CN110889598A - Decision-making method for behaviors of transaction subjects in distributed power generation marketization environment - Google Patents
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Abstract
The invention discloses a method for deciding behaviors of trading subjects in a distributed power generation marketization environment, which belongs to the technical field of new energy power generation optimal configuration and comprises the following steps: establishing a distributed generator response behavior model; establishing a power consumer response behavior model; establishing a power grid enterprise response behavior model; converting the response behavior model into a single-layer multi-objective optimization model by adopting a multi-lower-layer and double-layer planning model conversion method; and solving the single-layer multi-objective optimization model by using a multi-objective evolutionary algorithm to obtain a behavior decision result of each trading subject in the distributed power generation marketization environment. The method provided by the invention is combined with relevant policies and standard constraints of distributed power generation marketization, a response behavior model of each transaction subject is established, benefit requirements and behavior modes of each subject in the distributed power generation marketization environment can be reflected practically, and the subject behavior decision results obtained by the method provided by the invention are more in line with actual engineering requirements, so that the safety and economy of operation of the distributed power generation market are improved.
Description
Technical Field
The invention belongs to the technical field of new energy power generation optimization configuration, and particularly relates to a method for deciding behaviors of transaction subjects in a distributed power generation marketization environment.
Background
The distributed power generation has wide distribution points, can realize the production and consumption of clean energy nearby, and represents a new form and a new direction of energy development. However, the distributed power generation has high grid-connected cost and strong dependence on subsidy in the development process, and further promotion of the marketization application of the distributed power generation cannot be controlled for the salient problems of large-scale industrial production and power supply and the like.
The trading modes of the distributed generation marketization trading are divided into three types: the method comprises the steps that firstly, in a direct transaction mode, distributed power generators and power users directly perform power transaction, sign medium-term and long-term transaction contracts and pay 'network passing fees' to power grid enterprises; the distributed power generation business commissions and asks the power grid enterprise to sell power, the power grid enterprise settles the accounts with the distributed power generation business according to the local comprehensive power selling price, and transfers the residual income to the distributed power generation business after deducting the corresponding 'network passing fee'; and thirdly, a marker post electricity price purchasing mode, wherein the power grid enterprise purchases the electricity price of the power generation marker posts on the internet according to various national regulations, but the corresponding 'network passing fee' is borne by the power grid enterprise. According to the field implementation experience, the income of the distributed power generator is mainly from two parts, namely the income of participating in the market-oriented trading of the distributed power generation and the income provided by government agencies such as the state and province and city. In the case of a post price acquisition mode, for example, a power grid enterprise bears the economic expenditure of acquiring the distributed power generation electric quantity by using the coal-fired post price, and the difference between the grid-surfing electricity price of the distributed power generation post and the coal-fired post price is the electricity consumption subsidy provided by the government organization.
Currently, the current practice is. Relevant research has been carried out on the market of distributed power generation at home and abroad. The existing research mainly expounds the basic concept and characteristics of distributed transaction, compares the mechanism and mode of distributed generation transaction at home and abroad, and discusses the development of the market-oriented transaction of the future distributed generation in China; some students analyze the distributed power generation marketing mode in the distributed power generation marketing environment from the perspective of distributed power generation access planning, and even analyze the influence of the distributed power generation marketing policy from the perspective of participation of a microgrid in marketing trading. However, the above studies have three problems. Firstly, the above documents focus on analyzing and interpreting contents of policies in distributed power generation marketization, and more documents belong to the category of policy statement. After the distributed generation marketization transaction is implemented, the response behaviors of all the main bodies lack corresponding analysis and research; secondly, although the research of the partial documents relates to distributed generation marketization trading, most documents only use the concept of 'trading mode' or 'internet fee', do not really give the trading mode option to the distributed generator, and lack the panoramic display analysis of the distributed generation marketization trading; thirdly, with the development of distributed generation marketization trading at each test point, each test point provides a new settlement rule and a deviation assessment method, but most of the existing documents only relate to the content of 'notice' documents, and do not relate to new trading rules at all places basically.
Disclosure of Invention
Aiming at the defects or the improvement requirements of the prior art, the invention provides a method for deciding the behavior of each trading subject in a distributed power generation marketization environment, and aims to solve the technical problem that the behavior decision of each trading subject in the distributed power generation market at the present stage does not accord with the actual engineering requirements, so that the running safety and economy of the distributed power generation market are lower.
In order to achieve the above object, the present invention provides a method for deciding the behavior of each transaction subject in a distributed power generation marketization environment, comprising:
(1) the method comprises the steps that the minimum economic cost of a distributed generator is taken as a first objective function, the built-in capacity and the transaction mode of a distributed generation project are taken as variables to be decided, and a response behavior model of the distributed generator is constructed according to the built-in installation quantity constraint of a region to be researched, the net charge constraint and the electricity subsidy constraint under different transaction modes;
(2) the minimum electricity utilization cost of the power consumer is taken as a second objective function, the electric energy trading volume of each distributed power generator is taken as a variable to be decided, and a power consumer response behavior model is established according to the electricity selling price and the estimated tradeable electric quantity all the year round which are provided by each distributed power generator;
(3) constructing a power grid enterprise response behavior model according to power grid operation constraint conditions by taking the minimum economic cost of power grid operation as a third objective function, the minimum reduction of distributed generation as a fourth objective function and the access nodes and the capacity of distributed generation projects as variables to be decided;
(4) converting the response behavior model into a single-layer multi-objective optimization model by adopting a multi-lower-layer and double-layer planning model conversion method;
(5) and solving the single-layer multi-objective optimization model by using a multi-objective evolutionary algorithm to obtain a behavior decision result of each trading subject in the distributed power generation marketization environment.
Further, the transaction pattern includes: the system comprises a direct transaction mode, a power grid electricity-selling-replacing mode and a pole-surfing electricity price purchasing mode.
Further, the first objective function is:
Objdg,k＝min(Cins,k+Cyw,k+Cgwf,k-Bbt,k-Bsell,k)k∈Cdg
in the formula, Objdg,kThe minimum objective function of economic cost of the kth distributed power generator is obtained; cins,kAnnual total cost of distributed power generation projects built for the kth distributed power generator; cyw,kThe annual operation and maintenance cost of the kth distributed power generator is saved; cgwf,kThe annual network charge cost paid to the power grid enterprise by the kth distributed generator in the corresponding transaction mode is saved; b isbt,kSubsidizing a renewable energy power generation project acquired from the government all year round by a kth distributed power generator; b issell,kTrading earnings for the kth distributed generator for annual electric energy; cdgIs a set of all distributed power generation merchants participating in the market trading of distributed power generation.
Further, in the direct transaction mode, the fee B of passing through the network is usedgwfzj,kAnd (3) constraint:
in the formula, sum is a matrix summation function; lambda [ alpha ]invoThe power transmission and distribution price, lambda, corresponding to the highest voltage level involved by accessing the power grid to the power consumer10、λ35、λ110For the corresponding transmission and distribution prices of 10kV, 35kV and 110kV in the area to be researched
Furthermore, in the power grid electricity selling mode, the user charges Bgwfdsd,kThe constraints are:
in the formula, λ1The power transmission and distribution price is corresponding to the voltage class of 1kV and below in the area to be researched.
Further, the kth distributed generator trades the income B of the annual electric energysell,kComprises the following steps:
in the formula, Bxy,kFor the kth distributed power generator adopting the direct trading mode, the trading agreement is signedConsulting the obtained revenue; lambda [ alpha ]rmbgThe electricity price is charged for the coal-fired marking pole to access the internet; lambda [ alpha ]xy,kThe agreement electricity price for the direct transaction of the kth distributed power generator and the power user; wsg,k、Wqf.kRespectively determining the actual settlement electric quantity of the kth distributed power generator in the direct transaction mode as the electric quantity which is generated more and less than the transaction electric quantity; lambda [ alpha ]rmbgThe electricity price is charged for the coal-fired marking pole to access the internet; lambda [ alpha ]zhThe comprehensive electricity selling price is achieved.
Further, the second objective function is:
Objload＝min(Cxyload+Cdwload-Bpcload)
in the formula, ObjloadAn objective function for minimizing the electricity consumption cost of the electricity consumers, Cxylaod、CdwloadThe purchase cost of the power consumer purchasing electricity through the distributed generation marketization transaction and directly purchasing electricity to the power grid enterprise is respectively BpcloadThe method comprises the following steps of collecting benefits for fine money acquired by power users due to power deviation in distributed power generation marketization transaction;
in the formula, WindloadThe total annual power consumption of the power consumer.
Further, the third objective function is:
Objdw＝min(Cdgsg+Cdsd+Csjbuy-Bsjsell-Bload-Bgwf)
in the formula, ObjdwFor operating the grid with a minimum objective function of economic cost, CdgsgThe annual generating capacity purchasing cost of the power grid enterprise aiming at the distributed power generation project in the direct transaction mode and the benchmarking electricity price purchasing mode is saved; cdsdThe annual generated energy online cost of a power grid enterprise aiming at a distributed power generation project under the electricity selling mode is saved; csjbuy、BsjsellRespectively charging the power cost and returning the power for the power grid enterprise all the year around for the superior power grid; b isloadNegative charge for power grid enterprises in areas to be researchedThe annual electricity selling income of the lotus; b isgwfThe annual network charge income is paid to the power grid enterprise by the distributed power generator;
Bgwfbg,kpaying a net charge to the power grid for the kth distributed power generator in a post-surfing electricity price purchasing mode;
further, the fourth objective function is:
in the formula, Pg,i,tThe active output value of the ith transformer node in the t-th time period is obtained; pdg,tThe output upper limit of unit installation capacity of the distributed power generation project at the t-th time period is set; pbyq,iAccessing capacity for distributed generation of an ith transformer node; byq is a set of transformer nodes accessible for distributed power generation; t isjs,tThe total number of time periods that can be represented throughout the year for the t-th time period; t isjsThe number of time segments is calculated for the total.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) the method provided by the invention combines relevant policies and standard constraints of distributed power generation marketization, establishes response behavior models of various transaction subjects, and can practically reflect interest demands and behavior modes of various subjects in the distributed power generation marketization environment, so that the behavior decision result of each subject is more in line with the actual engineering requirements, and the method is favorable for improving the safety and economy of the operation of the distributed power generation marketization.
(2) The invention utilizes the conversion method of the multi-lower layer double-layer planning model to convert the original multi-main-body model into the double-layer planning model with the single lower layer planning problem, thereby avoiding the complex programming process of multi-intelligent-body simulation, being more beneficial to displaying the benefit interaction relationship among the main bodies and being convenient for solving.
Drawings
FIG. 1 is a schematic diagram of a trading mechanism of each main body in a distributed power generation market according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for determining the behavior of a transaction subject in a distributed power generation marketization environment according to the present invention;
FIG. 3 is a schematic diagram of distributed generator accessible nodes and transformer capacity (MW) provided by an embodiment of the present invention;
FIG. 4 is a Pareto optimal scatter plot of economic costs for various distributed power producers provided by an embodiment of the present invention;
fig. 5 is a graph of percentage of response behavior parameters of each participating transaction body according to the reduction ratio of subsidies provided by the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The basic concept of the method of the invention is as follows: the distributed generation marketization trade participation main body mainly comprises a distributed generator, power users, a power grid enterprise, a power trading mechanism and a power dispatching mechanism. Considering that the distributed power generation market in China is in a starting construction stage, the responsibility and obligation of the three parts are all born by a power grid enterprise at present; in addition, in consideration of subsidies of high-volume renewable energy power generation projects acquired by distributed power generators from government agencies, subsidies of government agencies also have an influence on the behavior of each transaction subject in the distributed power generation marketization environment. Therefore, the trading subject studied in the distributed power generation marketization environment of the present invention includes: distributed power generators, power consumers, grid enterprises, and government agencies; the distributed power generator determines the investment amount of a distributed power generation project, and selects a transaction mode; the power consumer decides the electricity purchasing agreement electric quantity in the direct transaction mode; the power grid enterprise is responsible for distributed power generation project access and power grid safe and economic operation; the government agency decides different subsidy amounts obtained by the distributed power generation project in different built capacity ranges; the trading mechanism of each main body of the distributed power generation marketization is shown in figure 1.
Referring to fig. 2, the decision method for the behavior of each transaction subject in the distributed power generation marketization environment provided by the invention includes:
(1) the method comprises the steps that the minimum economic cost of a distributed generator is taken as a first objective function, the built-in capacity and the transaction mode of a distributed generation project are taken as variables to be decided, and a response behavior model of the distributed generator is constructed according to the built-in installation quantity constraint of a region to be researched, the net charge constraint and the electricity subsidy constraint under different transaction modes;
the distributed power generator is responsible for investing and constructing a distributed power generation project and selecting a trading mode by taking the minimum economic cost of the distributed power generator as a target. If the direct transaction mode is selected, the electricity selling agreement electricity price needs to be established, and the electricity selling agreement is signed and ordered with the power consumer. On one hand, the contradiction of electric quantity settlement among various transaction modes is considered, and at the present stage, no relevant rule how to divide the electric quantity of the various transaction modes exists; on the other hand, most distributed power generation marketization trading points execute a single distributed power generation project at present, and only one trading mode can be selected. Therefore, in order to simplify the model and combine the actual situation, the distributed power generator only selects one transaction mode. A plurality of distributed power generators have a competitive relationship with the load capacity resources and the power load resources of the power grid distributed power generation project.
(1.1) the first objective function is:
Objdg,k＝min(Cins,k+Cyw,k+Cgwf,k-Bbt,k-Bsell,k)k∈Cdg
in the formula, Objdg,kFor the k distributed generator economic cost minimum objective function, Cins,k、Cyw,kAnd Cgwf,kAnnual total cost, annual operation and maintenance cost and annual network charge cost paid to a power grid enterprise of a distributed power generation project built by a kth distributed power generator respectively; b isbt,kSubsidizing a renewable energy power generation project acquired from the government all year round by a kth distributed power generator; b issell,kTrading earnings for the kth distributed generator for annual electric energy; cdgThe method comprises the steps of collecting all distributed power generation merchants participating in the distributed power generation marketization transaction;
in the formula, Pins,kThe installation amount of the distributed power generation project is built for the kth distributed power generator; cdginvInstallation cost per distributed generation installation amount; r, TgcAnd α are respectively the discount rate, project period and equipment residual value rate;
Cyw,k＝λdgywWsum,k
in the formula, λdgywElectric operation and maintenance cost for a distributed power generation project; wsum,kThe annual total power generation of a project is built for the kth distributed power generator;
in the formula, Cdgzj、CdgdsdAnd CdgbgThe system comprises a distributed power generation merchant set, a power grid electricity-selling-substituting mode and a benchmarking electricity price purchasing mode, wherein the distributed power generation merchant set adopts a direct transaction mode, a power grid electricity-selling-substituting mode and a benchmarking electricity price purchasing mode respectively; b isgwfzj,kPaying the network fee to the power grid for the kth distributed generator in the direct transaction mode; b isgwfdsd,kPaying the power grid for the kth distributed generator in a power grid electricity-selling-replacing mode; if the post price acquisition mode is adopted, the distributed power generator does not need to pay the network charge to the power grid;
Bbt,k＝λbt,kWsum,k
in the formula, λbt,kObtaining from government agencies for the kth distributed generatorThe renewable energy power generation project electricity subsidy;
in the formula, Bxy,kThe benefits obtained for the kth distributed power generator adopting the direct trading mode through signing a trading agreement; lambda [ alpha ]rmbgThe electricity price is charged for the coal-fired marking pole to access the internet; lambda [ alpha ]xy,kThe agreement electricity price for the direct transaction of the kth distributed power generator and the power user; wsg,k、Wqf.kThe actual settlement electric quantity of the kth distributed power generator in the direct transaction mode is compared with the excessive electric quantity and the insufficient electric quantity of the transaction electric quantity, and the actual settlement electric quantity is generally called as deviation electric quantity; when the transaction settlement electric quantity of the distributed power generation project is lower than the agreement signing electric quantity, the default responsible party pays default compensation fee to the other transaction party according to the default electric quantity and 10% of the coal-fired benchmarking electric price; when the actual grid power is higher than the agreement signed power, the power grid enterprise buys the coal-fired benchmarking power price; lambda [ alpha ]rmbgThe electricity price is charged for the coal-fired marking pole to access the internet; lambda [ alpha ]zhThe comprehensive electricity selling price is achieved.
(1.2) responding to the behavioral model constraint conditions by the distributed generators;
1) and (3) construction and installation quantity constraint of a distributed power generation project:
wherein the min function is a minimum function, Ploadave10、Ploadave35、Ploadave110Respectively average electricity utilization load of the areas to be researched in the whole year in the power supply ranges of 10kV, 35kV and 110kV voltage levels; y isins10,k、Yins35,k、Yins110,kThe distributed generation projects built for the kth distributed generator are respectively connected to the zone bits at the voltage levels of 10kV, 35kV and 110kV, and epsilon is a minimum number; the maximum project capacity of the monomer is not more than 50MW, and when the project capacity of the monomer is less than 6MW, the voltage level of the accessed power grid is 10kV or below; when the monomer project capacity is 6MW to 20MW, the power grid is accessedThe voltage grade is 35 kV; when the monomer project exceeds 20MW, the voltage level of the accessed power grid does not exceed 110 kV;
2) in direct transaction mode, a net charge Bgwfzj,kAnd (3) constraint:
in the formula, sum is a matrix summation function; lambda [ alpha ]invoThe power transmission and distribution price, lambda, corresponding to the highest voltage level involved by accessing the power grid to the power consumer10、λ35、λ110Corresponding transmission and distribution prices of 10kV, 35kV and 110kV in the area to be researched;
3) passing the net charge B under the mode of power-supply-network-substitute sellinggwfdsd,kAnd (3) constraint:
in the formula, λ1The power transmission and distribution price is corresponding to the voltage class of 1kV or below in the area to be researched;
4) electricity consumption patch Bbt,kAnd (3) constraint:
in the formula, α110、α35、α10Respectively are proportional factors relative to the current power supply patch; lambda [ alpha ]btnowThe linear electricity subsidy price is obtained; the government organization is responsible for making subsidy policies, carrying out gradient subsidy on distributed power generation projects with different built-in capacities, and participating in distributed power generation projects of distributed power generation marketization trade. The method shows subsidy behaviors of government agencies to the distributed by continuously reducing government subsidiesInfluence brought by power generation marketization.
5) Agreement electricity price limit
0≤λxy,k≤λindk∈Cdgzj
In the formula, λindDirectly purchasing electricity price for industrial load to a power grid enterprise; in the current-stage distributed power generation marketization trading, medium-term and long-term trading contracts are encouraged by distributed power generation projects and stable industrial loads, and distributed power generators adopting a direct trading mode compete for contract electric quantity by quoting the industrial loads, so that the agreed electric price is the highest and cannot exceed the electric price of directly purchasing electricity to a power grid enterprise by the industrial loads.
6) And electric quantity deviation constraint:
if Wsum,k-Wxy,kWhen the ratio is less than or equal to 0, then
If Wsum,k-Wxy,kIs not less than 0, then
(2) The minimum electricity utilization cost of the power consumer is taken as a second objective function, the electric energy trading volume of each distributed power generator is taken as a variable to be decided, and a power consumer response behavior model is established according to the electricity selling price and the estimated tradeable electric quantity all the year round which are provided by each distributed power generator;
and the power consumer aims at minimizing the self power consumption cost, and decides the annual agreement power quantity with each distributed power generator according to the power selling price provided by each distributed power generator and the annual estimated tradeable power quantity. On one hand, the power users participating in the distributed power generation marketization transaction should have long-term stable and large power requirements, and the distributed power generation project unit should trade with the power users capable of accommodating all the internet electricity quantity; on the other hand, the load demand resources owned by the power consumers are relatively fixed, while the distributed power generator can expand the power supply by additional investment, and the power load demand is a scarce resource relative to the distributed power generator. Therefore, the present invention adopts a mode that a single large power consumer faces competition of a plurality of distributed power generators to describe the competition state in the distributed power generation marketization trade.
(2.1) the second objective function is:
Objload＝min(Cxyload+Cdwload-Bpcload)
in the formula, ObjloadAn objective function for minimizing the electricity consumption cost of the electricity consumers, Cxylaod、CdwloadThe purchase cost of the power consumer purchasing electricity through the distributed generation marketization transaction and directly purchasing electricity to the power grid enterprise is respectively BpcloadThe method comprises the following steps of collecting benefits for fine money acquired by power users due to power deviation in distributed power generation marketization transaction;
in the formula, WindloadThe total annual power consumption of the power consumer is calculated;
(2.2) the power consumer responds to the behavioral model constraint condition;
1) and (4) protocol electric quantity limiting:
in the formula, WindxyThe annual electricity consumption quantity available for distributed power generation marketization trading for power consumers is quoted along with distributed power generators when a plurality of distributed power generators competexy,kThe higher the electricity consumer has agreed upon the amount of electricity W with the distributed generatorxy,kThe lower.
(3) Constructing a power grid enterprise response behavior model by taking the minimum economic cost of power grid operation as a third objective function, the minimum reduction of distributed generation as a fourth objective function and the access nodes and the capacity of distributed generation projects as variables to be decided;
the power grid enterprises need to provide services such as power grid access, safe and stable operation of the power grid, market trading bottom, market trading settlement and the like for the distributed power generation project. The power grid enterprise considers two targets, namely the minimum economic cost of power grid operation is taken as a target, and the minimum reduction of distributed power generation is taken as a target, so that the access node and the capacity of a distributed power generation project are decided, and the safe and stable operation of the power grid is ensured.
(3.1) the third objective function is:
Objdw＝min(Cdgsg+Cdsd+Csjbuy-Bsjsell-Bload-Bgwf)
in the formula, ObjdwFor operating the grid with a minimum objective function of economic cost, CdgsgThe annual generating capacity purchasing cost of the power grid enterprise aiming at the distributed power generation project in the direct transaction mode and the benchmarking electricity price purchasing mode is saved; cdsdThe annual generated energy online cost of a power grid enterprise aiming at a distributed power generation project under the electricity selling mode is saved; csjbuy、BsjsellRespectively charging the power cost and returning the power for the power grid enterprise all the year around for the superior power grid; b isloadThe load collected for the power grid enterprise in the area to be researched is sold for the whole year; b isgwfThe annual network charge income is paid to the power grid enterprise by the distributed power generator;
in the formula, λsjvlevelThe power transmission and distribution price is the voltage grade of the connection point of the power grid and the superior power grid in the area to be researched; wsjbuy、WsjsellRespectively buying electric quantity and returning electric quantity for the power grid enterprise all the year for a superior power grid; welseloadAnnual power consumption (such as residential power, agricultural power and general industrial and commercial power) for other power loads except for power consumers in the area to be researched; lambda [ alpha ]elseAverage electricity prices for other power loads; b isgwfbg,kThe net charge which needs to be paid to the power grid for the kth distributed power generator in the post-surfing tariff purchase mode is paid, and the net surfing cost is caused by the purchasing of the distributed power generation amount in the distributed power generation project adopting the post-surfing tariff purchase modeThe system is born by a power grid enterprise;
(3.2) the fourth objective function is:
in the formula, Pg,i,tThe active output value of the ith transformer node in the t-th time period is obtained; pdg,tThe output upper limit of unit installation capacity of the distributed power generation project at the t-th time period is set; pbyq,iAccessing capacity for distributed generation of an ith transformer node; byq is a set of transformer nodes accessible for distributed power generation; t isjs,tThe total number of time periods that can be represented throughout the year for the t-th time period; t isjsCalculating the number of time segments for the total;
(3.3) power grid enterprise response behavior model constraint conditions:
1) power purchasing and selling constraint for upper-level power grid
In the formula, Pgsjbuy,t、Pgsjsell,tThe electricity purchasing power and the electricity selling power are respectively purchased from a power grid company to a superior power grid in the tth time period; pgsjbuy max、Pgsjsell maxThe upper limit values of the electricity purchasing power and the electricity selling power for the superior power grid are respectively; y issjbuy,t、Ysjsell,tThe power purchasing and selling flag bits are respectively the power purchasing and selling flag bits of the power grid company in the tth time period, and at the same time, the power grid company can only have one state of power purchasing or power selling for the superior power grid.
2) Power generation node constraints
0≤Pg,i,t≤Pdg,tPbyq,ii∈Byq
3) Distributed power generation project access transformer capacity constraint
In the formula, Pbyq,i,kThe distributed generation capacity is accessed to the ith transformer node for the kth distributed generator; y isbyq,i、Pbyqrl,iThe access zone bit and the transformer capacity of the ith transformer node are respectively; byqkA set of transformer nodes for which distributed generation is accessible for the kth distributed generator. According to the related technical standard of the distributed power generation power grid, the capacity of a transformer is 1.1-1.2 times of the capacity of a grid-connected point of the distributed power generation; the capacity requirement of a single grid-connected point in a 10kV grid-connected voltage level is 400 kW-6 MW; the capacity requirement of a single grid-connected point in the 35kV grid-connected voltage level is 6 MW-20 MW; the capacity requirement of a single grid-connected point in the 110kV grid-connected voltage class is 20MW or more. When the capacity of a single grid-connected point is less than 400kW, 380V/220V grid-connected voltage level can be directly accessed, and the method belongs to the household category and is out of the range considered by the method.
4) Electric quantity restraint
In the formula, Pg,k,i,tThe active output value of the ith transformer node in the t period is accessed to the kth distributed generator, Cdg,kA set of transformer nodes is accessed for the kth distributed generator.
5) Comprehensive electricity selling price constraint
6) Flow equation constraints
In the formula, Pij,t、Qij,tAnd thetai,j,tThe active power, the reactive power and the branch voltage phase angle difference of a branch (hereinafter referred to as a branch ij) connected with the node i and the node j in the tth time period are respectively; gijAnd bijConductance and susceptance of branch ij, respectively; v. ofi,tThe voltage amplitude of node i is the t-th period.
7) Other constraints
Besides the constraints, the power grid enterprise response model still needs to consider the constraint conditions such as line current constraint, load flow out-of-limit constraint, node voltage constraint, node balance constraint and reactive power constraint.
(4) Converting the behavior response model into a single-layer model by adopting a multi-lower-layer and double-layer planning model conversion method;
the response behavior model of each transaction main body in the distributed power generation marketization environment has the problems of multiple main bodies, high dimensionality, nonlinearity and the like. The method adopts multi-lower layer double-layer programming model conversion and a multi-target evolutionary algorithm to solve. Multi-lower layer two-layer linear programming means that the decision of each lower layer subject is not only limited by the decision of the upper layer subject, but also influenced by the decision of other lower layer subjects. The general form is:
s.t. x is not less than 0, wherein y1,…ykNeed to satisfy
Wherein, F, Fi:Rn×Rm→R1,c,ci∈Rn,
Wherein F, x are the upper layer objective function and decision variable, respectively, fi、yiRespectively is the objective function and the decision variable of the ith lower main body. In the multi-lower layer double-layer planning model, the decision effect of the upper layer main body decision x is influenced by the decision variables y of the lower layer main bodies1y2…ykThe influence of (a); lower ith subject decision yiIs subject to upper layer decision and other lower layer subject yt(t ≠ i).
Considering that the commissioning behavior of the distributed power generator in the distributed power generation marketization transaction has the advantage of moving first, and other main bodies make decisions according to the commissioning quantity of the distributed power generation project, the response behavior model of the distributed power generator is used as an upper-layer model, and the response behavior model of other main bodies is used as a lower-layer model. Therefore, the behavior response model is converted into a multi-lower-layer double-layer planning model with mutually influenced lower-layer decision variables.
The upper layer model can be abbreviated as:
min Objdg,kk∈Cdg
s.t.fdg,i(xdg,xload,xdw)≤0,i＝1,…,mdg
hdg,i(xdg,xload,xdw)＝0,i＝1,…,pdg
in the formula (f)dg、hdgInequality constraints and equality constraints of the distributed generator quotient response model are respectively; m isdg、pdgThe number of inequality constraints and the number of equality constraints are respectively set; x is the number ofdgThe decision variables of the upper model are influenced by the decisions of the lower power consumer and the power grid enterprise.
The underlying model can be abbreviated as:
s.t.fk,i(xk,xt,xdg)≤0,i＝1,…,mkk,t∈C t≠k
hk,i(xk,xt,xdg)＝0,i＝1,…,pkk,t∈C t≠k
c ═ power consumer, grid enterprise }
In the formula (f)k,i、hk,iInequality constraints and equality constraints of the kth lower-layer main body response model are respectively; m isk、pkThe number of inequality constraints and the number of equality constraints are respectively set; x is the number ofkThe decision variables of the kth main body response model of the lower layer are used as parameters to influence the decision of the kth main body. Considering that the power consumer model is relatively simple, the power consumer behavior response model can be subjected to equivalent transformation mathematically by using a KKT (Karush-Kuhn-Tucker) condition, and finally a double-layer planning model with a single lower-layer planning problem is formed for solving.
(5) And solving the single-layer model by using a multi-objective evolutionary algorithm to obtain a behavior decision result of each trading subject in the distributed power generation marketization environment.
The converted model is a multi-objective optimization model, and the Pareto optimal solution of the model can reflect various results of competition of different distributed power generators. Therefore, the solution is performed by using a multi-objective evolutionary algorithm (MOEA/D) of the Tchebycheff decomposition strategy. The algorithm flow is as follows:
1) and setting parameters. Setting the population number N, the neighbor number T, the maximum iteration number Maxgen and the range of N independent variables;
2) the weight vector is initialized. Generating N weight vectors, and acquiring the first T weight vectors as adjacent weight vectors B of each weight vector according to the sequence of Euclidean distances from small to large;
3) an individual is initialized. Respectively initializing the argument value X according to the N weight vectors, i.e.
4) Is provided withAnd setting a reference point. Solving the model of formula (39) to obtain the fitness value Fit (x)i) Initialization reference point Z ═ Z1z2z3]TSetting the initial iteration number gen as 0;
5) and acquiring a new individual. For each individual xiFrom its neighboring weight vector BiRandomly selecting two serial numbers k and l, and generating a new individual y by using a genetic operator;
6) improving new individuals. Checking whether the individual y meets the requirement of the independent variable range, and correcting the individuals beyond the range;
7) and updating the reference point and the adjacent solution. If Fitj(y) is less than zjUpdating the reference point; for each weight vector BiIf Fit (y) is compared to Fit (x)i) Closer to the reference point, update BiCorresponding adjacent solutions;
8) and (4) terminating the conditions. If gen is Maxgen, stopping calculation, and outputting an adaptive value Fit (X) and an optimal solution X, otherwise, if gen is gen +1, returning to the step 5 to continue calculation;
on the premise of fully considering distributed generation marketization trading policies and standard constraints, a response behavior model of each trading subject under a distributed generation marketization environment is established according to actual requirements of each subject participating in the distributed generation marketization trading, interest interaction relations among the subjects are analyzed, and theoretical support is provided for further popularization of the distributed generation marketization trading. And converting the original model into a double-layer planning model with a single lower-layer planning problem by adopting a KKT condition conversion method, and solving and obtaining the optimal decision result of each trading subject under the distributed power generation marketization environment by utilizing a multi-objective evolutionary algorithm.
In the following, an actual distribution network system in a certain town is taken as an example. In order to simplify calculation, three distributed power generators are arranged to participate in competition, and all three distributed power generation projects are photovoltaic power generation. The load data is time sequence data of the local power distribution network running all the year round by adopting the actual power distribution network of a certain village and town for example analysis, the load ratio of local large-power users is 50.44%, and the method can be used for large-power use of electric power in transaction with distributed photovoltaic power generatorsThe household load proportion is 34.24%. The distribution network main line is a 35kV outgoing line, 11 35kV/10kV transformers are provided in total, the annual peak load of the line is 43.45MW, the maximum allowable current is 408A, and the structure of a power supply line and the capacity of the transformers are shown in figure 3. The original state is that the power grid enterprise is not accessed by distributed power generation, and the power grid operation cost is minus 3.36 multiplied by 106And (5) Yuan. In the multi-objective evolutionary algorithm, the population number is set to be 100, the neighbor number is set to be 20, and the maximum iteration number is set to be 250 generations. The example analysis analyzes the response behaviors of all main bodies in the distributed power generation marketization environment from four aspects of distributed power generators, power grid enterprises, power consumers and governments. Some values of key parameters and line parameters which can be directly obtained and are used in the embodiment of the invention are respectively shown in table 1 and table 2.
TABLE 1
TABLE 2
The effectiveness of the invention is analyzed by the embodiment from four main body response behaviors of distributed generators, power grid enterprises, power consumers and government agencies:
(1) distributed generator response behavior analysis
(1.1) economic cost Pareto optimal analysis of distributed power generators:
the power grid enterprise aims at minimizing the economic cost of operation of the power grid enterprise, government subsidy behaviors are executed according to the current administrative policy, the Pareto frontier for solving the model to obtain the economic cost of the distributed power generators is shown in fig. 4, and the response behaviors of the distributed power generators are compared and shown in table 3 under the condition that a certain distributed power generator obtains the optimal solution.
TABLE 3
Because the investment of load resources is a scarce resource relative to the investment of distributed power generators and the bearing capacity of a power grid to a distributed power generation project is limited, the distributed power generators compete for the limited resources in a distributed power generation marketization environment, and each solution in a Pareto solution set represents an optimal decision result of the distributed power generators in a specific competition allocation mode. As can be seen from fig. 4 and table 3:
1) when the economic cost of one distributed power generator is the minimum, the economic cost of the other two distributed power generators faces an increase of an order of magnitude and even a loss state. Thus, a single distributed generator economic cost optimum does not mean that all distributed generator economic costs are optimum;
2) in the aspect of trading mode selection, when the distributed power generator obtains the optimal solution, the selected trading modes are direct trading modes. This is mainly because the present embodiment is simulated in the case where the grid enterprise aims to minimize the running economic cost of the grid enterprise, and the grid enterprise prefers the distributed power generator to select the direct trading mode or the benchmarking electricity price purchasing mode. Compared with the low electricity price of the benchmarking electricity price acquisition mode, the distributed generator selects the direct transaction mode to further reduce the economic cost.
(1.2) impact analysis of distributed generator commissioning behavior
Considering that the competitive relationship among the distributed generators is already described in section (1.1), from the perspective of control variables, the section assumes that the three distributed generators are put into operation in the same amount, and increases the total amount in turn, and analyzes the change of interest of other subjects caused by the putting into operation of the distributed generators. The decision-making target of the power grid enterprise is that the economic cost of the operation of the power grid enterprise is minimum, the policy subsidy action is executed according to the current policy, and the weight vector of the upper model is set to be [1/3,1/3 and 1/3 ]. Other subject benefit changes resulting from distributed generator commissioning activities are shown in table 4.
TABLE 4
As can be seen from the analysis of table 4, when the grid enterprise decides the target according to the economic operation of the grid:
1) in actual operation, the distributed power generation projects built by each distributed power generator have different geographic positions, the range of the nodes connected to the power grid is also different, and the accessible nodes of each distributed power generator are shown in fig. 3. As can be seen from the cases 1 to 4, even when the distributed power generator investment amount and the trading mode selection are the same, the economic operation cost is different due to different access nodes;
2) when the investment amount of the distributed power generation project is the same, the economic cost minimum modes of the distributed power generators are direct transaction modes, which is consistent with the analysis conclusion of section (1.1).
3) When the investment amount of the distributed power generation project is gradually increased, the economic total cost of a distributed power generator is reduced, but the economic cost of a single distributed power generator is not necessarily reduced along with the difference of the grid access position and the selected trading mode;
4) when the distributed power generation project investment amount is gradually increased, the net economic operation cost of the power grid enterprise is also reduced, mainly because the controllable resources of the power grid enterprise are more as the distributed power generation project investment amount is increased (in a direct transaction mode, the power grid can purchase the electric quantity which is more than the agreed electric quantity in the distributed power generation). From the economic operation angle, compared with the power purchasing from a superior power grid, a power grid enterprise prefers to purchase power from distributed power generation suppliers, and the economic operation cost of the power grid is reduced;
5) when the investment of the distributed power generation project is gradually increased, the load power consumption cost is reduced, mainly because the average agreement power price of the large power users trading through the marketization of the distributed power generation is reduced along with the increase of the investment of the distributed power generation project, and further the total power consumption cost of the load is reduced.
(2) Power grid enterprise response behavior analysis
The power grid enterprise as an important participant of distributed power generation marketization trading has a great influence on the benefits of the power grid enterprise and other benefit agents by the response behavior formed from different targets. Considering that the opportunity of the power grid enterprise is equal for each distributed power generation project, from the perspective of simplifying model analysis and control variables, the weight vector of the upper-layer model is set to [1/3,1/3 and 1/3], and the distributed power generators still take consistent construction amount behaviors and pay more attention to analyzing the response behaviors of the power grid enterprise. The distributed power generator aims at minimizing economic cost, and government subsidy behaviors are executed according to the current policy. Table 5 shows a comparison of the response behavior of each participating subject in two cases, namely, the power grid enterprise aims at minimizing the economic cost of power grid operation and minimizing the amount of distributed power generation reduction.
TABLE 5
From the analysis in table 5, it can be seen that:
1) when the distributed power generation investment amount is not changed, the decision-making behavior of the power grid enterprise directly influences the profits of distributed power generators and load users. Considering the national enterprise attributes and the requirements of related law and law regulations of power grid enterprises in China, when the power grid is selected to aim at the minimum distributed power generation reduction, the economic operation cost of the power grid enterprises is increased compared with that of the power grid enterprises without the access of distributed power generation projects, but the income of the distributed power generators is greatly increased, the load power consumption cost is further reduced, the distributed power generation reduction is also reduced to a greater extent, and the construction of an environment-friendly society is facilitated;
2) when the power grid aims at the minimum of the reduction amount of the distributed power generation, the power grid is more prone to dispersedly accessing the distributed power generation project so as to absorb the amount of electricity generated by the distributed power generation through local loads as much as possible;
3) when the power grid enterprise aims at the minimum running economic cost, the power grid enterprise guides the distributed power generator to firstly select a direct transaction mode and secondly a benchmarking power price purchasing mode. This is mainly due to two reasons: on one hand, the power grid electricity-substituting selling mode is equivalent to directly cutting off high-price load resources of a power grid enterprise, and the economic benefit of the power grid enterprise is reduced; on the other hand, the part exceeding the protocol electricity quantity in the direct transaction mode can be purchased by the power grid enterprises at the price of the coal-fired benchmarking electricity. Therefore, from the economic benefit of the power grid enterprise, the power grid enterprise prefers to purchase the electric quantity of the distributed power generation project at the coal-fired benchmarking electric price, namely prefers that the distributed power generator selects the benchmarking electric price purchasing mode or the direct trading mode. Meanwhile, for the distributed power generator, the profit of the direct trading mode is greater than that of the benchmarking electricity price purchasing mode. Therefore, under the guidance of the decision-making behavior of the power grid enterprise, the distributed power generator selects a direct trading mode, and the profit is larger;
4) when the power grid enterprise aims at the minimum of the reduction amount of the distributed power generation, the economic guidance of the power grid enterprise does not exist any more, and the income obtained by the distributed power generator in the direct transaction mode is not more prominent compared with the electricity selling mode; when the investment amount of the distributed power generator is large, the power grid electricity-substitute selling mode becomes a mode with large profit for the distributed power generator;
5) under the condition that the investment amount is kept unchanged, when a power grid enterprise aims at the minimum of the distributed power generation reduction amount, the power utilization price of the power load obtained through a direct trading mode in the distributed power generation marketization trading is lower. The distributed power generation system is mainly characterized in that when a power grid enterprise aims at distributed power generation reduction, distributed power generators can get more electric quantity on the grid; in the direct trading mode, the distributed power generator achieves the purpose of acquiring more load resources by reducing the price of the protocol electricity.
(3) Analysis of large power user influence
The power consumer faces two types of power supply in distributed power generation marketization, one type is power supply of a power grid enterprise; the other is distributed generator supply. Because the power supply of the power grid enterprise is a fixed power price and the distributed power generators can freely quote, theoretically, as long as the quoted price of the distributed power generators is lower than the power price of the power grid enterprise, power users can select the power supply from the distributed power generators from the goal of minimum power utilization cost. The only decision needed by the power consumer is to reasonably select the electric quantity agreed with different distributed power generators when multiple distributed power generators are presented. In consideration of the monopoly occupied by the large power users in the distributed power generation marketization transaction, the electric quantity of the tradeable load of the large power users is important for the distributed power generation marketization transaction. Therefore, the influence of the tradeable load capacity of the large power consumer on the marketization of the distributed power generation is intensively analyzed, so that the influence change of the large power consumer on the marketization of the distributed power generation is researched. The decision objective of the power grid enterprise is that the economic cost of the power grid enterprise is minimum, the policy subsidy action is executed according to the current policy, the minimum economic cost of the distributed power generator is taken as the objective, and the weight vector of the upper model is set to be [1/3,1/3 and 1/3 ]. A comparison of the response behavior of each participating trading entity for different tradeable load ratios is shown in table 6.
TABLE 6
From the analysis in table 6, it can be seen that:
1) from a distributed generator perspective, the total cost of a distributed generator does not always remain monotonically increasing with tradeable load. When the tradeable load occupies a small area, the distributed power generator selects the power grid electricity-substituting mode to have a larger profit, mainly because the load oriented by the power grid electricity-substituting mode is the load of the whole area, and when the tradeable load occupies a small area, the trading volume advantage of the power grid electricity-substituting mode compared with the direct trading mode is highlighted; with the increase of the tradable load ratio, considering that the agreement electricity price of the direct trading mode is higher than the comprehensive electricity selling price, the increase of the trading volume leads the direct trading mode to become a mode with larger profit; the benchmarking electricity price purchasing mode is the mode with the minimum profit of the distributed power generator regardless of the proportion of the tradeable load;
2) from the perspective of the power grid enterprise, with the increase of the proportion of the tradeable load, the economic operation cost of the power grid enterprise is reduced, mainly because the industrial electricity price adopted by the load of the large power consumer is far higher than the electricity price of the resident electricity consumption. Under the condition that the total load of the area is unchanged, the higher the load ratio of the large-power user is, the larger the income of a power grid enterprise is; but when the tradable load is increased to a certain degree, the trading electric quantity of the direct trading mode in the distributed power generation project is further increased, and a certain load resource of a power grid enterprise is seized. Therefore, the revenue of the grid enterprise may drop slightly;
3) from the perspective of power consumers, with the increase of the tradeable load ratio, the monopoly of the power consumers in the distributed power generation marketization trading is enhanced, and the power consumers can further reduce the average price of the load power utilization by using the competition of a plurality of distributed power generators for load resources.
(4) Government subsidy behavior analysis
Considering the national tendency of subsidy for new energy power generation, comparison analysis is carried out on five conditions of increasing subsidy reduction proportion to 0, 25%, 50%, 75% and 100% of the current subsidy policy respectively. The decision objective of the power grid enterprise is that the economic cost of the operation of the power grid enterprise is minimum, the economic cost of the distributed power generator is minimum, and the weight is [1/3,1/3,1/3 ]. As the subsidy reduction ratio increases, the ratio of each participating transaction body response behavior parameter to the condition that the subsidy is not reduced is as shown in fig. 5.
From the analysis of fig. 5, it can be seen that: along with the increase of subsidy reduction proportion, the total income of the distributed power generator is greatly reduced, and other parameters are not changed greatly. The situation shows that under the existing technical conditions, the distributed power generation trader can still obtain the benefit under the condition of no subsidy, and the participation enthusiasm of the distributed power generation marketization trade can still be kept, so that the method conforms to the existing new energy power generation subsidy trend of backing a slope.
In conclusion, the modeling method for response behavior of each trading subject in the distributed power generation marketized environment can practically reflect benefit requirements and behavior modes of each subject in the distributed power generation marketized environment, and lays a theoretical foundation for further development of distributed power generation marketized trading.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (9)
1. A method for deciding the behavior of each trading subject in a distributed power generation marketization environment is characterized by comprising the following steps:
(1) the method comprises the steps that the minimum economic cost of a distributed generator is taken as a first objective function, the distributed generation project investment capacity and a transaction mode are taken as variables to be decided, and a distributed generator response behavior model is constructed according to investment installation quantity constraints of an area to be researched, net charge constraints and electricity subsidy constraints under different transaction modes;
(2) the minimum electricity utilization cost of the power consumer is taken as a second objective function, the electric energy trading volume of each distributed power generator is taken as a variable to be decided, and a power consumer response behavior model is constructed according to the electricity selling price and the estimated tradeable electric quantity all the year round which are provided by each distributed power generator;
(3) constructing a power grid enterprise response behavior model according to power grid operation constraint conditions by taking the minimum economic cost of power grid operation as a third objective function, the minimum reduction of distributed generation as a fourth objective function and the access nodes and the capacity of distributed generation projects as variables to be decided;
(4) converting the response behavior model into a single-layer multi-objective optimization model by adopting a multi-lower-layer and double-layer planning model conversion method;
(5) and solving the single-layer multi-objective optimization model by using a multi-objective evolutionary algorithm to obtain a behavior decision result of each trading subject in the distributed power generation marketization environment.
2. The method according to claim 1, wherein the trading patterns comprise: the direct transaction mode, the power grid electricity-selling-replacing mode and the pole-surfing electricity price purchasing mode.
3. The method for decision-making of each trading subject behavior under distributed power generation marketization environment according to claim 2, wherein the first objective function is:
Objdg,k＝min(Cins,k+Cyw,k+Cgwf,k-Bbt,k-Bsell,k)k∈Cdg
in the formula, Objdg,kThe minimum objective function of economic cost of the kth distributed power generator is obtained; cins,kAnnual total cost of distributed power generation projects built for the kth distributed power generator; cyw,kThe annual operation and maintenance cost of the kth distributed power generator is saved; cgwf,kThe annual network charge cost paid to the power grid enterprise by the kth distributed generator in the corresponding transaction mode is saved; b isbt,kSubsidizing a renewable energy power generation project acquired from the government all year round by a kth distributed power generator; b issell,kTrading earnings for the kth distributed generator for annual electric energy; cdgThe method is a distributed power generation commerce set for all the distributed power generation marketization trades.
4. The method for decision-making of each trading subject behavior under distributed power generation marketization environment according to claim 3, characterized in that in direct trading mode, the net charge B is passedgwfzj,kAnd (3) constraint:
in the formula, sum is a matrix summation function; lambda [ alpha ]invoThe power transmission and distribution price, lambda, corresponding to the highest voltage level involved in the power user accessing the power grid10、λ35、λ110The corresponding power transmission and distribution prices of 10kV, 35kV and 110kV in the area to be researched.
5. The method as claimed in claim 3, wherein the power generation marketing environment is a distributed power generation marketing environment, and the power generation marketing environment is characterized in that the power generation marketing environment is a power generation marketing environment with a power grid charging rate of Bgwfdsd,kThe constraints are:
in the formula, λ1The power transmission and distribution price is corresponding to the voltage class of 1kV and below in the area to be researched.
6. The method for decision-making of each trading subject behavior under distributed power generation marketization environment according to claim 3, characterized in that the kth distributed power generator trades electric energy for a year round with income Bsell,kComprises the following steps:
in the formula, Bxy,kThe benefits obtained for the kth distributed generator adopting the direct trading mode through signing a trading agreement; lambda [ alpha ]rmbgThe electricity price is charged for the coal-fired marking pole to access the internet; lambda [ alpha ]xy,kThe agreement electricity price for the direct transaction of the kth distributed power generator and the power consumer; wsg,k、Wqf.kRespectively determining the actual settlement electric quantity of the kth distributed power generator in the direct transaction mode as the electric quantity which is generated more and less than the transaction electric quantity; lambda [ alpha ]rmbgThe electricity price is charged for the coal-fired marking pole to access the internet; lambda [ alpha ]zhThe comprehensive electricity selling price is achieved.
7. The method for decision-making on the behavior of each trading subject in distributed power generation marketization environment according to any one of claims 1 to 6, wherein the second objective function is:
Objload＝min(Cxyload+Cdwload-Bpcload)
in the formula, ObjloadAn objective function for minimizing the electricity consumption cost of the electricity consumers, Cxylaod、CdwloadThe electricity purchasing cost of purchasing electricity for the power users through the distributed generation marketization trade and directly purchasing electricity for the power grid enterprises, BpcloadObtaining fine income for power users in distributed power generation marketization transaction due to electric quantity deviation;
in the formula, WindloadThe total annual power consumption of the power consumer.
8. The method for decision-making on the behavior of each trading subject in distributed power generation marketization environment according to any one of claims 1 to 7, wherein the third objective function is:
Objdw＝min(Cdgsg+Cdsd+Csjbuy-Bsjsell-Bload-Bgwf)
in the formula, ObjdwFor operating the grid with a minimum objective function of economic cost, CdgsgThe annual generating capacity purchasing cost of the power grid enterprise aiming at the distributed power generation project in the direct transaction mode and the benchmarking electricity price purchasing mode is saved; cdsdThe annual generated energy online cost of a power grid enterprise aiming at a distributed power generation project under the electricity selling mode is saved; csjbuy、BsjsellRespectively returning the annual electricity buying cost and the annual electricity return income for a power grid enterprise aiming at a superior power grid; b isloadThe load collected for the power grid enterprise in the area to be researched is sold for the whole year; b isgwfThe annual network charge income is paid to the power grid enterprise by the distributed power generator;
Bgwfbg,kthe network-passing fee to be paid to the power grid by the kth distributed power generator in the post-surfing electricity price purchasing mode is paid;
9. the method for decision-making on the behavior of each trading subject in distributed power generation marketization environment according to any one of claims 1 to 8, wherein the fourth objective function is:
in the formula, Pg,i,tThe active output value of the ith transformer node in the t-th time period is obtained; pdg,tThe output upper limit of unit installation capacity of the distributed power generation project at the t-th time period is set; pbyq,iAccessing capacity for distributed generation of an ith transformer node; byq is a set of transformer nodes accessible for distributed power generation; t isjs,tThe total number of time periods that can be represented throughout the year for the t-th time period; t isjsThe number of time segments is calculated for the total.
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