CN115102231A - Wind and light storage station optimization control method and system under multi-scale electricity-carbon mode - Google Patents

Wind and light storage station optimization control method and system under multi-scale electricity-carbon mode Download PDF

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CN115102231A
CN115102231A CN202210953564.1A CN202210953564A CN115102231A CN 115102231 A CN115102231 A CN 115102231A CN 202210953564 A CN202210953564 A CN 202210953564A CN 115102231 A CN115102231 A CN 115102231A
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wind
storage station
time
node
light storage
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王凯
延肖何
刘念
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North China Electric Power University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

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Abstract

The invention relates to a method and a system for optimally controlling a wind-solar energy storage station in a multi-scale electricity-carbon mode. According to the method, the carbon cost of the unit is deduced according to the carbon emission characteristics of the unit in the system, and an electricity-carbon coupling quotation model of the unit is constructed; considering the internal energy flow of the wind and light storage station, designing the internal cooperative regulation and control of the wind and light storage station, and constructing an internal cooperative regulation and control model of the wind and light storage station; constructing a day-ahead-real-time stage profit model of the wind and light storage station participating in the multi-time scale power service based on a day-ahead and real-time two-stage model and considering wind and light uncertainty; according to the electricity-carbon quotations of other units, establishing a clearing model in a day-ahead real-time stage by taking the maximum social welfare as a target respectively; the method comprises the steps of taking a day-ahead-real-time stage income model of the wind and light storage station as an upper layer, and taking a day-ahead-real-time two-stage output model as a lower layer to construct a double-layer model, restating and solving a double-layer problem, so that theoretical support is provided for improving the overall economy after large-scale new energy is matched with energy storage grid connection.

Description

Wind and light storage station optimization control method and system under multi-scale electricity-carbon mode
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for optimizing and controlling a wind-solar energy storage station in a multi-scale electricity-carbon mode.
Background
Under the background that the loading amount of new energy resources is continuously increased, new energy resources such as wind and light and flexible resources such as energy storage become important supports of a novel power system, a traditional electric energy market model is modeled by aiming at the minimum power generation cost of all equipment in a scheduling period, and the influence of system carbon emission is rarely considered. In addition, the regulation mechanism and strategy of wind-solar energy storage under multiple time scales are further elucidated, and the economical efficiency of wind-solar energy storage under a double-carbon target is improved by considering the influence caused by carbon emission. Therefore, the carbon emission characteristic of the system needs to be considered, a regulation decision and optimization control method for the wind and light storage station to participate in the multi-time scale power service is researched, and theoretical support is provided for improving the overall economy after large-scale new energy is matched with energy storage grid connection.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method and a system for optimally controlling a wind-solar energy storage station in a multi-scale electricity-carbon mode.
In order to achieve the purpose, the invention provides the following scheme:
a wind-solar energy storage station optimization control method in a multi-scale electricity-carbon mode comprises the following steps:
determining the carbon cost of the unit according to the carbon emission characteristics of the unit, and constructing an electricity-carbon coupling quotation model of the unit based on the electricity quotation of the unit; the unit includes: a coal-fired unit, a gas-fired unit and a fuel-fired unit;
considering the energy flow inside the wind and light storage station, designing an internal cooperative regulation and control method of the wind and light storage station, and constructing an internal cooperative regulation and control model of the wind and light storage station;
the total output and the total input of the wind and light storage station described by the internal cooperative regulation and control model are adopted, uncertainty of wind and light output is represented in different scenes based on a day-ahead-real-time two-stage model, and a profit model of the wind and light storage station participating in the day-ahead-real-time two-stage model is constructed;
constructing a clearance model of the current stage according to the maximum social welfare of the current stage as a target based on an electric-carbon coupling quotation model of the unit and quotations and user quotations of the wind-solar storage station;
establishing a clearing model of a real-time stage according to the maximum social welfare of the real-time stage as a target based on an electric-carbon coupling quotation model of a unit and quotations and user quotations of a wind-light storage station;
constructing a double-layer model of the wind and light storage station participating in multi-time scale power service by taking a day-ahead and real-time two-stage profit model as an upper layer and taking a clearance model of the day-ahead stage and the clearance model of the real-time stage as a lower layer;
and expressing and solving the double-layer model by adopting a KKT theorem and a large M method to obtain the optimal decision quotation and the optimal output of the wind and light storage station.
Preferably, the unit electricity-carbon coupling quotation model is as follows:
Figure BDA0003790147480000021
wherein the content of the first and second substances,
Figure BDA0003790147480000022
to account for the quoted price of the i unit k quoted section for carbon emission intensity,
Figure BDA0003790147480000023
for the initial power quote for the i unit k quote segment,
Figure BDA00037901474800000215
and c, quoting the carbon emission cost of the unit i, wherein i represents a unit node, and k is an electric power quoting section.
Preferably, in the day-ahead phase, the internal cooperative regulation model should satisfy the following constraints:
Figure BDA0003790147480000024
Figure BDA0003790147480000025
Figure BDA0003790147480000026
Figure BDA0003790147480000027
Figure BDA0003790147480000028
Figure BDA0003790147480000029
Figure BDA00037901474800000210
Figure BDA00037901474800000211
Figure BDA00037901474800000212
Figure BDA00037901474800000213
Figure BDA00037901474800000214
in the formula (I), the compound is shown in the specification,
Figure BDA0003790147480000031
storing the electricity purchasing power from the wind and light storage station of the v node to the power grid in the day-ahead stage at the time t,
Figure BDA0003790147480000032
the discharge power of the v-node wind and light storage station stored in the day-ahead stage at the moment t is shown,
Figure BDA0003790147480000033
for the power sold by the wind power to the power grid at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure BDA0003790147480000034
for the photovoltaic power selling power of the v-node wind and light storage station to the power grid at the day-ahead stage at the time t,
Figure BDA0003790147480000035
the total charging power stored in the day-ahead stage at the moment t of the v-node wind and light storage station,
Figure BDA0003790147480000036
the discharge power stored in the day-ahead stage at the time t of the v-node wind and light storage station,
Figure BDA0003790147480000037
for charging power from wind power to stored energy at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure BDA0003790147480000038
for charging power from photovoltaic to stored energy at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure BDA0003790147480000039
for the energy storage loss coefficient of the v-node wind and light storage station,
Figure BDA00037901474800000310
the energy storage and charging efficiency of the wind and light storage station at the v node is improved,
Figure BDA00037901474800000311
for the v-node wind-solar energy storage field energy storage discharge efficiency,
Figure BDA00037901474800000312
wind-solar energy storage yard for v nodeThe rated capacity of the station's stored energy,
Figure BDA00037901474800000313
is the energy storage initial charge state of the v-node wind and light storage station at the day-ahead stage,
Figure BDA00037901474800000314
is the energy storage end charge state of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800000315
is a binary variable used for restricting the energy storage not to be charged and discharged simultaneously, T is the moment, T is the time period,
Figure BDA00037901474800000316
for the discharge power of the v-node wind and light storage station at the moment t in the day-ahead stage,
Figure BDA00037901474800000317
the energy storage SOC of the v-node wind and light energy storage station at the moment t in the day-ahead stage is shown, delta t is a unit time interval,
Figure BDA00037901474800000318
for the upper limit of electricity purchasing for the stored energy of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800000319
for the lower limit of electricity purchasing for the stored energy of the v-node wind and light storage station in the previous stage,
Figure BDA00037901474800000320
the node wind and light storage station t is at the upper limit of the energy storage SOC at the day-ahead stage,
Figure BDA00037901474800000321
for the lower limit of the energy storage SOC of the v-node wind and light energy storage station at the moment t in the day-ahead stage,
Figure BDA00037901474800000322
wind power of wind and light storage station at t moment of v node in day-ahead stageThe total predicted output force is obtained,
Figure BDA00037901474800000323
the total photovoltaic output of the v-node wind and light storage station at the moment t in the day-ahead stage is predicted;
in the real-time stage, the regulation and control of the wind-solar energy storage interior should meet the following constraints:
Figure BDA00037901474800000324
Figure BDA00037901474800000325
Figure BDA00037901474800000326
Figure BDA00037901474800000327
Figure BDA00037901474800000328
Figure BDA00037901474800000329
Figure BDA00037901474800000330
Figure BDA0003790147480000041
Figure BDA0003790147480000042
Figure BDA0003790147480000043
Figure BDA0003790147480000044
where s denotes multi-scene, rt denotes real-time phase variable,
Figure BDA0003790147480000045
for storing the electricity purchasing power to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA0003790147480000046
the discharge power stored in the real-time stage under the scene of t time s of the v-node wind and light storage station is shown,
Figure BDA0003790147480000047
for the power sold from the wind power to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA0003790147480000048
for the electricity selling power from photovoltaic to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA0003790147480000049
for the total charging power of the energy stored in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800000410
for the discharge power stored in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800000411
for the charging power from wind power to energy storage in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800000412
for charging power from photovoltaic to stored energy in a real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800000413
for the energy storage loss coefficient of the v-node wind and light storage station,
Figure BDA00037901474800000414
the energy storage and charging efficiency of the wind and light storage station at the v node is improved,
Figure BDA00037901474800000415
for the v-node wind-solar energy storage field energy storage discharge efficiency,
Figure BDA00037901474800000416
the rated capacity of the wind and light storage station is stored for the v node,
Figure BDA00037901474800000417
for the energy storage initial charge state of the v-node wind and light storage station in the real-time stage,
Figure BDA00037901474800000418
the energy storage end charge state of the v-node wind and light storage station in the real-time stage,
Figure BDA00037901474800000419
is a binary variable, s is different wind and light output scenes, T is time, T is a time period,
Figure BDA00037901474800000420
for the discharge power of the v-node wind and light storage station in the real-time stage under the scene of t time s,
Figure BDA00037901474800000421
is the energy storage SOC of the v-node wind and light storage station in the real-time stage at the time of t and s, delta t is a unit time interval,
Figure BDA00037901474800000422
the node wind and light storage station has the upper limit of energy storage and electricity purchase in the real-time stage,
Figure BDA00037901474800000423
for the lower limit of the energy storage and electricity purchase of the v-node wind and light storage station in the real-time stage,
Figure BDA00037901474800000424
the energy storage SOC upper limit of the v-node wind and light storage station at the moment t in the real-time stage,
Figure BDA00037901474800000425
for the lower limit of the energy storage SOC of the v-node wind and light energy storage station in the real-time stage at the moment t,
Figure BDA00037901474800000426
the total predicted output of wind power in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800000427
and the total photovoltaic output is predicted for the v-node wind and light storage station at the real-time stage in the scene of t time s.
Preferably, the pre-day-real-time phase revenue model is:
Figure BDA00037901474800000428
in the formula (I), the compound is shown in the specification,
Figure BDA00037901474800000429
for the benefit of v-node sites during the day-ahead period,
Figure BDA00037901474800000430
for the income of the v-node station in the real-time stage, v represents the node of the wind and light storage station,
Figure BDA0003790147480000051
the node set is a node set of the wind and light storage station.
Preferably, the birth model of the day-ahead stage is expressed as an objective function of social welfare maximization:
Figure BDA0003790147480000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003790147480000053
for the charging quote of v node stations in quote section b/k,
Figure BDA0003790147480000054
for discharge quotes for the v-node sites in quote section b/k,
Figure BDA0003790147480000055
for the offer of user j in offer segment b/k,
Figure BDA0003790147480000056
for the unit i quoted price in the quote section b/k,
Figure BDA0003790147480000057
for the power of user j in quote section b/k in the previous day period,
Figure BDA0003790147480000058
for the power of the unit i in the quotation section b/k in the previous stage,
Figure BDA0003790147480000059
is a node set of the wind and light storage station,
Figure BDA00037901474800000510
is a machine group node set,
Figure BDA00037901474800000511
Is a collection of nodes for a user and,
Figure BDA00037901474800000512
is a set of all nodes of the system, v is a node of the wind and light storage station, and i is a unit nodeThe point, j, is the user's node, where,
Figure BDA00037901474800000513
t is the time of day, Δ t is the unit period, and da is the day-ahead phase.
Preferably, the real-time phase cleaning model is:
Figure BDA00037901474800000514
where rt is the real-time phase,
Figure BDA00037901474800000515
for the power of the b quoted section of the v-node wind and light storage station in the scene of t time s in the real-time stage,
Figure BDA00037901474800000516
b quotes the power of the section for the real-time phase j node user in the scene of time t s,
Figure BDA00037901474800000517
for the power of the k quoted section of the v-node wind and light storage station in the scene of t time s in the real-time stage,
Figure BDA00037901474800000518
and (4) the power of the k quotation section of the i-node unit at the real-time stage under the scene of t time s.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
1. the method considers different carbon emission characteristics of generating sets such as coal-fired, gas-fired and fuel-fired power sets, considers the transaction cost of the carbon quota into the power quotation, and constructs the quotation model of electric-carbon coupling, so that the wind and light storage station considers the influence of the carbon emission characteristics of the system in the quotation participation process.
2. The invention designs an internal cooperative regulation and control method of a wind and light storage station, constructs a cooperative regulation and control model between wind power-photovoltaic-energy storage inside the station, increases the arbitrage means of energy storage in the wind and light storage station, considers wind and light uncertainty, and constructs an optimal quotation strategy of the wind and light storage station by taking the maximum daily and real-time total profit of the wind and light storage station as a target.
3. The invention establishes a day-ahead-real-time two-stage model of electric-carbon cooperative optimization, considers the uncertainty of wind and light output in a real-time stage through multiple scenes, establishes a double-layer model of the income of an upper wind and light storage station and the yield of a lower layer in a day-ahead-real-time two-stage mode, and accurately solves the problem through a KKT theory and a large M method.
Corresponding to the provided wind and light storage station optimization control method under the multi-scale electricity-carbon mode, the invention also provides the following implementation systems:
a wind-solar storage station optimization control system in a multi-scale electricity-carbon mode comprises:
the electric carbon quotation model building module is used for determining the carbon cost of the unit according to the carbon emission characteristics of the unit and building an electric-carbon coupling quotation model of the unit based on the electric power quotation of the units such as coal, gas and fuel; the unit includes: a coal-fired unit, a gas-fired unit and a fuel oil unit;
the station regulation and control model building module is used for designing an internal cooperative regulation and control method of the wind and light storage station and building an internal cooperative regulation and control model of the wind and light storage station by considering the energy flow inside the wind and light storage station;
the station profit model building module is used for adopting the total output and the total input of the wind and light storage station described by the internal cooperative regulation and control model, representing the uncertainty of wind and light output by different scenes based on a day-ahead-real-time two-stage model, and building a profit model of the wind and light storage station participating in the day-ahead-real-time two-stage model;
the day-ahead clearing model building module is used for building a day-ahead clearing model with the social welfare of the day-ahead stage as a maximum target based on the electricity-carbon coupling quotation model of the unit, the quotation of the wind and light storage station and the quotation of the user;
the real-time clearing model building module is used for building a clearing model in a real-time stage according to the maximum social welfare of the real-time stage as a target on the basis of an electric-carbon coupling quotation model of the unit and quotations and user quotations of the wind and light storage station;
the double-layer model building module is used for building a double-layer model of the wind and light storage station participating in multi-time scale power service by taking the day-ahead and real-time two-stage income model as an upper layer and taking the coming-out model of the day-ahead stage and the coming-out model of the real-time stage as a lower layer;
and the model solving module is used for expressing and solving the double-layer model by adopting a KKT theorem and a large M method to obtain the optimal decision quotation and the optimal output of the wind and light storage station.
An electronic device, comprising: a processor and a memory;
the processor is connected with the memory; the processor retrieves and executes the computer program stored in the memory; the computer program is used for realizing the optimal control method of the wind-solar energy storage station in the multi-scale electricity-carbon mode.
The implementation system provided by the invention has the same technical effect as the provided method for optimizing the decision of the wind and light storage station participating in the multi-time scale power service, so the implementation system is not repeated herein.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a method for optimizing control of a wind-solar energy storage station in a multi-scale electricity-carbon mode according to the present invention;
fig. 2 is a power generation quotation diagram of a thermal power generating unit considering carbon trading according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of internal regulation and control of a wind-solar energy storage station provided by an embodiment of the invention;
FIG. 4 is a schematic diagram of a wind-solar energy storage station participating in a multi-time-scale power service provided by an embodiment of the invention;
FIG. 5 is a schematic structural diagram of a wind and light storage yard optimization control system in a multi-scale electricity-carbon mode provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for optimizing and controlling a wind and light storage station in a multi-scale electricity-carbon mode, and provide theoretical support for improving the overall economy after large-scale new energy is matched with energy storage grid connection.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the method for optimally controlling a wind-solar energy storage station in a multi-scale electricity-carbon mode provided by the invention comprises the following steps:
step 100: the unit carbon cost is determined according to the unit carbon emission characteristics, and an electricity-carbon coupling quotation model of the units such as the coal-fired, gas-fired and fuel-fired units is constructed based on the electricity quotation of the units such as the coal-fired, gas-fired and fuel-fired units. Specifically, in the current power trading background, the dispatching of the power system is completed by the power trading center and the power dispatching center together. Firstly, each power generation enterprise reports respective corresponding power generation quotation; then the transaction center determines the bid amount of each enterprise according to the principle that the system power consumption cost is the lowest; and finally, checking and executing by a dispatching center. In the Carbon Emission right trade in China, a competent department can freely allocate Carbon Emission quota (CEA) corresponding to the generated energy according to the specified Carbon Emission Intensity (CEI) reference value. The thermal power enterprises need to provide CEA equal to the actual carbon emission at the end of each obligation period, otherwise, administrative and economic penalties are faced. Thus, when the actual CEI is below (above) the baseline CEI, the free-to-dispense CEA is more (less) than the actual required amount of performance, and the net remaining (required) CEA may be sold (purchased), making the thermal power unit a winner (loser).
The original quotation of the units such as the coal, the gas and the fuel oil is the marginal power generation cost, and because the CEI of the units such as the coal, the gas and the fuel oil is generally related to the power generation amount, the CEI of the units such as the coal, the gas and the fuel oil is reduced along with the power generation power according to the difference of combustion products, the unit efficiency and the like. The relation between the CEI of units such as coal-fired units, gas units, fuel oil units and the like and the power generation power thereof is expressed as a linear function:
Figure BDA0003790147480000081
in the formula:
Figure BDA0003790147480000082
representing the CEI actual value of units such as i-node coal-fired, gas-fired, fuel oil and the like with the unit of t/MWh and P i G Representing the generating power of the i-node unit, a i 、b i Respectively represents the carbon emission coefficient, omega, of the i-node unit g Representing a set of team nodes.
The actual power quote is required in a step-wise incremental fashion, so the carbon cost/carbon revenue generated by the actual CEI is superimposed on the response quote segment, as shown in fig. 2.
The actual power quote is required to be in the form of a step non-decreasing curve, which makes the electricity-carbon coupled quote as shown by the dashed line in fig. 2 not realistic. Thus, the present invention calculates the average CEI for each quote segment from the total carbon emissions for each quote segment:
Figure BDA0003790147480000083
Figure BDA0003790147480000084
Figure BDA0003790147480000085
in the formula:
Figure BDA0003790147480000091
the total carbon emission amount in the unit quotation section k of i-node fire coal, gas, fuel oil and the like is represented by t,
Figure BDA0003790147480000092
the unit of the power generation in the i-node unit quotation section k is MWh,
Figure BDA0003790147480000093
and the discrete CEI value corresponding to the i-node unit power quotation section k is represented, and the unit is t/MWh. As shown in equation (2): total carbon emissions in quote section k
Figure BDA0003790147480000094
Is equivalent to the CEI value at the end of the quote section
Figure BDA0003790147480000095
The power generation capacity of the quoted price section
Figure BDA0003790147480000096
The product of (a). However, since the CEI decreases with increasing power, the amount of electricity generated for each of the 1 st to k th quote periods should be calculated as the total amount of carbon emissions for the corresponding CEI of the k-th quote period. Therefore, the discrete CEI value corresponding to each power price section k can be obtained by iterating the equations (3) to (4)
Figure BDA0003790147480000097
As shown in fig. 2.
The unit quotations of coal, gas, fuel oil and the like considering the carbon emission intensity are represented as follows:
Figure BDA0003790147480000098
Figure BDA0003790147480000099
in the formula:
Figure BDA00037901474800000910
the unit price of coal, gas, fuel oil and the like considering the carbon emission intensity is unit/MWh, wherein
Figure BDA00037901474800000911
Is equal to the initial power quote,
Figure BDA00037901474800000912
in order to provide a carbon cost price,
Figure BDA00037901474800000913
is the actual value of the CEI of the i unit with the unit of t/MWh,
Figure BDA00037901474800000914
to correspond to the reference value, p c Is carbon number, in units of units/t.
Step 101: and (3) considering the energy flow inside the wind and light storage station, designing an internal cooperative regulation and control method of the wind and light storage station, and constructing an internal cooperative regulation and control model of the wind and light storage station. Specifically, the method comprises the following steps:
due to the fluctuation and randomness of wind and light output, the internal cooperative regulation and control of the wind and light storage station are mainly realized through energy storage. The stored energy in the wind and light storage station can coordinate wind and light output and stabilize the fluctuation of the wind and light storage station. In addition, the wind and light output should be a profit sharing means capable of being charged to enrich energy storage. As shown in fig. 3.
In the day-ahead stage, the internal cooperative regulation model should satisfy the following constraints:
Figure BDA00037901474800000915
Figure BDA00037901474800000916
Figure BDA00037901474800000917
Figure BDA00037901474800000918
Figure BDA00037901474800000919
Figure BDA0003790147480000101
Figure BDA0003790147480000102
Figure BDA0003790147480000103
Figure BDA0003790147480000104
Figure BDA0003790147480000105
Figure BDA0003790147480000106
in the formula (I), the compound is shown in the specification,
Figure BDA0003790147480000107
storing the electricity purchasing power from the wind and light storage station of the v node to the power grid in the day-ahead stage at the time t,
Figure BDA0003790147480000108
the discharge power stored in the V-node wind and light storage station at the moment t in the day-ahead stage is shown,
Figure BDA0003790147480000109
for the power sold by the wind power to the power grid at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800001010
for the photovoltaic power selling power of the v-node wind and light storage station to the power grid at the day-ahead stage at the time t,
Figure BDA00037901474800001011
the total charging power stored in the day-ahead stage at the moment t of the v-node wind and light storage station,
Figure BDA00037901474800001012
the discharge power stored in the day-ahead stage at the time t of the v-node wind and light storage station,
Figure BDA00037901474800001013
for charging power from wind power to stored energy at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800001014
for charging power from photovoltaic to stored energy at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800001015
for the energy storage loss coefficient of the v-node wind and light storage station,
Figure BDA00037901474800001016
the energy storage and charging efficiency of the wind and light storage station at the v node is improved,
Figure BDA00037901474800001017
for the v-node wind-solar energy storage field energy storage discharge efficiency,
Figure BDA00037901474800001018
the rated capacity of the wind and light storage station is stored for the v node,
Figure BDA00037901474800001019
is the energy storage initial charge state of the v-node wind and light storage station at the day-ahead stage,
Figure BDA00037901474800001020
is the energy storage end charge state of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800001021
is a binary variable, T is different time, T is a time period,
Figure BDA00037901474800001022
for the discharge power of the v-node wind and light storage station at the moment t in the day-ahead stage,
Figure BDA00037901474800001023
the delta t is a unit time interval for the energy storage SoC of the v-node wind and light energy storage station at the time t in the day-ahead stage,
Figure BDA00037901474800001024
for the upper and lower limits of the energy and electricity purchasing of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800001025
the upper and lower limits of the energy storage SoC of the v-node wind and light storage station at the moment t in the day-ahead stage,
Figure BDA00037901474800001026
for the total predicted output of wind power at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure BDA00037901474800001027
the total photovoltaic output of the v-node wind and light storage station at the moment t in the day-ahead stage is predicted; in the real-time stage, the regulation and control of the wind-solar energy storage interior should meet the following constraints:
Figure BDA00037901474800001028
Figure BDA00037901474800001029
Figure BDA0003790147480000111
Figure BDA0003790147480000112
Figure BDA0003790147480000113
Figure BDA0003790147480000114
Figure BDA0003790147480000115
Figure BDA0003790147480000116
Figure BDA0003790147480000117
Figure BDA0003790147480000118
Figure BDA0003790147480000119
in the formula, s represents multiple scenes, and superscripts represent real-time phase variables in real time. In the formula (I), the compound is shown in the specification,
Figure BDA00037901474800001110
for storing the electricity purchasing power to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001111
the discharge power stored in the real-time stage under the scene of t time s of the v-node wind and light storage station is shown,
Figure BDA00037901474800001112
for the power sold from the wind power to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001113
for the electricity selling power from photovoltaic to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001114
for the total charging power of the energy stored in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001115
for the discharge power stored in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001116
for charging power from wind power to stored energy in a real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001117
for charging power from photovoltaic to stored energy in real time under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001118
wind-light storage station for v-nodeThe coefficient of energy loss is calculated by the following formula,
Figure BDA00037901474800001119
the energy storage and charging efficiency of the wind and light storage station at the v node is improved,
Figure BDA00037901474800001120
for the v-node wind-solar energy storage field energy storage discharge efficiency,
Figure BDA00037901474800001121
the rated capacity of the wind and light storage station is stored for the v node,
Figure BDA00037901474800001122
for the energy storage initial charge state of the v-node wind and light storage station in the real-time stage,
Figure BDA00037901474800001123
is the energy storage end charge state of the v-node wind and light storage station in the real-time stage,
Figure BDA00037901474800001124
is binary variable, s is different scene of wind and light output, T is different time, T is a time period,
Figure BDA00037901474800001125
for the discharge power of the v-node wind and light storage station in the real-time stage under the scene of t time s,
Figure BDA00037901474800001126
is an energy storage SoC in a real-time stage under the scene of t time s of a v-node wind and light storage station, delta t is a unit time interval,
Figure BDA00037901474800001127
for the upper and lower limits of the energy storage and electricity purchasing of the v-node wind and light storage station in the real-time stage,
Figure BDA00037901474800001128
is the upper and lower limits of the energy storage SoC of the v-node wind and light storage station in the real-time stage at the moment t,
Figure BDA00037901474800001129
the total predicted output of wind power in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure BDA00037901474800001130
the method comprises the steps of (1) predicting the total photovoltaic output of a v-node wind and light storage station in a real-time stage under a scene of t time s;
step 102: and constructing a revenue model of the wind and light storage station participating in the day-ahead and real-time two-stage by adopting the total output and the total input of the wind and light storage station described by the internal cooperative regulation and control model and representing the uncertainty of wind and light output in different scenes based on the day-ahead and real-time two-stage model. Specifically, the method comprises the following steps:
in the process of participating in spot market of the wind and light storage station, income mainly comes from day-ahead real-time electricity selling, and cost mainly comes from day-ahead real-time electricity purchasing and self electricity generation cost. The uncertainty of the wind-solar output is represented by a plurality of scenes obtained through clustering, and the income and the cost of the wind-solar storage station in the real-time stage are the weighted sum of the cost and the income of each scene. The income and the quotation of the wind and light storage station participating in the multi-time scale power service are as follows:
Figure BDA0003790147480000121
wherein:
Figure BDA0003790147480000122
Figure BDA0003790147480000123
Figure BDA0003790147480000124
Figure BDA0003790147480000125
Figure BDA0003790147480000126
Figure BDA0003790147480000127
in the formula:
Figure BDA0003790147480000128
and
Figure BDA0003790147480000129
respectively represents the gains of the v-node wind and light storage station in the day-ahead and real-time stages, v represents the nodes of the wind and light storage station,
Figure BDA00037901474800001210
a set of nodes representing stations;
Figure BDA00037901474800001211
representing the charging/discharging power of the v node wind and light storage station in the b/k quotation section at the time t in the day,
Figure BDA00037901474800001212
represents the daily Marginal node price (LMP) of the v node,
Figure BDA00037901474800001213
representing the cost of the v-node wind and light storage station in a quotation section k, wherein the cost comprises the power generation cost and the equipment loss cost of the wind and light storage station;
Figure BDA00037901474800001214
representing the charging/discharging power of the v-node wind and light storage station at t moment in a scene of s in real time in a b/k quotation section,
Figure BDA00037901474800001215
the real-time LMP of the v node is shown, and the settlement capacity and the LMP of the wind and light storage station in the upper formula both depend on the clearing result of the lower model;
Figure BDA0003790147480000131
representing the quotation of the v-node wind and light storage station in the quotation section b/k,
Figure BDA0003790147480000132
Figure BDA0003790147480000133
respectively, the upper and lower limits thereof.
Step 103: and constructing a clearing model of the day-ahead stage by taking the social welfare of the day-ahead stage as a maximum target based on the electricity-carbon coupling quotation models of the units such as the coal, the gas and the fuel oil, the quotation of the wind and light storage station and the quotation of the users. Specifically, the method comprises the following steps:
the power dispatching mechanism adopts a day-ahead power clearing model to perform optimization calculation based on information declared by each member and power grid operation boundary conditions of an operation day, clearing is performed to obtain a day-ahead stage transaction result, day-ahead node marginal price and a medium scalar are issued to each member, and a target function of day-ahead stage clearing is social welfare maximization:
Figure BDA0003790147480000134
wherein:
Figure BDA0003790147480000135
Figure BDA0003790147480000136
Figure BDA0003790147480000137
Figure BDA0003790147480000138
Figure BDA0003790147480000139
Figure BDA00037901474800001310
Figure BDA00037901474800001311
Figure BDA00037901474800001312
Figure BDA00037901474800001313
Figure BDA00037901474800001314
Figure BDA00037901474800001315
in the formula:
Figure BDA00037901474800001316
representing the charging/discharging quotation of the v-node wind and light storage station in the quotation section b/k,
Figure BDA00037901474800001317
indicating the quotation of the user j/unit i in the quotation section b/k,
Figure BDA00037901474800001318
representing the power of the user j/unit i in the quoted section b/k,
Figure BDA0003790147480000141
respectively representing the node sets of the wind and light storage station, the units and the users, wherein
Figure BDA0003790147480000142
v, i, j represent different nodes; b n,m Is the admittance value of the line between nodes n and m,
Figure BDA0003790147480000143
is the voltage phase at the time of n node t;
Figure BDA0003790147480000144
is the upper limit for power transfer for the line between nodes n and m. Equation (32) defines node 1 as the balanced node and the phase as 0. In addition, each constraint right [. X [ ]]Inside is its corresponding dual variable. Wherein the dual variables
Figure BDA0003790147480000145
Indicating the LMP of node n at the day before time t.
Step 104: and constructing a real-time clearing model with the maximum social welfare of the real-time stage as a target based on the electricity-carbon coupling quotation models of the units such as the coal, the gas and the fuel oil, the quotation of the wind and light storage station and the quotation of the users. The uncertainty of the wind-solar output in the real-time stage is represented by a plurality of scenes obtained through clustering, and each scene needs to be cleared by a real-time clearing model. Specifically, the method comprises the following steps:
the real-time phase output is similar to the day-ahead phase, and the main difference is that the uncertainty of wind and light output is considered in the real-time phase. The uncertainty of wind-solar output is represented by a plurality of scenes, and the total load in the real-time stage fluctuates randomly according to a certain proportion. The real-time rendering model is represented as:
Figure BDA0003790147480000146
wherein:
Figure BDA0003790147480000147
Figure BDA0003790147480000148
Figure BDA0003790147480000149
Figure BDA00037901474800001410
Figure BDA00037901474800001411
Figure BDA00037901474800001412
Figure BDA00037901474800001413
Figure BDA00037901474800001414
Figure BDA00037901474800001415
Figure BDA00037901474800001416
Figure BDA0003790147480000151
where the superscript rt represents the real-time phase variable and the subscript s represents the different scenes in the real-time phase. The equations and constraints in the real-time phase are similar to those defined in the day-ahead phase.
Step 105: and constructing a double-layer model of the wind and light storage station participating in the multi-time scale power service by taking the day-ahead and real-time two-stage profit model as an upper layer and taking the clearance model of the day-ahead stage and the clearance model of the real-time stage as a lower layer. On the upper layer, the wind and light storage station optimizes the quotation strategy by considering the internal regulation of the wind and light storage station, so that the profit is maximized; on the lower layer, an independent system operator receives a bidding price-power curve from a wind and light storage station and other electric-carbon coupling quotations of the unit and bidding parameters of a user, then the wind and light output uncertainty is considered, clearing is carried out based on a day-ahead-real-time two-stage clearing model, and the result is transmitted to the upper layer.
The wind and photovoltaic storage station is used as a supply side and a demand side in the quotation process: as a supply side, the wind and light storage station and units such as coal-fired, gas-fired and fuel-fired units submit power generation quotations to independent system operators in a curve format with price-power step increasing; as a demand side, the station and the user submit power utilization quotations to an independent system operator in a price-power step descending curve format in the charging state of the stored energy, and then the independent system operator gives a day-ahead stage in a unified way. When the real-time stage is cleared, the system needs to consider the wind and light processing and the load fluctuation of different scenes, and an independent system operator can clear the real-time stage through the bid of the previous stage in each scene.
Step 106: and expressing and solving the double-layer model by adopting a KKT theorem and a large M method to obtain the optimal decision quotation and the optimal output of the wind and light storage station. Specifically, the method comprises the following steps:
the double-layer model provided in the step is a double-layer mixed integer nonlinear model, and simple and quick solution cannot be carried out. The original problem is thus first converted to a single-layer non-linearity problem based on the KKT condition. Take the day-ahead questions as an example:
Figure BDA0003790147480000152
Figure BDA0003790147480000153
Figure BDA0003790147480000154
Figure BDA0003790147480000155
Figure BDA0003790147480000156
Figure BDA0003790147480000157
Figure BDA0003790147480000161
Figure BDA0003790147480000162
Figure BDA0003790147480000163
Figure BDA0003790147480000164
Figure BDA0003790147480000165
Figure BDA0003790147480000166
Figure BDA0003790147480000167
Figure BDA0003790147480000168
Figure BDA0003790147480000169
Figure BDA00037901474800001610
Figure BDA00037901474800001611
Figure BDA00037901474800001612
Figure BDA00037901474800001613
Figure BDA00037901474800001614
Figure BDA00037901474800001615
Figure BDA00037901474800001616
the real-time phase may be converted in a similar manner and thus a detailed conversion process thereof will not be described. And the two converted lower-layer problems are used as constraint conditions of the upper-layer problems, and the original double-layer model is converted into a single-layer model for solving.
The complementary relaxation constraints are linearized using the large M method, as exemplified by equation (65):
Figure BDA00037901474800001617
Figure BDA00037901474800001618
other methods of linearization of complementary relaxation constraints are the same.
And finally, the wind and light storage station can solve to obtain self optimal decision quotation and optimal output by considering self internal regulation and simulation clearing, and declares according to a solving result.
Based on the implementation process provided above, the application interaction process of the wind and light storage station participating in the multi-time scale power service is shown in fig. 4.
Corresponding to the provided wind and light storage station optimization control method under the multi-scale electricity-carbon mode, the invention also provides the following implementation systems:
as shown in fig. 5, the system for optimizing and controlling the wind-solar energy storage station in the multi-scale electricity-carbon mode includes:
the electric carbon quotation model building module 1 is used for determining the unit carbon cost according to the unit carbon emission characteristics and building an electric-carbon coupling quotation model of units such as fire coal, gas and fuel oil based on the electric quotation of the units such as the fire coal, the gas and the fuel oil;
the site regulation and control model building module 2 is used for designing an internal cooperative regulation and control method of the wind and light storage site and building an internal cooperative regulation and control model of the wind and light storage site in consideration of energy flow inside the wind and light storage site;
the station profit model building module 3 is used for building a profit model of the wind and light storage station participating in the day-ahead and real-time two-stage by adopting the total output and the total input of the wind and light storage station described by the internal cooperative regulation and control model and representing the uncertainty of wind and light output in different scenes based on the day-ahead and real-time two-stage model;
the day-ahead clearing model building module 4 is used for building a day-ahead clearing model with the maximum social welfare of the day-ahead stage as a target based on the electricity-carbon coupling quotation models of the units such as the coal, the gas and the fuel, the quotation of the wind and light storage stations and the quotation of the users;
the real-time clearing model building module 5 is used for building a clearing model in a real-time stage according to the maximum social welfare of the real-time stage as a target on the basis of the electricity-carbon coupling quotation models of the units such as the coal, the gas and the fuel, the quotation of the wind and light storage stations and the quotation of users;
the double-layer model building module 6 is used for building a double-layer model of the wind and light storage station participating in multi-time scale power service by taking the day-ahead and real-time two-stage income model as an upper layer and taking the clearance model of the day-ahead stage and the clearance model of the real-time stage as a lower layer;
and the model solving module 7 is used for expressing and solving the double-layer model by adopting a KKT theorem and a large M method to obtain the optimal decision quotation and the optimal output of the wind and light storage station.
Another is an electronic device, comprising: a processor and a memory.
The processor is connected with the memory. The processor retrieves and executes the computer program stored in the memory. And the computer program is used for realizing the optimal control method of the wind-solar energy storage station in the multi-scale electricity-carbon mode.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (8)

1. A wind-solar energy storage station optimal control method in a multi-scale electricity-carbon mode is characterized by comprising the following steps:
determining the carbon cost of the unit according to the carbon emission characteristics of the unit, and constructing an electricity-carbon coupling quotation model of the unit based on the electricity quotation of the unit; the unit includes: a coal-fired unit, a gas-fired unit and a fuel oil unit;
considering the energy flow inside the wind and light storage station, designing an internal cooperative regulation and control method of the wind and light storage station, and constructing an internal cooperative regulation and control model of the wind and light storage station;
the total output and the total input of the wind and light storage station described by the internal cooperative regulation and control model are adopted, uncertainty of wind and light output is represented in different scenes based on a day-ahead-real-time two-stage model, and a profit model of the wind and light storage station participating in the day-ahead-real-time two-stage model is constructed;
establishing a clearing model of the day-ahead stage with the maximum social welfare of the day-ahead stage as a target based on the electricity-carbon coupling quotation model of the unit and the quotation and user quotation of the wind-light storage station;
establishing a clearing model of a real-time stage according to the maximum social welfare of the real-time stage as a target based on an electric-carbon coupling quotation model of a unit and quotations and user quotations of a wind-light storage station;
constructing a double-layer model of the wind and light storage station participating in multi-time scale power service by taking a day-ahead and real-time two-stage profit model as an upper layer and taking a clearance model of the day-ahead stage and the clearance model of the real-time stage as a lower layer;
and expressing and solving the double-layer model by adopting a KKT theorem and a large M method to obtain the optimal decision quotation and the optimal output of the wind and light storage station.
2. The wind-solar energy storage station optimal control method under the multi-scale electricity-carbon mode according to claim 1, wherein the unit electricity-carbon coupling quotation model is as follows:
Figure FDA0003790147470000011
wherein the content of the first and second substances,
Figure FDA0003790147470000012
to account for the quoted price of the i unit k quoted section for carbon emission intensity,
Figure FDA0003790147470000013
for the initial power quote for the i unit k quote segment,
Figure FDA0003790147470000014
and c, quoting the carbon emission cost of the unit i, wherein i represents a unit node, and k is an electric power quoting section.
3. The method for optimizing and controlling the wind and light storage station under the multi-scale electricity-carbon mode according to claim 1, wherein in a day-ahead stage, the internal cooperative regulation and control model satisfies the following constraints:
Figure FDA0003790147470000021
Figure FDA0003790147470000022
Figure FDA0003790147470000023
Figure FDA0003790147470000024
Figure FDA0003790147470000025
Figure FDA0003790147470000026
Figure FDA0003790147470000027
Figure FDA0003790147470000028
Figure FDA0003790147470000029
Figure FDA00037901474700000210
Figure FDA00037901474700000211
in the formula (I), the compound is shown in the specification,
Figure FDA00037901474700000212
storing the electricity purchasing power from the wind and light storage station of the v node to the power grid in the day-ahead stage at the time t,
Figure FDA00037901474700000213
the discharge power of the v-node wind and light storage station stored in the day-ahead stage at the moment t is shown,
Figure FDA00037901474700000214
for the power sold by the wind power to the power grid at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure FDA00037901474700000215
for the photovoltaic power selling power of the v-node wind and light storage station to the power grid at the day-ahead stage at the moment t,
Figure FDA00037901474700000216
for the total charging power stored in the wind and light storage station at the v node at the moment t in the day-ahead stage,
Figure FDA00037901474700000217
the discharge power stored in the day-ahead stage at the time t of the v-node wind and light storage station,
Figure FDA00037901474700000218
charging power from wind power to stored energy at the day-ahead stage at the time t of the v-node wind and light storage station,
Figure FDA00037901474700000219
for charging power from photovoltaic to stored energy at the moment t of the v-node wind and light storage station in the day-ahead stage,
Figure FDA00037901474700000220
for the energy storage loss coefficient of the v-node wind and light storage station,
Figure FDA00037901474700000221
the energy storage and charging efficiency of the wind and light storage station at the v node is improved,
Figure FDA00037901474700000222
for the v-node wind-solar energy storage field energy storage discharge efficiency,
Figure FDA00037901474700000223
for v node wind-lightThe storage station stores the rated capacity of energy,
Figure FDA00037901474700000224
is the energy storage initial charge state of the v-node wind and light storage station at the day-ahead stage,
Figure FDA00037901474700000225
is the energy storage end charge state of the v-node wind and light storage station in the day-ahead stage,
Figure FDA00037901474700000226
is a binary variable used for restricting the energy storage not to be charged and discharged simultaneously, T is the moment, T is the time period,
Figure FDA00037901474700000227
for the discharge power of the v-node wind and light storage station at the moment t in the day-ahead stage,
Figure FDA00037901474700000228
the energy storage SOC of the v-node wind and light energy storage station at the moment t in the day-ahead stage is shown, delta t is a unit time interval,
Figure FDA00037901474700000229
for the upper limit of electricity purchasing for the stored energy of the v-node wind and light storage station in the day-ahead stage,
Figure FDA00037901474700000230
for the lower limit of electricity purchasing of the v-node wind and light storage station in the previous stage,
Figure FDA00037901474700000231
the upper limit of the energy storage SOC of the v-node wind and light energy storage station at the moment t in the day-ahead stage,
Figure FDA0003790147470000031
for the lower limit of the energy storage SOC of the v-node wind and light energy storage station at the moment t in the day-ahead stage,
Figure FDA0003790147470000032
for the total predicted wind power output of the v-node wind and light storage station at the moment t in the day-ahead stage,
Figure FDA0003790147470000033
the total photovoltaic output of the v-node wind and light storage station at the moment t in the day-ahead stage is predicted;
in the real-time stage, the regulation and control of the wind-solar energy storage interior should meet the following constraints:
Figure FDA0003790147470000034
Figure FDA0003790147470000035
Figure FDA0003790147470000036
Figure FDA0003790147470000037
Figure FDA0003790147470000038
Figure FDA0003790147470000039
Figure FDA00037901474700000310
Figure FDA00037901474700000311
Figure FDA00037901474700000312
Figure FDA00037901474700000313
Figure FDA00037901474700000314
where s denotes multi-scene, rt denotes real-time phase variable,
Figure FDA00037901474700000315
for storing the electricity purchasing power to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000316
representing the discharge power stored in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000317
for the electricity selling power from wind power to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000318
for the electricity selling power from photovoltaic to the power grid in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000319
for the total charging power of the energy stored in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000320
for the discharge power stored in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000321
for charging power from wind power to stored energy in a real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000322
for charging power from photovoltaic to stored energy in real time under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000323
for the energy storage loss coefficient of the v-node wind and light storage station,
Figure FDA00037901474700000324
the energy storage and charging efficiency of the wind and light storage station at the v node is improved,
Figure FDA00037901474700000325
for the v-node wind-solar energy storage field energy storage discharge efficiency,
Figure FDA00037901474700000326
the rated capacity of the wind and light storage station is stored for the v node,
Figure FDA0003790147470000041
for the energy storage initial charge state of the v-node wind and light storage station in the real-time stage,
Figure FDA0003790147470000042
the energy storage end charge state of the v-node wind and light storage station in the real-time stage,
Figure FDA0003790147470000043
is binary variable, s is different scene of wind and light output, T is time, T is time period,
Figure FDA0003790147470000044
for the discharge power of the v-node wind and light storage station in the real-time stage at the time s scene,
Figure FDA0003790147470000045
is the energy storage SOC of the v-node wind and light storage station in the real-time stage under the scene of t time s, delta t is a unit time interval,
Figure FDA0003790147470000046
the upper limit of energy storage and electricity purchase of the v-node wind and light storage station in the real-time stage,
Figure FDA0003790147470000047
for the lower limit of the energy storage and electricity purchase of the v-node wind and light storage station in the real-time stage,
Figure FDA0003790147470000048
for the upper limit of the energy storage SOC of the v-node wind and light energy storage station in the real-time stage at the moment t,
Figure FDA0003790147470000049
for the lower limit of the energy storage SOC of the v-node wind and light energy storage station in the real-time stage at the moment t,
Figure FDA00037901474700000410
the total predicted output of wind power in the real-time stage under the scene of t time s of the v-node wind and light storage station,
Figure FDA00037901474700000411
and the total photovoltaic output is predicted for the photovoltaic in the real-time stage under the scene of t time s of the v-node wind and light storage station.
4. The method for optimizing control over a wind and light storage station in a multi-scale electricity-carbon mode according to claim 1, wherein the day-ahead-real time period profit model is:
Figure FDA00037901474700000412
in the formula (I), the compound is shown in the specification,
Figure FDA00037901474700000413
for the benefit of v-node sites during the day-ahead period,
Figure FDA00037901474700000414
and is the benefit of the v node station in the real-time phase, v represents the wind and light storage station node,
Figure FDA00037901474700000415
the node set is a node set of the wind and light storage station.
5. The wind-solar energy storage station optimal control method under the multi-scale electricity-carbon mode as claimed in claim 1, wherein the coming-out model of the day-ahead stage takes social welfare maximization as an objective function, and is expressed as follows:
Figure FDA00037901474700000416
in the formula (I), the compound is shown in the specification,
Figure FDA00037901474700000417
for the charging quote of v node stations in quote section b/k,
Figure FDA00037901474700000418
for discharge quotes for the v-node sites in quote section b/k,
Figure FDA00037901474700000419
for the offer of user j in offer segment b/k,
Figure FDA00037901474700000420
for the unit i quoted price in the quote section b/k,
Figure FDA00037901474700000421
for the power of user j in quote section b/k in the previous day period,
Figure FDA00037901474700000422
for the power of the unit i in the quotation section b/k in the previous stage,
Figure FDA00037901474700000423
is a node set of the wind and light storage station,
Figure FDA00037901474700000424
is a machine group node set,
Figure FDA00037901474700000425
Is a collection of nodes for a user and,
Figure FDA00037901474700000426
is a set of all nodes of the system, v is a node of the wind and light storage station, i is a unit node, j is a node of a user, wherein,
Figure FDA00037901474700000427
t is the time of day, Δ t is the unit period, and da is the day-ahead phase.
6. The optimal control method for the wind and light storage station under the multi-scale electricity-carbon mode according to claim 5, wherein the real-time phase clearance model is as follows:
Figure FDA0003790147470000051
where, rt is the real-time phase,
Figure FDA0003790147470000052
wind-solar energy storage station for v nodes in real timeB quote the power of the segment in the scenario of time t s,
Figure FDA0003790147470000053
b quotes the power of the section for the real-time phase j node user in the scene of time t s,
Figure FDA0003790147470000054
for the power of the k quoted section of the v-node wind and light storage station in the scene of t time s in the real-time stage,
Figure FDA0003790147470000055
and (4) the power of the k quotation section of the i-node unit at the real-time stage under the scene of t time s.
7. A wind-solar energy storage station optimization control system in a multi-scale electricity-carbon mode is characterized by comprising:
the electric carbon quotation model building module is used for determining the carbon cost of the unit according to the carbon emission characteristics of the unit and building an electric-carbon coupling quotation model of the unit based on the electric power quotation of the units such as coal, gas and fuel; the unit includes: a coal-fired unit, a gas-fired unit and a fuel oil unit;
the station regulation and control model building module is used for designing an internal cooperative regulation and control method of the wind and light storage station and building an internal cooperative regulation and control model of the wind and light storage station by considering the energy flow inside the wind and light storage station;
the station profit model building module is used for adopting the total output and the total input of the wind and light storage station described by the internal cooperative regulation and control model, representing the uncertainty of wind and light output by different scenes based on a day-ahead-real-time two-stage model, and building a profit model of the wind and light storage station participating in the day-ahead-real-time two-stage model;
the day-ahead clearing model building module is used for building a day-ahead clearing model with the social welfare of the day-ahead stage as a maximum target based on the electricity-carbon coupling quotation model of the unit, the quotation of the wind and light storage station and the quotation of the user;
the real-time clearing model building module is used for building a clearing model in a real-time stage according to the maximum social welfare of the real-time stage as a target on the basis of an electric-carbon coupling quotation model of the unit and quotations and user quotations of the wind and light storage station;
the double-layer model building module is used for building a double-layer model of the wind and light storage station participating in multi-time scale power service by taking the day-ahead and real-time two-stage income model as an upper layer and taking the clearance model of the day-ahead stage and the clearance model of the real-time stage as a lower layer;
and the model solving module is used for expressing and solving the double-layer model by adopting a KKT theorem and a large M method to obtain the optimal decision quotation and the optimal output of the wind and light storage station.
8. An electronic device, comprising: a processor and a memory;
the processor is connected with the memory; the processor retrieves and executes the computer program stored in the memory; the computer program is used for implementing the optimal control method for the wind-solar storage station in the multi-scale electricity-carbon mode according to any one of claims 1 to 6.
CN202210953564.1A 2022-08-10 2022-08-10 Wind and light storage station optimization control method and system under multi-scale electricity-carbon mode Pending CN115102231A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117200342A (en) * 2023-09-06 2023-12-08 上海勘测设计研究院有限公司 Wind-solar-energy-storage integrated operation method, system, medium and device with cross time scale
CN117639022A (en) * 2024-01-25 2024-03-01 华北电力大学 Energy storage multiplex regulation and control method, system and electronic equipment

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117200342A (en) * 2023-09-06 2023-12-08 上海勘测设计研究院有限公司 Wind-solar-energy-storage integrated operation method, system, medium and device with cross time scale
CN117639022A (en) * 2024-01-25 2024-03-01 华北电力大学 Energy storage multiplex regulation and control method, system and electronic equipment
CN117639022B (en) * 2024-01-25 2024-05-03 华北电力大学 Energy storage multiplex regulation and control method, system and electronic equipment

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