pbt,iThe charging and discharging power of the storage battery in the ith time period is positive, and the charging is negative; pdxe,iFor the power of the electric load which can not be shifted in the period i, a fixed value is taken according to the actual situation of the agricultural production activity of the micro energy grid; pejThe j type time-shiftable electric load power; x is the number ofej,iScheduling the working state quantity of the time-shiftable electrical load of the jth type for the period i.
② heat energy balance constraint
In the formula: pZO、PXORespectively showing the rated output power of the methane tank and the phase change heat storage device. x is the number ofZOi、xXOiRespectively representing the output state quantities of the methane tank and the phase change heat storage device in the period i; pdxq,iTaking a fixed value according to the actual situation of the agricultural production activity of the micro energy network for the heat load power which can not be shifted in the period i; pqjTime-shiftable thermal load power of jth; x is the number ofqj,iScheduling period j for i the workload amount of the jth type of timeshiftable thermal load.
Third, potential energy balance constraint
In the formula: pSOIndicating the rated output power of the reservoir. x is the number ofSOiRespectively representing the output state quantity of the reservoir in the period i; pdxp,iAcquiring a fixed value for potential energy load power which can not be time-shifted in the period i according to the actual situation of the agricultural production activity of the micro energy network; ppjThe j type time-shiftable potential energy load power; x is the number ofpj,iScheduling the working state quantity of the jth type time-shiftable potential energy load for the period i.
Power exchange constraint with distribution network
Pcx,min≤Pcx,i≤Pcx,max i＝1...24 (16)
In the formula: pcx,minAnd Pcx,maxRespectively exchange power between the system and the distribution networkThe allowable lower limit and upper limit of the rate.
Energy storage space constraint of each form
In the formula: eXU i-1、EZHAO i-1、ERE i-1、Ebt i-1Respectively representing the energy stored in the reservoir, the methane tank, the phase change heat storage device and the storage battery at the end of the previous time period (i.e. the i-1 time period), in kWh; zXU、ZZHAO、ZRE、ZbtRespectively determining the built capacities of a reservoir, a methane tank, a phase change heat storage device and a storage battery in the upper layer planning model; delta t is a time interval, and is taken for 1 hour; etaSI、ηZI、ηXIRespectively the energy storage efficiency of the water storage tank, the methane tank and the phase change heat storage device. The remaining variables have the same meaning as in formula (11).
Sixthly, constraint of continuous working state of time-shifting load
Due to process flow limitations, some time-shiftable loads must be operated continuously for several periods of time, for example, a physical insecticidal load must be operated continuously for 2 hours to achieve an effect. Therefore, the following continuous working load constraints are established for the time-shiftable loads needing continuous working:
xLi·xLi+1…xLi+k-11(i 1 … 24, L e G) (18)
In the formula: x is the number ofLt、xLt+1And xLt+k-1Respectively representing the working state quantities of the time-shiftable loads L in the ith, i +1 and i + k-1 periods, and G is a continuous working time-shiftable load set.
Seventhly, constraint of non-working time interval of time-shiftable load
Due to production limitations, certain time-shiftable loads cannot operate for certain specific periods of time, such as watering irrigation loads, due to plant growth effects ranging from 0:00 to 10:00, 19: 00-24: 00 can not work. Thus, a down-time period status constraint is established for such time-shiftable loads.
xLi0 (i.n and L e F) (19)
In the formula: x is the number ofltRepresenting the working state quantity of the time-shiftable load L in the ith period; n is the set of time-shiftable loads L that are not operable for a particular period of time, and F is the set of time-period-of-operation limiting time-shiftable loads.
Energy storage input and output state quantity mutual exclusion constraint of every form
Energy storage of each form cannot be in an input state and an output state at the same time, namely the energy storage input and output state quantity needs to meet mutual exclusion constraint:
the variables in the formula (II) have the same meanings as in formula (11).
Based on the content of the foregoing embodiment, as an optional embodiment, a method for obtaining prediction data of several typical scene days of a photovoltaic greenhouse micro-energy network system is provided, including but not limited to:
And 2, acquiring photovoltaic power generation data of the photovoltaic greenhouse micro-energy network system in each hour in one year according to the relational expression of the solar irradiance and the photovoltaic output. The photovoltaic power generation data can also be called photovoltaic output data, namely the power value of photovoltaic power generation.
And 3, according to the photovoltaic power generation data of each hour, counting by adopting a clustering method to obtain photovoltaic output curves of a plurality of typical scene days, the load condition of production activities of the photovoltaic greenhouse and the number of days of each typical scene day within one year. The statistical method may specifically adopt a clustering method to perform statistics.
According to the method provided by the embodiment of the invention, the planned prediction data of a plurality of typical scene days in one year and the occurrence days of each typical scene day in one year are constructed by utilizing the solar irradiance historical data of the photovoltaic greenhouse micro-energy grid system, so that the prediction basic data is provided for optimizing the energy storage configuration.
Based on the content of the above embodiment, as an optional embodiment, a method for solving a double-layer planning model according to prediction data of a typical scene day to obtain the optimal energy storage capacity of each form of energy storage device of a photovoltaic greenhouse micro-energy network system is provided, which includes but is not limited to: randomly generating a plurality of energy storage capacity chromosomes according to the constraint conditions of the upper-layer planning model; for each energy storage capacity chromosome, solving the lower-layer planning model according to the energy storage capacity chromosome, the prediction data of the typical scene days and the occurrence days of each typical scene day in one year to obtain the optimal state quantity of each time-shiftable load of the typical scene day and the optimal input and output power of each form of energy storage device; calculating annual operation maintenance cost according to the optimal state quantity of each time-shiftable load and the optimal input and output power of each form of energy storage device in each typical scene day; calculating an objective function value of an upper-layer planning model corresponding to the energy storage capacity chromosome according to the annual operation maintenance cost; and taking the energy storage capacity of each form of energy storage device corresponding to the energy storage capacity chromosome with the optimal objective function value as the optimal configuration capacity of each form of energy storage device.
Before the step 1 is executed, a solution method of the double-layer programming model is described below, and the solution method may specifically adopt a double-layer iterative programming method based on an improved genetic algorithm.
(1) Solving method of upper-layer planning model
The solution of the upper-layer planning model can be specifically solved by adopting a real number coded genetic algorithm, and the control variable is the built-in capacity (namely the storage capacity) of each energy storage device such as a reservoir, a methane tank, a phase change heat storage device, a storage battery and the like, so that the chromosome coding mode of the upper-layer planning model is as follows:
Z＝[Z1,Z2,Z3,Z4] (21)
in the formula Z1、Z2、Z3、Z4And (3) coding the capacities of the reservoir, the methane tank, the phase change heat storage device and the storage battery, and taking real numbers meeting the constraint of the formula (7).
(2) Solving method of lower-layer planning model
The underlying planning model may be solved based on a matrix binary-coded genetic algorithm. The control variables of the lower layer planning model are: the photovoltaic greenhouse micro-energy network system can shift the working state quantities of the electric load, the thermal load and the potential energy load in each time interval, and the input and output state quantities (namely the input and output powers of the energy storage devices in each form) of various energy storage devices such as a storage battery, a water storage tank, a methane tank and a phase change heat storage device in each time interval. Solving by adopting a genetic algorithm, and decomposing a control variable code into two relatively independent sub-chromosome codes by using a binary coding mode of a grouping matrix, namely a sub-chromosome L of a working state quantity matrix of each type of time-shiftable load in each time period and an input and output state quantity matrix Q of each form of energy storage in each time period. The matrix C formed by the control variables is represented as:
l is a working state quantity matrix of each type of time-shiftable load in each time interval:
in the formula: x is the number ofej,iThe working state quantity of the jth type time-shiftable electric load is the ith period; x is the number ofpj,iWorking state quantity of the jth type time-shiftable potential energy load in the ith period; x is the number ofqj,iFor the working state quantity of the jth type time-shiftable thermal energy load in the ith period, 0 represents in a non-working state, and 1 represents in a working state.
Q is a matrix of the input and output state quantities of the energy storage of each form in each time period, and is represented by formula (24):
in the formula: x is the number ofSIi、xZIi、xXIi、xBIiRespectively representing input state quantities of the reservoir, the methane tank, the phase change heat storage device and the storage battery during the ith time period, wherein 0 represents that the reservoir, the methane tank, the phase change heat storage device and the storage battery are in an off state, and 1 represents that the reservoir, the methane tank, the phase change heat storage device and the storage battery are in an input state; x is the number ofSOi、xZOi、xXOiAnd xBOiRespectively showing the output state quantities of the reservoir, the methane tank, the phase change heat storage device and the storage battery during the ith time period, wherein 0 shows the non-working state, and 1 shows the output state.
(3) Double-layer iterative optimization solving process
The upper layer planning model in the double-layer planning problem belongs to the planning problem, and the lower layer planning model belongs to the production simulation problem. The upper layer chromosome is the planning capacity, and the lower layer chromosome is the energy storage input and output state quantity of each form and the working state quantity of each time-shifting load. In the iteration process, the upper layer chromosome is a constraint condition of the lower layer chromosome, and then the loop iteration optimization is carried out according to the fitness function value; the objective function value formed by the optimal solution of the lower layer chromosome is a part of the upper layer objective function value, and influences the optimizing direction of the upper layer chromosome.
Specifically, according to each energy storage capacity chromosome Z as a capacity boundary of energy storage, an initial population C, namely a time-shifting load state quantity matrix L and an energy storage input and output state quantity matrix Q, under each typical scene day is obtained through a lower-layer planning model by solving, and the solving method adopts a binary genetic algorithm to obtain the optimal solution of the lower-layer planning model, namely L and Q. After each typical state day is solved in sequence, the optimal state quantity L of each time-shiftable load and the optimal input and output power Q of each form energy storage device in each typical scene day can be obtained. Then substituting the time-shiftable load state quantity matrix L and the energy storage input and output state quantity matrix Q of N typical days obtained by optimizing the lower layer model into the upper layerPlan model solution annual operation maintenance cost C1Further obtain the objective function value f of the upper layer planning model1. Thereby obtaining the upper layer planning model objective function value f corresponding to the energy storage capacity chromosome1. And after cyclic processing, the objective function value corresponding to each energy storage capacity chromosome can be obtained.
To illustrate the solution process of the two-layer planning model, referring to fig. 2 and 3, the solution process of the two-layer planning model is illustrated, which includes the following steps:
And 2, inputting the data and parameters such as time-of-use electricity price, cost coefficient, energy storage rated power, load power and the like required in the double-layer planning model.
And 3, randomly generating an initial population of the upper-layer planning model, namely an energy storage capacity chromosome Z according to the constraint range of the formula (7), and juxtaposing the upper-layer planning iteration number t as 1.
And 4, aiming at each chromosome Z in the upper-layer planning population as a capacity boundary of energy storage, generating an initial population C to be solved by the lower-layer model under the typical day scene, namely a time-shifting load state quantity matrix L and an energy storage input and output state quantity matrix Q, obtaining the optimal solution of the lower-layer planning by using a binary genetic algorithm until the lower-layer planning model under N typical days is solved, and finally obtaining the optimal solution of the time-shifting load state quantity and the energy storage input and output state quantity under each typical day scene.
And 5, substituting the time-shiftable load state quantity matrix L and the energy storage input and output state quantity matrix Q of the N typical days obtained by optimizing the lower layer model into the upper layer model to solve the annual operation and maintenance cost C1Further obtain the objective function value f of the upper layer planning model1。
And 6, carrying out selection, crossing and mutation genetic operations on the upper planning population to generate an upper planning new population.
Step 7, judging the upper-layer planning loop termination condition, and stopping and outputting an optimization result if the loop iteration reaches the set maximum iteration frequency; otherwise, the upper layer planning iteration time t ═ t +1, and go to step 4.
Based on the content of the above embodiment, the embodiment of the invention provides a polymorphic energy storage configuration system of a photovoltaic greenhouse micro-energy grid system, and the polymorphic energy storage configuration system of the photovoltaic greenhouse micro-energy grid system is used for executing the polymorphic energy storage configuration method of the photovoltaic greenhouse micro-energy grid system in the above method embodiment. Referring to fig. 4, the system includes: an obtaining module 401, an establishing module 402 and a solving module 403, wherein:
the acquisition module 401 is used for acquiring prediction data of a plurality of typical scene days of the photovoltaic greenhouse micro-energy network system, wherein the prediction data comprises photovoltaic power generation prediction data and various types of load prediction data; the solving module 403 is configured to solve the double-layer planning model according to the prediction data of the typical scene day to obtain optimal energy storage capacities corresponding to the energy storage devices in the forms of the photovoltaic greenhouse micro-energy grid system; the double-layer planning model established by the establishing module 402 comprises an upper-layer planning model and a lower-layer planning model, the upper-layer planning model is used for optimally configuring the energy storage capacity of each energy storage device in various forms by taking low operation cost of the photovoltaic greenhouse micro-energy network system in one year as a target, and the lower-layer planning model is used for optimally configuring the time-shiftable load working state quantity and the input and output power of each energy storage device in one day by taking the photovoltaic power supply local consumption degree as a target.
Wherein, the polymorphic energy storage at least comprises the following forms: the invention relates to a water storage tank, a methane tank, a phase change heat storage device and a storage battery. Based on the established double-layer planning model, when the solving module 402 solves the double-layer planning model according to the prediction data of the typical scene day, because in a broad sense, the solution of the lower-layer planning model in the double-layer planning model is the calculation parameter of the upper-layer planning model, in the solving process, the solving module 402 can calculate and obtain the working state quantity of each time-shiftable load and the input and output power of each shape energy storage device in one day based on the lower-layer planning model, and then solve the upper-layer planning model, and finally obtain the optimal energy storage capacity.
According to the polymorphic energy storage configuration system of the photovoltaic greenhouse micro-energy network system, the double-layer planning model is solved through the prediction data of a plurality of typical scene days of the photovoltaic greenhouse micro-energy network system, and the optimal energy storage capacity corresponding to each morphological energy storage device of the photovoltaic greenhouse micro-energy network system is obtained, so that the energy storage capacities of the energy storage devices in various energy forms can be configured, the polymorphic load requirements can be met, the energy storage investment and the operation and maintenance cost can be reduced, and the high-proportion local consumption of photovoltaic power generation under various meteorological scenes and greenhouse operation scenes all the year around is realized.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A polymorphic energy storage configuration method for a photovoltaic greenhouse micro-energy grid system is characterized by comprising the following steps:
establishing a double-layer planning model, wherein the double-layer planning model comprises an upper-layer planning model and a lower-layer planning model, the upper-layer planning model is used for optimizing and configuring the energy storage capacity of a polymorphic energy storage device by taking the low investment and operation cost of the photovoltaic greenhouse micro-energy network system within one year as a target, and the lower-layer planning model is used for optimizing the working state quantity of each time-shiftable load and the input and output power of each morphological energy storage device within one day by taking the local consumption degree of a photovoltaic power supply as a target; the polymorphic energy storage comprises at least two of a reservoir, a methane tank, a phase change heat storage device and a storage battery;
acquiring prediction data of a plurality of typical scene days and scene day occurrence days of a photovoltaic greenhouse micro-energy network system, wherein the prediction data comprises photovoltaic power generation prediction data and various types of load prediction data of production activities of a photovoltaic greenhouse;
solving a double-layer planning model according to the prediction data of the typical scene day to obtain the optimal energy storage capacity corresponding to each form of energy storage device of the photovoltaic greenhouse micro-energy network system; in the solving process, the working state quantity of each time-shiftable load and the input and output power of each form of energy storage device in one day are calculated and obtained on the basis of the lower-layer planning model, and then the upper-layer planning model is solved.
2. The method according to claim 1, wherein the objective function of the upper layer planning model is the sum of the annual investment cost corresponding to each energy storage device and the annual operation and maintenance cost of the photovoltaic greenhouse micro-energy network system; and the constraint condition of the upper-layer planning model is the range of the energy storage capacity corresponding to each energy storage device.
3. The method according to claim 2, wherein the annual operating maintenance cost of the photovoltaic greenhouse micro energy grid system is:
in the formula: dsTotal days of occurrence of scene s in a year; n is the total number of scenes; pcx,iPower is purchased and sold for the micro-energy network system and the power distribution network at the ith time period, wherein the power is purchased positively and sold negatively; cp,iIs the actual time-of-use electricity price in the i period, Pcx,iWhen the time is less than 0, the electricity selling time-sharing price P of the time interval is takencx,iWhen the time is more than 0, the electricity purchasing time-of-use price of the time period is taken; pXU,iReservoir power for the ith time period; cXU_omFor maintenance of reservoir unit powerA cost; pZHAO,iThe power of the methane tank in the ith period; cZHAO_omThe production and maintenance cost of the methane tank per unit power is saved; pRE,iThe power of the phase change heat storage device is the ith time period; cRE_omThe maintenance cost for the unit power of the phase change heat storage device; pbt,iThe charging and discharging power of the storage battery in the ith time period is positive, and the charging is negative; cbt_omMaintenance cost for battery unit power; ppv,iPhotovoltaic power generation power for the ith time period; cpv_omThe maintenance cost of the unit power of the photovoltaic power generation is saved.
4. The method of claim 1, wherein the objective function of the underlying planning model is the sum of squares of the difference between the energy usage of the load and the photovoltaic power generation during a day; the constraint conditions of the lower-layer planning model comprise electric energy balance constraint, heat energy balance constraint, potential energy balance constraint, power exchange constraint between the photovoltaic greenhouse micro-energy network system and the power distribution network, space constraint of energy storage devices in various forms, continuous working state constraint of time-shifting load, constraint of non-working time period of time-shifting load and input and output state quantity mutual exclusion constraint of the energy storage devices in various forms.
5. The method of claim 4, wherein the objective function of the underlying planning model is:
in the formula: piLoad power in the optimized period i; ppviPredicting the power generation power of the photovoltaic power supply in the period i;
Pithe system consists of a non-time-shifting load, a time-shifting load and stored input and output energy in an i period:
Pi＝Pdxi+Pmovei+Pci
in the formula, PdxiThe total energy of the load is not time-shiftable in the period i; pmoveiTime-shifting electric, thermal and potential energy for i periodLoad power; pciStoring the total input power for the i period.
6. The method of claim 5, wherein P ismoveiComprises the following steps:
in the formula, n is the number of time-shifting electric loads; m is the number of potential energy loads capable of time shifting; v is the number of time-shiftable thermal loads; pejThe power of the jth timeshiftable load is kW; ppjThe power of the jth potential energy load capable of time shifting, kW; pqjRespectively corresponding to the power, kW, of the jth timeshiftable thermal load; x is the number ofej,iFor the working state quantity of the jth type time-shiftable electric load in the ith scheduling period, 0 represents that the electric load is in a non-working state, and 1 represents that the electric load is in a working state; x is the number ofpj,iWorking state quantity of the jth type time-shiftable potential energy load in the i period; x is the number ofqj,iThe operating state quantity of the j-th type time-shiftable thermal load is a period i.
7. The method of claim 5, wherein P isciComprises the following steps:
Pci＝PSIxSIi-PSOxSOi+PZIxZIi-PZOxZOi+PXIxXIi-PXOxXOi+PBIxBIi-PBOxBOi
in the formula: x is the number ofSIi、xZIi、xXIiAnd xBIiRespectively representing input state quantities of the reservoir, the methane tank, the phase change heat storage device and the storage battery during the ith time period, wherein 0 represents that the reservoir, the methane tank, the phase change heat storage device and the storage battery are in an off state, and 1 represents that the reservoir, the methane tank, the phase change heat storage device and the storage battery are in an input state; x is the number ofSOi、xZOi、xXOiAnd xBOiRespectively representing the output state quantities of the reservoir, the methane tank, the phase change heat storage device and the storage battery during the period i, wherein 0 represents the non-working state, and 1 represents the output state; pSI、PZI、PXI、PBIRespectively representing the rated input power of the water storage pool, the methane tank, the phase change heat storage device and the storage battery; pSO、PZO、PXO、PBORespectively showing the rated output power of the water storage tank, the methane tank, the phase change heat storage device and the storage battery.
8. The method as claimed in claim 1, wherein the obtaining of the prediction data of a plurality of typical scene days of the photovoltaic greenhouse micro energy network system comprises:
according to the historical solar irradiance data of the location of the photovoltaic greenhouse micro-energy grid system, predicting to obtain solar irradiance data in a planning period within one year;
acquiring photovoltaic power generation data of the photovoltaic greenhouse micro-energy grid system per hour in one year according to the relational expression between the solar irradiance and the photovoltaic output;
and according to the hourly photovoltaic power generation data, counting by adopting a clustering method to obtain the photovoltaic output curves of the plurality of typical scene days, the load condition of the production activities of the photovoltaic greenhouse and the number of days of each typical scene day within one year.
9. The method according to claim 2, wherein solving a double-layer planning model according to the prediction data of the typical scene day to obtain the optimal energy storage capacity of each form of energy storage device of the photovoltaic greenhouse micro-energy network system comprises:
randomly generating a plurality of energy storage capacity chromosomes according to the constraint conditions of the upper layer planning model;
for each energy storage capacity chromosome, solving the lower-layer planning model according to the energy storage capacity chromosome, the prediction data of the typical scene days and the occurrence days of each typical scene day in one year to obtain the optimal state quantity of each time-shiftable load of the typical scene days and the optimal input and output power of each form of energy storage device;
calculating the annual operation maintenance cost according to the optimal state quantity of each time-shifting load and the optimal input and output power of each form of energy storage device in each typical scene day;
calculating an objective function value of the upper-layer planning model corresponding to the energy storage capacity chromosome according to the annual operation maintenance cost;
and taking the energy storage capacity of each form of energy storage device corresponding to the energy storage capacity chromosome with the optimal objective function value as the optimal configuration capacity of each form of energy storage device.
10. A polymorphic energy storage configuration system of photovoltaic greenhouse micro-energy network system is characterized by comprising:
the system comprises an establishing module, a planning module and a planning module, wherein the establishing module is used for establishing a double-layer planning model, the double-layer planning model comprises an upper-layer planning model and a lower-layer planning model, the upper-layer planning model is used for optimizing and configuring the energy storage capacity of the polymorphic energy storage devices with the aim of low investment and operation cost in one year of the photovoltaic greenhouse micro-energy network system, and the lower-layer planning model is used for optimizing the working state quantity of each time-shifting load and the input and output power of each morphological energy storage device in one day with the aim of large local consumption degree of a photovoltaic power; the polymorphic energy storage comprises at least two of a reservoir, a methane tank, a phase change heat storage device and a storage battery;
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring the prediction data of a plurality of typical scene days and the occurrence days of the scene days of a photovoltaic greenhouse micro-energy network system, and the prediction data comprises photovoltaic power generation prediction data and various types of load prediction data of production activities of the photovoltaic greenhouse;
the solving module is used for solving the double-layer planning model according to the prediction data of the typical scene day to obtain the optimal energy storage capacity corresponding to each form of energy storage device of the photovoltaic greenhouse micro-energy network system; in the solving process, the working state quantity of each time-shiftable load and the input and output power of each form of energy storage device in one day are calculated and obtained on the basis of the lower-layer planning model, and then the upper-layer planning model is solved.
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