CN113496298A - Optimization method and device of comprehensive energy system and electronic equipment - Google Patents
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Abstract
The embodiment of the invention provides an optimization method and device of an integrated energy system and electronic equipment, wherein the method comprises the following steps: acquiring constraint conditions of the comprehensive energy system; determining initial design point parameters of the comprehensive energy system according to the constraint conditions; according to the constraint conditions, performing first iterative optimization on the initial design point parameters by adopting a first preset optimization model, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by adopting a second preset optimization model; and acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model, and acquiring a target equipment output value of each equipment in the comprehensive energy system according to the output result of the second preset optimization model. The embodiment of the invention can improve the energy utilization rate of the comprehensive energy system.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to an optimization method and device of a comprehensive energy system and electronic equipment.
Background
The electric power system can provide electric energy in various modes such as hydroelectric power, coal power, tidal power generation, energy storage equipment power generation and the like to form a comprehensive energy system combining various energy modes, wherein the power generation ratio of renewable energy sources is higher and higher.
Because the output of the renewable energy is greatly influenced by weather and has uncertainty, in the related technology, the output condition and the load condition of the renewable energy in the whole year are described by data of a typical day, but the output condition and the load condition described by the typical day are too comprehensive, and the output condition and the load condition of the renewable energy in the whole year are not accurate, so that in the process of optimizing the operation mode of the power system by adopting the typical day mode, the actual output condition and the actual load condition of the renewable energy are possibly different from the output condition and the load condition reflected by the typical day in the optimization result, and the energy utilization rate of the comprehensive energy system is reduced.
From the above, the optimization method for the integrated energy system in the related art has a drawback of reducing the energy utilization rate of the integrated energy system.
Disclosure of Invention
The embodiment of the invention provides an optimization method and device of an integrated energy system and electronic equipment, and aims to solve the problem that the energy utilization rate of the integrated energy system is reduced in the integrated energy system optimization method in the related art.
In order to solve the above technical problem, the embodiment of the present invention is implemented as follows:
in a first aspect, an embodiment of the present invention provides a method for optimizing an integrated energy system, including:
acquiring constraint conditions of the comprehensive energy system;
determining an initial design point parameter of the integrated energy system according to the constraint condition, wherein the initial design point parameter comprises an initial value of the equipment capacity of each equipment in the integrated energy system;
according to the constraint conditions, performing first iterative optimization on the initial design point parameters by adopting a first preset optimization model, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by adopting a second preset optimization model, wherein the device output value output by the second preset optimization model is used as an input parameter of the next iteration of the first preset optimization model, and the design point parameters output by the first preset optimization model are used as input parameters of the next iteration of the second preset optimization model;
and acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model, and acquiring a target equipment output value of each equipment in the comprehensive energy system according to the output result of the second preset optimization model.
In a second aspect, an embodiment of the present invention further provides an optimization apparatus for an integrated energy system, including:
the first acquisition module is used for acquiring constraint conditions of the comprehensive energy system;
the determining module is used for determining an initial design point parameter of the integrated energy system according to the constraint condition, wherein the initial design point parameter comprises an initial value of the equipment capacity of each equipment in the integrated energy system;
the optimization module is used for performing first iterative optimization on the initial design point parameter by adopting a first preset optimization model according to the constraint condition, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by adopting a second preset optimization model, wherein the device output value output by the second preset optimization model is used as an input parameter of the next iteration of the first preset optimization model, and the design point parameter output by the first preset optimization model is used as an input parameter of the next iteration of the second preset optimization model;
and the second acquisition module is used for acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model and acquiring a target equipment output value of each equipment in the comprehensive energy system according to the output result of the second preset optimization model.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps in the method for optimizing an integrated energy system according to the first aspect of the embodiment of the present invention.
In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the optimization method of the integrated energy system provided in the first aspect of the embodiments of the present invention.
The optimization method of the comprehensive energy system provided by the embodiment of the invention comprises the following steps: acquiring constraint conditions of the comprehensive energy system; determining an initial design point parameter of the integrated energy system according to the constraint condition, wherein the initial design point parameter comprises an initial value of the equipment capacity of each equipment in the integrated energy system; according to the constraint conditions, performing first iterative optimization on the initial design point parameters by adopting a first preset optimization model, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by adopting a second preset optimization model, wherein the device output value output by the second preset optimization model is used as an input parameter of the next iteration of the first preset optimization model, and the design point parameters output by the first preset optimization model are used as input parameters of the next iteration of the second preset optimization model; and acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model, and acquiring a target equipment output value of each equipment in the comprehensive energy system according to the output result of the second preset optimization model. Therefore, the first iterative optimization can be carried out on the design point parameters meeting the constraint conditions, and the second iterative optimization can be carried out on the device output value corresponding to the design point parameters, so that the target design point parameters obtained by the optimization of the first preset optimization model and the target device output value obtained by the optimization of the second preset optimization model are closer to the actual condition of the comprehensive energy system, and the energy utilization rate of the comprehensive energy system is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart of an optimization method of an integrated energy system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an optimization method of an integrated energy system according to an embodiment of the present invention;
fig. 3 is a block diagram of an optimization apparatus of an integrated energy system according to an embodiment of the present invention;
fig. 4 is a structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flowchart of an optimization method of an integrated energy system according to an embodiment of the present invention is shown in fig. 1, where the method may include the following steps:
and 101, acquiring constraint conditions of the comprehensive energy system.
In a specific implementation, the above-mentioned integrated energy system can be understood as: the system comprises a renewable energy source, an energy storage device and a power system on a power utilization side.
In addition, the constraint condition may be a constraint condition defined according to an electric quantity supply and demand balance principle, and according to a device parameter in the integrated energy system, for example: if the integrated energy system includes a renewable energy device and an energy storage device, the constraints of the integrated energy system may include: the comprehensive energy system can ensure the stable operation of the energy system under the condition that the comprehensive energy system meets the constraint conditions.
Specifically, the above-mentioned electric power flow constraint condition may be expressed as the following formula:
wherein, PIKRepresenting the active power injected by the node k at the time t; pkj(t) represents the active power transmitted between node k and node j at time t; gkjRepresenting the power flow coefficient between the node k and the node j; bkjAn imaginary part representing the admittance between node k and node j; vk(t) represents the voltage at node k at time t; vj(t) represents the voltage at node j at time t; thetakj(t) represents the phase angle difference of the voltage between node k and node j at time t; qIKRepresenting the reactive power injected by the node k at the moment t; qkj(t) represents the reactive power transmitted between node k and node j at time t; n isDRepresenting a total number of nodes in the integrated energy system; j ∈ n (k) indicates that j takes any node that has a transmission relationship with the k node.
The thermal power flow constraint described above can be expressed as the following equation:
wherein, PMT,i(t) represents the active power of the ith micro-combustion engine (MT) at time t; thetaMT,iRepresenting the reactive power of the ith micro-combustion engine; hboil,j(t) represents the total amount of heat load generated by the jth boiler (boiler) at time t;
represents the power absorbed by the thermal energy store at time t;
representing the power released by the thermal energy storage at time t; hload(t) represents the total amount of heat load at time t; hHS(t) represents the transferable thermal load at time t; hloss(t) represents heat loss at time t; n isMTRepresenting the number of micro-combustion engines; n isBIndicating the total number of boilers.
The node voltage constraint described above can be expressed as the following equation:
wherein,
represents the minimum allowable voltage value of the node k; vk(t) represents the voltage value of node k at time t;
representing the maximum allowable voltage value for node k. In practical application, the above
And
the number of the nodes is constant, and the specific value of the nodes is determined according to the structural parameters of the nodes k and the operation condition of the comprehensive energy system.
The above line transmission power constraint can be expressed as the following equation:
wherein,
represents the minimum allowed transmission power between node k and node j; pkj(t) represents the transmission power between node k and node j at time t;
representing the maximum allowed transmission power between node k and node j. In practical application, the above
And
the number of the nodes is constant, and the specific value of the node is determined according to the structural parameters of the node k and the node j and the operation condition of the comprehensive energy system.
The operation constraint condition of the distributed power generation device can be expressed as the following formula:
wherein u isMT,i(t) represents the start-stop state of the ith micro-combustion engine (MT) at the time t;
representing the maximum allowable output power of the ith micro-combustion engine; pMT,i(t) represents the output power of the ith micro-combustion engine at time t;
the minimum allowable output power of the ith micro-combustion engine is represented; r isgiIndicating a rise rate limit of the ith micro-combustion engine;
representing the operation time length of the ith micro-combustion engine at the time t;
representing the maximum allowable shutdown time length of the ith micro-combustion engine;
the time length of shutdown of the ith micro-combustion engine at the time t is shown;
the minimum allowable shutdown period of the ith micro-combustion engine is indicated.
In a specific implementation, in uMT,i(t) is equal to 1, indicating that the ith micro-combustion engine is in a starting state at the moment t; at uMT,iWhen (t) is equal to 0, it indicates that the ith micro-combustion engine is in a shutdown state at time t, and of course, in a specific implementation, the state of the micro-combustion engine may further include: standby state, auxiliary state, etc., and uMT,iThe value of (t) may be preset accordingly, and is not limited herein.
In addition, the above
And
the specific value of the constant is determined according to the structural parameters of the ith micro-combustion engine and the operation condition of the comprehensive energy system.
The above stored energy operating constraints may be expressed as the following equation:
wherein,
representing the charging power of the energy storage device at time t;
represents a maximum allowable charging power of the energy storage device;
representing the discharge power of the energy storage device at time t;
representing a maximum allowable discharge power representative of the energy storage device; SOC (0) represents an initial state of charge of the energy storage device during operation; SOC (T)N) Representing a final state of charge of the energy storage device during operation; soc (t) represents the state of charge of the energy storage device at time t; SOCminRepresents a minimum allowable state of charge of the energy storage device; SOCmaxRepresents a maximum allowable state of charge of the energy storage device; σ represents the self-discharge rate of the energy storage device; Δ t represents a scheduled time period;
representing the charging capacity of the energy storage device at the moment t; etacRepresenting a charge coefficient of the energy storage device; eesRepresenting an electrical energy storage capacity;
representing the discharge capacity of the energy storage device at the time t; etadRepresents a discharge coefficient of the energy storage device;
represents the maximum allowable absorbed power of the thermal energy storage;
indicating maximum allowable heat storageAllowing power to be discharged; sth(0) Representing the day initial nuclear power state of heat energy storage; sth(TN) Representing the final daily nuclear power state of heat energy storage;
representing a minimum allowable nuclear power state of thermal energy storage; sth(t) represents the nuclear power state of the thermal energy storage at the time t;
representing a maximum allowable nuclear power state of thermal energy storage; sigmathRepresents the self-discharge rate of the heat storage energy; eHSSRepresenting a heat energy storage capacity;
represents the charging efficiency of the heat storage energy;
indicating the discharge efficiency of the thermal energy storage.
In a specific embodiment, the above
SOCmin、SOCmax、σ、ηc、Ees、ηd、
σth、
EHSSAnd
the values may be constants, and the specific values are determined according to the structural parameters of the energy storage device and the operating conditions of the integrated energy system.
The demand-side response constraint described above may be expressed as the following equation:
wherein,
showing the switching state of the r-th load at the time t; pLR(t ') represents the amount of load actually transferred at an actual time t ', where t ' may be equal to (t + 1); n is a radical ofLSRepresenting the total number of transferable coincidences; thetaMT,iThe electrothermal ratio of the ith micro-combustion engine is expressed;
representing the transferable load quantity of the r-th transferable load at the moment t; pLC(t) represents the amount of reducible load at time t; ε represents the proportion of the reducible load; pLoad(t) represents the amount of load at time t; n is a radical ofHSRepresenting the total number of transferable loads;
representing the amount of thermal load the r-th transferable load is allowed to transfer at time t;
a switching state representing the kth thermal load transferred at time t; sr,t,t'Indicating whether the thermal load is shifted from time t to time t'; k represents the heat dissipation coefficient of the region; f denotes the regional heat dissipation coefficient; t isoutRepresents the outdoor temperature of the area; t isinRepresents the indoor temperature of the area; a represents a temperature difference correction coefficient; LS (least squares)r,t,t'Indicating a time period during which the r-th transferable load can undergo load transfer; v. ofr,t,t'Indicating whether the r-th electrical load is transferred from the time t to the time t'; HSr,t,t'Indicating a time period during which the r-th transferable thermal load can undergo load transfer; hHR(t ') represents the amount of thermal load actually transferred at the actual time t'.
In a specific implementation, in
When the load is equal to 1, the r-th load is in a starting state at the time t; in that
When the value is equal to 0, it indicates that the r-th load is in the shutdown state at time t, and of course, in a specific implementation, the states of the loads may further include: a standby state, an auxiliary state, etc., and
the value of (a) can be preset correspondingly, and is not limited herein.
In addition, at Sr,t,t'Equal to 1, indicates that the thermal load shifts from time t to time t'; at Sr,t,t'When the value is equal to 0, it means that the heat load does not shift from the time t to the time t'.
In addition, in
When the k-th heat load is equal to 1, the k-th heat load is in a starting state at the time t; in that
When the k-th thermal load is equal to 0, it indicates that the k-th thermal load is in the shutdown state at time t, and of course, in a specific implementation, the state of the thermal load may further include: a standby state, an auxiliary state, etc., and
the value of (a) can be preset correspondingly, and is not limited herein.
In addition, the K, F and a can be constants respectively, and the specific value is determined according to the structure and the operation condition of the integrated energy system.
As an alternative embodiment, the constraints of the integrated energy system are determined according to the following procedure:
determining scene sets and the occurrence probability of each scene in the scene sets according to the operation data of the comprehensive energy system in a target historical time period;
performing scene reduction on the scene set by adopting a synchronous back substitution subtraction method to obtain a typical scene and the occurrence probability of the typical scene, wherein the scene set comprises the typical scene;
determining an output curve of renewable energy in the integrated energy system and a load curve of the integrated energy system according to the typical scene and the occurrence probability of the typical scene;
and determining the constraint condition of the comprehensive energy system according to the output curve of the renewable energy and the load curve of the comprehensive energy system.
In a specific implementation, the scene set may include a large number of scenes, for example: the number of the operation scenes in summer, the operation scenes in spring, the operation scenes in rainy days, the operation scenes in sunny days and the like can reach hundreds, and the occurrence probability of each operation scene is different, for example: if a year includes 365 days, the number of rainy days in the year is 150 days, and a sunny day includes 100 days, the occurrence probability of the operation scene corresponding to the sunny day may be (150 × 100%)/365, and the occurrence probability of the operation scene corresponding to the sunny day may be (100 × 100%)/365. In addition, the above synchronous back-substitution subtraction method can combine similar operation scenes to obtain a typical scene and the occurrence probability of the typical scene. Therefore, the number of scenes can be reduced, and calculation amount in the process of determining the output curve of the renewable energy sources in the integrated energy system and the load curve of the integrated energy system according to the typical scenes and the occurrence probability of the typical scenes is facilitated to be simplified.
In addition, the constraint condition for determining the integrated energy system according to the output curve of the renewable energy and the load curve of the integrated energy system may be: determining a typical operation mode of the comprehensive energy system according to the typical scene and the occurrence probability of the typical scene, and determining various parameter values in the constraint conditions according to the typical operation mode, such as: in the case that the typical scene includes three (respectively: the typical scene a, the typical scene B, and the typical scene C), and the probability of the typical scene a is 50%, the probability of the typical scene B is 30%, and the probability of the typical scene C is 20%, the target parameter in the typical operation manner may be equal to the sum of the value of the target parameter in the typical scene a multiplied by 50%, the value of the target parameter in the typical scene B multiplied by 30%, and the value of the target parameter in the typical scene C multiplied by 20%, so that the constraint condition may be determined according to the occurrence probabilities of the typical scene and the typical scene.
And 102, determining an initial design point parameter of the integrated energy system according to the constraint condition, wherein the initial design point parameter comprises an initial value of the equipment capacity of each equipment in the integrated energy system.
In a specific implementation, the design point parameter meeting the constraint condition may be a value range, for example: the equipment capacity of the new power generation equipment a may be between 100MW (megawatt) and 200MW, and the equipment capacity of the new power generation equipment B may be between 15MW and 200MW, and the initial design point parameters may include: the initial value of the equipment capacity of the power generation equipment A is any constant between 100MW and 200MW, and the initial value of the equipment capacity of the power generation equipment B is any constant between 150MW and 200 MW.
The device capacity in the design point parameter may be a device capacity of any one of a power generation device, a power utilization device, an energy storage device, and the like.
And 103, performing first iterative optimization on the initial design point parameter by using a first preset optimization model according to the constraint condition, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by using a second preset optimization model, wherein the device output value output by the second preset optimization model is used as an input parameter of the next iteration of the first preset optimization model, and the design point parameter output by the first preset optimization model is used as an input parameter of the next iteration of the second preset optimization model.
In a specific implementation, the objective function of the first preset optimization model is that the total cost of the integrated energy system per unit time is the lowest, and the objective function of the second preset optimization model is that the operation cost of the integrated energy system per unit time is the lowest.
In addition, the above first iterative optimization of the initial design point parameter by using the first preset optimization model can be understood as: inputting the initial design point parameter and the last iteration result of the second preset optimization model into the first preset optimization model to obtain whether the initial design point parameter meets the requirement of the objective function of the first preset optimization model, if not, inputting the changed design point parameter into the second preset optimization model to obtain the second iteration result of the second preset optimization model, then inputting the second iteration result of the second preset optimization model and the changed design point parameter into the first preset optimization model to obtain whether the changed design point parameter meets the second preset condition, and continuing to iterate the first preset optimization model and the second preset optimization model under the condition that the changed design point parameter does not meet the requirement, and stopping iteration until the obtained design point parameter and the obtained device output value meet a second preset condition, or until the iteration times reach the maximum allowable times, or until the iteration time of the application program executing the iteration process reaches the maximum allowable time, and selecting one design point parameter and one device output value which are closest to the second preset condition and obtained in the iteration process as the iteration results of the first preset optimization model and the second preset optimization model under the condition that the iteration times reach the maximum allowable times, or the iteration time of the application program executing the iteration process reaches the maximum allowable time.
Wherein, the requirement of the objective function of the first preset optimization model may be understood as: the total cost of the comprehensive energy system corresponding to the design point parameter of the iteration in unit time is less than the total cost of the comprehensive energy system corresponding to the design point parameter of the previous iteration in unit time. In particular, the total cost per unit time may be an annual total cost, which may be the sum of annual equal investment costs and annual operational maintenance costs. In addition, the second preset condition may be that the total annual cost obtained in the iterative process is the lowest.
Of course, the unit time may be monthly or quarterly, and the like, and is not particularly limited herein.
As an alternative embodiment, it is determined that the target design point parameter meets the second preset condition by the following process:
determining that the target design point parameter meets a second preset condition under the condition that the total cost of the comprehensive energy system corresponding to the target design point parameter in unit time is the minimum value, wherein the total cost of the comprehensive energy system in unit time is as follows: the sum of the equal investment cost of the comprehensive energy system in unit time and the operation and maintenance cost of the comprehensive energy system in unit time.
In a specific implementation, the minimum value of the total cost of the integrated energy system per unit time can be calculated by the following formula:
min Ctotal＝Cinv+Cope
wherein, CtotalRepresenting a total cost of the integrated energy system per unit time; cinvRepresenting an equal investment cost of the integrated energy system per unit time; copeRepresents the operation and maintenance cost of the integrated energy system in unit time. Specifically, the equal investment cost of the integrated energy system in a unit time is a cost value of the total investment cost of the integrated energy system distributed to each unit time in the operation cycle in an equal amount, and can be calculated by the following formula:
wherein I represents the total number of devices in the integrated energy system; wiIndicating the rated capacity of the ith device;
unit throw for indicating ith equipmentCost of materials; m represents interest rate per unit time and L is the lifetime of the device.
Of course, in a specific implementation, it may also be determined that the target design point parameter meets the second preset condition in the case that the total cost of the integrated energy system per unit time is less than or equal to the preset total cost.
In addition, the above second iterative optimization of the device output value of each device in the integrated energy system by using the second preset optimization model can be understood as: updating boundary conditions of a distributed power generation device, an energy storage device and a demand response device in the comprehensive energy system according to design point parameters of a first preset optimization model, updating parameters of a second preset optimization model according to the updated boundary conditions, solving an objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain a device output value of each device in the comprehensive energy system, updating the device output value in the second preset optimization model under the condition that the device output value does not accord with the first preset condition until a result calculated by the second preset optimization model accords with the first preset condition, otherwise, continuously updating design point parameters of next iteration in the first preset optimization model, executing the iteration process of the second preset optimization model according to the design point parameters until the device output value accords with the first preset condition, and inputting the equipment output force value meeting the first preset condition into a first preset optimization model.
Wherein, the first preset condition may be: and when the running cost of the comprehensive energy system in unit time is the minimum value, determining that the equipment output value corresponding to the running cost meets a first preset condition, thereby finishing the iteration process of the second preset optimization model, and inputting the equipment output value meeting the first preset condition into the first preset optimization model to calculate the total cost of the comprehensive energy system in unit time under the condition of the equipment output value and the design point parameter.
As an alternative embodiment, it is determined that the target apparatus output value meets the first preset condition by the following process:
and under the condition that the running cost of the comprehensive energy system in unit time corresponding to the target equipment output force value is the minimum value, determining that the target equipment output force value meets a first preset condition.
In a specific implementation, the minimum value of the operation cost of the integrated energy system in unit time can be calculated by the following formula:
wherein S represents the total number of typical scenes; s represents the s-th renewable energy and load scenario (i.e., a typical scenario); p is a radical ofsRepresenting the occurrence probability of different scenes; t isNRepresenting the total run time of each typical scenario;
representing the total fuel cost of the integrated energy system at time t;
representing the total maintenance cost of the integrated energy system at the moment t;
representing the total network loss cost of the comprehensive energy system at the time t;
representing the total demand side response cost of the integrated energy system at time t.
In this embodiment, the updated objective function of the second preset optimization model is solved by using an improved second-order cone optimization algorithm, and the updated objective function of the first preset optimization model is solved by using a differential evolution algorithm, so that the calculation process of the device output value and the design point parameter can be simplified, and the complexity of the comprehensive energy system optimization method is simplified.
And 104, acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model, and acquiring a target device output value of each device in the comprehensive energy system according to the output result of the second preset optimization model.
In a specific implementation, the obtained design point parameters may include: the rated capacity values of a plurality of devices, and the device output value of each device may include: the output value of each device in the integrated energy system in a certain unit time is as follows: hourly force values were taken.
In a specific implementation, the total cost (often, the annual total cost) and the operation and maintenance cost of the integrated energy system in a unit time can be determined according to the rated capacity values of the plurality of devices and the output value of each device in the integrated energy system in a certain unit time, so that the cost of the integrated energy system can be reduced under the condition that the target design point parameter and the target device output value corresponding to the minimum value of the total cost and the operation and maintenance cost in the unit time are increased by the device corresponding to the target design point parameter, and the output value of each device in the integrated energy system is controlled to be matched with the target device output value.
As an optional implementation manner, the performing, according to the constraint condition, a first iterative optimization on the initial design point parameter by using a first preset optimization model, and performing a second iterative optimization on a device output value of each device in the integrated energy system by using a second preset optimization model includes:
updating the objective function of the second preset optimization model according to the design point parameters output by the first preset optimization model, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized device output value;
under the condition that the device output value output by the second preset optimization model meets a first preset condition, updating the objective function of the first preset optimization model according to the optimized device output value, and solving the updated objective function of the first preset optimization model by adopting a differential evolution algorithm to obtain an optimized design point parameter;
the acquiring a target design point parameter of the integrated energy system according to the output result of the first preset optimization model and acquiring a target device output value of each device in the integrated energy system according to the output result of the second preset optimization model includes:
and under the condition that the optimized design point parameter meets a second preset condition, determining a target design point parameter as the optimized design point parameter, and determining the output of the target equipment as the output value of the optimized equipment.
The following integrated energy system includes: the renewable energy device, the energy storage device, the distributed power generation device, and the demand-side response device are used as examples, and the working principle of the optimization method of the integrated energy system is illustrated, specifically as shown in fig. 2, the working principle of the optimization method of the integrated energy system includes the following steps:
the first step is as follows: all parameters of the two-layer optimization are initialized.
In this step, the double-layer optimization represents a first preset optimization model and a second preset optimization model, and the initialized parameters include: network parameters and equipment parameters of the comprehensive energy system, parameters in the first preset optimization model and the second preset optimization model and the like. In a specific implementation, a constraint condition may be determined according to the typical scenario and the occurrence probability of the typical scenario provided in the embodiment of the present invention, and each of the initialized parameters is a parameter that meets the constraint condition.
The second step is that: initial design point parameters are generated based on device capacity constraints.
In this step, the device capacity constraint may be a constraint condition determined according to the typical scenario and the occurrence probability of the typical scenario in this embodiment of the present invention. In a specific implementation, the device capacity meeting the constraint condition may be a value range, and then any value in the value range may be taken as an initial value of the device capacity in the initial design point parameter.
The third step: and optimizing and evaluating initial design point parameters.
In this step, the optimization is an iterative process, in each iteration, at least one device capacity value in the design point parameters is changed within a constraint range, and the changed design point parameters are evaluated. In a specific implementation, the evaluation may be to determine a total annual cost of the integrated energy system corresponding to the changed design point parameter, and before determining the total annual cost, a fourth step may be performed to determine the total annual cost based on the hourly equipment capacity value obtained in the fourth step.
The fourth step: run optimization was performed based on design point parameters.
In this step, the above operation optimization based on the design point parameter may also be expressed as operation optimization based on the design point parameter by using a second preset optimization model, so as to obtain an hourly device output value of each device in the integrated energy system. Specifically, the optimization process includes the following steps:
the first small step: and updating boundary conditions of a distributed generation Device (DG), an energy storage device (ES) and a demand side response Device (DR).
In a specific implementation, the boundary condition is updated according to the design point parameters of the first preset model.
The second small step: and updating the second preset optimization model.
In specific implementation, the parameters in the second preset optimization model are updated according to the boundary conditions updated in the first small step.
And a third small step: and solving the second preset optimization model by adopting an improved second-order cone optimization algorithm (SOCP algorithm for short).
In a specific implementation, the improved second-order cone optimization algorithm is an algorithm adopted by the second preset optimization model, and the output result of the algorithm is as follows: and integrating the equipment output value of each equipment in the energy system, wherein in addition, the objective function of the second preset optimization model is as follows: the annual operating costs of the integrated energy system are minimal.
The fourth small step: whether an end condition is satisfied.
In a specific implementation, whether the constraint condition is satisfied is as follows: detecting whether the equipment output value calculated by the improved second-order cone optimization algorithm meets the condition that the annual running cost of the comprehensive energy system is minimum, if so, ending the iteration process and outputting the equipment output value calculated by the improved second-order cone optimization algorithm; if the output value does not meet the requirement, the next equipment output value is continuously calculated by iteration through the improved second-order cone optimization algorithm until the equipment output value calculated by the improved second-order cone optimization algorithm meets the end condition.
In addition, the iteration process may be stopped after the preset number of iterations, the annual operation cost corresponding to the device output value obtained by the preset number of iterations is respectively calculated, and the device output value corresponding to the smallest annual operation cost is taken as the output result of the second preset optimization model.
In addition, the iteration process may also be stopped after iterating for a preset time length, and is not specifically limited herein.
The fifth step: whether the newly generated design point parameter is better than the original design point parameter.
In this step, the original design point parameter is a design point parameter used by the first preset optimization model in the last iteration, and the newly generated design point parameter is better than the original design point parameter by: when the comprehensive energy system configures the equipment capacity according to the newly generated design point parameters, the annual total cost is less than the annual total cost when the equipment capacity is configured according to the original design point parameters. And if the newly generated design point parameter is superior to the original design point parameter, executing the step six, otherwise, executing the step four to update the design point parameter.
And a sixth step: the new generated design point parameter replaces the original design point parameter.
In this step, replacing the original design point parameter with the new design point parameter can be understood as follows: and (3) calculating the annual total cost of the comprehensive energy system when the capacity of the equipment is configured according to the newly generated design point parameters, and particularly calculating the annual total cost of the comprehensive energy system by adopting a differential evolution algorithm.
The seventh step: whether an end condition is satisfied.
In a specific implementation, whether the constraint condition is satisfied is as follows: detecting whether the design point parameters calculated by the differential evolution algorithm meet the condition that the annual total cost of the comprehensive energy system is minimum, if so, ending the iteration process and outputting the design point parameters calculated by the differential evolution algorithm; if not, continuing to iteratively calculate the next design point parameter by adopting the differential evolution algorithm until the design point parameter calculated by the differential evolution algorithm meets the end condition.
In addition, the above-mentioned manner of whether the iterative process stops the iteration may refer to the iterative process of the second preset optimization model, which is not described herein again.
As an optional implementation manner, when the optimized design point parameter does not meet the second preset condition, a differential evolution algorithm is used to adjust the design point parameter;
the updating the objective function of the second preset optimization model according to the design point parameter output by the first preset optimization model, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized device output value, includes:
and updating the objective function of the second preset optimization model according to the adjusted design point parameters, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized equipment output value.
In a specific implementation, the adjusting of the design point parameter by using the differential evolution algorithm may be randomly adjusting at least one device capacity in the design point parameter, and the adjusted design point parameter meets a constraint condition of the integrated energy system.
In the embodiment, each design point parameter which accords with the constraint condition of the comprehensive energy system can be facilitated by adopting a differential evolution algorithm so as to obtain the final value of the target function, so that the result of the optimization method of the comprehensive energy system is more comprehensive and reliable.
It should be noted that, in a specific implementation, the double-layer optimization model including the first preset optimization model and the second preset optimization model may be stored in a computer program, and historical operating data of the integrated energy system is input to the computer program, so that a target design point parameter and a target device output value may be determined according to an execution result of the program, and further, an optimization scheme of the integrated energy system may be determined according to the target design point parameter and the target device output value, for example: the equipment capacity is increased, the output per hour of equipment is increased or adjusted, and the like, so that the cost of the comprehensive energy system can be reduced, and the energy utilization rate of the comprehensive energy system is improved.
The optimization method of the comprehensive energy system provided by the embodiment of the invention comprises the following steps: acquiring constraint conditions of the comprehensive energy system; determining an initial design point parameter of the integrated energy system according to the constraint condition, wherein the initial design point parameter comprises an initial value of the equipment capacity of each equipment in the integrated energy system; according to the constraint conditions, performing first iterative optimization on the initial design point parameters by adopting a first preset optimization model, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by adopting a second preset optimization model, wherein the device output value output by the second preset optimization model is used as an input parameter of the next iteration of the first preset optimization model, and the design point parameters output by the first preset optimization model are used as input parameters of the next iteration of the second preset optimization model; and acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model, and acquiring a target equipment output value of each equipment in the comprehensive energy system according to the output result of the second preset optimization model. Therefore, the first iterative optimization can be carried out on the design point parameters meeting the constraint conditions, and the second iterative optimization can be carried out on the device output value corresponding to the design point parameters, so that the target design point parameters obtained by the optimization of the first preset optimization model and the target device output value obtained by the optimization of the second preset optimization model are closer to the actual condition of the comprehensive energy system, and the energy utilization rate of the comprehensive energy system is improved.
Referring to fig. 3, an optimization apparatus of an integrated energy system according to an embodiment of the present invention is shown in fig. 3, where the optimization apparatus 300 of the integrated energy system may include the following modules:
a first obtaining module 301, configured to obtain constraint conditions of the integrated energy system;
a determining module 302, configured to determine an initial design point parameter of the integrated energy system according to the constraint condition, where the initial design point parameter includes an initial value of a device capacity of each device in the integrated energy system;
the optimization module 303 is configured to perform first iterative optimization on the initial design point parameter by using a first preset optimization model according to the constraint condition, and perform second iterative optimization on a device output value of each device in the integrated energy system by using a second preset optimization model, where the device output value output by the second preset optimization model is used as an input parameter of a next iteration of the first preset optimization model, and the design point parameter output by the first preset optimization model is used as an input parameter of a next iteration of the second preset optimization model;
a second obtaining module 304, configured to obtain a target design point parameter of the integrated energy system according to an output result of the first preset optimization model, and obtain a target device output value of each device in the integrated energy system according to an output result of the second preset optimization model.
Optionally, the constraint condition of the integrated energy system is determined according to the following process:
determining scene sets and the occurrence probability of each scene in the scene sets according to the operation data of the comprehensive energy system in a target historical time period;
performing scene reduction on the scene set by adopting a synchronous back substitution subtraction method to obtain a typical scene and the occurrence probability of the typical scene, wherein the scene set comprises the typical scene;
determining an output curve of renewable energy in the integrated energy system and a load curve of the integrated energy system according to the typical scene and the occurrence probability of the typical scene;
and determining the constraint condition of the comprehensive energy system according to the output curve of the renewable energy and the load curve of the comprehensive energy system.
Optionally, the constraint conditions of the integrated energy system include:
the system comprises an electric power flow constraint condition, a thermal power flow constraint condition, a node voltage constraint condition, a line transmission power constraint condition, a distributed power generation device operation constraint condition, an energy storage operation constraint condition and a demand side response constraint condition.
Optionally, the optimization module includes:
the first updating unit is used for updating the objective function of the second preset optimization model according to the design point parameters output by the first preset optimization model, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized device output value;
the second updating unit is used for updating the objective function of the first preset optimization model according to the optimized equipment output value under the condition that the equipment output value output by the second preset optimization model meets a first preset condition, and solving the updated objective function of the first preset optimization model by adopting a differential evolution algorithm to obtain an optimized design point parameter;
the second obtaining module is specifically configured to:
and under the condition that the optimized design point parameter meets a second preset condition, determining a target design point parameter as the optimized design point parameter, and determining the output of the target equipment as the output value of the optimized equipment.
Optionally, the optimization device of the integrated energy system further includes:
the adjusting module is used for determining that the target design point parameter is the optimized design point parameter under the condition that the optimized design point parameter meets a second preset condition, and adjusting the design point parameter by adopting a differential evolution algorithm under the condition that the optimized design point parameter does not meet the second preset condition before determining that the output of the target equipment is the optimized equipment output value;
the first updating unit is specifically configured to:
and updating the objective function of the second preset optimization model according to the adjusted design point parameters, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized equipment output value.
Optionally, determining that the output value of the target device meets the first preset condition through the following process:
determining that the output value of the target equipment meets a first preset condition under the condition that the running cost of the comprehensive energy system corresponding to the output value of the target equipment in unit time is the minimum value;
determining that the target design point parameter meets the second preset condition by the following process:
determining that the target design point parameter meets a second preset condition under the condition that the total cost of the comprehensive energy system corresponding to the target design point parameter in unit time is the minimum value, wherein the total cost of the comprehensive energy system in unit time is as follows: the sum of the equal investment cost of the comprehensive energy system in unit time and the operation and maintenance cost of the comprehensive energy system in unit time.
The optimization device of the integrated energy system provided by the embodiment of the invention can execute each process of the optimization method of the integrated energy system provided by the embodiment of the invention, and can obtain the same beneficial effects, and in order to avoid repetition, the details are not repeated.
Referring to fig. 4, fig. 4 is a schematic diagram of a hardware structure of an electronic device for implementing various embodiments of the present invention, and as shown in fig. 4, the electronic device includes: the processor 401, the memory 402, and the computer program stored in the memory 402 and capable of running on the processor 401 are executed by the processor 401 to implement the processes of the above-mentioned embodiment of the optimization method of the integrated energy system, and can achieve the same technical effects, and are not described herein again to avoid repetition.
In a specific implementation, the electronic device may be a computer or a computer cluster, and the computer or the computer cluster may further control an operation mode of the integrated energy system or an output device capacity adjustment suggestion according to an execution result of a program corresponding to an optimization method of the integrated energy system that is stored in the computer or the computer cluster.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling an electronic device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (9)
1. A method for optimizing an integrated energy system, the method comprising:
acquiring constraint conditions of the comprehensive energy system;
determining an initial design point parameter of the integrated energy system according to the constraint condition, wherein the initial design point parameter comprises an initial value of the equipment capacity of each equipment in the integrated energy system;
according to the constraint conditions, performing first iterative optimization on the initial design point parameters by adopting a first preset optimization model, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by adopting a second preset optimization model, wherein the device output value output by the second preset optimization model is used as an input parameter of the next iteration of the first preset optimization model, and the design point parameters output by the first preset optimization model are used as input parameters of the next iteration of the second preset optimization model;
and acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model, and acquiring a target equipment output value of each equipment in the comprehensive energy system according to the output result of the second preset optimization model.
2. The method of claim 1, wherein the constraints of the integrated energy system are determined according to the following process:
determining scene sets and the occurrence probability of each scene in the scene sets according to the operation data of the comprehensive energy system in a target historical time period;
performing scene reduction on the scene set by adopting a synchronous back substitution subtraction method to obtain a typical scene and the occurrence probability of the typical scene, wherein the scene set comprises the typical scene;
determining an output curve of renewable energy in the integrated energy system and a load curve of the integrated energy system according to the typical scene and the occurrence probability of the typical scene;
and determining the constraint condition of the comprehensive energy system according to the output curve of the renewable energy and the load curve of the comprehensive energy system.
3. The method of claim 2, wherein the constraints of the integrated energy system include:
the system comprises an electric power flow constraint condition, a thermal power flow constraint condition, a node voltage constraint condition, a line transmission power constraint condition, a distributed power generation device operation constraint condition, an energy storage operation constraint condition and a demand side response constraint condition.
4. The method according to any one of claims 1 to 3, wherein the performing, according to the constraint condition, a first iterative optimization on the initial design point parameter by using a first preset optimization model and a second iterative optimization on the plant output value of each plant in the integrated energy system by using a second preset optimization model comprises:
updating the objective function of the second preset optimization model according to the design point parameters output by the first preset optimization model, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized device output value;
under the condition that the device output value output by the second preset optimization model meets a first preset condition, updating the objective function of the first preset optimization model according to the optimized device output value, and solving the updated objective function of the first preset optimization model by adopting a differential evolution algorithm to obtain an optimized design point parameter;
the acquiring a target design point parameter of the integrated energy system according to the output result of the first preset optimization model and acquiring a target device output value of each device in the integrated energy system according to the output result of the second preset optimization model includes:
and under the condition that the optimized design point parameter meets a second preset condition, determining a target design point parameter as the optimized design point parameter, and determining the output of the target equipment as the output value of the optimized equipment.
5. The method of claim 4, wherein before determining the target design point parameter as the optimized design point parameter and determining the target device output force as the optimized device output force value if the optimized design point parameter meets a second predetermined condition, the method further comprises:
adjusting the design point parameter by adopting a differential evolution algorithm under the condition that the optimized design point parameter does not accord with the second preset condition;
the updating the objective function of the second preset optimization model according to the design point parameter output by the first preset optimization model, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized device output value, includes:
and updating the objective function of the second preset optimization model according to the adjusted design point parameters, and solving the updated objective function of the second preset optimization model by adopting an improved second-order cone optimization algorithm to obtain an optimized equipment output value.
6. The method of claim 5, wherein the determination that the target device output value meets the first preset condition is made by:
determining that the output value of the target equipment meets a first preset condition under the condition that the running cost of the comprehensive energy system corresponding to the output value of the target equipment in unit time is the minimum value;
determining that the target design point parameter meets the second preset condition by the following process:
determining that the target design point parameter meets a second preset condition under the condition that the total cost of the comprehensive energy system corresponding to the target design point parameter in unit time is the minimum value, wherein the total cost of the comprehensive energy system in unit time is as follows: the sum of the equal investment cost of the comprehensive energy system in unit time and the operation and maintenance cost of the comprehensive energy system in unit time.
7. An optimization apparatus of an integrated energy system, comprising:
the first acquisition module is used for acquiring constraint conditions of the comprehensive energy system;
the determining module is used for determining an initial design point parameter of the integrated energy system according to the constraint condition, wherein the initial design point parameter comprises an initial value of the equipment capacity of each equipment in the integrated energy system;
the optimization module is used for performing first iterative optimization on the initial design point parameter by adopting a first preset optimization model according to the constraint condition, and performing second iterative optimization on the device output value of each device in the comprehensive energy system by adopting a second preset optimization model, wherein the device output value output by the second preset optimization model is used as an input parameter of the next iteration of the first preset optimization model, and the design point parameter output by the first preset optimization model is used as an input parameter of the next iteration of the second preset optimization model;
and the second acquisition module is used for acquiring a target design point parameter of the comprehensive energy system according to the output result of the first preset optimization model and acquiring a target equipment output value of each equipment in the comprehensive energy system according to the output result of the second preset optimization model.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method for optimizing an integrated energy system according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for optimizing an integrated energy system according to any one of claims 1 to 6.
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