CN112952908B - Distributed coordination transaction method for multi-cooperation micro-grid main body - Google Patents
Distributed coordination transaction method for multi-cooperation micro-grid main body Download PDFInfo
- Publication number
- CN112952908B CN112952908B CN202110344743.0A CN202110344743A CN112952908B CN 112952908 B CN112952908 B CN 112952908B CN 202110344743 A CN202110344743 A CN 202110344743A CN 112952908 B CN112952908 B CN 112952908B
- Authority
- CN
- China
- Prior art keywords
- micro
- main body
- grid
- grid main
- transaction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000005457 optimization Methods 0.000 claims abstract description 55
- 230000005611 electricity Effects 0.000 claims abstract description 41
- 238000004146 energy storage Methods 0.000 claims abstract description 41
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 16
- 230000004044 response Effects 0.000 claims abstract description 15
- 238000004088 simulation Methods 0.000 claims abstract description 6
- 238000004590 computer program Methods 0.000 claims description 9
- 230000009977 dual effect Effects 0.000 claims description 6
- 238000005265 energy consumption Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 230000003247 decreasing effect Effects 0.000 claims description 4
- 238000007599 discharging Methods 0.000 claims description 3
- 230000003190 augmentative effect Effects 0.000 claims description 2
- 230000009194 climbing Effects 0.000 claims description 2
- 238000012804 iterative process Methods 0.000 claims description 2
- 230000009191 jumping Effects 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 claims description 2
- 239000002356 single layer Substances 0.000 claims description 2
- 238000013076 uncertainty analysis Methods 0.000 claims description 2
- 239000013598 vector Substances 0.000 claims description 2
- 230000006399 behavior Effects 0.000 abstract 1
- 230000000295 complement effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000000354 decomposition reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003763 carbonization Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006735 deficit Effects 0.000 description 1
- 238000012887 quadratic function Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of electricity market at the electricity selling side, and aims to provide a distributed coordination trading method for a main body of a multi-cooperation micro-grid, which relates to the field of electricity market at the electricity selling side and comprises the steps of modeling operation constraints of internal elements of the main body of the multi-type micro-grid, so that the simulation of market trading behaviors under the cooperation sharing of regional multi-micro-grids is realized. The method comprises the steps of constructing a multi-cooperation micro-grid distributed coordination trading framework based on an objective function cascading algorithm, coordinating the trading electric quantity among micro-grid main bodies, constructing a micro-grid main body two-stage self-adaptive robust optimization decision model by considering the uncertainty of wind-solar renewable energy output in the micro-grid main bodies, wherein the main body internal decision variables comprise demand response adjustment quantity, the output of a micro gas turbine, the charge and discharge quantity of an energy storage system and the exchange power with a power distribution network and other micro-grid main bodies, and solving the decision model by adopting a column and constraint generation algorithm.
Description
Technical Field
The invention relates to the technical field of electricity market at the electricity selling side, in particular to a distributed coordination transaction method for a main body of a multi-cooperation micro-grid.
Background
Under the development background of low-carbonization transformation of electric power, a large amount of distributed energy sources with a distributed type and a small scale are integrated to the electricity selling side, but the distributed energy sources are difficult to participate in the electric power wholesale market of the traditional power grid level due to the characteristics of strong output randomness, small single machine capacity, huge quantity, wide distribution and the like. Meanwhile, the micro-grid is one of the most effective solutions for integrating distributed energy into an electric power system, and an area micro-grid group integrating differences in the types, capacities, load electricity utilization characteristics and the like of the distributed energy is formed at the electricity selling side. In recent years, the sharing economy is rising, the 'sharing' thinking is introduced into the electric power field, the direct transaction of the micro-grid is allowed, the multi-micro-grid has strong complementary characteristics, and a plurality of micro-grid bodies in the area possibly sign a specific cooperation agreement to form a cooperation alliance.
However, the existing designs have the following problems:
1. the lack of skills protects the privacy of the micro-grid main body and can quickly coordinate the electric quantity transaction among the regional multi-cooperation micro-grid main bodies.
2. The risk that the uncertainty of the wind and light renewable energy output inside each micro-grid main body possibly causes to the micro-grid 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 distributed coordination transaction method for a multi-cooperation micro-grid main body.
The method is realized by the following technical scheme: a distributed coordination transaction method for a multi-cooperation micro-grid main body comprises the following steps:
step 1: performing uncertainty analysis on each parameter of each type of micro-grid main body, constructing a micro-grid main body two-stage self-adaptive robust optimization decision model, and executing the step 2;
step 2: acquiring a demand response, energy storage and energy supply elements in the micro-grid main body, constructing a micro-grid main body market decision model by combining the two-stage self-adaptive robust optimization decision model in the step 1, and executing the step 2;
step 3: and constructing a multi-cooperation micro-grid main body distributed coordination trading framework according to cooperation alliances formed by the multi-micro-grid main bodies in the region, and solving a coordination trading electric quantity and a two-stage self-adaptive robust optimization decision model between the multi-micro-grid main bodies through an objective function association method.
Preferably, in the step 1, each parameter includes each parameter of renewable energy output, and uncertainty of description of renewable energy output of a polyhedron uncertainty set is constructed to obtain a two-stage adaptive robust optimization decision model of the micro-grid main body.
Preferably, in the step 2, the micro-grid main body market decision model includes a plurality of constraints, specifically, energy-consumption adjustment constraint, energy storage charge-discharge constraint, micro-gas turbine output constraint, electricity purchase constraint, internal energy consumption balance constraint, and objective function includes electricity purchase cost, demand response adjustment cost, gas turbine operation cost, and penalty cost of wind and light rejection.
Preferably, the distributed coordination trading framework of the multi-cooperation micro-grid main body adds punishment items on objective functions to enable the multi-micro-grid main body to achieve joint agreements, and the coordination trading electric quantity among the multi-micro-grid main body is solved in an extended Lagrange form of a two-stage self-adaptive robust optimization decision model of the micro-grid main body.
Preferably, the objective function cascading algorithm solves a distributed coordination trading framework of a multi-cooperation micro-grid main body, and specifically comprises the following steps:
step 51: input initial parameters including first-order multiplier ρ and second-order multiplier γ, and coordinate transaction electric quantitySetting an initial iteration value n=0 and a maximum iteration number N, and setting convergence adjustment epsilon=0.01;
step 52: updating coordinated transaction electricity values between the micro-grid m and other micro-grid 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 coordinated transaction electric quantity values with other micro-grid main bodies
Step 54: updating first-order, second-order multipliers
Update n=n+1 and then go to step 52.
In another aspect, there is also provided a computer readable storage medium having stored thereon one or more computer programs which when executed by one or more processors implement the distributed coordinated transaction method of any of claims 1 to 5.
In another aspect, there is also provided 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 a distributed coordinated transaction method as described above.
In another aspect, a distributed coordinated transaction system for a plurality of micro-grids is provided, where the micro-grids include: the system comprises a wind turbine generator, a photovoltaic unit, a controllable power supply of a miniature gas turbine, an energy storage system and an internal load; the main controller in the system performs simulation on distributed coordination transaction of the micro-grid;
the main controller stores one or more computer programs, and the one or more computer programs realize the distributed coordination transaction method when being executed by one or more processors of the main controller;
the distributed coordination trading method is used for distributing electricity consumption in each micro-grid, modeling and accounting electricity prices.
The beneficial effects of the invention are as follows:
(1) The load energy utilization characteristics among the micro-grid main bodies are considered to have complementary characteristics, the privacy protection requirements of the multi-micro-grid main bodies are considered, a multi-cooperation micro-grid distributed coordination trading framework is built based on an objective function cascading algorithm, and coordination trading electric quantity among the multi-cooperation micro-grids can be quickly coordinated;
(2) The risk possibly caused by the uncertainty of wind power and photovoltaic renewable energy sources in the micro-grid main body to the participation of the micro-grid main body in the transaction is considered, a two-stage robust optimization decision model of the micro-grid main body is constructed, the main problem and the sub problem of model decomposition are solved in an iterative mode by means of a column and constraint generation algorithm, and the existing solving tool package CPLEX is adopted for effective solving.
Drawings
FIG. 1 is a schematic diagram of a transaction flow in the present invention;
FIG. 2 is a block diagram of a decision variable of a main body of a power distribution network according to one embodiment of the present invention;
FIG. 3 illustrates a multi-microgrid system configuration in accordance with one embodiment of the present invention;
FIG. 4 shows wind power, photovoltaic units, and load predictions for an embodiment of the present invention;
FIG. 5 illustrates a trade power rate between a net power rate and a micro grid in accordance with one embodiment of the present invention;
FIG. 6 shows a micro grid 1 power transaction detail according to one embodiment of the present invention;
FIG. 7 illustrates a micro-grid 2 power transaction detail in accordance with one embodiment of the present invention;
FIG. 8 illustrates a micro grid 3 power transaction detail in accordance with one embodiment of the present invention;
FIG. 9 shows that the overall MG2 cost is relatively poor for different fluctuation ranges for an embodiment of the invention;
fig. 10 illustrates unbalanced power in the MG2 real-time market for one embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made more fully with reference to the accompanying drawings 1-10, in which it is apparent that the embodiments described are only some, but not all embodiments of the invention. Based on the embodiments of the present invention, one of ordinary skill in the art would obtain all other implementations that may be obtained without undue burden.
In the description of the present invention, it should be understood that the terms "counterclockwise," "clockwise," "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, are merely for convenience in describing the present invention, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Please refer to fig. 1, 2 and 3
A distributed coordination transaction method for a multi-cooperation micro-grid main body comprises the following steps:
step 1, analyzing uncertainties of wind, light and the like in various micro-grid main bodies, and constructing a micro-grid main body two-stage self-adaptive robust optimization decision model;
step 2, modeling elements such as a demand response, energy storage, micro gas turbine and the like 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 alliance aiming at the regional multi-micro-grid main body, constructing a multi-cooperation micro-grid main body distributed coordination trading framework, and solving coordination trading electric quantity among the multi-micro-grid main bodies through an objective function cascading method to solve;
and 4, solving a two-stage robust optimization decision model of the micro-grid main body through a column and constraint generation algorithm.
Preferably, in the step 1, the uncertain characteristics of renewable energy sources such as wind and light inside the micro-grid main body are analyzed, a general two-stage robust optimization decision model is constructed, and because the penalty term comprises a quadratic function, the purchase and sale quantity of the micro-grid main body, the power distribution network and other micro-grid main bodies is set as a first-stage decision variable and is recorded as { xm }; the demand response adjustment quantity, the energy storage charge and discharge quantity, the micro gas turbine output and the like are set as second-stage decision variables, and are marked as { ym }, and a general robust optimization mathematical model is adopted.
The physical meaning of the robust optimization model is that a robust decision scheme which enables the total running cost to be the lowest under the worst working condition is found, and the second stage max-min model is used for finding the worst scene of the maximum total running cost in the uncertain parameter range. Meanwhile, the robust optimization solution can ensure any value in the range of the uncertainty set, and the whole model is feasible.
The key of robust optimization is how to use uncertainty sets to characterize the uncertainty of source-charge, we use ζ respectively PV And xi L To describe uncertainty of output and load electricity of the photovoltaic unit. Wherein the parameter Γ Wind And Γ PV Named as uncertainty adjustment parameter, the value range is 0-N T The whole number in the range 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, the total number can be used for adjusting the conservation of the optimal solution, and the decision scheme obtained by the larger value is more conservative, otherwise, the decision scheme is more risky.
In the middle ofAnd->An uncertainty set for wind and light output inside a micro-grid main body, wherein +.>And->Is the predicted value of wind power and photovoltaic output, < ->Is the up-and-down fluctuation range of wind power and photovoltaic output,are all 0-1 auxiliary variables, e.g. when +.>When (I)> And->And respectively collecting wind power and photovoltaic unit nodes in the MG m main body, wherein i is the node number of the micro-grid system, and t is the operation period.
Preferably, in the step 2, the internal elements of the micro-grid main body are modeled in detail, and a micro-grid main body market robust optimization decision model is built based on a general two-stage robust optimization decision model.
The microgrid body decision model objective function:
the first stage objective function for the decision of the micro-grid main body comprises the electricity purchasing cost for trading with the distribution network and other micro-grid main bodies, whereinCost coefficients of electricity purchase and sale for trading with distribution networks and other micro-grids, respectively, < >>And the purchase and sales electric quantity is respectively traded with the power distribution network and other micro-grids.
The objective function is a second stage objective function in the micro-grid main body decision model, and comprises the adjustment cost of demand response, the output cost of the micro gas turbine, and the penalty cost of wind discarding and light discarding, whereinThe unit cost coefficients of load adjustment, micro gas turbine output, wind abandon and light abandon punishment are respectively adopted,
operational constraints of the micro gas turbine:
wherein,for the output value of the micro gas turbine, +.>Is the upper output limit of the micro gas turbine. The main consideration of the operation plan of the hour level is that the climbing constraint of the unit is ignored when the operation constraint modeling of the micro gas turbine is carried out,and the nodes are assembled for the miniature gas turbine unit in the MG m main body.
Operational constraints of the energy storage system:
the operation constraint of the energy storage system comprises a charge-discharge constraint of unit time, whereinCharge amount per unit time, +.>The maximum charge and discharge amount of the energy storage system in unit time is set; the state of charge of the energy storage system is maintained within a limit, wherein the SOC i，t For the charge of a certain period of time of the energy storage system, < ->For the charge and discharge efficiency of the energy storage system,storing electric quantity for the maximum and minimum of the energy storage system; />In a charging and discharging state of the energy storage system for a certain period of time, the energy storage system can only be charged or discharged in a certain period of time, +>And the energy storage system nodes are integrated in the MG m main body.
The demand response constraint:
the adjustment of the adjustable load must be kept within an allowable range, whereinFor a certain period of time, the load is increased or decreased by +.>The maximum range in which the load can be increased or decreased for a certain period of time,for a predicted value of the adjustable load for a certain period of time, < >>Node set for flexible load inside MG m main body
The wind power and photovoltaic output constraint:
the output value of the wind power and photovoltaic units is smaller than the predicted scene, whereinThe actual output value of the wind power generation and photovoltaic units.
The micro-grid main body purchase electricity selling constraint:
the amount of electricity traded by the micro-grid body with the distribution network and other micro-grid bodies should remain within the safety constraints for a certain period of time, and the micro-grid bodies can only choose to purchase or sell electricity for the same period of time. Wherein the method comprises the steps ofFor purchasing electricity, the person is in the state of->Maximum purchase power for micro-grid body, < ->For a set of micro-grids within a federation, +.>The collection of micro-grid MG m is divided within the federation.
The microgrid body internal power balance constraint:
and in any time period, the electricity purchasing quantity of the micro-grid main body, the wind power and photovoltaic output values, the micro-gas turbine output and the energy storage and discharge quantity are required to be balanced with the load electricity consumption quantity, and the energy storage and charge quantity is required to be kept.
Preferably, in the step 3, a multi-cooperation micro-grid distributed coordination transaction solving framework is adopted, and for convenience of description of a later solving process, a two-stage robust optimization decision model of a micro-grid main body is expressed in a matrix form, wherein the two-stage robust optimization decision model comprises an objective function, load-transferable energy-consumption adjustment constraint, energy storage charge-discharge constraint, micro-gas turbine output constraint, electricity purchase constraint, internal energy consumption balance constraint and the like.
s.t.A m x m ≤c m
B m x m ＝0
C m y m ≤d m
D m x m +E m y m ≤e m
F m y m ≤w m ,G m y m ≤p m
The matrix form of the two-stage robust optimization decision model of the micro-grid main body is shown above, x and y are decision variables, and a, b, c, d, w and p are column vectors of an objective function and constraint conditions respectively; A. b, C, D, E, F, G represents a coefficient matrix of constraints.
The basic idea of the multi-cooperative micro-grid body distributed coordination transaction is to coordinate the decentralized optimization of the multi-system by adding penalty terms to the objective function. Adding penalty terms to the objective functions of the individual microgrid bodies may force them to gradually reach a joint agreement. The decision model of the microgrid body is thus converted into an augmented lagrangian form as:
wherein ρ and γ are the first order and second order multipliers of a penalty function representing the penalty cost of the deviation of the transaction power between different microgrid bodies, wherein y mk The 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 achieves agreement through iterative updating, wherein the updating result is as follows:
determining whether to determine a final coordinated transaction result by calculating a residual, wherein the calculation result of the nth residual is:
the distributed coordination transaction framework of the multi-cooperation micro-grid main body is solved by adopting an objective function cascading algorithm, and the specific calculation steps are as follows:
the first step: input initial parameters, packageThe electric quantity of the transaction is coordinated by the first-order multiplier rho and the second-order multiplier gammaAn initial iteration value n=0 and a maximum iteration number N are set. Setting convergence adjustment ε 1 ＝0.001。
And a second step of: and updating the coordinated transaction electric quantity value between the micro-grid m and other micro-grid main bodies.
And a third step of: each micro-grid main body adopts a column and constraint generation algorithm to carry out respective robust optimization decision problem solving to obtain coordinated transaction electric quantity values with other micro-grid main bodies
Fourth step: and calculating residual errors of coordinated transaction electric quantity values among the micro-grids, terminating the iterative process if convergence conditions are met, outputting an optimal decision result, and otherwise, turning to a fifth step.
Fifth step: and updating the first-order and second-order multipliers.
Update n=n+1, and then go to the second step.
The two-stage robust optimization model in the micro-grid main body considers the transaction risk possibly caused by the uncertainty of wind and solar renewable energy sources in each micro-grid main body to the participation of the micro-grid main body in the transaction, takes the purchase and sales electric quantity of a power distribution network and other micro-grid main bodies as a first-stage variable, takes the demand response adjustment quantity, the output of a micro gas turbine and the energy storage charge and discharge quantity as a second-stage variable according to the economical efficiency of the internal components of a micro-grid main body operator, and is characterized by being divided into main problems and sub-problems:
obtaining decision variables by solving the master problem MPSubstituting the sub-problem into the SP to solve the worst scene +.>And in this scenario the internal controllable unit is forced to guarantee robustness by the worst scenario generated +.>The min model in the main problem MP is solved, so that the iteration solution between the main problem and the sub-problem is realized, and the sub-problem model is linear and can be processed by a dual method because the sub-problem needs to be converted into a single layer before the solution, whereinThen 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 utilizing a strong dual theory.
In the second stage objective functionAnd->As nonlinear terms, the Big-M method is required to be applied to linearize the sub-problem,
equivalent expression of bilinear terms by binary variables and a series of linear constraints, whereinIs 0-1 auxiliary variable,>is a continuous auxiliary variable.
And the two-order robust optimization decision model of the micro-grid main body adopts a column and constraint generation algorithm to carry out iterative solution on the main problem MP and the sub-problem SP, and the specific solution steps are as follows.
The first step: the relevant variables are initialized and the process is performed,taking ub=1e8, lb=0, s=1, ε 2 ＝0.01,Γ；
And a second step of: solving the main problem to obtain a decision resultUpdating lower bound->
And a third step of: according to the main problem resultSolving the sub-problem to obtain a decision result +.>Then update the upper bound ++>If UB is s -LB s ≤ε 2 Stopping iteration, outputting an optimization decision result, and otherwise, jumping to the fourth step.
Fourth step: update s=s+1 and then go to the second step.
By adopting the technical scheme, the objective function cascading method is utilized to carry out coordinated solution on the transaction electric quantity of the multi-cooperation micro-grid, 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 existing solution tool package CPLEX is adopted to carry out effective solution.
Assuming that the multi-cooperation micro-grid system is specifically configured as shown in fig. 2, each micro-grid main body comprises a wind power unit, a photovoltaic unit, a controllable power supply of a micro gas turbine, 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 units and loads are shown in fig. 4, and the residual electricity on-line electricity price of the micro grid main body and the transaction electricity price between the micro grid main bodies are shown in fig. 5.
TABLE 1 demand response operating parameters
TABLE 2 micro gas turbine operating parameters
TABLE 3 energy storage system operating parameters
According to the multi-micro-grid system, the multi-cooperation micro-grid distributed coordination transaction is simulated, and the independent operation simulation condition of the multi-micro-grid system and the distributed coordination transaction model provided by the patent are adopted to perform non-analysis, and the non-analysis is denoted as Case 2, and the model provided by the patent is denoted as Case 1. Stroke of the patientUncertainty adjustment parameter Γ for electrical and photovoltaic units Wind 、Γ PV The maximum fluctuation range of the wind power and photovoltaic units upwards and downwards is 10 percent by setting the wind power and photovoltaic units as 12.
The simulation is performed according to the simulation scheme, the total operation cost of the MG1-MG3 is shown in the table 4, wherein the saved operation in the Case1 mainly comprises the electric quantity transaction among micro-grid main bodies, and the electric quantity transaction results of the MG1-MG3 are shown in the figures 6-8. For MG1, there is excess power during the 12:00-17:00 period, which can be used to cope with peak loads of MG2 and MG3 during this period. The charge surplus and the charge deficit of MG2 and 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 costs of microgrid bodies
| MG1/￥ | MG2/￥ | MG3/￥ | |
| Case 1 | 2446.27 | 5735.56 | 4641.8 |
| Case 2 | 2713.79 | 5934.34 | 4945.49 |
The total cost difference deltac of the robust optimization method and the deterministic method defined by us is
Wherein C is RO And C DA The method is respectively the robust optimization model under the worst scene and the daily total running cost under the deterministic method. Setting an uncertainty adjustment parameter Γ Wind 、Γ PV The difference in total running cost of MG2 when changed is as shown in fig. 9. With Γ Wind 、Γ PV The period of time during which the wind turbine generator photovoltaic generator output can reach the fluctuation range boundary increases, and therefore, the difference in total daily cost between the robust optimization model and the deterministic method may increase. This shows that when the uncertainty adjustment parameter setting is large, the trading plan of the micro-grid body is more robust, and the micro-grid body can adjust the conservation of the daily operation trading plan by adjusting the uncertainty parameter.
As described above, when the uncertainty adjusts the parameter Γ Wind 、Γ PV When the value is set to zero, the robust optimization model is the same as the deterministic method. It should be noted that the total daily operational cost of the deterministic method is lower than that of the robust optimization model, but this does not mean that the operational trade plan obtained by the deterministic optimization model is better than that obtained by the robust optimization model. Power imbalances in the real-time market due to predicted deviations and actual output require the distribution network to compensate. In addition, the real-time market's buy/sell price is generally higher/lower than the market's buy/sell price in the day-ahead market. From this point of view, the operational trading plan obtained by the robust optimization model has strong robustness and capability of processing uncertainty of renewable energy output. To demonstrate the performance of the proposed two-stage tunable robust optimization method, it is assumed that the real-time market buying/selling price is 1.5/0 of the corresponding price in the market in the past.5 times and based on the actual/predicted output of wind and photovoltaic power generation shown in fig. 4.
The results of the operating costs of the microgrid bodies (including real-time balance and total daily costs) at different fluctuation ranges are shown in table 5. FIG. 10 shows the position at alpha Wind When=0.1, MG2 is unbalanced in the real-time market. When the power is positive, it indicates that the MG2 needs to additionally purchase electric energy to make up for the power shortage, and when the power is negative, it indicates that the MG2 can sell the remaining power in the real market. It can be seen that while the cost of running a robust optimization model daily is greater than that of the deterministic method, the unbalanced power of the robust optimization model in the real-time market is lower. Therefore, under the condition of considering proper prediction errors, the robust optimization model can effectively improve the robustness of decisions and reduce the balance cost in the real-time market.
Table 5 robust optimization method and deterministic method running cost vs
In summary, the invention mainly builds a multi-cooperation micro-grid main body distributed coordination trading framework, and elaborates the distributed coordination trading process of the multi-cooperation micro-grid, wherein the distributed coordination trading of the multi-cooperation micro-grid is solved by adopting an objective function cascading algorithm, on the basis, the characteristics of renewable energy sources such as wind power, photovoltaic and the like contained in the micro-grid main body are considered, and by combining with the wind power, photovoltaic typical scene data and the regulation characteristics of decision variables, a two-stage robust optimization decision model in the micro-grid main body is built, and iterative solution is performed by adopting a column and constraint generation algorithm.
Claims (5)
1. The distributed coordination transaction method for the multi-cooperation micro-grid main body is characterized by comprising the following steps of:
step 1: performing uncertainty analysis on each parameter of each type of micro-grid main body, constructing a micro-grid main body two-stage self-adaptive robust optimization decision model, and executing the step 2;
step 2: acquiring a demand response, energy storage and energy supply elements in the micro-grid main body, constructing a micro-grid main body market decision model by combining the two-stage self-adaptive robust optimization decision model in the step 1, and executing the step 2;
step 3: constructing a multi-cooperation micro-grid main body distributed coordination trading framework according to cooperation alliances formed by the multi-micro-grid main bodies in the region, and solving a coordination trading electric quantity and a two-stage self-adaptive robust optimization decision model between the multi-micro-grid main bodies through an objective function association method;
in the step 1, each parameter comprises each parameter of renewable energy output, and uncertainty of description of renewable energy output of a polyhedron uncertainty set is constructed to obtain a micro-grid main body two-stage self-adaptive robust optimization decision model; analyzing uncertain characteristics of wind-solar renewable energy sources in a micro-grid main body, constructing a general two-stage robust optimization decision model, setting purchase and sales electric quantity of the micro-grid main body, a power distribution network and other micro-grid main bodies as a first-stage decision variable, and recording as { x } m -a }; the demand response adjustment quantity, the energy storage charge and discharge quantity and the micro gas turbine output are set as second stage decision variables and recorded as { y } m A general robust optimization mathematical model is:
with xi m To describe uncertainty of output and load electricity consumption of photovoltaic units in micro-grid, z m As an uncertain parameter, the variation range of the method is in the set xi m Within the range, parameter Γ Wind And Γ PV Is an uncertainty adjustment parameter, and has a value range of 0 to N T An integer in the schedule period, which 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 middle ofAnd->An uncertainty set for wind and light output inside a micro-grid main body, wherein +.>And->Is the predicted value of wind power and photovoltaic output, < ->Is the up-and-down fluctuation range of wind power and photovoltaic output,are all 0-1 auxiliary variables, i is the node number of the micro-grid system, t is the operation period, and the weight is added>And->Respectively collecting wind power and photovoltaic unit nodes in the MG m main body;
in the step 2, the micro-grid main body market decision model comprises a plurality of constraints, in particular energy-consumption adjustment constraints capable of transferring loads, energy storage charging and discharging constraints, micro-gas turbine output constraints, electricity purchasing and selling constraints, internal energy consumption balance constraints, an objective function comprises electricity purchasing and selling costs, demand response adjustment costs, gas turbine operation costs, and punishment costs of wind abandoning and light abandoning; the microgrid body decision model objective function:
the first stage objective function for the decision of the micro-grid main body comprises the electricity purchasing cost for trading with the distribution network and other micro-grid main bodies, whereinThe electricity purchasing and selling cost coefficients respectively for trading with the distribution network and other micro-grids, wherein Deltat is the accumulated period of time,/->Purchase of electric power from the distribution network for the microgrid body m in period t +.>Selling electric power to a power distribution network for a micro-grid main body m in a period t, < >>The micro-grid main body m purchases electric power from the micro-grid main body k in the period t,/for the micro-grid main body k>The micro-grid main body m sells electric power to the micro-grid main body k in the period t;
the objective function is a second stage objective function in the micro-grid main body decision model, and comprises the adjustment cost of demand response, the output cost of the micro gas turbine, and the penalty cost of wind discarding and light discarding, wherein The unit cost coefficients of load adjustment, micro gas turbine output, wind abandon and light abandon punishment are respectively adopted;
operational constraints of micro gas turbines:
wherein,for the output value of the micro gas turbine, +.>For the upper output limit of the micro gas turbine, an hour-level operation plan is considered, and the climbing constraint of a unit is ignored in modeling the operation constraint of the micro gas turbine, wherein ∈>The method comprises the steps that a node set of a micro gas turbine unit in an MG m main body is provided;
operational constraints of the energy storage system:
the operation constraint of the energy storage system comprises a charge-discharge constraint of unit time, whereinCharge amount per unit time, +.>The maximum charge and discharge amount of the energy storage system in unit time is set; the state of charge of the energy storage system is maintained within a limit, wherein the SOC i，t For the charge of a certain period of time of the energy storage system, < ->For the charge and discharge efficiency of the energy storage system,storing electric quantity for the maximum and minimum of the energy storage system; />In a charging and discharging state of the energy storage system for a certain period of time, the energy storage system can only be charged or discharged in a certain period of time, +>Is MG m main bodyAn internal energy storage system node set;
demand response constraints:
the adjustment of the adjustable load must be kept within an allowable range, whereinFor a certain period of time, the load is increased or decreased by +.>Maximum range in which the load can be increased or decreased for a certain period of time, < >>For a predicted value of the adjustable load for a certain period of time, < >>And->Maximum ranges of load energy increase and decrease over the operating period, respectively, +.>Node set for flexible load in MG m main body;
wind power and photovoltaic output constraint:
the output value of the wind power and photovoltaic units is smaller than the predicted scene, whereinThe actual output value of the wind power and photovoltaic unit;
micro-grid main body electricity purchasing constraint:
in a certain time period, the transaction electric quantity of the micro-grid main body and 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 electricity purchasing or selling; wherein the method comprises the steps ofFor purchasing electricity, the person is in the state of->Maximum purchase power for micro-grid body, < ->For a set of micro-grids within a federation, +.>A collection of micro-grid MG m is divided into alliances;
micro-grid body internal power balance constraint:
in any time period, the electricity purchasing quantity of the micro-grid main body, the wind power and photovoltaic output values, the micro-gas turbine output and the energy storage and discharge quantity are required to be balanced with the load electricity consumption quantity, and the energy storage and charge quantity is maintained;
the distributed coordination trading framework of the multi-cooperation micro-grid main body is characterized in that penalty items are added to an objective function to enable the multi-micro-grid main body to achieve joint agreements, and coordination trading electric quantity among the multi-micro-grid main bodies is solved in an extended Lagrange form of a two-stage self-adaptive robust optimization decision model of the micro-grid main body;
in the step 3, a multi-cooperation micro-grid distributed coordination transaction solving framework comprises an objective function, energy utilization adjustment constraint, energy storage charge-discharge constraint, micro gas turbine output constraint, electricity purchase constraint and internal energy utilization balance constraint of transferable load:
s.t.A m x m ≤c m
B m x m ＝0
C m y m ≤d m
D m x m +E m y m ≤e m
F m y m ≤w m ,G m y m ≤p m
the matrix form of the two-stage robust optimization decision model of the micro-grid main body is shown above, x and y are decision variables, and a, b, c, d, w and p are column vectors of an objective function and constraint conditions respectively; A. b, C, D, E, F, G represents a coefficient matrix of constraint conditions;
the distributed coordination transaction of the multi-cooperation micro-grid main body is to coordinate the decentralized optimization of the multi-system by adding punishment items on objective functions, and a decision model of the micro-grid main body is converted into an augmented Lagrange form:
wherein ρ and γ are the first order and second order multipliers of a penalty function representing the penalty cost of the deviation of the transaction power between different microgrid bodies, wherein y mk For the coordinated transaction electric quantity between the micro-grid m and the micro-grid k, the coordinated transaction electric quantity between the micro-grids finally achieves agreement through iterative updating, wherein the updating result is as follows:
determining whether to determine a final coordinated transaction result by calculating a residual, wherein the calculation result of the nth residual is:
the distributed coordination transaction framework of the multi-cooperation micro-grid main body is solved by adopting an objective function cascading algorithm, and the specific calculation steps are as follows:
the first step: input initial parameters including first-order multiplier ρ and second-order multiplier γ, and coordinate transaction electric quantitySetting an initial iteration value n=0 and a maximum iteration number N, and setting convergence adjustment epsilon 1 ＝0.001；
And a second step of: updating coordinated transaction electricity values between the micro-grid m and other micro-grid main bodies;
and a third step of: each micro-grid main body adopts a column and constraint generation algorithm to carry out respective robust optimization decision problem solving to obtain coordinated transaction electric quantity values with other micro-grid main bodies
Fourth step: calculating residual errors of coordinated transaction electric quantity values among micro-grids, terminating the iterative process if convergence conditions are met, outputting an optimal decision result, and otherwise, turning to a fifth step;
fifth step: updating a first-order multiplier and a second-order multiplier;
updating n=n+1, and then turning to the second step;
the two-stage robust optimization model in the micro-grid main body considers the transaction risk possibly caused by the uncertainty of wind and solar renewable energy sources in each micro-grid main body to the participation of the micro-grid main body in the transaction, takes the purchase and sales electric quantity of a power distribution network and other micro-grid main bodies as a first-stage variable, takes the demand response adjustment quantity, the output of a micro gas turbine and the energy storage charge and discharge quantity as a second-stage variable according to the economical efficiency of the internal components of a micro-grid main body operator, and is characterized by being divided into main problems and sub-problems:
MP
SP
obtaining decision variables by solving the master problem MPSubstituting the problem into the subproblem SP to solve the problem to obtain the worst sceneAnd in this scenario the internal controllable unit is forced to pass the worst field generated in order to ensure robustnessScene->The min model in the main problem MP is solved, so that the iteration solution between the main problem and the sub-problem is realized, and the sub-problem model is linear and can be processed by a dual method because the sub-problem needs to be converted into a single layer before the solution, whereinA 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 utilizing a strong dual theory:
in the second stage objective functionAnd->As nonlinear terms, linearizing the sub-problem by using a Big-M method;
equivalent expression of bilinear terms by binary variables and a series of linear constraints, wherein Is 0-1 auxiliary variable,>as a continuous auxiliary variable, M is a relatively large constant;
the two-order robust optimization decision model of the micro-grid main body adopts a column and constraint generation algorithm to carry out iterative solution on a main problem MP and a sub-problem SP, and the specific solution steps are as follows:
the first step: the relevant variables are initialized and the process is performed,taking ub=1e8, lb=0, s=1, ε 2 ＝0.01,Γ；
And a second step of: solving the main problem to obtain a decision resultUpdating lower bound->
And a third step of: according to the main problem resultSolving the sub-problem to obtain a decision result +.>Then updating the upper bound valueIf UB is s -LB s ≤ε 2 Stopping iteration, outputting an optimization decision result, otherwise, jumping to the fourth step;
fourth step: update s=s+1 and then go to the second step.
2. A computer readable storage medium having stored thereon one or more computer programs which when executed by one or more processors implement the distributed coordinated transaction method of claim 1.
3. A distributed coordinated transaction device, 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 coordination transaction method of claim 1.
4. A multi-cooperative micro-grid body distributed coordination trading system, comprising, in a plurality of micro-grids: the system comprises a wind turbine generator, a photovoltaic unit, a controllable power supply of a miniature gas turbine, an energy storage system and an internal load; wherein,
a main controller in the system performs simulation on the distributed coordination transaction of the micro-grid;
the master controller having stored therein one or more computer programs which when executed by one or more processors of the master controller implement the distributed coordinated transaction method of claim 1.
5. Use of the distributed coordination trading method according to claim 1 for distribution of electricity consumption, modeling, accounting of electricity prices in individual micro-grids.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110344743.0A CN112952908B (en) | 2021-03-29 | 2021-03-29 | Distributed coordination transaction method for multi-cooperation micro-grid main body |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110344743.0A CN112952908B (en) | 2021-03-29 | 2021-03-29 | Distributed coordination transaction method for multi-cooperation micro-grid main body |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112952908A CN112952908A (en) | 2021-06-11 |
| CN112952908B true CN112952908B (en) | 2024-02-02 |
Family
ID=76231132
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110344743.0A Active CN112952908B (en) | 2021-03-29 | 2021-03-29 | Distributed coordination transaction method for multi-cooperation micro-grid main body |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112952908B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113822707B (en) * | 2021-09-10 | 2024-04-30 | 国网冀北电力有限公司电力科学研究院 | Output decision method and device for electric power market, computer equipment and storage medium |
| CN115204562B (en) * | 2022-05-10 | 2023-06-30 | 浙江工业大学 | Multi-energy sharing-based distributed collaborative optimization scheduling method and system for interconnected micro energy networks |
| CN115187316B (en) * | 2022-09-14 | 2022-11-29 | 国网湖北省电力有限公司经济技术研究院 | Wind storage micro-grid distributed transaction method suitable for spot market transaction |
| CN116646973B (en) * | 2023-04-28 | 2024-05-03 | 国网山东省电力公司日照供电公司 | Information interaction method of micro-grid, micro-grid and storage medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107622324A (en) * | 2017-09-01 | 2018-01-23 | 燕山大学 | A kind of robust environmental economy dispatching method for considering more microgrid energy interactions |
| CN107979111A (en) * | 2017-07-21 | 2018-05-01 | 天津大学 | A kind of energy management method for micro-grid based on the optimization of two benches robust |
| CN109274134A (en) * | 2018-11-08 | 2019-01-25 | 东南大学 | A kind of active distribution network robust active reactive coordination optimizing method based on time series scene analysis |
| CN109884888A (en) * | 2018-12-30 | 2019-06-14 | 国网天津市电力公司电力科学研究院 | A kind of more building microgrid model predictions regulation method based on non-cooperative game |
| CN109980685A (en) * | 2019-04-02 | 2019-07-05 | 东南大学 | It is a kind of to consider probabilistic active distribution network distributed optimization operation method |
| CN110544957A (en) * | 2019-05-23 | 2019-12-06 | 东南大学 | Multi-microgrid-containing power distribution network distributed optimization scheduling method considering source-load correlation |
| CN111080022A (en) * | 2019-12-23 | 2020-04-28 | 国网四川省电力公司经济技术研究院 | Partition distributed coordination optimization method containing multiple benefit agents |
| CN111192164A (en) * | 2019-12-23 | 2020-05-22 | 国网四川省电力公司经济技术研究院 | Micro-grid combined game optimization sharing and benefit distribution method considering uncertain wind power |
| CN111242392A (en) * | 2020-03-06 | 2020-06-05 | 上海电力大学 | Double-layer and two-stage operation method for multi-virtual power plant participating in active power distribution network |
| CN111355265A (en) * | 2020-04-10 | 2020-06-30 | 中国人民解放军国防科技大学 | Micro-grid energy two-stage robust optimization method and system |
| CN111668877A (en) * | 2020-04-28 | 2020-09-15 | 中国电力科学研究院有限公司 | Active power distribution network distribution robust optimization scheduling method and system |
| CN112072641A (en) * | 2020-08-19 | 2020-12-11 | 国网江苏省电力有限公司扬州供电分公司 | Source network load storage flexible coordination control and operation optimization method |
| CN112257229A (en) * | 2020-09-18 | 2021-01-22 | 西安理工大学 | Two-stage robust scheduling method for microgrid |
| CN112332461A (en) * | 2020-11-13 | 2021-02-05 | 上海电力大学 | Multi-energy micro-grid group control method based on two-stage robust model |
-
2021
- 2021-03-29 CN CN202110344743.0A patent/CN112952908B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107979111A (en) * | 2017-07-21 | 2018-05-01 | 天津大学 | A kind of energy management method for micro-grid based on the optimization of two benches robust |
| CN107622324A (en) * | 2017-09-01 | 2018-01-23 | 燕山大学 | A kind of robust environmental economy dispatching method for considering more microgrid energy interactions |
| CN109274134A (en) * | 2018-11-08 | 2019-01-25 | 东南大学 | A kind of active distribution network robust active reactive coordination optimizing method based on time series scene analysis |
| CN109884888A (en) * | 2018-12-30 | 2019-06-14 | 国网天津市电力公司电力科学研究院 | A kind of more building microgrid model predictions regulation method based on non-cooperative game |
| CN109980685A (en) * | 2019-04-02 | 2019-07-05 | 东南大学 | It is a kind of to consider probabilistic active distribution network distributed optimization operation method |
| CN110544957A (en) * | 2019-05-23 | 2019-12-06 | 东南大学 | Multi-microgrid-containing power distribution network distributed optimization scheduling method considering source-load correlation |
| CN111080022A (en) * | 2019-12-23 | 2020-04-28 | 国网四川省电力公司经济技术研究院 | Partition distributed coordination optimization method containing multiple benefit agents |
| CN111192164A (en) * | 2019-12-23 | 2020-05-22 | 国网四川省电力公司经济技术研究院 | Micro-grid combined game optimization sharing and benefit distribution method considering uncertain wind power |
| CN111242392A (en) * | 2020-03-06 | 2020-06-05 | 上海电力大学 | Double-layer and two-stage operation method for multi-virtual power plant participating in active power distribution network |
| CN111355265A (en) * | 2020-04-10 | 2020-06-30 | 中国人民解放军国防科技大学 | Micro-grid energy two-stage robust optimization method and system |
| CN111668877A (en) * | 2020-04-28 | 2020-09-15 | 中国电力科学研究院有限公司 | Active power distribution network distribution robust optimization scheduling method and system |
| CN112072641A (en) * | 2020-08-19 | 2020-12-11 | 国网江苏省电力有限公司扬州供电分公司 | Source network load storage flexible coordination control and operation optimization method |
| CN112257229A (en) * | 2020-09-18 | 2021-01-22 | 西安理工大学 | Two-stage robust scheduling method for microgrid |
| CN112332461A (en) * | 2020-11-13 | 2021-02-05 | 上海电力大学 | Multi-energy micro-grid group control method based on two-stage robust model |
Non-Patent Citations (3)
| Title |
|---|
| Distributed Optimal Operation of AC/DC Hybrid Distribution Network Considering Wind Power Uncertainty;Youbo Liu等;《2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2)》;20210215;3791-3796页 * |
| 基于二阶段鲁棒博弈模型的微电网群及混合交直流配电***协调能量管理策略研究;符杨等;《中国电机工程学报》;20200220;第40卷(第4期);1226-1236页 * |
| 基于动态能耗模型的微电网经济运行优化;闫占新等;《四川大学学报（工程科学版）》;第46卷;140-144页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112952908A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112952908B (en) | Distributed coordination transaction method for multi-cooperation micro-grid main body | |
| Du et al. | A cooperative game approach for coordinating multi-microgrid operation within distribution systems | |
| CN108599373B (en) | Cascade analysis method for transmission and distribution coordination scheduling target of high-proportion renewable energy power system | |
| Foruzan et al. | Reinforcement learning approach for optimal distributed energy management in a microgrid | |
| Li et al. | Optimal operation of multimicrogrids via cooperative energy and reserve scheduling | |
| Dehghanpour et al. | An agent-based hierarchical bargaining framework for power management of multiple cooperative microgrids | |
| Wu et al. | Dynamic economic dispatch of a microgrid: Mathematical models and solution algorithm | |
| Zhang et al. | Distributed online optimal energy management for smart grids | |
| Chen et al. | Research on day-ahead transactions between multi-microgrid based on cooperative game model | |
| Toutounchi et al. | A stochastic bilevel model to manage active distribution networks with multi-microgrids | |
| CN107392395A (en) | A kind of power distribution network and micro electric network coordination optimization method based on price competition mechanism | |
| CN109713673B (en) | Method for configuring and optimizing operation of grid-connected micro-grid system in electricity selling environment | |
| Zhang et al. | Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO | |
| CN112217195B (en) | Cloud energy storage charging and discharging strategy forming method based on GRU multi-step prediction technology | |
| CN111934360B (en) | Virtual power plant-energy storage system energy collaborative optimization regulation and control method based on model predictive control | |
| CN113869927B (en) | Time-sharing pricing method for promoting energy sharing of multiple producers and consumers in community micro-grid | |
| CN115115096A (en) | Active power distribution network game optimization scheduling method considering multi-microgrid energy storage sharing | |
| CN112821470A (en) | Micro-grid group optimization scheduling strategy based on niche chaos particle swarm algorithm | |
| Zhang et al. | Three-level day-ahead optimal scheduling framework considering multi-stakeholders in active distribution networks: Up-to-down approach | |
| CN112529276A (en) | Interconnected micro-grid layered distributed optimization scheduling method | |
| CN115693779A (en) | Multi-virtual power plant and distribution network collaborative optimization scheduling method and equipment | |
| CN111987717B (en) | Internet of vehicles charging and discharging management method and system, electric power system and storage medium | |
| CN117477552A (en) | Micro-grid level collaborative optimization regulation and control method and system with distributed energy storage at power distribution side | |
| CN116054241B (en) | Robust energy management method for new energy micro-grid group system | |
| CN114399369B (en) | Multi-benefit-body random dynamic transaction game method at power distribution network side |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |