A DEA-based building new energy equipment investment cost allocation method

Abstract

The present application relates to a kind of new energy equipment investment cost apportionment method based on DEA of building, DEA refers to Data envelopment analysis data envelope analysis, from the whole, multiple buildings installed with new energy equipment form a collection, form "self-production and self-marketing, on-site consumption" in building group, can reduce the direct impact of photovoltaic access to power grid.The method of the present application can be more fair cost allocation according to the size of user contribution, on the one hand, it can reduce the construction or modification cost of single investment party, on the other hand, it can enable the user with poor photovoltaic or energy storage device installation conditions to use clean green energy at a lower cost, so as to realize multi-win, sharing.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of renewable energy buildings, in particular to a multi-user collective construction of renewable energy buildings, and more particularly to a DEA-based building new energy equipment investment cost allocation method. BACKGROUND

[0002] Promoting new energy buildings equipped with photovoltaic and energy storage devices is an effective measure to promote carbon reduction in the construction industry. On the one hand, it can promote clean energy use and local consumption of buildings, and on the other hand, it can greatly save electricity costs during building operation. However, the cost of constructing or renovating new energy buildings equipped with photovoltaic and energy storage devices is high, and a single user may not be able to afford the high cost at one time. High new energy equipment cost may limit the development of new energy in buildings.

[0003] With the development of the sharing economy, "wall-to-wall electricity sales" is being further promoted, that is, energy producers can directly sell surplus electricity to nearby energy consumers, thereby realizing the sharing of new energy equipment. Therefore, the one-time investment cost of renewable energy buildings can be allocated among multiple users in a building group. When multiple buildings form a building group, photovoltaic and energy storage devices will become shared resources, and the allocation of their investment costs needs to be analyzed comprehensively in combination with the loads, installation conditions, etc. of each user.

[0004] When multiple buildings want to jointly "sell electricity across walls", the existing technology usually uses the average cost allocation method or allocates costs according to the actual installation of new energy equipment by each building. The average cost allocation method means that each user in the building group has the same allocation cost regardless of their contribution, which is obviously unfair to some users who have made greater contributions. Allocating costs according to the actual installation of new energy equipment by each building is also unreasonable for users with good photovoltaic installation conditions but low electricity consumption, as the one-time investment cost is too high and the capital recovery period is too long. Therefore, a more fair and reasonable cost allocation method is needed to solve this problem. SUMMARY

[0005] The purpose of the present application is to provide a DEA-based building new energy equipment investment cost allocation method that can allocate costs in a more fair and reasonable manner and take into account multiple factors to solve the problem of allocating costs for investing in new energy buildings.

[0006] To achieve the above purpose, the present application provides a DEA-based building new energy equipment investment cost allocation method, which comprises the following steps:

[0007] Data collection; collect the relevant information of each user who wants to join the new energy building group, including the maximum installable photovoltaic component capacity of each user, the maximum installable energy storage system capacity, the historical electricity load curve of each user, and the light resource data;

[0008] Optimizing the energy consumption mode of the users in the building group, obtaining the optimized load curve of each user, the photovoltaic component capacity to be laid, the energy storage system capacity to be installed, the overall reconstruction or installation cost, and the annual electricity bill that each user can save after sharing and apportioning;

[0009] Based on the load curve before and after optimization, define the sharing contribution index of each user for the building group resource sharing;

[0010] Data analysis and calculation; based on the foregoing steps, calculate the total cost of all users in the building group for investing in new energy equipment construction or reconstruction, the actual photovoltaic component capacity laid by each user, and the actual energy storage system capacity installed;

[0011] Define the input index and output index of DEA, and solve the efficiency of each user;

[0012] Establish a model to solve the apportioned cost, and apply a mixed integer linear programming algorithm to solve the cost that each user needs to apportion.

[0013] As a preferred solution, in order to realize the maximum sharing of resources, it is necessary to further analyze the contribution of each user in the sharing, i.e. the sharing contribution index, before apportioning the cost;

[0014] Before system optimization, at time t, the relationship between the overall load L t of the building group and the load L 1,t ~L n,t of n users is:

[0015] L t = L 1,t + L 2,t +... + L n,t (1)

[0016] After system optimization, the load of each user changes, and at time t, the relationship between the load R t of the building group and the load R 1,t ~R n,t of n users is:

[0017] R t = R 1,t + R 2,t +... + R n,t (2)

[0018] Therefore, the sharing contribution index P of user i (i = 1, 2, 3, ..., n) for resource sharing after installing new energy equipment is c,i Defined as:

[0019]

[0020] As a preferred solution, based on the DEA method, a preliminary calculation of the DEA efficiency is performed, and the efficiency η of each user is:

[0021]

[0022] In formula (4), the user has a total of b output indicators and d input indicators; α a is the weight of output indicator a, β c is the weight of the input indicator c, q a is the output indicator, p c For input indicators.

[0023] Furthermore, the DEA input index includes the construction or renovation cost C that each user needs to invest. c,i , the actual photovoltaic module capacity E installed by each user pv,i , Actual installed energy storage system capacity E ess,i ; Among them, the cost C that each user needs to share c,i is the decision variable, that is, the unknown quantity to be solved, the total investment cost C total It is known that the cost C that each user needs to share c,i With the known total investment cost C total The relationship is:

[0024]

[0025] DEA output indicators include: the electricity cost that each user can save P e,i , each user's shared contribution index P c,I .

[0026] Furthermore, by substituting the input index and output index into formula (4), we can obtain formula (6), which is the corresponding DEA efficiency calculation formula:

[0027]

[0028] In formula (6), u i 、v i The electricity cost P that each user can save is e,i , each user's shared contribution index P c,i The weight of a i 、b i 、c i The actual installed photovoltaic module capacity E for each userpv,i , the construction or renovation costs required by each user C c,i , Actual installed energy storage system capacity E ess,i The weight of .

[0029] Furthermore, solving the model for shared cost includes defining optimization objectives, decision variables, and constraints;

[0030] The cost C that each user needs to share c,i is the decision variable, and the sum of the technical efficiencies of each user calculated based on the DEA method is The maximum is the optimization target f, and the constraint condition is the total cost C that all users need to invest total The cost constraint range [x1, x2] that each user can invest remains unchanged, and the cost C that each user should share is finally obtained. c,i .

[0031] Furthermore, after establishing the optimization model, the mixed integer linear programming algorithm is used to finally return the iterative calculation to obtain the cost C that each user needs to share. c,i , as shown in formula (7):

[0032]

[0033] Beneficial effects of the present invention:

[0034] On the whole, multiple buildings equipped with new energy equipment such as photovoltaics and energy storage form a cluster, forming "self-production and self-sales, and on-site consumption" within the building complex, which can reduce the direct impact of photovoltaic access on the power grid.

[0035] In addition, compared with the average cost sharing method or the cost sharing based on the actual new energy equipment installed in each building, the method of the present invention can not only fairly share costs based on the size of contributions, but also calculate a more reasonable cost sharing method for users with good photovoltaic installation conditions and low electricity consumption. For building users, the method of the present invention can reduce the construction or renovation costs of a single investor, while allowing users who do not meet the installation conditions of new energy equipment such as photovoltaics and energy storage to use clean green energy at a lower cost, achieving win-win and sharing for all parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of the solution steps of the present invention;

[0037] Figure 2 A graph of the total load before and after sharing optimization for a building complex. DETAILED DESCRIPTION

[0038] In order to make the technical problems solved by the present application, the technical solutions adopted and the technical effects achieved more clear, the technical solutions of the present application are further described below in conjunction with the accompanying drawings and specific embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application. In addition, it should be noted that, for the purpose of description, only the parts related to the present application are shown in the drawings, not all.

[0039] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance. Among them, the terms "first position" and "second position" are two different positions.

[0040] In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixed connection, or it can be detachable connection; it can be mechanical connection, or it can be electrical connection; it can be directly connected, or it can be indirectly connected through intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0041] The present application relates to a DEA-based building new energy equipment investment cost allocation method, which is an investment cost allocation method for transforming existing ordinary buildings into renewable energy buildings. The method can solve the cost allocation problem of investing in new energy buildings in a more fair and reasonable way by considering various factors. The present application combines multiple buildings with renewable energy equipment installation and transformation conditions or buildings that want to apply clean energy but do not meet the equipment installation conditions into a building group. Through joint laying of photovoltaic or configuration of energy storage system, each building in the group is both a power generation participant and a power consumption participant.

[0042] DEA (Data Envelopment Analysis), DEA theory is originally applied in production activities, which is an evaluation method for calculating input and output efficiency. The application improves the application of DEA theory, defines the DEA input index and output index related to the building group, takes the cost allocation of each user in the building group as the decision variable, and takes the sum of the overall DEA efficiency of the building group as the optimization target. Finally, the new energy equipment investment cost allocated by each user in the building group is obtained, so that the cost allocation is more fair and reasonable.

[0043] The method comprises the following steps, as shown in Figure 1

[0044] Step one: data collection. Collect the relevant information of each user who wants to join the new energy building group, including but not limited to the maximum installable photovoltaic component capacity of each user, the maximum installable energy storage system capacity, the historical electricity load curve of each user, and the light resource data.

[0045] Step two: system optimization by using existing technology. On the basis of step one, the optimized load curve of each user, the photovoltaic component capacity to be laid, the energy storage system capacity to be installed, the overall reconstruction or installation cost, and the annual electricity savings of each user after sharing are obtained by using existing technology.

[0046] Step three: based on the load curve before and after optimization, define the contribution index of each user to the building group resource sharing. In order to realize the maximum sharing of resources, the contribution of each user in sharing needs to be further analyzed before cost allocation, which is defined as the sharing contribution index P c,i Before system optimization at time t, the overall load L t of the building group and the load L 1,t ~ L n,t of n users are related as follows:

[0047] L t = L 1,t + L 2,t +... + L n,t (1)

[0048] After system optimization based on step two, the load of each user changes, and at time t, the load R t of the building group and the load R 1,t ~ R n,t of n users are related as follows:

[0049] R t = R 1,t + R 2,t +... + R n,t ​(2)

[0050] Therefore, the contribution index P of the user i (i=1, 2, 3,..., n) to the resource sharing after installing the new energy equipment is c,i is defined as:

[0051]

[0052] Step four: data analysis and calculation. On the basis of the foregoing steps, the total cost C of investment construction or reconstruction of all users in the building group is calculated total , the actual PV module capacity E laid by each user pv,i , the actual installed energy storage system capacity E ess,i , and i represents the number of users in the building group.

[0053] Step five: defining the input and output indexes of DEA and solving the efficiency η1~η n of each user. This step is to preliminarily calculate the DEA efficiency involved in the present application based on the existing DEA method (formula (4)). The DEA model itself is suitable for evaluating the efficiency of enterprises considering multiple inputs or multiple outputs, that is, the efficiency η of each user is:

[0054]

[0055] In formula (4), the enterprise has b output indexes and d input indexes in total. α a is the weight of the output index a, β c is the weight of the input index c, q a is the output index, and p c is the input index. In the present application, the subscript i represents the user, and n is the total number of users participating in the sharing.

[0056] The DEA input indexes of the present application include: the construction or reconstruction cost C c,i to be invested by each user, the actual PV module capacity E pv,i laid by each user, and the actual installed energy storage system capacity E ess,i , wherein the cost C c,i to be shared by each user is a decision variable, that is, an unknown quantity to be solved, and the total investment cost C total is known. The relationship between the cost C c,i to be shared by each user and the known total investment cost C total is:

[0057]

[0058] The DEA output indexes of the present application include: the electricity bill P e,i saved by each user and the sharing contribution index Pc,i .

[0059] Substituting the input index and output index in the present invention into formula (4), formula (6) can be obtained, which is the corresponding DEA efficiency calculation formula:

[0060]

[0061] In formula (6), u i 、v i The electricity cost P that each user can save is e,i , each user's shared contribution index P c,i The weight of a i 、b i 、c i The actual installed photovoltaic module capacity E for each user pv,i , the construction or renovation costs required by each user C c,i , Actual installed energy storage system capacity E ess,i The weight of .

[0062] Step 6: Establish a model for solving the shared cost and apply the mixed integer linear programming algorithm in the existing technology to solve the cost C that each user needs to share c,i Since the cost C that each user needs to share in step 5 c,i It is an unknown quantity to be solved and is also the final result to be obtained by the present invention. Therefore, in step six, an optimization model for solving the shared cost is established, which includes defining the optimization goal, decision variables, and constraints.

[0063] The details are as follows: Based on step 5, the cost C that each user needs to share c,i is the decision variable, i.e. the quantity to be solved, and the sum of the technical efficiencies calculated by the DEA method using formula (6) in step 5 The maximum is the optimization target f, and the constraint condition is the total cost C that all users need to invest total The cost constraint range [x1, x2] that each user can invest remains unchanged. Based on formula (7), the cost C that each user should share is finally obtained. c,i .

[0064] The specific solution model is shown in formula (7). The corresponding parameter explanation of formula (7) has been indicated in the previous description. After establishing the optimization model, the mixed integer linear programming algorithm in the existing technology can be used to finally return to the iterative calculation to obtain the cost C that each user needs to share c,i .

[0065]

[0066] Embodiments:

[0067] The analysis is conducted by taking 8 single-family residential buildings that want to participate in the "wall-to-wall electricity sales" building complex as an example.

[0068] like Figure 2 As shown, load curve 4 is defined as the total load curve of a building complex after sharing optimization, and load curve 5 is the total load curve of the building complex before sharing optimization. The total load curve is obtained by accumulating the load curves of each user.

[0069] Point A(t) represents the total load at time t after shared optimization, and point B(t) represents the total load at time t before shared optimization. If the load value at point A(t) is 12kWh and the value at point A(t) is 5kWh at time t, the load adjusted before and after user shared optimization, as well as the contribution index of each user in the building complex, can be obtained based on step 3 of the present invention, as shown in Table 1 below:

[0070]

[0071] Combining the collected data and the existing technical analysis, the known data that each user needs to solve using the method of the present invention is shown in Table 2. A ~C H The shared cost to be solved is , and the known total cost is 500,000 yuan.

[0072] Table 2 Known data

[0073]

[0074] After consultation among the building complex's users, considering a total investment of 500,000 yuan, each household's contribution ranged from 20,000 to 90,000 yuan. Applying the cost allocation method proposed in this invention yielded the cost allocation results for each user, as detailed in Table 2. Table 2 compares the actual investment cost calculated using this method with the results obtained using the conventional average allocation method.

[0075] Table 3 Comparison results

[0076]

[0077] It can be seen from Table 3 that the total DEA efficiency obtained by applying the cost-sharing method of the present invention reaches 6.79, which is 16.7% higher than the total DEA efficiency of 5.82 obtained by applying the conventional average cost-sharing method. Among them, the highest efficiency of a single user is 1, while the efficiency of four users all reaches 1 by applying the method of the present invention, indicating that the cost-sharing method proposed by the present invention is more fair and reasonable.

[0078] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the patent scope of the present application. It should be noted that for ordinary skilled persons in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A DEA-based building new energy equipment investment cost allocation method, characterized in that: Comprising the following steps: Data collection; Collecting the relevant information of each user who wants to join the new energy building group, including the maximum installable photovoltaic component capacity, the maximum installable energy storage system capacity, the historical electricity load curve of each user, and the light resource data; Optimizing the energy consumption mode of the users in the building group, obtaining the optimized load curve of each user, the photovoltaic component capacity to be laid, the energy storage system capacity to be installed, the overall reconstruction or installation cost, and the annual electricity savings of each user after realizing sharing and apportionment; Defining the sharing contribution index of each user for the building group resource sharing based on the load curve before and after optimization; Data analysis and calculation; Based on the foregoing steps, the total cost of investment in new energy equipment construction or reconstruction of all users in the building group, the actual photovoltaic component capacity laid by each user, and the actual energy storage system capacity installed are calculated; Defining the input index and output index of DEA, and solving the efficiency of each user; A model for solving the apportionment cost is established, and a mixed integer linear programming algorithm is applied to solve the cost to be apportioned by each user. 2.The DEA-based building new energy equipment investment cost apportionment method according to claim 1, characterized in that: In order to realize the maximum sharing of resources, the contribution of each user in the sharing, i.e. the sharing contribution index, needs to be further analyzed before the cost apportionment; At time t, before system optimization, the overall load L of the building group t The load L of n users 1,t ~L n,t The relationship between them is: L t = L 1,t + L 2,t +... + L n,t (1) After the system is optimized, the load of each user changes, and at time t, the load R t of the building group and the load R 1,t of n users are related as follows: n,t ​ R t = R 1,t + R 2,t +... + R n,t (2) Thus, a user i, i = 1, 2, 3,..., n, makes a sharing contribution index P for resource sharing after installing a new energy device c,i is defined as: 3.The DEA-based building new energy equipment investment cost apportionment method according to claim 1, characterized in that: Based on the DEA method, the DEA efficiency is preliminarily calculated, and the efficiency η of each user is: In formula (4), the user has b output indicators and d input indicators in total; α a is the weight of the output indicator a, β c is the weight of the input indicator c, q a is the output indicator, and p c is the input indicator. 4.The DEA-based building new energy equipment investment cost apportionment method according to claim 3, characterized in that: DEA input indicators include the construction or renovation costs C that each user needs to invest c,i , the actual photovoltaic module capacity E installed by each user pv,i , Actual installed energy storage system capacity E ess,I ; Among them, the cost C that each user needs to share c,i is the decision variable, that is, the unknown quantity to be solved, the total investment cost C total It is known that the cost C that each user needs to share c,i With the known total investment cost C total The relationship is: The DEA output indicators include: electricity bill P that each user can save e,i , shared contribution index P of each user c,i . 5.The DEA-based building new energy equipment investment cost apportionment method according to claim 4, characterized in that: Substituting the input index and output index into equation (4), equation (6) is obtained, i.e. the corresponding DEA efficiency calculation formula: In formula (6), u i , v i respectively represent the weight of the electricity bill P e,i that user i can save, the shared contribution index P c,i of each user, a i , b i , c i respectively are the weight of the actual installed photovoltaic module capacity E pv,i of each user, the construction or renovation cost C c,i that each user needs to invest, and the actual installed energy storage system capacity E ess,i . 6.The DEA-based building new energy equipment investment cost apportionment method according to claim 5, characterized in that: An optimization model for solving the cost allocation is established, including defining optimization objectives, decision variables, and constraint conditions, wherein the optimization objectives are the sum of the technical efficiencies of the users calculated by the DEA method maximum; The cost C to be shared by each user c,i The cost C to be shared by each user total The cost C to be shared by each user c,i The cost C to be shared by each user 7.The DEA-based building new energy equipment investment cost apportionment method according to claim 6, characterized in that: After the optimization model is established, a mixed integer linear programming algorithm is adopted to finally return the cost C that each user needs to share calculated through iteration c,i As formula (7):

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