CN106355344A - Method for robustly and optimally operating micro-grids on basis of orthogonal arrays - Google Patents

Abstract

The invention discloses a method for robustly and optimally operating micro-grids on the basis of orthogonal arrays. The method includes steps of extracting static data of micro grid architectures, power types, operation costs of the power types, energy storage system unit operation cots, interaction costs, time-of-use electricity prices and the like; building micro-grid uncertainty set models; screening test scenarios; building micro-grid robust and optimal operation models on the basis of the orthogonal arrays and designing solution strategies on the basis of the test scenarios so as to obtain ultimate micro-grid robust and optimal operation schemes. The method has the advantages that the grid-connection micro-grids with consideration of output of renewable energy distributed power sources and load demand uncertainty can be optimally operated by the aid of the method, and dispatching reference can be provided for operation personnel of the micro-grids.

Description

A Robust Optimal Operation Method of Microgrid Based on Orthogonal Array

technical field

The invention relates to a method for robust optimal operation of a microgrid based on an orthogonal array, and belongs to the technical field of optimal scheduling and operation of electric power systems.

Background technique

Compared with the regulation of non-renewable energy distributed power generation, the power generation form of renewable energy distributed power generation is easily affected by factors such as climate and environment, and has obvious randomness and intermittent nature. Large-scale access systems will inevitably Increasing the uncertainty in the operation of the power distribution system will also make the relationship between objectives and constraints more complicated in the research and modeling. How to adapt to the working conditions of various renewable energy distributed power sources and uncertain load demand, and realize the benign interaction and economic operation of multiple resources are the research difficulties in the energy management and optimization of microgrids. Therefore, how to effectively establish a multi-period economic model and integrate the worst scenario with the largest interaction cost to guide the realization of a robust operation plan for regional units has become an important issue that the dispatching department needs to solve urgently.

Contents of the invention

The purpose of the present invention is to provide a robust optimal operation method for a microgrid based on an orthogonal array, which realizes the optimal allocation of renewable energy distributed power sources and power distribution system resources with load uncertainty.

In order to achieve the above object, the present invention provides a method for robust optimal operation of a microgrid based on an orthogonal array, comprising steps:

(1) Extract static data such as microgrid architecture, power supply type and its operating cost, energy storage system unit operating cost, interaction cost, and time-of-use electricity price;

(2) Construct an uncertain set of renewable energy distributed power output and load demand;

(3) Screening robust test scenarios based on orthogonal arrays;

(4) Construct a robust optimization operation model of the microgrid;

(5) Design a two-stage solution strategy based on the test scenario, and obtain the final robust optimization operation scheme of the microgrid.

2. The micro-grid architecture includes: power source type, distributed energy storage system, power load, dispatch control center, and upper-level grid interface.

3. The types of distributed power supply include: non-renewable energy distributed power supply and renewable energy distributed power supply.

4. The interaction cost model represents the economic cost generated by the interaction power transmission between the microgrid and the upper-level grid.

5. The construction steps of the uncertain set of output and load demand of the renewable energy distributed power generation are:

(1) Determine the upper and lower boundary reference quantities of the uncertain set of renewable energy distributed power output according to the historical data of distributed power generation output:

According to the operating constraints of the actual renewable energy distributed power generation unit, the upper and lower boundary values of its output are corrected:

In the formula, , Respectively, the upper and lower boundary reference values of renewable energy distributed power generation, , Respectively, the upper and lower boundary reference values of the revised renewable energy distributed power generation output, Predict the base value for the distributed power output point of renewable energy, is the prediction error probability density function, for The inverse function representation method of , , Respectively, the maximum and minimum values of renewable energy distributed power output, is the confidence level parameter, , Respectively, the upper and lower bounds of the confidence level parameters;

(2) Thus, an uncertain set of distributed power generation is formed:

Similarly, the uncertain set of available load demands is:

In the formula, For renewable energy distributed power generation in the time period actual output, for the load demand in the time period the actual demand value of for the load demand in the time period point forecast base value, , Respectively, the load demand in the time period The upper and lower boundary reference values of .

6. The screening steps for robust test scenarios based on orthogonal arrays are:

(1) Divide a scheduling cycle into T periods, then the distributed renewable energy output has T discrete values in the scheduling cycle, which is recorded as: , the load demand has a total of T discrete values in the scheduling period, which is recorded as: , so there are 2×T input parameters of renewable energy distributed power generation and load demand in one scheduling cycle;

(2) Screening test scenarios: Screening through orthogonal arrays and corresponding parameter value level assignment rules;

The so-called orthogonal array (orthogonal array, OA) matrix refers to an A×B matrix composed of different input parameters corresponding to C, and an orthogonal array of strength D (0≤D≤B). If any of the matrix A×B In an A×D sub-matrix, any permutation with intensity D happens to be in Appears in a row, recorded as:

Among them, A is the size of the matrix array, which is used to refer to the number of scenarios to be tested under the current input parameter value level; B is the total number of parameters, which refers to the total number of input parameters of renewable energy distributed power generation and load demand ; C is a single set of input parameters corresponding to a value level; D is the intensity coefficient of OA;

Thus, the total number of test scenarios is obtained according to the value levels of multiple sets of different input parameters.

7. The robust optimization operation model of the microgrid is:

(1) The objective function of the microgrid robust optimization operation model is:

in, represents the total cost of the microgrid; is the unit power generation cost of a conventional generating set, with ; , is the unit power generation cost coefficient, the specific value is related to the selected generator type and its parameters, For conventional generator sets in the time period output; To control the cost of the energy storage system, there are ; Regulatory cost per unit of energy storage device; is the output power of the energy storage system; the general mathematical expression of the interaction cost is: , , , Corresponding time period The 0-1 state variables of the microgrid’s purchase and transmission to the main network, set the current hour, ,when hour, , with: ; is the interaction cost in the worst scenario, satisfying , s represents the current test scenario; S is the total test scenario; the objective function Expressed in terms of interaction costs and scenario sets, the robust optimization operation mode of the microgrid can be transformed into:

in, is the optimal value of comprehensive economic operation cost under scenario s ;

(2) The constraints of the objective function of the microgrid robust optimization operation model are:

The upper and lower limits of power that the conventional generating set needs to meet in different periods of time are:

in, , Respectively, the upper and lower limits of conventional generating set output;

The conventional unit ramp power constraint is:

in, , is the power limit for climbing up and down;

When considering the expansion model of multiple units (multiple units undertake power generation tasks) for conventional generator sets, the power generation cost model of the i -th unit is:

in, is the set of conventional genset numbers, is the start-stop state of the unit No. 1 in the period, Indicate time period No. The unit is in the power-on state, and the corresponding , Indicate time period No. The unit is in shutdown state, the corresponding ; for the first Unit start-up cost;

When the type of conventional power source is extended to multi-conventional generator sets, the relevant variable styles are modified:

in, is the unit power generation cost of multiple conventional generator sets, is the number of conventional generator sets, for the first The unit power generation cost of a conventional generating set;

The relationship between the charging and discharging power of the energy storage system and the capacity of the energy storage system is:

in, is the current capacity status of the energy storage device;

The energy storage system charging and discharging power constraints and capacity constraints are:

in, , Respectively, the upper and lower limits of the charging and discharging power of the energy storage device per unit time, , are the upper and lower limits of the capacity of the energy storage device, , is the charge-discharge cut-off rate

The constraints that the interaction power needs to satisfy are:

in, , Respectively, the upper and lower limits of interactive power transmission in time intervals;

Standby Power Constraints:

in, is the reserve power of the interactive power, reserve power for conventional generation costs, is the backup power of the energy storage system, On behalf of the system in the time period The minimum standby power that needs to be achieved is set according to the capacity of the microgrid.

8. The interaction cost reflects the consumption and utilization of renewable energy power generation resources in the multi-period system, and its influencing factors include: the interactive power between the microgrid and the upper-level grid, the price of electricity purchased, and the price of electricity sold; Influencing factors include: conventional unit cost economic indicators, electricity purchase and sale prices, and the interaction and operation status of the microgrid and the upper-level grid.

9. The steps of the two-stage solution strategy based on the test scenario are:

(1) Initialization ,for For each test scenario in , consider various constraints and optimize the solution, thus obtaining the , , and calculate ,in: Represents the initial feasible solution of the decision variable; Represents the optimal solution of the decision variable in the scenario s ; Represents the interaction cost corresponding to the optimal solution in scenario s ;

(2) order , ,according to renew , and its corresponding , ,in, Indicates the "worst" test scenario; Represents the interaction cost under the "worst scenario" of the optimal operation scheme; Represents the final solution of the decision variable; Represents the optimal economic operating cost considering the "worst scenario" robust objective;

The operation scheme corresponding to the maximum interaction cost is used as the robust optimal operation scheme of the microgrid.

The invention proposes a grid-connected micro-grid-oriented robust optimization operation model and a solution method thereof under the consideration of renewable energy type distributed power source output and load demand uncertainty. Through the interval prediction method, the uncertainty interval quantification of the renewable energy distributed power output and load demand is carried out, and the uncertainty set used for the optimization model is generated; the coordinated "source-storage" scheduling model can improve the flexibility of microgrid operation Sex and economy; using orthogonal array matrix to generate test scenarios is a simple and effective method for screening simulation scenarios.

Description of drawings

Fig. 1 is a schematic diagram of a typical microgrid architecture of the present invention;

The meanings of the symbols in the drawings and text: for the distributed renewable energy class in the time period output power, For the energy storage system in the time period output power, is the interactive power between the regional grid and the superior grid, Contribute to the conventional power unit in the regional system, is the total load demand of the system.

Specific implementation method

In the following, the orthogonal array-based robust optimization operation method of the microgrid of the present invention will be further described in detail with reference to the accompanying drawings.

The present invention provides a robust optimization operation method for a microgrid based on an orthogonal array, comprising steps:

(1) Extract static data such as microgrid architecture, power supply type and its operating cost, energy storage system unit operating cost, interaction cost, and time-of-use electricity price;

(2) Construct an uncertain set of renewable energy distributed power output and load demand;

(3) Screening robust test scenarios based on orthogonal arrays;

(4) Construct a robust optimization operation model of the microgrid;

(5) Design a two-stage solution strategy based on the test scenario, and obtain the final robust optimization operation scheme of the microgrid.

The micro-grid architecture includes: power source type, distributed energy storage system, power load, dispatch control center, and upper-level grid interface.

The types of distributed power supply include: non-renewable energy distributed power supply and renewable energy distributed power supply.

The interaction cost model represents the economic cost generated by the interaction power transmission between the microgrid and the superordinate grid.

The construction steps of the uncertain set of output and load demand of the distributed power generation of renewable energy are:

(1) Determine the upper and lower boundary reference quantities of the uncertain set of renewable energy distributed power output according to the historical data of distributed power generation output:

According to the operating constraints of the actual renewable energy distributed power generation unit, the upper and lower boundary values of its output are corrected:

In the formula, , Respectively, the upper and lower boundary reference values of renewable energy distributed power generation, , Respectively, the upper and lower boundary reference values of the revised renewable energy distributed power generation output, Predict the base value for the distributed power output point of renewable energy, is the prediction error probability density function, for The inverse function representation method of , , Respectively, the maximum and minimum values of renewable energy distributed power output, is the confidence level parameter, , Respectively, the upper and lower bounds of the confidence level parameters;

(2) Thus, an uncertain set of distributed power generation is formed:

Similarly, the uncertain set of available load demands is:

In the formula, For renewable energy distributed power generation in the time period actual output, for the load demand in the time period the actual demand value of for the load demand in the time period point forecast base value, , Respectively, the load demand in the time period The upper and lower boundary reference values of .

The screening steps of the robust test scene based on the orthogonal array are:

(1) Divide a scheduling cycle into T periods, then the distributed renewable energy output has T discrete values in the scheduling cycle, which is recorded as: , the load demand has a total of T discrete values in the scheduling period, which is recorded as: , so there are 2×T input parameters of renewable energy distributed power generation and load demand in one scheduling cycle;

(2) Screening test scenarios: Screening through orthogonal arrays and corresponding parameter value level assignment rules;

The so-called orthogonal array (orthogonal array, OA) matrix refers to an A×B matrix composed of different input parameters corresponding to C, and an orthogonal array of strength D (0≤D≤B). If any of the matrix A×B In an A×D sub-matrix, any permutation with intensity D happens to be in Appears in a row, recorded as:

Among them, A is the size of the matrix array, which is used to refer to the number of scenarios to be tested under the current input parameter value level; B is the total number of parameters, which refers to the total number of input parameters of renewable energy distributed power generation and load demand ; C is a single set of input parameters corresponding to a value level; D is the intensity coefficient of OA;

Thus, the total number of test scenarios is obtained according to the value levels of multiple sets of different input parameters.

The robust optimization operation model of the microgrid is:

(1) The objective function of the microgrid robust optimization operation model is:

in, represents the total cost of the microgrid; is the unit power generation cost of a conventional generating set, with ; , is the unit power generation cost coefficient, the specific value is related to the selected generator type and its parameters, For conventional generator sets in the time period output; To control the cost of the energy storage system, there are ; Regulatory cost per unit of energy storage device; is the output power of the energy storage system; the general mathematical expression of the interaction cost is: , , , Corresponding time period The 0-1 state variables of the microgrid’s purchase and transmission to the main network, set the current hour, ,when hour, , with: ; is the interaction cost in the worst scenario, satisfying , s represents the current test scenario; S is the total test scenario; the objective function Expressed in terms of interaction costs and scenario sets, the robust optimization operation mode of the microgrid can be transformed into:

in, is the optimal value of comprehensive economic operation cost under scenario s ;

(2) The constraints of the objective function of the microgrid robust optimization operation model are:

The upper and lower limits of power that the conventional generating set needs to meet in different periods of time are:

in, , Respectively, the upper and lower limits of conventional generating set output;

The conventional unit ramp power constraint is:

in, , is the power limit for climbing up and down;

When considering the expansion model of multiple units (multiple units undertake power generation tasks) for conventional generator sets, the power generation cost model of the i -th unit is:

in, is the set of conventional genset numbers, is the start-stop state of the unit No. 1 in the period, Indicate time period No. The unit is in the power-on state, and the corresponding , Indicate time period No. The unit is in shutdown state, the corresponding ; for the first Unit start-up cost;

When the type of conventional power source is extended to multi-conventional generator sets, the relevant variable styles are modified:

in, is the unit power generation cost of multiple conventional generator sets, is the number of conventional generator sets, for the first The unit power generation cost of a conventional generating set;

The relationship between the charging and discharging power of the energy storage system and the capacity of the energy storage system is:

in, is the current capacity status of the energy storage device;

The energy storage system charging and discharging power constraints and capacity constraints are:

in, , Respectively, the upper and lower limits of the charging and discharging power of the energy storage device per unit time, , are the upper and lower limits of the capacity of the energy storage device, , is the charge-discharge cut-off rate;

The constraints that the interaction power needs to satisfy are:

in, , Respectively, the upper and lower limits of interactive power transmission in time intervals;

Standby Power Constraints:

in, is the reserve power of the interactive power, reserve power for conventional generation costs, is the backup power of the energy storage system, On behalf of the system in the time period The minimum standby power that needs to be achieved is set according to the capacity of the microgrid.

The interaction cost reflects the consumption and utilization of renewable energy power generation resources in the multi-period system, and its influencing factors include: the interactive power between the microgrid and the upper-level grid, the price of electricity purchased, and the price of electricity sold; Influencing factors include: conventional unit cost economic indicators, electricity purchase and sale prices, and the interaction and operation status of the microgrid and the upper-level grid.

The steps of the two-stage solution strategy based on the test scenario are:

(1) Initialization ,for For each test scenario in , consider various constraints and optimize the solution, thus obtaining the , , and calculate ,in: Represents the initial feasible solution of the decision variable; Represents the optimal solution of the decision variable in the scenario s ; Represents the interaction cost corresponding to the optimal solution in scenario s ;

(2) order , ,according to renew , and its corresponding , ,in, Indicates the "worst" test scenario; Represents the interaction cost under the "worst scenario" of the optimal operation scheme; Represents the final solution of the decision variable; Represents the optimal economic operating cost considering the "worst scenario" robust objective;

The operation scheme corresponding to the maximum interaction cost is used as the robust optimal operation scheme of the microgrid.

The specific implementation method of the invention described above has further described the purpose, technical solution and beneficial effect of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and does not constitute protection for the present invention. Scope limitation. Any modifications, equivalent replacements and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims (9)

1. A microgrid robust optimization operation method based on an orthogonal array, characterized in that it comprises steps: (1) Extract static data such as microgrid architecture, distributed power unit operating cost, energy storage system unit operating cost, interaction cost, and time-of-use electricity price; (2) Construct an uncertain set of renewable energy distributed power output and load demand; (3) Screening robust test scenarios based on orthogonal arrays; (4) Construct a robust optimization operation model of the microgrid; (5) Design a two-stage solution strategy based on the test scenario, and obtain the final robust optimization operation scheme of the microgrid. 2. A method for robust optimal operation of a microgrid based on an orthogonal array according to claim 1, wherein the microgrid architecture includes: distributed power sources, distributed energy storage systems, power loads, and dispatch control Center, superior grid interface. 3. A method for robust optimal operation of a microgrid based on an orthogonal array according to claim 1, wherein the type of distributed power includes: non-renewable energy distributed power and renewable energy distributed power supply. 4. The method for robust optimal operation of a microgrid based on an orthogonal array according to claim 1, wherein the interaction cost model represents the economic cost generated by the interactive power transmission between the microgrid and the upper-level grid. 5. A method for robust optimal operation of a microgrid based on an orthogonal array according to claim 1, wherein the step of constructing an uncertain set of output and load demand of the renewable energy type distributed power supply is: (1) Determine the upper and lower boundary reference quantities of the uncertain set of renewable energy distributed power output according to the historical data of distributed power generation output: According to the operating constraints of the actual renewable energy distributed power generation unit, the upper and lower boundary values of its output are corrected: In the formula, , Respectively, the upper and lower boundary reference values of renewable energy distributed power generation, , Respectively, the upper and lower boundary reference values of the revised renewable energy distributed power generation output, Predict the base value for the distributed power output point of renewable energy, is the prediction error probability density function, for The inverse function representation method of , , Respectively, the maximum and minimum values of renewable energy distributed power output, is the confidence level parameter, , Respectively, the upper and lower bounds of the confidence level parameters; (2) Thus, an uncertain set of distributed power generation is formed: Similarly, the uncertain set of available load demands is: In the formula, For renewable energy distributed power generation in the time period actual output, for the load demand in the time period the actual demand value of for the load demand in the time period point forecast base value, , Respectively, the load demand in the time period The upper and lower boundary reference values of . 6. A kind of orthogonal array-based microgrid robust optimization operation method according to claim 1, characterized in that, the orthogonal array-based robust test scene screening step is: (1) Divide a scheduling cycle into T periods, then the distributed renewable energy output has T discrete values in the scheduling cycle, which is recorded as: , the load demand has a total of T discrete values in the scheduling period, which is recorded as: , so there are 2×T input parameters of renewable energy distributed power generation and load demand in one scheduling cycle; (2) Screening test scenarios: Screening through orthogonal arrays and corresponding parameter value level assignment rules; The so-called orthogonal array (orthogonal array, OA) matrix refers to an A×B matrix composed of different input parameters corresponding to C, and an orthogonal array of strength D (0≤D≤B). If any of the matrix A×B In an A×D sub-matrix, any permutation with intensity D happens to be in Appears in a row, recorded as: Among them, A is the size of the matrix array, which is used to refer to the number of scenarios to be tested under the current input parameter value level; B is the total number of parameters, which refers to the total number of input parameters of renewable energy distributed power generation and load demand ; C is a single set of input parameters corresponding to a value level; D is the intensity coefficient of OA; Thus, the total number of test scenarios is obtained according to the value levels of multiple sets of different input parameters. 7. A kind of orthogonal array-based microgrid robust optimization operation method according to claim 1, characterized in that, the robust optimization operation model of the microgrid is: (1) The objective function of the microgrid robust optimization operation model is: in, represents the total cost of the microgrid; is the unit power generation cost of a conventional generating set, with ; , is the unit power generation cost coefficient, the specific value is related to the selected generator type and its parameters, For conventional generator sets in the time period output; To control the cost of the energy storage system, there are ; Regulatory cost per unit of energy storage device; is the output power of the energy storage system; the general mathematical expression of the interaction cost is: , , , Corresponding time period The 0-1 state variables of the microgrid’s purchase and transmission to the main network, set the current hour, ,when hour, , with: ; is the interaction cost in the worst scenario, satisfying , s represents the current test scenario; S is the total test scenario; the objective function Expressed in terms of interaction costs and scenario sets, the robust optimization operation mode of the microgrid can be transformed into: in, is the optimal value of comprehensive economic operation cost under scenario s ; (2) The constraints of the objective function of the microgrid robust optimization operation model are: The upper and lower limits of power that the conventional generating set needs to meet in different periods of time are: in, , Respectively, the upper and lower limits of conventional generating set output; The conventional unit ramp power constraint is: in, , is the power limit for climbing up and down; When considering the expansion model of multiple units (multiple units undertake power generation tasks) for conventional generator sets, the power generation cost model of the i -th unit is: in, is the set of conventional genset numbers, is the start-stop state of the unit No. 1 in the period, Indicate time period No. The unit is in the power-on state, and the corresponding , Indicate time period No. The unit is in shutdown state, the corresponding ; for the first Unit start-up cost; When the type of conventional power source is extended to multi-conventional generator sets, the relevant variable styles are modified: in, is the unit power generation cost of multiple conventional generator sets, is the number of conventional generator sets, for the first The unit power generation cost of a conventional generating set; The relationship between the charging and discharging power of the energy storage system and the capacity of the energy storage system is: in, is the current capacity status of the energy storage device; The energy storage system charging and discharging power constraints and capacity constraints are: in, , Respectively, the upper and lower limits of the charging and discharging power of the energy storage device per unit time, , are the upper and lower limits of the capacity of the energy storage device, , is the charge-discharge cut-off rate; The constraints that the interaction power needs to satisfy are: in, , Respectively, the upper and lower limits of interactive power transmission in time intervals; Standby Power Constraints: in, is the reserve power of the interactive power, reserve power for conventional generation costs, is the backup power of the energy storage system, On behalf of the system in the time period The minimum standby power that needs to be achieved is set according to the capacity of the microgrid. 8. A method for robust optimal operation of a microgrid based on an orthogonal array according to claim 7, wherein the interaction cost is used to reflect the consumption and utilization of renewable energy power generation resources in a multi-period system, and its impact Factors include: the interactive power between the microgrid and the upper-level grid, electricity purchase price, and electricity sales price; among them, the influencing factors of the interactive power between the micro-grid and the upper-level grid include: conventional unit cost economic indicators, electricity purchase and sale prices, and the interaction between the micro-grid and the upper-level grid. and operating status. 9. a kind of orthogonal array-based microgrid robust optimization operation method according to claim 1, is characterized in that, described two-stage solution strategy step based on test scene is: (1) Initialization ,for For each test scenario in , consider various constraints and optimize the solution, thus obtaining the , , and calculate ,in: Represents the initial feasible solution of the decision variable; Represents the optimal solution of the decision variable in the scenario s ; Represents the interaction cost corresponding to the optimal solution in scenario s ; (2) order , ,according to renew , and its corresponding , ,in, Indicates the "worst" test scenario; Represents the interaction cost under the "worst scenario" of the optimal operation scheme; Represents the final solution of the decision variable; Represents the optimal economic operating cost considering the "worst scenario" robust objective; The operation scheme corresponding to the maximum interaction cost is used as the robust optimal operation scheme of the microgrid.