CN108134403A - The micro- energy dispatching method that can be netted of industrialized agriculture and system - Google Patents

The micro- energy dispatching method that can be netted of industrialized agriculture and system Download PDF

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CN108134403A
CN108134403A CN201711475664.3A CN201711475664A CN108134403A CN 108134403 A CN108134403 A CN 108134403A CN 201711475664 A CN201711475664 A CN 201711475664A CN 108134403 A CN108134403 A CN 108134403A
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energy
time
scheduling
shiftable
energy storage
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CN108134403B (en
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王维洲
刘福潮
郑晶晶
牛焕娜
王钰竹
井天军
张大品
张建华
彭晶
张钰
张韵
廖志军
殷平
陈婷
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China Agricultural University
State Grid Jiangxi Electric Power Co Ltd
State Grid Gansu Electric Power Co Ltd
Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
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China Agricultural University
State Grid Jiangxi Electric Power Co Ltd
State Grid Gansu Electric Power Co Ltd
Electric Power Research Institute of State Grid Gansu Electric Power Co Ltd
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Abstract

本发明提供了一种设施农业微能网的能量调度方法及系统，所述方法包括：若判断获知在设施农业微能网的调度周期内，设施农业微能网的光伏出力总量满足第一预设条件时，基于设施农业微能网内各能量存储装置的输入输出功率模型和可时移负荷模型，以所有可时移负荷消耗的功率总和与设施农业微能网的光伏发电功率之间的偏差程度最小为第一目标，建立设施农业微能网的优化调度模型；基于优化调度模型，在调度周期内，调整设施农业微能网内每一可时移负荷对应的工作时段和各能量存储装置的存储状态，确定满足第一目标时每一可时移负荷对应的工作时段，以及各能量存储装置的存储状态。通过本发明方案，可以实现光伏发电的最大化就地消纳。

The present invention provides an energy scheduling method and system for a facility agricultural micro-energy network, the method comprising: if it is determined that within the scheduling period of the facility agriculture micro-energy network, the total photovoltaic output of the facility agriculture micro-energy network satisfies the first Under preset conditions, based on the input and output power models of each energy storage device and the time-shiftable load model in the facility agricultural micro-energy grid, the relationship between the sum of the power consumed by all time-shiftable loads and the photovoltaic power generation power of the facility agriculture micro-energy network The minimum degree of deviation is the first goal, and an optimal scheduling model of the facility agricultural micro-energy network is established; based on the optimal scheduling model, within the scheduling period, the working period and each energy corresponding to each time-shiftable load in the facility agriculture micro-energy network are adjusted. The storage state of the storage device determines the working period corresponding to each time-shiftable load when the first target is met, and the storage state of each energy storage device. Through the solution of the invention, the maximum on-site consumption of photovoltaic power generation can be realized.

Description

Energy scheduling method and system for facility agriculture micro-energy network
Technical Field
The invention relates to the technical field of smart power grids and energy Internet, in particular to an energy scheduling method and system of a facility agriculture micro-energy grid.
Background
Photovoltaic power generation is a technology of directly converting light energy into electric energy by using the photovoltaic effect of a semiconductor interface. The solar energy power generation system mainly comprises three parts, namely a solar panel (or a solar cell module), a controller and an inverter, and the main part is composed of electronic components. The solar cells are connected in series and then are packaged and protected to form a large-area solar cell module, and then the photovoltaic power generation device is formed by matching with components such as a power controller and the like. Because the cost of photovoltaic power generation is low, and sunlight resources can be obtained at any time, the photovoltaic power generation is gradually widely applied.
The photovoltaic greenhouse is a typical application of combining a photovoltaic power generation technology and facility agriculture, and forms a facility agriculture micro-energy network system mainly based on modern agriculture planting industry. The facility agriculture micro-energy network system fully exerts the advantages of solar energy, biomass energy and novel agricultural load by effectively combining a multi-energy complementary control technology with a modern agricultural planting technology, realizes the on-site collection, on-site storage and on-site use of the solar energy, and finally converts the solar energy into an energy form required by the growth of crops. Through reasonable control, the purpose of effectively absorbing the peak output of photovoltaic power generation is achieved, and a new choice is provided for photovoltaic poverty alleviation. Therefore, how to optimize and schedule various energy sources, energy storage and agricultural time-shifting loads in the facility agricultural micro-energy network system so as to realize local consumption of the photovoltaic power supply by the multi-energy source and time-shifting loads becomes the key for successful implementation of the technology.
However, at present, no report is found on the research of achieving photovoltaic power supply local consumption through energy optimization scheduling of the facility agriculture micro-energy network at home and abroad. Compared with the traditional microgrid system widely researched at present, the load of the facility agriculture microgrid mostly has the time-shifting characteristic and has the energy storage forms of multiple energy types, so that the energy optimization scheduling method of the traditional microgrid is not applicable to the facility agriculture microgrid any more. For this reason, it is urgently needed to provide an energy scheduling method capable of realizing a facility agriculture microgrid including a time-shiftable load.
Disclosure of Invention
To overcome the above problems or at least partially solve the above problems, the present invention provides an energy scheduling method and system for a facility agriculture micro-energy network.
In one aspect, the invention provides an energy scheduling method for a facility agriculture micro-energy network, which comprises the following steps:
s11, if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a first preset condition in a scheduling period of the facility agricultural micro-energy network is judged and known, based on an input and output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network, a first optimized scheduling model of the facility agricultural micro-energy network is established by taking the minimum deviation degree between the sum of power consumed by all time-shifting loads in the facility agricultural micro-energy network and the photovoltaic power generation power of the facility agricultural micro-energy network as a first target;
s12, based on the first optimized scheduling model, adjusting the working time interval corresponding to each time-shifting load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network in the scheduling cycle, and determining the working time interval corresponding to each time-shifting load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the first target is met;
the first preset condition is that the total photovoltaic output amount meets and only meets the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network; the input and output power model is used for calculating the input power and the output power of each energy storage device; the time-shiftable load model is used to determine the power consumed by the time-shiftable load operating at each scheduling period within the scheduling cycle after adjustment.
Preferably, the first preset condition is specifically that: and the ratio of the total energy consumed by all time-shifting loads in the facility agricultural micro-energy network to the total photovoltaic output is within a preset interval range.
Preferably, the method further comprises:
if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a second preset condition in the scheduling period is judged and known, a second optimized scheduling model of the facility agricultural micro-energy network is established on the basis of an input and output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network, the minimum daily operation cost of the facility agricultural micro-energy network serves as a second target, and the accumulation and minimum power variation of all scheduling periods in adjacent scheduling periods serve as a third target;
based on the second optimized scheduling model, in the scheduling cycle, adjusting the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network, and determining the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the second target and the third target are met simultaneously;
the second preset condition is that the total photovoltaic output cannot meet the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network, or the total photovoltaic output has a sale allowance after meeting the energy consumed by all the time-shifting loads.
Preferably, the method further comprises:
acquiring power consumed by each energy storage device as a time-shiftable load, and taking the power as input power of the corresponding energy storage device;
determining the output power of each energy storage device based on the energy storage space of each energy storage device and the corresponding energy output maximum time;
an input-output power model is constructed for each energy storage device based on the input power and the output power of each energy storage device.
Preferably, the constructing an input-output power model of each energy storage device specifically includes: constructing an input-output power model of each energy storage device by the following formula;
in the formula, Pk.IIs the input power, P, of an energy storage device kk.pumpPower consumed for energy storage device k as a timeshiftable load; pk.OIs the output power of an energy storage device k, AkEnergy storage space, t, for an energy storage device kk.O_MAXMaximum output time, t, for energy storage device kk.I_MAXMaximum input time for energy storage device k, ηk.IIs the energy storage efficiency of the energy storage device k.
Preferably, the energy storage space of each energy storage device satisfies an energy storage space constraint condition, which is as follows:
Ek t-1+xk_i.t·Pk.I·ηk.I-xk_o.t·Pk.O≤Ak，(t＝1,2…T)
wherein T is the scheduling cycle, T is a scheduling time interval in the scheduling cycle, ESHUI t-1Representing the energy stored in an energy storage device k during a t-1 scheduling period of the scheduling cycle, xk_i.tRepresenting the input state quantity, x, of the energy storage device k during the scheduling period tk_o.tRepresenting the output state quantity of the energy storage means k for the scheduled period t.
Preferably, the method further comprises:
the method further comprises the following steps:
acquiring the sum of the power consumed by all the time-shiftable loads in each scheduling period before adjustment, the sum of the power consumed by the time-shiftable loads transferred into each scheduling period after adjustment, and the sum of the power consumed by the time-shiftable loads transferred out of each scheduling period after adjustment;
calculating the total power consumed by the time-shiftable loads working at each scheduling period after adjustment based on the following formula, and constructing a time-shiftable load model in the facility agriculture micro-energy network;
Lt＝Lfore.t+LIN.t-LOUT.t
in the formula, Lfore.tTo adjust the sum of the power consumed by all time-shiftable loads in the pre-scheduling period t, LIN.tSum of power consumed by time-shiftable loads adjusted to shift into scheduling period t, LOUT.tThe sum of the power consumed by the time-shiftable loads for the adjusted transition out of the scheduling period t.
Preferably, the energy storage devices within the facility agricultural micro-grid comprise potential energy storage devices, biomass chemical energy storage devices and thermal energy storage devices, and the time-shiftable loads comprise time-shiftable electrical loads, time-shiftable thermal loads and time-shiftable potential energy loads; l isIN.tAnd LOUT.tCalculated by the following formula:
wherein L isin_E.t、Lin_Q.tAnd Lin_P.tRespectively representing the sum of power consumed by the time-shiftable electric load, the time-shiftable heat load and the time-shiftable potential energy load which are shifted into the scheduling time period t after adjustment, Lout_E.t、Lout_Q.tAnd Lout_P.tRespectively indicating adjusted roll-out scheduling periodst, the sum of the power consumed by the time-shiftable electrical load, the time-shiftable thermal load, and the time-shiftable potential energy load.
Preferably, the working time period corresponding to the time-shiftable load and the storage state of each energy storage device in the facility agriculture micro-energy network can construct a state quantity matrix as follows;
wherein, L is a state quantity matrix of each scheduling time interval of the time-shifting load in a scheduling cycle, S is a storage state matrix of each energy storage device in each scheduling time interval, N is the number of the scheduling time intervals in a scheduling cycle, and M is the total number of all the time-shifting loads in the facility agriculture micro-energy network.
In another aspect, the present invention further provides an energy scheduling system for a facility agriculture micro-energy grid, including: the device comprises a model building module and a determining module. Wherein,
the model building module is used for building a first optimized scheduling model of the facility agricultural micro-energy network based on an input and output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network and taking the minimum deviation degree between the sum of the power consumed by all the time-shifting loads in the facility agricultural micro-energy network and the photovoltaic power generation power of the facility agricultural micro-energy network as a first target if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a first preset condition in a scheduling period of the facility agricultural micro-energy network is judged and known;
a determining module, configured to adjust, based on the first optimized scheduling model, a working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and a storage state of each energy storage device in the facility agricultural micro-energy network in the scheduling cycle, and determine the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the first target is met;
the first preset condition is that the total photovoltaic output amount meets and only meets the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network; the input and output power model is used for calculating the input power and the output power of each energy storage device; the time-shiftable load model is used to determine the power consumed by the time-shiftable load for each scheduling period of the adjusted operation within the scheduling cycle.
In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the above-described method.
According to the energy scheduling method and system of the facility agriculture micro energy network, the time-shifting load and the input power and the output power of the various energy storage devices are reasonably optimized and scheduled according to the actual load characteristics in the facility agriculture micro energy network, the maximum local consumption of photovoltaic power generation is realized, the energy generated by the photovoltaic power generation is fully utilized, the energy is fundamentally saved, and the energy consumption is controlled. Meanwhile, the energy storage cost can be reduced by utilizing the existing energy storage device in the facility agriculture micro-energy network to participate in the scheduling optimization of the facility agriculture micro-energy network for photovoltaic consumption, and various resources of the facility agriculture micro-energy network can be effectively utilized.
Drawings
Fig. 1 is a schematic flow chart of an energy scheduling method for a facility agricultural micro-energy grid according to an embodiment of the present invention;
fig. 2 is a schematic view of a complete flow chart of an energy scheduling method for a facility agricultural micro-energy grid according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart of an energy scheduling method of a facility agriculture micro-energy network based on a genetic algorithm according to an embodiment of the invention;
fig. 4 is a schematic diagram illustrating a comparison of optimization results in a scene 1 in the energy scheduling method for the facility agriculture micro-energy grid according to the embodiment of the present invention;
fig. 5 is a schematic diagram illustrating a comparison of optimization results in a scene 2 in the energy scheduling method for the facility agriculture micro-energy grid according to the embodiment of the present invention;
fig. 6 is a schematic structural diagram of an energy scheduling system of a facility agriculture microgrid provided in an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides an energy scheduling method for a facility agricultural micro-energy grid, including:
s11, if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a first preset condition in a scheduling period of the facility agricultural micro-energy network is judged and known, based on an input and output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network, a first optimized scheduling model of the facility agricultural micro-energy network is established by taking the minimum deviation degree between the sum of power consumed by all time-shifting loads in the facility agricultural micro-energy network and the photovoltaic power generation power of the facility agricultural micro-energy network as a first target;
s12, based on the first optimized scheduling model, adjusting the working time interval corresponding to each time-shifting load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network in the scheduling cycle, and determining the working time interval corresponding to each time-shifting load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the first target is met;
the first preset condition is that the total photovoltaic output amount meets and only meets the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network; the input and output power model is used for calculating the input power and the output power of each energy storage device; the time-shiftable load model is used to determine the power consumed by the time-shiftable load for each scheduling period of the adjusted operation within the scheduling cycle.
Specifically, the method is mainly used for optimizing and scheduling the energy generated through photovoltaic power generation in the facility agriculture micro-energy grid. According to the actual load characteristics in the facility agriculture micro-energy network, the time-shiftable load and the input power and the output power of various energy storage devices are reasonably optimized and scheduled, and the maximum local consumption of photovoltaic power generation is realized. It should be noted that the facility agriculture according to the present invention is a modern agriculture mode for efficient production of animals and plants by engineering means under relatively environmentally controlled conditions. The main source of energy in the facility agriculture micro-energy network is photovoltaic power generation, and the facility agriculture micro-energy network can realize self-sufficiency under certain conditions. If not, electricity needs to be purchased from a power supply station to provide energy to the load in the facility agricultural micro-grid, and there is also a case where the energy generated by photovoltaic power generation in the facility agricultural micro-grid is not only self-sufficient but also has a margin for sale. The invention starts from the three conditions, and optimally schedules the working time of each time-shiftable load and the storage state of each energy storage device in the facility agriculture micro-energy network so as to realize the maximum local consumption of photovoltaic power generation. The local consumption refers to digestion and absorption, namely that the electric energy generated by photovoltaic power generation is utilized in time.
In the invention, the load in the facility agricultural micro-energy network is a time-shifting load, namely the actual working time interval of the load can be adjusted according to the requirement, namely a first target, the first optimized scheduling model of the facility agricultural micro-energy network is constructed according to the requirement which is met by scheduling requirement, the working time interval corresponding to each time-shifting load when the adjusted function can be met is determined by adjusting the working time intervals of all the time-shifting loads in the facility agricultural micro-energy network each time, and finally the working time interval corresponding to each time-shifting load in the facility agricultural micro-energy network when the first target is met and the storage state of each energy storage device in the facility agricultural micro-energy network are determined. It should be noted that "first" in the first optimized scheduling model herein does not have a sequential meaning, but is only for distinguishing from "second" in the following.
Here, it is first required to determine whether the total photovoltaic output of the facility agricultural micro energy grid in the scheduling period meets a first preset condition, that is, whether the total photovoltaic output can meet and only can meet the energy sum consumed by all time-shiftable loads in the facility agricultural micro energy grid, and if the first preset condition is met, it is described that the energy consumed by the time-shiftable loads in the facility agricultural micro energy grid can be basically met by the energy obtained by photovoltaic power generation in the facility agricultural micro energy grid, that is, the facility agricultural micro energy grid can achieve self-sufficiency, and a main objective is how to more reasonably and effectively utilize the energy generated by the photovoltaic power generation, that is, the substantial content of the first objective. Generally, when the total photovoltaic output meets a first preset condition, the ratio of the total energy consumed by all time-shiftable loads in the facility agricultural micro-energy network to the total photovoltaic output needs to be within a preset interval range. Preferably, the predetermined interval range may be [0.7,1.3], that is, as shown in formula (1).
Wherein T is a scheduling period; l isfore.tThe power consumed by all time-shiftable loads is summed for a scheduling period t before adjusting the working period of the time-shiftable loads and the storage state of each energy storage device in the facility agriculture micro-energy network,for scheduling time t at schedulingThe sum of the energy consumed by all the time-shiftable loads before the working time of the whole time-shiftable load and the storage state of each energy storage device in the facility agriculture micro-energy network; l ispvtFor scheduling the photovoltaic power generation power for the time period t,the total photovoltaic output in the scheduling time t. It should be noted that, the scheme of optimizing the scheduling provided by the present invention is to implement reasonable and effective utilization of the energy generated by photovoltaic power generation in a certain scheduling period in the future, so L herefore.tAnd LpvtThe method is obtained by prediction, and specifically comprises the following steps:
the method comprises the steps of obtaining historical data of solar irradiance of a geographical position where a facility agricultural micro-energy network is located, selecting a proper prediction model to obtain a predicted value of solar irradiance in a certain scheduling period in the future, and obtaining photovoltaic power generation power in a scheduling time period t according to the predicted value of the solar irradiance. Meanwhile, historical data of energy consumed by the time-shiftable load in the facility agricultural micro-energy network is obtained, and the characteristics of the time-shiftable load in the facility agricultural micro-energy network and the predicted value of power consumed by the time-shiftable load in a certain scheduling period in the future can be obtained by selecting a proper prediction model.
The relation between the solar irradiance and the photovoltaic output is shown as a formula (2).
Lpvt＝Eλ·Qrated·η·Eλ_max(2)
In the formula, LpvtRepresenting the photovoltaic power generation power during a scheduling period t, EλRepresenting the intensity of solar radiation, Eλ_maxRepresenting maximum solar irradiance, η representing conversion efficiency of solar irradiance, QratedRepresenting the photovoltaic rated capacity.
When the total photovoltaic output meets a first preset condition, the photovoltaic power generation can be maximally absorbed for the time-shiftable load in the facility agricultural micro-energy network, namely the facility agricultural micro-energy networkThe energy utilization curve of the energy network is similar to the photovoltaic output curve to the maximum extent, so that the first target is described by adopting a least square method, and the formula (3) is a concrete representation of the first target, namely an objective function f of the first optimization scheduling model1。
Wherein f1 is an objective function of the first optimized scheduling model, T is the scheduling period, and L istIs the sum of the power consumed by all time-shiftable loads in the scheduling period T in the adjusted scheduling period T, LpvtAnd scheduling the photovoltaic power generation power of the facility agriculture micro-energy network in the time period T in the scheduling period T.
The final purpose of the invention is to meet the first objective by adjusting the working time period of the time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network. The working time period corresponding to each time-shiftable load in the facility agriculture micro-energy network and the storage state of each energy storage device in the facility agriculture micro-energy network are obtained as the optimal selection. The result of the adjustment can fully utilize the energy generated by the photovoltaic power generation in the whole facility agricultural micro-energy grid.
It should be noted that the input/output power model of each energy storage device in the facility agricultural micro-grid and the time-shiftable load model in the facility agricultural micro-grid in S13 are constraints that the first optimal scheduling model needs to satisfy. It should be noted that the storage states of each energy storage device include: an output state in which the energy storage unit supplies energy as an energy source, an input state in which the energy storage unit stores energy as a time-shiftable load, and an off state (i.e., a 0-output state and a 0-input state).
The energy storage device in the facility agriculture micro-energy net mainly comprises: the potential energy storage device, the biomass chemical energy storage device and the thermal energy storage device can realize local consumption of energy generated by photovoltaic power generation through the time-shifting characteristic of load, namely, the potential energy storage device is used as an electric load to consume electric energy and store the electric energy into energy in other forms during the peak output period of photovoltaic power generation, and is used as energy in other forms during the valley output period of photovoltaic power generation to directly provide energy for the time-shifting load. Preferably, the potential energy storage device is mainly a reservoir, and can convert electric energy into potential energy of water for storage by utilizing pumped storage. The biomass chemical energy storage device is mainly a methane tank, and can promote methane production by utilizing a methane tank heat pump, so that electric energy and chemical energy of rural biomass waste are converted into biomass chemical energy of methane. The heat energy storage device is mainly a phase-change heat storage greenhouse, and converts electric energy into heat energy for storage by utilizing a phase-change heat storage heat pump.
According to the invention, the time-shiftable load and the input power and the output power of various energy storage devices are reasonably optimized and scheduled according to the actual load characteristics (namely the time-shiftable characteristic) in the facility agriculture micro-energy network, so that the maximum local consumption of photovoltaic power generation is realized, the energy generated by the photovoltaic power generation is fully utilized, the energy is fundamentally saved, and the energy consumption is controlled. Meanwhile, the energy storage cost can be reduced by utilizing the existing energy storage device in the facility agriculture micro-energy network to participate in the scheduling optimization of the facility agriculture micro-energy network for photovoltaic consumption, and various resources of the facility agriculture micro-energy network can be effectively utilized.
On the basis of the above embodiment, the method further includes:
if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a second preset condition in the scheduling period is judged and known, based on an input-output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network, a second optimized scheduling model of the facility agricultural micro-energy network is established by taking the minimum calculation result of the daily operation cost function of the facility agricultural micro-energy network as a second target and taking the minimum calculation result of the power variation cumulative sum function of all scheduling periods in the adjacent scheduling period as a third target;
based on the second optimized scheduling model, in the scheduling cycle, adjusting the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network, and determining the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the second target and the third target are met simultaneously;
the second preset condition is that the total photovoltaic output cannot meet the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network, or the total photovoltaic output has a sale allowance after meeting the energy consumed by all the time-shifting loads.
Specifically, the foregoing description needs to judge whether the total photovoltaic output of the facility agricultural micro energy grid in the scheduling period meets a first preset condition, that is, whether the total photovoltaic output meets and only meets the energy sum consumed by all time-shiftable loads in the facility agricultural micro energy grid. If the total photovoltaic output capacity does not meet the first preset condition, two conditions are adopted, wherein in one condition, the total photovoltaic output capacity is not enough to meet the energy sum consumed by all time-shiftable loads in the facility agricultural micro-energy network, and at the moment, electric energy needs to be purchased from the power grid to be provided for the time-shiftable loads in the facility agricultural micro-energy network. In another case, after the total photovoltaic output meets the energy sum consumed by all time-shiftable loads in the facility agricultural micro-energy network, the remaining energy can be sold, namely, a sale margin exists. That is to say, the second preset condition is just met when the total photovoltaic output does not meet the first preset condition, that is, the first preset condition and the second preset condition are mutually exclusive conditions.
Generally, when the total photovoltaic output meets a first preset condition, the ratio of the total energy consumed by all time-shifting loads in the facility agricultural micro-grid to the total photovoltaic output meets formula (1), and when the total photovoltaic output meets a second preset condition, the ratio of the total energy consumed by all time-shifting loads in the facility agricultural micro-grid to the total photovoltaic output meets formula (4).
Or
When the total photovoltaic output of the facility agricultural micro-energy network meets a second preset condition, electricity is purchased or sold from the power grid to achieve energy supply and demand balance according to the condition that the energy form presented to the outside by the facility agricultural micro-energy network is a load or a source, and meanwhile the daily running cost of the facility agricultural micro-energy network is minimized by combining the real-time electricity price condition, namely, the second target is met. Wherein the daily operating cost can be expressed by a daily operating cost function, as shown in equation (5).
Wherein f is20For daily operating costs, atFor the electricity purchase price corresponding to the scheduling period t, btFor electricity selling price, x, corresponding to scheduling time period tdjAndis a mutually exclusive state variable, and takes the values as follows:
the final purpose of the invention is to minimize the value of the daily operation cost function by adjusting the working time of the time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network, that is, the objective function f2 of the second optimized scheduling model is:
in the actual process, the electricity price a is purchasedtAnd selling electricity price btThe method can be adjusted according to needs, can be the same or different, and specifically can be valued according to the following three models:
model one, peak-to-valley electricity price model: 6: 00-22: 00 is peak, electricity price 1 yuan/kWh; 22: 00-6: 00 is valley, and the price of electricity is 0.3 yuan/kWh. As in equation (8), where ftIs the electricity price.
Model two, peak-to-valley electricity price model: 11: 00-12: 00 is peak, electricity price 1 yuan/kWh; 6: 00-11: 00, 20: 00-22: 00 is flat, and the electricity price is 0.6 yuan/kWh; 22: 00-24: 00,0: 00-6: 00 is valley, and the price of electricity is 0.3 yuan/kWh. As shown in formula (9).
Model three, multimodal electricity price model: 10: 00-12: 00, 18: 00-20: 00 is peak, electricity price 1 yuan/kWh; 6: 00-10: 00, 12: 00-18: 00, 20: 00-22: 00 is flat, and the electricity price is 0.6 yuan/kWh; 22: 00-24: 00,0: 00-6: 00 is valley, and the price of electricity is 0.3 yuan/kWh. As shown in formula (11).
It should be noted that, when the total photovoltaic output of the facility agricultural micro-energy grid is not equal to the total energy consumed by all time-shiftable loads in the facility agricultural micro-energy grid, the facility agricultural micro-energy grid needs to generate exchange power with the power grid, and in order to ensure the electric energy quality of the power grid, the exchange power between the facility agricultural micro-energy grid and the power grid needs to be as small as possible. When the total photovoltaic output of the facility agricultural micro-energy network meets the first preset condition, the difference between the total photovoltaic output and the total energy consumed by all time-shifting loads in the facility agricultural micro-energy network is not large, and the generated exchange power is small and can be ignored. And only when the total photovoltaic output of the facility agriculture micro-energy network meets the second preset condition, the consideration is needed. Therefore, when the total photovoltaic output of the facility agricultural micro-energy network meets the second preset condition, the value of the daily operation cost function formula (5) is required to be minimum, and the accumulated sum of the power variation of all scheduling periods in the adjacent scheduling period T is required to be minimum, namely the third target is met. The power variation cumulative sum may be expressed by a power variation cumulative sum function, which is shown in formula (11 a).
Wherein f is30Accumulating the power variation of all scheduling periods in the adjacent scheduling period, LtThe method is used for adjusting the sum of the power consumed by all time-shiftable loads in a scheduling time t after the working time corresponding to each time-shiftable load in the facility agriculture micro-energy network.
The corresponding formula when the values of the power variation accumulation sum functions of all scheduling periods in the adjacent scheduling period T are minimum is formula (11b), i.e., a second objective function f3 of the second optimized scheduling model.
On the basis of the above embodiment, the method further includes:
acquiring the total power consumed by all the time-shiftable loads in each scheduling period before adjustment, the energy consumed by the time-shiftable loads shifted into each scheduling period after adjustment, and the energy consumed by the time-shiftable loads shifted out of each scheduling period after adjustment;
calculating the energy consumed by the time-shiftable load working at each scheduling time interval after adjustment based on the following formula, and constructing a time-shiftable load model in the facility agriculture micro-energy network;
Lt＝Lfore.t+LIN.t-LOUT.t(12)
in the formula, Lfore.tTo adjust the sum of the power consumed by all time-shiftable loads in the pre-scheduling period t, LIN.tSum of power consumed by time-shiftable loads adjusted to shift into scheduling period t, LOUT.tThe sum of the power consumed by the time-shiftable loads for the adjusted transition out of the scheduling period t.
Specifically, when the time-shiftable loads are optimally scheduled, not only the cases of transferring in and transferring out the time-shiftable loads in a scheduling period of the scheduling cycle, but also the cases of transferring in and transferring out the time-shiftable loads in a scheduling period greater than one scheduling period, and the cases of mutually converting between different types of energy loads (i.e., the types of loads to which the energy storage device belongs as the time-shiftable loads) are considered, considering that the work durations (work durations refer to work periods) of the different types of time-shiftable loads are different and the difference of energy compatibility is considered. Thus, LIN.tAnd LOUT.tThe expression of (a) is:
in the formula, Lin_E.tAnd Lout_E.tThe switching-in value and the switching-out value of the electric load type which only participates in the electric energy in the facility agriculture micro-energy network in the scheduling time period t are represented; l isin_Q.tAnd Lout_Q.tThe method comprises the following steps of (1) representing a transfer-in value and a transfer-out value of a heat load type within scheduling time t under the participation of a heat energy storage device methane tank (finally providing heat energy through combustion) and a phase change heat storage greenhouse; l isin_P.tAnd Lout_P.tIndicating energy storage in reservoirAnd the rotating-in value and the rotating-out value of the potential energy load participating in the scheduling period t.
Wherein mel.1 is the total number of time-shiftable electric load types; mel.2 is the number of types of time-shifting electric loads with the working time interval larger than one scheduling time interval; mql.1 is the total number of time-shiftable heat load types; mql.2 is the number of the types of the time-shiftable heat loads with the working period larger than one scheduling period; mpl.1 is the total number of the types of the time-shiftable potential energy loads; mpl.2 is the number of potential energy load types which can be time-shifted and have the working time interval larger than one scheduling time interval; h isemaxThe maximum value of the sub-working period number in the working period of the time-shifting electric load, if 1h is taken as a scheduling period, then hemax≥2；hqmaxAnd hpmaxMaximum values of working periods of the time-shiftable thermal load and the time-shiftable potential energy load respectively; x is the number ofke.t′.tNumber of k-class time-shiftable electrical loads, x, for transferring from scheduling period t' to scheduling period tkq.t′.tNumber of k-class timeshiftable thermal loads, x, for transferring from scheduling period t' to scheduling period tkp.t′.tThe load number of k types of time-shiftable potential energy is transferred from the scheduling time interval t' to the scheduling time interval t; e.g. of the type1.kFor the power of the kth-class timeshiftable electrical load during the 1 st sub-operating period, q1.kPower in the 1 st sub-operation period for a kth-class timeshiftable thermal load, p1.kThe power of the potential energy load in the 1 st sub-working period is time-shifted for the k-th class. e.g. of the type(h+1).kFor the power of the h +1 th sub-working period of the kth-type time-shiftable electrical load in the working period thereof, q(h+1).kFor the power of h +1 sub-working period of k-th class timeshiftable thermal load in its working period, p(h+1).kThe power of the h +1 th sub-working period in the working period of the kth type time-shiftable potential energy load is calculated. x is the number ofZHAO_i.tIn order to schedule the input state quantity of the methane tank in the time period t, the input is 1 in the process, and other state values are 0. x is the number ofXIANG_i.tFor the input state quantity of the phase change heat storage greenhouse in the scheduling time period t, the input is 1 in the process, and other state values are 0. x is the number ofSHUI_i.tFor the input state quantity of the scheduling time t reservoir, the ongoing input is 1, and the other state values are 0. x is the number ofZHAO_o.tIn order to schedule the output state quantity of the methane tank in the time period t, the output is 1 in progress, and other state values are 0. x is the number ofXIANG_o.tIn order to schedule the output state quantity of the phase change heat storage greenhouse in the time period t, the ongoing output is 1, and other state values are 0. x is the number ofSHUI_o.tFor the output state quantity of the scheduling time t reservoir, the ongoing output is 1, and other state values are 0. PSHUI.IFor input power of reservoir, PSHUI.OThe output power of the reservoir. PZHAO.IFor the input power of the biogas digester, PZHAO.OIs the output power of the methane tank. PXIANG.IFor the input power, P, of phase-change heat-accumulating greenhousesXIANG.OThe output power of the phase-change heat storage greenhouse is obtained.
It should be noted here that the types of time-shiftable loads in the present invention may include: the time-shiftable electric load, the time-shiftable potential energy load and the time-shiftable heat load are three categories, and for all the time-shiftable loads in the facility agriculture micro-energy network, a plurality of time-shiftable loads belong to one category, namely, a plurality of time-shiftable loads belong to the time-shiftable electric load, the time-shiftable potential energy load or the time-shiftable heat load. The working period of the time-shiftable load refers to the working duration, and the working sub-period refers to the working sub-period obtained by subdividing the working duration.
Since in the present embodiment, based on equations (12), (13) and (14), the power consumed by the time-shiftable loads operating in each scheduling period within the scheduling cycle after adjustment is determined, this embodiment actually constructs a time-shiftable load model, and provides an effective method for constructing the time-shiftable load model.
On the basis of the embodiment, the time-shifting load model is further used for determining the working period constraint conditions of each time-shifting load in the facility agricultural micro-energy network, wherein the working period constraint conditions comprise continuous working period constraint conditions and discontinuous working period constraint conditions, and the continuous working period constraint conditions are shown in formula (15).
xl.i·xl.i+1·…·xl.i+k＝1 (15)
Where l represents the ith time-shiftable load, and i is the first scheduling period during which the time-shiftable load l starts to operate. If the time-shiftable load l has to work for k scheduling periods due to production flow restrictions, equation (15) needs to be satisfied.
The intermittent duty cycle constraint is shown in equation (16).
xl.i＝0，i≠h,j,…,f (16)
Where equation (16) indicates that due to production flow limitations, time-shiftable load l must work during the h, j, …, f scheduling period.
On the basis of the above embodiment, the method further includes:
acquiring power consumed by each energy storage device as a time-shiftable load, and taking the power value as the input power of the corresponding energy storage device;
determining the output power of each energy storage device based on the energy storage space of each energy storage device and the corresponding energy output maximum time;
an input-output power model is constructed for each energy storage device based on the input power and the output power of each energy storage device.
The building of the input-output power model of each energy storage device specifically includes: constructing an input-output power model of each energy storage device by the following formula;
in the formula, Pk.IIs the input power, P, of an energy storage device kk.pumpPower consumed for energy storage device k as a timeshiftable load; pk.OIs the output power of an energy storage device k, AkEnergy storage space, t, for an energy storage device kk.O_MAXMaximum output time, t, for energy storage device kk.I_MAXMaximum input time for energy storage device k, ηk.IIs the energy storage efficiency of the energy storage device k.
Specifically, the embodiment provides a method for constructing an input/output power model of each energy storage device in the facility agriculture micro energy network. The following description is respectively provided for the impounding reservoir energy storage input and output power model, the methane tank energy storage input and output power model and the phase change heat storage greenhouse energy storage input and output power model. Wherein, the values of k in the formula (17) are SHII, ZHAO and XIANG, which respectively represent a potential energy storage device, a biomass chemical energy storage device and a thermal energy storage device.
1) Reservoir energy storage input and output power model
The reservoir in the modern facility agriculture micro-energy network can convert electric energy into potential energy of water by utilizing pumped storage to be stored, and an input and output power model is established according to the following formula:
in the formula, PSHUI.IThe input power of the reservoir is equal to the power consumed by the reservoir as a time-shiftable load, i.e. the electric water pump pumping power P of the reservoirSHUI.pump；PSHUI.OThe output power of the water reservoir, namely the energy supply power when the water reservoir is used as an energy source; a. theSHUIThe energy storage space of the water storage tank is equal to the total electric energy required when the full water storage tank is used for irrigation instead of an electric pumping device for direct irrigation; t is tSHUI.O_MAXThe maximum output time of the reservoir, namely the time required for discharging water to be empty when the full water reservoir is used for irrigation; t is tSHUI.I_MAXThe maximum input time of the reservoir, i.e. the time required by the electric pump to fill the reservoir from empty to full ηSHUI.IThe energy storage efficiency of the water reservoir is improved.
2) Energy storage input and output power model of methane tank
The methane tank in the modern facility agriculture micro-energy net can promote the production of methane by utilizing a methane tank heat pump, and convert the electric energy and the chemical energy of rural biomass waste into the biomass chemical energy of the methane. When the greenhouse needs to be heated, the methane is directly combusted to provide heat energy. Based on the method, a methane tank input and output power model is established according to the following formula:
in the formula, PZHAO.IThe input power of the methane tank is equal to the power consumed by the methane tank as a time-shifting load, namely the power P of the heat pump of the methane tank for promoting the methane productionZHAO.pump(ii) a Output power P of methane tankZHAO.OSupplying power to the biogas digester; energy storage space AZHAOThe value is equal to the total electric energy required when the temperature of the greenhouse is raised by the full gas stored in the methane tank to be burnt instead of directly raising the same temperature by using electric heating equipment; t is tSHUI.O_MAXThe maximum output time of the methane tank is the time required by the process of only using the methane tank to heat the greenhouse from the full gas storage amount to 0 gas storage amount; t is tZHAO.I_MAXη maximum input time for the biogas digester, i.e. the time required for gas storage from 0 to full gas storage when the biogas production is promoted by the biogas digester heat pumpZAHO.IThe energy storage efficiency of the methane tank is improved.
3) Phase-change heat storage greenhouse energy storage input and output power model
The phase change heat storage greenhouse in the modern facility agriculture micro-energy network can convert electric energy into heat energy for storage by utilizing a phase change heat storage heat pump, and an input and output power model is established according to the following formula:
in the formula, PXIANG.IThe input power of the phase-change heat storage greenhouse is equal to the power consumed by the phase-change heat storage greenhouse as a time-shifting load, namely the phase-change heat storage heat pump power P of the phase-change heat storage greenhouseXIANG.pump(ii) a Output power P of phase change heat storage greenhouseXIANG.OSupplying energy power; energy storage space AXIANGThe method is characterized in that the total equivalent electric energy required when the phase-change heat storage greenhouse is filled with heat energy to heat the greenhouse is replaced by directly heating the same temperature by using electric heating equipment; t is tXIANG.O_MAXThe maximum output time of the phase-change heat storage greenhouse is the time required by the energy consumption process of heating the greenhouse from full heat storage to 0 heat storage only by using the phase-change heat storage greenhouse; t is tXIANG.I_MAXη, the maximum input time of the phase-change heat-accumulating greenhouse, i.e. the time from 0 heat accumulation amount to full heat accumulation amount when the heat pump is used for accumulating heat in the phase-change heat-accumulating greenhouseXIANG.IThe energy storage efficiency of the phase change heat storage greenhouse is improved.
The facility agriculture micro-energy net not only has time-shifting load, but also has non-time-shifting load. The non-time-shifting loads include non-time-shifting electrical loads, non-time-shifting potential energy loads, and non-time-shifting thermal loads. For the whole facility agriculture micro-energy net, energy balance constraint conditions need to be met. The energy balance constraint is shown in equation (21).
Wherein L isdex.t、Ldpx.tAnd Ldqx.tNon-time-shifting electric load, non-time-shifting potential energy load and non-time-shifting potential energy load of scheduling time interval tThe power consumed by the thermal load may be time shifted. L ismove_e.t、Lmove_p.tAnd Lmove_q.tThe power consumed by the time-shiftable electrical load, the time-shiftable potential energy load and the time-shiftable thermal load of the scheduling period t are respectively. S'pAnd Sq' the stored energy of the potential energy storage device and the thermal energy storage device, respectively, at the end of this period T.Andrespectively the energy stored by the potential energy storage device and the thermal energy storage device at the beginning of the present period T. Sigma Le、∑LpAnd sigma LqRespectively is the accumulated value of the power consumed by the electric load, the accumulated value of the power consumed by the potential energy load and the accumulated value of the power consumed by the heat load in the period T.
Here, the time-shiftable heat load or the non-time-shiftable heat load according to the present invention is not limited to a heat load alone, but may be a temperature-dependent load, or may be a time-shiftable cooling load or a non-time-shiftable cooling load.
Here, it should be noted that the energy storage space of each energy storage device needs to satisfy the corresponding storage space constraint condition, that is, the formula (22) is satisfied.
Ek t-1+xk_i.t·Pk.I·ηk.I-xk_o.t·Pk.O≤Ak，(t＝1,2…T) (22)
Wherein T is the scheduling cycle, T is a scheduling time interval in the scheduling cycle, ESHUI t-1Representing the energy stored in an energy storage device k during a t-1 scheduling period of the scheduling cycle, xk_i.tRepresenting the input state quantity, x, of the energy storage device k during the scheduling period tk_o.tRepresenting the output state quantity of the energy storage means k for the scheduled period t.
The spatial constraints of the reservoir are shown in equation (23):
ESHUI t-1+xSHUI_i.t·PSHUI.I·ηSHUI.I-xSHUI_o.t·PSHUI.O≤ASHUI，(t＝1,2…T) (23)
in the formula, ESHUI t-1Representing the energy stored by the reservoir during the last scheduling period of the scheduling cycle. Formula (23) shows that after the current scheduling period T is over, the total energy of the reservoir after energy input and output is less than the water storage space of the reservoir, and for any scheduling period in the scheduling period, the energy stored in the reservoir is less than the water storage space of the reservoir.
The constraint condition of the energy storage space of the methane tank is shown as a formula (24):
EZHAO t-1+xZHAO_i.t·PZHAO.I·ηZHAO.I-xZHAO_o.t·PZHAO.O≤AZHAO(t＝1,2…T) (24)
in the formula, ESHUI t-1The energy stored in the methane tank in the last scheduling period in the scheduling period is shown, namely the initial energy of the methane tank in the current scheduling period. The formula (24) shows that after the current scheduling period T is finished, the total energy of the methane tank after energy input and output is less than the gas storage space of the methane tank, and for any scheduling period in the scheduling period, the energy stored in the methane tank also needs the gas storage space of the methane tank in a cell.
The energy storage space constraint condition of the phase change heat storage greenhouse is shown as a formula (25):
EXIANG t-1+xxiang_i.t·PXIANG.I·ηXIANG.I-xxiang_o.t·PXIANG.O≤AXIANG(t＝1,2…T) (25)
in the formula, ESHUI t-1Representing the energy stored in the phase change heat storage greenhouse in the previous scheduling period, namely the initial energy of the phase change heat storage greenhouse in the current scheduling periodAmount of the compound (A). After the current scheduling period is finished, the total energy of the phase change heat storage greenhouse after energy input and output is smaller than the energy storage space of the phase change heat storage greenhouse. And for each time period in the scheduling cycle, the energy stored in the methane tank is smaller than the energy storage space of the phase change heat storage greenhouse.
The invention can be actually understood as constructing an optimized scheduling model of the facility agricultural micro-energy network, wherein the optimized scheduling model comprises a first optimized scheduling model and a second optimized scheduling model, an optimization function of the optimized scheduling model is selected according to the condition that the total photovoltaic output of the facility agricultural micro-energy network meets a first preset condition or a second preset condition, when the total photovoltaic output of the facility agricultural micro-energy network meets the first preset condition, an optimized objective function is formula (3), and a constraint condition is specifically that formulas (12) - (25) are met. When the total photovoltaic output of the facility agricultural micro-energy network meets a second preset condition, the optimization objective function is the formulas (7) and (11b), and the constraint condition is the same as the formulas (12) to (25).
According to the invention, energy stored by the existing energy storage device of the facility agriculture micro-energy network is used for participating in energy scheduling optimization to carry out photovoltaic consumption, so that the energy storage cost can be reduced, and various resources in the facility agriculture micro-energy network are effectively utilized. Meanwhile, the time-shifting load and energy storage device in the facility agricultural micro-energy network is optimized and scheduled, so that the load characteristic and the energy storage characteristic in the facility agricultural micro-energy network system can be fully utilized, and the maximum photovoltaic consumption is realized as far as possible.
As shown in fig. 2, another embodiment of the present invention provides an energy scheduling method for a facility agricultural micro-energy grid, which includes the following specific steps:
and (3) predicting according to the solar irradiance historical data in the local facility agricultural database to obtain solar irradiance data in a scheduling period, and obtaining the photovoltaic power generation predicted power in the scheduling period according to the relation between the solar irradiance and the photovoltaic output (namely, a formula (2)).
And predicting and obtaining the energy sum consumed by all time-shiftable loads in a scheduling cycle before the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network is adjusted according to historical load data in the local facility agricultural database.
Comparing the photovoltaic output total (the photovoltaic output total is the cumulant of the photovoltaic power generation predicted power in the scheduling period) with the total energy consumed by all time-shiftable loads, and classifying scenes, wherein the scene 1 meets a first preset condition, and the optimization objective function of the optimization scheduling model is a formula (3); in case 2, in order to satisfy the second preset condition, the optimization objective function of the optimized scheduling model is equations (7) and (11 b).
And taking the constructed input and output power models of the energy storage devices and the time-shiftable load model in the facility agriculture micro-energy network as constraint conditions, establishing an optimization scheduling model according to an optimization objective function, obtaining a working time period corresponding to each time-shiftable load meeting the optimization objective function based on the energy scheduling optimization model, determining the storage state of each energy storage device in the facility agriculture micro-energy network corresponding to the minimum value of the scheduling function, and finishing optimization.
The working time period corresponding to each type of time-shiftable load and the storage state of each energy storage device in the facility agriculture micro-energy network can construct a state quantity matrix shown as a formula (26).
Wherein L represents a state quantity matrix of each scheduling period of each type of load in a scheduling cycle, as shown in equation (27):
in the formula, N represents the number of scheduling time intervals in a scheduling cycle, and M represents a place including a time-shifting electric load, a time-shifting heat load and a time-shifting potential energy load in the facility agriculture micro energy networkThere is a total number of time-shiftable loads,respectively representing the state quantities of the 1 st type time-shiftable electric load, the time-shiftable heat load and the time-shiftable electric potential load in the 1 st scheduling period (the state quantity takes a value of 0 or 1, 0 represents a non-running state, 1 represents a running state), respectively representing the state quantities of the type 1 time-shiftable electrical load, the time-shiftable thermal load and the time-shiftable potential load in the ith scheduling period,respectively representing the state quantities of the type 1 time-shiftable electric load, the time-shiftable thermal load and the time-shiftable potential load in the nth scheduling period,respectively representing the state quantities of the potential energy load which can be time-shifted by the type v at the 1 st, i th and N th scheduling periods.
S is a storage state matrix of each energy storage device at each time period, as shown in equation (28):
wherein N represents the number of scheduling periods within a scheduling cycle,respectively representing the input state quantities (value 0 or 1, respectively representing the state of not working or input) of the reservoir, the methane tank and the phase change heat storage greenhouse in the 1 st scheduling period, respectively representing the output state quantities (value 0 or 1, respectively representing the state of not working or output) of the reservoir, the methane tank and the phase change heat storage greenhouse in the 1 st scheduling period.Respectively representing the input state quantities of the reservoir, the methane tank and the phase change heat storage greenhouse in the ith scheduling period,respectively representing the output state quantities of the reservoir, the methane tank and the phase change heat storage greenhouse in the ith scheduling period. Respectively representing the input state quantities of the reservoir, the methane tank and the phase change heat storage greenhouse in the ith scheduling period,respectively representing the output state quantities of the reservoir, the methane tank and the phase change heat storage greenhouse in the Nth scheduling period. The values of the input and output state quantities of each energy storage device cannot be in the input and output states at the same time, that is, the values of the input and output state quantities cannot be 1 at the same time, that is, the formula (29) holds.
In another embodiment of the present invention, an energy scheduling method for a facility agriculture micro-energy network is provided, in which a genetic algorithm is used to solve an optimized scheduling model, and the specific steps are shown in fig. 3 and described as follows:
1) coding of control variables
The state quantities of the time-shifting electric load, the time-shifting heat load, the time-shifting potential energy load, the input and output of the water storage pool energy storage, the input and output of the methane pool energy storage and the input and output of the phase-change heat storage greenhouse energy storage in each scheduling period are used as control variables. The value of the state quantity is 0 or 1, which respectively represents that the time-shiftable load is in a non-operation state or an operation state in the scheduling period or the energy storage device is in a non-operation state or an input/output state in the scheduling period. The optimization problem is a multi-objective non-linear integer programming of N x (M +6) control variables, wherein N represents the number of scheduling time intervals in a scheduling cycle, and M represents the total number of time-shiftable electric loads, time-shiftable thermal loads and time-shiftable potential energy loads.
Controlling variable XN×(M+6)Forming N x (M +6) 0-1 matrices.
2) Generation of initial population
XN×(M+6)The number of 1 s in each column is determined randomly, and the positions are also random.
3) Calculation of individual fitness
The objective functions f1, f2 and f3 are respectively used for seeking that the difference between the total photovoltaic output and the total energy consumed by time-shifting loads is minimum, the daily cost of the facility agriculture micro-energy network is minimum and the accumulated amount of power change of the facility agriculture micro-energy network is minimum, namely the smaller the values of the objective functions f1, f2 and f3 are, the better the individual characteristics in the population are, and the larger the corresponding fitness value is. Therefore, the present invention employs a method of taking the reciprocal of the objective function values f1, f2, f3 to evaluate the fitness of an individual, and constructs a fitness function, as shown in equation (30).
For scenario 1:for scenario 2:where λ is a weight coefficient.
4) Genetic operator and genetic operation
① intersection
The crossing adopts the point crossing of parents and parents. Crossover points were randomly selected for the two parent chromosomes and the gene segments of the parent chromosomes were then interchanged to generate two new generation chromosomes.
② replication
And setting a flag vector pair to flag whether the constraint condition is met. Kicking out individuals which do not meet the constraint conditions, calculating and storing the fitness value of each individual in the current population, and selecting n better individuals to copy to the next generation.
③ variation
Whether or not to mutate by the mutation probability PmAnd (4) determining that variant individuals are randomly selected, randomly selecting variant bits, and judging whether the storage space constraint conditions are met, if the storage space constraint conditions are not met, judging that the variant is invalid.
The graph of the facility agriculture micro energy network load before the optimized scheduling and the graph of the facility agriculture micro energy network load after the optimized scheduling are shown in fig. 4 and fig. 5. The difference between the photovoltaic output and the load after the optimal scheduling is obviously smaller than that before the optimal scheduling, and the photovoltaic consumption with good effect is realized. Before optimized scheduling in scene 2, the user cost is calculated according to the peak-valley electricity price and is 625.28 yuan, and after optimized scheduling, the user cost is 300.41 yuan, so that 324.87 yuan is reduced.
As shown in fig. 6, on the basis of the above embodiment, another embodiment of the present invention provides an energy scheduling system for a facility agriculture microgrid, comprising: a model building module 61 and a determination module 62. Wherein,
the adjusting module 61 is configured to, if it is determined that the total photovoltaic output of the facility agricultural micro-energy network in the scheduling period of the facility agricultural micro-energy network meets a first preset condition, establish a first optimized scheduling model of the facility agricultural micro-energy network based on an input/output power model of each energy storage device in the facility agricultural micro-energy network and a time-shiftable load model in the facility agricultural micro-energy network, and with a minimum deviation degree between a total power consumed by all time-shiftable loads in the facility agricultural micro-energy network and the photovoltaic power generation power of the facility agricultural micro-energy network as a first target;
the determining module 62 is configured to adjust, based on the first optimized scheduling model, a working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and a storage state of each energy storage device in the facility agricultural micro-energy network in the scheduling cycle, and determine a working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and a storage state of each energy storage device in the facility agricultural micro-energy network when the first target is met;
the first preset condition is that the total photovoltaic output amount meets and only meets the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network; the input and output power model is used for calculating the input power and the output power of each energy storage device; the time-shiftable load model is used to determine the power consumed by the time-shiftable load for each scheduling period of the adjusted operation within the scheduling cycle.
Specifically, the functions and operation flows of the modules in this embodiment correspond to those in the above method embodiments one to one, and this embodiment is not described herein again.
On the basis of the above embodiments, another embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method described in any of the above embodiments.
Finally, the method of the present invention is only a preferred embodiment and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An energy scheduling method of a facility agriculture micro-energy network is characterized by comprising the following steps:
s11, if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a first preset condition in a scheduling period of the facility agricultural micro-energy network is judged and known, based on an input and output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network, a first optimized scheduling model of the facility agricultural micro-energy network is established by taking the minimum deviation degree between the sum of power consumed by all time-shifting loads in the facility agricultural micro-energy network and the photovoltaic power generation power of the facility agricultural micro-energy network as a first target;
s12, based on the first optimized scheduling model, adjusting the working time interval corresponding to each time-shifting load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network in the scheduling cycle, and determining the working time interval corresponding to each time-shifting load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the first target is met;
the first preset condition is that the total photovoltaic output amount meets and only meets the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network; the input and output power model is used for calculating the input power and the output power of each energy storage device; the time-shiftable load model is used to determine the power consumed by the time-shiftable load operating at each scheduling period within the scheduling cycle after adjustment.
2. The method according to claim 1, wherein the first preset condition is specifically: and the ratio of the total energy consumed by all time-shifting loads in the facility agricultural micro-energy network to the total photovoltaic output is within a preset interval range.
3. The method of claim 1, further comprising:
if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a second preset condition in the scheduling period is judged and known, a second optimized scheduling model of the facility agricultural micro-energy network is established on the basis of an input and output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network, the minimum daily operation cost of the facility agricultural micro-energy network serves as a second target, and the accumulation and minimum power variation of all scheduling periods in adjacent scheduling periods serve as a third target;
based on the second optimized scheduling model, in the scheduling cycle, adjusting the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network, and determining the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the second target and the third target are met simultaneously;
the second preset condition is that the total photovoltaic output cannot meet the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network, or the total photovoltaic output has a sale allowance after meeting the energy consumed by all the time-shifting loads.
4. The method of claim 1, further comprising:
acquiring power consumed by each energy storage device as a time-shiftable load, and taking the power as input power of the corresponding energy storage device;
determining the output power of each energy storage device based on the energy storage space of each energy storage device and the corresponding energy output maximum time;
an input-output power model is constructed for each energy storage device based on the input power and the output power of each energy storage device.
5. The method of claim 4, wherein constructing the input-output power model for each energy storage device specifically comprises: constructing an input-output power model of each energy storage device by the following formula;
wherein, Pk.IIs the input power, P, of an energy storage device kk.pumpPower consumed for energy storage device k as a timeshiftable load; pk.OIs the output power of an energy storage device k, AkEnergy storage space, t, for an energy storage device kk.O_MAXMaximum output time, t, for energy storage device kk.I_MAXMaximum input time for energy storage device k, ηk.IIs the energy storage efficiency of the energy storage device k.
6. The method of claim 5, wherein the energy storage space of each energy storage device satisfies an energy storage space constraint, wherein the energy storage space constraint is defined by the following equation:
Ek t-1+xk_i.t·Pk.I·ηk.I-xk_o.t·Pk.O≤Ak，(t＝1,2…T+
wherein T is the scheduling cycle, T is a scheduling time interval in the scheduling cycle, ESHUI t-1Representing the energy stored in an energy storage device k during a t-1 scheduling period of the scheduling cycle, xk_i.tRepresenting the input state quantity, x, of the energy storage device k during the scheduling period tk_o.tRepresenting the output state quantity of the energy storage means k for the scheduled period t.
7. The method according to any one of claims 1-6, further comprising:
acquiring the sum of the power consumed by all the time-shiftable loads in each scheduling period before adjustment, the sum of the power consumed by the time-shiftable loads transferred into each scheduling period after adjustment, and the sum of the power consumed by the time-shiftable loads transferred out of each scheduling period after adjustment;
calculating the total power consumed by the time-shiftable loads working at each scheduling period after adjustment based on the following formula, and constructing a time-shiftable load model in the facility agriculture micro-energy network;
Lt＝Lfore.t+LIN.t-LOUT.t
wherein L isfore.tTo adjust the sum of the power consumed by all time-shiftable loads within the pre-scheduling period t,LIN.tsum of power consumed by time-shiftable loads adjusted to shift into scheduling period t, LOUT.tThe sum of the power consumed by the time-shiftable loads for the adjusted transition out of the scheduling period t.
8. The method of claim 7, wherein the energy storage devices within the facility agricultural micro-energy grid comprise potential energy storage devices, biomass chemical energy storage devices, and thermal energy storage devices, and wherein the time-shiftable loads comprise time-shiftable electrical loads, time-shiftable thermal loads, and time-shiftable potential energy loads; l isIN.tAnd LOUT.tCalculated by the following formula:
wherein L isin_E.t、Lin_Q.tAnd Lin_P.tRespectively representing the sum of power consumed by the time-shiftable electric load, the time-shiftable heat load and the time-shiftable potential energy load which are shifted into the scheduling time period t after adjustment, Lout_E.t、Lout_Q.tAnd Lout_P.tRespectively representing the sum of the power consumed by the time-shiftable electrical load, the time-shiftable thermal load and the time-shiftable potential energy load transferred out of the scheduling period t after adjustment.
9. The method of claim 8, wherein the working hours corresponding to the timeshiftable loads and the storage states of the energy storage devices within the facility agricultural micro-grid construct a state quantity matrix;
wherein, L is a state quantity matrix of each scheduling time interval of the time-shifting load in a scheduling cycle, S is a storage state matrix of each energy storage device in each scheduling time interval, N is the number of the scheduling time intervals in a scheduling cycle, and M is the total number of all the time-shifting loads in the facility agriculture micro-energy network.
10. An energy scheduling system for a facility agricultural micro-energy grid, comprising:
the model building module is used for building a first optimized scheduling model of the facility agricultural micro-energy network based on an input and output power model of each energy storage device in the facility agricultural micro-energy network and a time-shifting load model in the facility agricultural micro-energy network and taking the minimum deviation degree between the sum of the power consumed by all the time-shifting loads in the facility agricultural micro-energy network and the photovoltaic power generation power of the facility agricultural micro-energy network as a first target if the fact that the total photovoltaic output of the facility agricultural micro-energy network meets a first preset condition in a scheduling period of the facility agricultural micro-energy network is judged and known;
a determining module, configured to adjust, based on the first optimized scheduling model, a working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and a storage state of each energy storage device in the facility agricultural micro-energy network in the scheduling cycle, and determine the working time period corresponding to each time-shiftable load in the facility agricultural micro-energy network and the storage state of each energy storage device in the facility agricultural micro-energy network when the first target is met;
the first preset condition is that the total photovoltaic output amount meets and only meets the energy sum consumed by all time-shifting loads in the facility agricultural micro-energy network; the input and output power model is used for calculating the input power and the output power of each energy storage device; the time-shiftable load model is used to determine the power consumed by the time-shiftable load for each scheduling period of the adjusted operation within the scheduling cycle.
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