CN111967647A - Cooperative game-based multi-subject investment proportion optimization method and system - Google Patents
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Abstract
The invention discloses a cooperative game-based multi-subject investment proportion optimization method and system, wherein the method comprises the following steps: establishing an energy hub model; calculating a total investment cost of an incremental power distribution project based on the energy hub model; acquiring initial investment proportions of various investment main bodies according to the total investment cost, and determining related influence factors for redistributing the initial investment proportions; and taking the related influence factors and the satisfaction evaluation standard as correlation factors, and carrying out re-optimization on the initial investment proportion through an asymmetric Nash negotiation model. The embodiment of the invention can solve the problem of unfair allocation of the investment cost of the alliance and improve the marketization degree of the power distribution network from the side.
Description
Technical Field
The invention relates to the field of investment optimization of a power distribution network, in particular to a cooperative game-based multi-subject investment proportion optimization method and system.
Background
For a long time, the power distribution network is always invested, built and operated by power grid enterprises, but with continuous deepening of reform of the Chinese power system, the national development committee and the national energy source bureau encourage the orderly investment of social capital and operation increment power distribution network by issuing an ordered release power distribution network service management method, so that the construction and development of the power distribution network are promoted, and the operation efficiency of the power distribution network is improved. At present, the incremental power distribution project is developed mainly by establishing an incremental power distribution company with all systems mixed, however, the property attribution problem existing in the establishment process of the company is not clear, the investment proportion or the share ratio mostly belongs to artificial agreement, and the doped subjective factor is strong. In view of the related research proposed at present, much attention is focused on discussing the investment decision problem in the field of incremental power distribution on a macroscopic level, and little attention is paid to the fairness of the investment proportion obtained by various investment subjects after investment. Therefore, how to effectively optimize the investment proportion of various investment subjects becomes a problem to be solved by the invention.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a cooperative game-based multi-subject investment proportion optimization method and system.
Correspondingly, the invention provides a cooperative game-based multi-subject investment proportion optimization method, which comprises the following steps:
establishing an energy hub model;
calculating a total investment cost of an incremental power distribution project based on the energy hub model;
acquiring initial investment proportions of various investment main bodies according to the total investment cost, and determining related influence factors for redistributing the initial investment proportions;
and taking the related influence factors and the satisfaction evaluation standard as correlation factors, and carrying out re-optimization on the initial investment proportion through an asymmetric Nash negotiation model.
Optionally, the total investment cost of the incremental power distribution project includes a total construction cost of the incremental power distribution project and a total operation cost of the incremental power distribution project.
Optionally, the total cost of construction of the incremental power distribution project
Comprises the following steps:
wherein the content of the first and second substances,
in order to reduce the construction cost of the power distribution network,
for the construction cost of the energy conversion equipment in the energy hub model,
and the construction cost for energy storage is saved.
Optionally, the construction cost of the power distribution network
Comprises the following steps:
wherein, C0tFor a fixed construction cost per year of the distribution network,0tannual coefficient of fixed construction costs for distribution networks, CtranFor investment costs of the substation, ClineTo build up new line costs, C1tIn order to achieve the cost of operation and maintenance,1tfor operation and maintenance cost rate, C2tTo the loss cost of the network, betatThe comprehensive loss rate, Q, of the incremental distribution network in the t yeartTo increment the predicted capacity of the distribution grid for the t year,
the average electricity price of the incremental distribution network in the t year C3tFor loss cost of power failure, M is the load point number of the distribution network, RovenTo produce electricity ratio, EmExpected power shortage of the load nodes in the research period.
Optionally, the energy conversion equipment construction costs
Comprises the following steps:
wherein S isMTIs the plant capacity of a micro gas turbine, SEBIs the plant capacity of the hot boiler, SGBIs the equipment capacity, xi, of the gas boilerMTUnit price, ξ, of the capacity of a micro gas turbineEBUnit price, xi, of the capacity of the hot boilerGBIs the unit price of the capacity of the gas boiler,ehthe investment annual coefficient of the energy conversion equipment.
Optionally, the energy storage construction cost
Comprises the following steps:
wherein the content of the first and second substances,esfor the purpose of energy storage and annual investment coefficient,
is the rated capacity of the energy storage system, sesIn order to be a unit price per capacity,
for the rated power of the energy storage system, pesIs a unit price per power, betaesThe replacement rate of the energy storage battery.
Optionally, the total cost of operation of the incremental power distribution project
Comprises the following steps:
wherein the content of the first and second substances,
in order to purchase the cost of the electrical energy to the grid,
in order to save the purchase cost of the stored energy,
in order to account for the cost of the natural gas input to the system,
electric energy xi required to be purchased by the distribution network in the t-th timebetFor the unit price of purchasing power during the t-th time,
rated power, ξ, of the energy storage system during the t-th timevetThe electricity price at the bottom time in the t-th time,
total amount of natural gas, ξ, required for the distribution network during the t-th timebgtThe unit price of purchasing natural gas for the t-th time.
Optionally, the asymmetric Nash negotiation model is as follows:
wherein the content of the first and second substances,
is the minimum value of the investment proportion of the ith investment entity, yiIs the increasing coefficient of the ith investment entity to the occupied investment proportion,
is the maximum value, x, of the investment proportion of the ith investment entityiThe weight occupied by the ith investment entity,
is the final investment proportion occupied by the ith investment entity, and n is the total number of investment entities in the coalition.
Optionally, the satisfaction evaluation criterion includes:
wherein λ isiTo the satisfaction of the ith investment entity,
is the negotiation benchmark of the ith investment entity.
Correspondingly, the embodiment of the invention also provides a cooperative game-based multi-subject investment proportion optimization system, which comprises:
the establishing module is used for establishing an energy hub model;
a calculation module for calculating a total investment cost for an incremental power distribution project based on the energy hub model;
the analysis module is used for acquiring the initial investment proportion of each type of investment main body according to the total investment cost and determining related influence factors for redistributing the initial investment proportion;
and the optimization module is used for carrying out re-optimization on the initial investment proportion through an asymmetric Nash negotiation model by taking the relevant influence factors and the satisfaction evaluation standard as correlation factors.
In the embodiment of the invention, under the open environment of the incremental power distribution service, the investment proportion of various investment subjects is optimized by using the asymmetric Nash negotiation model, and the stability degree of the model is restricted by setting the satisfaction degree evaluation standard, so that the problem of unfair allocation of the investment cost of the alliance can be solved, more investment subjects can be attracted to enter the incremental power distribution project for fair competition, the marketization degree of the power distribution side is improved, and the operation efficiency of the power distribution network is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a cooperative game-based multi-principal investment proportion optimization method disclosed in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a topology of an energy hub model according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of the fluctuation of the investment ratio before and after negotiation as disclosed in the embodiment of the present invention;
fig. 4 is a schematic composition diagram of a cooperative game-based multi-agent investment proportion optimization system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows a schematic flow chart of a cooperative game-based multi-agent investment proportion optimization method in an embodiment of the present invention, where the method includes the following steps:
s101, establishing an energy hub model;
in the embodiment of the invention, based on a cooperative game mode of an incremental distribution network environment, the A-type, B-type and C-type investment subjects with cooperative relations realize mutual conversion between electricity, heat and gas through an energy conversion device, and an energy hub model is established for internal overall optimization by taking unit prices corresponding to three types of energy of electricity, heat and gas at different time intervals as guidance, so that the overall investment cost is minimized. The investment of class A is mainly power grid enterprises, the investment of class B is mainly local government, social capital, energy storage enterprises and the like, and the investment of class C is mainly cold/heat supply enterprises and gas supply enterprises.
Fig. 2 shows a schematic topology of an energy hub model in an embodiment of the present invention, the energy hub model is formed by an electric transformer, a micro gas turbine (MT), a Gas Boiler (GB), and a thermal boiler (EB). As shown in fig. 2, the purchased electric energy
As an input variable of the model, a pure electric load can be generated after the action of the power transformer
(output) and convertible to thermal energy after acting via said thermal boiler
(output quantity); total amount of natural gas purchased
As another input of the model, heat energy can be generated after the action of the gas boiler
And respectively generating heat energy after the action of the micro gas turbine
And pure electric load
On the basis of the model, the two input quantities and the two output quantities can be expressed by a matrix as follows:
wherein alpha isMTAn energy distribution coefficient, alpha, for said micro gas turbineEBAn energy distribution coefficient for the heat boiler,
in order to be efficient for the transformer,
for the conversion efficiency of the natural gas into electric energy through the micro gas turbine,
is the total energy of the energy storage device,
in order to achieve the conversion efficiency of the natural gas into heat energy through the micro gas turbine,
the energy efficiency ratio of the refrigeration/heat of the gas boiler.
In the energy hub model, the cost associated with the electric energy flow is the cost of investment required by the class a investment entity, the cost associated with the natural gas flow and the heat energy flow is the cost of investment required by the class C investment entity, and the investment of public equipment such as energy conversion equipment is the cost of investment required by the class B investment entity.
S102, calculating the total investment cost of the incremental power distribution project based on the energy hub model;
in an embodiment of the present invention, the total investment cost of the incremental power distribution project includes total construction cost of the incremental power distribution project and total operation cost of the incremental power distribution project, which are respectively as follows:
(1) total cost of construction of the incremental power distribution project
Comprises the following steps:
in the formula:
in order to reduce the construction cost of the power distribution network,
for the construction cost of the energy conversion equipment in the energy hub model,
and the construction cost for energy storage is saved. Wherein:
a. construction cost of the power distribution network
Comprises the following steps:
in the formula: ctran＝atran+btranS，Cline＝Lline×(aline+blineAline)
Wherein, C0tFor a fixed construction cost per year of the distribution network,0tannual coefficient of fixed construction costs for distribution networks, CtranFor investment costs of the substation, ClineTo build up new line costs, C1tFor the operation and maintenance costs (including material cost, repair cost and compensation),1tfor operation and maintenance cost rate, C2tTo the loss cost of the network, betatThe comprehensive loss rate, Q, of the incremental distribution network in the t yeartTo increment the predicted capacity of the distribution grid for the t year,
the average electricity price of the incremental distribution network in the t year C3tFor loss cost of power failure, M is the load point number of the distribution network, RovenTo produce electricity ratio, EmThe expected value of the electric quantity shortage of the load node in the research period is obtained; a istranCoefficients (of fixed value) for the parts of the investment that are not related to the capacity of the substation, btranIs a coefficient (belonging to a variable value) in the investment and having a linear relation with the capacity of the transformer substation, S is the capacity of the transformer substation, LlineFor the length of the newly-built line, alineCoefficient (belonging to a fixed value) of a portion of the investment that is independent of the cross-sectional area of the wire, blineThe coefficient (of variable value) of investment which is linear with the cross-sectional area of the wirelineIs the cross-sectional area of the wire;
b. the construction cost of the energy conversion equipment
Comprises the following steps:
wherein S isMTIs the plant capacity of a micro gas turbine, SEBIs the plant capacity of the hot boiler, SGBIs the equipment capacity, xi, of the gas boilerMTUnit price, ξ, of the capacity of a micro gas turbineEBUnit price, xi, of the capacity of the hot boilerGBIs the unit price of the capacity of the gas boiler,ehthe annual investment coefficient of the energy conversion equipment;
c. the energy storage construction cost
Comprises the following steps:
wherein the content of the first and second substances,esfor the purpose of energy storage and annual investment coefficient,
is the rated capacity of the energy storage system, sesIn order to be a unit price per capacity,
for the rated power of the energy storage system, pesIs a unit price per power, betaesThe replacement rate of the energy storage battery.
(2) Total cost of operation of the incremental power distribution project
Comprises the following steps:
wherein the content of the first and second substances,
in order to purchase the cost of the electrical energy to the grid,
in order to save the purchase cost of the stored energy,
in order to account for the cost of the natural gas input to the system,
electric energy xi required to be purchased by the distribution network in the t-th timebetFor the unit price of purchasing power during the t-th time,
rated power, ξ, of the energy storage system during the t-th timevetThe electricity price at the bottom time in the t-th time,
total amount of natural gas, ξ, required for the distribution network during the t-th timebgtThe unit price of purchasing natural gas in the tth time is t, and the value of t is taken at intervals of every hour.
S103, acquiring initial investment proportions of various investment main bodies according to the total investment cost, and determining related influence factors for redistributing the initial investment proportions;
in the embodiment of the present invention, since the investment degrees of each investment entity on the incremental power distribution project are different, more investment costs should be borne according to the fairness principle, and less investment costs should be borne, based on the investment costs mentioned in step S102, the initial investment proportion occupying the total investment cost is calculated according to the investment properties of each investment entity, and the sum of the initial investment proportions corresponding to each investment entity needs to be 1. Furthermore, determining relevant influencing factors for the redistribution of the initial investment proportion comprises the following points:
(1) risk sharing factor
In the process of incremental power distribution project from construction to operation, various investment subjects need to bear various risks, such as social risk, construction risk, operation risk, policy risk and the like, and if m different risks exist, the risk sharing coefficients R of the various investment subjectsiComprises the following steps:
in the formula:
wherein alpha isjWeight for the jth risk, RijThe coefficients for the jth risk are shared for the ith investor.
(2) Core capacity coefficient
The core capability of the incremental power distribution project is formed by innovation capability, core technology and cooperation capability together, wherein the innovation capability depends on the value embodiment of various investment subjects on the alliance according to talent reserve, information acquisition and the like, the core technology depends on the unique technology, management experience and the like of various investment subjects, and the cooperation capability depends on the cooperation degree of various investment subjects in the project investment, construction and operation processes. Core capability coefficient of various investment subjects is assumedIs EiIn order to keep the initial investment proportion and the risk sharing coefficient consistent, the normalization processing is as follows: sigma Ei＝1。
(3) Weight occupied by investment entity
In the incremental power distribution project, the weight x occupied by various investment subjects can be determined according to the initial investment proportion, the risk sharing coefficient and the core capacity coefficientiComprises the following steps:
wherein, ω isIWeight coefficient, ω, of initial investment proportion for the ith investment entityRWeighting factor, ω, of risk-sharing factors for the ith investment entityEWeight coefficient of core ability coefficient, I, for the ith investment entityiInitial investment proportion, R, for the ith investment entityiRisk-sharing factor for the ith investment entity, EiIs the core capacity coefficient of the ith investment entity.
And S104, re-optimizing the initial investment proportion by using the related influence factors and the satisfaction evaluation standard as correlation factors through an asymmetric Nash negotiation model.
In the embodiment of the present invention, it is assumed that there are n investment bodies in a federation, and each of the investment bodies proposes an optimization scheme for its initial investment proportion: di＝{d1i,d2i,…,dniAnd therefore, all investment proportion optimization schemes proposed by n investment subjects are expressed by a coefficient matrix as follows:
wherein d isniIs the ith investment entityProposed optimization scheme DiThe nth investment entity and the ith line of data are the investment proportion set which is considered by all the investment entities as the ith investment entity to be responsible for
Defining as the optimal value of the proportion of the investment to be charged by the ith investment entity, taking the maximum value in the set
Defined as the least desirable value of the proportion of the investment that the ith investment entity should afford, namely:
however, because the optimal scheme cannot meet the constraint condition that the sum of the distribution coefficients corresponding to n investment subjects is 1 in actual conditions, the ith investment subject needs to receive an increase coefficient after the operation of the asymmetric Nash negotiation model, and the final investment proportion of the ith investment subject is as follows:
wherein the asymmetric Nash negotiation model is as follows:
in the formula:
is the minimum value of the investment proportion of the ith investment entity, yiIs the increasing coefficient of the ith investment entity to the occupied investment proportion,
is the maximum value, x, of the investment proportion of the ith investment entityiThe weight occupied by the ith investment entity,
is the final investment proportion occupied by the ith investment entity, and n is the total number of investment entities in the coalition.
It should be noted that, whether the negotiation of the n investment subjects via the asymmetric Nash negotiation model is agreed (i.e. federation stability) should depend on the satisfaction evaluation criteria, including:
wherein λ isiTo the satisfaction of the ith investment entity,
is the negotiation benchmark of the ith investment entity.
Based on the multi-principal investment proportion optimization method shown in fig. 1, the embodiment of the present invention performs simulation analysis by taking an incremental power distribution project of an industrial park as an example, wherein relevant parameter information required for calculating the total investment cost is shown in table 1, and meanwhile, a relevant peak-valley time-of-use electricity price of the industrial park is called and is shown in table 2.
TABLE 1 Total cost of investment parameters
TABLE 2 Peak-to-valley time of use electricity price
(1) Calculating the initial investment proportion of various investment subjects
According to the related information provided in tables 1 and 2, and combining with the different cost calculation formulas in step S102, the investment cost situations of the investment subjects of class a, class B and class C in the cooperative mode and the noncooperative mode can be obtained, as shown in table 3: in the cooperation mode, the initial investment proportion required to be borne by the class A investment body is 55.14% (including the electricity purchasing cost and the construction cost of a power distribution network), the initial investment proportion required to be borne by the class B investment body is 14.34% (including the energy conversion equipment cost and the energy storage construction cost), and the initial investment proportion required to be borne by the class C investment body is 30.52% (including only the gas purchasing cost); in the non-cooperative mode, the total investment cost is higher than that in the cooperative mode, which shows that in the environment of releasing the incremental distribution network, a plurality of types of investment subjects are encouraged to enter the incremental distribution project and adopt a cooperative mode, and meanwhile, the project structure is optimized, so that the benefit maximization can be realized.
TABLE 3 various investment cost cases
(2) Analyzing the relevant influence factors on the redistribution of the initial investment proportion
The incremental power distribution project of the industrial park mainly has four categories of social risk, construction risk, operation risk and policy risk, wherein the weight of the social risk is 0.2, the weight of the construction risk is 0.3, the weight of the operation risk is 0.4, and the weight of the policy risk is 0.1, and the risk sharing conditions and the core capacity coefficients of various investment subjects to different categories are shown in table 4:
TABLE 4 relevant influencing factors (Risk and core Capacity)
In the embodiment of the present invention, the influence weight of the initial investment proportion is set to 0.8, the influence weight of the risk share coefficient is set to 0.1, and the influence weight of the core capacity coefficient is set to 0.1, and then the weight occupied by each kind of index corresponding to each kind of investment subject is determined according to the initial investment proportion, the risk share coefficient and the core capacity coefficient of each kind of investment subject, as shown in table 5:
TABLE 5 weight occupied by various indexes
(3) Re-optimizing the initial investment proportion of various investment subjects
The optimization schemes proposed based on the investment subjects of class A, class B and class C according to the respective initial investment proportions are as follows:
and the weights of the indexes provided by the table 5 are used, the asymmetric Nash negotiation model is used for carrying out balance analysis and negotiation, and the final investment proportion of the A-class investment body is 52.57%, the final investment proportion of the B-class investment body is 14.57%, and the final investment proportion of the C-class investment body is 32.85% respectively; meanwhile, the negotiation conditions of various investment subjects are analyzed in combination with the satisfaction evaluation criteria proposed in step S104, as shown in table 6:
TABLE 6 negotiation of various investment subjects
As can be seen from table 6, the final investment proportions obtained by the investment entities after passing through the asymmetric Nash negotiation model are all between the least ideal investment proportion before negotiation and the most ideal investment proportion, and the satisfaction degrees of the investment entities are all greater than the corresponding negotiation base points, which indicates that the negotiation results are accepted by the investment entities, so as to ensure the benefit maximization of the federation.
In addition, because the optimization methods proposed by various investment bodies according to respective initial investment proportions have subjectivity, and the distribution proportion output by the asymmetric Nash negotiation model may fluctuate, the embodiment of the invention proposes that the fluctuation conditions of the final optimization results of various investment bodies under different fluctuation proportions are respectively considered by taking the change conditions of the initial investment proportions corresponding to various investment bodies before negotiation as input values and the change conditions of the final investment proportions corresponding to various investment bodies after negotiation as output values of the model. At this time, it is assumed that the investment proportion assumed by the investment entity of class a when the initial investment proportion is provided can be respectively reduced by 2%, 5%, 10%, 20% and 30%, and the variation of the final investment proportion of the investment entities of the classes under different fluctuation conditions is analyzed, as shown in fig. 3.
Wherein the final investment proportion of the investment entity of class B may increase with the increase of the fluctuation of the initial allocation proportion proposed by the investment entity of class A, and the final investment proportion of the investment entity of class C may decrease with the increase of the fluctuation of the initial allocation proportion proposed by the investment entity of class A; that is, in the case of input fluctuation caused by the class a investment entity, the influence on the final investment proportion of the class B investment entity is the largest, and the influence on the final investment proportion of the class C investment entity is small. Specifically, the embodiment of the present invention extracts two representative fluctuation situations (a slight decrease situation of 2% input and a large fluctuation decrease situation of 30% input) from fig. 3 for visual data analysis, as shown in table 7.
TABLE 7 investment proportion values of various investment subjects before and after different fluctuations
As can be seen from Table 7: under the condition that the input quantity is reduced by 2%, the maximum value of fluctuation generated by the output quantity of the model is about 5%, which indicates that the final investment proportion of various investment subjects is still maintained near the initial investment proportion, namely the model has good stability against small disturbance; in the case of a 30% drop in input, the maximum value of the fluctuation produced by the output of the model has reached 41%, indicating that the model is less stable against large fluctuations. Therefore, in consideration of the defect problem of the model in the case of large fluctuation, the embodiment of the present invention defines the relative difference between the satisfaction and the negotiation base point as the relative deviation degree, and performs comparative analysis on the relative deviation degree in the two cases of no fluctuation and large fluctuation, as shown in table 8:
TABLE 8 comparison of the degree of relative deviation between the case of no fluctuation and large fluctuation
| Investment body | Degree of deviation without fluctuation | Large fluctuation deviation degree |
| A | 2.4352 | 1.7074 |
| B | 0.1693 | 0.2254 |
| C | 0.2799 | 0.6579 |
| Total degree of relative deviation | 2.8844 | 2.6237 |
As can be seen from Table 8: with the non-fluctuation condition as a reference, the satisfaction in the large fluctuation condition is higher than the negotiation base point, but there is a case that the satisfaction is lower than the total relative deviation degree of the negotiation base point. When the initial investment proportion proposed by the investment entity in the class A fluctuates greatly, the final investment proportion of the investment entity in the class A can still be accepted by the investment entities in other classes through the model, but the overall satisfaction degree of the alliance is reduced. Therefore, a minimum alliance satisfaction degree can be set in the negotiation process of the model to further increase the stability of the model, and if the final optimization result enables the alliance satisfaction degree to be lower than the minimum alliance satisfaction degree, various investment subjects are required to re-propose an initial investment proportion distribution scheme, and the model is used for re-optimization.
Fig. 4 is a schematic diagram illustrating a cooperative game-based multi-agent investment proportion optimization system in an embodiment of the present invention, where the system includes:
an establishing module 201, configured to establish an energy hub model;
a calculation module 202 for calculating a total investment cost of the incremental power distribution project based on the energy hub model;
the analysis module 203 is used for acquiring the initial investment proportion of each type of investment subject according to the total investment cost and determining related influence factors for redistributing the initial investment proportion;
and the optimizing module 204 is configured to re-optimize the initial investment proportion through an asymmetric Nash negotiation model by using the relevant influence factors and the satisfaction evaluation criteria as association factors.
For the specific implementation of each module in the system, please refer to the method flowchart and specific implementation content shown in fig. 1, which are not described herein again.
In the embodiment of the invention, under the open environment of the incremental power distribution service, the investment proportion of various investment subjects is optimized by using the asymmetric Nash negotiation model, and the stability degree of the model is restricted by setting the satisfaction degree evaluation standard, so that the problem of unfair allocation of the investment cost of the alliance can be solved, more investment subjects can be attracted to enter the incremental power distribution project for fair competition, the marketization degree of the power distribution side is improved, and the operation efficiency of the power distribution network is improved.
The method and the system for optimizing the multi-principal investment proportion based on the cooperative game are introduced in detail, a specific example is adopted to explain the principle and the implementation mode of the method, and the description of the embodiment is only used for helping to understand the method and the core idea of the method; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A cooperative game-based multi-subject investment proportion optimization method, comprising:
establishing an energy hub model;
calculating a total investment cost of an incremental power distribution project based on the energy hub model;
acquiring initial investment proportions of various investment main bodies according to the total investment cost, and determining related influence factors for redistributing the initial investment proportions;
and taking the related influence factors and the satisfaction evaluation standard as correlation factors, and carrying out re-optimization on the initial investment proportion through an asymmetric Nash negotiation model.
2. The method of claim 1, wherein the total investment cost of the incremental power distribution project comprises a total construction cost of the incremental power distribution project and a total operational cost of the incremental power distribution project.
3. The method of optimizing multi-agent investment proportions of claim 2 wherein the aggregate cost of construction of the incremental power distribution project
Comprises the following steps:
wherein the content of the first and second substances,
in order to reduce the construction cost of the power distribution network,
for the construction cost of the energy conversion equipment in the energy hub model,
and the construction cost for energy storage is saved.
4. The method of claim 3, wherein the power distribution network construction cost is optimized according to the multi-agent investment proportion
Comprises the following steps:
wherein, C0tFor a fixed construction cost per year of the distribution network,0tannual coefficient of fixed construction costs for distribution networks, CtranFor investment costs of the substation, ClineTo build up new line costs, C1tIn order to achieve the cost of operation and maintenance,1tfor operation and maintenance cost rate, C2tTo the loss cost of the network, betatThe comprehensive loss rate, Q, of the incremental distribution network in the t yeartTo increment the predicted capacity of the distribution grid for the t year,
the average electricity price of the incremental distribution network in the t year C3tFor loss cost of power failure, M is the load point number of the distribution network, RovenTo produce electricity ratio, EmExpected power shortage of the load nodes in the research period.
5. The method of claim 3, wherein the energy conversion facility construction costs
Comprises the following steps:
wherein S isMTIs the plant capacity of a micro gas turbine, SEBIs the plant capacity of the hot boiler, SGBIs the equipment capacity, xi, of the gas boilerMTUnit price, ξ, of the capacity of a micro gas turbineEBUnit price, xi, of the capacity of the hot boilerGBIs the unit price of the capacity of the gas boiler,ehthe investment annual coefficient of the energy conversion equipment.
6. The multi-subject investment proportion optimization method of claim 3, wherein the energy storage construction costs
Comprises the following steps:
wherein the content of the first and second substances,esfor the purpose of energy storage and annual investment coefficient,
is the rated capacity of the energy storage system, sesIn order to be a unit price per capacity,
for the rated power of the energy storage system, pesIs a unit price per power, betaesThe replacement rate of the energy storage battery.
7. The method of optimizing multi-agent investment proportions of claim 2 wherein the total cost of operation of the incremental power distribution project
Comprises the following steps:
wherein the content of the first and second substances,
in order to purchase the cost of the electrical energy to the grid,
in order to save the purchase cost of the stored energy,
in order to account for the cost of the natural gas input to the system,
for distribution network in the tth timeElectric energy, xi, required to be purchasedbetFor the unit price of purchasing power during the t-th time,
rated power, ξ, of the energy storage system during the t-th timevetThe electricity price at the bottom time in the t-th time,
total amount of natural gas, ξ, required for the distribution network during the t-th timebgtThe unit price of purchasing natural gas for the t-th time.
8. The multi-subject investment proportion optimization method of claim 1, wherein the asymmetric Nash negotiation model is:
wherein the content of the first and second substances,
is the minimum value of the investment proportion of the ith investment entity, yiIs the increasing coefficient of the ith investment entity to the occupied investment proportion,
is the maximum value, x, of the investment proportion of the ith investment entityiThe weight occupied by the ith investment entity,
is the final investment proportion occupied by the ith investment entity, and n is the total number of investment entities in the coalition.
9. The multi-subject investment proportion optimization method of claim 8, wherein the satisfaction criterion comprises:
wherein λ isiTo the satisfaction of the ith investment entity,
is the negotiation benchmark of the ith investment entity.
10. A cooperative game based multi-agent investment proportion optimization system, the system comprising:
the establishing module is used for establishing an energy hub model;
a calculation module for calculating a total investment cost for an incremental power distribution project based on the energy hub model;
the analysis module is used for acquiring the initial investment proportion of each type of investment main body according to the total investment cost and determining related influence factors for redistributing the initial investment proportion;
and the optimization module is used for carrying out re-optimization on the initial investment proportion through an asymmetric Nash negotiation model by taking the relevant influence factors and the satisfaction evaluation standard as correlation factors.
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| CN112560284A (en) * | 2020-12-24 | 2021-03-26 | 国网河北省电力有限公司经济技术研究院 | Power distribution network planning method for multi-subject game and terminal equipment |
| WO2022011968A1 (en) * | 2020-07-17 | 2022-01-20 | 广西电网有限责任公司电力科学研究院 | Multi-agent investment proportion optimization method and system based on cooperative game |
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| CN108805449A (en) * | 2018-06-11 | 2018-11-13 | 南方电网科学研究院有限责任公司 | Cooperative game method towards integrated energy system cost sharing and distribution of income |
| CN111353128B (en) * | 2020-01-17 | 2023-07-25 | 浙江工业大学 | Multi-energy hub optimization operation method based on non-cooperative game |
| CN111476470A (en) * | 2020-03-30 | 2020-07-31 | 广西电网有限责任公司崇左供电局 | Multi-main-body investment cost allocation method based on asymmetric Nash negotiation model |
| CN111967647A (en) * | 2020-07-17 | 2020-11-20 | 广西电网有限责任公司电力科学研究院 | Cooperative game-based multi-subject investment proportion optimization method and system |
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| WO2022011968A1 (en) * | 2020-07-17 | 2022-01-20 | 广西电网有限责任公司电力科学研究院 | Multi-agent investment proportion optimization method and system based on cooperative game |
| CN112560284A (en) * | 2020-12-24 | 2021-03-26 | 国网河北省电力有限公司经济技术研究院 | Power distribution network planning method for multi-subject game and terminal equipment |
| CN112560284B (en) * | 2020-12-24 | 2022-04-08 | 国网河北省电力有限公司经济技术研究院 | Power distribution network planning method for multi-subject game and terminal equipment |
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