A new energy site selection and capacity optimization method considering the impact of electricity substitution

Abstract

The present invention provides a new energy site selection and sizing optimization method taking into account the influence of electric energy substitution, firstly collect relevant parameters and economic parameters; fit the independent probability density function of wind and light according to the historical data of wind and light; obtain the rigid load of each node of the system; fit the independent probability density distribution function of electric energy substitution load; establish the joint probability distribution function of wind, light and electric energy substitution load; generate typical output scenarios of wind, light and electric energy substitution load; establish a demand response model for electric energy substitution flexible load; establish the objective function of the new energy site selection and sizing optimization model taking into account the influence of electric energy substitution; establish the constraint conditions of the new energy site selection and sizing optimization model taking into account the influence of electric energy substitution; optimize and solve the new energy site selection and sizing optimization model taking into account the influence of electric energy substitution. When selecting the site and sizing of new energy, the present invention takes into account the interaction between electric energy substitution and new energy, which can reduce the occurrence of system wind and light abandonment and improve the system stability operation capability.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a new energy site selection and capacity optimization method taking into account the impact of electric energy substitution. Background Art

[0002] Under the dual carbon background, my country has vigorously promoted the development of electric energy to replace loads, and the load characteristics of the power system have gradually changed.

[0003] Most of the electric energy replacement loads are flexible loads, which have the ability to interact with the power grid in both directions. By participating in demand response, they can promote the consumption of new energy power generation in the system and improve the peak load regulation capacity of the power grid. When conducting site selection and capacity planning for new energy power sources, considering the interaction between electric energy replacement and new energy can reduce the occurrence of wind and solar power abandonment in the system and improve the system's stable operation capability.

[0004] Therefore, studying an optimization method for the site selection and sizing of renewable energy power sources that takes into account the impact of electricity substitution is of vital importance to the construction of a high-proportion renewable energy power grid under the future dual-carbon background. Summary of the invention

[0005] In view of this, the present invention proposes a new energy site selection and capacity optimization method that takes into account the impact of electric energy substitution, which is aimed at the failure of traditional new energy power site selection and capacity optimization technology to consider the impact of electric energy substitution. This method can be used to optimize the site selection and capacity of new energy for regional power grids containing electric energy substitution loads. The evaluation process gives priority to the role of electric energy substitution loads in absorbing new energy. Compared with the evaluation results of traditional methods, the optimization scheme has a lower wind and solar power abandonment rate and is more economical.

[0006] The technical solution adopted by the present invention is as follows:

[0007] A new energy site selection and capacity optimization method taking into account the impact of electric energy substitution comprises the following steps:

[0008] Step 1: Collect relevant parameters of wind and photovoltaic power plants and the system to be evaluated, as well as economic parameters. Relevant parameters of wind and photovoltaic power plants include historical data of wind speed and light intensity, historical output data of wind and light, cost coefficients of wind and light new energy construction, operation and maintenance cost coefficients, and subsidy gains for wind and light energy consumption; historical data of different types of electric energy replacement loads in the area where the system to be evaluated is located, topological parameters of the system to be evaluated, power supply parameters, and historical data of traditional loads; economic parameters include grid electricity prices, network loss cost coefficients, electric energy replacement demand response subsidy coefficients, and wind and light abandonment cost coefficients.

[0009] Step 2: Fit the wind-solar independent probability density function according to the historical data of wind and solar, where the wind speed is fitted by the two-parameter Weibull function, and the light intensity is fitted by the Beta function. Combined with the functional relationship between wind power and photovoltaic power and wind speed and photovoltaic respectively, the wind-solar independent output probability distribution function is obtained;

[0010] Step 3. According to the types and characteristics of multiple energy replacement loads, the energy replacement loads are divided into two categories: flexible loads and rigid loads. Flexible loads can be divided into curtailable loads, transferable loads, and shiftable loads, and the historical data of each load is accumulated. Among them, curtailable loads refer to loads that can participate in system demand response in real time by reducing power consumption; transferable loads refer to loads that can participate in system demand response in the form of orderly charging and discharging, and can transfer load access areas; shiftable loads refer to energy replacement loads that can shift power consumption time to another time period; rigid loads refer to traditional loads that cannot participate in demand response through the above three methods.

[0011] Step 4: Based on the traditional load, subtract the total amount of flexible load to obtain the rigid load of each node in the system.

[0012] Step 5: Based on the historical data of the load that can be cut, the load that can be transferred, and the load that can be translated, the independent probability density distribution functions of the three are fitted using the kernel function nonparametric estimation method;

[0013] Step 6: Use the vine-copula function to combine the independent output probability distribution of wind, solar, and three types of electric energy to replace flexible loads to establish a five-element joint probability distribution function.

[0014] Step 4: Use Latin hypercube sampling technology and the five-element joint probability distribution function of wind, light, and electric energy substitution loads to generate wind power output, photovoltaic power generation output, and electric energy substitution flexible load output scenarios within a year. Use the K-means clustering method to obtain a typical scenario set

[0015] The composition of the grid load can be expressed as:

[0016]

[0017] In the formula, is the wind power vector, is the photovoltaic power vector, is the reducible load vector, is the transferable load vector, P is the translatable load vector, and T is the time occupied by each typical scenario. L1i represents the rigid load component of the i-th load node, P LSjrepresents the rigid load component of the electric energy replacement load connected to the jth node with electric energy replacement load, P LSjk It represents the component of the kth type of flexible load in the electric energy substitution load connected to j nodes containing electric energy substitution load.

[0018] Step 5: Establish a model for three types of electric energy replacement flexible loads to participate in demand response. Assume that the time when the i-th node load participates in demand response occurs at time t0, the proportions of the three types of loads participating in demand response are r1, r2, and r3 respectively, and the three types of electric energy replacement loads at each node are

[0019] (1) Load reduction: Reduce the load The total load of the i-th node at time t0 becomes And it does not affect the total load at future times.

[0020] (2) Transferable load: the amount of load that can be transferred from node i The total load of the i-th node at time t0 becomes Assuming the transfer node is j, the total load of node j becomes And it does not affect the total load at future times.

[0021] (3) Translable load: Translative load of node i The total load of the i-th node at time t0 becomes Assuming the translation to time t1, the total load of node i at time t1 in the future is

[0022] Step 6: Establish the objective function for new energy site selection and capacity determination. The objective function consists of (1)-(12).

[0023] minαC1+βC2+γC3-κC4-μC5+θC6+ωdU+λP (1)

[0024] Among them, C1 is the investment and construction cost of new energy, C2 is the operation and maintenance cost of new energy, C3 is the network loss cost, C4 is the subsidy income for new energy consumption, C5 is the subsidy cost for electric energy substitution to participate in demand response, C6 is the loss cost of abandoned wind and solar power, dU is the voltage stability margin, P represents the amount of abandoned new energy, α, β, γ, κ, μ, θ are the weight coefficients of various costs, and λ is the penalty factor for abandoned new energy. The specific calculation formulas for various costs are as follows:

[0025]

[0026] In the formula, C 1WGi represents the installation cost of the i-th wind turbine, C 1PVi represents the installation cost of each PV power station, N WGRepresents the total number of wind farms, N PV represents the total number of photovoltaic power stations. r represents the discount rate.

[0027]

[0028] Among them, C 2WGi represents the operation and maintenance cost of the i-th wind power station, C 2PVi represents the operation and maintenance cost of each PV power station, T WGi represents the annual operating time of the i-th wind power station, T PVi Represents the annual operating time of the i-th photovoltaic unit.

[0029]

[0030] Among them, K represents the total number of scenes, P losst Indicates the cost of purchasing a unit of electricity, P losst represents the active network loss of the ith scenario, T i represents the time occupied by the i-th scene in a year, P i represents the probability of the i-th scenario occurring.

[0031]

[0032] Among them, σ represents the subsidy coefficient, which is based on the amount of new energy consumption. wg-sup represents wind power subsidy, C pv-sup Represents photovoltaic subsidies.

[0033]

[0034] P sijk =g(k,P wgj ,P pvj ,P Lj ) (7)

[0035]

[0036] Among them, C sk-sup It represents the subsidy cost coefficient for the kth new energy to participate in demand response.

[0037]

[0038] P dg-loss =h(k,P wgj ,P pvj ,P Lj ) (10)

[0039]

[0040] Among them, C dg-lossIt represents the cost coefficient of abandoned new energy loss.

[0041]

[0042] Step 5: Set constraints. The model constraints consist of the following equations (13)-(21).

[0043] (1) Power flow constraints

[0044]

[0045] Where P Gi,k and Q Gi,k are the active power and reactive power injected into node i respectively; P Li,k and Q Li,k are the active load and reactive load of node i respectively; U i,k and U j,k are the voltage amplitudes of nodes i and j respectively.

[0046] (2) Node power balance constraints

[0047]

[0048] Where P Sm→j2 It represents the load amount transferred from the mth node to the jth node by the second type of electric energy replacement load.

[0049] (3) System power balance

[0050]

[0051] (4) Node voltage constraints

[0052] U i,min ≤U i ≤U i,max (16)

[0053] Where U i,min and U i,max are the upper and lower limits of the voltage at node i respectively.

[0054] (5) Branch power constraints

[0055] 0≤S i ≤S i,max (17)

[0056] In the formula, s i,max represents the upper capacity limit of the i-th branch.

[0057] (4) Constraints on the upper limit of new energy installation capacity

[0058] ∑P DGi ≤Pi,max (18)

[0059]

[0060] Where P DGmax Indicates the upper limit of total installed capacity of renewable energy.

[0061] (5) New energy site selection constraints

[0062] N WG ,N PV ≤N (20)

[0063] (6) Constraints on the ability to respond to demand for electricity substitution

[0064] D Sjk ≤τ k P LSk (twenty one)

[0065] In the formula, τ k represents the maximum proportion of the k-th type of electric energy replacement load that can participate in demand response, P LSk represents the kth demand response load.

[0066] Step 6: Optimize and solve the above objective function based on the particle swarm algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a flow chart of one embodiment of a method for selecting a new energy site and determining the capacity of a new energy source taking into account the influence of electric energy substitution according to the present invention;

[0068] Figure 2 This is the IEEE-33 node system topology diagram.

[0069] Figure 3 This is a curve chart of the site selection and capacity optimization results.

[0070] Figure 4 This is a data chart of the site selection and capacity optimization results. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] like Figure 1FIG. 1 is a flow chart of an embodiment of a method for taking into account the influence of electric energy substitution according to the present invention, wherein the method comprises the following steps:

[0073] Step 1: Collect relevant parameters of wind and photovoltaic power plants and the system to be evaluated, as well as economic parameters. Relevant parameters of wind and photovoltaic power plants include historical data of wind speed and light intensity, historical output data of wind and light, cost coefficients of wind and light new energy construction, operation and maintenance cost coefficients, and subsidy gains for wind and light energy consumption; historical data of different types of electric energy replacement loads in the area where the system to be evaluated is located, topological parameters of the system to be evaluated, power supply parameters, and historical data of traditional loads; economic parameters include grid electricity prices, network loss cost coefficients, electric energy replacement demand response subsidy coefficients, and wind and light abandonment cost coefficients.

[0074] Step 2: Fit the wind-solar independent probability density function according to the historical data of wind and solar, where the wind speed is fitted by the two-parameter Weibull function, and the light intensity is fitted by the Beta function. Combined with the functional relationship between wind power and photovoltaic power and wind speed and photovoltaic respectively, the wind-solar independent output probability distribution function is obtained;

[0075] Step 3. According to the types and characteristics of multiple energy replacement loads, the energy replacement loads are divided into two categories: flexible loads and rigid loads. Flexible loads can be divided into curtailable loads, transferable loads, and shiftable loads, and the historical data of each load is accumulated. Among them, curtailable loads refer to loads that can participate in system demand response in real time by reducing power consumption; transferable loads refer to loads that can participate in system demand response in the form of orderly charging and discharging, and can transfer load access areas; shiftable loads refer to energy replacement loads that can shift power consumption time to another time period; rigid loads refer to traditional loads that cannot participate in demand response through the above three methods.

[0076] Step 4: Based on the traditional load, subtract the total amount of flexible load to obtain the rigid load of each node in the system.

[0077] Step 5: Based on the historical data of the load that can be cut, the load that can be transferred, and the load that can be translated, the independent probability density distribution functions of the three are fitted using the kernel function nonparametric estimation method;

[0078] Step 6: Use the vine-copula function to combine the independent output probability distribution of wind, solar, and three types of electric energy to replace flexible loads to establish a five-element joint probability distribution function.

[0079] Step 4: Use Latin hypercube sampling technology and the five-element joint probability distribution function of wind, light, and electric energy substitution loads to generate wind power output, photovoltaic power generation output, and electric energy substitution flexible load output scenarios within a year. Use the K-means clustering method to obtain a typical scenario set

[0080] The composition of the grid load can be expressed as:

[0081]

[0082] In the formula, is the wind power vector, is the photovoltaic power vector, is the reducible load vector, is the transferable load vector, P is the translatable load vector, and T is the time occupied by each typical scenario. L1i represents the rigid load component of the i-th load node, P LSj represents the rigid load component of the electric energy replacement load connected to the jth node with electric energy replacement load, P LSjk It represents the component of the kth type of flexible load in the electric energy substitution load connected to j nodes containing electric energy substitution load.

[0083] Step 5: Establish a model for three types of electric energy replacement flexible loads to participate in demand response. Assume that the time when the i-th node load participates in demand response occurs at time t0, the proportions of the three types of loads participating in demand response are r1, r2, and r3 respectively, and the three types of electric energy replacement loads at each node are

[0084] (1) Load reduction: Reduce the load The total load of the i-th node at time t0 becomes And it does not affect the total load at future times.

[0085] (2) Transferable load: the amount of load that can be transferred from node i The total load of the i-th node at time t0 becomes Assuming the transfer node is j, the total load of node j becomes And it does not affect the total load at future times.

[0086] (3) Translable load: Translative load of node i The total load of the i-th node at time t0 becomes Assuming the translation to time t1, the total load of node i at time t1 in the future is

[0087] Step 6: Establish the objective function for new energy site selection and capacity determination. The objective function consists of (1)-(12).

[0088] minαC1+βC2+γC3-κC4-μC5+θC6+ωdU+λP (1)

[0089] Among them, C1 is the investment and construction cost of new energy, C2 is the operation and maintenance cost of new energy, C3 is the network loss cost, C4 is the subsidy income for new energy consumption, C5 is the subsidy cost for electric energy substitution to participate in demand response, C6 is the loss cost of abandoned wind and solar power, dU is the voltage stability margin, P represents the amount of abandoned new energy, α, β, γ, κ, μ, θ are the weight coefficients of various costs, and λ is the penalty factor for abandoned new energy. The specific calculation formulas for various costs are as follows:

[0090]

[0091] In the formula, C 1WGi represents the installation cost of the i-th wind turbine, C 1PVi represents the installation cost of each PV power station, N WG Represents the total number of wind farms, N PV represents the total number of photovoltaic power stations. r represents the discount rate.

[0092]

[0093] Among them, C 2WGi represents the operation and maintenance cost of the i-th wind power station, C 2PVi represents the operation and maintenance cost of each PV power station, T WGi represents the annual operating time of the i-th wind power station, T PVi Represents the annual operating time of the i-th photovoltaic unit.

[0094]

[0095] Among them, K represents the total number of scenes, P losst Indicates the cost of purchasing a unit of electricity, P losst represents the active network loss of the ith scenario, T i represents the time occupied by the i-th scene in a year, P i represents the probability of the i-th scenario occurring.

[0096]

[0097] Among them, σ represents the subsidy coefficient, which is based on the amount of new energy consumption. wg-sup represents wind power subsidy, C pv-sup Represents photovoltaic subsidies.

[0098]

[0099] P sijk =g(k,P wgj ,P pvj ,P Lj ) (7)

[0100]

[0101] Among them, C sk-sup It represents the subsidy cost coefficient for the kth new energy to participate in demand response.

[0102]

[0103] P dg-loss =h(k,P wgj ,P pvj ,P Lj ) (10)

[0104]

[0105] Among them, C dg-loss It represents the cost coefficient of abandoned new energy loss.

[0106]

[0107] Step 5: Set constraints. The model constraints consist of the following equations (13)-(21).

[0108] (1) Power flow constraints

[0109]

[0110] Where P Gi,k and Q Gi,k are the active power and reactive power injected into node i respectively; P Li,k and Q Li,k are the active load and reactive load of node i respectively; U i,k and U j,k are the voltage amplitudes of nodes i and j respectively.

[0111] (2) Node power balance constraints

[0112]

[0113] Where P Sm→j2 It represents the load amount transferred from the mth node to the jth node by the second type of electric energy replacement load.

[0114] (3) System power balance

[0115]

[0116] (4) Node voltage constraints

[0117] U i,min ≤U i ≤U i,max (16)

[0118] Where Ui,min and U i,max are the upper and lower limits of the voltage at node i respectively.

[0119] (5) Branch power constraints

[0120] 0≤S i ≤S i,max (17)

[0121] In the formula, s i,max represents the upper capacity limit of the i-th branch.

[0122] (4) Constraints on the upper limit of new energy installation capacity

[0123] ∑P DGi ≤P i,max (18)

[0124]

[0125] Where P DGmax Indicates the upper limit of total installed capacity of renewable energy.

[0126] (5) New energy site selection constraints

[0127] N WG ,N PV ≤N (20)

[0128] (6) Constraints on the ability to respond to demand for electricity substitution

[0129] D Sjk ≤τ k P LSk (twenty one)

[0130] In the formula, τ k represents the maximum proportion of the k-th type of electric energy replacement load that can participate in demand response, P LSk represents the kth demand response load.

[0131] Step 6: Optimize and solve the above objective function based on the particle swarm algorithm.

[0132] The beneficial effects of the present invention are as follows: The present invention proposes a new energy site selection and sizing method that takes into account the impact of electric energy substitution. Compared with the traditional new energy site selection and sizing method, this method can be used to optimize the new energy site selection and sizing of regional power grids containing electric energy substitution loads. The evaluation process gives priority to the effect of electric energy substitution loads on the absorption of new energy. Compared with the evaluation results of traditional methods, the optimization scheme has lower wind and solar power abandonment rates and is more economical.

[0133] Taking the IEEE-33 node system as an example, a new energy site selection and capacity determination method taking into account the impact of electric energy substitution determined by the present invention is described in detail. Figure 2 This is the original topology diagram of the IEEE-33 node system. The rated power of the wind turbine is set to 2kW, the rated wind speed of each wind turbine is 12m / s, and the cut-in wind speed is 3m / s. The wind, light, electric energy substitution and other load data are from the historical data of a certain place in 2019. The electric energy substitution in this area is mainly divided into three types: air conditioning, electric vehicles and large industrial loads. The cost coefficients of wind and light new energy are 18,000 yuan / kW and 12,000 yuan / kW respectively, the operation and maintenance cost coefficients are 0.12 yuan / W and 0.18 yuan / W, and the wind and light subsidy gains are 0.1 yuan / W and 0.08 yuan / W respectively. The parameters of the IEEE-33 node system refer to the relevant documents, the grid electricity price is 0.55 yuan / kwh, the network loss cost coefficient is 0.2 yuan / kW, the electric energy substitution demand subsidy coefficient is 0.1 yuan / kW, and the wind and light abandonment cost coefficients are 0.08 yuan / kW respectively.

[0134] (1) Use historical wind and light data to fit independent probability distribution functions.

[0135] (2) The electric energy replacement loads in the research object of this example are divided into three categories, among which air conditioning load belongs to the first category of flexible load, electric vehicles belong to the second category of flexible load, and large industrial loads belong to the third category of flexible load.

[0136] (3) Subtract the three types of flexible loads from the total amount of electric energy substitution load to obtain the rigid electric energy substitution load.

[0137] (4) Based on the historical data of the three types of loads, the independent probability density functions of the three types of flexible loads at each node are obtained.

[0138] (5) Based on the fitted independent probability distribution functions of wind, solar and three flexible electric energy alternatives, the multivariate probability distribution function is established using the vine-copula theory. In this example, the Archimedean function family is used as the fitting copula function of the single vine function.

[0139] (6) Based on the Latin hypercube sampling method and the established multivariate probability distribution function, the wind, solar, and three types of flexible load output scenarios are simulated, and the typical scenarios are reduced based on the k-means clustering algorithm. During the reduction process, the DIS parameters are calculated as the basis for scenario reduction.

[0140] (7) Set the parameters of particle swarm optimization. Set the number of particles to 90, the upper limit of the single-node new energy capacity to 6 kW, and the upper limit of the total amount of new energy access to the system to 300 kW.

[0141] (8) Through the particle swarm algorithm, the objective function and constraints proposed in steps 4 and 5 are combined to find the optimal solution and obtain the site selection and capacity optimization result, as shown in the figure. Figure 3 and Figure 4 shown.

[0142] The present invention proposes a new energy site selection and sizing optimization method taking into account the impact of electric energy substitution. The method can be used to optimize the new energy site selection and sizing of regional power grids containing electric energy substitution loads. The evaluation process gives priority to the consumption effect of electric energy substitution loads on new energy. Compared with the evaluation results of traditional methods, the optimization scheme has lower wind and solar power abandonment rates and is more economical.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A new energy site selection and capacity optimization method taking into account the impact of electric energy substitution, characterized by: The following steps are involved: Step 1: Collect relevant parameters of wind and photovoltaic fields, systems to be evaluated, and economic parameters; relevant parameters of wind and photovoltaic fields include historical data of wind speed and light intensity, historical output data of wind and light, cost coefficients of wind and light new energy construction, operation and maintenance cost coefficients, and subsidy gains for wind and light energy consumption; historical data of different types of electric energy replacement loads in the area where the system to be evaluated is located, topological parameters of the system to be evaluated, power supply parameters, and historical data of traditional loads; economic parameters include grid electricity prices, network loss cost coefficients, electric energy replacement demand response subsidy coefficients, and wind and light abandonment cost coefficients; Step 2: Fit the wind-solar independent probability density function according to the historical data of wind and solar; the wind speed is fitted by the two-parameter Weibull function, and the light intensity is fitted by the Beta function. Combined with the functional relationship between wind power and photovoltaic power and wind speed and photovoltaic respectively, the wind-solar independent output probability distribution function is obtained; Step 3: Classify the electric energy replacement loads, specifically, classify the electric energy replacement loads into two categories: flexible loads and rigid loads. Flexible loads can be further divided into curtailable loads, transferable loads, and shiftable loads, and accumulate historical data for each type of load; curtailable loads refer to loads that can participate in system demand response in real time by reducing power consumption; transferable loads refer to loads that can participate in system demand response in the form of orderly charging and discharging, and can transfer load access areas; shiftable loads refer to electric energy replacement loads that can shift power consumption time to another time period; rigid loads refer to traditional loads that cannot participate in demand response through curtailable loads, transferable loads, and shiftable loads; Step 4: Based on the traditional load, the rigid load of each node in the system is obtained; Step 5: Fit the independent probability density distribution function of the electric energy substitution load; establish the joint probability distribution function of wind, solar and electric energy substitution loads; generate typical output scenarios of wind, solar and electric energy substitution loads; Step 6: Establish a demand response model for electric energy substitution flexible loads; Step 7: Establish the objective function and constraints of the new energy site selection and capacity optimization model taking into account the impact of electricity substitution. The objective function is as follows: minαC1+βC2+γC3-κC4-μC5+θC6+ωdU+λP Among them, C1 is the investment and construction cost of new energy, C2 is the operation and maintenance cost of new energy, C3 is the network loss cost, C4 is the subsidy income for new energy consumption, C5 is the subsidy cost for electric energy substitution to participate in demand response, C6 is the loss cost of wind and solar abandonment, dU is the voltage stability margin, P represents the amount of abandoned new energy, α, β, γ, κ, μ, θ are the weight coefficients of various costs, and λ is the penalty factor for abandoning new energy; The definition of the subsidy cost C5 for electricity substitution participating in demand response in the objective function is as follows: P sijk =g(k,P wgj ,P pvj ,P Lj ) Among them, C sk-sup It represents the subsidy cost coefficient of the kth renewable energy participating in the demand response; The definition of the wind and solar power abandonment loss cost C6 in the objective function is as follows: P dg-loss =h(k,P wgj ,P pvj ,P Lj ) Among them, C dg-loss It represents the cost coefficient of the abandoned new energy loss; The model for electric energy substitution flexible load to participate in demand response is established as follows: Assume that the time when the i-th node load participates in the demand response occurs at time t0, the proportions of the three loads participating in the demand response are r1, r2, and r3 respectively, and the three types of electricity replacement loads at each node are (1) Load reduction: Reduce the load The total load of the i-th node at time t0 becomes And it does not affect the total load at future times; (2) Transferable load: the amount of load that can be transferred from node i The total load of the i-th node at time t0 becomes Assuming the transfer node is j, the total load of node j becomes And it does not affect the total load at future times; (3) Translable load: Translative load of node i The total load of the i-th node at time t0 becomes Assuming the translation to time t1, the total load of node i at time t1 in the future is 2. The node power balance constraint in the optimal constraint condition for new energy site selection and capacity determination taking into account the impact of electric energy substitution as claimed in claim 1 is defined as follows: Where P Sm→j2 It represents the load amount transferred from the mth node to the jth node by the second type of electric energy replacement load.

3. The constraint conditions of the new energy site selection and capacity optimization model taking into account the impact of electric energy substitution as claimed in claim 1 are characterized by: The definition of the system power balance constraint in the constraint condition is as follows: Step 8: Optimize and solve the new energy site selection and capacity optimization model taking into account the impact of electricity substitution.

4. A new energy site selection and capacity optimization method taking into account the impact of electric energy substitution as claimed in claim 1, characterized in that: The method for fitting the independent probability density distribution function of the electric energy replacement load described in step 5 is as follows: Based on the historical data of curtailable load, transferable load and shiftable load, the independent probability density distribution functions of the three are fitted by the kernel function nonparametric estimation method.

5. The new energy site selection and capacity optimization method taking into account the impact of electric energy substitution as claimed in claim 1, characterized in that: The method to establish the joint probability distribution function of wind, solar and electric energy substitution load is as follows: The vine-copula function is used to combine the independent output probability distribution of wind, solar and three types of electric energy to replace flexible loads, and a five-element joint probability distribution function is established.

Images