CN116523481A - Double-layer collaborative optimization method for independent decision-making of upper retailer pricing and lower micro-grid - Google Patents
Double-layer collaborative optimization method for independent decision-making of upper retailer pricing and lower micro-grid Download PDFInfo
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Abstract
A double-layer collaborative optimization method for independent decision-making of upper retailers and lower micro-networks belongs to the technical field of hierarchical energy collaborative optimization methods. Establishing an upper layer optimization problem aiming at maximizing the income of retailers and meeting the constraint of the electricity price purchasing interval; establishing a lower-layer daily market operation optimization model with the aim of minimizing the operation cost, and listing and writing lower-layer model constraint conditions considering independent decisions of the micro-grid; processing bilinear terms in an objective function by using a continuous variable equivalent substitution method, and under the condition of giving a feasible solution of 0-1 variable, giving an effective upper bound of the bilinear terms by using the dual values of a lower model, and relaxing a non-convex optimization problem containing the bilinear terms into a mixed integer programming problem which is easy to solve; the model after S3 relaxation is a double-layer mixed integer programming model. The method can quickly and effectively solve the double-layer mixed integer programming model containing bilinear terms, and can be widely applied to solving the non-convex model.
Description
Technical Field
A double-layer collaborative optimization method for independent decision-making of upper retailers and lower micro-networks belongs to the technical field of hierarchical energy collaborative optimization methods.
Background
With the release of the distribution side power market, end users select the appropriate power provider to meet their energy needs based on electricity prices and electricity usage preferences. As an intermediary between customers and power plants, independently operated power retailers are actively engaged in power market trading, purchasing energy from futures markets and energy pools and selling it to customers. In the energy trading process, the traditional centralized management model optimizes the running decision of the whole system by using a general objective function. However, in the current energy management process, a plurality of benefit-related micro-networks are generally involved, each micro-network controls a corresponding distributed unit to realize on/off decision, and the centralized management mode is difficult to meet the flexibility of energy transaction in a free electric power market. Therefore, in order to promote the collaborative optimization between retailers and the micro-grid and realize the energy management of the power distribution side, the distributed double-layer optimization model can be used for describing the energy transaction process between the retailers and users and realizing the flexible transaction between the retailers and the users.
Based on the difference in the decision level and decision sequence between the retailer and the user, the Stackelberg game model is commonly used to solve the energy transaction problem between the retailer and the micro-net. In the existing model, the lower micro-grid adopts a linear programming model to describe energy transaction, and a dual theory and KKT conditions are utilized to realize the solution of a double-layer linear optimization model containing bilinear terms. And adding a 0-1 variable into an energy transaction model of a lower micro-grid to construct a double-layer mixed integer model containing bilinear terms by considering the influence of on/off decision states such as buying, selling electricity, charging and discharging and the like in the micro-grid on energy transaction. Although the model effectively describes the operating state of the micro-grid and the energy transaction process between the retailer and the micro-grid, the presence of upper bilinear terms and lower binary variables makes the model non-convex, resulting in difficult model solutions.
Disclosure of Invention
The invention aims to solve the technical problems that: the method has the advantages that the defects of the prior art are overcome, a double-layer collaborative optimization method for independent decision of upper retailer pricing and lower microgrid is provided, and the double-layer mixed integer programming model containing bilinear terms can be quickly and effectively solved.
The technical scheme adopted for solving the technical problems is as follows: the double-layer collaborative optimization method for independent decision of the upper retailer and the lower micro-network is characterized by comprising the following steps of: the method comprises the following steps:
s1: establishing an upper layer optimization problem aiming at maximizing the income of retailers and meeting the constraint of the electricity price purchasing interval;
s2: establishing a lower-layer daily market operation optimization model with the aim of minimizing the operation cost, and writing lower-layer model constraint conditions considering independent decisions of the microgrid under the condition of meeting power balance constraint, electricity purchase and selling constraint and energy storage charge and discharge constraint;
s3: processing bilinear terms in an objective function by using a continuous variable equivalent substitution method, and under the condition of giving a feasible solution of 0-1 variable, giving an effective upper bound of the bilinear terms by using the dual values of a lower model, and relaxing a non-convex optimization problem containing the bilinear terms into a mixed integer programming problem which is easy to solve;
s4: the model after S3 relaxation is a double-layer mixed integer programming model, the double-layer mixed integer programming model is decomposed into a main problem and a sub problem by a reconstruction decomposition method, and the main problem and the sub problem are solved, and the model is calculated until the model converges by an iterative calculation method and a cutting set adding method.
Preferably, an upper optimization model is established which aims at maximizing the benefits of retailers and meets the constraint of the electricity purchasing price interval, 24 hours are divided into T time periods, and the transaction electricity price with the micro-grid is determined according to the upper optimization modelAnd->
The constraint is as follows:
wherein:for the given internet power price->The electricity price and the electricity quantity of electricity purchased and sold by the micro-grid i to retailers respectively are +.>Upper and lower boundaries of power supply to retailers for the micro-grid, respectively,)>The upper and lower boundaries of the electricity sold by the micro-grid to retailers, respectively.
Preferably, the objective function of the lower layer day-ahead market operation optimization model is:
wherein, the liquid crystal display device comprises a liquid crystal display device,the electricity price and the electricity quantity of electricity purchased and sold by the micro-grid i to retailers respectively,net fee for the micro-net transaction with retailer, < ->For the depreciation of the battery, the charge and discharge power of the battery is +.>And-> Is a degradation cost of energy storage.
Preferably, the method further comprises, in the optimization process, that the microgrid needs to satisfy the following power balance constraints:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Predictive value of new energy power generation and load in micro-grid respectively, < >>The electricity buying and selling amounts of electricity between the micro-grids are respectively.
Preferably, in order to ensure safe and stable operation of the system, the following transmission capacity constraints are required to be satisfied when the micro-grid and the retailer conduct energy transactions:
wherein, the liquid crystal display device comprises a liquid crystal display device,the upper limit of electricity purchasing and electricity selling is respectively +.>Is a 0-1 variable and satisfies the following constraint:
preferably, the method further comprises, in view of the energy sharing problem between the micro-networks, the energy transaction satisfies the following transmission capacity constraints:
0-1 variableThe following energy transaction constraints need to be satisfied:
preferably, the method further comprises that the power of the battery inside the microgrid satisfies the following constraint:
wherein, the liquid crystal display device comprises a liquid crystal display device,respectively represent the maximum value of charge and discharge power in micro-grid i,/, respectively>Is a 0-1 variable;
state of charge SOC of a battery i，t Is that the variation of (a) satisfies the constraint:
wherein eta Ec 、η Ed Respectively the charge and discharge efficiency and Cap of the storage battery i Is the capacity of the storage battery.
Preferably, the method further comprises implementing an effective relaxation of bilinear terms in the upper layer pricing model using the following constraints:
wherein the continuous variable X 1 、X 2 The electricity price of the upper layer in the original model is obtained; continuous variable Y of lower layer 1 、Y 2 、Y 3 、Y 4 And a discrete variable w 1 、w 2 、w 3 、w 4 Representing an operational decision of the micro-grid; constant matrix Y 1 max 、T 2 max 、T 3 max 、T 4 max Is a parameter in the original model and has the same dimension as the variable; to achieve bilinear term X 1 Y 1 And X 2 Y 2 Replacing the bilinear term with a variable η; by Lagrange multiplierThe composed dual objective function gives the upper bound of the relaxation objective function with the first iteration.
Preferably, the double-layer mixed integer programming model is decomposed into a main problem and a sub-problem by using a reconstruction decomposition method, and is solved by the main problem and the sub-problem, and the model convergence is calculated by an iterative calculation and a method for adding a cutset, wherein the method comprises the following steps of:
s3.1: solving the Main Problem (MP) to obtain the optimal solution of the upper layer problemWherein the Main Problems (MP) are:
wherein, to distinguish the variables of the main and sub problems in the iterative process, the variable Y is utilized 1 0 、Y 2 0 、Respectively replace the original variable Y 1 、Y 2 、w 3 、w 4 And adding corresponding constraint to ensure feasibility of the main problem after relaxation;
s3.2: in determining the solution of the upper-layer master problemAfter that, solve the sub-problem (SP 1):
s3.3: solution of given upper-layer electricity price and sub-problem (SP 1)Then solving the sub-problem (SP 2) to obtain the objective function value psi of the sub-problem (SP 2) * And effective solution of variables->Providing an effective boundary for solving the upper layer problem in the subsequent iteration process;
s3.4, adding a feasible cutting set to the main problem to tighten a feasible domain in the iteration process, and after multiple iterations, terminating the iteration when the gap between the upper bound and the lower bound of the objective function is smaller than a small value E;
wherein the constant matrix A T 、B T 、C T 、D T Representing parameters in the master model.
Compared with the prior art, the invention has the following beneficial effects:
the double-layer non-male model solving method based on the reconstruction decomposition algorithm of the double-layer collaborative optimization method for independently deciding the upper retailer pricing and the lower microgrid utilizes variable equivalent substitution and dual theory to carry out linearization relaxation on bilinear terms and give an objective function upper bound, and then utilizes model reconstruction and decomposition to convert complex non-male problems into a plurality of mixed integer programming problems capable of being directly solved, so that iterative solving of a model is realized, solving of a double-layer mixed integer programming model containing bilinear terms can be rapidly and effectively realized, and the double-layer non-male model solving method can be widely applied to solving of the non-male model.
Drawings
FIG. 1 is a hierarchical interaction graph of a two-tier energy management problem;
FIG. 2 is a flow chart of a reconfiguration decomposition;
fig. 3 is a graph of iterative computation results.
Detailed Description
The present invention will be further described with reference to specific embodiments, however, it will be appreciated by those skilled in the art that the detailed description herein with reference to the accompanying drawings is for better illustration, and that the invention is not necessarily limited to such embodiments, but rather is intended to cover various equivalent alternatives or modifications, as may be readily apparent to those skilled in the art.
Fig. 1 to 3 are preferred embodiments of the present invention, and the present invention will be further described with reference to fig. 1 to 3.
The double-layer collaborative optimization method for independent decision-making of the upper retailer and the lower micro-network comprises the following steps:
s1: and establishing an upper layer optimization problem aiming at maximizing the income of retailers and meeting the constraint of the electricity price interval of purchase and sale.
S2: establishing a lower-layer daily market operation optimization model with the aim of minimizing the operation cost, and writing lower-layer model constraint conditions considering independent decisions of the microgrid under the condition of meeting power balance constraint, electricity purchase and selling constraint and energy storage charge and discharge constraint;
s3: processing bilinear terms in an objective function by using a continuous variable equivalent substitution method, and under the condition of giving a feasible solution of 0-1 variable, giving an effective upper bound of the bilinear terms by using the dual values of a lower model, and relaxing a non-convex optimization problem containing the bilinear terms into a mixed integer programming problem which is easy to solve;
s4: the model after S3 relaxation is a double-layer mixed integer programming model, the double-layer mixed integer programming model is decomposed into a main problem and a sub problem by a reconstruction decomposition method, and the main problem and the sub problem are solved, and the model is calculated until the model converges by an iterative calculation method and a cutting set adding method.
Specifically, based on a Stackelberg game, a double-layer optimization model with maximized benefits of an upper-layer retailer and minimized running cost of a lower-layer micro-grid is constructed, and the two-layer model obeys corresponding constraint; relaxing bilinear terms in an upper-layer objective function by utilizing equivalent relaxation and adding an upper bound to ensure the feasibility of solution of a model; and converting and decomposing the double-layer mixed integer programming problem into a mixed integer programming problem which can be solved iteratively by utilizing reconstruction decomposition, and realizing effective calculation of a model by adopting commercial solvers such as Gurobi and the like.
The double-layer optimization model provides an optimal energy trading strategy for retailers and micro-networks by means of continuous and discrete variables and realizes energy trading of the electric power market.
Fig. 1 depicts the relationship between the upper retailer and the lower micro-network, step S1 taking into account the energy transaction problem that the retailer participates as an intermediary between the distribution network and the micro-network. At a given internet power priceAnd->On the basis of (1) establishing an upper optimization model aiming at the maximization of the income of retailers and meeting the constraint of the price interval of electricity purchase and sale, dividing 24 hours into T time periods, and determining the trade price with the micro-grid by the upper optimization model>And->
The objective function is:
the constraint is as follows:
wherein:for the given internet power price->The electricity price and the electricity quantity of electricity purchased and sold by the micro-grid i to retailers are respectively, and the business of the level between the retailers and the micro-grid is smoothly carried out, namely +.> Upper and lower boundaries of power supply to retailers for the micro-grid, respectively,)>The upper and lower boundaries of the electricity sold by the micro-grid to retailers, respectively.
The expected benefits of retailers are mainly derived from energy transactions with distribution networksEnergy trading by determining retail electricity prices and microgrids>Constraint (2) and constraint (3) respectively represent electricity price intervals of micro-grid electricity purchase and selling.
The objective function of the lower layer day-ahead market operation optimization model is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,the electricity price and the electricity quantity of electricity purchased and sold by the micro-grid i to retailers respectively,net fee for the micro-net transaction with retailer, < ->For the depreciation of the battery, the charge and discharge power of the battery is +.>And->Is a degradation cost of energy storage.
In the process of operation optimization, the microgrid needs to meet the power balance constraint:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Predictive value of new energy power generation and load in micro-grid respectively, < >>The electricity buying and selling amounts of electricity between the micro-grids are respectively.
In order to ensure safe and stable operation of the system, the micro-grid and retailers need to meet transmission capacity constraints (6) and (7), namely: cannot exceed the upper limit of electricity purchasing and sellingFurthermore, the 0-1 variable in constraint (8) is utilizedThe situation that the micro-network buys and sells electricity to retailers simultaneously is avoided, and the corresponding energy transaction constraint is as follows:
considering the problem of energy sharing between micro-networks, its energy transactions satisfy the transmission capacity constraints (9) and (10). To avoid simultaneous purchase and sale of electricity by the microgrid, the transaction decision is constrained by the 0-1 variables in equations (11), (12) and (13).
In order to realize the smooth operation of the power system or achieve the purpose of peak clipping and valley filling, the storage battery is arranged inside the micro-grid as an important distributed unit. Limited by the system capacity, its power needs to meet constraints:
wherein, the liquid crystal display device comprises a liquid crystal display device,respectively represent the maximum value of charge and discharge power in micro-grid i,/, respectively>Is a variable of 0-1And the condition of simultaneous charge and discharge of the storage battery is avoided. Cap based on battery capacity i The change in State of Charge (SOC) satisfies the constraint:
wherein eta Ec 、η Ed Respectively the charge and discharge efficiency and Cap of the storage battery i Is the capacity of the storage battery.
Considering the service life of the storage battery, the storage battery cannot be overdischarged or overcharged, namely: minimum and maximum boundary constraints of the SOC are required to meet the constraint (17). At the time of known charge and dischargeEfficiency eta Ec And eta Ed On the basis of (1), the equation relationship between the SOC state and the charge-discharge power can be expressed as a constraint (18).
For convenience of description, the above model is rewritten into a compact form:
Y 1 ，Y 2 ，Y 3 ，Y 4 ，w 1 ，w 2 ，w 3 ，w 4 ∈argmin{
X 1 Y 1 -X 2 Y 2 +C T (Y 3 +Y 4 )+D T (w 3 +w 4 )； (22)
s.t.Y 1 -Y 2 +Y 3 -Y 4 ≤r：λ Y ； (23)
w 1 +w 2 ≤1； (26)
w 3 +w 4 ≤1，w 3 ，w 4 ∈{0,1}； (29)
wherein the continuous variable X 1 、X 2 The electricity price of the upper layer in the original model is obtained; continuous variable Y of lower layer 1 、Y 2 、Y 3 、Y 4 And a discrete variable w 1 、w 2 、w 3 、w 4 Representing an operational decision of the micro-grid; constant matrix (A) T 、B T 、C T 、D T 、X 1 min 、X 1 max 、/>r、Y 1 max 、Y 2 max 、Y 3 max 、Y 4 max ) Is a parameter in the original model and has the same dimension as the variable; />Representing vector X 1 、X 2 Is n R Positive real number of dimension.
Step S3, relaxing bilinear terms in an upper layer objective function by utilizing equivalent relaxation and adding an upper bound to ensure the feasibility of a solution of a model, and specifically comprises the following steps:
solution of 0-1 variable after determining relaxationThe underlying problem can be regarded as a linear programming problem. The use of dual transforms provides an effective upper bound for objective functions that contain bilinear terms.
The bilinear objective function is relaxed to a linear function that is easy to handle by replacing the bilinear term with the variable η. On the basis of which; by Lagrange multiplierThe composed dual objective function gives the upper bound of the relaxation objective function with the first iteration.
Inspired by the above idea, let η=x 1 Y 1 -X 2 Y 2 And adding constraints (30) to the models (19) - (21) to relax the bilinear term-containing dual-layer mixed integer programming model into a solveable dual-layer mixed integer programming problem.
Step S3, converting and decomposing the double-layer mixed integer programming problem into a mixed integer programming problem which can be solved iteratively by utilizing reconstruction decomposition, wherein the specific steps comprise:
the double-layer mixed integer programming problem is decomposed into a Main Problem (MP) and two sub-problems (SP 1), (SP 2) by using a reconstruction decomposition algorithm. On the basis of the structural decomposition, each problem is solved iteratively by utilizing Gurobi, and the solving process is shown in figure 2.
S3.1: and initializing a model. Replacing bilinear terms with η in the upper-level master problem, obtaining a feasible solution of the model by copying the lower-level constraint into the Master Problem (MP), and updating the upper bound UB. In addition, the upper boundary is largerIs added into the model for constraint variable eta to ensure that a feasible solution exists in the model. By solving the Main Problem (MP):
obtaining an optimal solution of the upper layer problem
Wherein, to distinguish the variables of the main and sub problems in the iterative process, the variable Y is utilized 1 0 、Y 2 0 、Respectively replace the original variable Y 1 、Y 2 、w 3 、w 4 And corresponding constraints are added to ensure the feasibility of the main problem after relaxation.
S3.2: the sub-problem is solved (SP 1). In determining the solution of the upper-layer master problemAfter that, solve the sub-problem (SP 1)
A set of possible solutions to the underlying sub-problem is obtained.
S3.3: the sub-problem is solved (SP 2). Since the sub-problem (SP 1) may get multiple possible solutions to the underlying problem, to get the currentThe next best solution requires solving the sub-problem (SP 2) and updating the lower bound LB. Solution of given upper-layer electricity price and sub-problem (SP 1)>Then solve the sub-problem (SP 2):
obtaining an effective solution of the objective function value ψ and the variable of the sub-problem (SP 2)An effective boundary is provided for solving the upper layer problem in the subsequent iteration process.
S3.4, adding a cutset constraint. In the iterative process, a feasible cut set tightening feasible region is added to the main problem. After a number of iterations, the iteration is terminated when the gap between the upper and lower bounds of the objective function is smaller than a small value co.
Table 1 shows the parameter settings of the examples. In this embodiment, a typical day-ahead forecast is selected for analysis to consider the impact of the microgrid switch decision state on the level energy transactions.
Table 1 node parameter variables
And when the 0-1 variable is ignored, the energy trading model of the lower micro-grid is a linear programming model. In this model, the net traffic between micro-networks can be expressed as:namely: />Positive values indicate electricity purchases to other micro networks, and conversely electricity sells to other micro networks. After 0-1 variable is introduced, the lower mixed integer programming model effectively describes the decision process of the micro-networks, allows each micro-network to participate in energy sharing as an independent individual, and realizes the adjustment of internal supply and demand balance by adjusting electricity purchasing decision and energy storage charging and discharging depth. Therefore, the double-layer optimization model added with 0-1 variable effectively describes the energy transaction process between micro-networksAnd the influence of the change of the lower micro-grid model on the dispatching result is verified.
The result of the model iterative calculation is shown in fig. 3, and is regarded as model convergence when the relative error of the upper and lower bounds is co. According to the graph, the method realizes effective solution of the model after 7 iterations, and the solution time is 79.95s.
The double-layer energy collaborative optimization model containing bilinear terms and 0-1 variables provided by the method considers the influence of the lower-layer on/off decision state on the hierarchical energy transaction. The upper layer retailers price and the lower layer micro-networks participate in demand response according to electricity prices, and collaborative optimization between retailers and the micro-networks in the area is achieved through hierarchical energy transaction. In the process, the independent decision state of the micro-grid is considered, so that the engineering practicability of the double-layer model is improved.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the invention in any way, and any person skilled in the art may make modifications or alterations to the disclosed technical content to the equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present invention still fall within the protection scope of the technical solution of the present invention.
Claims (9)
1. The double-layer collaborative optimization method for independent decision of the upper retailer and the lower micro-network is characterized by comprising the following steps of: the method comprises the following steps:
s1: establishing an upper layer optimization problem aiming at maximizing the income of retailers and meeting the constraint of the electricity price purchasing interval;
s2: establishing a lower-layer daily market operation optimization model with the aim of minimizing the operation cost, and writing lower-layer model constraint conditions considering independent decisions of the microgrid under the condition of meeting power balance constraint, electricity purchase and selling constraint and energy storage charge and discharge constraint;
s3: processing bilinear terms in an objective function by using a continuous variable equivalent substitution method, and under the condition of giving a feasible solution of 0-1 variable, giving an effective upper bound of the bilinear terms by using the dual values of a lower model, and relaxing a non-convex optimization problem containing the bilinear terms into a mixed integer programming problem which is easy to solve;
s4: the model after S3 relaxation is a double-layer mixed integer programming model, the double-layer mixed integer programming model is decomposed into a main problem and a sub problem by a reconstruction decomposition method, and the main problem and the sub problem are solved, and the model is calculated until the model converges by an iterative calculation method and a cutting set adding method.
2. The method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 1, wherein: establishing an upper optimization model aiming at the maximization of the income of retailers and meeting the constraint of the price interval of electricity purchase and sale, dividing 24 hours into T time periods, and determining the electricity price of transaction with the micro-grid according to the time periodsAnd->
The constraint is as follows:
wherein:for the given internet power price->The electricity price and the electricity quantity of electricity purchased and sold by the micro-grid i to retailers respectively are +.>Upper and lower boundaries of power supply to retailers for the micro-grid, respectively,)>The upper and lower boundaries of the electricity sold by the micro-grid to retailers, respectively.
3. The method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 1, wherein: the objective function of the lower layer day-ahead market operation optimization model is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,the electricity price and the electricity quantity of electricity purchased and sold by the micro-grid i to retailers respectively are +.>Net fee for the micro-net transaction with retailer, < ->For the depreciation of the battery, the charge and discharge power of the battery is +.>Andis a degradation cost of energy storage.
4. The method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 3, wherein: the method further comprises, during the optimization process, the micro-grid needs to satisfy the following power balance constraints:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Predictive value of new energy power generation and load in micro-grid respectively, < >>The electricity buying and selling amounts of electricity between the micro-grids are respectively.
5. The method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 3, wherein: in order to ensure the safe and stable operation of the system, the following transmission capacity constraints need to be satisfied when the micro-network and the retailers conduct energy transaction:
wherein, the liquid crystal display device comprises a liquid crystal display device,the upper limit of electricity purchasing and electricity selling is respectively +.>Is a 0-1 variable and satisfies the following constraint:
6. the method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 3, wherein: the method further comprises, considering the problem of energy sharing between micro-networks, the energy transaction satisfying the following transmission capacity constraints:
0-1 variableThe following energy transaction constraints need to be satisfied:
7. the method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 3, wherein: the method further comprises that the power of the storage battery inside the micro-grid satisfies the following constraint:
wherein, the liquid crystal display device comprises a liquid crystal display device,respectively represent the maximum value of charge and discharge power in micro-grid i,/, respectively>Is a 0-1 variable;
state of charge SOC of a battery i，t Is that the variation of (a) satisfies the constraint:
wherein eta Ec 、η Ed Respectively the charge and discharge efficiency and Cap of the storage battery i Is the capacity of the storage battery.
8. The method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 1, wherein: the method further includes implementing an effective relaxation of bilinear terms in the upper layer pricing model with the following constraints:
wherein the continuous variable X 1 、X 2 The electricity price of the upper layer in the original model is obtained; continuous variable Y of lower layer 1 、Y 2 、Y 3 、Y 4 And a discrete variable w 1 、w 2 、w 3 、w 4 Representing an operational decision of the micro-grid; constant matrix Is a parameter in the original model and has the same dimension as the variable; to achieve bilinear term X 1 Y 1 And X 2 Y 2 Replacing the bilinear term with a variable η; by Lagrangian multiplier->The composed dual objective function gives the upper bound of the relaxation objective function with the first iteration.
9. The method for double-layer collaborative optimization of upper retailer pricing and lower microgrid independent decisions according to claim 1, wherein: decomposing the double-layer mixed integer programming model into a main problem and a sub problem by using a reconstruction decomposition method, solving the main problem and the sub problem, and calculating the main problem and the sub problem until the model converges by using an iterative calculation method and a cutting set adding method, wherein the method comprises the following steps of:
s3.1: solving the Main Problem (MP) to obtain the optimal solution of the upper layer problemWherein the method comprises the steps ofThe Main Problems (MP) are:
wherein, to distinguish the variables of the main and sub problems in the iterative process, the variables are utilizedRespectively replace the original variable Y 1 、Y 2 、w 3 、w 4 And adding corresponding constraint to ensure feasibility of the main problem after relaxation;
s3.2: in determining the solution of the upper-layer master problemAfter that, solve the sub-problem (SP 1):
s3.3: solution of given upper-layer electricity price and sub-problem (SP 1)Then solving the sub-problem (SP 2) to obtain the objective function value psi of the sub-problem (SP 2) * And effective solution of variables->Providing an effective boundary for solving the upper layer problem in the subsequent iteration process;
s3.4, adding a feasible cutting set to the main problem to tighten a feasible domain in the iteration process, and after multiple iterations, terminating the iteration when the gap between the upper bound and the lower bound of the objective function is smaller than a small value E;
wherein the constant matrix A T 、B T 、C T 、D T Representing parameters in the master model.
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