CN107358307A - A kind of power grid enterprises investment budgey method under multi-constraint condition based on electricity sales amount prediction - Google Patents
A kind of power grid enterprises investment budgey method under multi-constraint condition based on electricity sales amount prediction Download PDFInfo
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Abstract
The invention discloses power grid enterprises' investment budgey measuring method under a kind of multi-constraint condition based on electricity sales amount prediction, it is related to enterprise investment budget studies field, based on electricity sales amount prediction in following 1 year, calculate the investment budgey under general constraints, under target profit constraint and under the constraint of desired asset debt ratio and take the minimum value in three to determine power grid enterprises' investment budgey.The present invention has taken into full account a variety of constraintss, more scientific and reasonable, as a result more accurate based on grey forecasting model.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of enterprise investment budget research, in particular to a power grid enterprise investment budget calculating method under the constraint condition based on power sales amount prediction.
[ background of the invention ]
Along with the steady development of national economy of China, the living standard of people is improved year by year, the demand of the whole society on electric power is increased day by day, and the investment scale of a power grid is also increased continuously. During the 'twelve-five' period, the fixed asset investment of the power enterprise is doubled compared with the 'eleven-five' period, and the total amount of the assets is developed in a crossing manner. It is expected that the fixed asset investment will remain at a higher level for the duration of "thirteen five".
With the advancement of the innovation of the power industry, investment decisions are gradually shifting towards benefit guidance. The calculation of the power grid enterprise investment budget can provide reference for the formulation of capital investment budget, meet the theoretical requirements in the aspect of power grid industrial investment decision, meet the transition from 'epitaxial expansion' to 'content promotion' of benefit guidance in accordance with the power investment mode, and provide effective reference for the related investment decision analysis of other industries.
At present, the research on the investment budget of a power grid enterprise is less, the theories of financial management, technical economy and the like are applied in the prior art, the concepts of return on investment, operation coefficients and the like are introduced, and a quantitative model of the investment budget of the power grid enterprise is constructed; a power grid investment measurement model based on asset liability rate limitation is constructed; the foreign investment budget analysis and economic technology evaluation mainly focus on the engineering technical problems of electric power market fund integration and withdrawal, risk control, power supply reliability and the like.
In fact, the investment budget is constrained by a number of factors and is not a linear relationship. However, the above studies are mostly investment budgets under a single constraint condition, and the studies on the investment budgets under multiple constraint conditions are lacked. Therefore, the future investment budget of the power grid enterprise needs to be quantitatively evaluated under different constraint conditions.
[ summary of the invention ]
Aiming at the problems in the prior art, the invention provides a power grid enterprise investment budget method under the multi-constraint condition based on power sale amount prediction, the calculation method is based on prediction income, prediction cost and prediction cost obtained by power sale amount prediction in the next year, and is used for calculating the power grid enterprise investment budget by taking a cash flow constant equation as a means under the constraint conditions of target profit and target asset liability rate, so as to provide decision basis for reasonably determining investment scale and improving investment benefit of power grid enterprises.
The invention adopts the following technical scheme: a power grid enterprise investment budgeting method under the multi-constraint condition based on power selling amount prediction comprises the following steps:
step one, obtaining a predicted value of the electricity selling amount of the next year according to original data of the electricity selling amount of a target power grid enterprise, and predicting based on a grey prediction model GM (1, 1);
step two, calculating a first investment budget LA of the target enterprise under common constraint according to the predicted value of the electricity sales amount obtained in the step one1；
Step three, calculating a second investment budget LA of the target enterprise under the target profit constraint according to the predicted value of the electricity sales amount obtained in the step one2；
Step four, calculating a third investment budget LA of the target enterprise under the constraint of the target asset liability rate according to the predicted value of the electricity sales amount obtained in the step one3；
Step five, selecting the first investment budget LA1The second investment budget LA2And the third investment budget LA3The minimum value in the above is the investment budget of the target enterprise in the next year.
Further, the first step includes:
step one, listing the original data Q of the power selling amount of the target power grid enterprise(0)＝{Q(0)(1)， Q(0)(2)，…，Q(0)(k) And f, wherein the value range of k is a positive integer, and the first-order accumulation is carried out to obtain an accumulation generation sequence Q(1)＝{Q(1)(1)，Q(1)(2)，…，Q(1)(k) And (c) the step of (c) in which,k is positive integer, and the sequence Q is generated by accumulation(1)Satisfy the formula one
A second substep of generating a sequence Q from the accumulation(1)And formula twoCombine to obtainWhere a and u are parameters in the equation,is the obtained parameter value;
in substep two, the sequence Q is generated as a result of the accumulation(1)With exponential growth law, while the solution of the first order differential equation is a solution in the form of exponential growth, so the sequence Q is considered to be(1)The following first-order linear differential equation model, equation one, is satisfied, in terms of derivative definition, if expressed in discrete form, the differential term of the differential equation may beWriting into:wherein Q(1)Can only take the average value of time k and k +1, i.e.Will Q(1)Value of andsubstitution intoIn, can push outAnd the formula II is obtained by rewriting the matrix form.
A third substep of obtaining the parameter values obtained in the second substepCombining with the first formula to obtain a third formulaWherein the value range of k is positive integer, and Q in the third substep(0)And (k +1) is a predicted value of the electricity sales amount of the target enterprise in the next year.
In the third substep, the parameter values obtained in the second substep are usedSubstituting into formula one to obtain equationSolving the equation to obtain formula fourWherein the value range of k is a positive integer, and the formula IV is the time response function of the gray prediction model GM (1,1)Then, the formula four is reduced by accumulative subtraction to obtain the formula threeFormula three is the original data sequence Q of the electricity sold in the past years(0)The gray prediction model of (1).
Further, the second step includes:
substep one, calculating profit before tax interestWherein,the operation profit of the year is realized,in order to account for the financial cost of the year,for funding interest of the year, Q(0)(k +1) is the electricity sales in the next year,. DELTA.p is the purchase-sale price difference, AzjFor increasing depreciation of capital and production scale, RzcFor asset scale growth rate, RzjThe comprehensive depreciation rate is obtained;
and a second substep: calculating interest and fund rate of borrowing
And a third substep: calculating depreciation cost Frz＝Azj×(1+Rzc/2)×Rzj；
And a fourth substep: calculating operating capital Change Δ Ayy＝ΔAld-ΔDldWherein Δ AldFor moving assets, Δ DldIs a mobile liability shift;
and a fifth substep: calculating balance D of interest and debt under common constrainti dx1＝Di-1 dx+(Di-1+Ei-1)×Rzc×RzcfzWherein D isi-1 dxBalance of debt charged for the last year, Di-1Is the annual liability, Ei-1For the owner's right of the year, RzcfzIs the rate of assets liability;
and a sixth substep: calculating the cost of the charged interest F under the common constraintlx1＝Frz×(1-Rzbh)×Di dx1In which F isrzAverage financing cost;
and a seventh substep: calculating the net amount of cash flow OCF generated by business activities under common constraints1＝(Wxsq-Flx1)×(1-RIT)+Frz-ΔAyy+Flx1+Sjz-Sgy-StzWherein R isITFor the income tax rate, SjzFor asset depreciation losses, SgyThe earnings are changed for the fair value, StzTo return on investment;
and a substep eight: calculating cash inflow for financing activities under common constraintsWherein, CIyhFor the clear inflow of cash to the customer engineering, SsjIn order to pay for the investment income,for this annual equity fund inflow,increasing equity fund inflow for the next year;
and a substep nine: calculating cash inflow for funding activities under common constraints
And a substep of ten: computing the target business inFirst investment budget LA under general constraints1,
Wherein, Δ MCR1The lowest safety reserve payment under the common constraint is changed.
Further, the third step includes:
the first substep: calculating interest balance D under target profit constrainti dx2＝(Wxsq-Wys)/[Frz×(1-Rzbh)]Wherein W isysA constraint value for the total profit;
and a second substep: calculating a charged interest fee F under the constraint of target profitlx2＝Di dx2×Frz×(1-Rzbh)；
And a third substep: calculating the net cash flow generated by the business activities under the constraint of target profit
OCF2＝(Wxsq-Flx2)×(1-RIT)+Frz-ΔAyy+Flx2+Sjz-Sgy-Stz；
And a fourth substep: calculating net cash flow generated by financing activities under target profit constraints
And a fifth substep: funding activity cash inflow under calculated target profit constraints
And a sixth substep: calculating a second investment budget LA of the target enterprise under the target profit constraint2,
Wherein, Δ MCR2The minimum safe reserve payment amount changes under the constraint of target profit.
For the second investment budget LA2Constraint value W on the gross profitysCalculating a partial derivative:
it can be seen that the derivative function is always negative, i.e. the profit constraint value is negatively related to the investment budget, i.e. the higher the constraint value of the total profit, the higher the second investment budget LA of the target power grid enterprise is under the target profit constraint2The lower.
Further, the fourth step includes:
the first substep: calculating the net amount OCF of cash flow born by the operation under the constraint of the asset liability rate3Net amount of cash flow FCF generated by financing activities under the constraint of capital and debt rate3The sum of the total weight of the components,
wherein the total amount of the option interest is Ei-1The amount of the given assets transferred is AszThe rest-to-debt ratio is Rdxfz；
And a second substep: calculating investment activity cash inflow under asset liability rate constraints
And a third substep: calculating a third investment budget LA of the target enterprise under the constraint of the asset liability rate3,
Wherein, Δ MCR3The change of the minimum safety reserve payment under the constraint of the asset load rate.
For the third investment capacity LA3On the rate of assets liability RzcfzDerivation:
it can be seen that the derivative function is always positive, that is, the asset liability ratio constraint value is positively correlated with the investment budget, that is, the higher the asset liability ratio constraint value is, the third investment LA of the target power grid enterprise under the asset liability ratio constraint3The higher the budget.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the invention fully considers various constraint conditions, determines the investment budgets under the constraint of the common constraint, the target asset liability rate constraint and the target profit constraint according to the minimum principle after respectively measuring and calculating the investment budgets of three calibers, and is more scientific and reasonable.
(2) The method is based on the grey prediction model, and based on the prediction income, the prediction cost and the prediction expense which are obtained by predicting the electricity sold in the next year, the accuracy of the calculation result is fully ensured.
[ detailed description ] embodiments
The technical solutions of the embodiments of the present invention are explained and illustrated below, but the following embodiments are only preferred embodiments of the present invention, and not all of them. Based on the embodiments in the embodiment, other embodiments obtained by those skilled in the art without any creative efforts belong to the protection scope of the invention.
The invention provides a power grid enterprise investment budgeting method under a multi-constraint condition based on power sales amount prediction. The power grid enterprise investment budgeting method under the multi-constraint condition based on power selling amount prediction comprises the following steps:
step one, obtaining a predicted value of the electricity selling amount of the next year according to original data of the electricity selling amount of a target power grid enterprise, and predicting based on a grey prediction model GM (1, 1);
step two, calculating a first investment budget LA of the target enterprise under common constraint according to the predicted value of the electricity sales amount obtained in the step one1；
Step three, calculating a second investment budget LA of the target enterprise under the target profit constraint according to the predicted value of the electricity sales amount obtained in the step one2；
Step four, calculating a third investment budget LA of the target enterprise under the constraint of the target asset liability rate according to the predicted value of the electricity sales amount obtained in the step one3；
Step five, selecting the first investment budget LA1The second investment budget LA2And the third investment budget LA3The minimum value in the above is the investment budget of the target enterprise in the next year.
Further, the first step includes:
step one, listing the original data Q of the power selling amount of the target power grid enterprise(0)＝{Q(0)(1)， Q(0)(2)，…，Q(0)(k) And f, wherein the value range of k is a positive integer, and the first-order accumulation is carried out to obtain an accumulation generation sequence Q(1)＝{Q(1)(1)，Q(1)(2)，…，Q(1)(k) And (c) the step of (c) in which,k is positive integer, and the accumulation generation sequenceColumn Q(1)Satisfy the formula one
A second substep of generating a sequence Q from the accumulation(1)And formula twoCombine to obtainWhere a and u are parameters in the equation,is the obtained parameter value;
in substep two, the sequence Q is generated as a result of the accumulation(1)With exponential growth law, while the solution of the first order differential equation is a solution in the form of exponential growth, so the sequence Q is considered to be(1)The following model of a first-order linear differential equation, equation one, is satisfied, and the differential terms of the differential equation, if expressed in discrete form, can be written as:wherein Q(1)Can only take the mean of the times k and k +1, i.e.Will Q(1)Value of andsubstitution intoIn, can push outAnd the formula II is obtained by rewriting the matrix form.
A third substep, which is to say, the second substepThe obtained parameter valueCombining with the first formula to obtain a third formulaWherein the value range of k is positive integer, and Q in the third substep(0)And (k +1) is a predicted value of the electricity sales amount of the target enterprise in the next year.
In the third substep, the parameter values obtained in the second substep are usedSubstituting into formula one to obtain equationSolving the equation to obtain formula fourWherein the value range of k is a positive integer, the formula IV is a time response function of the gray prediction model GM (1,1), and the formula IV is subjected to subtraction reduction to obtain the formula IIIFormula three is the original data sequence Q of the electricity sold in the past years(0)The gray prediction model of (1).
Further, the second step includes:
substep one, calculating profit before tax interestWherein,the operation profit of the year is realized,is the property of the yearThe cost of the service is increased, and the service cost is increased,for capitalization interest of the year, Q(0)(k +1) is the electricity sales in the next year,. DELTA.p is the purchase-sale price difference, AzjFor increasing depreciation of capital and production scale, RzcFor asset scale growth rate, RzjThe comprehensive depreciation rate is obtained;
and a second substep: calculating interest and fund rate of borrowing
And a third substep: calculating depreciation cost Frz＝Azj×(1+Rzc/2)×Rzj；
And a fourth substep: calculating operating capital Change Δ Ayy＝ΔAld-ΔDldWherein Δ AldFor moving assets, Δ DldIs a mobile liability shift;
and a fifth substep: calculating balance D of interest and debt under common constrainti dx1＝Di-1 dx+(Di-1+Ei-1)×Rzc×RzcfzWherein D isi-1 dxBalance of debt charged for the last year, Di-1Is the annual liability, Ei-1For the owner's right of the year, RzcfzIs the rate of assets liability;
and a sixth substep: calculating the cost of the charged interest F under the common constraintlx1＝Frz×(1-Rzbh)×Di dx1In which F isrzAverage financing cost;
and a seventh substep: calculating the net amount of cash flow OCF generated by business activities under common constraints1＝(Wxsq-Flx1)×(1-RIT)+Frz-ΔAyy+Flx1+Sjz-Sgy-StzWherein R isITFor the income tax rate, SjzAs an assetLoss of reduction, SgyThe earnings are changed for the fair value, StzTo return on investment;
and a substep eight: calculating cash inflow for financing activities under common constraintsWherein, CIyhFor the clear inflow of cash to the customer engineering, SsjIn order to pay for the investment income,for this annual equity fund inflow,increasing equity fund inflow for the next year;
and a substep nine: calculating cash inflow for funding activities under common constraints
And a substep of ten: calculating a first investment budget LA of the target enterprise under a common constraint1,
Wherein, Δ MCR1The lowest safety reserve payment under the common constraint is changed.
Further, the third step includes:
the first substep: calculating interest balance D under target profit constrainti dx2＝(Wxsq-Wys)/[Frz×(1-Rzbh)]Wherein W isysA constraint value for the total profit;
and a second substep: calculating a charged interest fee F under the constraint of target profitlx2＝Di dx2×Frz×(1-Rzbh)；
And a third substep: calculating the net amount of cash flow OCF generated by business activities under the constraint of target profit2＝(Wxsq-Flx2)×(1-RIT)+Frz-ΔAyy+Flx2+Sjz-Sgy-Stz；
And a fourth substep: calculating net cash flow generated by financing activities under target profit constraints
And a fifth substep: funding activity cash inflow under calculated target profit constraints
And a sixth substep: calculating a second investment budget LA of the target enterprise under the target profit constraint2,
Wherein, Δ MCR2The minimum safe reserve payment amount changes under the constraint of target profit.
For the second investment budget LA2Constraint value W on the gross profitysCalculating a partial derivative:
it can be seen that the derivative function is always negative, i.e. the profit constraint value is negatively related to the investment budget, i.e. the higher the constraint value of the total profit, the higher the second investment budget LA of the target power grid enterprise is under the target profit constraint2The lower.
Further, the fourth step includes:
the first substep: calculating the net amount OCF of cash flow born by the operation under the constraint of the asset liability rate3Net cash flow FCF generated by financing activities under the constraint of asset liability rate3The sum of the total weight of the components,
wherein the total amount of the option interest is Ei-1The amount of the given assets transferred is AszThe rest-to-debt ratio is Rdxfz；
And a second substep: calculating investment activity cash inflow under asset liability rate constraints
And a third substep: calculating a third investment budget LA of the target enterprise under the constraint of the asset liability rate3,
Wherein, Δ MCR3The change of the minimum safety reserve payment under the constraint of the asset load rate.
For the third investment capacity LA3On the rate of assets liability RzcfzDerivation:
it can be seen that the derivative function is always positive, that is, the asset liability rate constraint value is positively correlated with the investment budget, that is, the higher the asset liability rate constraint value is, the third investment LA of the target power grid enterprise under the asset liability rate constraint3The higher the budget.
For further explanation of the method provided by the invention, the investment capacity of a certain power grid enterprise is measured and calculated according to the operation data of the enterprise.
The method comprises the following steps: the historical data of annual electricity sales are shown in table 1, and the electricity sales of the power grid enterprise are predicted by using a grey prediction technology. Firstly, the original data is accumulated to generate:
table 1 raw data accumulation table
Next, the parameter values of the differential equation are calculated. Calculating parameters of a differential equation according to a second linear equation formulaThe value of (c):
then, a GM (1,1) model is established, and the values of the parameters are substituted to obtain a gray prediction model with accumulated number series: q(0)(k+1)＝1457.04e0.0738k。
Finally, the predicted value of the electricity selling amount of the enterprise in 2017 is predicted to be 365.03 multiplied by 104 KW.h through a gray prediction model with the accumulated number series.
Step two: under normal constraints, the enterprise financial statement data is shown in table 2:
table 2 input parameters table 1
According to the formula in the second step, intermediate parameters such as profit before tax interest, interest borrowing rate, depreciation cost and the like under common constraint can be respectively calculated, and the summary is shown in a table 3:
table 3 intermediate parameters table 1
Further, under the general constraint, the net cash flow generated by the operation activity, the net cash flow generated by the financing activity, the cash inflow of the investment activity and the investment budget of the power grid enterprise can be respectively calculated, and the results are shown in table 4:
table 4 calculation results table 1
Step three: under the constraint of the target profit, the balance of the debt and the charged interest fee under the constraint of the target profit can be respectively calculated according to the formula in step three, as shown in table 5:
table 5 intermediate parameters table 2
Further, the net cash flow generated by the business activities under the constraint of the target profit, the net cash flow generated by the financing activities, the cash inflow of the investment activities, and the investment budget of the power grid enterprises under the constraint of the target profit can be respectively calculated, and the results are shown in table 6:
table 6 calculation results table 2
Step four: under the constraint of the asset liability ratio, the input parameters are as follows:
table 7 input parameters table 3
According to the formula in the fourth step, the net cash flow generated by the operation activities and the financing activities under the constraint of the asset liability rate, the cash inflow of the investment activities and the investment budget of the power grid enterprises under the constraint of the asset liability rate can be respectively calculated, and the results are shown in table 8:
table 8 calculation results table 3
In the step 5: and (3) respectively calculating the investment budgets under the three constraint conditions of common constraint, target profit constraint and asset liability ratio constraint according to the steps, and finally determining the investment budgets under the three constraint conditions according to the minimum principle, wherein the investment capacity of the power grid enterprise is 539623 ten thousand yuan in the example, and the calculation is stopped.
In conclusion, the invention has the following advantages and beneficial effects:
(1) the invention fully considers various constraint conditions, determines the investment budgets under the constraint of the common constraint, the target asset liability rate constraint and the target profit constraint according to the minimum principle after respectively measuring and calculating the investment budgets of three calibers, and is more scientific and reasonable.
(2) The method is based on the grey prediction model, and based on the prediction income, the prediction cost and the prediction expense which are obtained by predicting the electricity sold in the next year, the accuracy of the calculation result is fully ensured.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in other forms without departing from the spirit or essential characteristics thereof. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (5)
1. A power grid enterprise investment budgeting method under multiple constraint conditions based on power sale amount prediction is characterized by comprising the following steps: the power grid enterprise investment budgeting method under the multi-constraint condition based on power selling amount prediction comprises the following steps:
step one, obtaining a predicted value of electricity selling amount of the next year according to original data of the electricity selling amount of a target power grid enterprise;
step two, calculating a first investment budget LA of the target enterprise under common constraint according to the predicted value of the electricity sales amount obtained in the step one1；
Step three, calculating a second investment budget LA of the target enterprise under the target profit constraint according to the predicted value of the electricity sales amount obtained in the step one2；
Step four, calculating a third investment budget LA of the target enterprise under the constraint of the target asset liability rate according to the predicted value of the electricity sales amount obtained in the step one3；
Step five, selecting the first investment budget LA1The second investment budget LA2And the third investment budget LA3The minimum value in the above is the investment budget of the target enterprise in the next year.
2. The method for budgeting investment of power grid enterprises under multiple constraints based on power sales amount prediction according to claim 1, wherein the first step comprises:
step one, listing the original data Q of the power selling amount of the target power grid enterprise(0)＝{Q(0)(1)，Q(0)(2)，…，Q(0)(k) And f, wherein the value range of k is a positive integer, and the first-order accumulation is carried out to obtain an accumulation generation sequence Q(1)＝{Q(1)(1)，Q(1)(2)，…，Q(1)(k) And (c) the step of (c) in which,k is positive integer, and the sequence Q is generated by accumulation(1)Satisfy the formula one
A second substep of generating a sequence Q from the accumulation(1)And formula twoCombine to obtainWhere a and u are parameters in the equation,is the obtained parameter value;
a third substep of obtaining the parameter values obtained in the second substepCombining with the first formula to obtain a third formulaWherein the value range of k is positive integer, and Q in the third substep(0)And (k +1) is a predicted value of the electricity sales amount of the target enterprise in the next year.
3. The power grid enterprise investment budgeting method under the multi-constraint condition based on the power sales amount prediction according to claim 2, wherein the second step comprises:
substep one, calculating profit before tax interestWherein,the operation profit of the year is realized,in order to account for the financial cost of the year,for capitalization interest of the year, Q(0)(k +1) is the electricity sales in the next year,. DELTA.p is the purchase-sale price difference, AzjFor reckoning the size of depreciated assets, RzcFor asset scale growth rate, RzjThe comprehensive depreciation rate is obtained;
and a second substep: calculating interest and fund rate of borrowing
And a third substep: calculating depreciation cost Frz＝Azj×(1+Rzc/2)×Rzj；
And a fourth substep: calculating operating capital Change Δ Ayy＝ΔAld-ΔDldWherein Δ AldFor moving assets, Δ DldIs a mobile liability shift;
and a fifth substep: calculating balance D of interest and debt under common constrainti dx1＝Di-1 dx+(Di-1+Ei-1)×Rzc×RzcfzWherein D isi -1 dxBalance of debt charged for the last year, Di-1Is the annual liability, Ei-1For the owner's right of the year, RzcfzIs the rate of assets liability;
and a sixth substep: calculating the cost of the charged interest F under the common constraintlx1＝Frz×(1-Rzbh)×Di dx1In which F isrzAverage financing cost;
and a seventh substep: calculating the net amount of cash flow OCF generated by business activities under common constraints1＝(Wxsq-Flx1)×(1-RIT)+Frz-ΔAyy+Flx1+Sjz-Sgy-StzWherein R isITFor the income tax rate, SjzFor asset depreciation losses, SgyThe earnings are changed for the fair value, StzTo return on investment;
and a substep eight: calculating cash inflow for financing activities under common constraintsWherein, CIyhFor the clear inflow of cash to the customer engineering, SsjIn order to pay for the investment income,for this annual equity fund inflow,increasing equity fund inflow for the next year;
and a substep nine: calculating cash inflow for funding activities under common constraints
And a substep of ten: calculating a first investment budget LA of the target enterprise under a common constraint1,
Wherein, Δ MCR1The lowest safety reserve payment under the common constraint is changed.
4. The power grid enterprise investment budgeting method under the multi-constraint condition based on the power sales amount prediction according to claim 3, wherein the third step comprises:
the first substep: calculating interest balance D under target profit constrainti dx2＝(Wxsq-Wys)/[Frz×(1-Rzbh)]Wherein W isysA constraint value for the total profit;
and a second substep: calculating a charged interest fee F under the constraint of target profitlx2＝Di dx2×Frz×(1-Rzbh)；
And a third substep: calculating the net amount of cash flow OCF generated by business activities under the constraint of target profit2＝(Wxsq-Flx2)×(1-RIT)+Frz-ΔAyy+Flx2+Sjz-Sgy-Stz；
And a fourth substep: calculating net cash flow generated by financing activities under target profit constraints
And a fifth substep: funding activity cash inflow under calculated target profit constraints
And a sixth substep: calculating a second investment budget LA of the target enterprise under the target profit constraint2,
Wherein, Δ MCR2The minimum safe reserve payment amount changes under the constraint of target profit.
5. The power grid enterprise investment budgeting method under the multi-constraint condition based on the power sales amount prediction according to claim 4, wherein the fourth step comprises:
the first substep: calculating the net amount OCF of cash flow born by the operation under the constraint of the asset liability rate3Net cash flow FCF generated by financing activities under the constraint of asset liability rate3The sum of the total weight of the components,
wherein the total amount of the option interest is Ei-1The amount of the given assets transferred is AszThe rest-to-debt ratio is Rdxfz；
And a second substep: calculating investment activity cash inflow under asset liability rate constraints
And a third substep: calculating a third investment budget LA of the target enterprise under the constraint of the asset liability rate3,
Wherein, Δ MCR3The change of the minimum safety reserve payment under the constraint of the asset load rate.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109214449A (en) * | 2018-08-28 | 2019-01-15 | 华北电力大学 | A kind of electric grid investment needing forecasting method |
| CN109598618A (en) * | 2018-09-12 | 2019-04-09 | 阿里巴巴集团控股有限公司 | Data processing method, the determination method and apparatus of mobility time limit notch |
| CN110288141A (en) * | 2019-06-18 | 2019-09-27 | 国网上海市电力公司 | A kind of construction investment neural network based turns tariff prediction technique |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109214449A (en) * | 2018-08-28 | 2019-01-15 | 华北电力大学 | A kind of electric grid investment needing forecasting method |
| CN109598618A (en) * | 2018-09-12 | 2019-04-09 | 阿里巴巴集团控股有限公司 | Data processing method, the determination method and apparatus of mobility time limit notch |
| CN109598618B (en) * | 2018-09-12 | 2022-12-16 | 创新先进技术有限公司 | Data processing method, and method and device for determining fluidity deadline gap |
| CN110288141A (en) * | 2019-06-18 | 2019-09-27 | 国网上海市电力公司 | A kind of construction investment neural network based turns tariff prediction technique |
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