CN113888348B - Source load intelligent matching method taking multilateral benefit balance as target - Google Patents
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Abstract
The invention provides a source load intelligent matching method taking multilateral benefit balance as a target, which comprises the following steps of: constructing an electric power market structure containing various market main bodies under the distribution network level; step 2: establishing a source load intelligent matching model which aims at multilateral benefit balance according to market subject characteristics; step 3: and solving the source load intelligent matching model, and executing a source load intelligent matching process aiming at multilateral benefit balance. The intelligent matching method aims at multilateral benefit balance and can promote the nearby digestion of new energy, maintain the safe and stable operation of the power distribution network and realize multilateral benefit balance including the new energy generator, the flexible load and the power distribution network operators.
Description
Technical Field
The invention belongs to the field of power market matching transaction under the distribution network level, and particularly relates to an intelligent source load matching method aiming at multilateral benefit balance.
Background
In recent years, the rapid penetration of distributed renewable energy sources such as photovoltaic and flexible loads such as energy storage in a power distribution network brings impact to the safe and stable operation of the power distribution network, and the problem of the absorption of new energy sources at the power distribution network side is also more and more remarkable. The source-load matching transaction can effectively promote new energy consumption and alleviate the impact of the power grid to a certain extent. However, the conventional source load matching transaction method with the maximum satisfaction degree as the target does not consider the security preference of the power distribution network operators, and the security and stability of the power grid are difficult to effectively guarantee.
Therefore, in order to promote the effective consumption of new energy and ensure the safe and stable operation of the power distribution network under the condition of multi-type flexible load access, it is highly desirable to design a source load intelligent matching method capable of considering the security preference of the power distribution network. In addition, the method needs to balance the multilateral interests to ensure the executability of the matching result.
Disclosure of Invention
The invention aims to provide an intelligent source load matching method aiming at multilateral benefit balance so as to promote effective consumption of new energy and ensure safe and stable operation of a power distribution network under the condition of high-proportion new energy and multi-type flexible load access.
The intelligent source load matching method for the polygonal benefit balance comprises the following steps:
Step 1: constructing an electric power market structure containing various market subjects under the distribution network level, wherein the market subjects comprise a distribution network operator, a new energy power generator and a flexible load manufacturer;
Step 2: establishing a source load intelligent matching model aiming at multilateral benefit balance according to market subject characteristics: calculating the matching satisfaction of each new energy generator, flexible load and distribution network operators, and establishing a source load intelligent matching model with multilateral benefit balance as a target, wherein the target function of the source load intelligent matching model is to maximize the total matching satisfaction of the new energy generator, the flexible load and the distribution network operators, and the constraint condition is the matching transaction amount constraint;
Step 3: and solving the source load intelligent matching model, and executing a source load intelligent matching process aiming at multilateral benefit balance.
Further, the set of new energy power generators is recorded asThe new energy power generator i is represented by P i, wherein/>The set of flexible loaders is denoted/>The flexible loader j is denoted by C j, where/>Each new energy generator and each flexible load quotient are matched with a plurality of flexible load quotients, and transaction electric quantity is determined; as an independent third party, the power distribution network operators are mainly responsible for organizing the matching transaction in the market, and preferably match the new energy generator with the flexible load business, if the matching is successful, the transaction is carried out according to the matching result, otherwise, the transaction main body with failed matching carries out the transaction with the power grid.
Further, calculating the matching satisfaction degree of each new energy generator, each flexible loader and each power distribution network operator in the step 2 specifically includes:
the satisfaction degree calculation formulas of the new energy generator for quotation and electricity utilization flexibility of the flexible load manufacturer are respectively as follows:
Wherein, Quotation/>, for the new energy generator P i, of the flexible loader C j P sell,G is the online electricity price of the new energy generator, formulated by the power grid,/>The highest quotation for all flexible loaders in the market, M is a positive number approaching infinity; /(I)For the satisfaction degree of the new energy generator P i on the electricity utilization flexibility f j of the flexible load manufacturer C j, f min、fmax is the minimum value and the maximum value of the commercial electricity flexibility of all flexible loads in the market; the comprehensive satisfaction degree of the new energy generator P i to the flexible load quotient C j is/>
The satisfaction degree calculation formulas of the flexible load quotient on the quotation of the new energy generator and the power generation prediction precision are respectively as follows:
Wherein, Quotation/>, for flexible loader C j, for New energy Generator P i P buy,G is the electricity selling price of the power grid,/>The lowest quotation of all new energy power generators in the market is achieved; /(I)For the satisfaction degree of the flexible load quotient C j on the historical average power generation prediction error e i of the new energy power generator P i, the error e i is used for representing the power generation prediction precision of the new energy power generator, the smaller the e i is, the higher the power generation prediction precision is, and the e min、emax is the minimum value and the maximum value of the power generation historical average prediction errors of all new energy power generators in the market; the integrated satisfaction of the flexible loader C j to the new energy generator P i is/>
Distribution network operators also have their own preferences for matching transactions in the market, expressed in terms of matching satisfaction:
Wherein, The satisfaction degree of the power distribution network operator on the power utilization flexibility f j of the flexible loader C j, the historical average power generation prediction error e i of the new energy power generator and the distance d ji between the two transaction parties is respectively that d min、dmax is the minimum value and the maximum value of the distance d ji in the matching situation of all the flexible loaders C j and the new energy power generator P i in the market; the comprehensive satisfaction of the distribution network operator on the matching of the flexible loader C j and the new energy generator P i is/>
Further, in the step2, the objective function of the source load intelligent matching model targeting the multilateral benefit balance is to maximize the total matching satisfaction degree of the new energy generator, the flexible load manufacturer and the power distribution network operator, namely:
Wherein y ij is a decision variable, which represents the matched electric quantity of the flexible loader C j and the new energy generator P i.
Further, in the step 2, the constraint condition of the source load intelligent matching model targeting the multilateral benefit balance is a matching transaction amount constraint, which specifically includes:
yij≥0 (12)
Wherein y ij is a decision variable, which represents the matched electric quantity of the flexible loader C j and the new energy generator P i, The total power generation amount of the new energy generator P i and the total power consumption amount of the flexible load carrier C j are respectively represented.
Further, in the step 3, the intelligent matching process of the source load with the goal of multilateral benefit balance is as follows:
1) Each new energy power generator publishes self quotation and historical average power generation prediction errors, and each flexible load provider publishes self quotation and power utilization flexibility;
2) Each new energy power generator reports the self power generation amount and each flexible load provider reports the self power consumption amount to the power grid;
3) Based on the data published in the step 1), each new energy generator, flexible load manufacturer and distribution network operator calculate satisfaction degree of each source load matching scheme according to formulas (1) - (7);
4) Based on the data reported in the step 2) and the satisfaction calculated in the step 3), the power distribution network operators solve the matching models shown in the formulas (8) - (13) to obtain and publish matching results, each new energy generator and each flexible load provider execute a matching scheme, and the matching process is finished.
Compared with the prior art, the invention has the beneficial effects that:
1. the preference of the distribution network operators to matching in the market is considered, and the preference is quantized into matching satisfaction, so that safe and stable operation of the distribution network can be maintained;
2. The distance between the two matching parties is considered when the matching satisfaction degree of the power distribution network operators is calculated, and the satisfaction degree of the power distribution network operators is higher for matching between the source charges with the relatively close distance, so that the near consumption of new energy sources can be promoted.
Drawings
The accompanying drawings, in which like reference numerals refer to identical or similar parts throughout the several views and which are included to provide a further understanding of the application, are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application and not to limit the application unduly. In the drawings:
FIG. 1 is a flow chart of a source load intelligent matching method targeting multilateral benefit balance according to the present invention;
FIG. 2 is a diagram of the power market structure of the present invention;
FIG. 3 is a flow chart of the intelligent matching of source charges according to the present invention;
FIG. 4 is a block diagram of a power system according to one embodiment of the present invention;
Fig. 5 is a diagram of a source load intelligent matching result according to one embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below. It will be apparent that the described embodiments are only a part of the invention, but not all. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a source load intelligent matching method aiming at multilateral benefit balance, the general framework of which is shown in figure 1, and the specific implementation modes are as follows:
Step 1: an electric power market structure is constructed that includes a variety of market principals. As shown in fig. 2, the electric power market is composed of various types of market subjects of distribution network operators, new energy power generators and flexible load operators; the collection of new energy power generators is recorded as The new energy power generator i is represented by P i, wherein/>The set of flexible loaders is denoted/>The flexible loader j is denoted by C j, where/>Each new energy generator can be matched with a plurality of flexible load merchants and determine transaction electric quantity, and the same is true for the flexible load merchants; as an independent third party, the power distribution network operators are mainly responsible for organizing the matching transaction in the market, and preferably match the new energy generator with the flexible load business, if the matching is successful, the transaction is carried out according to the matching result, otherwise, the transaction main body with failed matching needs to carry out the transaction with the power grid.
Step 2: and establishing a source load intelligent matching model aiming at multilateral benefit balance according to the characteristics of the market main body. Specifically, the matching satisfaction degree of each new energy generator, each flexible load provider and each power distribution network operator is calculated according to formulas (1) - (7), a source load intelligent matching model which aims at multilateral benefit balance and is shown in formulas (8) - (13) is established, the objective function is to maximize the total matching satisfaction degree of the new energy generator, the flexible load provider and the power distribution network operator, and the constraint condition is the matching transaction amount constraint.
Step 3: and solving the source load intelligent matching model, and executing a source load intelligent matching flow aiming at multilateral benefit balance, as shown in fig. 3. Firstly, each new energy generator publishes self quotation and historical average power generation prediction errors, each flexible load provider publishes self quotation and power utilization flexibility, and the self generated energy or power utilization amount is reported to a power grid by the two; secondly, based on the published data, each new energy generator, flexible load manufacturer and distribution network operator calculate the satisfaction degree of each source load matching scheme according to formulas (1) - (7); then, based on the published data and the calculated satisfaction, the power distribution network operators solve the matching models shown in the formulas (8) - (13) to obtain and publish matching results, each new energy generator and each flexible load provider execute a matching scheme, and the matching process is finished.
The following is one embodiment of the invention:
The embodiment is taken to illustrate a source load intelligent matching method aiming at multilateral benefit balance, as shown in an IEEE 9 node power system shown in fig. 4 for example, and MATLAB is used for solving a source load intelligent matching model aiming at multilateral benefit balance. Parameters of each generator and each loader are shown in tables 1 and 2, and distances between each generator and each loader are shown in table 3.
Table 1 New energy Generator parameters for IEEE 9 node Power System
Table 2 flexible loaders parameters for IEEE 9 node power systems
TABLE 3 distance between IEEE 9 node Power System generators and loaders
According to the data, matching satisfaction degrees of the new energy power generators, the flexible load suppliers and the power distribution network operators are calculated, and as shown in fig. 5, the satisfaction degrees of the power generators on the load suppliers C 7 are the highest, and the satisfaction degrees of the power generators on the power generator P 1 are the highest. Therefore, when the preference of the power distribution network operator and the distance factor between the two matching parties are not considered, in order to achieve the optimization target with the maximum total matching satisfaction, the matching between P 1 and C 7 should be preferentially promoted. However, when considering both the preference of the distribution network operator and the distance factor of the matching, as shown in the matching result in fig. 5, P 1 finally preferentially matches with C 4、C5 closest thereto, and C 7 finally preferentially matches with P 2 closest thereto, and the other is the same. Therefore, the intelligent source load matching method with the polygonal benefit balance as the target can promote the nearby consumption of new energy, consider the preference of a power distribution network operator, and maintain the safe, stable and efficient operation of the power distribution network.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (3)
1. The intelligent source load matching method with polygonal benefit balance as target is characterized by comprising the following steps:
Step 1: constructing an electric power market structure containing various market subjects under the distribution network level, wherein the market subjects comprise a distribution network operator, a new energy power generator and a flexible load manufacturer;
Step 2: establishing a source load intelligent matching model aiming at multilateral benefit balance according to market subject characteristics: calculating the matching satisfaction of each new energy generator, flexible load and distribution network operators, and establishing a source load intelligent matching model with multilateral benefit balance as a target, wherein the target function of the source load intelligent matching model is to maximize the total matching satisfaction of the new energy generator, the flexible load and the distribution network operators, and the constraint condition is the matching transaction amount constraint;
step 3: solving the source load intelligent matching model, and executing a source load intelligent matching process taking multilateral benefit balance as a target;
In the step 2, calculating the matching satisfaction degree of each new energy generator, each flexible loader and each power distribution network operator specifically includes:
the satisfaction degree calculation formulas of the new energy generator for quotation and electricity utilization flexibility of the flexible load manufacturer are respectively as follows:
Wherein, Quotation/>, for the new energy generator P i, of the flexible loader C j P sell,G is the online electricity price of the new energy generator, formulated by the power grid,/>The highest quotation for all flexible loaders in the market, M is a positive number approaching infinity; /(I)For the satisfaction degree of the new energy generator P i on the electricity utilization flexibility f j of the flexible load manufacturer C j, f min、fmax is the minimum value and the maximum value of the commercial electricity flexibility of all flexible loads in the market; the comprehensive satisfaction degree of the new energy generator P i to the flexible load quotient C j is/>
The satisfaction degree calculation formulas of the flexible load quotient on the quotation of the new energy generator and the power generation prediction precision are respectively as follows:
Wherein, Quotation/>, for flexible loader C j, for New energy Generator P i P buy,G is the electricity selling price of the power grid,/>The lowest quotation of all new energy power generators in the market is achieved; /(I)For the satisfaction degree of the flexible load quotient C j on the historical average power generation prediction error e i of the new energy power generator P i, the error e i is used for representing the power generation prediction precision of the new energy power generator, the smaller the e i is, the higher the power generation prediction precision is, and the e min、emax is the minimum value and the maximum value of the power generation historical average prediction errors of all new energy power generators in the market; the integrated satisfaction of the flexible loader C j to the new energy generator P i is/>
Distribution network operators also have their own preferences for matching transactions in the market, expressed in terms of matching satisfaction:
Wherein, The satisfaction degree of the power distribution network operator on the power utilization flexibility f j of the flexible loader C j, the historical average power generation prediction error e i of the new energy power generator and the distance d ji between the two transaction parties is respectively that d min、dmax is the minimum value and the maximum value of the distance d ji in the matching situation of all the flexible loaders C j and the new energy power generator P i in the market; the comprehensive satisfaction of the distribution network operator on the matching of the flexible loader C j and the new energy generator P i is/>
In the step 2, the objective function of the source load intelligent matching model with polygonal benefit balance as the objective is to maximize the total matching satisfaction of the new energy generator, the flexible load manufacturer and the power distribution network operator, namely:
Wherein y ij is a decision variable, which represents the matched electric quantity of the flexible load quotient C j and the new energy power generator P i;
In the step 2, constraint conditions of the source load intelligent matching model targeting polygonal benefit balance are matched transaction amount constraints, and the method specifically comprises the following steps:
yij≥0 (13)
Wherein y ij is a decision variable, which represents the matched electric quantity of the flexible loader C j and the new energy generator P i, The total power generation amount of the new energy generator P i and the total power consumption amount of the flexible load carrier C j are respectively represented.
2. The intelligent matching method for source load targeting multilateral interest balance according to claim 1, wherein the set of new energy generator is recorded asThe new energy power generator i is represented by P i, wherein/>The set of flexible loaders is denoted/>The flexible loader j is denoted by C j, where/>Each new energy generator and each flexible load quotient are matched with a plurality of flexible load quotients, and transaction electric quantity is determined; as an independent third party, the power distribution network operators are mainly responsible for organizing the matching transaction in the market, and preferably match the new energy generator with the flexible load business, if the matching is successful, the transaction is carried out according to the matching result, otherwise, the transaction main body with failed matching carries out the transaction with the power grid.
3. The intelligent matching method for source charges targeting multilateral interest balance according to claim 1, wherein in the step 3, the intelligent matching flow for source charges targeting multilateral interest balance is as follows:
1) Each new energy power generator publishes self quotation and historical average power generation prediction errors, and each flexible load provider publishes self quotation and power utilization flexibility;
2) Each new energy power generator reports the self power generation amount and each flexible load provider reports the self power consumption amount to the power grid;
3) Based on the data published in the step 1), each new energy generator, flexible load manufacturer and distribution network operator calculate satisfaction degree of each source load matching scheme according to formulas (1) - (7);
4) Based on the data reported in the step 2) and the satisfaction calculated in the step 3), the power distribution network operators solve the matching models shown in the formulas (8) - (13) to obtain and publish matching results, each new energy generator and each flexible load provider execute a matching scheme, and the matching process is finished.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110942191A (en) * | 2019-11-22 | 2020-03-31 | 国网陕西省电力公司电力科学研究院 | Multi-target bilateral matching method in medium-and-long-term power market |
| WO2020155515A1 (en) * | 2019-01-30 | 2020-08-06 | 广东电网有限责任公司电力调度控制中心 | Blockchain-based dual-source energy internet transaction method and device |
| CN111754308A (en) * | 2020-06-28 | 2020-10-09 | 铭数科技(青岛)有限公司 | Multi-index many-to-many bilateral matching method in energy trading market |
| CN112163687A (en) * | 2020-06-28 | 2021-01-01 | 国家电网公司西北分部 | User side two-stage peak shaving response method based on load aggregation quotient |
| CN112365193A (en) * | 2020-12-01 | 2021-02-12 | 国网湖北省电力有限公司电力科学研究院 | Centralized clearing method and device for power market considering line transmission safety |
| CN112465303A (en) * | 2020-11-06 | 2021-03-09 | 上海交通大学 | Multi-agent-based bilateral power market optimization decision method considering demand response |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11055732B2 (en) * | 2018-09-12 | 2021-07-06 | Mitsubishi Electric Research Laboratories, Inc. | Day-ahead joint generation scheduling and bidding optimization for power plants |
-
2021
- 2021-09-16 CN CN202111087623.3A patent/CN113888348B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020155515A1 (en) * | 2019-01-30 | 2020-08-06 | 广东电网有限责任公司电力调度控制中心 | Blockchain-based dual-source energy internet transaction method and device |
| CN110942191A (en) * | 2019-11-22 | 2020-03-31 | 国网陕西省电力公司电力科学研究院 | Multi-target bilateral matching method in medium-and-long-term power market |
| CN111754308A (en) * | 2020-06-28 | 2020-10-09 | 铭数科技(青岛)有限公司 | Multi-index many-to-many bilateral matching method in energy trading market |
| CN112163687A (en) * | 2020-06-28 | 2021-01-01 | 国家电网公司西北分部 | User side two-stage peak shaving response method based on load aggregation quotient |
| CN112465303A (en) * | 2020-11-06 | 2021-03-09 | 上海交通大学 | Multi-agent-based bilateral power market optimization decision method considering demand response |
| CN112365193A (en) * | 2020-12-01 | 2021-02-12 | 国网湖北省电力有限公司电力科学研究院 | Centralized clearing method and device for power market considering line transmission safety |
Non-Patent Citations (2)
| Title |
|---|
| "多源异荷"配电网分布式能源交易方法研究;张虎;《中国优秀硕士学位论文全文数据库》;20220520;全文 * |
| 主动配电网双层实时优化博弈研究;姜冬;刘鲲鹏;;电测与仪表;20180925(18);全文 * |
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