CN114118529A - New energy locating and sizing optimization method considering electric energy substitution influence - Google Patents
New energy locating and sizing optimization method considering electric energy substitution influence Download PDFInfo
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Abstract
The invention provides a new energy locating and sizing optimization method considering electric energy substitution influence, which comprises the steps of firstly, collecting relevant parameters and economic parameters; fitting a wind-solar independent probability density function according to wind-solar historical data; obtaining the rigid load of each node of the system; fitting an electric energy substitution load independent probability density distribution function; establishing a wind, light and electric energy alternative load joint probability distribution function; generating wind, light and electric energy to replace a load typical output scene; establishing an electric energy substitution flexible load participation demand response model; establishing an objective function of a new energy locating constant volume optimization model considering electric energy substitution influence; establishing a constraint condition of a new energy locating and sizing optimization model considering electric energy substitution influence; and carrying out optimization solution on the new energy locating constant volume optimization model considering the electric energy substitution influence. According to the invention, when the new energy is selected and fixed in location, the interaction between the electric energy substitution and the new energy is considered, the phenomena of wind and light abandonment of the system can be reduced, and the stable operation capability of the system is improved.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a new energy locating and sizing optimization method considering electric energy substitution influence.
Background
Under the double-carbon background, China vigorously advances the development process of replacing loads with electric energy, and the load characteristics of an electric power system are gradually changed.
The electric energy replacing load is mostly flexible load, has the capability of bidirectional interaction with the power grid, can promote the consumption of the new energy power generation of the system in a mode of participating in demand response, and improves the peak regulation capability of the power grid. When the new energy power supply is subjected to site selection and volume fixing planning, the interaction between electric energy substitution and new energy is considered, the phenomena of wind and light abandonment of the system can be reduced, and the stable operation capacity of the system is improved.
Therefore, the research of the new energy power source location and volume optimization method considering the electric energy substitution influence is of great significance to the construction of a high-proportion new energy power grid under the future double-carbon background.
Disclosure of Invention
In view of this, the invention provides a new energy localization and sizing optimization method considering the influence of electric energy substitution aiming at the problem that the influence of electric energy substitution cannot be considered in the traditional new energy power localization and sizing optimization technology. The method can be used for carrying out new energy site selection and volume determination optimization on the regional power grid containing the electric energy replacing load, the consumption effect of the electric energy replacing load on new energy is preferentially considered in the evaluation process, and compared with the evaluation result of the traditional method, the optimization scheme has the advantages of less wind and light abandoning rate and higher economy.
The technical scheme adopted by the invention is as follows:
a new energy locating and sizing optimization method considering electric energy substitution influence comprises the following steps:
the method comprises the steps of collecting wind, a photoelectric field, relevant parameters of a system to be evaluated and economic parameters. The wind and photoelectric field related parameters comprise historical wind speed and illumination intensity data, historical wind and light output data, new wind and light energy construction cost coefficients, operation and maintenance cost coefficients and wind and photoelectric energy absorption subsidy gains; different types of electric energy in the area where the system to be evaluated is located replace load historical data, topological parameters of the system to be evaluated, power supply parameters and traditional load historical data; the economic parameters comprise the power grid electricity price, the grid loss cost coefficient, the electric energy replacement demand response subsidy coefficient and the wind and light abandoning cost coefficient.
Fitting a wind-solar independent probability density function according to wind-solar historical data, wherein wind speed is fitted by using a two-parameter Weibull function, illumination intensity is fitted by using a Beta function, and a wind-solar independent output probability distribution function is obtained by combining the functional relations between wind power and photovoltaic power generation power and wind speed and photovoltaic power generation respectively;
step three, according to the type and the characteristics of the multi-element electric energy replacing load, the electric energy replacing load is divided into two categories: flexible loads, which in turn can be classified as reducible loads, transferable loads, translatable loads, and accumulate historical data for each load, are compared to rigid loads. The reducible load refers to a load which can participate in system demand response in real time in a mode of reducing power consumption; the transferable load refers to a load which can participate in system demand response in an ordered charging and discharging mode and can be transferred to an access area; the translatable load refers to a mode of replacing the load by translating the electric energy from the power utilization time to another time period; rigid loads refer to traditional class loads that cannot participate in demand response in the three ways described above.
And step four, subtracting the total flexible load amount on the basis of the traditional load amount to obtain the rigid load amount of each node of the system.
Fifthly, fitting independent probability density distribution functions of the reducible load, the transferable load and the translatable load by utilizing a kernel function nonparametric estimation method based on historical data of the reducible load, the transferable load and the translatable load;
and step six, replacing the independent output probability distribution of the flexible load with three types of electric energy, namely wind, light and power by using a vine-copula function, and establishing a five-element combined probability distribution function.
And fourthly, generating a scene of wind power output, photovoltaic power generation output and electric energy alternative flexible load output within one year by combining a wind, light and electric energy alternative load quinary combined probability distribution function through a Latin hypercube sampling technology. Obtaining a typical scene set by using a K-means clustering method
The composition of the grid load may be expressed as:
in the formula (I), the compound is shown in the specification,
is a vector of the wind power,
in order to be a photovoltaic power vector,
in order to be able to reduce the load vector,
in order to be able to transfer the load vector,
to translate the load vector, T is the typical scene occupancy time for each. PL1iRepresenting the rigid load component, P, of the ith load nodeLSjRepresenting the rigid load component, P, in the electric energy alternative load quantity connected to the jth node containing the electric energy alternative loadLSjkAnd the component of the kth flexible load in the electric energy replacing load quantity connected with j nodes containing the electric energy replacing load is shown.
And step five, establishing three electric energy to replace the flexible load to participate in the demand response model. Suppose that the time when the ith node load participates in the demand response occurs at t0At the moment, the three load proportions participating in the demand response are r1、r2、r3The three electric energy substitution loads of each node are
(1) The load can be reduced: reducing the load
t0The total load of the ith node becomes
And does not affect the total amount of load at future times.
(2) The transferable load: node i transfer load
t0The total load of the ith node becomes
Assuming that the transfer node is j, the total load of the node j becomes
And does not affect the total amount of load at future times.
(3) Translatable load: node i translation load
t0The total load of the ith node becomes
Suppose a shift to time t1Then t is the future1The total load of the time node i is
And step six, establishing a new energy locating constant volume objective function, wherein the objective function consists of (1) - (12).
minαC1+βC2+γC3-κC4-μC5+θC6+ωdU+λP (1)
Wherein, C1Investment and construction costs for new energy C2For the operation and maintenance cost of new energy C3For loss of network, C4Subsidy income for new energy consumption C5Subsidizing costs for the substitution of electric energy for participation in demand response, C6And in order to abandon wind and light loss cost, dU is a voltage stability margin, P represents the abandoned new energy quantity, alpha, beta, gamma, kappa, mu and theta are cost weight coefficients, and lambda is a abandoned new energy penalty factor. The specific calculation formula for each charge is as follows:
in the formula, C1WGiIndicates the ith wind-power installation cost, C1PViRepresents the ith eachInstallation cost of photovoltaic power station, NWGRepresenting the total number of wind farms, NPVRepresenting the total number of photovoltaic power plants. And r represents the discount rate.
Wherein, C2WGiRepresents the i-th wind power operation maintenance cost, C2PViRepresents the operation and maintenance cost, T, of the ith photovoltaic power stationWGiRepresenting the ith annual wind turbine running time, TPViAnd the ith photovoltaic annual running time is shown.
Where K denotes the total number of scenes, PlosstRepresenting the charge required to purchase a unit of electricity, PlosstRepresenting the active network loss, T, of the ith sceneiRepresents the time occupied by the ith scene in one year, PiIndicating the probability of the occurrence of the ith scene.
Wherein σ represents a subsidy coefficient, subsidy is performed according to the new energy consumption, and Cwg-supIndicating a wind power subsidy, Cpv-supRepresenting a photovoltaic patch.
Psijk＝g(k,Pwgj,Ppvj,PLj) (7)
Wherein, Csk-supIs shown asAnd the k new energy sources participate in the demand response subsidy cost coefficient.
Pdg-loss＝h(k,Pwgj,Ppvj,PLj) (10)
Wherein, Cdg-lossAnd expressing a cost coefficient of the first-time new energy loss.
And step five, setting constraint conditions. The model constraints include the following equations (13) to (21).
(1) Constraint condition of power flow
In the formula, PGi,kAnd QGi,kRespectively injecting active power and reactive power into the node i; pLi,kAnd QLi,kRespectively an active load and a reactive load of a node i; u shapei,kAnd Uj,kThe voltage amplitudes of nodes i, j, respectively.
(2) Node power balance constraints
(3) System power balance
(4) Node voltage constraint
Ui,min≤Ui≤Ui,max (16)
In the formula of Ui,minAnd Ui,maxThe upper limit and the lower limit of the voltage of the node i are respectively.
(5) Branch power constraint
0≤Si≤Si,max (17)
In the formula, si,maxThe upper capacity limit of the ith branch is indicated.
(4) New energy installation capacity upper limit constraint
∑PDGi≤Pi,max (18)
In the formula, PDGmaxAnd representing the upper limit of the total capacity of the new energy installation.
(5) New energy site selection constraint
NWG,NPV≤N (20)
(6) Electric energy replacement demand response capability constraint
DSjk≤τkPLSk (21)
In the formula, τkRepresents the maximum participative demand response proportion of the kth electric energy substitution load, PLSkRepresenting the kth demand response load amount.
And sixthly, carrying out optimization solution on the objective function according to a particle swarm algorithm.
Drawings
FIG. 1 is a schematic flow chart illustrating a new energy localization and sizing method considering electric energy substitution effects according to an embodiment of the present invention;
FIG. 2 is a topology diagram of an IEEE-33 node system.
Fig. 3 is a graph of the result of site selection and volume determination optimization.
FIG. 4 is a data diagram of the result of site selection and volume determination optimization.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly 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, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is a schematic flow chart of a method for considering an influence of electric energy substitution according to an embodiment of the present invention, the method includes the following steps:
the method comprises the steps of collecting wind, a photoelectric field, relevant parameters of a system to be evaluated and economic parameters. The wind and photoelectric field related parameters comprise historical wind speed and illumination intensity data, historical wind and light output data, new wind and light energy construction cost coefficients, operation and maintenance cost coefficients and wind and photoelectric energy absorption subsidy gains; different types of electric energy in the area where the system to be evaluated is located replace load historical data, topological parameters of the system to be evaluated, power supply parameters and traditional load historical data; the economic parameters comprise the power grid electricity price, the grid loss cost coefficient, the electric energy replacement demand response subsidy coefficient and the wind and light abandoning cost coefficient.
Fitting a wind-solar independent probability density function according to wind-solar historical data, wherein wind speed is fitted by using a two-parameter Weibull function, illumination intensity is fitted by using a Beta function, and a wind-solar independent output probability distribution function is obtained by combining the functional relations between wind power and photovoltaic power generation power and wind speed and photovoltaic power generation respectively;
step three, according to the type and the characteristics of the multi-element electric energy replacing load, the electric energy replacing load is divided into two categories: flexible loads, which in turn can be classified as reducible loads, transferable loads, translatable loads, and accumulate historical data for each load, are compared to rigid loads. The reducible load refers to a load which can participate in system demand response in real time in a mode of reducing power consumption; the transferable load refers to a load which can participate in system demand response in an ordered charging and discharging mode and can be transferred to an access area; the translatable load refers to a mode of replacing the load by translating the electric energy from the power utilization time to another time period; rigid loads refer to traditional class loads that cannot participate in demand response in the three ways described above.
And step four, subtracting the total flexible load amount on the basis of the traditional load amount to obtain the rigid load amount of each node of the system.
Fifthly, fitting independent probability density distribution functions of the reducible load, the transferable load and the translatable load by utilizing a kernel function nonparametric estimation method based on historical data of the reducible load, the transferable load and the translatable load;
and step six, replacing the independent output probability distribution of the flexible load with three types of electric energy, namely wind, light and power by using a vine-copula function, and establishing a five-element combined probability distribution function.
And fourthly, generating a scene of wind power output, photovoltaic power generation output and electric energy alternative flexible load output within one year by combining a wind, light and electric energy alternative load quinary combined probability distribution function through a Latin hypercube sampling technology. Obtaining a typical scene set by using a K-means clustering method
The composition of the grid load may be expressed as:
in the formula (I), the compound is shown in the specification,
is a vector of the wind power,
in order to be a photovoltaic power vector,
in order to be able to reduce the load vector,
in order to be able to transfer the load vector,
to translate the load vector, T is the typical scene occupancy time for each. PL1iRepresenting the rigid load component, P, of the ith load nodeLSjRepresenting the rigid load component, P, in the electric energy alternative load quantity connected to the jth node containing the electric energy alternative loadLSjkAnd the component of the kth flexible load in the electric energy replacing load quantity connected with j nodes containing the electric energy replacing load is shown.
And step five, establishing three electric energy to replace the flexible load to participate in the demand response model. Suppose that the time when the ith node load participates in the demand response occurs at t0At the moment, the three load proportions participating in the demand response are r1、r2、r3The three electric energy substitution loads of each node are
(1) The load can be reduced: reducing the load
t0The total load of the ith node becomes
And does not affect the total amount of load at future times.
(2) The transferable load: node i transfer load
t0The total load of the ith node becomes
Assuming that the transfer node is j, the total load of the node j becomes
And does not affect the total amount of load at future times.
(3) Translatable load: node i translation load
t0The total load of the ith node becomes
Suppose a shift to time t1Then t is the future1The total load of the time node i is
And step six, establishing a new energy locating constant volume objective function, wherein the objective function consists of (1) - (12).
minαC1+βC2+γC3-κC4-μC5+θC6+ωdU+λP (1)
Wherein, C1Investment and construction costs for new energy C2For the operation and maintenance cost of new energy C3For loss of network, C4Subsidy income for new energy consumption C5Subsidizing costs for the substitution of electric energy for participation in demand response, C6And in order to abandon wind and light loss cost, dU is a voltage stability margin, P represents the abandoned new energy quantity, alpha, beta, gamma, kappa, mu and theta are cost weight coefficients, and lambda is a abandoned new energy penalty factor. The specific calculation formula for each charge is as follows:
in the formula, C1WGiIndicates the ith wind-power installation cost, C1PViRepresents installation cost of ith photovoltaic power station, NWGRepresenting the total number of wind farms, NPVRepresenting the total number of photovoltaic power plants. And r represents the discount rate.
Wherein, C2WGiRepresents the i-th wind power operation maintenance cost, C2PViRepresents the operation and maintenance cost, T, of the ith photovoltaic power stationWGiRepresenting the ith annual wind turbine running time, TPViAnd the ith photovoltaic annual running time is shown.
Where K denotes the total number of scenes, PlosstRepresenting the charge required to purchase a unit of electricity, PlosstRepresenting the active network loss, T, of the ith sceneiRepresents the time occupied by the ith scene in one year, PiIndicating the probability of the occurrence of the ith scene.
Wherein σ represents a subsidy coefficient, subsidy is performed according to the new energy consumption, and Cwg-supIndicating a wind power subsidy, Cpv-supRepresenting a photovoltaic patch.
Psijk＝g(k,Pwgj,Ppvj,PLj) (7)
Wherein, Csk-supAnd the kth new energy participation demand response subsidy cost coefficient is shown.
Pdg-loss＝h(k,Pwgj,Ppvj,PLj) (10)
Wherein, Cdg-lossAnd expressing a cost coefficient of the first-time new energy loss.
And step five, setting constraint conditions. The model constraints include the following equations (13) to (21).
(1) Constraint condition of power flow
In the formula, PGi,kAnd QGi,kRespectively injecting active power and reactive power into the node i; pLi,kAnd QLi,kRespectively an active load and a reactive load of a node i; u shapei,kAnd Uj,kThe voltage amplitudes of nodes i, j, respectively.
(2) Node power balance constraints
(3) System power balance
(4) Node voltage constraint
Ui,min≤Ui≤Ui,max (16)
In the formula of Ui,minAnd Ui,maxThe upper limit and the lower limit of the voltage of the node i are respectively.
(5) Branch power constraint
0≤Si≤Si,max (17)
In the formula, si,maxThe upper capacity limit of the ith branch is indicated.
(4) New energy installation capacity upper limit constraint
∑PDGi≤Pi,max (18)
In the formula, PDGmaxAnd representing the upper limit of the total capacity of the new energy installation.
(5) New energy site selection constraint
NWG,NPV≤N (20)
(6) Electric energy replacement demand response capability constraint
DSjk≤τkPLSk (21)
In the formula, τkRepresents the maximum participative demand response proportion of the kth electric energy substitution load, PLSkRepresenting the kth demand response load amount.
And sixthly, carrying out optimization solution on the objective function according to a particle swarm algorithm.
The invention has the following beneficial effects: compared with the traditional new energy locating and sizing method, the new energy locating and sizing method considering the electric energy substitution influence can be used for carrying out new energy locating and sizing optimization on a regional power grid containing electric energy substitution loads, the absorption effect of the electric energy substitution loads on new energy is preferentially considered in the evaluation process, and compared with the evaluation result of the traditional method, the optimization scheme has the advantages of less wind and light abandonment rate and higher economy.
Taking an IEEE-33 node system as an example, a detailed description will be given to a new energy location determination method considering electric energy substitution influence, as shown in fig. 2, which is an original topology diagram of the IEEE-33 node system. The rated power of the wind turbine generator is set to be 2kW, the rated wind speed of each fan is 12m/s, the cut-in wind speed is 3m/s, wind, light and electric energy substitution and other load data come from 2019 historical data in a certain area, and the electric energy substitution in the area is mainly divided into three types, namely air conditioners, electric vehicles and large industrial loads. The cost coefficients of wind and light new energy are 18000 yuan/kW and 12000 yuan/kW respectively, the operation and maintenance cost coefficients are 0.12 yuan/W and 0.18 yuan/W, the wind and light subsidy gains are 0.1 yuan/W and 0.08 yuan/W respectively, the system parameters of the IEEE-33 node refer to related files, the electricity price of a power grid is 0.55 yuan/kwh, the network loss cost coefficient is 0.2 yuan/kW, the electric energy replacement demand marketing subsidy coefficient is 0.1 yuan/kW, and the wind and light abandonment cost coefficients are 0.08 yuan/kW respectively.
(1) And fitting the independent probability distribution function by using historical wind and light data.
(2) The electric energy substitution load in the study object in this example is classified into three categories, wherein the air conditioning load belongs to a first category flexible load, the electric vehicle belongs to a second category flexible load, and the large industrial load belongs to a third category flexible load.
(3) And subtracting the three types of flexible load quantities from the total quantity of the electric energy replacing load to obtain the rigid electric energy replacing load quantity.
(4) And based on the three types of load historical data, obtaining three types of flexible load independent probability density functions of each node.
(5) Based on the independent probability distribution functions substituted by the fitted wind, light and three flexible electric energy, a vine-copula theory is utilized to establish a multivariate probability distribution function, and an Archimedes function family is adopted as the fitted copula function of a single-vine function in the embodiment.
(6) Based on a Latin hypercube sampling method, combining with the established multivariate probability distribution function, simulating wind, light and three types of flexible load output scenes, and based on a k-means clustering algorithm, reducing to obtain a typical scene. And in the reduction process, calculating the DIS parameter as a scene reduction basis.
(7) And setting parameters of particle swarm optimization. The number of particles is set to be 90, the upper limit of the capacity of the single-node new energy is set to be 6kW, and the upper limit of the total amount of the new energy accessed to the system is set to be 300 kW.
(8) Optimizing by a particle swarm algorithm, and solving by combining the objective function and the constraint condition proposed in the fourth step and the fifth step to obtain a locating and sizing optimization result, as shown in fig. 3 and 4.
The new energy locating and sizing optimization method considering the electric energy substitution influence can be used for conducting new energy locating and sizing optimization on a regional power grid containing electric energy substitution loads, the absorption effect of the electric energy substitution loads on new energy is considered preferentially in the evaluation process, and compared with the evaluation result of a traditional method, the optimization scheme is less in wind and light abandonment rate and higher in economical efficiency.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (6)
1. A new energy locating and sizing optimization method considering electric energy substitution influence is characterized by comprising the following steps: the method comprises the following steps:
step 1, collecting wind, a photoelectric field, relevant parameters of a system to be evaluated and economic parameters; the wind and photoelectric field related parameters comprise historical wind speed and illumination intensity data, historical wind and light output data, new wind and light energy construction cost coefficients, operation and maintenance cost coefficients and wind and photoelectric energy absorption subsidy gains; different types of electric energy in the area where the system to be evaluated is located replace load historical data, topological parameters of the system to be evaluated, power supply parameters and traditional load historical data; the economic parameters comprise the power grid price, the grid loss cost coefficient, the electric energy replacement demand response subsidy coefficient and the wind and light abandoning cost coefficient
Step 2, fitting a wind-solar independent probability density function according to wind-solar historical data; the wind speed is fitted by using a two-parameter Weibull function, the illumination intensity is fitted by using a Beta function, and a wind-solar independent output probability distribution function is obtained by combining the functional relations between the wind power and the photovoltaic power generation power and the wind speed and the photovoltaic power generation power respectively;
step 3, classifying the electric energy replacing loads, specifically classifying the electric energy replacing loads into two categories: flexible load and rigid load, the flexible load can be divided into reducible load, transferable load, translatable load again, and accumulate each kind of load historical data; the reducible load refers to a load which can participate in system demand response in real time in a mode of reducing power consumption; the transferable load refers to a load which can participate in system demand response in an ordered charging and discharging mode and can be transferred to an access area; the translatable load refers to a mode of replacing the load by translating the electric energy from the power utilization time to another time period; rigid load refers to traditional class load which cannot participate in demand response in the three ways;
step 4, obtaining the rigid load of each node of the system on the basis of the traditional load;
step 5, fitting an electric energy substitution load independent probability density distribution function; establishing a wind, light and electric energy alternative load joint probability distribution function; generating wind, light and electric energy to replace a load typical output scene;
step 6, establishing an electric energy substitution flexible load participation demand response model;
step 7, establishing an objective function and constraint conditions of the new energy locating constant volume optimization model considering the electric energy substitution influence, wherein the objective function is as follows:
minαC1+βC2+γC3-κC4-μC5+θC6+ωdU+λP
wherein, C1Investment and construction costs for new energy C2For the operation and maintenance cost of new energy C3For loss of network, C4Subsidy income for new energy consumption C5Subsidizing costs for the substitution of electric energy for participation in demand response, C6Discarding the loss cost of light for wind abandonment, dU is a voltage stability margin, P represents the amount of abandoned new energy, alpha, beta, gamma, kappa, mu and theta are cost weight coefficients of various items, and lambda is a penalty factor for abandoning new energy;
subsidy cost C for electric energy substitution participation demand response in objective function5Is defined as follows:
Psijk＝g(k,Pwgj,Ppvj,PLj)
wherein, Csk-supRepresenting the kth new energy participation demand response subsidy cost coefficient;
wind curtailment and light loss cost C in objective function6Is defined as follows:
Pdg-loss＝h(k,Pwgj,Ppvj,PLj)
wherein, Cdg-lossRepresenting a first new energy loss cost coefficient;
2. the node power balance constraint in the new energy siting optimization constraint considering the electric energy substitution influence as recited in claim 1 is defined as follows:
in the formula，PSm→j2And the load quantity of the second type of electric energy to replace the load transferred to the jth node by the mth node is represented.
3. The constraint condition of the new energy localization volume optimization model considering the electric energy substitution influence according to claim 1, characterized in that: the system power balance constraint in the constraint is defined as follows:
and 8, carrying out optimization solution on the new energy locating constant volume optimization model considering the electric energy substitution influence.
4. The new energy localization volume optimization method considering the electric energy substitution influence as claimed in claim 1, characterized in that: the method for fitting the electric energy alternative load independent probability density distribution function in the step 5 comprises the following steps:
based on the historical data of the reducible load, the transferable load and the translatable load, fitting the independent probability density distribution functions of the reducible load, the transferable load and the translatable load by using a kernel function nonparametric estimation method.
5. The new energy localization volume optimization method considering the electric energy substitution influence as claimed in claim 1, characterized in that: the method for establishing the wind, light and electric energy alternative load combined probability distribution function comprises the following steps:
and (3) utilizing a vine-copula function, combining wind, light and three electric energy to replace the independent output probability distribution of the flexible load, and establishing a five-element combined probability distribution function.
6. The new energy localization volume optimization method considering the electric energy substitution influence as claimed in claim 1, characterized in that: the electric energy replacing flexible load participation demand response model is established as follows:
suppose that the time when the ith node load participates in the demand response occurs at t0Time of day, negation of three participating demand responsesThe ratio of the charges is r1、r2、r3The three electric energy substitution loads of each node are
(1) The load can be reduced: reducing the load
t0The total load of the ith node becomes
The total load at the future moment is not influenced;
(2) the transferable load: node i transfer load
t0The total load of the ith node becomes
Assuming that the transfer node is j, the total load of the node j becomes
The total load at the future moment is not influenced;
(3) translatable load: node i translation load
t0The total load of the ith node becomes
Suppose a shift to time t1Then t is the future1The total load of the time node i is
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