CN112329230A - Multi-microgrid main body non-cooperative game transaction method - Google Patents

Multi-microgrid main body non-cooperative game transaction method Download PDF

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CN112329230A
CN112329230A CN202011211419.3A CN202011211419A CN112329230A CN 112329230 A CN112329230 A CN 112329230A CN 202011211419 A CN202011211419 A CN 202011211419A CN 112329230 A CN112329230 A CN 112329230A
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高红均
王雷雷
李驰宇
向月
刘友波
刘俊勇
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Abstract

The invention discloses a non-cooperative game transaction method for multiple micro-grid bodies, which relates to the field of power distribution network terminal power markets, and comprises the steps of carrying out market behavior characteristic analysis and classified modeling on various types of micro-grid bodies formed in different interiors of power distribution network terminals, realizing simulation of different market behaviors of various types of micro-grid bodies of the power distribution network terminals in the market, constructing a distributed transaction platform among the multiple micro-grid bodies, a multi-microgrid-body non-cooperative game transaction mechanism is constructed for market transaction behaviors among a plurality of microgrid bodies through a distributed transaction platform, a two-stage robust optimization model in the microgrid body is constructed by considering characteristics of the microgrid body containing wind power inside and combining typical scene data of the wind power and adjustment characteristics of decision variables, a CCG algorithm is adopted to solve the model, and after optimization is realized, non-cooperative game among the multiple microgrid agents is realized through market behavior selection of different microgrid agents in the transaction process.

Description

Multi-microgrid main body non-cooperative game transaction method
Technical Field
The invention relates to the field of power distribution network terminal power markets, in particular to a multi-microgrid main body non-cooperative game transaction method.
Background
The Micro-Grid (Micro-Grid) is also translated into a Micro-Grid, which refers to a small power generation and distribution system composed of a distributed power supply, an energy storage device, an energy conversion device, a load, a monitoring and protecting device and the like. The appearance of the micro-grid realizes flexible and efficient application of the distributed power supply, so that various renewable energy sources with different distributions can make up for each other, and the resource utilization efficiency is optimized. The micro-grid can participate in auxiliary regulation of the power grid through price signals or excitation signals, and also can participate in electric power market regulation in a multi-party game mode, so that reasonable optimization and configuration of resources are facilitated.
However, the existing design has the following problems:
1. a platform capable of providing transactions for individual microgrid parties is lacking.
2. The microgrid risk possibly caused by uncertainty of clean energy inside each microgrid main body to participate in the transaction is not considered.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a multi-microgrid main body non-cooperative game transaction method.
The purpose of the invention is realized by the following technical scheme:
a multi-microgrid main body non-cooperative game transaction method comprises the following steps:
step 1, carrying out market behavior characteristic analysis and classified modeling on various types of microgrid main bodies formed in different interiors of a power distribution network terminal, and simulating different market behaviors of various types of microgrid main bodies of the power distribution network terminal in the market;
step 2, constructing a distributed transaction platform among the multiple micro-grid main bodies according to simulation of different market behaviors of various types of micro-grid main bodies of the power distribution network terminal in the market;
step 3, a multi-microgrid-body non-cooperative game transaction mechanism is constructed for market transaction behaviors among a plurality of microgrid bodies through a distributed transaction platform;
step 4, in a multi-microgrid-main-body non-cooperative game transaction mechanism, a two-stage robust optimization model in a microgrid main body is constructed by adopting a two-stage robust optimization method;
step 5, solving the two-stage robust optimization model in the microgrid main body by using a column and constraint generation algorithm, and deciding a self-transaction strategy by each microgrid main body after realizing multiple iterations of the solved result;
and 6, each micro-grid main body continuously carries out self-optimization according to the game situation until Nash equilibrium is reached and presents different market trading behaviors.
Preferably, the step 1 of performing market behavior characteristic analysis and classification modeling on various types of microgrid main bodies formed in different interiors of the power distribution network terminal is to aim at achieving game balance in transactions among the microgrid main bodies of the power distribution network terminal, and the steps of performing modeling on internal formations of different types of microgrid main bodies, internal economic targets of different microgrid main bodies and running costs and profits of different microgrid main bodies participating in electricity purchasing and selling processes are carried out by taking the internal power balance of the microgrid main bodies, the balance of electricity purchased and sold among the microgrid main bodies, the balance of electricity purchased and sold settlement limit, the regulation and running limit of a gas turbine and a flexible load adjustable unit and the game balance among the microgrid main bodies as constraint conditions;
the microgrid subject operator objective function:
Figure BDA0002758864980000021
wherein the content of the first and second substances,
Figure BDA0002758864980000022
internal electricity selling earnings obtained by selling electricity to other microgrid main body operators, microgrid electricity purchasing and selling operators and traditional energy consumption users are obtained for the microgrid main body operator i at the moment t;
Figure BDA0002758864980000023
obtaining external electricity selling income for selling electricity to other participants of the electricity market by a microgrid main body operator i at the time t;
Figure BDA0002758864980000024
generating subsidy income for clean energy acquired by a microgrid main body operator i in a 'self-generating self-using and surplus internet surfing' mode at the moment t;
Figure BDA0002758864980000025
internal electricity purchasing cost required to be paid to other microgrid main body operators and microgrid electricity purchasing and selling operators in the electricity purchasing process at the moment t for the microgrid main body operator i;
Figure BDA0002758864980000026
external electricity purchasing cost required to be paid for electricity purchasing of other participants in the electricity market by a microgrid main body operator i at the time t;
Figure BDA0002758864980000027
the production cost of the microgrid main body operator i is the production cost when the gas turbine is adopted for power generation in order to guarantee the self internal load power consumption requirement at the moment t;
Figure BDA0002758864980000028
adjusting the cost generated when the flexible load is adjusted for ensuring the power supply stability for the microgrid main body operator i at the moment t;
Figure BDA0002758864980000029
for microgrid main body operator i at time tAnd paying the power grid company for passing the network when the electricity is sold to other microgrid main body operators, microgrid electricity-selling operators and traditional energy-using users.
The power generation subsidy and the power selling income obtained by the microgrid main body operator are as follows:
Figure BDA00027588649800000210
wherein λ isreThe method is characterized in that the method is a clean energy electricity subsidy when a wind turbine generator adopts a 'spontaneous self-use and surplus internet surfing' mode;
Figure BDA00027588649800000211
selling electricity quoted prices for the rest microgrid main body operators, the microgrid electricity purchasing and selling operators and the traditional energy consumption users at the moment t for the microgrid main body operator i;
Figure BDA00027588649800000212
external electricity selling price when the microgrid main body operator, the microgrid electricity purchasing and selling operator and the traditional energy using user sell electricity to the electricity market at the time t;
Figure BDA00027588649800000213
generating capacity of a wind generating set in a microgrid main body operator i at the moment t;
Figure BDA00027588649800000214
Figure BDA00027588649800000215
respectively selling electricity quantity of electricity to other microgrid main operators, microgrid electricity purchasing and selling operators and traditional energy consumption users at the moment t for the microgrid main operator i; n isIThe number of the microgrid main body operators in the microgrid main body operator set I is set; n isNThe number of the microgrid electricity purchasing and selling operators in the microgrid electricity purchasing operator set N is equal to the number of the microgrid electricity purchasing operators in the microgrid electricity purchasing operator set N; n isLThe number of the traditional energy using users in the traditional energy using user set L is set;
Figure BDA0002758864980000031
and the electricity selling quantity is the electricity selling quantity when the microgrid main body operator i sells electricity to the electricity market at the moment t.
The electricity purchasing cost and the network passing fee required by the operator of the micro-grid main body are as follows:
Figure BDA0002758864980000032
wherein the content of the first and second substances,
Figure BDA0002758864980000033
respectively offering electricity sales prices of the microgrid main body operator j and the microgrid electricity purchasing and selling operator n to other microgrid main body operators, microgrid electricity purchasing and selling operators and traditional energy consumption users at the moment t;
Figure BDA0002758864980000034
external electricity purchasing price when a micro-grid main operator, a micro-grid electricity purchasing and selling operator and a traditional energy using user purchase electricity to the electricity market at the time t; lambda [ alpha ]TDPaying power consumption and grid cost to a power grid company in the power selling process for a micro-grid main operator;
Figure BDA0002758864980000035
respectively purchasing electricity quantity of electricity from the rest microgrid main body operators and the microgrid electricity purchasing and selling operators by the microgrid main body operator i at the time t;
Figure BDA0002758864980000036
and (4) the electricity purchasing quantity is the electricity purchasing quantity when the microgrid main body operator i purchases electricity from the electricity market at the time t.
The production cost of the gas turbine of the microgrid main body operator is as follows:
Figure BDA0002758864980000037
the production cost of the gas turbine is mainly determined by the startup and shutdown cost of the gas turbine, the power generation cost of the gas turbine and the pollution gas exhaust of the gas turbineThe system comprises a discharge cost component, wherein lambda FIC, lambda SUC and lambda SUD are respectively the fixed cost and the start-stop cost of the gas turbine in the operation process; the method is characterized in that the power generation cost of the gas turbine is represented in a piecewise linearization mode, and Nn is the number of segments in the power generation process; bn is the cost slope of the gas turbine when the power generation output is in the nth section; δ n, t is the power generation output of the gas turbine on the nth section at the time t;
Figure BDA0002758864980000038
the sum of the generated output of the gas turbine in the micro-grid main body i at the moment t is obtained;
Figure BDA0002758864980000039
the emission amount of the k-th type pollution gas emitted by the gas turbine in the microgrid main body i at the moment t; yk and Vk are penalty cost and environmental value which need to be paid by unit kth type of pollution gas emitted by the gas turbine in the microgrid main body i at the moment t respectively; n iskThe number of types of pollution gas;
Figure BDA00027588649800000310
and
Figure BDA00027588649800000311
and the variables are 0-1, and respectively indicate whether a gas turbine in the microgrid main body i is in a startup state, a shutdown state or a working state at the moment t.
The flexible load cost of the microgrid main operator is as follows:
Figure BDA0002758864980000041
the flexible load is mainly used for cutting off part of load in a compensation mode to reduce the power supply load under the condition that the power supply of a microgrid main operator is insufficient, wherein,
Figure BDA0002758864980000042
the load quantity is reduced by a compensation means under the condition of insufficient power supply; a. b, c are prices to be considered in the compensation processFactor coefficient.
The micro-grid main body operator purchases and sells electricity constraint:
Figure BDA0002758864980000043
wherein d isitThe shortage electric quantity of the microgrid main body operator i at the moment t is obtained; qitThe surplus electric quantity of the internal energy unit of the microgrid main body operator i at the moment t is obtained.
The microgrid subject operator power balance constraint:
Figure BDA0002758864980000044
wherein the content of the first and second substances,
Figure BDA0002758864980000045
the internal load of the microgrid main body i at the moment t is shown.
The related operation constraints of the gas turbine unit in the microgrid main body operator are as follows:
Figure BDA0002758864980000046
wherein the content of the first and second substances,
Figure BDA0002758864980000047
respectively representing the minimum output power and the maximum output power of the gas turbine in the operation process;
Figure BDA0002758864980000048
representing the upper output limit of the nth section of the gas turbine in the operation process; r isi u、ri dRespectively representing the upward and downward climbing rates of the gas turbine in the operation process.
The target function of the micro-network electricity purchasing and selling operator is as follows:
Figure BDA0002758864980000049
wherein the content of the first and second substances,
Figure BDA00027588649800000410
internal electricity selling income and external electricity selling income of the microgrid electricity purchasing and selling operators are respectively obtained;
Figure BDA00027588649800000411
respectively carrying out internal electricity purchasing cost and external electricity purchasing cost of the microgrid electricity purchasing and selling operator;
Figure BDA00027588649800000412
the energy storage operation cost of the electricity selling operators is purchased for the micro-grid;
Figure BDA0002758864980000051
and paying a network passing fee to the power grid company for the microgrid electricity-selling operator in the electricity-selling process.
The electricity selling income of the micro-network electricity purchasing and selling operator is as follows:
Figure BDA0002758864980000052
wherein the content of the first and second substances,
Figure BDA0002758864980000053
the electricity selling price is quoted for the electricity selling operation business n of the microgrid to other microgrid electricity purchasing operation business, microgrid main body operation business and traditional energy consumption users at the time of t;
Figure BDA0002758864980000054
respectively purchasing electricity selling electric quantity of the electricity selling operators, the microgrid main body operators and the traditional energy consumption users for the rest microgrids at the time t for the microgrid electricity purchasing and selling operators n;
Figure BDA0002758864980000055
and (4) selling electricity quantity when the electricity selling operator n sells electricity to the electricity market at the time t for the microgrid.
The electricity purchasing cost of the microgrid electricity purchasing and selling operators is as follows:
Figure BDA0002758864980000056
wherein the content of the first and second substances,
Figure BDA0002758864980000057
electricity selling quotations are provided for the microgrid electricity purchasing and selling operator m and the microgrid main body operator i when the rest microgrid main body operators, the microgrid electricity purchasing and selling operators and the traditional energy consumption users sell electricity at the time t;
Figure BDA0002758864980000058
respectively purchasing electricity quantity of electricity from the rest microgrid electricity purchasing and selling operators and the microgrid main operator for the microgrid electricity purchasing and selling operator n at the time t;
Figure BDA0002758864980000059
and (4) the electricity purchasing quantity is obtained when the electricity purchasing operator n purchases electricity from the electricity market at the time t.
The energy storage operation cost of the microgrid electricity purchasing and selling operator is as follows:
Figure BDA00027588649800000510
wherein λ isESRIs the unit operating cost of the energy storage unit;
Figure BDA00027588649800000511
and (4) operating capacity of the microgrid electricity-selling operator n when the microgrid operates at the time t.
The network fee payment of the micro-network electricity purchasing and selling operator is as follows:
Figure BDA00027588649800000512
wherein λ isTDDegree to be paid to power grid company by power grid electricity purchasing and selling operator in electricity selling processThe cost of electricity passing through the network.
The microgrid electricity purchasing and selling operator purchases electricity selling constraints:
Figure BDA00027588649800000513
wherein the content of the first and second substances,
Figure BDA0002758864980000061
the method comprises the steps that electric quantity stored by an internal energy storage unit of an electricity selling operator n at t-1 moment is purchased for the microgrid;
Figure BDA0002758864980000062
the maximum value and the minimum value of the stored electric quantity of the energy storage unit are respectively. The constraint is mainly used for limiting the microgrid electricity purchasing and selling operator not to exceed the storage capacity of the energy storage unit in the transaction process.
The power balance constraint of the microgrid electricity purchasing and selling operator is as follows:
Figure BDA0002758864980000063
this constraint primarily limits transactions made by the microgrid purchase electricity vendor to only the current day.
The legacy energy user objective function:
Figure BDA0002758864980000064
wherein the content of the first and second substances,
Figure BDA0002758864980000065
the internal electricity purchasing cost of the traditional energy consumption user L for purchasing electricity from the microgrid main body operator and the microgrid electricity purchasing and selling operator and the external electricity purchasing cost for participating in electricity purchasing in the electricity market are respectively the t time.
The electricity purchasing cost of the traditional energy consumption user is as follows:
Figure BDA0002758864980000066
wherein the content of the first and second substances,
Figure BDA0002758864980000067
respectively obtaining the electricity purchasing price and electricity purchasing quantity of electricity purchased from a microgrid main body operator i by a traditional energy consumption user L at the time t;
Figure BDA0002758864980000068
respectively obtaining the electricity purchasing price and the electricity purchasing quantity of electricity purchased from a microgrid electricity purchasing operator n by a traditional energy consumption user l at the time t;
Figure BDA0002758864980000069
the external electricity purchasing quantity of the traditional energy consumption user L at the time t is realized.
The conventional user power balance constraint:
Figure BDA00027588649800000610
wherein the content of the first and second substances,
Figure BDA00027588649800000611
the internal load of the user L at the time t is used for the tradition.
And different microgrid main body settlement constraints:
Figure BDA00027588649800000612
Figure BDA00027588649800000613
Figure BDA0002758864980000071
Figure BDA0002758864980000072
the constraint mainly represents the balance of the electricity purchased and sold by different microgrid main bodies in the transaction process.
Figure BDA0002758864980000073
The constraint is mainly characterized in that electricity selling quotations of different micro-grid bodies are limited through platform transactions.
Preferably, the distributed transaction platform among the multiple microgrid main bodies is constructed by adopting a block chain technology, the characteristics of decentralization, intelligent contracts, traceability and the like of the block chain technology are matched with the requirements of a distributed transaction mode, and an admission mechanism, an encryption mechanism, an intelligent contract mechanism, a common recognition mechanism and an internal and external level electricity purchasing and selling mechanism are constructed for reasonably and effectively completing market transactions among the multiple microgrid main bodies;
the admission mechanism is an admission mechanism which is set for users joining the trading platform and ensures that the trading on the trading platform is reasonably and orderly completed, specifically comprises the power generation capacity, load demand, geographical area where the users are located, information interaction capacity and the like of the users, the reliability of the trading can be ensured to a certain extent by adopting the admission mechanism, and in order to protect the privacy of the users, the auditing of the admission mechanism is carried out by a local government or a power grid company.
The encryption mechanism is that each user on the platform can generate a private and public key through the characteristics of the block chain technology, and when the user publishes a message on the platform, only the public key of the user needs to be provided, so that the information interaction of other users when responding to the requirements of the users can be ensured, only the public key of the user can be decrypted, the danger of information leakage can be effectively reduced, and the public and private key can be used as a basis for verifying mutual identities of both transaction parties.
The intelligent contract mechanism is a core mechanism of a distributed transaction platform, the essence of the intelligent contract mechanism is an automatically executed program, in the transaction process, two parties of the transaction can input conditions (the amount of transaction electric quantity, negotiated power price, basic information of the two parties of the transaction and the like) which are preset in an agreement into the platform and transfer the conditions into related transaction fees, and in the actual operation process, the platform processes the transaction fees in the electronic wallets of the two parties of the transaction according to the transaction conditions preset by the two parties of the transaction.
The consensus mechanism is one of the bases of the distributed transaction platform, after the transaction of the user is settled, the platform publishes basic information such as transaction electric quantity, both sides ID and the like for the inspection of other users, the mechanism can effectively ensure the authenticity and the validity of the transaction information on the platform, and provides reliable basis for reducing the trust cost of the user newly added into the platform.
The internal and external level electricity purchasing and selling mechanism is based on that in the electricity market transaction process, two transaction parties only need to pay network fees to a power grid company, and pricing of the two transaction parties is not influenced by electricity prices of the power grid, so that the transaction electricity prices of the two transaction parties can be regarded as internal transaction electricity prices, values can be between the electricity purchasing and selling prices of the power grid, and surplus or shortage of electricity can be used as a whole to participate in electricity purchasing of the electricity market after the internal transaction of the platform is finished. The internal and external level electricity purchasing and selling mechanism can promote pricing of both trading parties to a certain extent and is beneficial to improving economy of both trading parties.
Preferably, the transaction flow on the distributed transaction platform is as follows:
and an information release stage: the traditional energy consumption user predicts a next-day demand curve according to the historical energy consumption data of the traditional energy consumption user and issues demands through a trading platform; the microgrid main body operator predicts a next-day demand curve of the operator according to weather prediction and data analysis of the load of the operator, and releases the demand of the operator after internal optimization is carried out according to the existing information of the platform;
a transaction negotiation stage: a micro-grid main operator, a traditional energy utilization user and a micro-grid electricity purchasing and selling operator select a proper transaction object for negotiation according to information published on the platform, and adjust self requirements in the negotiation process until an agreement is reached;
a transaction confirmation stage: after each micro-grid main body on the trading platform reaches an agreement, signing an intelligent contract through the platform and transferring the asset amount related to the intelligent contract into a platform electronic purse to wait for settlement;
a transaction execution stage: each micro-grid main body executes transaction according to contract content;
and (3) transaction settlement stage: after each micro-grid main body executes the contract, the platform settles the fund according to the intelligent contract;
and an information disclosure stage: and after the transaction settlement is finished, recording the transaction related basic information to the block chain for later reference.
Preferably, the microgrid risk possibly caused by uncertainty of clean energy inside each microgrid main body to the participation in the transaction is considered by the two-stage robust optimization model in the microgrid main body, according to the economy of internal components of an operator of the microgrid main body, the electricity purchased and sold at the inner layer and the outer layer, the running state of a gas turbine and the like are used as first-stage variables, the load shedding electricity and the flexible load reduction are used as second-stage variables, and the variables are divided into main problems and sub-problems to be characterized:
Figure BDA0002758864980000081
wherein x and y are decision variables; a. b, c and h, m and d are column vectors of the target function and the constraint condition of the formula respectively; A. b, C, D, E, H, M represents the coefficient matrix of the constraint.
Figure BDA0002758864980000082
Figure BDA0002758864980000091
Deriving decision variables x by solving the main problem MP*Substituting the sub-problem into a sub-problem SP to solve the worst scene
Figure BDA0002758864980000092
And in the scene, the output of the internal controllable unit for ensuring the robustness passes through the worst generated scene
Figure BDA0002758864980000093
The solution of the min model in the main problem MP is realized, so that the iterative solution between the main problem and the sub-problems is realized, the sub-problems need to be converted into a single layer before the solution, the sub-problem model is linear, so that the dual method can be adopted for processing, and lambda, pi and gamma are corresponding variables;
the fluctuation scene of the wind power output is mainly characterized by an uncertain set, and is optimized on the basis, wherein the uncertain set Z isiComprises the following steps:
Figure BDA0002758864980000094
Figure BDA0002758864980000095
Figure BDA0002758864980000096
wherein Z isiRepresenting each output condition of wind turbine generator set in micro-grid main body operator i
Figure BDA0002758864980000097
A set of (a);
Figure BDA0002758864980000098
respectively obtaining a predicted value of the output of the wind turbine generator and upper and lower limits of the output fluctuation range contained in the microgrid main body operator i; gamma is an adjusting parameter of wind power uncertainty, and the value range is [0, 1%]Therefore, the actual scene can be obtained by selecting a proper gamma value.
Preferably, the two-stage robust optimization model in the microgrid main body carries out linearization processing on the subproblems by using a strong dual theory and a Big-M method, and then adopts a CCG algorithm to realize iterative solution of MP and SP so as to obtain an optimal operation strategy.
Preferably, after the reasonable convergence precision epsilon is selected, the solving step of the two-stage robust optimization model in the microgrid main body is as follows:
the method comprises the following steps: let the upper bound U ═ infinity and the lower bound L ═ infinity iteration number be n, where the initial value is 1 and the maximum value is nmax；
Step two: solving the main problem to obtain a decision result
Figure BDA0002758864980000099
And solving the objective function value aTx*+L*Update the model lower bound to L ═ max { L, aTx*+L*}；
Step three: the sub-problem is based on the main problem decision result x*Solving uncertain parameter key scene xi*And y*Calculating the objective function value b of the subproblemTξ*+cTy*And updating the model upper bound to U-min { U, a ═Tx*+bTξ*+cTy*}；
Step four: if (U-L) is less than or equal to epsilon, the iteration is ended, and x is returned*And y*(ii) a Otherwise, let n be n +1, update the worst scene to xin＝ξ*And (4) the descendant enters the main problem to be solved again, and is solved according to the flow from the step two until convergence is realized or the iteration number reaches nmax.
By adopting the technical scheme, the model is divided into the main problem and the sub problem by using the decomposition algorithm to carry out repeated iterative solution, the solution speed can be accelerated, and the CPLEX of the conventional solution toolkit is adopted to carry out effective solution.
The invention has the beneficial effects that:
1. a distributed transaction platform among the micro-grid main bodies is constructed by adopting a block chain technology, the characteristics of decentralization, intelligent contracts, traceability and the like of the block chain technology are matched with the requirements of a distributed transaction mode, and an admission mechanism, an encryption mechanism, an intelligent contract mechanism, a common identification mechanism and an internal and external level electricity purchasing and selling mechanism are constructed for reasonably and effectively completing market transactions among the micro-grid main bodies.
2. Under the micro-grid risk condition that the uncertainty of clean energy inside each micro-grid main body possibly causes the participation of the micro-grid main body in a transaction, a two-stage robust optimization model including the inside of an operator of the micro-grid main body is constructed according to the historical wind power output data inside the micro-grid main body, the model is divided into a main problem and a sub problem by a decomposition algorithm to be repeatedly and iteratively solved, the solving speed can be increased, and the CPLEX of the conventional solving tool kit is adopted to effectively solve.
Drawings
Fig. 1 is a detailed electricity transaction statement of a microgrid main operator 1 in a gaming process;
fig. 2 is a detailed electricity transaction statement of the microgrid main operator 2 in the gaming process;
fig. 3 is a detailed electricity transaction statement of the microgrid main operator 3 in the gaming process;
fig. 4 is details of electric quantity transaction of a microgrid electricity purchasing and selling operator in a game process;
FIG. 5 is a detailed description of the power transaction of a conventional energy user during a gaming process;
FIG. 6 is a system configuration;
fig. 7 is an exchange process of different microgrid bodies on a distributed platform;
fig. 8 is a detailed process of non-cooperative gaming on a distributed transaction platform.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
The modeling method is characterized in that the modeling is carried out on the controllable factors such as a gas turbine and a flexible load contained in the traditional micro-grid main body, market behavior characteristic analysis and classified modeling are mainly carried out on various types of micro-grid main bodies formed in different interiors of the power distribution network terminals, the simulation of different market behaviors of various types of micro-grid main bodies of the power distribution network terminals in the market is realized, a distributed transaction platform among multiple micro-grid main bodies is built on the basis, and a non-cooperative game transaction mechanism of the multiple micro-grid main bodies is built on the market transaction behaviors among the multiple micro-grid main bodies through the distributed transaction platform. On the basis, the characteristics of a micro-grid main body internally containing wind power are considered, a two-stage robust optimization model in the micro-grid main body is constructed by combining typical scene data of the wind power and the adjustment characteristics of decision variables, the model is solved by adopting a CCG algorithm, non-cooperative gaming among the multi-micro-grid main body is realized by selecting market behaviors of different micro-grid main bodies in the transaction process after optimization is realized, and finally the effectiveness of the method is verified by calculation.
It is assumed that the system is formed by different microgrid main bodies as shown in fig. 5, and the internal structures of the microgrid main body operators are different from each other as shown in table 1; the internal gas turbine unit of the microgrid main body operator adopts a TAU5670 model, and specific operation parameters and pollution discharge parameters are shown in tables 2-3; the reference load of the selected PSDR accounts for 10% of the total load of the microgrid main body.
Table 1 internal controllable unit composition of each microgrid main body operator
Figure BDA0002758864980000111
TABLE 2 gas turbine unit operating parameters
Figure BDA0002758864980000112
TABLE 3 gas turbine unit blowdown parameters
Figure BDA0002758864980000113
The internal components of the microgrid main body operators are different, and different microgrid main body operators, traditional energy consumption user load prediction and wind power output prediction under a typical scene are randomly generated.
Simulation is performed according to the simulation scheme, and the electricity transaction condition generated by the microgrid main operator in the gaming process is shown in fig. 1-5:
as shown in table 4, by analyzing and comparing fig. 1-5, it can be seen that the user behaviors of different microgrid subjects are influenced by their own constitutions when the non-cooperative gaming transaction is conducted. The microgrid main body operator has the special composition that the inside of the microgrid main body operator contains both an energy production unit and an energy consumption unit, so that the user behavior is the most complex, and the fluctuation of the internal clean energy further deepens the complexity, so that the uncertain risk resistance is improved by adopting a robust optimization mode, and the microgrid main body operator has important significance in participating in distributed transactions;
table 4 different microgrid subject gains
Figure BDA0002758864980000121
The foregoing is merely a preferred embodiment of the invention, it being understood that the embodiments described are part of the invention, and not all of it. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. The invention is not intended to be limited to the forms disclosed herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. A multi-microgrid main body non-cooperative game transaction method is characterized by comprising the following steps:
step 1, carrying out market behavior characteristic analysis and classified modeling on various types of microgrid main bodies formed in different interiors of a power distribution network terminal, and simulating different market behaviors of various types of microgrid main bodies of the power distribution network terminal in the market;
step 2, constructing a distributed transaction platform among the multiple micro-grid main bodies according to simulation of different market behaviors of various types of micro-grid main bodies of the power distribution network terminal in the market;
step 3, a multi-microgrid-body non-cooperative game transaction mechanism is constructed for market transaction behaviors among a plurality of microgrid bodies through a distributed transaction platform;
step 4, in a multi-microgrid-main-body non-cooperative game transaction mechanism, a two-stage robust optimization model in a microgrid main body is constructed by adopting a two-stage robust optimization method;
step 5, solving the two-stage robust optimization model in the microgrid main body by using a column and constraint generation algorithm, and deciding a self-transaction strategy by each microgrid main body after realizing multiple iterations of the solved result;
and 6, each micro-grid main body continuously carries out self-optimization according to the game situation until Nash equilibrium is reached and presents different market trading behaviors.
2. The non-cooperative game transaction method for multiple micro-grid bodies according to claim 1, wherein the step 1 of performing market behavior characteristic analysis and classification modeling on various types of micro-grid bodies formed in different interiors of the power distribution network terminals aims at achieving game balance in transactions among the micro-grid bodies of the power distribution network terminals, and the constraint conditions of internal power balance of the micro-grid bodies, balance of electricity purchased and sold among the micro-grid bodies, settlement limitation of electricity purchased and sold, regulation and operation limitation of a gas turbine and a flexible load adjustable unit and game balance among the micro-grid bodies comprise modeling on internal formation of the micro-grid bodies of different types, internal economic targets of the micro-grid bodies of different types, and operation cost and income of the micro-grid bodies participating in electricity purchase and sale processes.
3. The non-cooperative game transaction method for the multiple micro-grid bodies according to claim 1, wherein a distributed transaction platform among the multiple micro-grid bodies is constructed by adopting a block chain technology, and an admission mechanism, an encryption mechanism, an intelligent contract mechanism, a common identification mechanism and an internal and external level electricity purchasing and selling mechanism are constructed;
the admission mechanism is set for ensuring that the transaction on the transaction platform is reasonably and orderly completed and users joining the platform comprise the power generation capacity of the users, the load demand, the geographical area where the users are located and the information interaction capacity;
the encryption mechanism is used for generating a private and public key for each user on the platform through the characteristics of a block chain technology, and the private and public key is used as a basis for verifying the mutual identity of two transaction parties;
the intelligent contract mechanism is a core mechanism of the distributed transaction platform, and in the transaction process, two transaction parties can input conditions for agreement preset transaction achievement into the platform and transfer the conditions into related transaction fees;
the consensus mechanism is that after the user transaction is settled, the platform publishes the transaction electric quantity and the ID information of both parties for the other users to check;
the internal and external level electricity purchasing and selling mechanism is based on that in the electricity market transaction process, two transaction parties only need to pay network fees to a power grid company, and pricing of the two transaction parties is not influenced by electricity prices of the power grid, so that the transaction electricity prices of the two transaction parties can be regarded as internal transaction electricity prices, the value is between the electricity purchasing and selling prices of the power grid, and surplus or scarce electricity can participate in electricity purchasing of the electricity market as a whole after the internal transaction of the platform is finished.
4. The non-cooperative gaming transaction method for the multi-microgrid subject of claim 3, wherein the transaction flow on the distributed transaction platform is as follows:
and an information release stage: the traditional energy consumption user predicts a next-day demand curve according to the historical energy consumption data of the traditional energy consumption user and issues demands through a trading platform; the microgrid main body operator predicts a next-day demand curve of the operator according to weather prediction and data analysis of the load of the operator, and releases the demand of the operator after internal optimization is carried out according to the existing information of the platform;
a transaction negotiation stage: a micro-grid main operator, a traditional energy utilization user and a micro-grid electricity purchasing and selling operator select a proper transaction object for negotiation according to information published on the platform, and adjust self requirements in the negotiation process until an agreement is reached;
a transaction confirmation stage: after each micro-grid main body on the trading platform reaches an agreement, signing an intelligent contract through the platform and transferring the asset amount related to the intelligent contract into a platform electronic purse to wait for settlement;
a transaction execution stage: each micro-grid main body executes transaction according to contract content;
and (3) transaction settlement stage: after each micro-grid main body executes the contract, the platform settles the fund according to the intelligent contract;
and an information disclosure stage: and after the transaction settlement is finished, recording the transaction related basic information to the block chain for later reference.
5. The non-cooperative game transaction method for the multiple micro-grid bodies according to claim 2, wherein the micro-grid risk possibly caused by uncertainty of clean energy inside each micro-grid body to participate in the transaction is considered by the two-stage robust optimization model inside the micro-grid body, according to economy of internal components of an operator of the micro-grid body, the electricity purchased and sold at the inner layer and the outer layer, the operation state of a gas turbine and the like are used as first-stage variables, the cut-load electricity and the flexible load reduction are used as second-stage variables, and the variables are divided into main problems and sub-problems to be characterized:
Figure FDA0002758864970000021
wherein x and y are decision variables; a. b, c and h, m and d are column vectors of the objective function and the constraint condition respectively; A. b, C, D, E, H, M represents the coefficient matrix of the constraint.
Figure FDA0002758864970000031
Figure FDA0002758864970000032
Deriving decision variables x by solving the main problem MP*Substituting the sub-problem into a sub-problem SP to solve the worst scene
Figure FDA0002758864970000033
And in this scenario the internal controllable unit is for ensuring robustnessForce, through the worst scenario of production
Figure FDA0002758864970000034
The solution of the min model in the main problem MP is realized, so that the iterative solution between the main problem and the sub-problems is realized, the sub-problems need to be converted into a single layer before the solution, the sub-problem model is linear, so that the dual method can be adopted for processing, and lambda, pi and gamma are corresponding variables;
the fluctuation scene of the wind power output is mainly characterized by an uncertain set, and is optimized on the basis, wherein the uncertain set Z isiComprises the following steps:
Figure FDA0002758864970000035
Figure FDA0002758864970000036
Figure FDA0002758864970000037
wherein Z isiRepresenting each output condition of wind turbine generator set in micro-grid main body operator i
Figure FDA0002758864970000038
A set of (a);
Figure FDA0002758864970000039
respectively obtaining a predicted value of the output of the wind turbine generator and upper and lower limits of the output fluctuation range contained in the microgrid main body operator i; gamma is an adjusting parameter of wind power uncertainty, and the value range is [0, 1%]Therefore, the actual scene can be obtained by selecting a proper gamma value.
6. The non-cooperative gaming transaction method for the multi-microgrid main body according to claim 5, characterized in that a two-stage robust optimization model in the microgrid main body linearizes a subproblem by using a strong dual theory and a Big-M method, and then adopts a CCG algorithm to realize iterative solution of MP and SP so as to obtain an optimal operation strategy.
7. The non-cooperative game transaction method for the multiple micro-grid bodies according to claim 6, wherein after a reasonable convergence precision epsilon is selected, the solving step of the two-stage robust optimization model in the micro-grid body comprises the following steps:
the method comprises the following steps: taking U ═ infinity, L ═ infinity as the upper and lower bounds of the model respectively, and the iteration number as n, wherein the initial value is 1, and the maximum value is nmax；
Step two: solving the main problem to obtain a decision result x*,
Figure FDA0002758864970000041
L*And solving the objective function value aTx*+L*Update the model lower bound to L ═ max { L, aTx*+L*}；
Step three: the sub-problem is based on the main problem decision result x*Solving uncertain parameter key scene xi*And y*Calculating the objective function value b of the subproblemTξ*+cTy*And updating the model upper bound to U-min { U, a ═Tx*+bTξ*+cTy*}；
Step four: if (U-L) is less than or equal to epsilon, the iteration is ended, and x is returned*And y*(ii) a Otherwise, let n be n +1, update the worst scene to xin＝ξ*And (4) the descendant enters the main problem to be solved again, and is solved according to the flow from the step two until convergence is realized or the iteration number reaches nmax.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113344249A (en) * 2021-05-14 2021-09-03 合肥工业大学 Block chain-based cooling, heating and power combined supply multi-microgrid optimal scheduling method and system
CN115423622A (en) * 2022-08-12 2022-12-02 国网江苏省电力有限公司淮安供电分公司 Block chain-based power demand response transaction settlement method and system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113344249A (en) * 2021-05-14 2021-09-03 合肥工业大学 Block chain-based cooling, heating and power combined supply multi-microgrid optimal scheduling method and system
CN113344249B (en) * 2021-05-14 2022-09-30 合肥工业大学 Block chain-based cooling, heating and power combined supply multi-microgrid optimal scheduling method and system
CN115423622A (en) * 2022-08-12 2022-12-02 国网江苏省电力有限公司淮安供电分公司 Block chain-based power demand response transaction settlement method and system

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