CN113537601A - Distributed photovoltaic investment decision optimization method and system - Google Patents
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Abstract
The invention discloses a distributed photovoltaic investment decision optimization method and a distributed photovoltaic investment decision optimization system, wherein an optimization model is built based on two investment modes of full-amount internet surfing and surplus electricity internet surfing, and the method comprises the following specific steps: s1, determining an investment mode and a loan mode, and inputting basic data; s2, calculating the annual average power generation amount, the total investment and the spontaneous self-use proportion according to a decision index system; s3, calculating the benefit, cost, investment recovery period and annual average investment profitability of the investment users in the two modes; s4, determining an optimization target formula by taking benefit/cost maximization as a target; s5, determining the constraint conditions of the investment decision model by analyzing the influence factors; s6: and outputting result data, and selecting and outputting index data of the optimal scheme according to the mode. By adopting the distributed photovoltaic investment decision optimization method and system with the structure, the decision process is simplified, the investment scheme which best meets the user is screened out, and the benefit/cost maximization is realized.
Description
Technical Field
The invention relates to the technical field of photovoltaic investment, in particular to a distributed photovoltaic investment decision optimization method and system.
Background
The building roof resources in China are rich and widely distributed, and the development and construction of the roof distributed photovoltaic has great potential. The distributed photovoltaic project is generally built near the location of a user, such as a residential roof, a factory building roof and the like, has the advantages of being close to the user side, high in electric energy utilization rate, free of extra land occupation indexes and the like, and the operation mode adopts a full-amount internet surfing mode and a surplus electricity internet surfing mode. How to guide a user to make reasonable investment decision on the distributed photovoltaic is helpful for guiding the green energy consumption of residents, and is helpful for realizing the high-quality development of the distributed photovoltaic, thereby promoting the aims of carbon peak reaching and carbon neutralization in China.
The related open source software application systems for distributed project investment decision optimization in the prior market are fewer, and the functions are relatively fixed. At present, software systems related to distributed project investment in China mainly comprise the following types: firstly, a user inputs basic parameters such as the installed scale and the like, and the economic benefit index of the investment distributed photovoltaic is measured and calculated. For example: a photovoltaic electricity price calculator and a photovoltaic power station investment return calculator. Secondly, inputting key parameters (such as roof area and the like), measuring and calculating the investment scale of the distributed photovoltaic, and further evaluating the economy of the distributed photovoltaic project. For example: seeder photovoltaic calculator. These software focus on assessing the economics of distributed projects and lack consideration of the economics of the users. From the perspective of distributed users, the economic status of the users themselves is a key factor affecting their investment in distributed photovoltaics, for example: return on investment requirements, initial investment amount limits, etc. In addition, factors such as subsidy policy, power grid price, loan rate, weather conditions and the like are involved in the decision making process, and the decision making process is complex. Under the influence and limitation of the factors, how a user selects a distributed photovoltaic investment scheme suitable for the economic condition of the user becomes a primary problem for participating in distributed photovoltaic.
Disclosure of Invention
The method considers two distributed operation modes of full-amount internet surfing and surplus electricity internet surfing, analyzes key factors influencing photovoltaic investment decisions of distributed users, simplifies decision making process, and constructs investment decision optimization models in different operation modes by taking benefit/cost maximization as an optimization target. And setting an input module, customizing parameter values by a user, and outputting an optimal investment scheme.
In order to achieve the purpose, the invention provides the following technical scheme:
a distributed photovoltaic investment decision optimization method is based on two investment modes of full-amount internet surfing and surplus electricity internet surfing, an optimization model is built, and the method specifically comprises the following steps:
s1, determining an investment mode and a loan mode, and inputting basic data;
s2, calculating the annual average power generation amount, the total investment and the spontaneous self-use proportion according to a decision index system;
s3, calculating the benefit, cost, investment recovery period and annual average investment profitability of the investment users in the two modes;
s4, determining an optimization target formula by taking benefit/cost maximization as a target;
s5, determining the constraint conditions of the investment decision model by analyzing the influence factors;
s6: and outputting result data, and selecting and outputting index data of the optimal scheme according to the mode.
Preferably, in the step S3, in the full internet access mode, the benefits include total power selling income and annual subsidy income generated by power generation; in the residual electricity internet access mode, benefits comprise the income of internet power, the cost saving due to spontaneous self-electricity utilization and the annual subsidy income generated due to electricity generation;
the full internet surfing benefit formula is as follows:
Bx＝n×Ep×(p0+ps)+(t-n)×Ep×p0
the surplus electricity network-surfing benefit formula is as follows:
By＝n×[λEp×(p1+ps)+(1-λ)Ep×(p0+ps)]+(t-n)[λEp×p1+(1-λ)Ep×p0]
wherein n is the subsidy year, EpGenerated power for annual surfing0For local photovoltaic benchmarks on-line electricity prices, p1Price of electricity for user, psFor subsidy investment, t is the investment period, and lambda is the spontaneous self-use proportion.
Preferably, the cost of both modes in the step S3 is determined by the investment cost and the operation and maintenance cost, and the cost formula is as follows:
Ci＝TIi+co×t，i∈{1,2}
total investment/annual net income
Total investment/(annual electricity income-operation and maintenance cost)
Annual average investment yield ═ total income-total investment/life cycle)/total investment
Wherein, TIiAs a total investment cost, coFor annual maintenance cost, t is an investment period, i-1 represents an equal-amount principal payment mode, i-2 represents an equal-amount principal payment mode, j-x represents a full-amount internet access mode, and j-y represents an excess electricity internet access mode.
Preferably, the optimization target formula in step S4 is:
maxBj/Ci，j∈{x,y}，i∈{1,2}
wherein, i-1 represents an equal money repayment mode, i-2 represents an equal money repayment mode, j-x represents a full internet access mode, and j-y represents an excess power internet access mode.
Preferably, the constraint conditions in step S5 include:
the ratio of benefit to cost is more than or equal to 1: b isj/Ci≥1，j∈{x,y}，i∈{1,2}；
The upper limit of the area of the roof or the field is as follows:
rounding down;
third, the upper limit of initial investment: cP (personal computer)AZ×(1-r1)≤I0；
Fourthly, the upper limit of the investment recovery period is as follows:
j∈{x,y}；
the parameters are all more than or equal to 0.
Constraint conditions I, II and V are constraint conditions which must be met in the distributed photovoltaic investment decision problem; the constraint conditions (c) and (d) are constraint conditions which can be selected by a user and can define parameter values by the user;
wherein s is the area of the roof or the field, a, b,
Respectively, the photovoltaic module specification (length, width) and the corresponding module installation capacity, PAZInvesting photovoltaic capacity for users, c investment cost per unit capacity, r1As a proportion of loan I0The initial upper limit of the investment is set,
the term (year) is the upper limit of the investment recovery period, i-1 represents an equal-amount principal payment mode, i-2 represents an equal-amount principal payment mode, j-x represents a full-amount internet access mode, and j-y represents a surplus-electricity internet access mode.
A distributed photovoltaic investment decision optimization system comprises a UI (user interface), a controller and an optimizer, wherein the UI is provided with an operation mode, a loan mode option box, an input component and an output component. The controller responds to the UI interface and encapsulates the input data, and the controller transfers the encapsulated input data to the optimizer for optimization; an input function, an output function and an optimization module are arranged in the optimizer, and functions such as an annual average photovoltaic power generation function, a cost function, a total investment scale function, a total cost function, a spontaneous self-use proportion function and a benefit function are arranged in the optimization module; the method comprises the steps that an investment decision optimization function needs to be calculated, a total investment function, a total cost function and a benefit function need to be called respectively, the total investment function needs to be called when the total cost function is calculated, a cost function and a function need to be called when the total investment function is calculated, an annual average photovoltaic power generation function and a spontaneous self-use proportion function need to be called when the benefit function is calculated, and the annual average photovoltaic power generation function needs to be called when the spontaneous self-use proportion function is calculated.
The distributed photovoltaic investment decision optimization method and system adopting the structure have the following beneficial effects:
1. and providing a personalized optimal investment scheme aiming at different users. Firstly, the invention provides two investment optimization model options of full-amount internet surfing and surplus electricity internet surfing, and a user can respectively calculate and compare the optimal investment schemes in two modes and select an investment operation mode and an optimal scheme which meet the economic requirements of the user. Secondly, parameter options such as an upper limit of investment amount, an upper limit of a static investment recovery period, photovoltaic module specifications, module capacity and the like are set in the input module, and a user can select investment constraint conditions and define parameter values by himself according to own economic conditions so as to provide an individualized and optimal investment scheme.
2. Achieving maximum benefit/cost. The method gives consideration to the cost and the benefit of the distributed photovoltaic investment decision, and constructs an investment decision optimization model by using a cost/benefit analysis method and aiming at maximizing the benefit/cost of the investment distributed photovoltaic.
3. And by using the singleton mode, the resource loss is reduced, and the calculation efficiency is improved. In the programming process, the method uses a new method to intervene in the creation of the instance so as to realize a single-case mode, avoid creating an optimizer object for many times and improve the calculation efficiency.
4. Promote the popularization of distributed photovoltaic. The design of the invention is based on the distributed photovoltaic investment optimization modeling of benefit cost, and the design of an optimization system is based on the distributed photovoltaic investment optimization modeling, thereby being beneficial to guiding various users to rationally invest distributed photovoltaic projects.
Drawings
FIG. 1 is a flow chart of a distributed photovoltaic investment decision optimization method;
FIG. 2 is an illustration of a distributed photovoltaic investment decision optimization system;
FIG. 3 is a diagram of the call relationship of functions in a distributed photovoltaic investment decision optimization system;
FIG. 4 is a diagram illustrating exemplary operational results in a full Internet mode;
fig. 5 shows an exemplary operation result in the power-remaining internet mode.
Detailed Description
The technical scheme of the invention is further explained by combining the drawings and the embodiment.
Description of the parameters:
PAZinvesting photovoltaic capacity for users;
a、b、
the photovoltaic module is respectively in length and width and corresponding module installation capacity;
c is the unit volume investment cost;
d is the average monthly electricity consumption (kwh/month);
s is the area of the roof or the field (m)2)；
t is the life cycle (reference value: 20-25 years);
r1is the loan proportion; r is2The loan annual interest rate; n is loan age;
TIitotal investment cost;
I0is the initial upper investment limit;
the upper limit (year) of the investment recovery period;
p0the electricity price is bought for the local photovoltaic marker post; p is a radical of1The price of electricity used for the user;
pssubsidizing the investment; n is the subsidy year;
Epthe annual network power generation (kwh);
c0maintenance charge for year (Yuan/year)；
Lambda is the spontaneous self-use proportion;
i belongs to {1,2}, wherein 1 represents an equal-amount principal information payment mode, and 2 represents an equal-amount principal money payment mode; j belongs to { x, y }, wherein j equals x represents the full rate internet access mode, and j equals y represents the residual power internet access mode.
As shown in fig. 1, a distributed photovoltaic investment decision optimization method builds an optimization model based on two investment modes of full-rate internet surfing and surplus electricity internet surfing, and specifically includes the following steps:
s1, determining an investment mode and a loan mode, and inputting basic data;
the basic data includes a must fill part and a select fill part. The compulsory filling part comprises the total local annual irradiation amount, the electricity price of a local post, the electricity price of a user, the average monthly electricity consumption, the specification of a photovoltaic panel, the constructable area, the area of the photovoltaic panel, the unit capacity cost, the system efficiency, the service life cycle, the electricity price subsidy and the subsidy year limit; the optional filling part comprises an initial investment upper limit, an investment recovery period upper limit, a loan proportion, a loan annual interest rate and a repayment upper limit.
S2, calculating the annual average power generation amount, the total investment and the spontaneous self-use proportion according to a decision index system;
annual average power generation:
according to a repayment mode, calculating the sum of the instinct:
money repayment with equal amount:
and (2) equal-amount fund repayment:
self-contained cash: cP (personal computer)AZ×(1-r1)；
Total investment (self-contained cash + instinct sum): TIi＝cPAZ×(1-r1)+Ii；
Residual electricity network-connecting mouldThe spontaneous self-use scale function is as follows: λ 12d/Ep(λ ═ λ when λ < 1, λ ═ 1);
s3, calculating the benefit, cost, investment recovery period and annual average investment profitability of the investment users in the two modes;
in the full-amount internet access mode, the benefits include total electricity sales income and annual subsidy income generated by electricity generation (subsidy policy year is n, subsidy income exists in n years, and no subsidy income exists in t-n years), and the benefit formula is as follows:
Bx＝n×Ep×(p0+ps)+(t-n)×Ep×p0
under the surplus electricity internet mode, the benefit includes the income of internet power, the expense of saving because of spontaneous self-power consumption and the income of annual subsidy because of electricity generation, and the benefit formula is:
By＝n×[λEp×(p1+ps)+(1-λ)Ep×(p0+ps)]+(t-n)[λEp×p1+(1-λ)Ep×p0]
secondly, the cost of the two modes is determined by investment cost and operation and maintenance cost, and the cost formula is as follows:
Ci＝TIi+co×t，i∈{1,2}
③ period of investment recovery (total investment/annual net income)
Total investment/(annual electricity income-operation and maintenance cost)
(iv) annual average investment yield ═ total income-total investment)/life cycle/total investment
S4, determining an optimization target formula by taking benefit/cost maximization as a target;
maxBj/Ci，j∈{x,y}，i∈{1,2}
s5, determining the constraint conditions of the investment decision model by analyzing the influence factors;
the ratio of benefit to cost is more than or equal to 1: b isj/Ci≥1，j∈{x,y}，i∈{1,2}；
The upper limit of the area of the roof or the field is as follows:
rounding down;
third, the upper limit of initial investment: cP (personal computer)AZ×(1-r1)≤I0；
Fourthly, the upper limit of the investment recovery period is as follows:
j∈{x,y}；
the parameters are all more than or equal to 0.
Constraint conditions I, II and V are constraint conditions which must be met in the distributed photovoltaic investment decision problem; the constraint conditions (c) and (d) are constraint conditions which can be selected by a user and can define parameter values by the user;
s6: and outputting result data, and selecting and outputting index data of the optimal scheme according to the mode.
The result data comprises index data such as optimal investment capacity, the ratio of benefit cost, annual average generated energy, total investment cost, spontaneous self-using proportion, investment recovery period, annual average investment profitability and the like.
Based on the method, the invention also designs a set of system which comprises the following components:
fig. 2-3 show a distributed photovoltaic investment decision optimization system, which includes a UI interface, a controller, and an optimizer, where the UI interface is configured with an operation mode and loan mode option box, an input component, and an output component. The controller responds to the UI interface and encapsulates the input data, and the controller transfers the encapsulated input data to the optimizer for optimization. An input function, an output function and an optimization module are arranged in the optimizer, and functions such as an annual average photovoltaic power generation function, a cost function, a total investment scale function, a total cost function, a spontaneous self-use proportion function and a benefit function are arranged in the optimization module.
The calling relation of each function is shown in fig. 2, and the calling sequence is as follows: the total investment function, the total cost function and the benefit function need to be called respectively to calculate the investment decision optimization function, the total investment function needs to be called to calculate the total cost function, the cost function and the function need to be called to calculate the total investment function, the annual average photovoltaic power generation function and the spontaneous self-use proportion function need to be called to calculate the benefit function, and the annual average photovoltaic power generation function needs to be called to calculate the spontaneous self-use proportion function.
Based on the system, the invention further discloses a system building method.
1. A design UI interface is created based on PyQt 5. Setting the attribute value and the attribute name of the Qwidget window component by using a Qt Designer design interface, converting Puic 5 Tool into a Python file, writing an event response function by using Python, and finally performing unit test. The UI interface designed by the invention mainly comprises an input module, an output module, an operation mode and loan mode selection box.
2. And writing a software program code based on Python to realize an optimization function. The specific process is as follows:
firstly, according to a distributed photovoltaic optimization model, an entity class of Python packaging input data and output data is designed and realized.
Secondly, carding the software operation flow, and designing and implementing a Controller class for controlling software operation and instruction distribution; and designing and realizing an Optimizer class for optimizing investment decision based on the optimization model. The execution sequence is as follows:
firstly, a program Controller assigns an initial value to an optimizer Opti mizer through a setInput () function;
decomposing the calculation process of the optimal investment decision into a function of annual average photovoltaic power generation (calculated and estimated) production (pvivvet), a function of intrinsic information and a function of intrinsic information (calculated and estimated) (pvivvet), a function of total investment scale (calculated and estimated), a function of total cost (calculated and estimated), a function of spontaneous self-use ratio (calculated and estimated) and a function of benefit (calculated and estimated) based on a distributed investment decision optimization model, and compiling corresponding functions in an Optimizer class based on a Python language;
calling each function in the optimizeInvest () function by using an optimize optimization module in the script software package, wherein the calling relationship of each function is shown in fig. 2, and the calling sequence is as follows: calculating an investment decision optimization function needs to call a total investment function, a total cost function and a benefit function respectively; the total cost function is calculated by calling the total investment function; the calculation of the total investment function needs to call the instinct and the function; the calculation of the benefit function requires calling an annual average photovoltaic power generation function and a spontaneous self-use proportion function; the calculation of the spontaneous self-use proportional function requires calling an annual average photovoltaic power generation function;
and fourthly, calculating the relevant economic indexes of the optimal investment decision as output data, and encapsulating the output data in an output entity class so that the program Controller can call the output entity class through a getOptimalResult () function.
In the Optimizer class, to reduce resource consumption, the present invention uses the _ new _ method to intervene in creating instances when programming to implement singleton schema. And (4) using the instance to deposit the instance, if the instance is None, newly creating the instance, and otherwise, directly returning to the instance to deposit the instance.
3. Packing the program into an executable file by utilizing a PyInstaller, wherein the packing flow of the PyInstaller is as follows: and reading the written Python program, analyzing a module called by the program, collecting file copies such as a Python interpreter and the like, and packaging the copies and the Python program files into an executable file.
And running the file, and opening the distributed photovoltaic investment decision optimization software. Relevant parameter values are filled in an input module, a 'confirm' button is clicked, and an optimal investment decision scheme of a user is output, wherein the optimal investment decision scheme comprises the following steps: the method comprises the following steps of optimizing economic indexes such as investment capacity, annual average network electricity quantity, total investment cost, spontaneous self-using proportion, static investment recovery period, investment income rate in an investment cycle, average annual investment income rate and the like.
Case operation. The scenario parameters are set as follows: total solar energy exposure in horizontal plane (kwh/m)2)：HA1370; local photovoltaic post grid electricity price (yuan/kwh): p is a radical of0＝0.49; price of electricity consumed by user (yuan/kWh): p is a radical of10.62; monthly average power usage (kwh/month): d is 500; component mounting capacity (kWp):
photovoltaic module specification (area): ab 1.5; comprehensive efficiency of the photovoltaic system: k is 80%; roof or floor area (m)2): s is 50; investment cost per unit volume (meta/kWp): c is 3800; annual maintenance costs (yuan/year): c. Co300; subsidy of electricity prices (yuan/kWh) in "full-rated internet" mode: p is a radical ofs0.2; subsidy in "balance surf" mode (yuan/kWh): p is a radical ofs0.1; subsidy year (year): n is 5; investment cycle (years): t is 25; and (3) loan proportion: r is150 percent; annual loan rate: r is2Not more than 5%; loan year (year): n is 10; initial upper investment limit (yuan): i is010000 ═ 10000; the upper limit of the investment recovery period is as follows:
and respectively calculating the optimal investment capacity and the related economic indexes in the two modes by adopting an equal-amount interest loan mode, wherein the calculation result is shown in figures 4-5.
And (3) displaying an optimization result: the optimal investment capacity of a user is 2.03kW (7 photovoltaic modules with 290W specification are installed), the annual power generation amount reaches 2224.88kWh, and the total investment amount is 8774.67 yuan (self-contained cash 3857 yuan, Ben and 4917.67 yuan). In addition, under the full internet access mode, the benefit-cost ratio of the user is 181.14%, the investment recovery period is 9.98 years, and the annual average investment income is 9.44%; under the residual electricity internet access mode, the benefit-cost ratio of the user is 218.73%, the investment recovery period is 7.81 years, the annual average investment income is 12.23%, and the spontaneous self-consumption proportion reaches 100%. Based on the above data, the user can select an appropriate investment pattern and an optimal solution according to the decision preference.
The above is a specific embodiment of the present invention, but the scope of the present invention should not be limited thereto. Any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are included in the protection scope of the present invention, and therefore, the protection scope of the present invention is subject to the protection scope defined by the appended claims.
Claims (6)
1. A distributed photovoltaic investment decision optimization method is characterized in that an optimization model is built based on two investment modes of full-amount internet surfing and surplus electricity internet surfing, and the method comprises the following specific steps:
s1, determining an investment mode and a loan mode, and inputting basic data;
s2, calculating the annual average power generation amount, the total investment and the spontaneous self-use proportion according to a decision index system;
s3, calculating the benefit, cost, investment recovery period and annual average investment profitability of the investment users in the two modes;
s4, determining an optimization target formula by taking benefit/cost maximization as a target;
s5, determining the constraint conditions of the investment decision model by analyzing the influence factors;
s6: and outputting result data, and selecting and outputting index data of the optimal scheme according to the mode.
2. The distributed photovoltaic investment decision optimization method according to claim 1, wherein in the step S3, in the full internet access mode, the benefits include total power selling income and annual subsidy income generated by power generation; in the residual electricity internet access mode, benefits comprise the income of internet power, the cost saving due to spontaneous self-electricity utilization and the annual subsidy income generated due to electricity generation;
the full internet surfing benefit formula is as follows:
Bx＝n×Ep×(p0+ps)+(t-n)×Ep×p0
the surplus electricity network-surfing benefit formula is as follows:
By＝n×[λEp×(p1+ps)+(1-λ)Ep×(p0+ps)]+(t-n)[λEp×p1+(1-λ)Ep×p0]
wherein n is the subsidy year, EpGenerated power for annual surfing0For on the local photovoltaic marker postGrid price, p1Price of electricity for user, psFor subsidy investment, t is the investment period, and lambda is the spontaneous self-use proportion.
3. The distributed photovoltaic investment decision optimization method of claim 1, wherein the cost of both modes in the step S3 is determined by investment cost and operation and maintenance cost, and the cost formula is as follows:
Ci＝TIi+co×t，i∈{1,2}
total investment/annual net income
Total investment/(annual electricity income-operation and maintenance cost)
Annual average investment yield ═ total income-total investment/life cycle)/total investment
Wherein, TIiAs a total investment cost, coFor annual maintenance cost, t is an investment period, i-1 represents an equal-amount principal payment mode, i-2 represents an equal-amount principal payment mode, j-x represents a full-amount internet access mode, and j-y represents an excess electricity internet access mode.
4. The distributed photovoltaic investment decision optimization method of claim 1, wherein the optimization objective formula in the step S4 is:
maxBj/Ci，j∈{x,y}，i∈{1,2}
wherein, i-1 represents an equal money repayment mode, i-2 represents an equal money repayment mode, j-x represents a full internet access mode, and j-y represents an excess power internet access mode.
5. The distributed photovoltaic investment decision optimization method of claim 1, wherein the constraints in the step S5 include:
the ratio of benefit to cost is more than or equal to 1: b isj/Ci≥1，j∈{x,y}，i∈{1,2}；
The upper limit of the area of the roof or the field is as follows:
rounding down;
third, the upper limit of initial investment: cP (personal computer)AZ×(1-r1)≤I0；
Fourthly, the upper limit of the investment recovery period is as follows:
the parameters are all more than or equal to 0.
Constraint conditions I, II and V are constraint conditions which must be met in the distributed photovoltaic investment decision problem; the constraint conditions (c) and (d) are constraint conditions which can be selected by a user and can define parameter values by the user;
wherein s is the area of the roof or the field, a, b,
Respectively, the photovoltaic module specification (length, width) and the corresponding module installation capacity, PAZInvesting photovoltaic capacity for users, c investment cost per unit capacity, r1As a proportion of loan I0The initial upper limit of the investment is set,
the term (year) is the upper limit of the investment recovery period, i-1 represents an equal-amount principal payment mode, i-2 represents an equal-amount principal payment mode, j-x represents a full-amount internet access mode, and j-y represents a surplus-electricity internet access mode.
6. A distributed photovoltaic investment decision optimization system comprises a UI (user interface), a controller and an optimizer, wherein the UI is provided with an operation mode, a loan mode option box, an input component and an output component. The controller responds to the UI interface and encapsulates the input data, and the controller transfers the encapsulated input data to the optimizer for optimization; an input function, an output function and an optimization module are arranged in the optimizer, and functions such as an annual average photovoltaic power generation function, a cost function, a total investment scale function, a total cost function, a spontaneous self-use proportion function and a benefit function are arranged in the optimization module; the method comprises the steps that an investment decision optimization function needs to be calculated, a total investment function, a total cost function and a benefit function need to be called respectively, the total investment function needs to be called when the total cost function is calculated, a cost function and a function need to be called when the total investment function is calculated, an annual average photovoltaic power generation function and a spontaneous self-use proportion function need to be called when the benefit function is calculated, and the annual average photovoltaic power generation function needs to be called when the spontaneous self-use proportion function is calculated.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657789A (en) * | 2021-08-24 | 2021-11-16 | 国网（天津）综合能源服务有限公司 | Distributed photovoltaic roof resource multi-dimensional evaluation method |
| CN117745435A (en) * | 2024-02-21 | 2024-03-22 | 厦门橙序科技有限公司 | Transaction investment decision-making system and method based on artificial intelligence |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080183638A1 (en) * | 2003-02-20 | 2008-07-31 | Itg Software Solutions, Inc. | Method and system for multiple portfolio optimization |
| CN109784725A (en) * | 2019-01-16 | 2019-05-21 | 国家电网有限公司 | A kind of investment decision method and system of power grid project |
-
2021
- 2021-07-20 CN CN202110817278.8A patent/CN113537601A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080183638A1 (en) * | 2003-02-20 | 2008-07-31 | Itg Software Solutions, Inc. | Method and system for multiple portfolio optimization |
| CN109784725A (en) * | 2019-01-16 | 2019-05-21 | 国家电网有限公司 | A kind of investment decision method and system of power grid project |
Non-Patent Citations (1)
| Title |
|---|
| 邵景钰等: "基于MVC框架的个人资产配置研究", 《中国市场》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113657789A (en) * | 2021-08-24 | 2021-11-16 | 国网（天津）综合能源服务有限公司 | Distributed photovoltaic roof resource multi-dimensional evaluation method |
| CN117745435A (en) * | 2024-02-21 | 2024-03-22 | 厦门橙序科技有限公司 | Transaction investment decision-making system and method based on artificial intelligence |
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