CN112734451B - Green house multi-energy system based on non-cooperative game and optimization method - Google Patents
Green house multi-energy system based on non-cooperative game and optimization method Download PDFInfo
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Abstract
The disclosure provides a comprehensive energy system optimization method and system based on an agricultural greenhouse, comprising the following steps: according to uncertainty influence factors of the agricultural greenhouse as input data, predicting photovoltaic output, electricity purchase price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity and influence of weather on greenhouse illumination; based on the prediction data, calculating an optimal output decision selected in a prediction time domain, enabling a self cost function to be minimum, predicting again after entering the next moment, performing iterative convergence of Nash equilibrium again, deciding an optimal output, and performing rolling time domain optimization in sequence. According to the technical scheme, the agricultural greenhouse production and the comprehensive energy system are combined, so that the environmental problem of agricultural production garbage can be solved, the energy utilization efficiency can be improved, and the effects of environmental protection and energy conservation are achieved.
Description
Technical Field
The disclosure belongs to the technical field of comprehensive energy optimization scheduling, and particularly relates to an agricultural greenhouse comprehensive energy system output optimization method based on non-cooperative game.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Energy and environmental protection issues have long been hot topics of interest. From the aspect of the energy structure of China, the proportion of coal-fired power generation is about 60%, although China is a large country for producing and consuming coal resources, the cost of coal power generation is low, the heat quantity is high, and the power generation mode can bring about a small environmental problem. The development of green renewable energy sources is a fundamental approach to solve the energy problem, whether energy sources are safe or environmental pollution. However, there are various problems and barriers to the utilization of renewable energy sources, such as wind power cannot be used in agricultural greenhouses, photovoltaic power generation randomly fluctuates, and coupling factors on the demand side cause some disturbance to the system. For such conventional systems of agricultural greenhouses, existing control decisions do not take into account the capacity issues of the biogas storage tanks.
The method specifically comprises the following steps:
1. the utilization efficiency is to be improved, the system digestion capability is to be improved, the resource endowment of China is characterized by reverse distribution of the load center, and the resource and load matching is relatively poor.
2. The technology level is low, the utilization cost is high, and the cost needs to be reduced through further technical progress and industrial upgrading.
3. In terms of biomass energy, the utilization mode mainly comprises power generation, but raw materials of the biomass energy are uncontrollable, and large-area loss occurs in biomass power generation. The energy stored by biomass energy is calculated to be twice larger than the consumption total amount of fossil energy at the present stage, and the biomass energy is extremely small in the energy structure in China at present, and is mainly caused by a rough production mode and energy utilization. Currently, the more effective biomass energy utilization forms are biogas production and alcohol production by using biomass.
Disclosure of Invention
In order to overcome the defects of the prior art, the disclosure provides a comprehensive energy system optimization method of an agricultural greenhouse based on non-cooperative game, which utilizes a distributed non-cooperative game theory to realize multi-energy flow prediction, reduces maintenance and operation cost of the agricultural greenhouse, and increases stability and reliability of the system.
To achieve the above object, one or more embodiments of the present disclosure provide the following technical solutions:
in a first aspect, an integrated energy system optimization method based on an agricultural greenhouse is disclosed, comprising:
according to uncertainty influence factors of the agricultural greenhouse as input data, predicting photovoltaic output, electricity purchase price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity and influence of weather on greenhouse illumination;
based on the prediction data, calculating an optimal output decision selected in a prediction time domain, enabling a self cost function to be minimum, predicting again after entering the next moment, performing iterative convergence of Nash equilibrium again, deciding an optimal output, and performing rolling time domain optimization in sequence.
According to the technical scheme, when the temperature is predicted, the optimization targets and constraint conditions of natural gas, electric energy, cold energy and heat energy are determined based on the requirements of the temperature greenhouse on temperature, humidity and illumination.
According to the technical scheme, when the optimal output decision is selected in the prediction time domain, under the condition that the output conditions of electric energy, cold energy, heat energy and natural gas are not in cooperation, an optimization target is solved according to a distributed non-cooperative game control algorithm, and forward rolling optimization is continuously carried out.
According to the technical scheme, in the non-cooperative game control algorithm, the players mainly output natural gas, electric power, cold power and heat power, the players minimize own loss functions, and because the loss functions are closely related to the output of the four players, the Lagrange multiplier method is adopted to minimize own income functions in consideration of mutual constraint, nash equilibrium points of the game are calculated in a prediction time domain, and the output condition of each energy source at the Nash equilibrium points is used as an optimal state at the next moment.
According to the technical scheme, the method further comprises the step of monitoring the methane storage tank in real time, wherein the ratio of the residual gas of the methane storage tank to the maximum capacity is monitored in real time, if the ratio is larger than a first set value, the gas generator does not exert force, otherwise, whether the ratio is smaller than a second set value is continuously judged, if not, the methane generator set and the combustion generator exert force simultaneously, and if so, the methane generator set does not exert force.
In a second aspect, an integrated energy system optimization system based on an agricultural greenhouse is disclosed, comprising:
the data prediction module is used for predicting photovoltaic output, electricity purchasing price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity and influence of weather on greenhouse illumination according to uncertainty influence factors of the agricultural greenhouse as input data;
the output optimization module is used for calculating an optimal output decision selected in a prediction time domain based on the prediction data, so that a self cost function is minimum, when the next moment is entered, the prediction is performed again, the iteration convergence of Nash equalization is performed again, the optimal output is decided, and the rolling time domain optimization is performed sequentially.
The one or more of the above technical solutions have the following beneficial effects:
according to the technical scheme, the agricultural greenhouse production and the comprehensive energy system are combined, so that the environmental problem of agricultural production garbage can be solved, the energy utilization efficiency can be improved, and the effects of environmental protection and energy conservation are achieved.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
FIG. 1 is a schematic diagram of energy conversion according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of the composition of a system according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a deep long and short duration memory of a system according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of an optimization prediction algorithm according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of a reservoir capacity monitoring in accordance with an embodiment of the present disclosure;
FIG. 6 is a flow chart of a gaming algorithm according to an embodiment of the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
Example 1
The embodiment discloses a comprehensive energy system optimization method based on an agricultural greenhouse, which considers the coupling factors among various input data in the face of uncertainty of photovoltaic power generation, weather conditions, electricity prices and the like, and provides a depth LSMT algorithm for predicting factors such as photovoltaic output, electricity purchase price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity, influence of weather on greenhouse illumination and the like. Based on the predicted data (the cold-hot load P obtained through LSTM network D And initial value x of game participant 1 ,x 2 ,x 3 ,x 4 ) And calculating how to select the optimal output decision in the prediction time domain, so as to minimize the self cost function. And when the next moment is reached, predicting again, carrying out iterative convergence of Nash equalization again, deciding the optimal output, and carrying out rolling time domain optimization in sequence. And a control decision is provided for the situations of insufficient reserve gas and insufficient gas storage tank capacity possibly occurring in the biogas gas storage tank, see H in the formula (3) BG 。
It should be noted that, the game algorithm in the prior art has no constraint condition, the constraint condition is considered in the algorithm, and the specific position is g in the step three of optimizing and solving the summary j (x)。
When the technical scheme is implemented, the prediction is obtained firstlyThe data of the week before the moment, including illumination intensity, electricity purchase price, local humidity, local temperature, local wind speed and load data. And obtaining actual load data at the moment to be predicted and an initial value x of the game algorithm through the LSTM network. Wherein the obtained actual load data at the moment to be predicted is used as the user load P in the load balance constraint D Wherein P is D Included
Gaming algorithm initial values x, including x 1 ,x 2 ,x 3 ,x 4 There is specifically set forth in the specific embodiments.
The depth LSMT algorithm is used for predicting factors such as photovoltaic output, electricity purchase price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity, influence of weather on greenhouse illumination and the like, and specifically comprises the following steps:
the first step, the reading and processing of the data comprises the normalization and division of the training set;
second, the framework model determines parameters (the number of output neurons of the first hidden layer and the dimension of each sample);
thirdly, training the data by using a training set, testing by using a testing set, dividing a part of the training set to obtain a verification set, and giving an early warning for the over-fitting problem;
fourth, obtaining prediction data.
Specifically, the hardware system based on the system consists of a methane tank, a methane storage tank, a methane generator set, a solar photovoltaic power generation device, a gas turbine, a waste heat recovery boiler, a gas boiler, a storage battery, an electric refrigerator, an absorption refrigerator, a heat storage water tank and other devices. The production of the agricultural greenhouse has special requirements, and the temperature, humidity and illumination in the greenhouse need to be maintained within a certain range.
The scheme is suitable for the production mode of co-production of the cultivation greenhouse and the planting greenhouse, the raw materials of the biogas engineering are sewage, excrement and vegetable garbage of the cultivation greenhouse and the planting greenhouse, and the biogas generating set has the characteristics of more sufficient and stable sources, so that the upper limit and the lower limit of the power generation capacity of the biogas generating set are easier to estimate, and the uncertainty factor is smaller. The daily manure yield of the cultivation greenhouse and the daily vegetable garbage yield of the planting greenhouse can be obtained according to the past records of the agricultural greenhouse, the biogas yield of the biogas digester can be estimated, and the capacity of the biogas generator set can be calculated.
Four energy forms are respectively electric energy, heat energy, cold energy and clean energy in the comprehensive energy system. The energy forms are converted through each unit, as shown in figure 1. The specific conversion process is as follows: the clean energy source comprises natural gas and methane, the methane is prepared from agricultural waste of the agricultural greenhouse through a methane tank, and the methane is stored in a gas storage tank and is converted into electric energy through a methane generator set. Natural gas is obtained from a gas pipeline and is converted into electric energy through a gas generator set. The electric energy is generated by a main power grid, a biogas generator set, a gas generator set, a photovoltaic power generation device and a battery pack according to the load of a user. The cold energy is generated by the electric refrigerator and the absorption refrigerator according to the load of the user. Wherein the electric refrigerator can convert electric energy into cold energy, and the absorption refrigerator can convert heat energy into cold energy. The heat energy is obtained from the waste heat absorbing device and the gas boiler via the heat storage water tank according to the user load. The waste heat recovery boiler can recycle the flue gas generated by the combustion of the gas turbine unit, and the waste heat is recovered to reduce the waste of energy sources, so that the waste heat is recycled. The gas boiler can convert clean energy into heat energy so as to meet the heat supply requirement of a user, as shown in fig. 2.
The comprehensive energy system is applied to the agricultural greenhouse, and the agricultural greenhouse has higher requirements on temperature, humidity and illumination, so the following requirements on temperature, humidity and illumination are listed according to the requirements of the agricultural greenhouse, and the part is used as coupling constraint (electric energy, cold energy and heat energy constraint) and is g in optimization solution summary j (x) Is a part of the same.
Temperature conditions:
wherein T is 1 And T 2 The upper and lower limits of the temperature to be kept for the agricultural greenhouse are kept at T 1 To T 2 Between T out P is the outside temperature of the day disc,tst,t Is the output thermal power of the heat storage water tank,
for the cooling power of the electric refrigerator, +.>
K being the refrigerating power of the absorption refrigerator out ，K tst ，K EC ，K AC Respectively the heat transfer coefficients.
Illumination conditions:
L 1 ≤L out ·l out +P L,t ·l e ≤L 2
wherein L is 1 ，L 2 The upper limit and the lower limit of the illumination intensity required to be maintained for the agricultural greenhouse are L out For the outside illumination intensity of the same day, P L,t Output power of light supplementing lamp in agricultural greenhouse, l out ，l e Is the constant of light energy loss.
Humidity conditions:
S 1 ≤S t ≤S 2
wherein S is 1 ，S 2 S is the upper and lower limits of humidity required to be kept for the agricultural greenhouse t For the inside humidity of the green house of t moment, the humidity of green house passes through fan and spouts a little and adjusts.
The optimization problem is described as follows:
optimization objective for natural gas:
wherein C is G To consume natural gas costs, R ng Price of natural gas in cubic meter, V G Is the equipment set of the gas boiler, P GT,i,t Power, η, of natural gas for gas turbine GT H is the thermal efficiency of the gas turbine ng Is the heat value of natural gas, P b,t Is burnt byOutput heat power of gas boiler, eta b Is the thermal efficiency of the gas boiler.
Constraint conditions for natural gas:
the gas turbine is limited by gas consumption
P GT,i,t ＝η GT ·H ng ·f GT,i,t (1)
Wherein P is GT,i,t For the output power, eta of the gas turbine GT H is the thermal efficiency of the gas turbine ng Is the heat value of natural gas, f GT,i,t Is the gas consumption of natural gas.
The gas consumption constraint of the gas boiler is
P b,t ＝η b ·H ng ·f b,t (2)
Wherein P is b,t Is the output heat power of the gas boiler, eta b Is the heat efficiency of the gas boiler, H ng Is the heat value of natural gas, f b,t Is the gas consumption of the gas boiler.
Therefore, the optimization problem for natural gas can be described as:
min C G (P GT,i,t ,P b,t )
s.t.(1)～(2)
optimization objective for electrical energy:
wherein C is E K is the cost of electricity generation and purchase om,GT For operating and maintaining costs of gas turbines, P GT,i,t For the output of the gas turbine, P pv,t K is the output power of the photovoltaic power generation device om,pv For the operation and maintenance cost of the photovoltaic power generation device, H BG To determine whether the biogas generating set starts or not, a decision weight value, P BG,t K is the output power of the biogas generator set om,BG The operation and maintenance cost of the biogas generator set is P c,BT,t For charging power of battery pack, P disc,BT,t K is the discharge power of the battery pack om,BT For transporting battery packsLine maintenance cost, P g,t For purchasing electric power from main electric network, R p,t Is the electricity price.
Constraint conditions for electrical energy:
the electric power balance constraint is that
Wherein C is E K is the cost of electricity generation and purchase om,GT For operating and maintaining costs of gas turbines, P GT,i,t For the output of the gas turbine, P pv,t K is the output power of the photovoltaic power generation device om,pv For the operation and maintenance cost of the photovoltaic power generation device, H BG To determine whether the biogas generating set starts or not, a decision weight value, P BG,t K is the output power of the biogas generator set om,BG The operation and maintenance cost of the biogas generator set is P c,BT,t For charging power of battery pack, P disc,BT,t K is the discharge power of the battery pack om,BT For the operation and maintenance cost of the battery pack, P g,t For purchasing electric power from main electric network, R p,t Is the electricity price.
The output constraint of the biogas generator set is that
Wherein the method comprises the steps of BG PIs the lower limit of the output force of the biogas generator set,
is the upper limit of the output force of the methane generator set, P BG,i,t Is the current output power of the methane generator set.
The constraint of the gas generator set is that
Wherein the method comprises the steps of GT PIs the lower limit of the output power of the gas generator set,
is the upper limit of the output power of the gas generator set.
The constraint of the battery pack is that
Wherein the method comprises the steps of
For the lower limit of the discharge capacity, +.>
P, the upper limit of the battery charging capability disc,BT For discharging power of battery, P c,BT,t For charging power of battery, U disc,BT,t To determine the decision weight of whether the battery is discharged, U c,BT,t Decision weights for determining whether or not to charge the battery.
In summary, the optimization problem for electrical energy can be described as:
minC E (P GT,i,t ,P BG,t ,P c,BT,t ,P disc,BT,t ,P PV,t ,P g,t )
s.t.(3)～(6)
optimization objective for cold energy:
wherein C is c For the cold energy operation the maintenance costs are high,
k for absorbing refrigerating power of refrigerator om,AC Maintenance costs for the operation of absorption refrigerators, < >>
K is the refrigerating power of the electric refrigerator om,EC And the cost is maintained for the operation of the electric refrigerator.
Constraint conditions for cold energy:
the cold power balance constraint is that
Wherein the method comprises the steps of
Is the refrigerating power of the electric refrigerator, +.>
For the cooling power of the absorption chiller, +.>
Is the cooling load power of the user.
The coupling constraint of the electric refrigerator is that
Wherein the method comprises the steps of
Is the refrigerating power of the electric refrigerator, +.>
Is the power consumption of the electric refrigerator, eta EC Is the refrigeration coefficient of the electric refrigerator.
The coupling constraint of the absorption refrigerator is that
Wherein the method comprises the steps of
For the cooling power of the absorption chiller, +.>
For absorbing the heat-absorbing power of the refrigerator, eta AC Is the refrigeration coefficient of the absorption refrigerator.
Therefore, the optimization problem for cold energy can be described as:
s.t.(7)～(9)
the optimization targets for thermal energy are:
wherein C is H Maintenance cost for thermal energy operation, P b,t Is the output power of the gas boiler, K om,b For operating and maintaining the gas turbine, P c,tst,t The heat storage power of the heat storage water tank is P disc,tst,t K is the heat release power of the heat storage water tank om,tst For the operation and maintenance costs of the heat storage water tank,
for the output heat power of the waste heat boiler, K om,WHB The method is the operation and maintenance cost of the waste heat boiler.
Constraint on thermal energy:
the thermal power balance constraint is that
Wherein the method comprises the steps of
Is the output heat power of the waste heat boiler, P b,t U is the output heat power of the gas boiler disc.tst,t To determine the decision weight of whether the heat storage water tank releases heat, P disc,tst,t For the output power of the heat storage water tank, < >>
U for absorbing heat absorption power of refrigerator c,tst,t The heat storage power of the heat storage water tank is P c,tst,t For the heat storage power of the heat storage water tank, < >>
Power is demanded for the thermal energy of the user.
The coupling constraint of the gas turbine is that
Wherein the method comprises the steps of
For the power generation of a gas turbine, +.>
The heat release power of the gas turbine is a constant coefficient.
The thermal power constraint of the waste heat boiler is that
Wherein the method comprises the steps of
Is the output heat power of the waste heat boiler, eta WHB For the efficiency of the waste heat boiler>
The output heat power of the gas boiler.
The thermal power constraint of the heat storage tank is that
Wherein U is disc,tst,t In order to determine the decision weight of whether the heat storage water tank releases heat,
p is the lower limit of heat release of the heat storage water tank disc,tst,t Heat release power of the heat storage water tank, P c,tst,t U is the heat storage power of the heat storage water tank c,tst,t For determining the decision weight of whether the heat storage water tank stores heat, < ->
U is the upper limit of heat storage of the heat storage water tank c,tst,t The decision weight is used for deciding whether the heat storage water tank stores heat or not.
Therefore, the optimization problem for thermal energy can be described as:
s.t.(10)～(13)
the optimization objective is equal to the optimization objective function mentioned below, and the decision variable x is biased. Similarly, constraint and g mentioned below j (x) Equal, decision variable x is biased.
Overview of optimization solutions
Step one: because a single prediction method is difficult to reflect the coupling relation among multiple energy sources, an improved LSTM (Long short-term memory) method is adopted to predict factors such as photovoltaic output, electricity purchase price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity, influence of weather on greenhouse illumination and the like, as shown in figure 3.
Step two: the gambler is mainly the output of natural gas, electricity, cold and heat. All four players want to minimize their own loss function, and because the loss function and the four's output are closely related, the Lagrangian multiplier method is used to minimize their own profit function, taking into account the mutual constraints. And in the prediction time domain, calculating Nash equilibrium points of the game, and taking the output condition of each energy source at the Nash equilibrium points as an optimization state at the next moment.
Step three: consider a distributed gaming system consisting of n players using the following distributed gaming theory algorithm: note that optimization problem C C ,C H ,C E Is equal to f herein i (x) Constraint in the optimization problem is equal to g here j (x)。
Wherein x is i Representing the status, k, of the ith player i Representing the control gain, y i ＝[y i1 ,y i2 ,...,y in ] T Representing an estimate of the state of the other players by the ith bettor, i.e. y ij Represents an estimate of the status of the ith player for the jth player, lambda ij Lagrangian multiplier, f, representing the ith player i (x) To optimize the objective function g j (x) Represents the j-th constraint satisfied, j e K, K represents the set of constraints.
For the first player's system, the Lagrangian multiplier needs to satisfy
λ 1j (k+ΔT)＝λ 1j (k)+k 1j λ 1j (k)g j (y 1 )ΔT
Wherein k is 1j To control the gain.
The lagrangian multiplier of the other players needs to satisfy the consistency protocol as follows:
wherein gamma is ij To control the gain, a ij The jth element of the ith row of the laplace matrix representing the communication map. The estimation of the state of the jth player by the ith player is expressed as
y ii (k)＝x i (k),i∈V
Wherein w is ij Representing the control gain.
Step four: and step three, iterating until the system converges to a Nash equilibrium point.
Step five: the rolling time domain optimization is continuously carried out forwards.
Under the condition that the power output conditions of electric energy, cold energy, heat energy and natural gas in the system are not in cooperation, forward rolling optimization is continuously carried out according to the control algorithm.
Detailed description of the preferred embodiments
Step 1: before the LSTM algorithm is adopted to predict the photovoltaic energy source to output electricity, firstly, input and output variables are required to be determined, an input and output data set is divided into a training set, a verification set and a test set, and normalization preprocessing is carried out, as shown in fig. 4.
Step 1.1: if the data of the previous week of the predicted time is adopted, let U= [ U ] 1 ,u 2 ,...,u 168 ]，u i Data representing the i-th hour of the day before prediction, u 1 ＝[u 11 ,u 12 ,...,u 1n ] T ,u 11 Indicating the illumination intensity, u 12 Indicating the price of electricity purchase, u 13 Indicating local weather humidity, u 14 Indicating the local weather temperature, u 15 Represents the local wind speed, Z= [ Z ] 1 ,z 2 ,...,z n ] T ，z j Represents the j-th data in the predicted data for 1 hour in the future, where z 1 Representation of
z 2 Representation->
z 3 Representation->
z 4 Represents x 1 ，z 5 Represents x 2 ,z 6 Represents x 2 ，z 7 Represents x 3 ，z 8 Represents x 4 。
Step 2: initializing. Initial values of game participants are set. According to the data such as photovoltaic output in the predicted data in the step 1, the method comprises the following steps of
x 2 ＝[P GT,i,t ,P b,t ] T ，
Setting a prediction time domain as p days, and designing the maximum iteration number as p m . Setting initial ERROR and final ERROR in game process end . The communication diagram formed by the communication states of the four in the game process, which can communicate with the neighbors, is a communication diagram. The element of the ith row and jth column of the Laplace matrix is a ij 。
Step 3:
step 3.1: calculating the state of the system by adopting a gradient descent method:
step 3.2: updating system state calculation ERROR ERROR, judging whether ERROR is smaller than ERROR end If the difference is smaller than the preset value, the step 4 is entered; otherwise, the next step is carried out.
Step 3.3: judging whether the iteration times of the system reach the maximum value, if so, entering step 4; otherwise, the next step is carried out.
Step 3.4: updating the Lagrangian multiplier.
λ 1j (k+ΔT)＝λ 1j (k)+k 1j λ 1j (k)g j (y 1 )ΔT
Updating the Lagrangian multipliers of the other players using the consistency protocol:
wherein gamma is ij To control the gain, a ik The ith row and kth element of the laplace matrix representing the communication map.
Step 4:
step 4.1: and updating the estimation of each player on the states of other players according to the consistency algorithm.
y ii (k)＝x i (k),i∈V
Step 4.2: updating the estimation information, and proceeding to step 3. The iterative process is shown in fig. 6.
Step 5: updating the state of the current step according to the prediction information
x 2 ＝[P GT,i,t+1 ,P b,t+1 ] T
The depth LSMT algorithm can accurately predict photovoltaic output, electricity purchase price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity and influence of weather on greenhouse illumination.
As shown in fig. 5, a control strategy is provided for the situations of insufficient gas storage capacity and insufficient gas storage capacity possibly occurring in the biogas storage tank, and the decision can better ensure the stability and reliability of the comprehensive energy system.
As shown in fig. 6, a multi-energy flow prediction optimization algorithm based on a distributed non-cooperative game theory is provided, and the provided method can be used for processing load and uncertainty of new energy, increasing stability of a control strategy and reducing cost of system operation and maintenance.
The comprehensive energy system is applied to the agricultural greenhouse, biomass energy is added into the comprehensive energy system, and the uncertainty of biomass energy power generation in the system is small.
Example two
It is an object of the present embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing specific steps in the above method when executing the program.
Example III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs specific steps in the above method.
The steps involved in the devices of the second, third and fourth embodiments correspond to those of the first embodiment of the method, and the detailed description of the embodiments can be found in the related description section of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media including one or more sets of instructions; it should also be understood to include any medium capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any one of the methods of the present disclosure.
It will be appreciated by those skilled in the art that the modules or steps of the disclosure described above may be implemented by general-purpose computer means, alternatively they may be implemented by program code executable by computing means, so that they may be stored in storage means and executed by computing means, or they may be fabricated separately as individual integrated circuit modules, or a plurality of modules or steps in them may be fabricated as a single integrated circuit module. The present disclosure is not limited to any specific combination of hardware and software.
The foregoing description of the preferred embodiments of the present disclosure is provided only and not intended to limit the disclosure so that various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
While the specific embodiments of the present disclosure have been described above with reference to the drawings, it should be understood that the present disclosure is not limited to the embodiments, and that various modifications and changes can be made by one skilled in the art without inventive effort on the basis of the technical solutions of the present disclosure while remaining within the scope of the present disclosure.
Claims (7)
1. An integrated energy system optimization method based on an agricultural greenhouse is characterized by comprising the following steps:
according to uncertainty influence factors of the agricultural greenhouse as input data, predicting photovoltaic output, electricity purchase price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity and influence of weather on greenhouse illumination;
based on the prediction data, calculating an optimal output decision in a prediction time domain, enabling a self cost function to be minimum, predicting again after entering the next moment, performing iterative convergence of Nash equalization again, deciding an optimal output, and sequentially performing rolling time domain optimization;
when the optimal output decision is selected in the prediction time domain, under the condition that the output conditions of electric energy, cold energy, heat energy and natural gas are not cooperative, solving an optimization target according to a distributed non-cooperative game control algorithm, and continuously performing forward rolling optimization;
the method comprises the steps that in the non-cooperative game control algorithm, a game player is natural gas, electric power, cold and hot power, the game player minimizes own loss function, and because the loss function is closely related to the output of the four players, a Lagrange multiplier method is adopted to minimize own profit function in consideration of mutual constraint, nash equilibrium points of the game are calculated in a prediction time domain, and the output condition of each energy source at the Nash equilibrium points is used as an optimal state of the next moment;
the method further comprises a step of monitoring the methane gas storage tank in real time, wherein the ratio of the residual gas of the methane gas storage tank to the maximum capacity is monitored in real time, if the ratio is larger than a first set value, the gas generator does not exert force, otherwise, whether the ratio is smaller than a second set value is continuously judged, if the ratio is smaller than the first set value, the methane generator set and the combustion generator exert force simultaneously, and if the ratio is larger than the first set value, the methane generator set does not exert force.
2. The comprehensive energy system optimization method based on the agricultural greenhouse as claimed in claim 1, wherein the optimization targets and constraint conditions of the natural gas, the electric energy, the cold energy and the heat energy are determined based on the requirements of the temperature greenhouse on temperature, humidity and illumination during prediction.
3. The method for optimizing an integrated energy system based on an agricultural greenhouse as claimed in claim 1, wherein the uncertainty influencing factors of the agricultural greenhouse include: the method comprises the steps of light intensity, electricity purchase price, local humidity, local temperature, local wind speed and load data, wherein the data are obtained through an information platform.
4. The method for optimizing the comprehensive energy system based on the agricultural greenhouse according to claim 1, wherein actual load data at the moment to be predicted and initial values of a game algorithm are obtained through an LSTM network based on received input data, and the obtained actual load data at the moment to be predicted is used as user loads in load balance constraint.
5. An integrated energy system optimizing system based on an agricultural greenhouse is characterized by comprising:
the data prediction module is used for predicting photovoltaic output, electricity purchasing price, influence of weather on greenhouse temperature, influence of weather on greenhouse humidity and influence of weather on greenhouse illumination according to uncertainty influence factors of the agricultural greenhouse as input data;
the output optimization module is used for calculating an optimal output decision selected in a prediction time domain based on the prediction data, so that a self cost function is minimum, when the next moment is entered, the prediction is performed again, the iterative convergence of Nash equilibrium is performed again, the optimal output is decided, and the rolling time domain optimization is performed sequentially;
when the optimal output decision is selected in the prediction time domain, under the condition that the output conditions of electric energy, cold energy, heat energy and natural gas are not cooperative, solving an optimization target according to a distributed non-cooperative game control algorithm, and continuously performing forward rolling optimization;
the method comprises the steps that in the non-cooperative game control algorithm, a game player is natural gas, electric power, cold and hot power, the game player minimizes own loss function, and because the loss function is closely related to the output of the four players, a Lagrange multiplier method is adopted to minimize own profit function in consideration of mutual constraint, nash equilibrium points of the game are calculated in a prediction time domain, and the output condition of each energy source at the Nash equilibrium points is used as an optimal state of the next moment;
the real-time monitoring module is used for monitoring the ratio of the residual gas of the gas storage tank to the maximum capacity in real time, if the ratio is larger than a first set value, the gas generator does not exert force, otherwise, whether the ratio is smaller than a second set value is continuously judged, if the ratio is smaller than the second set value, the gas generator set and the combustion generator exert force simultaneously, and if the ratio is larger than the first set value, the gas generator set does not exert force.
6. A computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the specific steps of the method of any of the preceding claims 1-4 when the program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, performs the specific steps of the method of any of the preceding claims 1-4.
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