CN114358361A - Multi-time scale optimization scheduling method for micro-grid considering demand response - Google Patents

Multi-time scale optimization scheduling method for micro-grid considering demand response Download PDF

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CN114358361A
CN114358361A CN202111188389.3A CN202111188389A CN114358361A CN 114358361 A CN114358361 A CN 114358361A CN 202111188389 A CN202111188389 A CN 202111188389A CN 114358361 A CN114358361 A CN 114358361A
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程超
马瑞光
陈博
李达
魏俊
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State Grid Sichuan Economic Research Institute
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract

The invention discloses a micro-grid multi-time scale optimization scheduling method considering demand response, and relates to the field of micro-grid optimization. The invention provides a micro-grid multi-time scale optimization scheduling method considering demand response, which comprises the following steps of: s101, reading day-ahead prediction data and constructing a day-ahead scheduling optimization model by taking reduction of operation cost in a first operation period of the microgrid as an optimization target; s102, discretizing the first operation period into a plurality of second operation periods in equal time length, taking the operation cost in the second operation period of the microgrid as an optimization target, reading real-time prediction data, and constructing a real-time scheduling optimization model; s103, solving a day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme; and S104, solving the real-time scheduling optimization model based on the day-ahead scheduling optimization scheme to obtain the real-time scheduling optimization scheme of each power supply. The method provided by the invention can effectively improve the autonomous performance of the microgrid and ensure that the microgrid can run more safely and economically.

Description

Multi-time scale optimization scheduling method for micro-grid considering demand response
Technical Field
The invention relates to the technical field of optimization of a micro-grid, in particular to a micro-grid multi-time scale optimization scheduling method and system considering demand response and electronic equipment.
Background
In recent years, distributed power generation resources represented by wind power and photovoltaic are applied on a large scale due to the fact that problems such as shortage of traditional fossil energy and pollution are becoming serious. However, the output of distributed power sources such as wind and light has randomness and uncertainty, and when the distributed power sources are incorporated into a power grid in a large scale, the distributed power sources can bring huge challenges to the operation of a power system. The micro-grid is used as a controllable unit integrating control devices of a distributed power supply, a load, energy storage and the like, self management and control can be realized, and the problems of wind abandonment, light abandonment and the like caused by the fact that the high-permeability distributed power supply is connected into the power grid can be effectively solved by accurately predicting the load size and the new energy output and formulating a reasonable scheduling plan. However, due to the shortage of the prediction technology at the present stage, the prediction error of the day-ahead output of new energy such as wind, light and the like usually reaches 20-30%. Therefore, it is difficult to cope with the real-time occurrence of "source-load" mismatch only by day-ahead scheduling of the microgrid.
Disclosure of Invention
In order to overcome the above problems or partially solve the above problems, an object of the present invention is to provide a method, a system, and an electronic device for optimizing and scheduling a microgrid with multiple time scales, which take demand responses into consideration, so as to improve the autonomous performance of the microgrid and ensure that the microgrid can operate more safely and economically.
The invention is realized by the following technical scheme:
in a first aspect, the invention provides a microgrid multi-time scale optimization scheduling method considering demand response, which includes the following steps: s101, reading day-ahead prediction data and constructing a day-ahead scheduling optimization model by taking reduction of operation cost in a first operation period of the microgrid as an optimization target; s102, dispersing the first operation period into a plurality of second operation periods in an equal-time-length mode, taking the operation cost in the second operation period of the micro-grid as an optimization target, reading real-time prediction data, and constructing a real-time scheduling optimization model; s103, solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme; and S104, solving the real-time scheduling optimization model based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
Based on the first aspect, in some embodiments of the present invention, the objective function of the day-ahead scheduling optimization model is minC1＝min(Cunits+Cbess+Cbuy) In the formula, minC1Represents the lowest operating cost, C, in the first operating cycleunitsRepresenting the operating cost of the internal unit of the microgrid; cbessRepresents the operating cost of the storage battery; cbuyRepresenting the cost of the micro grid to purchase electricity from the grid.
Based on the first aspect, in some embodiments of the present invention, the constraints of the day-ahead scheduling optimization model include: the method comprises the following steps of power balance constraint, unit output constraint, tie line power constraint, unit start-stop time constraint, unit climbing constraint and storage battery operation constraint.
Based on the first aspect, in some embodiments of the present invention, before the building the real-time scheduling optimization model, the method further includes: and constructing an excitation demand response dynamic capacity model.
Based on the first aspect, in some embodiments of the present invention, the expression of the incentive demand response dynamic capacity model is:
Figure RE-GDA0003466659610000021
wherein E is an incentive-type demand response capacity incentive price elastic matrix, PtIndicating the excitation at time t in the second operating cycleExcitation type demand response capacity, Δ PtRepresenting the variation of the excitation-type demand response capacity at time t, C, in the second operating periodtRepresenting the price of excitation, Δ C, at time t during the second operating cycletRepresenting the amount of change in incentive price at time t during the second operational period.
Based on the first aspect, in some embodiments of the present invention, the objective function of the real-time scheduling optimization model is: MinC2＝min(Cunits+Cbess+Cbuy+Cibdr+Cpunish),CunitsRepresenting the operating cost of the internal unit of the microgrid; cbessRepresents the operating cost of the storage battery; cbuyRepresenting the purchase of electricity from the microgrid to the grid, CibdrRepresenting incentive-type demand response scheduling cost, CpunishRepresenting a penalty cost to constrain the real-time contribution adjustment sequence.
Based on the first aspect, in some embodiments of the invention, the incentive type demand response scheduling cost CibdrThe expression of (a) is:
Figure RE-GDA0003466659610000022
where n represents the total number of users participating in the incentive-type demand response,
Figure RE-GDA0003466659610000023
and
Figure RE-GDA0003466659610000024
respectively representing the scheduled demand response down-regulation capacity and the scheduled demand response up-regulation capacity of the user i at the time t;
Figure RE-GDA0003466659610000025
and
Figure RE-GDA0003466659610000026
showing that the user i adjusts the incentive price downwards and increases the incentive price at the moment t; t represents a second operating cycle; Δ T represents a time interval;
the penalty cost CpunishThe expression of (a) is:
Figure RE-GDA0003466659610000027
in the formula, S represents a micro-grid distributed power supply set; mu.sS、μbuyAnd muibdrOutput deviation punishment coefficients respectively representing the distributed power supply, the tie line power and the excitation type demand response of the microgrid, and used for constraining the real-time output adjustment sequence to meet the requirement of mubuy＞μS＞μibdr；Ps(t)、Pbuy(t) respectively representing the output of the micro-grid power supply and the power purchased by the power grid at the real-time t moment;
Figure RE-GDA0003466659610000028
and
Figure RE-GDA0003466659610000029
and respectively representing the output of the micro-grid power supply and the purchased electric quantity of the power grid at the moment t before the day.
In a second aspect, the present invention provides a microgrid multi-time scale optimized scheduling system considering demand response, including: a first model building module: the method is used for reading day-ahead prediction data and constructing a day-ahead scheduling optimization model; a second model building module: the real-time scheduling optimization model is constructed by reading real-time prediction data; the first solving module: the system is used for solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme; the second solving module: and the real-time scheduling optimization model is used for solving based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
In a third aspect, the present invention provides an electronic device comprising: at least one processor, at least one memory, and a data bus; the processor and the memory complete mutual communication through the data bus; the memory stores program instructions executable by the processor, which are called by the processor to perform one or more of the procedures or methods described above, such as performing: s101, reading day-ahead prediction data and constructing a day-ahead scheduling optimization model by taking reduction of operation cost in a first operation period of the microgrid as an optimization target; s102, dispersing the first operation period into a plurality of second operation periods in an equal-time-length mode, taking the operation cost in the second operation period of the micro-grid as an optimization target, reading real-time prediction data, and constructing a real-time scheduling optimization model; s103, solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme; and S104, solving the real-time scheduling optimization model based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
Compared with the prior art, the invention at least has the following advantages and beneficial effects:
(1) the output adjustment sequence of the dispatching plan is considered in the real-time stage, the real-time tie line plan can be guaranteed to follow the day-ahead dispatching plan, the influence on the power grid production and the dispatching plan is reduced, and meanwhile the autonomous performance of the micro-power grid is improved.
(2) The dynamic incentive type demand response participation real-time scheduling strategy is provided, the enthusiasm of users participating in incentive type demand response (IBDR) is mobilized to a greater extent, the users are encouraged to participate in the incentive type demand response (IBDR) to the greatest extent, the power deviation of a microgrid is further eliminated, the user benefit is improved, and the real-time operation cost is reduced.
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In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort. In the drawings:
FIG. 1 is a block flow diagram of an embodiment of a micro grid multi-time scale optimization scheduling method that accounts for demand response;
FIG. 2 is a time line diagram of a microgrid two-stage optimization scheduling model in an embodiment of a microgrid multi-time scale optimization scheduling method considering demand response;
FIG. 3 is a schematic diagram illustrating incentive demand response incentive price adjustment in an embodiment of a demand response-based microgrid multi-time scale optimized scheduling method;
FIG. 4 is a graph of stimulated demand response declared capacity in a test example;
FIG. 5 is a graph of predicted power curves of wind power, photovoltaic and load at various time intervals in a test example;
FIG. 6 is a graph of a result of a day-ahead scheduling optimization in a test example;
FIG. 7 is a graph showing a result of real-time scheduling optimization in a test example;
FIG. 8 is a graph of tie line power tracking in the experimental example;
FIG. 9 is a graph of user-stimulated demand response capacity calls for a test example;
FIG. 10 is a graph showing a comparison of the tie-line tracking effect in the comparative example;
FIG. 11 is a block diagram of a micro-grid multi-time scale optimization scheduling system that accounts for demand response;
fig. 12 is a block diagram of an electronic device.
Icon: 1-a processor; 2-a memory; 3-a data bus; 100-a first model building module; 200-a second model building module; 300-a first solving module; 400-second solving module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
Referring to fig. 1, fig. 2 and fig. 3, an embodiment of the present invention provides a micro grid multi-time scale optimization scheduling method considering demand response, including the following steps:
s101, reading day-ahead prediction data and constructing a day-ahead scheduling optimization model by taking reduction of operation cost in a first operation period of the microgrid as an optimization target;
for example, in this embodiment, the day-ahead scheduling plan takes 1h as a resolution, according to day-ahead prediction data, takes the minimum total operation cost of the microgrid within one day (24h) in the first operation period as an optimization target, takes the operation constraint conditions of each power supply in the microgrid into consideration, and makes an hourly output plan of each power supply of the microgrid on the day of operation (24h) in the day-ahead stage, so as to provide a reference for real-time scheduling. The day-ahead prediction data comprises the day-ahead prediction data of wind energy and light energy.
Constructing a day-ahead scheduling optimization model:
1) day-ahead scheduling optimization objective function
The day-ahead economic dispatching takes the minimum total operation cost of the micro-grid in an operation period (24h) as an optimization target, and the objective function is as follows:
minC1＝min(Cunits+Cbess+Cbuy) (1)
in the above formula, CunitsRepresenting the operating cost of the internal unit of the microgrid; cbessRepresents the operating cost of the storage battery; cbuyThe system represents the cost of the micro-grid for purchasing electricity from the power grid, and does not consider the condition that the micro-grid sells electricity from the power grid. Operating cost of the unit CunitsThe method mainly comprises the fuel cost, the maintenance cost, the starting cost and the pollutant treatment cost of the unit, as shown in the formula (2).
Figure RE-GDA0003466659610000041
In the formula, Pi(t) represents the power of the unit i at the time t;
Figure RE-GDA0003466659610000042
representing the maintenance cost of the unit i of power; cfuel(Pi(t)) is with respect to Pi(t) a non-linear function representing the fuel cost of the unit i over a period t; siRepresenting the starting cost of the unit i;
Figure RE-GDA0003466659610000043
represents the starting state of the unit i at the moment t and is 0-1 variable, wherein 1 represents that the unit is in a starting state, and 0 represents that the unit is not in the starting state; vjRepresents the treatment cost of the unit discharge amount of the jth pollutant, WijRepresenting the class j pollutant emission of the unit i.
Operating cost of accumulator CbessThe method mainly comprises the maintenance cost and the service life loss cost of the storage battery, as shown in the formula (3).
Figure RE-GDA0003466659610000051
Wherein the content of the first and second substances,
Figure RE-GDA0003466659610000052
and
Figure RE-GDA0003466659610000053
respectively representing the maintenance cost and depreciation cost of the unit power of the storage battery; pdis(t) and Pch(t) represents the discharge power and the charge power of the battery at time t, respectively.
The electricity purchasing cost of the power grid is as follows:
Cbuy＝Pbuy(t)Cgrid(t)ΔT (4)
wherein, Pbuy(t) power purchasing from the microgrid to the power grid at the moment t, CgridAnd (t) is the unit electricity price of electricity purchased from the microgrid to the power grid at the moment t.
2) Day-ahead scheduling optimization model constraint conditions
Power balance constraint
Figure RE-GDA0003466659610000054
Tie line power constraint
Figure RE-GDA0003466659610000055
Unit start-stop time constraints
Figure RE-GDA0003466659610000056
Figure RE-GDA0003466659610000057
Unit climbing restraint
Figure RE-GDA0003466659610000058
Battery operating constraints
Figure RE-GDA0003466659610000059
In the formula, Pw(t)、Ppv(t) and PL(t) respectively representing the wind power, photovoltaic output and load at the time t; u shapei(t) represents the running state of the unit at the moment t;
Figure RE-GDA0003466659610000061
and
Figure RE-GDA0003466659610000062
representing the minimum and maximum output of the unit i;
Figure RE-GDA0003466659610000063
representing the maximum purchasing power of the microgrid;
Figure RE-GDA0003466659610000064
and
Figure RE-GDA0003466659610000065
respectively representing the minimum continuous startup and shutdown time of the unit i;
Figure RE-GDA0003466659610000066
and
Figure RE-GDA0003466659610000067
representing the starting state and the shutdown state of the unit at the moment t;
Figure RE-GDA0003466659610000068
and
Figure RE-GDA0003466659610000069
representing the maximum climbing rate and the maximum descending rate of the unit; e (t) represents the capacity of the battery at time t; eminAnd EmaxRepresenting the minimum and maximum capacities of the battery;
Figure RE-GDA00034666596100000610
and
Figure RE-GDA00034666596100000611
represents the maximum value of the charging power and the discharging power of the storage battery; u shapech(t) and Udis(t) represents the state of charge and the state of discharge of the battery at time t.
S102, dispersing the first operation period into a plurality of second operation periods in an equal-time-length mode, taking the operation cost in the second operation period of the micro-grid as an optimization target, reading real-time prediction data, and constructing a real-time scheduling optimization model;
for example, as shown in fig. 2, in the present embodiment, the first operation period (24h) is discretized into 24 second operation periods (1h), and the real-time prediction data includes real-time prediction data of wind energy and light energy. And performing rolling optimization on the real-time scheduling plan every 1h by taking 15min as resolution and 1h as an operation period according to the real-time prediction data. And the real-time scheduling is to correct the day-ahead scheduling output plan by taking the minimum operating cost in each operating period as a target on the basis of day-ahead scheduling to obtain a corrected value of the power output plan in each operating period.
When the micro-grid has real-time power deviation, the adjustment of the load side resource does not influence the interior of the micro-grid and the dispatching plan of the power grid, so that excitation type demand response (IBDR) is firstly put into to stabilize power fluctuation. In addition, the influence of large-range fluctuation of the power of the tie line on the scheduling and production plan of the power grid is reduced, and meanwhile the autonomous performance of the micro-power grid is improved. When the excitation type demand response resources are not enough to stabilize fluctuation, the day-ahead output plan of the internal power supply of the microgrid is ensured to be adjusted on the basis of not changing the operation state of the internal power supply of the microgrid. When the above measures still cannot stabilize the power fluctuation, the power deviation is eliminated by finally considering the change of the day-ahead tie line power. The order of adjustment of the real-time dispatch plan presented herein is therefore: and (4) invoking an excitation type demand response > adjusting the output of the internal unit of the microgrid and the charging and discharging power of the storage battery > changing the power of the day-ahead tie line.
Preferably, before constructing the real-time scheduling optimization model, the method further comprises: constructing an excitation demand response dynamic capacity model;
1. constructing an incentive-type demand response (IBDR) model
The IBDR is under contract with users by the micro-grid, and the capacity up-regulation and the capacity down-regulation are directly controlled by a dispatching center, so that the real-time power deviation can be effectively stabilized. Considering that the actual IBDR calling condition is related to the response speed of the IBDR, in order to fully improve the positivity of the user for participating in the IBDR, the real-time power deviation of the micro-grid is better stabilized. The embodiment of the invention provides a new idea for a user to participate in IBDR: for the IBDR with a high response speed, the micro-grid adjusts the incentive price of the IBDR according to the real-time power deviation, and allows a user to report the IBDR capacity of the future 1h operation period again 1h before the rolling optimization every time, so that the purpose that the IBDR capacity can be increased or reduced along with the trend of the power deviation is achieved; and for the IBDR with the slower response speed, the IBDR is executed according to the contract capacity declared day before.
The incentive price adjustment for IBDR is related to its real-time power deviation, and the concept of payload increment is first defined to describe the power deviation:
Figure RE-GDA0003466659610000071
wherein P isL(t) and
Figure RE-GDA0003466659610000072
respectively representing the real-time load and the day-ahead load at the time t; pw(t) and
Figure RE-GDA0003466659610000073
respectively representing the real-time wind power output and the day-ahead output at the time t; ppv(t) and
Figure RE-GDA0003466659610000074
and respectively representing photovoltaic real-time and day-ahead output at the moment t.
ΔPL(t) > 0 represents that the load is greater than the power output at the moment t, so that the IBDR down-regulation incentive price needs to be increased, and the user is encouraged to reduce the power load; in the same way, Δ PL(t) < 0 indicates that the load is less than the power output at the time t, so that the up-regulation incentive price of the IBDR needs to be increased, and the user is encouraged to increase the power load; the incentive price adjustment for the IBDR takes a stepwise form (as shown in figure 3).
The compensation cost for user i participating in the IBDR is as follows:
Figure RE-GDA0003466659610000075
wherein the content of the first and second substances,
Figure RE-GDA0003466659610000076
and
Figure RE-GDA0003466659610000077
respectively representing the scheduled demand response down-regulation capacity and the scheduled demand response up-regulation capacity of the user i at the time t;
Figure RE-GDA0003466659610000078
and
Figure RE-GDA0003466659610000079
and the user i is shown to adjust the incentive price downwards and upwards at the moment t.
Price type demand response affects the electricity usage of a consumer by adjusting the electricity price, and usually a price demand elastic coefficient is used to describe the impact of the rate of change of electricity price on the rate of change of load of the consumer. The analogy price demand elasticity coefficient introduces the IBDR capacity demand elasticity coefficient to describe the influence of the IBDR incentive price on the IBDR declaration capacity of the user after being changed.
Figure RE-GDA00034666596100000710
Figure RE-GDA00034666596100000711
εiiAnd εijRespectively, an auto-elasticity coefficient and a cross-elasticity coefficient, respectively representing the response of the user to the IBDR reported volume at the current moment and other moments after the incentive price is changed, wherein the auto-elasticity coefficient epsiloniiThe value should be positive, the cross-elastic coefficient εijThe value should be negative; delta PiAnd Δ CiRespectively representing the IBDR capacity P at time iiAnd incentive price CiThe amount of change in (c).
The response of the user's IBDR declared capacity to the incentive price can thus be derived as follows:
Figure RE-GDA00034666596100000712
wherein
Figure RE-GDA0003466659610000081
Incentive of price elastic matrix for IBDR Capacity, PtRepresenting the stimulated demand response Capacity, Δ P, at time t during the second operating cycletRepresenting the variation of the excitation-type demand response capacity at time t, C, in the second operating periodtRepresenting the price of excitation, Δ C, at time t during the second operating cycletRepresenting the amount of change in incentive price at time t during the second operational period.
2. Building real-time scheduling optimization model
1) Real-time scheduling optimized objective function
The objective function of the real-time scheduling optimization is still the minimum of the total operation cost of the micro-grid in the operation period, but the scheduling cost of the real-time operation IBDR and the penalty cost for additionally restricting the real-time output adjustment sequence need to be considered.
minC2＝min(Cunits+Cbess+Cbuy+Cibdr+Cpunish) (17)
Wherein, IBDR scheduling cost CibdrAnd a penalty cost CpunishThe expression is as follows:
Figure RE-GDA0003466659610000082
Figure RE-GDA0003466659610000083
wherein n represents the total number of users participating in the IBDR; s represents a micro-grid distributed power supply set; mu.sS、μbuyAnd muibdrRespectively representing output deviation punishment coefficients of the distributed power supply, the tie line power and the IBDR of the microgrid, and being used for constraining the real-time output adjustment sequence and meeting the requirement of mubuy＞μS＞μibdr；Ps(t)、Pbuy(t) respectively representing the output of the micro-grid power supply and the power purchased by the power grid at the real-time t moment;
Figure RE-GDA0003466659610000084
and
Figure RE-GDA0003466659610000085
and respectively representing the output of the micro-grid power supply and the purchased electric quantity of the power grid at the moment t before the day.
2) Real-time scheduling optimization model constraint conditions
Power balance constraint
Figure RE-GDA0003466659610000086
IBDR invocation capacity constraint
Figure RE-GDA0003466659610000087
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003466659610000088
and
Figure RE-GDA0003466659610000089
respectively representing the maximum IBDR down-regulation capacity and up-regulation capacity of the user i at the time t;
mutual exclusion constraint
Figure RE-GDA0003466659610000091
In the formula (I), the compound is shown in the specification,
Figure RE-GDA0003466659610000092
Figure RE-GDA0003466659610000093
respectively representing the IBDR down call state and up call state of the user i at the time t.
Distributed power supply output state constraint
Figure RE-GDA0003466659610000094
Figure RE-GDA0003466659610000095
Figure RE-GDA0003466659610000096
Representing the running state of the unit i at the moment t before the day;
Figure RE-GDA0003466659610000097
and
Figure RE-GDA0003466659610000098
respectively showing the charging and discharging states of the storage battery at the moment t before the day.
In addition, the unit operation constraint, the electricity purchasing constraint and the storage battery operation constraint which should meet the day-ahead scheduling are shown as follows.
Figure RE-GDA0003466659610000099
S103, solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme;
and converting the day-ahead scheduling optimization model of the micro-grid into a Mixed Integer Linear Programming (MILP) model after linearization treatment, and calling a CPLEX solver by using MATLAB to solve the day-ahead scheduling optimization model so as to obtain a day-ahead scheduling optimization scheme of each power supply.
And S104, solving the real-time scheduling optimization model based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
And converting the real-time scheduling optimization model into a Mixed Integer Quadratic Programming (MIQP) model. And (4) combining the day-ahead scheduling optimization scheme obtained in the step (S103), and calling a CPLEX solver by using MATLAB to solve the model so as to obtain a real-time scheduling optimization scheme of each power supply and perform rolling optimization.
Example 2
Referring to fig. 11, an embodiment of the present invention provides a micro-grid multi-time scale optimization scheduling system considering demand response, including a first model building module 100: the method is used for reading day-ahead prediction data and constructing a day-ahead scheduling optimization model; the second model building module 200: the real-time scheduling optimization model is constructed by reading real-time prediction data; the first solving module 300: the system is used for solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme; the second solving module 400: and the real-time scheduling optimization model is used for solving based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
The system provided by the embodiment of the present invention can be used for executing the method described in the above embodiment 1, specifically see embodiment 1. And will not be described in detail herein.
Example 3
Referring to fig. 12, an embodiment of the invention provides an electronic device, including: at least one processor 1, at least one memory 2 and a data bus 3; wherein, the processor 1 and the memory 2 complete the communication with each other through the data bus 3; the memory 2 stores program instructions executable by the processor 1, and the processor 1 calls the program instructions to execute the method in the embodiment, for example, to execute: s101, reading day-ahead prediction data and constructing a day-ahead scheduling optimization model by taking reduction of operation cost in a first operation period of the microgrid as an optimization target; s102, dispersing the first operation period into a plurality of second operation periods in an equal-time-length mode, taking the operation cost in the second operation period of the micro-grid as an optimization target, reading real-time prediction data, and constructing a real-time scheduling optimization model; s103, solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme; and S104, solving the real-time scheduling optimization model based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
Fig. 12 is a schematic structural block diagram of an electronic device according to an embodiment of the present application. The electronic device comprises a memory 2, a processor 1 and a data bus 3, the memory 2, the processor 1 and the data bus 3 being electrically connected to each other, directly or indirectly, to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 2 can be used for storing software programs and modules, such as program instructions/modules corresponding to the electronic device provided in the embodiments of the present application, and the processor 1 executes the software programs and modules stored in the memory 2, thereby executing various functional applications and data processing. The data bus 3 can be used for signaling or data communication with other node devices.
Test examples
The micro-grid scheduling optimization according to the embodiment of the invention is described in detail in the following by specific examples.
The test example of the invention adopts the data of a small-sized micro-grid in a certain area as an example for simulation, and the area is provided with 1 photovoltaic power generation system, a wind turbine generator set, a micro gas turbine, a fuel cell, a diesel generator and a storage battery. The relevant data are shown in table 1, table 2, fig. 4 and fig. 5:
TABLE 1 distributed Power supply parameters
Figure RE-GDA0003466659610000111
TABLE 2 Battery parameters
Figure RE-GDA0003466659610000112
And (4) optimizing the result:
1) scheduling results day ahead
According to the day-ahead scheduling optimization strategy, the output of each distributed power supply and the power of the tie line of the day-ahead micro-grid can be obtained as shown in fig. 6.
As can be seen from fig. 6, when the load power is low during the time from 01:00 to 07:00, the micro grid mainly maintains power balance by purchasing power to the grid, and simultaneously charges the storage battery. When the power rate is in the peak time period of the power rate at the moment of 08: 00-11: 00, the electricity purchasing power is obviously reduced, the load requirement is met mainly by the output of an internal unit of the microgrid, and meanwhile, the storage battery is in a discharging state, so that the purpose of 'peak valley profit sharing' is realized, and the running economy of the microgrid is improved. The time from 13:00 to 17:00 is in the electricity price flat period, because the fuel cell has the highest electricity generation cost in the period, and the load requirement is mainly met by the micro gas turbine, the diesel generator and the electricity purchasing power. The time from 21:00 to 23:00 is in the peak period of the electricity price, and the load power is low at the moment, so that the load balance can be met by the output of the internal unit of the microgrid completely, and the power purchasing to the microgrid is not needed any more.
2) Real-time scheduling results
And performing rolling optimization every 1h by taking 15min as resolution and 1h as an operation period according to the real-time prediction data in the real-time scheduling.
The real-time scheduling optimization of the micro-grid is essentially correction of real-time power deviation, and it can be seen from fig. 7 and 8 that the real-time scheduling optimization result of the micro-grid has high similarity with a day-ahead scheduling result, only a small part of changes occur in the output of a unit and the power of a storage battery, the power of a tie line is basically consistent with the day-ahead condition, the power deviation mainly adjusts through excitation type demand response (IBDR) and a power supply inside the micro-grid, the real-time tie line power is guaranteed to follow the day-ahead scheduling plan to the maximum extent, and the influence of the change of the tie line power on the scheduling and production plan of the grid is reduced. It can be seen from fig. 9 that after the incentive type demand response (IBDR) participates in the real-time scheduling of the microgrid, the net load increment Δ PLObviously reduces the power deviation and reduces the regulation pressure of the real-time scheduling of the micro-grid.
Comparative example
To verify the superiority of the method provided by the present invention, 2 comparison strategies were constructed for comparison.
Strategy 1: the dynamic incentive type demand response (IBDR) proposed herein participates in real-time scheduling.
Strategy 2: static incentive type demand response (IBDR) participates in real-time scheduling, with incentive prices and user capacity being fixed values.
Strategy 3: an incentive-free demand response (IBDR) participates in real-time scheduling.
To compare the effects of the 3 strategies described above, an analysis will be made of both the economics of operation and the tie-line power tracking effects. The economy is embodied by the operation cost of the micro-grid and the economic benefit obtained by the participation of a user in incentive type demand response (IBDR); the tie line power tracking effect is embodied by Root Mean Square Error (RMSE), which is a regression evaluation index in the field of statistics and is used for measuring the deviation degree of two groups of data, and the calculation formula is as follows:
Figure RE-GDA0003466659610000121
where m is the total number of data, yiThe ith data to be compared is compared with the data to be compared,
Figure RE-RE-GDA0003466659610000122
is the ith original data.
TABLE 3 comparison of the operating effects of different strategies
Figure RE-GDA0003466659610000123
From fig. 10 and table 3, it can be seen that the real-time dispatch tie line power without incentive demand response (IBDR) participation is much different from the day-ahead plan, the RMSE reaches 5.24kW, and the microgrid real-time operation cost is 67.73% and 60.44% higher than that of strategy 1 and strategy 2, respectively. This is mainly because the microgrid's own regulation capability is limited without the involvement of incentive type demand response (IBDR), which causes significant penalty costs by having to change the day-ahead tie power plan in order to maintain power balance. Strategy 1 not only reduces the operating cost and RMSE by 4.35%, 14.80%, respectively, compared to strategy 2, but also increases the revenue of user engagement in incentive-based demand response (IBDR) by 28.17%. The incentive price adjustment can fully mobilize the enthusiasm of the users for participating in incentive type demand response (IBDR), guide the users to participate in the incentive type demand response (IBDR) with the optimal capacity, eliminate the power deviation to a greater extent, and further reduce the operation cost caused by the future prediction error while improving the self-income.
And (4) conclusion: aiming at the uncertainty of the output and the load of the new energy in the optimized operation of the microgrid, the method provided by the invention initially obtains the following conclusion through example simulation:
(1) the constructed power grid two-stage optimization scheduling model can effectively stabilize power fluctuation caused by a prediction error in the day ahead, can ensure that a real-time tie line plan follows the day ahead scheduling plan, reduces the influence on power grid production and the scheduling plan, and improves the autonomous performance of the micro-power grid.
(2) Compared with static incentive type demand response (IBDR), the dynamic incentive type demand response (IBDR) can better mobilize the enthusiasm of the user for participating in incentive type demand response (IBDR), encourages the user to participate in incentive type demand response (IBDR) to the maximum extent, further eliminates the power deviation of the microgrid, improves the user benefit and reduces the real-time operation cost.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A multi-time scale optimization scheduling method of a micro-grid considering demand response is characterized by comprising the following steps:
with the reduction of the operation cost of the micro-grid in the first operation period as an optimization target, reading day-ahead prediction data and constructing a day-ahead scheduling optimization model;
the first operation period is subjected to equal-length discretization to form a plurality of second operation periods, the operation cost in the second operation period of the microgrid is reduced to serve as an optimization target, real-time prediction data are read, and a real-time scheduling optimization model is constructed;
solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme;
and solving the real-time scheduling optimization model based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
2. The microgrid multi-time scale optimized scheduling method considering demand response as claimed in claim 1, wherein an objective function of the day-ahead scheduling optimization model is minC1＝min(Cunits+Cbess+Cbuy) In the formula, minC1Represents the lowest operating cost, C, in the first operating cycleunitsRepresenting the operating cost of the internal unit of the microgrid; cbessRepresents the operating cost of the storage battery; cbuyPower purchase from the grid on behalf of the microgridThe cost of (2).
3. The microgrid multi-time scale optimized scheduling method considering demand response as claimed in claim 2, wherein the constraint conditions of the day-ahead scheduling optimization model include: the method comprises the following steps of power balance constraint, unit output constraint, tie line power constraint, unit start-stop time constraint, unit climbing constraint and storage battery operation constraint.
4. The microgrid multi-time scale optimized scheduling method considering demand response of claim 1, wherein before constructing the real-time scheduling optimization model, further comprising: and constructing an excitation demand response dynamic capacity model.
5. The microgrid multi-time scale optimized scheduling method considering demand response as claimed in claim 4, wherein the expression of the incentive demand response dynamic capacity model is as follows:
Figure FDA0003300224640000011
wherein E is an incentive-type demand response capacity incentive price elastic matrix, PtRepresenting the stimulated demand response Capacity, Δ P, at time t during the second operating cycletRepresenting the variation of the excitation-type demand response capacity at time t, C, in the second operating periodtRepresenting the price of excitation, Δ C, at time t during the second operating cycletRepresenting the amount of change in incentive price at time t during the second operational period.
6. The microgrid multi-time scale optimized scheduling method considering demand response of claim 1, wherein an objective function of the real-time scheduling optimization model is as follows: MinC2＝min(Cunits+Cbess+Cbuy+Cibdr+Cpunish),CunitsRepresenting the operating cost of the internal unit of the microgrid; cbessRepresents the operating cost of the storage battery; cbuyRepresenting the purchase of electricity from the microgrid to the grid, CibdrRepresentative stimulus type demand responseScheduling cost, CpunishRepresenting a penalty cost to constrain the real-time contribution adjustment sequence.
7. The microgrid multi-time scale optimized scheduling method considering demand responses as claimed in claim 6, characterized in that the incentive type demand response scheduling cost CibdrThe expression of (a) is:
Figure FDA0003300224640000021
where n represents the total number of users participating in the incentive-type demand response,
Figure FDA0003300224640000022
and
Figure FDA0003300224640000023
respectively representing the scheduled demand response down-regulation capacity and the scheduled demand response up-regulation capacity of the user i at the time t;
Figure FDA0003300224640000024
and
Figure FDA0003300224640000025
showing that the user i adjusts the incentive price downwards and increases the incentive price at the moment t; t represents a second operating cycle; Δ T represents a time interval;
the penalty cost CpunishThe expression of (a) is:
Figure FDA0003300224640000026
in the formula, S represents a micro-grid distributed power supply set; mu.sS、μbuyAnd muibdrOutput deviation punishment coefficients respectively representing the distributed power supply, the tie line power and the excitation type demand response of the microgrid, and used for constraining the real-time output adjustment sequence to meet the requirement of mubuy＞μS＞μibdr；Ps(t)、Pbuy(t) respectively representing the real-time t-timeThe output of a micro-grid power supply and the electric quantity purchased by a power grid;
Figure FDA0003300224640000027
and
Figure FDA0003300224640000028
and respectively representing the output of the micro-grid power supply and the purchased electric quantity of the power grid at the moment t before the day.
8. The microgrid multi-time scale optimization scheduling method considering demand responses, as claimed in claim 6, wherein the constraint conditions of the real-time scheduling optimization model include power balance constraint, incentive type demand response call capacity constraint, mutual exclusion constraint, distributed power supply output state constraint, unit operation constraint, electricity purchasing constraint and storage battery operation constraint.
9. A microgrid multi-time scale optimized dispatching system considering demand response is characterized by comprising:
a first model building module: the method is used for reading day-ahead prediction data and constructing a day-ahead scheduling optimization model;
a second model building module: the real-time scheduling optimization model is constructed by reading real-time prediction data;
the first solving module: the system is used for solving the day-ahead scheduling optimization model to obtain a day-ahead scheduling optimization scheme;
the second solving module: and the real-time scheduling optimization model is used for solving based on the day-ahead scheduling optimization scheme to obtain a real-time scheduling optimization scheme of each power supply.
10. An electronic device, comprising: at least one processor, at least one memory, and a data bus;
the processor and the memory complete mutual communication through the data bus; the memory stores program instructions executable by the processor, the processor calling the program instructions to perform the method of any of claims 1 to 8.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116739182A (en) * 2023-07-26 2023-09-12 国网数字科技控股有限公司 Electricity selling information output method and device, electronic equipment and storage medium
CN116739182B (en) * 2023-07-26 2023-12-22 国网数字科技控股有限公司 Electricity selling information output method and device, electronic equipment and storage medium

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