Electro-hydrogen energy storage configuration planning method and system based on photovoltaic hydrogen production

Abstract

The invention relates to the technical field of energy storage planning, in particular to an electricity hydrogen energy storage configuration planning method and system based on photovoltaic hydrogen production, and the method comprises the following steps: planning the optimal site selection of an electricity hydrogen energy storage station in a target region through employing a target optimization algorithm; based on the productivity data of the photovoltaic hydrogen production energy station and the demand data of the power supply load, constructing a configuration measurement and calculation model for measuring and calculating the optimal configuration of the electricity hydrogen energy storage station; and based on the optimal site selection of the electricity hydrogen energy storage station, planning the optimal configuration of the electricity hydrogen energy storage station by using a pre-established configuration measurement and calculation model. According to the invention, based on the position data of the photovoltaic hydrogen production energy station and the position data of the power supply load, the optimal site selection of the electricity hydrogen energy storage station is planned in the target area by using the target optimization algorithm, and the configuration measurement and calculation model for measuring and calculating the optimal configuration of the electricity hydrogen energy storage station is constructed. Site selection and configuration planning are carried out based on data support, the planning objectivity is high, and the planning effect and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage planning, and in particular to a method and system for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production. Background Art

[0002] Photovoltaic hydrogen energy storage is a new energy storage technology that converts solar energy into hydrogen energy. The stored hydrogen energy can be converted into electricity to supply the demand side when electricity is needed. Therefore, in the process of photovoltaic hydrogen production, energy storage planning is very important, especially in the two processes of hydrogen energy storage and power supply, which involve a large amount of transportation. Reasonable planning can effectively reduce energy storage costs and avoid energy losses.

[0003] Existing technologies usually rely on the experience and judgment of planners when planning the location and capacity of electric hydrogen storage, lack data support, and are not efficient, resulting in poor results in electric hydrogen energy storage configuration planning. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for planning the configuration of electric hydrogen energy storage based on photovoltaic hydrogen production, so as to solve the technical problems in the prior art that the planning effect of electric hydrogen energy storage configuration is poor due to the reliance on the experience and judgment of planners, lack of data support, and low efficiency.

[0005] In order to solve the above technical problems, the present invention specifically provides the following technical solutions:

[0006] A method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production comprises the following steps:

[0007] Collecting the location data of the photovoltaic hydrogen production energy sites in the target area, the production capacity data of the photovoltaic hydrogen production energy sites, and the location data of the power supply loads in the target area and the demand data of the power loads;

[0008] Based on the location data of the photovoltaic hydrogen production energy site and the location data of the power supply load, the optimal location of the electric hydrogen energy storage site is planned in the target area using a target optimization algorithm;

[0009] Based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load, a configuration calculation model for calculating the optimal configuration of the electric hydrogen energy storage site is constructed;

[0010] Based on the optimal location of the electric hydrogen energy storage site, the optimal configuration of the electric hydrogen energy storage site is planned using the pre-established configuration calculation model.

[0011] As a preferred solution of the present invention, the method for planning the optimal site for the electric hydrogen energy storage site includes:

[0012] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the photovoltaic hydrogen production energy site in the target area is used as the first optimization goal of the electric hydrogen energy storage site site selection to obtain the first optimization objective function, which is:

[0013]

[0014] Where Z1 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W i is the location data of the i-th photovoltaic hydrogen production energy site, m is the number of photovoltaic hydrogen production energy sites, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0015] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the power supply load in the target area is used as the second optimization goal of the site selection of the electric hydrogen energy storage site to obtain the second optimization objective function, which is:

[0016]

[0017] Where Z2 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W j is the location data of the jth power supply load, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0018] The target area is used as the solution space, and the first optimization algorithm and the second optimization algorithm are solved to obtain the location data of the optimal site for the electric hydrogen energy storage site.

[0019] As a preferred solution of the present invention, the method for establishing the configuration calculation model includes:

[0020] Randomly select multiple site selection locations for the electric hydrogen energy storage site to obtain location data of the random site selection for the electric hydrogen energy storage site;

[0021] The location data of the random site selection of the electric hydrogen energy storage site is used as the input of the BP neural network, and the configuration data of the random site selection of the electric hydrogen energy storage site is used as the output of the BP neural network;

[0022] Constructing a loss function of a BP neural network based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load;

[0023] The BP neural network is trained using the loss function of the BP neural network and the input of the BP neural network to obtain a configuration calculation model that outputs the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection;

[0024] The configuration calculation model is:

[0025] P out =BP(W in );

[0026] Where P out is the configuration data of the optimal configuration of the electric hydrogen energy storage site, W in It is the location data for site selection of electric hydrogen energy storage site, and BP is BP neural network.

[0027] As a preferred solution of the present invention, the construction of the loss function includes:

[0028] The difference between the configuration data of the electric hydrogen energy storage site and the production capacity data of the photovoltaic hydrogen energy site is taken as the first storage loss;

[0029] The difference between the configuration data of the electric hydrogen energy storage site and the demand data of the power supply load is taken as the second storage loss;

[0030] The first storage loss and the second storage loss are combined into a loss function, and the loss function is:

[0031]

[0032] In the formula, loss is the loss function value, P out,k is the configuration data of the kth electric hydrogen energy storage site output by the configuration calculation model, P i is the capacity data of the ith photovoltaic hydrogen production energy site, P j is the demand data of the jth power supply load, m is the number of photovoltaic hydrogen production energy sites, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, and Both are L2 norm formulas, Q1 is the weight of the first storage loss, and Q2 is the weight of the second storage loss.

[0033] As a preferred solution of the present invention, a method for determining the weight of the first storage loss and the weight of the second storage loss includes:

[0034] The stability of the production capacity data of the photovoltaic hydrogen production energy site is quantified within a time period, and the stability is used as the weight of the first storage loss. The weight of the first storage loss is:

[0035]

[0036] Where Q1 is the weight of the first storage loss, P i,t is the capacity data of the i-th photovoltaic hydrogen production energy site at the t-th time, T is the total number of times in the time period, m is the number of photovoltaic hydrogen production energy sites, is the L2 norm;

[0037] The stability of the demand data of the power supply load is quantified within a time period, and the stability is used as the weight of the second storage loss. The weight of the second storage loss is:

[0038]

[0039] Where Q2 is the weight of the first storage loss, P j,t is the capacity data of the ith photovoltaic hydrogen production energy station at the tth time, T is the total number of times in the time period, n is the number of power supply loads, is the L2 norm.

[0040] As a preferred solution of the present invention, the planning method for the optimal configuration of the electric hydrogen energy storage site includes:

[0041] The location data of the optimal location of the electric hydrogen energy storage site W k,best The configuration calculation model is used to output the configuration data P of the optimal configuration of the electric hydrogen energy storage site. k,best , where W k,best is the optimal location data of the kth electric hydrogen energy storage site, P k,best Configuration data for the optimal configuration of the kth electric hydrogen energy storage site.

[0042] As a preferred solution of the present invention, the present invention provides an electric hydrogen energy storage configuration planning system based on photovoltaic hydrogen production, which is applied to an electric hydrogen energy storage configuration planning method based on photovoltaic hydrogen production. The system includes:

[0043] A data collection unit is used to collect the location data of the photovoltaic hydrogen production energy site in the target area, the production capacity data of the photovoltaic hydrogen production energy site, and the location data of the power supply load in the target area and the demand data of the power load;

[0044] A site selection planning unit, for planning the most preferred site for the electric hydrogen energy storage site in the target area using a target optimization algorithm based on the location data of the photovoltaic hydrogen production energy site and the location data of the power supply load;

[0045] A model building unit, used to build a configuration calculation model for calculating the optimal configuration of the electric hydrogen energy storage site based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load;

[0046] The configuration planning unit is used to plan the optimal configuration of the electric hydrogen energy storage site based on the optimal site selection of the electric hydrogen energy storage site using a pre-established configuration calculation model.

[0047] As a preferred solution of the present invention, the method for planning the optimal site for the electric hydrogen energy storage site by the site selection planning unit includes:

[0048] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the photovoltaic hydrogen production energy site in the target area is used as the first optimization goal of the electric hydrogen energy storage site site selection to obtain the first optimization objective function, which is:

[0049]

[0050] Where Z1 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W i is the location data of the i-th photovoltaic hydrogen production energy site, m is the number of photovoltaic hydrogen production energy sites, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0051] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the power supply load in the target area is used as the second optimization goal of the site selection of the electric hydrogen energy storage site to obtain the second optimization objective function, which is:

[0052]

[0053] Where Z2 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W j is the location data of the jth power supply load, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0054] The target area is used as the solution space, and the first optimization algorithm and the second optimization algorithm are solved to obtain the location data of the optimal site for the electric hydrogen energy storage site.

[0055] As a preferred solution of the present invention, the method for establishing the configuration measurement model by the model building unit includes:

[0056] Randomly select multiple site selection locations for the electric hydrogen energy storage site to obtain location data of the random site selection for the electric hydrogen energy storage site;

[0057] The location data of the random site selection of the electric hydrogen energy storage site is used as the input of the BP neural network, and the configuration data of the random site selection of the electric hydrogen energy storage site is used as the output of the BP neural network;

[0058] Constructing a loss function of a BP neural network based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load;

[0059] The BP neural network is trained using the loss function of the BP neural network and the input of the BP neural network to obtain a configuration calculation model that outputs the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection;

[0060] The configuration calculation model is:

[0061] P out =BP(W in );

[0062] Where P out is the configuration data of the optimal configuration of the electric hydrogen energy storage site, W in It is the location data for site selection of electric hydrogen energy storage site, and BP is BP neural network.

[0063] As a preferred solution of the present invention, the planning method of the configuration planning unit for the optimal configuration of the electric hydrogen energy storage site includes:

[0064] The location data of the optimal location of the electric hydrogen energy storage site W k,best The configuration calculation model is used to output the configuration data P of the optimal configuration of the electric hydrogen energy storage site. k,best , where W k,best is the optimal location data of the kth electric hydrogen energy storage site, P k,best Configuration data for the optimal configuration of the kth electric hydrogen energy storage site.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] Based on the location data of the photovoltaic hydrogen production energy site and the location data of the power supply load, the present invention plans the optimal site for the electric hydrogen energy storage site in the target area using a target optimization algorithm, and combines the construction of a configuration calculation model for calculating the optimal configuration of the electric hydrogen energy storage site to achieve site selection and configuration planning based on data support, with strong objectivity in planning, thereby improving planning effect and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0068] Figure 1 A flow chart of a method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production provided in an embodiment of the present invention;

[0069] Figure 2A block diagram of a system for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0071] like Figure 1 As shown, the present invention provides a method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production, comprising the following steps:

[0072] Collecting the location data of the photovoltaic hydrogen production energy sites in the target area, the production capacity data of the photovoltaic hydrogen production energy sites, and the location data of the power supply loads in the target area and the demand data of the power loads;

[0073] Based on the location data of photovoltaic hydrogen production energy sites and the location data of power supply loads, the target optimization algorithm is used to plan the optimal location of the electric hydrogen energy storage site in the target area;

[0074] Based on the production capacity data of photovoltaic hydrogen production energy sites and the demand data of power supply load, a configuration calculation model for calculating the optimal configuration of electric hydrogen energy storage sites is constructed;

[0075] Based on the optimal location of the electric hydrogen energy storage site, the optimal configuration of the electric hydrogen energy storage site is planned using the pre-established configuration calculation model.

[0076] The present invention optimizes the site selection for electric hydrogen energy storage based on transportation cost, so that the planned electric hydrogen energy storage site can achieve the lowest transportation cost in both the energy storage and energy supply stages. Among them, the quantification of transportation cost in the present invention is quantified by transportation distance, and a short transportation distance corresponds to a low transportation cost.

[0077] The present invention aims to optimize the shortest transportation distance for measuring energy storage transportation costs on the one hand, and to optimize the shortest transportation distance for measuring energy supply transportation costs on the other hand. A multi-objective optimization method is used to optimize the two shortest transportation distance goals, and site selection planning of electric hydrogen energy storage sites is carried out to obtain the optimal site selection for the electric hydrogen energy storage site with the lowest cost.

[0078] The present invention determines the most preferred site after site selection planning for the electric hydrogen energy storage site, and then carries out storage capacity configuration planning for the electric hydrogen energy storage site at the most preferred site, so as to achieve capacity adaptation for energy output at the production end and energy demand at the user end, that is, the capacity of the electric hydrogen energy storage site at the most preferred site can achieve sufficient storage of energy output at the production end, and can fully meet the energy demand at the user end.

[0079] In order to realize the configuration planning of storage capacity for the electric hydrogen energy storage site at the most optimal site, the present invention establishes a data set by randomly selecting a site selection location in advance, and then trains a neural network with the data set to obtain a configuration calculation model, so as to calculate the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection.

[0080] When constructing the loss function of training the neural network, the present invention adopts two parts. The first part is the difference between the configuration data of the electric hydrogen energy storage site and the production capacity data of the photovoltaic hydrogen production energy site (the first energy storage loss). Using this part as the loss function can make the output value of the trained neural network (that is, the predicted storage capacity of the electric hydrogen energy storage site configuration) adapt to the production capacity of the photovoltaic hydrogen production energy site, and can store the energy output of the production end to the greatest extent possible. The second part is the difference between the configuration data of the electric hydrogen energy storage site and the demand data of the power supply load (the second energy storage loss). Using this part as the loss function can make the output value of the trained neural network (that is, the predicted storage capacity of the electric hydrogen energy storage site configuration) adapt to the demand of the power supply load, and can meet the energy demand of the user end to the greatest extent possible. These two parts together constitute the loss function, which can achieve capacity adaptation of the energy output of the production end and the energy demand of the user end, that is, the capacity of the electric hydrogen energy storage site at the optimal location can achieve the goal of fully storing the energy output of the production end, and can fully meet the energy demand of the user end, thereby achieving the optimal configuration based on the optimal location.

[0081] The present invention also sets weights for the two parts of the loss function. The weights are derived from the stability of the production capacity of the photovoltaic hydrogen production energy site and the demand for power supply load. Specifically, the higher the stability, the greater the weight. The greater the weight, the more the loss function focuses on the weighted part. For example, the more stable the production capacity of the photovoltaic hydrogen production energy site, the greater the weight of the first energy storage loss, and the closer the configuration is to the adaptation of the production capacity of the photovoltaic hydrogen production energy site when training the neural network. By adding weights to the two parts of the loss function, the storage capacity of the electric hydrogen energy storage site can be calculated to focus more on the adaptation of the stability item, thereby obtaining a relatively stable storage capacity of the electric hydrogen energy storage site and improving the stability of the optimal configuration of the electric hydrogen energy storage site.

[0082] In summary, the configuration calculation model constructed by the present invention can obtain an optimal configuration result with high stability, and combined with the optimal location, the optimal configuration based on the optimal location is achieved.

[0083] The present invention aims to optimize the shortest transportation distance for measuring the energy storage transportation cost on the one hand, and optimize the shortest transportation distance for measuring the energy supply transportation cost on the other hand. The multi-objective optimization method is used to optimize the shortest goals of the two transportation distances, and the site selection planning of the electric hydrogen energy storage site is carried out to obtain the optimal site selection of the electric hydrogen energy storage site with the lowest cost, which is specifically as follows:

[0084] Methods for planning the optimal location of electric hydrogen energy storage sites include:

[0085] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the photovoltaic hydrogen production energy site in the target area is used as the first optimization goal of the site selection of the electric hydrogen energy storage site (optimizing the shortest transportation distance to measure the energy storage transportation cost), and the first optimization objective function is obtained. The first optimization objective function is:

[0086]

[0087] Where Z1 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W i is the location data of the i-th photovoltaic hydrogen production energy site, m is the number of photovoltaic hydrogen production energy sites, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0088] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the power supply load in the target area is used as the second optimization goal of the site selection of the electric hydrogen energy storage site (optimizing the shortest transportation distance to measure the energy supply transportation cost), and the second optimization objective function is obtained. The second optimization objective function is:

[0089]

[0090] Where Z2 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W j is the location data of the jth power supply load, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0091] The target area is used as the solution space, and the first optimization algorithm and the second optimization algorithm are solved to obtain the location data of the optimal site selection of the electric hydrogen energy storage site.

[0092] In order to realize the configuration planning of storage capacity for the electric hydrogen energy storage site at the most optimal site, the present invention establishes a data set by randomly selecting the site selection location in advance, and then trains the neural network with the data set to obtain a configuration calculation model, so as to realize the calculation of the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection, as follows:

[0093] The method for establishing the configuration calculation model includes:

[0094] Randomly select multiple site selection locations for the electric hydrogen energy storage site to obtain location data of the random site selection for the electric hydrogen energy storage site;

[0095] The location data of the random site selection of the electric hydrogen energy storage site is used as the input of the BP neural network, and the configuration data of the random site selection of the electric hydrogen energy storage site is used as the output of the BP neural network;

[0096] The loss function of the BP neural network is constructed based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load;

[0097] The BP neural network is trained using the loss function of the BP neural network and the input of the BP neural network to obtain a configuration calculation model that outputs the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection;

[0098] The configuration calculation model is:

[0099] P out =BP(W in );

[0100] Where P out is the configuration data of the optimal configuration of the electric hydrogen energy storage site, W in It is the location data for site selection of electric hydrogen energy storage site, and BP is BP neural network.

[0101] The construction of the loss function includes:

[0102] The difference between the configuration data of the electric hydrogen energy storage site and the production capacity data of the photovoltaic hydrogen energy site is taken as the first storage loss;

[0103] The difference between the configuration data of the electric hydrogen energy storage site and the demand data of the power supply load is taken as the second storage loss;

[0104] The first storage loss and the second storage loss are combined into a loss function, and the loss function is:

[0105]

[0106] In the formula, loss is the loss function value, P out,k is the configuration data of the kth electric hydrogen energy storage site output by the configuration calculation model, P i is the capacity data of the ith photovoltaic hydrogen production energy site, P j is the demand data of the jth power supply load, m is the number of photovoltaic hydrogen production energy sites, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, and Both are L2 norm formulas, Q1 is the weight of the first storage loss, and Q2 is the weight of the second storage loss.

[0107] When constructing the loss function of training the neural network, the present invention adopts two parts. The first part is the difference between the configuration data of the electric hydrogen energy storage site and the production capacity data of the photovoltaic hydrogen production energy site (the first energy storage loss). Using this part as the loss function can make the output value of the trained neural network (that is, the predicted storage capacity of the electric hydrogen energy storage site configuration) adapt to the production capacity of the photovoltaic hydrogen production energy site, and can store the energy output of the production end to the greatest extent possible. The second part is the difference between the configuration data of the electric hydrogen energy storage site and the demand data of the power supply load (the second energy storage loss). Using this part as the loss function can make the output value of the trained neural network (that is, the predicted storage capacity of the electric hydrogen energy storage site configuration) adapt to the demand of the power supply load, and can meet the energy demand of the user end to the greatest extent possible. These two parts together constitute the loss function, which can achieve capacity adaptation of the energy output of the production end and the energy demand of the user end, that is, the capacity of the electric hydrogen energy storage site at the optimal location can achieve the goal of fully storing the energy output of the production end, and can fully meet the energy demand of the user end, thereby achieving the optimal configuration based on the optimal location.

[0108] The present invention also sets weights for the two parts of the loss function. The weights are derived from the stability of the production capacity of the photovoltaic hydrogen production energy site and the demand for power supply load. Specifically, the higher the stability, the greater the weight. The greater the weight, the more the loss function focuses on the weighted part. For example, the more stable the production capacity of the photovoltaic hydrogen production energy site, the greater the weight of the first energy storage loss, and the closer the configuration is to the adaptation of the production capacity of the photovoltaic hydrogen production energy site when training the neural network. By adding weights to the two parts of the loss function, the calculated storage capacity of the electric hydrogen energy storage site can be more focused on the adaptation of the stability item, thereby obtaining a relatively stable storage capacity of the electric hydrogen energy storage site, and improving the stability of the optimal configuration of the electric hydrogen energy storage site, as follows:

[0109] The method for determining the weight of the first storage loss and the weight of the second storage loss includes:

[0110] The stability of the production capacity data of the photovoltaic hydrogen energy site is quantified within a time period, and the stability is used as the weight of the first storage loss. The weight of the first storage loss is:

[0111]

[0112] Where Q1 is the weight of the first storage loss, P i,t is the capacity data of the i-th photovoltaic hydrogen production energy site at the t-th time, T is the total number of times in the time period, m is the number of photovoltaic hydrogen production energy sites, is the L2 norm;

[0113] The stability of the demand data of the power supply load is quantified within a time period, and the stability is used as the weight of the second storage loss. The weight of the second storage loss is:

[0114]

[0115] Where Q2 is the weight of the first storage loss, P j,t is the capacity data of the ith photovoltaic hydrogen production energy station at the tth time, T is the total number of times in the time period, n is the number of power supply loads, is the L2 norm.

[0116] The planning method for the optimal configuration of electric hydrogen energy storage sites includes:

[0117] The location data of the optimal location of the electric hydrogen energy storage site W k,best Input to the configuration calculation model, and the configuration calculation model outputs the configuration data P of the optimal configuration of the electric hydrogen energy storage site k,best , where W k,best is the optimal location data of the kth electric hydrogen energy storage site, P k,best Configuration data for the optimal configuration of the kth electric hydrogen energy storage site.

[0118] like Figure 2 As shown, the present invention provides an electric hydrogen energy storage configuration planning system based on photovoltaic hydrogen production, which is applied to an electric hydrogen energy storage configuration planning method based on photovoltaic hydrogen production. The system includes:

[0119] A data collection unit is used to collect the location data of the photovoltaic hydrogen production energy site in the target area, the production capacity data of the photovoltaic hydrogen production energy site, and the location data of the power supply load in the target area and the demand data of the power load;

[0120] A site selection and planning unit is used to plan the optimal site for the electric hydrogen energy storage site in the target area using a target optimization algorithm based on the location data of the photovoltaic hydrogen production energy site and the location data of the power supply load;

[0121] A model building unit, used to build a configuration calculation model for calculating the optimal configuration of the electric hydrogen energy storage site based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load;

[0122] The configuration planning unit is used to plan the optimal configuration of the electric hydrogen energy storage site based on the optimal site selection of the electric hydrogen energy storage site using a pre-established configuration calculation model.

[0123] The site selection planning unit's planning method for the optimal site selection of the electric hydrogen energy storage site includes:

[0124] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the photovoltaic hydrogen production energy site in the target area is used as the first optimization goal of the electric hydrogen energy storage site selection, and the first optimization objective function is obtained. The first optimization objective function is:

[0125]

[0126] Where Z1 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W i is the location data of the i-th photovoltaic hydrogen production energy site, m is the number of photovoltaic hydrogen production energy sites, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0127] Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the power supply load in the target area is used as the second optimization goal of the site selection of the electric hydrogen energy storage site, and the second optimization objective function is obtained. The second optimization objective function is:

[0128]

[0129] Where Z2 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W j is the location data of the jth power supply load, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, is the L2 norm;

[0130] The target area is used as the solution space, and the first optimization algorithm and the second optimization algorithm are solved to obtain the location data of the optimal site selection of the electric hydrogen energy storage site.

[0131] The method for establishing the configuration measurement model by the model building unit includes:

[0132] Randomly select multiple site selection locations for the electric hydrogen energy storage site to obtain location data of the random site selection for the electric hydrogen energy storage site;

[0133] The location data of the random site selection of the electric hydrogen energy storage site is used as the input of the BP neural network, and the configuration data of the random site selection of the electric hydrogen energy storage site is used as the output of the BP neural network;

[0134] The loss function of the BP neural network is constructed based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load;

[0135] The BP neural network is trained using the loss function of the BP neural network and the input of the BP neural network to obtain a configuration calculation model that outputs the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection;

[0136] The configuration calculation model is:

[0137] P out =BP(W in );

[0138] Where P out is the configuration data of the optimal configuration of the electric hydrogen energy storage site, W in It is the location data for site selection of electric hydrogen energy storage site, and BP is BP neural network.

[0139] The planning method of the configuration planning unit for the optimal configuration of the electric hydrogen energy storage site includes:

[0140] The location data of the optimal location of the electric hydrogen energy storage site W k,best Input to the configuration calculation model, and the configuration calculation model outputs the configuration data P of the optimal configuration of the electric hydrogen energy storage site k,best , where W k,best is the optimal location data of the kth electric hydrogen energy storage site, P k,best Configuration data for the optimal configuration of the kth electric hydrogen energy storage site.

[0141] Based on the location data of photovoltaic hydrogen production energy sites and the location data of power supply loads, the present invention plans the optimal site selection of electric hydrogen energy storage sites in the target area using a target optimization algorithm, and combines the construction of a configuration calculation model for calculating the optimal configuration of the electric hydrogen energy storage site to achieve site selection and configuration planning based on data support, with strong objectivity in planning, thereby improving planning effect and efficiency.

[0142] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production, characterized in that: The following steps are involved: Collecting the location data of the photovoltaic hydrogen production energy sites in the target area, the production capacity data of the photovoltaic hydrogen production energy sites, and the location data of the power supply loads in the target area and the demand data of the power loads; Based on the location data of the photovoltaic hydrogen production energy site and the location data of the power supply load, the optimal location of the electric hydrogen energy storage site is planned in the target area using a target optimization algorithm; Based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load, a configuration calculation model for calculating the optimal configuration of the electric hydrogen energy storage site is constructed; Based on the optimal location of the electric hydrogen energy storage site, the optimal configuration of the electric hydrogen energy storage site is planned using the pre-established configuration calculation model.

2. The method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production according to claim 1 is characterized in that: The method for planning the optimal site for the electric hydrogen energy storage site includes: Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the photovoltaic hydrogen production energy site in the target area is used as the first optimization goal of the electric hydrogen energy storage site site selection to obtain the first optimization objective function, which is: Where Z1 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W i is the location data of the i-th photovoltaic hydrogen production energy site, m is the number of photovoltaic hydrogen production energy sites, S is the number of electric hydrogen storage sites, is the L2 norm; Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the power supply load in the target area is used as the second optimization goal of the site selection of the electric hydrogen energy storage site to obtain the second optimization objective function, which is: Where Z2 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W j is the location data of the jth power supply load, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, is the L2 norm; The target area is used as the solution space, and the first optimization algorithm and the second optimization algorithm are solved to obtain the location data of the optimal site for the electric hydrogen energy storage site.

3. The method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production according to claim 2 is characterized in that: The method for establishing the configuration calculation model includes: Randomly select multiple site selection locations for the electric hydrogen energy storage site to obtain location data of the random site selection for the electric hydrogen energy storage site; The location data of the random site selection of the electric hydrogen energy storage site is used as the input of the BP neural network, and the configuration data of the random site selection of the electric hydrogen energy storage site is used as the output of the BP neural network; Constructing a loss function of a BP neural network based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load; The BP neural network is trained using the loss function of the BP neural network and the input of the BP neural network to obtain a configuration calculation model that outputs the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection; The configuration calculation model is: P out =BP(W in ); Where P out is the configuration data of the optimal configuration of the electric hydrogen energy storage site, W in It is the location data for site selection of electric hydrogen energy storage site, and BP is BP neural network.

4. The method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production according to claim 3 is characterized in that: The construction of the loss function includes: The difference between the configuration data of the electric hydrogen energy storage site and the production capacity data of the photovoltaic hydrogen energy site is taken as the first storage loss; The difference between the configuration data of the electric hydrogen energy storage site and the demand data of the power supply load is taken as the second storage loss; The first storage loss and the second storage loss are combined into a loss function, and the loss function is: In the formula, loss is the loss function value, P out,k is the configuration data of the kth electric hydrogen energy storage site output by the configuration calculation model, P i is the capacity data of the ith photovoltaic hydrogen production energy site, P j is the demand data of the jth power supply load, m is the number of photovoltaic hydrogen production energy sites, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, and Both are L2 norm formulas, Q1 is the weight of the first storage loss, and Q2 is the weight of the second storage loss.

5. The method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production according to claim 4 is characterized in that: The method for determining the weight of the first storage loss and the weight of the second storage loss includes: The stability of the production capacity data of the photovoltaic hydrogen production energy site is quantified within a time period, and the stability is used as the weight of the first storage loss. The weight of the first storage loss is: Where Q1 is the weight of the first storage loss, P i,t is the capacity data of the i-th photovoltaic hydrogen production energy site at the t-th time, T is the total number of times in the time period, m is the number of photovoltaic hydrogen production energy sites, is the L2 norm; The stability of the demand data of the power supply load is quantified within a time period, and the stability is used as the weight of the second storage loss. The weight of the second storage loss is: Where Q2 is the weight of the first storage loss, P j,t is the capacity data of the ith photovoltaic hydrogen production energy station at the tth time, T is the total number of times in the time period, n is the number of power supply loads, is the L2 norm.

6. The method for planning electric hydrogen storage configuration based on photovoltaic hydrogen production according to claim 5 is characterized in that: The planning method for the optimal configuration of the electric hydrogen energy storage site includes: The location data of the optimal location of the electric hydrogen energy storage site W k,best The configuration calculation model is used to output the configuration data P of the optimal configuration of the electric hydrogen energy storage site. k,best , where W k,best is the optimal location data of the kth electric hydrogen energy storage site, P k,best Configuration data for the optimal configuration of the kth electric hydrogen energy storage site.

7. A photovoltaic hydrogen production-based electric hydrogen storage configuration planning system, characterized in that: A method for planning electric hydrogen energy storage configuration based on photovoltaic hydrogen production as described in any one of claims 1 to 6, the system comprising: A data collection unit is used to collect the location data of the photovoltaic hydrogen production energy site in the target area, the production capacity data of the photovoltaic hydrogen production energy site, and the location data of the power supply load in the target area and the demand data of the power load; A site selection planning unit, for planning the most preferred site for the electric hydrogen energy storage site in the target area using a target optimization algorithm based on the location data of the photovoltaic hydrogen production energy site and the location data of the power supply load; A model building unit, used to build a configuration calculation model for calculating the optimal configuration of the electric hydrogen energy storage site based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load; The configuration planning unit is used to plan the optimal configuration of the electric hydrogen energy storage site based on the optimal site selection of the electric hydrogen energy storage site using a pre-established configuration calculation model.

8. The electric hydrogen energy storage configuration planning system based on photovoltaic hydrogen production according to claim 7 is characterized in that: The method for planning the optimal site selection of the electric hydrogen energy storage site by the site selection planning unit includes: Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the photovoltaic hydrogen production energy site in the target area is used as the first optimization goal of the electric hydrogen energy storage site site selection to obtain the first optimization objective function, which is: Where Z1 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W i is the location data of the i-th photovoltaic hydrogen production energy site, m is the number of photovoltaic hydrogen production energy sites, S is the number of electric hydrogen energy storage sites, is the L2 norm; Minimizing the difference between the location data of the electric hydrogen energy storage site and the location data of the power supply load in the target area is used as the second optimization goal of the site selection of the electric hydrogen energy storage site to obtain the second optimization objective function, which is: Where Z2 is the first optimization target value, W k is the location data of the kth electric hydrogen energy storage site, W j is the location data of the jth power supply load, n is the number of power supply loads, S is the number of electric hydrogen energy storage sites, ||W k -W j || 2 2 is the L2 norm; The target area is used as the solution space, and the first optimization algorithm and the second optimization algorithm are solved to obtain the location data of the optimal site for the electric hydrogen energy storage site.

9. The electric hydrogen energy storage configuration planning system based on photovoltaic hydrogen production according to claim 8 is characterized by: The method for establishing the configuration measurement model by the model building unit includes: Randomly select multiple site selection locations for the electric hydrogen energy storage site to obtain location data of the random site selection for the electric hydrogen energy storage site; The location data of the random site selection of the electric hydrogen energy storage site is used as the input of the BP neural network, and the configuration data of the random site selection of the electric hydrogen energy storage site is used as the output of the BP neural network; Constructing a loss function of a BP neural network based on the production capacity data of the photovoltaic hydrogen production energy site and the demand data of the power supply load; The BP neural network is trained using the loss function of the BP neural network and the input of the BP neural network to obtain a configuration calculation model that outputs the optimal configuration of the electric hydrogen energy storage site according to the location data of the electric hydrogen energy storage site selection; The configuration calculation model is: P out =BP(W in ); Where P out is the configuration data of the optimal configuration of the electric hydrogen energy storage site, W in It is the location data for site selection of electric hydrogen energy storage site, and BP is BP neural network.

10. The electric hydrogen energy storage configuration planning system based on photovoltaic hydrogen production according to claim 9 is characterized in that: The configuration planning unit plans the optimal configuration of the electric hydrogen energy storage site in a method comprising: The location data of the optimal location of the electric hydrogen energy storage site W k,best The configuration calculation model is used to output the configuration data P of the optimal configuration of the electric hydrogen energy storage site. k,best , where W k,best is the optimal location data of the kth electric hydrogen energy storage site, P k,best Configuration data for the optimal configuration of the kth electric hydrogen energy storage site.

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