CN112952908A - Multi-cooperation micro-grid main body distributed coordination transaction method - Google Patents

Multi-cooperation micro-grid main body distributed coordination transaction method Download PDF

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CN112952908A
CN112952908A CN202110344743.0A CN202110344743A CN112952908A CN 112952908 A CN112952908 A CN 112952908A CN 202110344743 A CN202110344743 A CN 202110344743A CN 112952908 A CN112952908 A CN 112952908A
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microgrid
grid
main body
transaction
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CN112952908B (en
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高红均
徐松
刘俊勇
刘友波
王乃永
吴子豪
王若谷
唐露甜
王辰曦
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Sichuan University
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shaanxi Electric Power Co Ltd
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Sichuan University
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shaanxi Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention relates to the technical field of power market at a power selling side, and aims to provide a distributed coordination trading method for a multi-cooperation micro-grid main body, which relates to the field of power market at the power selling side. A multi-cooperative micro-grid distributed coordination transaction framework is built based on an objective function cascade algorithm, the transaction electric quantity between micro-grid main bodies is coordinated, uncertainty of wind-light renewable energy output inside the micro-grid main bodies is considered, a two-stage self-adaptive robust optimization decision model of the micro-grid main bodies is built, decision variables inside the main bodies comprise demand response adjustment quantity, output of a micro gas turbine, charge and discharge quantity of an energy storage system and exchange power with a power distribution network and other micro-grid main bodies, and the decision model is solved by adopting a column and constraint generation algorithm.

Description

Multi-cooperation micro-grid main body distributed coordination transaction method
Technical Field
The invention relates to the technical field of power markets at power selling sides, in particular to a distributed coordination trading method for multiple cooperative microgrid main bodies.
Background
Under the background of the development of low-carbon transformation of electric power, a large amount of distributed and small-scale distributed energy resources are integrated to the electricity selling side, but the distributed energy resources are difficult to participate in the electric power wholesale market on the traditional power grid level due to the characteristics of strong output randomness, small single-machine capacity, large quantity, wide distribution and the like. Meanwhile, the microgrid is one of the most effective solutions for integrating distributed energy into an electric power system, and a regional microgrid group integrating differences in the types, capacities, load power utilization characteristics and the like of the distributed energy is formed on the power selling side. In recent years, share economy rises, a 'share' thought is introduced into the power field, micro-grids are allowed to trade directly, multiple micro-grids have strong complementary characteristics, and multiple micro-grid main bodies in a region can sign specific cooperation agreements to form cooperation alliances.
However, the existing design has the following problems:
1. and a decision framework which can protect the privacy of the microgrid main bodies and can quickly coordinate electric quantity transaction among the multiple cooperative microgrid main bodies in the area is lacked.
2. The risk that the wind-solar renewable energy output uncertainty inside each microgrid main body possibly causes the participation of each microgrid main body in a transaction is not considered.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a distributed coordination transaction method for multiple cooperative microgrid main bodies.
The method is realized by the following technical scheme: a multi-cooperation microgrid main body distributed coordination transaction method comprises the following steps:
step 1: performing uncertainty analysis on various parameters of each type of microgrid main body, constructing a two-stage adaptive robust optimization decision model of the microgrid main body, and executing the step 2;
step 2: acquiring demand response, energy storage and energy supply elements in the microgrid main body, combining the two-stage adaptive robust optimization decision model in the step 1, constructing a microgrid main body market decision model, and executing the step 2;
and step 3: and constructing a multi-cooperative microgrid main body distributed coordination transaction framework according to a cooperation union formed by a plurality of microgrid main bodies in the region, and solving coordination transaction electric quantity and a two-stage self-adaptive robust optimization decision model among the microgrid main bodies by an objective function connection method.
Preferably, in the step 1, each parameter includes each parameter of renewable energy output, a polyhedron uncertainty set is constructed to describe uncertainty of renewable energy output, and a two-stage adaptive robust optimization decision model of the microgrid main body is obtained.
Preferably, in the step 2, the microgrid main body market decision model includes multiple constraints, specifically, an energy consumption adjustment constraint, an energy storage charging and discharging constraint, a micro gas turbine output constraint, an electricity purchasing and selling constraint and an internal energy consumption balance constraint which can transfer loads, and the objective function includes an electricity purchasing cost, a demand response adjustment cost, a gas turbine operation cost and a penalty cost of wind abandoning and light abandoning.
Preferably, the multi-cooperative microgrid main body distributed coordination transaction framework is characterized in that penalty terms are added to objective functions to enable the multi-microgrid main bodies to achieve a joint agreement, and the coordination transaction electric quantity among the multi-microgrid main bodies is solved in an augmented Lagrange form of a two-stage adaptive robust optimization decision model of the microgrid main body.
Preferably, the method for solving the distributed coordination transaction framework of the multi-cooperation microgrid main body by the objective function cascade algorithm specifically comprises the following steps:
step 51: inputting initial parameters including a first-order multiplier rho and a second-order multiplier gamma, and coordinating transaction electric quantity
Figure BDA0002997766650000021
Setting an initial iteration value N to be 0 and a maximum iteration number N, and setting a convergence regulation epsilon to be 0.01;
step 52: updating the coordinated transaction electric quantity value between the microgrid m and other microgrid main bodies;
step 53: each micro-grid main body adopts a column and constraint generation algorithm to carry out respective robust optimization decision problem solving to obtain a coordinated trading electric quantity value with other micro-grid main bodies
Figure BDA0002997766650000022
Step 54: updating first and second order multipliers
Figure BDA0002997766650000023
Figure BDA0002997766650000024
Update n to n +1 and then go to step 52.
In another aspect, there is also provided a computer readable storage medium having one or more computer programs stored thereon which, when executed by one or more processors, implement the distributed coordinated transaction method according to any one of claims 1 to 5.
In another aspect, a distributed coordination transaction apparatus is also provided, including:
one or more processors;
a computer readable storage medium storing one or more computer programs; the one or more computer programs, when executed by the one or more processors, implement a distributed coordinated transaction method as described above.
In another aspect, a distributed coordination transaction system for multiple cooperative microgrid agents is further provided, wherein the multiple microgrid agents comprise: the system comprises a wind turbine generator set, a photovoltaic generator set, a controllable power supply of a micro gas turbine, an energy storage system and an internal load; the system comprises a main controller, a micro-grid distributed coordination transaction module and a micro-grid distributed coordination transaction module, wherein the main controller in the system carries out simulation on the micro-grid distributed coordination transaction;
the master controller having stored therein one or more computer programs that, when executed by one or more processors it has, implement a distributed coordinated transaction method as described above;
on the other hand, the distributed coordination transaction method is used for distribution of power consumption in each micro-grid, model building and accounting of electricity price.
The invention has the beneficial effects that:
(1) considering that the load energy consumption characteristics among microgrid main bodies have complementary characteristics and the privacy protection requirements of the multiple microgrid main bodies, constructing a multi-cooperation microgrid distributed coordination transaction framework based on a target function cascade algorithm, and quickly coordinating the coordination transaction electric quantity among the multiple cooperation microgrid main bodies;
(2) the method comprises the steps of considering risks possibly caused by uncertainty of wind power and photovoltaic renewable energy sources inside a microgrid main body to the participation of the microgrid main body in transactions, constructing a two-stage robust optimization decision model of the microgrid main body, utilizing a column and constraint generation algorithm to iteratively solve main problems and sub problems of model decomposition, and effectively solving by adopting the CPLEX of the conventional solving tool package.
Drawings
FIG. 1 is a schematic diagram of a transaction flow in the present invention;
FIG. 2 is a composition diagram of power distribution network subject decision variables according to one embodiment of the present invention;
FIG. 3 illustrates a multi-microgrid system configuration according to one embodiment of the present invention;
FIG. 4 illustrates wind power, photovoltaic plants, and load forecasts for one embodiment of the present invention;
FIG. 5 illustrates the power rates for the internet and the inter-microgrid trading according to one embodiment of the present invention;
fig. 6 is a diagram of a microgrid 1 power transaction detail according to an embodiment of the present invention;
FIG. 7 is a microgrid 2 power transaction detail of an embodiment of the present invention;
FIG. 8 is a diagram of a microgrid 3 power transaction detail in accordance with an embodiment of the present invention;
FIG. 9 illustrates the relative difference in total cost of MG2 for different ranges of fluctuation for an embodiment of the invention;
fig. 10 is an unbalanced power in the real-time market for MG2 of an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to fig. 1 to 10 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other implementations made by those of ordinary skill in the art based on the embodiments of the present invention are obtained without inventive efforts.
In the description of the present invention, it is to be understood that the terms "counterclockwise", "clockwise", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate orientations or positional relationships based on those shown in the drawings, and are used for convenience of description only, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be considered as limiting.
Please refer to fig. 1, 2 and 3
A multi-cooperation microgrid main body distributed coordination transaction method comprises the following steps:
step 1, analyzing uncertainty such as wind and light in various types of microgrid main bodies, and constructing a two-stage adaptive robust optimization decision model of the microgrid main body;
step 2, modeling elements such as demand response, energy storage and a micro gas turbine in the micro power grid main body, and constructing a micro power grid main body market decision model based on a two-stage robust model;
step 3, forming a cooperation union for multiple regional microgrid main bodies, constructing a multi-cooperation microgrid main body distributed coordination trading framework, and solving coordination trading electric quantity among the multiple microgrid main bodies through an objective function cascading method;
and 4, solving a two-stage robust optimization decision model of the microgrid main body through a column and constraint generation algorithm.
Preferably, in the step 1, uncertain characteristics of renewable energy sources such as wind, light and the like inside the microgrid main body are analyzed, a general two-stage robust optimization decision model is constructed, and since the penalty term comprises a quadratic function, the purchased and sold electric quantities of the microgrid main body, the power distribution network and other microgrid main bodies are set as first-stage decision variables and are marked as { xm }; and setting the demand response adjustment quantity, the energy storage charging and discharging quantity, the output of the micro gas turbine and the like as decision variables of the second stage, and marking as { ym }, wherein a general robust optimization mathematical model is as follows.
Figure BDA0002997766650000051
The physical meaning of the robust optimization model is to find a robust decision scheme which enables the total operation cost to be the lowest under the worst working condition, and the max-min model in the second stage is used for finding the worst scene which can obtain the maximum total operation cost within the uncertain parameter range. Meanwhile, the robust optimization solution can ensure any value in the uncertain set range, and the whole model is feasible.
The key of robust optimization is how to apply an uncertain set to characterize the source-load uncertainty, which we use separatelyξPVAnd xiLThe uncertainty of the output and the load electricity utilization of the photovoltaic unit is described. Wherein the parameter gammaWindAnd ΓPVNamed as 'uncertainty adjusting parameter' with the value range of 0-NTThe integer in the scheduling period represents the total number of time periods when the photovoltaic output and the load power take the minimum value or the maximum value in the fluctuation interval in the scheduling period, and can be used for adjusting the conservativeness of the optimal solution, the decision scheme obtained by the method is more conservative when the value is larger, and on the contrary, the decision scheme is more risky.
Figure BDA0002997766650000052
Figure BDA0002997766650000053
In the formula
Figure BDA0002997766650000054
And
Figure BDA0002997766650000055
is an uncertainty set of wind-solar forces within the microgrid body, wherein
Figure BDA0002997766650000056
And
Figure BDA0002997766650000057
is a predicted value of wind power and photovoltaic output,
Figure BDA0002997766650000058
the fluctuation range of wind power and photovoltaic output,
Figure BDA0002997766650000059
are all 0-1 auxiliary variables, e.g. when
Figure BDA00029977666500000510
When the temperature of the water is higher than the set temperature,
Figure BDA00029977666500000511
Figure BDA00029977666500000512
and
Figure BDA00029977666500000513
the method comprises the steps that wind power and photovoltaic unit node sets in an MG m main body are respectively, i is a node number of a micro-grid system, and t is an operation time period.
Preferably, in the step 2, a detailed modeling is performed on internal elements of the microgrid main body, and a microgrid main body market robust optimization decision model is constructed based on a general two-stage robust optimization decision model.
The decision model objective function of the microgrid body is as follows:
Figure BDA0002997766650000061
the first stage objective function for determining the microgrid body comprises the electricity purchasing and selling cost of trading with the power distribution network and other microgrid bodies, wherein
Figure BDA0002997766650000062
Respectively the cost coefficients of electricity purchasing and selling in transaction with the power distribution network and other micro-grids,
Figure BDA0002997766650000063
respectively the purchase and sale electricity quantity transacted with the power distribution network and other micro-grids.
Figure BDA0002997766650000064
The second stage objective function in the micro-grid body decision model comprises the adjustment cost of demand response, the output cost of the micro gas turbine and the penalty cost of wind and light abandonment, wherein
Figure BDA0002997766650000065
Respectively unit cost coefficients of load adjustment, micro gas turbine output, wind abandoning and light abandoning punishment,
the operation constraint of the micro gas turbine is as follows:
Figure BDA0002997766650000066
wherein the content of the first and second substances,
Figure BDA0002997766650000067
is the output value of the micro gas turbine,
Figure BDA0002997766650000068
the upper limit of the output of the micro gas turbine. The operation plan of the hour level is mainly considered, the climbing constraint of the unit is ignored when the operation constraint of the micro gas turbine is modeled,
Figure BDA0002997766650000069
the node set of the micro gas turbine engine group inside the MG m main body.
Operation constraint of the energy storage system:
Figure BDA0002997766650000071
Figure BDA0002997766650000072
Figure BDA0002997766650000073
Figure BDA0002997766650000074
Figure BDA0002997766650000075
Figure BDA0002997766650000076
the operation constraint of the energy storage system comprises charge and discharge constraint per unit time, wherein
Figure BDA0002997766650000077
Is the amount of charge per time unit,
Figure BDA0002997766650000078
the maximum charge and discharge capacity of the energy storage system in unit time; the state of charge of the energy storage system needs to be kept within a limited range, wherein the SOCi，tIs the charge of the energy storage system for a certain period of time,
Figure BDA0002997766650000079
in order to achieve the charging and discharging efficiency of the energy storage system,
Figure BDA00029977666500000710
maximum and minimum stored electricity quantities for the energy storage system;
Figure BDA00029977666500000711
is the charging and discharging state of the energy storage system in a certain time period, in the certain time period, the energy storage system can only be charged or discharged,
Figure BDA00029977666500000712
and the nodes of the internal energy storage system of the MG m main body are collected.
The demand response constraint is:
Figure BDA00029977666500000713
Figure BDA00029977666500000714
Figure BDA00029977666500000715
Figure BDA00029977666500000716
Figure BDA00029977666500000717
the adjustment of the adjustable load must be kept within a permissible range, wherein
Figure BDA00029977666500000718
The load can be increased or decreased for a certain period of time,
Figure BDA00029977666500000719
the maximum range over which the load can be increased or decreased for a certain period of time,
Figure BDA00029977666500000720
for the predicted value of the adjustable load for a certain period of time,
Figure BDA00029977666500000721
node set for internal flexible load of MG m main body
The wind power and photovoltaic output constraints are as follows:
Figure BDA0002997766650000081
Figure BDA0002997766650000082
the output value of the wind power and photovoltaic set is smaller than the predicted scene, wherein
Figure BDA0002997766650000083
The actual output values of the wind power and photovoltaic units are obtained.
The micro-grid main body electricity purchasing and selling constraint is as follows:
Figure BDA0002997766650000084
Figure BDA0002997766650000085
Figure BDA0002997766650000086
Figure BDA0002997766650000087
Figure BDA0002997766650000088
Figure BDA0002997766650000089
Figure BDA00029977666500000810
in a certain time period, the transaction electric quantity of the micro-grid main body, the power distribution network and other micro-grid main bodies is kept within a safety constraint range, and in the same time period, the micro-grid main body can only select to buy or sell electricity. Wherein
Figure BDA00029977666500000811
In order to purchase and sell the electricity,
Figure BDA00029977666500000812
as a microgridThe maximum purchase and sale electricity quantity of the main body,
Figure BDA00029977666500000813
for the collection of micro-grids within a federation,
Figure BDA00029977666500000814
the set of micro-grid MG m is divided for the federation.
The internal power balance constraint of the microgrid main body is as follows:
Figure BDA00029977666500000815
in any time period, the electricity purchasing quantity, the wind power and photovoltaic output values and the micro gas turbine output and the energy storage discharge quantity of the micro power grid main body are balanced with the load electricity consumption and the energy storage charging quantity.
Preferably, the step 3 is a multi-cooperative microgrid distributed coordination transaction solving framework, and for the convenience of description of a later solving process, a two-stage robust optimization decision model of a microgrid main body is expressed in a matrix form, wherein the two-stage robust optimization decision model comprises an objective function, a transferable load energy utilization adjustment constraint, an energy storage charging and discharging constraint, a micro gas turbine output constraint, a power purchasing and selling constraint, an internal energy utilization balance constraint and the like.
Figure BDA0002997766650000091
s.t.Amxm≤cm
Bmxm＝0
Figure BDA0002997766650000092
Cmym≤dm
Dmxm+Emym≤em
Fmym≤wm,Gmym≤pm
The matrix form of the two-stage robust optimization decision model of the microgrid main body is described above, x and y are decision variables, and a, b, c, d, w and p are column vectors of a target function and a constraint condition respectively; A. b, C, D, E, F, G represents the coefficient matrix of the constraint.
The basic idea of the multi-cooperation micro-grid main body distributed coordination transaction is to coordinate the distributed optimization of multiple systems by adding penalty terms on objective functions. Adding a penalty term to the objective function of each microgrid body can force them to gradually reach a joint agreement. Therefore, the decision model of the microgrid body is converted into an augmented lagrange form:
Figure BDA0002997766650000093
wherein rho and gamma are first-order multipliers and second-order multipliers of penalty functions, the penalty functions represent penalty costs of deviations of transaction electric quantity between different microgrid main bodies, and y ismkThe electric quantity is traded for coordination between the micro-grid m and the micro-grid k. The coordinated transaction electric quantity among the micro-grids finally reaches an agreement through iterative updating, wherein the updating result is as follows:
Figure BDA0002997766650000101
determining whether to determine a final coordination transaction result by calculating a residual error, wherein the calculation result of the nth residual error is as follows:
Figure BDA0002997766650000102
specifically, a target function cascade algorithm is adopted to solve a distributed coordination transaction framework of a multi-cooperation micro-grid main body, and the specific calculation steps are as follows:
the first step is as follows: inputting initial parameters including a first-order multiplier rho and a second-order multiplier gamma, and coordinating transaction electric quantity
Figure BDA0002997766650000103
And setting the initial iteration value N to be 0 and the maximum iteration number N. Setting convergence adjustment ε1＝0.001。
The second step is that: and updating the coordinated transaction electric quantity value between the microgrid m and other microgrid main bodies.
The third step: each micro-grid main body adopts a column and constraint generation algorithm to carry out respective robust optimization decision problem solving to obtain a coordinated trading electric quantity value with other micro-grid main bodies
Figure BDA0002997766650000104
The fourth step: and calculating the residual error of the coordinated transaction electric quantity value among the micro-grids, if the residual error meets the convergence condition, terminating the iteration process, and outputting an optimal decision result, otherwise, turning to the fifth step.
The fifth step: and updating the first-order multiplier and the second-order multiplier.
Figure BDA0002997766650000105
Figure BDA0002997766650000106
And updating n to n +1, and then turning to the second step.
The two-stage robust optimization model in the microgrid main body considers the transaction risk possibly caused by the uncertainty of wind-light renewable energy sources inside each microgrid main body to the participation in the transaction, according to the economy of internal components of a microgrid main body operator, the electricity purchasing and selling quantity of the power distribution network and other microgrid main bodies is taken as a first-stage variable, a demand response adjustment quantity, the output of a micro gas turbine and the energy storage charging and discharging quantity are taken as second-stage variables, and the main variable and the sub-variable are divided into main problems to be characterized:
Figure BDA0002997766650000107
Figure BDA0002997766650000111
deriving decision variables by solving a main problem MP
Figure BDA0002997766650000112
Substituting the sub-problem into a sub-problem SP to solve the worst scene
Figure BDA0002997766650000113
And in the scene, the output of the internal controllable unit for ensuring the robustness passes through the worst generated scene
Figure BDA0002997766650000114
And 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, and the sub-problems need to be converted into a single layer before the solution, and the sub-problem model is linear, so that the dual method can be adopted for processing, wherein
Figure BDA0002997766650000115
The corresponding dual variable.
The two-stage robust optimization model in the micro-grid main body converts a sub-problem max-min form into a min form by using a strong dual theory.
Figure BDA0002997766650000116
Figure BDA0002997766650000117
Figure BDA0002997766650000118
In the second stage objective function
Figure BDA0002997766650000119
And
Figure BDA00029977666500001110
for the nonlinear term, the sub-problem needs to be linearized by applying a Big-M method,
Figure BDA00029977666500001111
Figure BDA00029977666500001112
Figure BDA00029977666500001113
Figure BDA00029977666500001114
Figure BDA00029977666500001115
Figure BDA00029977666500001116
Figure BDA00029977666500001117
Figure BDA00029977666500001118
Figure BDA00029977666500001119
equivalently expressing bilinear terms through binary variables and a series of linear constraints, wherein
Figure BDA0002997766650000121
Is an auxiliary variable of 0 to 1,
Figure BDA0002997766650000122
is a continuous auxiliary variable.
The microgrid main body two-order robust optimization decision model adopts a column and constraint generation algorithm to iteratively solve a main problem MP and a sub problem SP, and the specific solving steps are as follows.
The first step is as follows: the relevant variables are initialized and the relevant variables are set,
Figure BDA0002997766650000123
take UB 1e8, LB 0, s 1, ε2＝0.01,Γ；
The second step is that: solving the main problem to obtain a decision result
Figure BDA0002997766650000124
Updating the lower bound value
Figure BDA0002997766650000125
The third step: according to the main question result
Figure BDA0002997766650000126
Solving the subproblems to obtain decision results
Figure BDA0002997766650000127
Then the upper bound value is updated
Figure BDA0002997766650000128
If UBs-LBs≤ε2Stopping iteration, outputting an optimization decision result, and otherwise, jumping to the fourth step.
The fourth step: s +1 is updated and then the process goes to the second step.
By adopting the technical scheme, the trade electric quantity of the multiple cooperative micro-grids is coordinated and solved by utilizing an objective function cascade method, wherein the robust optimization decision model of each micro-grid main body adopts a column and constraint decomposition algorithm to carry out iterative solution, and the CPLEX of the conventional solving toolkit is adopted to carry out effective solution.
Assuming that the multi-cooperative microgrid system is specifically constructed as shown in fig. 2, each microgrid main body comprises a wind power generation unit, a photovoltaic unit, a micro gas turbine controllable power supply, an energy storage system and an internal load. The operation parameters of the micro gas turbine and the energy storage system are shown in tables 1-3, the prediction scenes of wind power, photovoltaic sets and loads are shown in fig. 4, and the surplus power grid-connection electricity price of the micro power grid main body and the transaction electricity price between the micro power grid main bodies are shown in fig. 5.
TABLE 1 demand response operating parameters
Figure BDA0002997766650000129
Figure BDA0002997766650000131
TABLE 2 micro gas turbine operating parameters
Figure BDA0002997766650000132
TABLE 3 energy storage System operating parameters
Figure BDA0002997766650000133
According to the multi-microgrid system, simulation is carried out on multi-cooperative-microgrid distributed coordination transactions, the independent operation simulation condition of the multi-microgrid system and the distributed coordination transaction model provided by the patent are adopted for carrying out non-analysis and are marked as Case 2, and the model provided by the patent is marked as Case 1. Uncertainty adjusting parameter gamma of wind power and photovoltaic unitWind、Γ PV12, the wind power and photovoltaic units are arranged upwards,The downward maximum fluctuation range was 10%.
The total operation cost of the MGs 1-3 is shown in table 4 by performing simulation according to the simulation scheme, wherein the saved operation in Case1 mainly lies in the power trading among microgrid main bodies, and the power trading results of the MGs 1-MG3 are shown in fig. 6-8. For MG1, excess power during the period of 12:00-17:00 can be used to handle peak loads for MG2 and MG3 during this period. The power surplus and the power shortage of the MG2 and the MG3 in the period of 1:00-5:00 have complementary characteristics. As can be seen from table 4, the total operating costs of the three MGs under Case1 are 2628.2, 5610.2 and 4585.2 yuan, respectively, and the operating costs of the three MGs of Case1 are reduced by 3.15%, 5.46% and 7.29%, respectively, compared to the independent operating mode of Case 2.
TABLE 4 Total operating cost of microgrid Main body
MG1/￥ MG2/￥ MG3/￥
Case 1 2446.27 5735.56 4641.8
Case 2 2713.79 5934.34 4945.49
We define a robust optimization method and a deterministic method with a total cost difference Δ C of
Figure BDA0002997766650000134
Wherein, CROAnd CDAThe total day-ahead operating cost of the robust optimization model and the deterministic method in the worst scene is respectively. Setting an uncertainty adjustment parameter ΓWind、ΓPVThe difference in total operating cost of MG2 when varied is shown in fig. 9. With gammaWind、ΓPVAdditionally, the time period during which the wind turbine generator photovoltaic output can reach the fluctuation range boundary is also increased, and therefore, the day-ahead total cost difference between the robust optimization model and the deterministic method may be increased. This indicates that, when the uncertainty adjustment parameter setting is large, the trading plan of the microgrid main body has stronger robustness, and the microgrid main body can adjust the conservatism of the day-ahead operation trading plan by adjusting the uncertainty parameter.
As described above, when the uncertainty adjusts the parameter ΓWind、ΓPVThe robust optimization model is the same as the deterministic method when set to zero. It should be noted that the total running cost before the day using the deterministic method is lower than that using the robust optimization model, but this does not mean that the running trading plan obtained by the deterministic optimization model is better than the solution obtained by the robust optimization model. Power imbalances due to predicted deviations and actual output in the real-time market require power distribution grids to compensate. In addition, the buy/sell price of the real-time market is generally higher/lower than the buy/sell price of the market in the day ahead. From the perspective, the operation trading plan obtained by the robust optimization model has stronger robustness and the capability of processing the uncertainty of the output of the renewable energy. To demonstrate the performance of the proposed two-stage tunable robust optimization method, it is assumed that the buy/sell price of the real-time market is 1.5/0.5 times the corresponding price of the market at the day-ahead, and is based on the actual/predicted output of wind and photovoltaic power generation as shown in fig. 4.
The results of the operational costs of the microgrid body (including real-time balance and total day-ahead costs) at different fluctuation ranges are shown in table 5. FIG. 10 shows that at αWind0.1, MG2 imbalance in the real-time market. When the power is positive, the MG2 needs to buy extra electric energy to make up the power shortage, and when the power is negative, the MG2 can sell the surplus power on the real market. It can be seen that although the running cost of the robust optimization model is greater than that of the deterministic method day by day, the unbalanced power of the robust optimization model is lower in the real-time market. Therefore, under the condition of considering appropriate prediction errors, the robust optimization model can effectively improve the robustness of decision and reduce the balance cost in the real-time market.
TABLE 5 comparison of robust optimization method and deterministic method operating costs
Figure BDA0002997766650000141
In summary, the distributed coordination transaction framework of the multi-cooperation micro-grid main body is mainly constructed, the distributed coordination transaction process of the multi-cooperation micro-grid is elaborated in detail, the distributed coordination transaction of the multi-cooperation micro-grid is solved by adopting an objective function cascade algorithm, on the basis, the characteristic that renewable energy sources such as wind power and photovoltaic are contained in the micro-grid main body is considered, a two-stage robust optimization decision model in the micro-grid main body is constructed by combining the typical scene data of the wind power and the photovoltaic and the adjustment characteristic of decision variables, and iterative solution is carried out by adopting a column and constraint generation algorithm.

Claims (9)

1. A multi-cooperation micro-grid main body distributed coordination transaction method is characterized by comprising the following steps:
step 1: performing uncertainty analysis on various parameters of each type of microgrid main body, constructing a two-stage adaptive robust optimization decision model of the microgrid main body, and executing the step 2;
step 2: acquiring demand response, energy storage and energy supply elements in the microgrid main body, combining the two-stage adaptive robust optimization decision model in the step 1, constructing a microgrid main body market decision model, and executing the step 2;
and step 3: and constructing a multi-cooperative microgrid main body distributed coordination transaction framework according to a cooperation union formed by a plurality of microgrid main bodies in the region, and solving coordination transaction electric quantity and a two-stage self-adaptive robust optimization decision model among the microgrid main bodies by an objective function connection method.
2. The distributed coordination transaction method for multiple cooperative microgrid main bodies according to claim 1, characterized in that in step 1, parameters include parameters of renewable energy output, a polyhedral uncertain set is constructed to describe uncertainty of renewable energy output, and a two-stage adaptive robust optimization decision model of the microgrid main body is obtained.
3. The distributed coordination transaction method for multiple micro-grid entities in cooperation according to claim 2, wherein in the step 2, the micro-grid entity market decision model includes multiple constraints, specifically, an energy utilization adjustment constraint capable of transferring load, an energy storage charging and discharging constraint, a micro gas turbine output constraint, an electricity purchasing and selling constraint, an internal energy utilization balance constraint, and the objective function includes an electricity purchasing cost, a demand response adjustment cost, a gas turbine operation cost, and a wind abandonment and light abandonment penalty cost.
4. The distributed coordination transaction method for the multiple micro-grid bodies according to claim 3, characterized in that a penalty term is added to a target function of the distributed coordination transaction framework for the multiple micro-grid bodies to enable the multiple micro-grid bodies to achieve a joint agreement, and coordination transaction electric quantity among the multiple micro-grid bodies is solved in an augmented Lagrange form of a two-stage adaptive robust optimization decision model of the micro-grid bodies.
5. The distributed coordination transaction method for multiple cooperative microgrid main bodies according to claim 4, characterized in that the objective function cascade algorithm for solving the distributed coordination transaction framework for multiple cooperative microgrid main bodies specifically comprises the following steps:
step 51: inputting initial parameters including a first-order multiplier rho and a second-order multiplier gamma, and coordinating transaction electric quantity
Figure FDA0002997766640000011
Setting an initial iteration value N to be 0 and a maximum iteration number N, and setting a convergence regulation epsilon to be 0.01;
step 52: updating the coordinated transaction electric quantity value between the microgrid m and other microgrid main bodies;
step 53: each micro-grid main body adopts a column and constraint generation algorithm to carry out respective robust optimization decision problem solving to obtain a coordinated trading electric quantity value with other micro-grid main bodies
Figure FDA0002997766640000012
Step 54: updating first and second order multipliers
Figure FDA0002997766640000021
Figure FDA0002997766640000022
Update n to n +1 and then go to step 52.
6. A computer readable storage medium having one or more computer programs stored thereon which, when executed by one or more processors, implement the distributed coordinated transaction method of any one of claims 1 to 5.
7. A distributed coordinated transaction apparatus, comprising:
one or more processors;
a computer readable storage medium storing one or more computer programs; the one or more computer programs, when executed by the one or more processors, implement the distributed coordinated transaction method of any one of claims 1-3.
8. A multi-cooperation microgrid body distributed coordination transaction system is characterized in that a plurality of microgrids comprise: the system comprises a wind turbine generator set, a photovoltaic generator set, a controllable power supply of a micro gas turbine, an energy storage system and an internal load; wherein the content of the first and second substances,
a main controller in the system carries out simulation on the distributed coordination transaction of the micro-grid;
the master controller having stored therein one or more computer programs that, when executed by one or more processors it has, implement the distributed coordinated transaction method of claim 1 or 5.
9. Use of the distributed coordination transaction method according to any one of claims 1 to 5 for distribution of power consumption, model building, and accounting of electricity prices in individual micro-grids.
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