CN110889598B

CN110889598B - Distributed power generation project optimal configuration method

Info

Publication number: CN110889598B
Application number: CN201911095401.9A
Authority: CN
Inventors: 张迪; 苗世洪; 周宁; 孙芊; 赵健; 马建伟; 刘昊; 王磊
Original assignee: Huazhong University of Science and Technology; State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
Current assignee: Huazhong University of Science and Technology; State Grid Corp of China SGCC; Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
Priority date: 2019-11-11
Filing date: 2019-11-11
Publication date: 2022-07-12
Anticipated expiration: 2039-11-11
Also published as: CN110889598A

Abstract

The invention discloses a distributed power generation project optimal configuration method, 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-target optimization model by using a multi-target evolutionary algorithm to obtain the behavior decision results of each transaction subject in the distributed 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

Distributed power generation project optimal configuration method

Technical Field

The invention belongs to the technical field of new energy power generation optimal configuration, and particularly relates to a distributed power generation project optimal configuration method.

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 the prominent problems that the grid-connected cost is high, the dependence on subsidy is strong, the power cannot be supplied for large-scale industrial production and the like in the development process, and further promotion of market application of the distributed power generation is restricted. In order to solve the above problems, the national institute of energy and development committee introduced a policy document about "notice of development of distributed power generation marketization trial (hereinafter referred to as" notice ") in 2017, and started to promote the construction of the distributed power generation market in our country. Since the issue of Notification, several test points have been put forward in some provinces and cities. From the pilot promotion effect, the market utilization of the distributed power generation market is an intangible hand, and certain progress is made in the aspects of optimizing the power grid structure, guiding the benign competition of the clean energy market, releasing the innovation of the bonus for the manufacturing industry, deepening the structural innovation of the supply side and the like; however, it should be noted that some trial runs also have the problems that the market entities such as power grid enterprises, distributed power generators, power consumers and the like are not accurately positioned, the forms are administrative, and the market potential cannot be fully exploited. In the key stage of the construction of the distributed power generation market, how to clarify the behavior mode of each trading subject in the distributed power generation marketization environment and analyze the influence mechanism of the distributed power generation marketization trading policy is very important for the next construction of the distributed power generation market.

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, a distributed generator and a power consumer directly perform power transaction, sign medium and long term transaction contracts and pay 'network passing fee' to a power grid enterprise; 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 charge' 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 market trading of the distributed power generation and the electric subsidy provided by government agencies such as the country and the province and the 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 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 power generation transaction at home and abroad, and discusses the development of the future distributed power generation marketization transaction in China; some scholars analyze the behavior pattern of the distributed power generator under the distributed power generation marketization environment from the perspective of the distributed power generation access planning, and even analyze the influence of the distributed power generation marketization policy from the perspective of the microgrid participating in the marketization transaction. However, the above studies have three problems. Firstly, the above documents focus on analyzing and interpreting contents of distributed power generation marketing policies, and more belong to the category of policy statement documents. 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 transaction, most documents only use the concept of 'transaction mode' or 'internet fee', do not really give the transaction mode option to the distributed generation trader, and lack panoramic display analysis on the distributed generation marketization transaction; 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 distributed power generation project optimization configuration method, aiming at solving the technical problem that the operation safety and the economical efficiency of a distributed power generation market are lower due to the fact that the behavior decision of each trading subject of the distributed power generation market at the present stage does not accord with the actual engineering requirements.

In order to achieve the above object, the present invention provides a distributed power generation project optimization configuration method, including:

(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 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 direct transaction mode, the power grid electricity-selling-replacing mode and the pole-surfing electricity price purchasing mode.

Further, the first objective function is:

Obj_dg,k＝min(C_ins,k+C_yw,k+C_gwf,k-B_bt,k-B_sell,k)k∈C_dg

in the formula, Obj_dg,kThe minimum objective function of economic cost of the kth distributed power generator is obtained; c_ins,kAnnual total cost of distributed power generation projects built for the kth distributed power generator; c_yw,kThe annual operation and maintenance cost of the kth distributed power generator is saved; c_gwf,kThe annual network charge cost paid to the power grid enterprise by the kth distributed generator in the corresponding transaction mode is saved; b is_bt,kSubsidizing a renewable energy power generation project acquired from the government all year round by a kth distributed power generator; b is_sell,kTrading earnings for the kth distributed generator for annual electric energy; c_dgThe method is a distributed power generation commerce set for all the distributed power generation marketization trades.

Further, in the direct transaction mode, the fee B of passing through the network is used_gwfzj,kAnd (3) constraint:

in the formula, sum is a matrix summation function; lambda_invoThe power transmission and distribution price, lambda, corresponding to the highest voltage level involved in the power user accessing the power grid₁₀、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 B_gwfdsd,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 energy_sell,kComprises the following steps:

in the formula, B_xy,kThe benefits obtained for the kth distributed generator adopting the direct trading mode through signing a trading agreement; lambda_rmbgFor coal-fired marking post net-feedingA price; lambda [ alpha ]_xy,kThe agreement electricity price for the direct transaction of the kth distributed power generator and the power consumer; w_sg,k、W_qf.kRespectively comparing the actual settlement electric quantity of the kth distributed power generator in the direct transaction mode with the excessive electric quantity and the insufficient electric quantity of 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:

Obj_load＝min(C_xyload+C_dwload-B_pcload)

in the formula, Obj_loadAn objective function for minimizing the electricity consumption cost of the electricity consumers, C_xylaod、C_dwloadThe 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, B_pcloadObtaining fine income for power users in distributed power generation marketization transaction due to electric quantity deviation;

in the formula, W_indloadThe total annual power consumption of the power consumer.

Further, the third objective function is:

Obj_dw＝min(C_dgsg+C_dsd+C_sjbuy-B_sjsell-B_load-B_gwf)

in the formula, Obj_dwFor operating the grid with a minimum objective function of economic cost, C_dgsgThe 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; c_dsdThe annual generated energy online cost of a power grid enterprise aiming at a distributed power generation project under the electricity selling mode is saved; c_sjbuy、B_sjsellRespectively returning the annual electricity buying cost and the annual electricity return income for a power grid enterprise aiming at a superior power grid; b_loadThe load collected for the power grid enterprise in the area to be researched is sold for the whole year; b is_gwfIs distributedThe annual network charge income paid by the power generator to the power grid enterprise;

B_gwfbg,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;

further, the fourth objective function is:

in the formula, P_g,i,tThe active output value of the ith transformer node in the t-th time period is obtained; p_dg,tThe output upper limit of unit installation capacity of the distributed power generation project at the t-th time period is set; p_byq,iAccessing capacity for distributed generation of an ith transformer node; byq is a set of transformer nodes accessible for distributed power generation; t is_js,tThe total number of time periods that can be represented throughout the year for the t-th time period; t is_jsThe 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 is combined with relevant policies and standard constraints of distributed power generation marketization, establishes response behavior models of various transaction subjects, and can practically reflect interest requirements and behavior modes of various subjects in the distributed power generation marketization environment, so that the behavior decision result of various subjects is more in line with the actual engineering requirements, and the operation safety and economy of the distributed power generation marketization are favorably improved.

(2) The invention converts the original multi-subject model into the double-layer planning model with single lower-layer planning problem by using the conversion method of the multi-lower-layer double-layer planning model, thereby avoiding the complex programming process of multi-agent simulation, being more beneficial to displaying the benefit interaction relationship among subjects and being convenient to solve.

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 distributed power generation project optimization configuration method provided by 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 the percentage of response behavior parameters of each participating transaction body according to the change of the reduction ratio of subsidies according to 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 last three 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 distributed power generation project optimization configuration method provided by the present invention includes:

the distributed power generator is responsible for investing in building a distributed power generation project and selecting a trading mode by taking the minimum economic cost per se 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 competitive relationship between a plurality of distributed power generators and load carrying capacity resources and power load resources of a power grid distributed power generation project exists.

(1.1) the first objective function is:

Obj_dg,k＝min(C_ins,k+C_yw,k+C_gwf,k-B_bt,k-B_sell,k)k∈C_dg

in the formula, Obj_dg,kFor the k distributed generator economic cost minimum objective function, C_ins,k、C_yw,kAnd C_gwf,kAnnual universe of distributed power generation projects built for kth distributed power generator respectivelyCost, annual operation and maintenance cost, annual network charge cost paid to the power grid enterprise; b is_bt,kSubsidizing a renewable energy power generation project acquired from the government by the kth distributed power generator all year round; b is_sell,kTrading earnings for the kth distributed generator for annual electric energy; c_dgThe method comprises the steps of collecting all distributed power generation merchants participating in the distributed power generation marketization transaction;

in the formula, P_ins,kThe installation amount of the distributed power generation project is built for the kth distributed power generator; c_dginvInstallation cost per distributed generation installation amount; r, T_gcAnd alpha is the current rate, the project period and the residual value rate of the equipment respectively;

C_yw,k＝λ_dgywW_sum,k

in the formula, λ_dgywElectric operation and maintenance cost for a distributed power generation project; w_sum,kThe annual total power generation of a project is built for the kth distributed power generator;

in the formula, C_dgzj、C_dgdsdAnd C_dgbgThe 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 is_gwfzj,kPaying the network fee to the power grid for the kth distributed generator in the direct transaction mode; b is_gwfdsd,kPaying the power grid for the kth distributed generator in a power grid electricity-substituting 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;

B_bt,k＝λ_bt,kW_sum,k

in the formula, λ_bt,kThe electricity subsidy is the electricity subsidy of the renewable energy power generation project level obtained by the kth distributed power generator from the administrative department;

in the formula, B_xy,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; w_sg,k、W_qf.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 needs to pay default compensation cost to the other transaction party according to the default electric quantity by 10% of the electricity price of the fire coal benchmarking; when the actual grid power is higher than the agreement signed power, the power grid enterprise buys the coal-fired benchmarking power price; lambda_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, P_loadave10、P_loadave35、P_loadave110Average power loads of 10kV, 35kV and 110kV voltage class in the region to be researched all year round are respectively supplied; y is_ins10,k、Y_ins35,k、Y_ins110,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 highest project capacity of the monomer cannot exceed 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 voltage level of the accessed power grid is 35 kV; when in useWhen 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 B_gwfzj,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、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 selling_gwfdsd,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 B_bt,kAnd (3) constraint:

in the formula, alpha₁₁₀、α₃₅、α₁₀Respectively are proportional factors relative to the current power supply patch; lambda [ alpha ]_btnowThe price is subsidized for linearity electricity; 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, wherein the electric subsidy of the degree is in a gradient decreasing trend according to the increase of the built-in capacity and the increase of the voltage level of an access power grid, the higher the access voltage level is, the larger the access capacity is, the less the electric subsidy of the degree obtained by the distributed power generation projects is, and therefore, different distributed power generators can obtain different electric subsidies due to different built-in quantities. The method shows the government by continuously reducing government subsidiesThe subsidy behavior of the organization influences the marketization of the distributed power generation.

5) Agreement electricity price limit

0≤λ_xy,k≤λ_ind k∈C_dgzj

In the formula, λ_indDirectly 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 W_sum,k-W_xy,kWhen the ratio is less than or equal to 0, then

If W_sum,k-W_xy,kIs not less than 0, then

the power consumer aims at minimizing the self power consumption cost, and decides to agree with the all-year-round power of each distributed power generator according to the power selling price provided by each distributed power generator and the estimated tradeable power all year round. 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 generators can expand the power supply by additional investment, and the power load demand is a scarce resource relative to the distributed power generators. Therefore, the present invention adopts a mode in which 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:

Obj_load＝min(C_xyload+C_dwload-B_pcload)

in the formula, W_indloadThe total annual power consumption of the power consumers is calculated;

(2.2) the power consumer responds to the constraint condition of the behavior model;

1) and (4) protocol electric quantity limiting:

in the formula, W_indxyThe 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 compete_xy,kThe higher the electricity consumer has agreed upon the amount of electricity W with the distributed generator_xy,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 and the minimum reduction of distributed generation, and decides the access node and the capacity of a distributed generation project to ensure the safe and stable operation of the power grid.

(3.1) the third objective function is:

Obj_dw＝min(C_dgsg+C_dsd+C_sjbuy-B_sjsell-B_load-B_gwf)

in the formula, Obj_dwFor operating the grid with a minimum objective function of economic cost, C_dgsgThe annual generated energy acquisition cost of a power grid enterprise aiming at the distributed power generation project in a direct transaction mode and a benchmarking electricity price acquisition mode is saved; c_dsdThe annual generated energy online cost of a power grid enterprise aiming at a distributed power generation project under the electricity selling mode is saved; c_sjbuy、B_sjsellRespectively returning the annual electricity buying cost and the annual electricity return income for a power grid enterprise aiming at a superior power grid; b is_loadThe load collected for the power grid enterprise in the area to be researched is sold for the whole year; b_gwfThe annual net charge income is paid to the power grid enterprise for the distributed power generator;

in the formula, λ_sjvlevelThe 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; w_sjbuy、W_sjsellRespectively purchasing and returning electric quantity of a power grid enterprise all year round for a superior power grid; w_elseloadAnnual power consumption (such as residential power consumption, agricultural power consumption and general industrial and commercial power consumption) for other power loads except for power consumers in the area to be researched; lambda [ alpha ]_elseAverage electricity prices for other power loads; b is_gwfbg,kPaying to the power grid for the kth distributed power generator in the post-surfing electricity price purchasing modeThe network charge is borne by a power grid enterprise, and the network charge is caused by purchasing of distributed generation electric quantity in a distributed generation project adopting a benchmarking electricity price purchasing mode;

(3.2) the fourth objective function is:

in the formula, P_g,i,tThe active output value of the ith transformer node in the t-th time period is obtained; p_dg,tThe output upper limit of unit installation capacity of the distributed power generation project at the t-th time period is set; p is_byq,iAccessing capacity for distributed generation of an ith transformer node; byq is a set of transformer nodes accessible to distributed power generation; t is_js,tThe total number of time periods that can be represented throughout the year for the t-th time period; t is a unit of_jsCalculating 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, P_gsjbuy,t、P_gsjsell,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; p is_gsjbuymax、P_gsjsellmaxThe upper limit values of the electricity purchasing power and the electricity selling power for the superior power grid are respectively; y is_sjbuy,t、Y_sjsell,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≤P_g,i,t≤P_dg,tP_byq,i i∈Byq

3) Distributed power generation project access transformer capacity constraint

In the formula, P_byq,i,kDistributed generation capacity accessed to the ith transformer node for the kth distributed generator; y is_byq,i、P_byqrl,iThe access zone bit and the transformer capacity of the ith transformer node are respectively; byq_kA set of transformer nodes for which distributed generation is accessible for the kth distributed generator. According to the related technical standard of accessing the distributed power generation to a 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 a 110kV grid-connected voltage level is 20MW or more. When the capacity of a single grid-connected point is less than 400kW, the 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, P_g,k,i,tConnecting a successful output value C of the ith transformer node in the t period for the kth distributed generator_dg,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, P_ij,t、Q_ij,tAnd theta_i,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; g_ijAnd b_ijConductance and susceptance of branch ij, respectively; v. of_i,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 problem of solving in multiple aspects such as multiple main bodies, high dimensionality, nonlinearity and the like exists in each trading main body response behavior model in a distributed power generation marketization environment. 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,…y_kNeed to satisfy

Wherein,

i＝1,2,…,k,m＝m₁+m₂+…+m_k

wherein F, x are the upper layer objective function and decision variable, respectively, f_i、y_iRespectively an objective function and a decision variable of the ith main body of the lower layer. 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 bodies₁ y₂ … y_kThe influence of (c); lower ith subject decision y_iIs subject to upper layer decision and other lower layer subject y_t(t ≠ i).

Considering that the commissioning behavior of the distributed power generator in the distributed power generation marketization transaction has the advantage of moving ahead, other main bodies make decisions according to the commissioning quantity of the distributed power generation project, and therefore 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 Obj_dg,k k∈Cdg

s.t.f_dg,i(x_dg,x_load,x_dw)≤0,i＝1,…,m_dg

h_dg,i(x_dg,x_load,x_dw)＝0,i＝1,…,p_dg

in the formula (f)_dg、h_dgInequality constraints and equality constraints of the distributed generator quotient response model are respectively; m is a unit of_dg、p_dgThe number of inequality constraints and the number of equality constraints are respectively set; x is the number of_dgIs a decision variable of the upper model, a block of the upper modelPolicies are influenced by the decisions of the underlying power consumers and grid enterprises.

The underlying model can be abbreviated as:

s.t.f_k,i(x_k,x_t,x_dg)≤0,i＝1,…,m_k k,t∈C t≠k

h_k,i(x_k,x_t,x_dg)＝0,i＝1,…,p_k k,t∈C t≠k

c ═ power consumer, grid enterprise }

In the formula (f)_k,i、h_k,iInequality constraint and equality constraint of the kth main body response model of the lower layer are respectively; m is_k、p_kThe number of inequality constraints and the number of equality constraints are respectively set; x is the number of_kThe 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 a multi-objective evolutionary algorithm (MOEA/D) of a 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) A reference point is set. Solving the model of formula (39) to obtain the fitness value Fit (x)_i) Initialization reference point Z ═ Z₁ z₂z₃]^TSetting the initial iteration number gen as 0;

5) and acquiring a new individual. For each individual x_iFrom its neighboring weight vector B_iRandomly 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 Fit_j(y) is less than z_jUpdating the reference point; for each weight vector B_iIf Fit (y) is compared to Fit (x)_i) Closer to the reference point, update B_iCorresponding adjacent solutions;

8) and (4) terminating the conditions. If gen is Maxgen, stopping calculation, and outputting the adaptive value fit (X) and the optimal solution X, otherwise, if gen is gen +1, returning to step 5 to continue calculation;

on the premise of fully considering the distributed power generation marketization trading policy and the standard constraint, a response behavior model of each trading subject under the distributed power generation marketization environment is established according to the actual requirements of each subject participating in the distributed power generation marketization trading, the interest interaction relationship among the subjects is analyzed, and theoretical support is provided for further popularization of the distributed power 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.

Below, with a certainThe actual power distribution network system of villages and towns is taken as an embodiment. 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, the load ratio of local large power users is 50.44%, and the load ratio of large power users which can be used for achieving transactions with distributed photovoltaic power generators is 34.24%. The distribution network main line is a 35kV outgoing line, 11 kV/10kV transformers are provided in total, the annual peak load of the line is 43.45MW, the maximum allowable current is 408A, and the power supply line structure and the transformer capacity are shown in figure 3. The original state is that the power grid enterprise does not accept the access of distributed power generation, and the power grid operation cost is-3.36 multiplied by 10⁶And (5) Yuan. In the multi-objective evolutionary algorithm, the number of populations is set to be 100, the number of neighbors 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 generation merchants, power grid enterprises, power consumers and governments. Some directly-obtainable key parameters and line parameter values used in the embodiments of the present invention are shown in tables 1 and 2, respectively.

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 policy, the Pareto frontier for solving the model to obtain the economic cost of the distributed power generator 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 distribution 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 trading modes selected by the distributed power generator are direct trading modes. This is mainly because the present embodiment is obtained by simulation in the case where the grid enterprise aims at minimizing the economic cost of operation of the grid enterprise itself, 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) distributed power generator commissioning behavior influence analysis

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 the table 4, when the power grid enterprise decides the target according to the economic operation of the power 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 amount of the distributed power generator to be built and the selection of the transaction mode are the same, the economic operation cost is different due to the difference of the access nodes;

2) when the investment amount of the distributed power generation project is the same, the minimum economic cost modes of the distributed power generators are direct trading modes, and the minimum economic cost modes are 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 due to the fact that the power grid access position and the selected trading mode are different;

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 [1/3,1/3,1/3], 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 acts 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 investment of the distributed power generation is not changed, the decision-making behavior of the power grid enterprise directly influences the benefits of the distributed power generation suppliers and the load users. Considering national enterprise attributes and relevant legal and regulatory requirements of power grid enterprises in China, when the power grid is selected to take the minimum distributed power generation reduction as a target, it can be seen that although 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, the income of 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 electric quantity generated by the distributed power generation through the local load 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 electricity 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 electric quantity in the direct transaction mode can be purchased by the power grid enterprise 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 on the grid to obtain more electric quantity; in the direct transaction mode, the distributed power generator achieves the purpose of acquiring more load resources by reducing the agreement electricity price.

(3) Analysis of large power user influence

The power consumer faces two types of power supply in distributed power generation marketization, wherein 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. Considering the monopoly position 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 very important for the distributed power generation marketization transaction. Therefore, the influence of the tradeable load capacity of the large power user on the marketization of the distributed power generation is intensively analyzed, so that the influence change of the large power user 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 behavior is executed according to the current policy, the distributed power generator aims at the economic cost is minimum, 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 accounts for a relatively small amount, the distributed power generator selects the power grid electricity-substituting mode to have a larger income, mainly because the load oriented to the power grid electricity-substituting mode is the load of the whole area, and when the tradeable load accounts for a relatively small amount, the trading volume advantage of the power grid electricity-substituting mode compared with the direct trading mode is highlighted; with the increase of the proportion of the tradeable load, 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 enables the direct trading mode to become a mode with higher income; 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 power grid enterprises, with the increase of the proportion of tradeable loads, the economic operation cost of the power grid enterprises is reduced, mainly because the industrial electricity price adopted by the loads of large power users is far higher than the electricity price of residents. Under the condition that the total area load is unchanged, the higher the load proportion of a large-power user is, the larger the power grid enterprise income is; however, when the tradable load is increased to a certain extent, the tradable electric quantity in 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 slope-withdrawing subsidy trend of the state for new energy power generation, the method is used for carrying out comparative analysis on five conditions of increasing the subsidy reduction proportion to 0, 25%, 50%, 75% and 100% of the current subsidy policy. The decision objective of the power grid enterprise is that the economic cost of the operation of the power grid enterprise is the minimum, the distributed power generator aims at the minimum economic cost, and the weight is [1/3,1/3,1/3 ]. As the subsidy reduction rate increases, the proportion of each participating transaction body response behavior parameter relative 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 generator can still obtain a profit under the condition of no subsidy, can still keep participation enthusiasm for market-oriented transaction of distributed power generation, and accords with the existing new energy power generation subsidy trend.

In conclusion, the modeling method for the response behavior of each transaction subject in the distributed power generation marketization environment can practically reflect the benefit appeal and behavior mode of each subject in the distributed power generation marketization environment, and lays a theoretical foundation for further development of the distributed power generation marketization transaction.

It will be understood by those skilled in the art that the foregoing is only an exemplary embodiment of the present invention, and is not intended to limit the invention to the particular forms disclosed, since various modifications, substitutions and improvements within the spirit and scope of the invention are possible and within the scope of the appended claims.

Claims

1. A distributed power generation project optimal configuration method is characterized by comprising the following steps:

(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;

the constraint conditions of the power grid enterprise response behavior model comprise:

and (3) power generation node constraint:

0≤P_g,i,t≤P_dg,tP_byq,ii∈Byq

P_g,i,tthe active output value of the ith transformer node in the t-th time period is obtained; p_dg,tThe output upper limit of unit installation capacity of the distributed power generation project at the t-th time period is set; p_byq,iAccessing capacity for distributed generation for the ith transformer node, Byq represents a set of transformer nodes accessible to distributed generation;

capacity constraint of the distributed power generation project access transformer:

in the formula, P_byq,i,kDistributed generation capacity accessed to the ith transformer node for the kth distributed generator; y is_byq,i、P_byqrl,iThe access zone bit and the transformer capacity of the ith transformer node are respectively; byq_kSet of transformer nodes accessible to distributed generation for kth distributed generator, C_dgRepresenting the set of all distributed power generators participating in the marketized trade of distributed power generation, epsilon represents a minimal number, P_ins,kByq representing the installation amount of the distributed power generation project put into operation by the kth distributed power generator company_kA transformer node set representing that a kth distributed generator has access to distributed generation;

electric quantity constraint:

in the formula, P_g,k,i,tConnecting the active output value of the ith transformer node in the t period for the kth distributed generator, C_dg,kAccessing a set of transformer nodes for a kth distributed generator; tjs denotes the total number of computation time periods, W_sum,kRepresents the annual total power generation amount, P, of the kth distributed power generator project_gsjbuy,t、P_gsjsell,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; w is a group of_sjbuy、W_sjsellRespectively purchasing and returning electric quantity of a power grid enterprise all year round for a superior power grid; t is_js,tThe total number of time periods that can be represented throughout the year for the t-th time period;

and (3) restraining a power flow equation:

in the formula, P_ij,t、Q_ij,tAnd theta_i,j,tRespectively obtaining the active power and the reactive power of a branch circuit connected with the node i and the node j in the t-th time period and the voltage phase angle difference of the branch circuit; g_ijAnd b_ijConductance and susceptance of branch ij, respectively; v. of_i,tThe voltage amplitude of the node i in the t period;

reactive power constraint:

voltage constraint:

V_imin≤V_i,t≤V_imax

and (3) power flow constraint:

representing the square of the active power flow representing the branch to which node i and node j are connected during the t-th time period,

representing the square of the reactive power flow of the leg connected by node i and node j during the t-th period,

representing the square of the upper limit of the power flow of the branch connected with the node i and the node j;

current restraint:

representing the upper limit of reactive power generation of the node; _giQrepresenting the lower limit of reactive power generation of the node; q_gi,tRepresenting node reactive power generation power; v_i,tRepresents the node voltage; i is_maxRepresents the maximum allowable line current;

(5) and solving the single-layer multi-objective optimization model by using a multi-objective evolutionary algorithm to obtain access nodes and capacity of the distributed power generation project.

2. The distributed power generation project optimal configuration method according to claim 1, wherein the transaction mode comprises: the direct transaction mode, the power grid electricity-selling-replacing mode and the pole-surfing electricity price purchasing mode.

3. The distributed power generation project optimal configuration method according to claim 2, wherein the first objective function is:

Obj_dg,k＝min(C_ins,k+C_yw,k+C_gwf,k-B_bt,k-B_sell,k)k∈C_dg

in the formula, Obj_dg,kThe minimum objective function of economic cost of the kth distributed power generator is obtained; c_ins,kAnnual total cost of distributed power generation projects built for the kth distributed power generator; c_yw,kThe annual operation and maintenance cost of the kth distributed power generator is saved; c_gwf,kThe annual network charge cost paid to the power grid enterprise by the kth distributed generator in the corresponding transaction mode is saved; b_bt,kSubsidizing a renewable energy power generation project acquired from the government all year round by a kth distributed power generator; b is_sell,kTrading earnings for the kth distributed generator for annual electric energy; c_dgThe method is a distributed power generation commerce set for all the distributed power generation marketization trades.

4. The distributed power generation project optimal configuration method according to claim 3, wherein in the direct trading mode, the net charge B is paid_gwfzj,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、λ₁₁₀Corresponding transmission and distribution prices of 10kV, 35kV and 110kV in the area to be researched; y is_ins10,k、Y_ins35,k、Y_ins110,kThe distributed generation projects built for the kth distributed generator respectively have access to zone bits at voltage levels of 10kV, 35kV and 110kV, k represents the kth distributed generator, C_dgzjRepresenting a set of distributed power generators employing a direct trading mode.

5. According to the claimSolving 3 the optimal configuration method for the distributed power generation project is characterized in that under the power supply mode of the power grid for sale, the net charge B is passed_gwfdsd,kThe constraints are:

in the formula, λ₁For transmission and distribution prices corresponding to voltage classes of 1kV and below in the area to be researched, lambda₁₀、λ₃₅、λ₁₁₀For the transmission and distribution price, Y, corresponding to 10kV, 35kV and 110kV in the area to be researched_ins10,k、Y_ins35,k、Y_ins110,kThe distributed generation projects built for the kth distributed generator are respectively connected to zone bits at voltage levels of 10kV, 35kV and 110kV, k represents the kth distributed generator, C_dgdsdRepresenting a distributed set of power generators employing a grid electricity generation model.

6. The distributed power generation project optimal configuration method according to claim 3, wherein the kth distributed power generator trades electric energy all year round for profit B_sell,kComprises the following steps:

in the formula, B_xy,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; w_sg,k、W_qf.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_zhThe price of the comprehensive electricity selling price; w_xy,kRepresenting the amount of electricity agreed upon by the electricity consumer and the distributed power generator, C_dgzjRepresenting a set of distributed power generators using a direct trading mode, k representing the kth distributed power generator,C_dgdsdrepresenting a distributed power generator set adopting a power grid electricity-substituted selling mode, C_dgbgRepresenting a distributed generator set employing a benchmarking electricity price acquisition model.

7. A distributed power generation project optimal configuration method according to any of claims 1-6, wherein the second objective function is:

Obj_load＝min(C_xyload+C_dwload-B_pcload)

in the formula, W_indloadFor the total annual power consumption of the electricity consumers, B_xy,kRepresenting the revenue obtained by the kth distributed power generator in direct trading mode by contracting a trading agreement, C_dgzjRepresents a set of distributed power generators using a direct trade model, k represents the kth distributed power generator, λ_indRepresenting the electricity price, W, of the direct purchase of electricity by the industrial load to the grid enterprise_xy,kRepresenting the amount of agreed electricity, W, that the electricity consumer has with the distributed power generator_qf,kThe k distributed power generator is represented as the power generated by the k distributed power generator in the direct transaction mode, wherein the actual settlement power is less than the transaction power, lambda_rmbgAnd the price of the power on the internet of the fire coal marker post is shown.

8. The distributed power generation project optimization configuration method according to claim 1, wherein the third objective function is:

Obj_dw＝min(C_dgsg+C_dsd+C_sjbuy-B_sjsell-B_load-B_gwf)

in the formula, Obj_dwFor operating the grid with a minimum objective function of economic cost, C_dgsgThe 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; c_dsdThe annual generated energy online cost of a power grid enterprise aiming at a distributed power generation project under the electricity selling mode is saved; c_sjbuy、B_sjsellRespectively returning the annual electricity buying cost and the annual electricity return income for a power grid enterprise aiming at a superior power grid; b is_loadThe load for the power grid enterprise to collect in the area to be researched is sold for the whole year; b is_gwfThe annual network charge income is paid to the power grid enterprise by the distributed power generator;

B_gwfbg,kthe net charge to be paid to the power grid for the kth distributed generator in the post-surfing electricity price acquisition mode, C_dgzjRepresenting a set of distributed power generators using a direct trading mode, k representing the kth distributed power generator, B_gwfzj,kRepresenting the net charge that the kth distributed generator needs to pay to the power grid in the direct transaction mode, C_dgdsdRepresenting a distributed generator set using a grid electricity-substitution selling model, B_gwfdsd,kC, representing the net charge that the kth distributed generator needs to pay to the power grid under the power grid electricity-substituting mode_dgbgRepresenting a distributed power generator set adopting a benchmarking electricity price purchasing mode;

λ₁represents the corresponding power transmission and distribution price, lambda, of the voltage class of 1kV and below in the area to be researched₁₀、λ₃₅、λ₁₁₀For the transmission and distribution price, Y, corresponding to 10kV, 35kV and 110kV in the area to be researched_ins10,k、Y_ins35,k、Y_ins110,kAnd accessing flag bits for distributed generation projects built by the kth distributed generator respectively at voltage levels of 10kV, 35kV and 110 kV.

9. The distributed power generation project optimization configuration method according to claim 1, wherein the fourth objective function is:

in the formula, Obj_dwRepresenting the minimum objective function, P, of the economic cost of operating the grid_g,i,tThe active output value of the ith transformer node in the t-th time period is obtained; p_dg,tThe output upper limit of unit installation capacity of the distributed power generation project at the t-th time period is set; p_byq,iAccessing capacity for distributed generation of an ith transformer node; byq is a set of transformer nodes accessible for distributed power generation; tjs is the total number of calculation time segments.