CN112884572A - Multi-microgrid transaction optimization method and system under credit risk - Google Patents
Multi-microgrid transaction optimization method and system under credit risk Download PDFInfo
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Abstract
The invention provides a multi-microgrid transaction optimization method and system under credit risk, wherein the credit level of each microgrid main body is calculated based on historical transaction information of the microgrid main body; determining constraint conditions and optimization targets of a multi-microgrid transaction optimization problem under the credit risk, and establishing a transaction optimization model; considering the discreteness and continuity of variables, decomposing the multi-microgrid optimization model into a main problem for solving a microgrid transaction combination and a sub problem for solving a microgrid transaction plan considering the uncertainty of the credit level; and solving the decomposed main problem and sub-problem by using a column and constraint generation algorithm to obtain an optimal trading plan, and determining the electricity purchasing and selling state and the electricity quantity of each microgrid. The invention can improve the economy and fairness of multi-microgrid power transaction.
Description
Technical Field
The invention belongs to the technical field of micro-grid operation optimization, and particularly relates to a multi-micro-grid transaction optimization method and system under credit risk.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The dual problems of energy crisis and environmental deterioration promote all countries in the world to actively develop and utilize renewable energy, which has the advantages of high efficiency, cleanness, local consumption and the like, and the micro-grid can integrate various renewable energy and energy storage equipment, thereby effectively improving the utilization efficiency of the renewable energy and reducing the environmental pollution. With the large-scale access of new energy and a microgrid technology, a plurality of adjacent microgrids in a certain area realize interconnection and mutual assistance to form a plurality of microgrids, so that the internal interaction of energy can be promoted, the transaction with a distribution network is reduced, the electricity buying and selling cost and the electricity transmission and distribution cost of the microgrids are reduced, the energy utilization rate is further improved, and the benefit of a microgrid system is improved. The multi-microgrid interconnection operation improves the economy and brings hidden dangers, the dishonest behavior of the microgrid transaction main body affects the fairness and the reliability of the multi-microgrid power transaction, and therefore the credit risk is brought to the multi-microgrid transaction.
Currently, credit investigation characteristics and risk evaluation of a multi-microgrid power transaction main body under credit risk are still in an exploration stage, and an effective credit evaluation model and a transaction method are lacked. In a patent of 'a multistage credit risk early warning method in a regional power market', such as Hubo, and the like, a credit risk propagation model in the regional power market is given by adopting an expert evaluation method, so that the credit risk early warning problem in the power market is solved. In the patent of 'an electric power user credit risk early warning method based on SVM', Wangzaifeng and the like, an electric power user credit risk early warning model is provided by adopting an SVM method. The results only research the credit evaluation problem of the main body of the power market under medium and long-term trading, and the microgrid has duality, more flexible trading and shorter trading period, so the evaluation method and indexes are not suitable for use; on the other hand, a reasonable solution is not provided for the multi-microgrid transaction optimization problem under the credit risk.
In summary, for multi-microgrid transaction under credit risk, the existing method has the following two problems:
the characteristics of shorter multi-microgrid power transaction period and stronger flexibility are not considered, effective credit evaluation on a transaction subject is lacked, and a characterization method for accurate credit level of the multi-microgrid power transaction subject needs to be researched; secondly, the influence of credit level on multi-microgrid transaction is not considered, and particularly, reasonable description on the relationship between credit risk and a transaction optimization model is lacked, so that the transaction matching result is not ideal, and further the market is unfair.
Disclosure of Invention
The invention aims to solve the problems and provides a multi-microgrid transaction optimization method and system under the credit risk.
According to some embodiments, the invention adopts the following technical scheme:
a multi-microgrid transaction optimization method under credit risk comprises the following steps:
(1) calculating the credit level of each microgrid main body based on historical transaction information of the microgrid main bodies;
(2) determining constraint conditions and optimization targets of a multi-microgrid transaction optimization problem under the credit risk, and establishing a transaction optimization model;
(3) considering the discreteness and continuity of variables, decomposing the multi-microgrid optimization model into a main problem for solving a microgrid transaction combination and a sub problem for solving a microgrid transaction plan considering the uncertainty of the credit level;
(4) and solving the decomposed main problem and sub-problem by using a column and constraint generation algorithm to obtain an optimal trading plan, and determining the electricity purchasing and selling state and the electricity quantity of each microgrid.
In the step (1), the microgrid historical transaction information includes:
traffic demand, Δ P, for multiple piconetsiThe deviation between the microgrid transaction demand and the actual forecast is obtained; NS (server)sumSetting a deviation threshold value delta for the number of times of trading of the individual i, and when the deviation between the trading demand amount and the actual forecast amount exceeds the threshold value, namely delta PiWhen the number of times of one lost credit transaction is larger than or equal to delta, counting the number of times of one lost credit transaction to finally obtain Ne；
The historical average participation amount of the period; pr (Pr) ofiEvaluating the prediction accuracy degree of the renewable energy and the load for predicting the accuracy level; dpiAnd evaluating the energy transaction level of the microgrid for transaction quality, wherein the statistical period is preset.
In the step (1), according to the statistical historical information, converting the multiple indexes into default probability d by a logistic regression analysis methodiCredit level Pro of microgrid subject iiIs shown as
In the formula dmaxRepresenting a risk threshold.
In the step (1), if
The main body is identified as a malicious node, is removed out of a trading market and can only trade with a distribution network; counting the number N of continuous acceptable loss leveldnThe transaction deviation amount is within the acceptable range in a certain transaction period, and the credit level is recovered to be 1.
In the step (2), the constructed constraint conditions include the state and capacity constraint of energy transaction among the micro-grids, the state and capacity constraint of energy transaction between the micro-grids and the power grid, and the power balance constraint of the micro-grids and the multi-micro-grids.
In the step (2), U is an uncertain set of transaction clearing amounts under credit risk:
in the formula (I), the compound is shown in the specification,
giving out a numerical value of the clearance for the microgrid i at the moment t; deltaNL(i, t) is a deviation value of the micro-grid i clear output and the predicted clear output at the moment t; the credit level represents the historical transaction participation attribute of the microgrid body, influences the size of the uncertainty interval of the set U, and is expressed as
The higher the credit level, the smaller the credit risk, ΔNLThe smaller (i, t); conversely deltaNLThe larger (i, t) is, Γ (i) is the uncertainty margin of the risk.
In the step (2), the constructed optimization target is an objective function with the lowest total operating cost of the multiple micro-grids, and the total operating cost of the multiple micro-grids comprises energy transaction cost among the micro-grids, energy transaction cost between each micro-grid and a power grid, operating and maintaining cost of each micro-grid power generation unit and credit guarantee fund cost of each micro-grid.
In the step (3), the main problem only contains discrete decision variables and is used for solving the microgrid transaction combination; the sub-problems comprise continuous decision variables and uncertain parameters and are used for solving the microgrid trading plan considering the uncertainty of the credit level.
And (3) converting the main problem and the sub problem decomposed by the multi-microgrid optimization model into a mixed integer linear programming based on the KKT condition.
In the step (4), the converted main problem and sub-problem are solved by adopting a column and constraint generation algorithm to obtain a day-ahead trading plan of each microgrid, namely the electricity purchasing and selling state and the electricity quantity of each microgrid at each moment.
A multi-microgrid transaction optimization system under credit risk comprises:
a subject credit level determination module configured to calculate a credit level for each microgrid subject based on historical transaction information for the microgrid subject;
the model building module is configured to determine constraint conditions and optimization targets of the multi-microgrid transaction optimization problem under the credit risk and build a transaction optimization model;
the decomposition module is configured to consider the discreteness and the continuity of the variables, and decompose the multi-microgrid optimization model into a main problem for solving the microgrid transaction combination and a sub-problem for solving the microgrid transaction plan considering the uncertainty of the credit level;
and the calculation module is configured to solve the decomposed main problem and sub-problem by using a column and constraint generation algorithm to obtain an optimal transaction plan and determine the electricity purchasing and selling state and the electricity quantity of each microgrid.
Compared with the prior art, the invention has the beneficial effects that:
(1) aiming at the multi-microgrid power trading market, the credit level of a microgrid main body is carved based on historical statistical information, unreasonable subjective evaluation method can be solved, and fairness and reliability of multi-microgrid trading under credit risks are improved.
(2) By optimizing and managing multi-microgrid transaction, the economy of a multi-microgrid system can be improved, the robustness of system transaction decision can be improved, and the cost increase caused by uncertain factors of subjective behaviors is reduced.
(3) The method can be applied to a centralized multi-microgrid energy management system to coordinate multi-microgrid energy transactions and improve energy utilization efficiency.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a multi-piconet transaction optimization method under credit risk according to the present invention;
fig. 2 is a schematic diagram of a multi-piconet structure according to an embodiment of the invention;
fig. 3 is an information flow diagram of an energy service provider optimization process according to an embodiment of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Aiming at the operation optimization problem of multiple micro-grids, the embodiment provides a transaction optimization method of multiple micro-grids under the credit risk.
The method can still ensure that the multi-microgrid system has better economy and fairness when credit risk exists in the multi-microgrid transaction process, and can be popularized and applied in actual energy service providers.
The invention provides a multi-microgrid operation optimization method under credit risk, a flow chart of which is shown in figure 1, and the method comprises the following steps:
(1) acquiring the credit level of each microgrid main body based on historical transaction information of the microgrid main bodies;
(2) establishing a transaction optimization model according to the constraint conditions and the optimization target of the multi-microgrid transaction optimization problem under the credit risk;
(3) considering the discreteness and continuity of variables, and decomposing the multi-microgrid optimization model into a main problem and a sub problem;
(4) and solving the decomposed main problem and sub-problem through a column and constraint generation algorithm to obtain an optimal trading plan.
The embodiment describes a multi-microgrid transaction optimization method under the credit risk in the invention based on a multi-microgrid structure shown in fig. 2. A plurality of micro-grids in the region are interconnected and connected to the same power distribution network to form a region multi-micro-grid system, and each micro-grid is composed of a photovoltaic power generation unit and a local load. And reporting the power energy demand of each period of time in the next day to a service provider according to the predicted generated energy and load at a fixed time every day. The power energy generated by the photovoltaic power generation unit preferentially meets the load of each microgrid, if the power energy is left, electricity can be sold to other microgrids or power grids, and the microgrids are seller microgrids; if the energy generated by the photovoltaic is insufficient, electricity can be purchased from other micro-grids or a power grid to meet the load, and the micro-grid is the buyer micro-grid.
The regional energy service provider undertakes energy management on the multiple micro-grids, is responsible for coordinating power trading in the multiple micro-grids, and serves as an agent to conduct power trading with the power distribution network. The multi-microgrid transaction optimization method under the credit risk is applied to regional energy service providers.
Step (1): and acquiring the credit level of each microgrid main body based on the historical transaction information of the microgrid main bodies.
Calculating the credit level Pro of each subject by counting the historical transaction information of the participating subjectsiAnd converting the multiple attributes into quality indexes by a logistic regression analysis method according to the statistical historical information.
In the formula (d)iIs the individual violation probability, xkK is 1,2,3,4,5, which are the influence variables in the credit risk assessment and correspond to the 5 influence variables defined in the credit risk assessment, cjJ-0, 1.. and 6 are technical coefficients obtained by maximum likelihood estimation. Deviation delta P between actual participation amount and reported participation amount of microgridiCorresponding variable x1Counting and obtaining after the actual transaction task is finished; number of transaction lost NeCorresponding variable x2(ii) a Average participation
Corresponds to x3The indexes are quantitative indexes and represent historical credit of the microgrid body and contribution to energy trading; accurate level Pr predictioniCorresponds to x4Energy trade level DpiCorrespond tox5The two are qualitative indicators. The logistic regression value p ∈ (0,1) is the result of the credit analysis discrimination. The default probability can also be used as the basis for determining the credit rating of the electric power market main body.
Credit level Pro of microgrid subject iiUsing probability of breach diRepresents a piecewise function of:
in the formula (d)maxIndicates a risk threshold, if dmax≤diAnd (4) the main body is identified as a malicious node and is clearly cleared out of the trading market, and only can trade with a distribution network.
In order to encourage the participating entity to participate in the transaction as faithfully as possible, the number of consecutive acceptable levels of loss N is recordeddnAnd setting a threshold value NapIf N is present dn ≥NapThen, it means that the credit level is restored to 1, and the credit loss behavior is within the acceptable range in a certain transaction period.
Step (2): and establishing a transaction optimization model according to the constraint conditions and the optimization target of the multi-microgrid transaction optimization problem under the credit risk.
Fig. 3 is an information flow diagram of an energy service operator optimization process according to an embodiment of the present invention.
a. And constructing constraint conditions, wherein the constraint conditions comprise the state and capacity constraint of microgrid energy transaction, the state and capacity constraint of microgrid and power distribution network energy transaction, the power balance constraint of microgrid and multiple microgrids and the uncertain set of microgrid transaction amount.
The net clearing transaction amount of the microgrid per time period is expressed as pNL(i,t)＝ppv(i,t)-pload(i,t)，pload(i, t) and pPVAnd (i, t) is the photovoltaic power generation amount and the load power of the microgrid i at the moment t. p is a radical ofNLSeller microgrid p when (i, t) > 0NL(i,t)<And 0, the micro-grid is the buyer.
Energy transaction state constraint between micro grids:
in the formula, r (i, j, t) and s (i, j, t) are variables of 0 to 1 for purchasing or selling electricity from the microgrid j by the microgrid i at the time t.
And (3) state constraint of energy transaction between the micro-grid and the grid:
in the formula, u (i, t) and v (i, t) are variables of 0 to 1 for purchasing or selling electricity from the microgrid j by the microgrid i at the time t.
Energy transaction electric quantity constraint among each microgrid:
in the formula, pMG,buy(i, j, t) and pMG,sell(i, j, t) is the electricity purchasing quantity or the electricity selling quantity from the microgrid i to the microgrid j at the moment t,
and the upper limit of the transaction electric quantity between the micro grids.
Electric quantity constraint of energy transaction of each micro-grid and the power grid:
in the formula pgrid,buy(i, t) and pgrid,sell(i, t) is the electricity purchasing quantity or electricity selling quantity of the microgrid i power grid at the moment t;
and
and buying the upper limit and the lower limit of the electric quantity for the micro-grid and the power grid.
And (3) multi-microgrid power balance constraint:
and (3) micro-grid power balance constraint:
u is the uncertain set of transaction amounts at credit risk:
in the formula (I), the compound is shown in the specification,
giving out a numerical value of the clearance for the microgrid i at the moment t; deltaNLAnd (i, t) is a deviation value between the micro-grid i clear output and the predicted clear output at the time t. The credit level represents the historical participation attribute of the participating subject in the multi-microgrid transaction, and influences the size of the uncertainty interval of the set U, which is expressed as
The higher the credit level, the smaller the credit risk, ΔELThe smaller (i, t); conversely deltaELThe larger (i, t). Γ (i) is an uncertain margin of risk, the larger Γ (i) the less economical the system operates and the more resistant the system to risk.
Constructing an objective function:
in the formula, omega (r, s, u, v, p)NL)＝{(pmb,pms,pgb,pgs) (4) - (14) }, the bold variables in the set represent the vector form of the corresponding decision variables, e.g., r represents the variables r (i, j, t), pgsRepresents pgs(i,t)。
aserPaying a fixed service fee to a facilitator for inter-microgrid transactions, bserPaying fixed service fees to the regional energy service providers for the microgrid transaction and the power grid transaction; c. Csell(t) and csell(t) the electricity purchase price and the electricity sale price from the micro-grid to the power grid are respectively; a iscdtAnd ensuring the transaction deposit required to be paid for the microgrid.
And (3): considering the discreteness and continuity of variables, and decomposing the multi-microgrid optimization model into a main problem and a sub problem;
a. decomposing a multi-microgrid transaction optimization model into a transaction combination main problem and an economic optimization sub-problem under uncertainty, wherein the main problem only contains discrete decision variables, and the sub-problem contains continuous decision variables and uncertain parameters;
b. based on the KKT condition, converting the sub-problems from a double-layer optimization problem into a single-layer optimization problem, further linearizing nonlinear constraint through a large M method, and converting the original problem into mixed integer linear programming.
And (4): and solving the decomposed main problem and sub-problem through a column and constraint generation algorithm to obtain an optimal trading plan.
And solving the converted main problem and sub-problem by adopting a column and constraint generation algorithm to obtain a day-ahead trading plan of each microgrid, namely the electricity purchasing and selling state and the electricity quantity of each microgrid at each moment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A multi-microgrid transaction optimization method under credit risk is characterized by comprising the following steps: the method comprises the following steps:
(1) calculating the credit level of each microgrid main body based on historical transaction information of the microgrid main bodies;
(2) determining constraint conditions and optimization targets of a multi-microgrid transaction optimization problem under the credit risk, and establishing a transaction optimization model;
(3) considering the discreteness and continuity of variables, decomposing the multi-microgrid optimization model into a main problem for solving a microgrid transaction combination and a sub problem for solving a microgrid transaction plan considering the uncertainty of the credit level;
(4) and solving the decomposed main problem and sub-problem by using a column and constraint generation algorithm to obtain an optimal trading plan, and determining the electricity purchasing and selling state and the electricity quantity of each microgrid.
2. The method for optimizing multi-microgrid transaction under credit risk of claim 1, wherein: in the step (1), the microgrid historical transaction information includes:
traffic demand, Δ P, for multiple piconetsiThe deviation between the microgrid transaction demand and the actual forecast is obtained; NS (server)sumSetting a deviation threshold value delta for the number of times of trading of the individual i, and when the deviation between the trading demand amount and the actual forecast amount exceeds the threshold value, namely delta PiWhen the number of times of one malicious quotation is more than or equal to delta, counting the number of times of one malicious quotation to finally obtainNe；
The historical average participation amount of the period; pr (Pr) ofiEvaluating the prediction accuracy degree of the renewable energy and the load for predicting the accuracy level; dpiAnd evaluating the energy transaction level of the microgrid for transaction quality, wherein the statistical period is preset.
3. The method for optimizing multi-microgrid transaction under credit risk of claim 1, wherein: in the step (1), according to the statistical historical information, converting the multiple indexes into default probability d by a logistic regression analysis methodiCredit level Pro of microgrid subject iiIs shown as
In the formula dmaxRepresenting a risk threshold.
4. The method for optimizing multi-microgrid transaction under credit risk of claim 1, wherein: in the step (1), if
The main body is identified as a malicious node, is removed out of a trading market and can only trade with a distribution network; counting the number N of continuous acceptable loss leveldnThe transaction deviation amount is within the acceptable range in a certain transaction period, and the credit level is recovered to be 1.
5. The method for optimizing multi-microgrid transaction under credit risk of claim 1, wherein: in the step (2), the constructed constraint conditions include the state and capacity constraint of energy transaction among the micro-grids, the state and capacity constraint of energy transaction between the micro-grids and the power grid, and the power balance constraint of the micro-grids and the multi-micro-grids.
6. The method as claimed in claim 1, wherein in step (2), the optimization objective is constructed as an objective function with the lowest total operating cost of the microgrid, and the total operating cost of the microgrid comprises energy transaction fees between the microgrids, energy transaction fees between the microgrids and the power grid, operating and maintenance fees of the microgrid power generation units, and credit guarantee fee of the microgrids.
7. The method for optimizing multi-microgrid transaction under credit risk of claim 1, wherein: in the step (2), U is an uncertain set of transaction clearing amounts under credit risk:
in the formula (I), the compound is shown in the specification,
giving out a numerical value of the clearance for the microgrid i at the moment t; deltaNL(i, t) is a deviation value of the micro-grid i clear output and the predicted clear output at the moment t; the credit level represents the historical transaction participation attribute of the microgrid body, influences the size of the uncertainty interval of the set U, and is expressed as
The higher the credit level, the smaller the credit risk, ΔELThe smaller (i, t); conversely deltaELThe larger (i, t) is, Γ (i) is the uncertainty margin of the risk.
8. The method for optimizing multi-microgrid transaction under credit risk of claim 1, wherein: in the step (3), the main problem only contains discrete decision variables and is used for solving the microgrid transaction combination; the sub-problems comprise continuous decision variables and uncertain parameters and are used for solving the microgrid trading plan considering the uncertainty of the credit level;
and converting the main problem and the sub problem decomposed by the multi-microgrid optimization model into mixed integer linear programming based on the KKT condition.
9. The method for optimizing multi-microgrid transaction under credit risk of claim 1, wherein: in the step (4), the converted main problem and sub-problem are solved by adopting a column and constraint generation algorithm to obtain a day-ahead trading plan of each microgrid, namely the electricity purchasing and selling state and the electricity quantity of each microgrid at each moment.
10. A multi-microgrid transaction optimization system under credit risk is characterized in that: the method comprises the following steps:
a subject credit level determination module configured to calculate a credit level for each microgrid subject based on historical transaction information for the microgrid subject;
the model building module is configured to determine constraint conditions and optimization targets of the multi-microgrid transaction optimization problem under the credit risk and build a transaction optimization model;
the decomposition module is configured to consider the discreteness and the continuity of the variables, and decompose the multi-microgrid optimization model into a main problem for solving the microgrid transaction combination and a sub-problem for solving the microgrid transaction plan considering the uncertainty of the credit level;
and the calculation module is configured to solve the decomposed main problem and sub-problem by using a column and constraint generation algorithm to obtain an optimal transaction plan and determine the electricity purchasing and selling state and the electricity quantity of each microgrid.
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