CN111460390A - Energy storage output planning method, device, equipment and medium for comprehensive energy system - Google Patents

Energy storage output planning method, device, equipment and medium for comprehensive energy system Download PDF

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CN111460390A
CN111460390A CN202010460875.5A CN202010460875A CN111460390A CN 111460390 A CN111460390 A CN 111460390A CN 202010460875 A CN202010460875 A CN 202010460875A CN 111460390 A CN111460390 A CN 111460390A
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雷金勇
郭祚刚
袁智勇
徐敏
黎小林
王�琦
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China Southern Power Grid Co Ltd
Research Institute of Southern Power Grid Co Ltd
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Abstract

The application provides a method, a device, equipment and a medium for planning energy storage output of an integrated energy system, wherein the method comprises the following steps: inputting basic data; constructing a cost model of an energy storage system in the comprehensive energy system; constructing a multi-scenario analysis model of the net load power of the comprehensive energy system; constructing a CVaR-based linear programming model of the energy storage output of the comprehensive energy system according to a cost model of the energy storage system in the comprehensive energy system and a multi-scenario analysis model of the net load power of the comprehensive energy system; and solving and outputting the CVaR-based energy storage output linear programming result according to the basic data and the CVaR-based energy storage output linear programming model. The technical scheme of the application can effectively balance the operation risk and the operation efficiency of the comprehensive energy system, and has the advantage of maximizing the operation efficiency of the comprehensive energy system under the condition of considering the operation risk.

Description

Energy storage output planning method, device, equipment and medium for comprehensive energy system
Technical Field
The invention relates to the technical field of power grids, in particular to a method, a device, equipment and a medium for planning energy storage output of a comprehensive energy system.
Background
With the increasing severity of global energy crisis and environmental problems, it is of great importance to build a clean and efficient novel integrated energy system. Under the background, an integrated energy system is developed, and the integrated energy system integrates multiple energy sources such as coal, petroleum, natural gas, electric energy, heat energy and the like in a certain area by utilizing an advanced physical information technology and an innovative management mode, and realizes coordinated planning, optimized operation, cooperative management, interactive response and complementary mutual assistance among multiple heterogeneous energy subsystems. The energy utilization efficiency is effectively improved while the diversified energy utilization requirements in the system are met, and the sustainable energy development is promoted. The comprehensive energy system mainly comprises an energy supply network, an energy exchange link, an energy storage link, a terminal comprehensive energy supply and utilization unit and a large number of terminal users. The basic task of the energy storage link, i.e. the energy storage system, is to balance the differences between the supply and demand of electrical energy. After new energy power generation equipment with strong uncertainty such as wind power and photovoltaic is connected into the comprehensive energy system, the energy storage system plays an important role in promoting the consumption of new energy. When the new energy is sufficient in power generation, the comprehensive energy system can store redundant electric energy and can sell the electric energy to a power grid; when the new energy is not enough to supply the internal demand of the comprehensive energy system, the electric energy can be purchased from the power grid, and the electric energy in the energy storage system can also be used. The existence of the energy storage system also enables electric energy to flow bidirectionally between users and a power grid, and in the face of uncertainty of new energy power generation, how to arrange output planning of the energy storage system to reduce risks has important significance.
At present, for the planning of an energy storage system, it is generally assumed that there is no error in the prediction of new energy power generation and power load, which is seriously inconsistent with the actual situation. Aiming at the problem of planning output of an energy storage system with uncertain factors, in the prior art, robust optimization is mostly used for solving, but the processing of the uncertain factors by the robust optimization is too conservative, and the result obtained by the optimization often causes poor resource utilization rate of the comprehensive energy system, so that the balance between the operation risk and the operation efficiency of the comprehensive energy system is difficult to realize; the prior art solves the problem of planning the energy storage system under an uncertain condition by using a Risk-at-Risk (VaR) or variance to perform Risk optimization, and because VaR is not a consistent Risk measure and variance also requires that the probability distribution of uncertain factors is symmetrical about a mean Value, the method for performing Risk optimization by using VaR or variance has great limitations. Therefore, how to arrange the output planning of the energy storage system in one day so as to reduce the operation risk of the integrated energy system and improve the operation benefit is a current concern.
Disclosure of Invention
Based on the above, the invention aims to provide a method, a device, equipment and a medium for planning the energy storage output of the comprehensive energy system, and an optimization method based on a Conditional Risk-at-Risk (CVaR) theory is adopted, so that the operation Risk is reduced, and the operation efficiency of the comprehensive energy system is maximized.
In a first aspect, the present invention provides a method, an apparatus, a device and a medium for planning energy storage output of an integrated energy system, including:
inputting basic data;
constructing a cost model of an energy storage system in the comprehensive energy system;
constructing a multi-scenario analysis model of the net load power of the comprehensive energy system;
constructing a CVaR-based linear programming model of the energy storage output of the comprehensive energy system according to a cost model of the energy storage system in the comprehensive energy system and a multi-scenario analysis model of the net load power of the comprehensive energy system;
and solving and outputting the CVaR-based energy storage output linear programming result according to the basic data and the CVaR-based energy storage output linear programming model.
Preferably, the constructing a cost model of the energy storage system in the integrated energy system includes:
establishing an objective function of the cost of an energy storage system in the comprehensive energy system;
and establishing the constraint condition of the objective function.
Preferably, the establishing an objective function of the cost of the energy storage system in the integrated energy system includes:
C＝C1+C2＝ccp+-cdp-+cbuypb-csellps
in the formula, C1The running cost of the energy storage system comprises the running cost, p, required by each charge and discharge of the energy storage system+，p-Respectively refers to the charging electric quantity and the discharging electric quantity, p, planned in a certain period of time by the energy storage system+Is non-negative, p-Is not positive and one of the two must be 0, i.e. programming in a certain time period cannot both charge and discharge, cc，cdThe operating costs required for charging and discharging the unit electric energy for the energy storage system are respectively; c2The electric energy transaction cost of the comprehensive energy system refers to the expense of electric energy transaction between the comprehensive energy system and the power grid, wherein pb，psRespectively refers to the planned purchase electric quantity and sale electric quantity, p, of the integrated energy system in a certain periodbIs non-negative, psIs a non-positive number, and one of the two must be 0, i.e. the plan cannot both buy and sell power within a certain time period, cbuyUnit price of electrical energy for the integrated energy system, csellSelling the unit price of the electric energy for the comprehensive energy system; the operation cost can directly reflect the comprehensive energyThe operating efficiency of the system.
Preferably, the establishing the constraint condition of the objective function includes:
p+＝max(pbat,0)
p-＝min(pbat,0)
pb＝max(pd+pbat,0)
ps＝min(pd+pbat,0)
pb+ps＝pd+pbat
in the formula, pbat＝p++p-，pbatFor the variation of the electrical quantity of the energy storage system in a certain period of time, pdAnd subtracting the generated energy of the new energy in a certain time period from the electric energy demand of the user in the comprehensive energy system to obtain the net load demand.
Preferably, the nonlinear constraint of the objective function is converted into a linear constraint:
Figure BDA0002510910580000031
Figure BDA0002510910580000032
w1+w2+w3＝0
w1≤z1,w2≤z1+z2,w3≤z2
z1,z2＝0,1
pb≥pd+pbat
pb≥0
in the formula, w1,w2,w3,z1,z2Are auxiliary variables that convert a piecewise linear function to a linear function.
Preferably, the constructing a multi-scenario analysis model of the net load power of the integrated energy system includes:
setting the error of net load prediction to be normal distribution and the mean value of the error to be 0, and sampling the normal distribution by adopting equal step length to obtain 2NrPrediction error in +1 scenes;
maximum point f of probability density of normal distributionr(xr(0)) For sampling the center point, the mean value corresponding to the prediction error is xr(0)0; sampling point
Figure BDA0002510910580000041
Obtaining corresponding probability density
Figure BDA0002510910580000042
Sampling point xr(i)The corresponding scene probability is:
Figure BDA0002510910580000043
discretizing continuous uncertainty into 2Nr+1 scenarios for handling the serialization uncertainty; the more the number of scenes is, the more accurate the optimization of the CVaR is, but the calculation amount is also increased, and the specific number of scenes can be determined by combining the risk tolerance and the calculation capability of the integrated energy system.
Preferably, the constructing a CVaR-based linear programming model of the energy storage capacity and output of the integrated energy system according to the cost model of the energy storage system in the integrated energy system and the multi-scenario analysis model of the net load power of the integrated energy system is as follows:
Figure BDA0002510910580000044
subject to:
Figure BDA0002510910580000045
zi≥0,
Figure BDA0002510910580000046
Figure BDA00025109105800000411
Figure BDA0002510910580000048
Figure BDA0002510910580000049
w1+w2+w3＝0,
w1≤z1,w2≤z1+z2,w3≤z2,
z1,z2＝0,1
Figure BDA00025109105800000410
0≤p+≤pmax,
pbat＝p++p-,
in which i corresponds to the number from-NrTo NrTotal 2NrScene number of +1 scenes, α, pbat、ziFor optimized decision variables, α risk value VaR, pbatIs the charge and discharge capacity of the energy storage system, ziFor the auxiliary variables in the CVaR algorithm,
Figure BDA0002510910580000051
which represents the initial charge of the battery,
Figure BDA0002510910580000052
denotes the upper limit of the battery capacity, pmaxAn upper limit of the charging power is indicated,
Figure BDA0002510910580000053
and 4, providing an upper limit for energy storage of the energy storage system.
This application second aspect provides a comprehensive energy system energy storage optimizing apparatus, includes:
the input module is used for inputting the basic data into the comprehensive energy system energy storage optimization device;
the model building module is used for building a cost model of an energy storage system in the comprehensive energy system, building a multi-scenario analysis model of net load power of the comprehensive energy system, and building a linear programming model of the CVaR-based energy storage output of the comprehensive energy system;
the planning module is used for solving a linear planning model of the CVaR-based integrated energy system energy storage output;
and the output module is used for outputting the linear programming result of the CVaR-based integrated energy system energy storage output.
The third aspect of the present application provides an energy storage optimization device for an integrated energy system, comprising:
a memory for storing computer program code corresponding to a method for planning an energy storage capacity and output of an integrated energy system according to the first aspect of the present invention;
a controller for executing the computer program code to implement a method for planning an energy storage capacity of an integrated energy system according to the first aspect of the present invention.
A fourth aspect of the present application provides a computer-readable storage medium for storing a computer program; the storage medium stores program codes corresponding to the energy storage output planning method of the integrated energy system according to the first aspect of the invention; the computer program, when executed by the controller, implements a method for planning an energy storage output of an integrated energy system according to the first aspect of the present invention.
According to the technical scheme, the method has the following advantages:
the invention provides an energy storage output planning method of an integrated energy system, which comprises the steps of constructing a cost model of the energy storage system in the integrated energy system, constructing a multi-scene analysis model of net load power of the integrated energy system, and constructing a CVaR (constant current alternating current) -based linear planning model of the energy storage output of the integrated energy system; and solving and outputting the CVaR-based energy storage output linear programming result according to the input basic data and the CVaR-based energy storage output linear programming model.
Therefore, the cost model structure of the energy storage system in the comprehensive energy system is constructed, the operation cost and the electric energy transaction cost of the energy storage system in the comprehensive energy system are used as the optimized objective function, and the operation efficiency of the comprehensive energy system is directly reflected through the operation cost. When a multi-scenario analysis model of the net load power of the comprehensive energy system is constructed, risks caused by uncertainty of new energy power generation are considered, and the uncertainty is modeled by adopting a prediction error which follows normal distribution. And constructing a CVaR-based linear programming model of the energy storage output of the comprehensive energy system, and programming the operation cost on the premise of considering the risk. The method has the advantage of maximizing the operation efficiency of the comprehensive energy system under the condition of considering the operation risk.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a method for planning energy storage output of an integrated energy system according to the present application;
fig. 2 is a sub-flow chart of a method for planning energy storage output of an integrated energy system according to the present application;
fig. 3 is a schematic structural diagram of an apparatus of a method for planning energy storage output of an integrated energy system according to the present application.
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Specifically, an embodiment of the present application provides a method for planning energy storage output of an integrated energy system, please refer to fig. 1, where the method includes:
step S11: inputting basic data;
step S12: constructing a cost model of an energy storage system in the comprehensive energy system;
step S13: constructing a multi-scenario analysis model of the net load power of the comprehensive energy system;
step S14: constructing a CVaR-based linear programming model of the energy storage output of the comprehensive energy system according to a cost model of the energy storage system in the comprehensive energy system and a multi-scenario analysis model of the net load power of the comprehensive energy system;
step S15: and solving and outputting the CVaR-based energy storage output linear programming result according to the basic data and the CVaR-based energy storage output linear programming model.
Referring to fig. 2, the constructing a cost model of an energy storage system in an integrated energy system specifically includes:
step S21: establishing an objective function of the cost of an energy storage system in the comprehensive energy system;
step S22: and establishing the constraint condition of the objective function.
On the basis of the foregoing embodiments, the embodiments of the present application further explain and optimize the technical solutions, specifically as follows:
in this embodiment, the inputting the basic data specifically includes: cost coefficient, electricity transaction price, hyperbolic coefficient.
In this embodiment, the establishing an objective function of the cost of the energy storage system in the integrated energy system includes:
the objective function of the cost of the energy storage system in the comprehensive energy system comprises the operation cost of the energy storage system and the electric energy transaction cost of the comprehensive energy system.
The objective function is: c ═ C1+C2＝ccp+-cdp-+cbuypb-csellps；
In the formula, C1The running cost of the energy storage system comprises the running cost, p, required by each charge and discharge of the energy storage system+，p-Respectively refers to the charging electric quantity and the discharging electric quantity, p, planned in a certain period of time by the energy storage system+Is non-negative, p-Is not positive and one of the two must be 0, i.e. programming in a certain time period cannot both charge and discharge, cc，cdThe operating costs required for charging and discharging the unit electric energy for the energy storage system are respectively; c2The electric energy transaction cost of the comprehensive energy system refers to the expense of electric energy transaction between the comprehensive energy system and the power grid, wherein pb，psRespectively refers to the planned purchase electric quantity and sale electric quantity, p, of the integrated energy system in a certain periodbIs non-negative, psIs a non-positive number, and one of the two must be 0, i.e. the plan cannot both buy and sell power within a certain time period, cbuyUnit price of electrical energy for the integrated energy system, csellSelling the unit price of the electric energy for the comprehensive energy system; the operation cost can directly reflect the operation efficiency of the comprehensive energy system.
In this embodiment, the establishing the constraint condition of the objective function specifically includes:
p+＝max(pbat,0)
p-＝min(pbat,0)
pb＝max(pd+pbat,0)
ps＝min(pd+pbat,0)
pb+ps＝pd+pbat
in the formula, pbat＝p++p-，pbatFor the variation of the electrical quantity of the energy storage system in a certain period of time, pdAnd subtracting the generated energy of the new energy in a certain time period from the electric energy demand of the user in the comprehensive energy system to obtain the net load demand.
In this embodiment, the converting the nonlinear constraint condition of the objective function into a linear constraint condition specifically includes:
Figure BDA0002510910580000081
Figure BDA0002510910580000082
w1+w2+w3＝0
w1≤z1,w2≤z1+z2,w3≤z2
z1,z2＝0,1
pb≥pd+pbat
pb≥0
in the formula, w1,w2,w3,z1,z2Are auxiliary variables that convert a piecewise linear function to a linear function.
In this embodiment, the constructing a multi-scenario analysis model of the net load power of the integrated energy system specifically includes:
the characteristic that the new energy power generation amount is not accurately predicted is a main risk source of operation of the comprehensive energy system, and the estimation and the actual value of the net load demand in a certain period of time deviate, so that the prediction error of the new energy power generation amount is assumed to be in normal distribution in the embodiment. Because the precision of the load prediction is high, the error of the load prediction can be ignored; and the net load demand of the comprehensive energy system is obtained by subtracting the new energy generating capacity from the load demand in the comprehensive energy system, and the net load demand is also subjected to normal distribution.
The error of the net load prediction is normally distributed, the mean value of the error is 0, and the probability density function of the normal distribution is as follows:
Figure BDA0002510910580000083
in this embodiment, only the random variable of the net load power is considered, and the normal distribution is sampled in equal step length to obtain 2NrPrediction error in +1 scenes; taking the highest point f of the probability density of normal distributionr(xr(0)) For sampling the center point, the mean value corresponding to the prediction error is xr(0)0; the sampling points are sequentially
Figure BDA0002510910580000091
Obtaining corresponding probability density
Figure BDA0002510910580000092
Sampling point xr(i)The corresponding scene probability is:
Figure BDA0002510910580000093
as can be seen, this embodiment is used to deal with the continuous uncertainty, discretizing the continuous uncertainty into 2NrThe more scenes are in +1 scene, the more accurate the optimization of CVaR is, but the calculation amount is increased, and the specific number of scenes can be determined according to the comprehensive combination of the risk bearing capacity and the calculation capacity of the comprehensive energy system.
In this embodiment, the constructing a CVaR-based linear programming model of the energy storage capacity and output of the integrated energy system according to the cost model of the energy storage system in the integrated energy system and the multi-scenario analysis model of the net load power of the integrated energy system includes:
Figure BDA0002510910580000094
subject to:
Figure BDA0002510910580000095
zi≥0,
Figure BDA0002510910580000096
Figure BDA0002510910580000097
Figure BDA0002510910580000098
Figure BDA0002510910580000099
w1+w2+w3＝0,
w1≤z1,w2≤z1+z2,w3≤z2,
z1,z2＝0,1
Figure BDA00025109105800000910
0≤p+≤pmax,
pbat＝p++p-,
in which i corresponds to the number from-NrTo NrTotal 2NrScene number of +1 scenes, α, pbat、ziFor optimized decision variables, α risk value VaR, pbatIs the charge and discharge capacity of the energy storage system, ziFor the auxiliary variables in the CVaR algorithm,
Figure BDA0002510910580000101
which represents the initial charge of the battery,
Figure BDA0002510910580000102
denotes the upper limit of the battery capacity, pmaxAn upper limit of the charging power is indicated,
Figure BDA0002510910580000103
and 4, providing an upper limit for energy storage of the energy storage system.
According to the method, the cost model of the energy storage system in the comprehensive energy system is built, the multi-scenario analysis model of the net load power of the comprehensive energy system is built, the linear programming model of the energy storage output of the comprehensive energy system based on the CVaR is built, and the energy storage output linear programming result based on the CVaR is solved and output according to the basic data and the built energy storage output linear programming model based on the CVaR; by means of the CVaR theory-based energy storage output planning method of the comprehensive energy system, uncertainty modeling is conducted by adopting a prediction error which obeys normal distribution aiming at risks generated by uncertainty of new energy power generation, and operation cost is optimized under the condition that operation risks are considered. Therefore, the technical scheme of the invention can effectively balance the operation risk and the operation efficiency of the comprehensive energy system, and has the advantage of maximizing the operation efficiency of the comprehensive energy system under the condition of considering the operation risk.
An embodiment of the present application further provides a device for planning a user-side integrated energy system, please refer to fig. 3, which specifically includes:
the input module 11 is used for inputting the basic data into the energy storage output planning device of the integrated energy system;
the model building module 12 is configured to build a cost model of the energy storage system in the integrated energy system, build a multi-scenario analysis model of the net load power of the integrated energy system, and build a linear programming model of the CVaR-based integrated energy system energy storage output;
the planning module 13 is configured to solve a linear planning model of the CVaR-based integrated energy system energy storage output;
and the output module 14 is configured to output a linear programming result of the CVaR-based integrated energy system energy storage output.
Further, the present application further provides a comprehensive energy system energy storage output planning device, which specifically includes:
a memory for storing computer program code corresponding to the method for planning energy storage output of an integrated energy system according to the foregoing embodiment of the present invention;
and the controller is used for executing the computer program codes to realize the energy storage capacity output planning method of the integrated energy system according to the foregoing embodiment of the invention.
Further, the present application also provides a computer-readable storage medium for storing a computer program; the storage medium stores a program code corresponding to the energy storage output planning method of the integrated energy system in the embodiment; the computer program, when executed by the controller, implements a method for planning energy storage capacity and output of an integrated energy system according to the foregoing embodiments.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. An energy storage output planning method of an integrated energy system is characterized by comprising the following steps:
inputting basic data;
constructing a cost model of an energy storage system in the comprehensive energy system;
constructing a multi-scenario analysis model of the net load power of the comprehensive energy system;
constructing a CVaR-based linear programming model of the energy storage output of the comprehensive energy system according to a cost model of the energy storage system in the comprehensive energy system and a multi-scenario analysis model of the net load power of the comprehensive energy system;
and solving and outputting the CVaR-based energy storage output linear programming result according to the basic data and the CVaR-based energy storage output linear programming model.
2. The method for planning energy storage capacity output of an integrated energy system according to claim 1, wherein the constructing a cost model of the energy storage system in the integrated energy system comprises:
establishing an objective function of the cost of an energy storage system in the comprehensive energy system;
and establishing a corresponding constraint condition of the objective function.
3. The method for planning the energy storage capacity output of the integrated energy system according to claim 2, wherein the establishing an objective function of the cost of the energy storage system in the integrated energy system comprises:
C＝C1+C2＝ccp+-cdp-+cbuypb-csellps
in the formula, C1For operating costs, including the operating cost, p, required for each charging and discharging of the energy storage system+，p-Respectively refers to the charging electric quantity and the discharging electric quantity, p, planned in a certain period of time by the energy storage system+Is non-negative, p-Is not positive and one of the two must be 0, i.e. programming in a certain time period cannot both charge and discharge, cc，cdThe operating costs required for charging and discharging the unit electric energy for the energy storage system are respectively; c2For the cost of electric energy trade, the cost of electric energy trade between the comprehensive energy system and the power grid is referred, wherein pb，psRespectively refers to the planned purchase electric quantity and sale electric quantity, p, of the integrated energy system in a certain periodbIs non-negative, psIs a non-positive number, and one of the two must be 0, i.e. the plan cannot both buy and sell power within a certain time period, cbuyUnit price of electrical energy for the integrated energy system, csellThe unit price of electricity sold for the integrated energy system.
4. The method of claim 2, wherein the constraints associated with the objective function comprise:
p+＝max(pbat,0)
p-＝min(pbat,0)
pb＝max(pd+pbat,0)
ps＝min(pd+pbat,0)
pb+ps＝pd+pbat
in the formula, pbat＝p++p-，pbatFor the variation of the electrical quantity of the energy storage system in a certain period of time, pdAnd subtracting the generated energy of the new energy in a certain time period from the electric energy demand of the user in the comprehensive energy system to obtain the net load demand.
5. The integrated energy system energy storage output planning method according to claim 2, comprising:
converting the nonlinear constraint condition of the objective function into a linear constraint condition:
Figure FDA0002510910570000021
Figure FDA0002510910570000022
w1+w2+w3＝0
w1≤z1,w2≤z1+z2,w3≤z2
z1,z2＝0,1
pb≥pd+pbat
pb≥0
in the formula, w1,w2,w3,z1,z2Are auxiliary variables that convert a piecewise linear function to a linear function.
6. The method for planning energy storage output of integrated energy system according to claim 1, wherein the constructing a multi-scenario analysis model of the net load power of the integrated energy system comprises:
setting the error of net load prediction to be normal distribution and the mean value of the error to be 0, and sampling the normal distribution by adopting equal step length to obtain 2NrPrediction error in +1 scenes;
maximum point f of probability density of normal distributionr(xr(0)) For sampling the center point, the mean value corresponding to the prediction error is xr(0)0; sampling point
Figure FDA0002510910570000023
Obtaining corresponding probability density
Figure FDA0002510910570000024
Sampling point xr(i)The corresponding scene probability is:
Figure FDA0002510910570000025
7. the method for planning energy storage output of integrated energy system according to claim 1, wherein the constructing a multi-scenario analysis model of the net load power of the integrated energy system comprises:
Figure FDA0002510910570000031
Figure FDA0002510910570000032
zi≥0,
Figure FDA0002510910570000033
Figure FDA0002510910570000034
Figure FDA0002510910570000035
Figure FDA0002510910570000036
w1+w2+w3＝0,
w1≤z1,w2≤z1+z2,w3≤z2,
z1,z2＝0,1
Figure FDA0002510910570000037
0≤p+≤pmax,
pbat＝p++p-,
in which i corresponds to the number from-NrTo NrTotal 2NrScene number of +1 scenes, α, pbat、ziFor optimized decision variables, α risk value VaR, pbatIs the charge and discharge capacity of the energy storage system, ziFor the auxiliary variables in the CVaR algorithm,
Figure FDA0002510910570000038
which represents the initial charge of the battery,
Figure FDA0002510910570000039
denotes the upper limit of the battery capacity, pmaxAn upper limit of the charging power is indicated,
Figure FDA00025109105700000310
and 4, providing an upper limit for energy storage of the energy storage system.
8. An energy storage output planning device for an integrated energy system is characterized by comprising:
the input module is used for inputting the basic data into the energy storage optimization device of the comprehensive energy system;
the model building module is used for building a cost model of an energy storage system in the comprehensive energy system, building a multi-scenario analysis model of net load power of the comprehensive energy system, and building a linear programming model of the CVaR-based energy storage output of the comprehensive energy system;
the planning module is used for solving a linear planning model of the CVaR-based integrated energy system energy storage output;
and the output module is used for outputting the linear programming result of the CVaR-based integrated energy system energy storage output.
9. An integrated energy system energy storage output planning apparatus, comprising:
a memory for storing computer program code corresponding to a method of energy storage capacity output planning for an integrated energy system according to any of claims 1 to 7;
a controller for executing said computer program code to implement an integrated energy system stored energy output planning method according to any of claims 1 to 7.
10. A computer-readable storage medium, wherein the storage medium stores program code corresponding to the method for planning energy storage output of an integrated energy system according to any of claims 1 to 7; the computer program, when executed by a controller, implements a method for planning an integrated energy system stored energy output according to any of claims 1 to 7.
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