CN111030096A - Wind-solar-storage combined power generation system-based power generation and utilization integrated scheduling method - Google Patents

Wind-solar-storage combined power generation system-based power generation and utilization integrated scheduling method Download PDF

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CN111030096A
CN111030096A CN201911242410.6A CN201911242410A CN111030096A CN 111030096 A CN111030096 A CN 111030096A CN 201911242410 A CN201911242410 A CN 201911242410A CN 111030096 A CN111030096 A CN 111030096A
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wind
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CN111030096B (en
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侯慧
徐焘
吴细秀
李显强
唐爱红
刘鹏
陈洋洋
王晴
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Wuhan University of Technology WUT
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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
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/10—Photovoltaic [PV]
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a wind-solar-storage combined power generation system-based power generation and utilization integrated scheduling method, which comprises the following steps: respectively establishing a wind-solar-energy-storage combined power generation system model, a power grid side model and a user side model, and then establishing a power generation and utilization integrated scheduling model; when scheduling is prepared, collecting current load information, photovoltaic power generation conditions and wind power generation conditions, and calculating wind and light power generation amount according to the photovoltaic power generation conditions and the wind power generation conditions; determining the load type for reporting the electricity price according to the relation between the wind-solar power generation amount and the load and whether the electricity price of the power grid is higher than a threshold value, and reporting the electricity price by the load of the corresponding type; and according to the electricity price declaration information, taking the first objective function, the second objective function and the third objective function as optimization objectives, and scheduling by using the electricity generation and utilization integrated scheduling model to obtain a scheduling result. The invention can improve the dispatching effect and has important practical significance for improving the consumption capability of renewable energy sources and optimizing power generation and utilization resources.

Description

Wind-solar-storage combined power generation system-based power generation and utilization integrated scheduling method
Technical Field
The invention relates to the technical field of energy Internet, in particular to a wind-solar-storage combined power generation system-based power generation and utilization integrated scheduling method.
Background
The large-scale wind energy and solar energy are developed and utilized, so that the installed capacity of renewable energy sources is continuously increased, but wind and light are difficult to absorb, the problem of wind abandoning and light abandoning is serious, and meanwhile, great challenges are brought to the optimized scheduling of a power generation side.
The inventor of the present application finds that the method of the prior art has at least the following technical problems in the process of implementing the present invention:
in the prior art, the research on power generation and power utilization integrated scheduling at home and abroad mainly aims at reducing the operation cost of a power grid, and from the power generation side, most methods only consider a wind power system, a photovoltaic power generation system or a traditional thermal power generation system independently, and the wind-light-storage combined power generation system is used as a research object rarely; in addition, from the perspective of a user side, many researches are carried out on the scheduling problem of the controllable load and the electric vehicle load, but the controllable load and the electric vehicle are mostly considered independently in the optimization scheduling, so the scheduling effect is poor.
Therefore, the method in the prior art has the technical problem of poor scheduling effect.
Disclosure of Invention
In view of this, the invention provides a wind, light and energy storage combined power generation system-based power generation and utilization integrated scheduling method, which is used for solving or at least partially solving the technical problem of poor scheduling effect existing in the prior art.
In order to solve the technical problem, the invention provides a wind-solar-storage combined power generation system-based power generation and utilization integrated scheduling method, which comprises the following steps:
step S1: establishing a wind-solar-energy-storage combined power generation system model according to the power generation income, wherein the wind-solar-energy-storage combined power generation system model comprises a first objective function and corresponding constraint conditions;
step S2: establishing a power grid side model according to the difference value between the income for selling power to the system and the grid-connected cost, wherein the power grid side model comprises a second objective function and a corresponding constraint condition;
step S3: establishing a user side model according to the change condition of the user electricity consumption cost before and after scheduling, wherein the user side model comprises a third objective function and a corresponding constraint condition;
step S4: according to the wind-solar-storage combined power generation system model, the power grid side model and the user side model, taking the wind-solar-storage combined power generation system income, the power grid side income and the user side income as optimization targets, and constructing a power generation and power utilization integrated scheduling model;
step S5: when scheduling is prepared, collecting current load information, photovoltaic power generation conditions and wind power generation conditions, and calculating wind and light power generation amount according to the photovoltaic power generation conditions and the wind power generation conditions;
step S6: determining the load type for reporting the electricity price according to the relation between the wind-solar power generation amount and the load and whether the electricity price of the power grid is higher than a threshold value, and reporting the electricity price by the load of the corresponding type;
step S7: and according to the electricity price declaration information, taking the first objective function, the second objective function and the third objective function as optimization objectives, and scheduling by using the power generation and utilization integrated scheduling model to obtain a scheduling result, wherein the scheduling result comprises the user type and the user number.
In one embodiment, step S1 specifically includes:
step S1.1: first objective function F of wind-light-storage combined power generation system is optimally constructed according to power generation income1：
Figure BDA0002306628330000021
Wherein the content of the first and second substances,
Figure BDA0002306628330000022
representing the ith type of user load participating in scheduling at the time t, wherein i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents electric vehicle load; p (t) represents the user load not participating in scheduling; p is a radical ofb(t) is grid-connected electric quantity at the moment t; p is a radical ofm(t) represents the amount of power purchased to the grid at time t;
Figure BDA0002306628330000023
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t; c. Cm(t) represents the electricity purchase price at the time t; c. CbRepresenting the unit time operation cost of the wind-solar-energy storage combined power generation system; Δ t represents the operating time period;
step S1.2: determining constraint conditions of the wind-solar-energy-storage combined power generation system, wherein the constraint conditions comprise:
the residual capacity is within a set range:
SOCmin≤SOC(t)≤SOCmax
wherein, SOC (t) is the state of charge of the energy storage system at the time t; SOCmin、SOCmaxRespectively the upper limit and the lower limit of the charge state of the energy storage system;
the wind-solar power generation utilization rate is less than or equal to the rated wind-solar power generation utilization rate:
Figure BDA0002306628330000031
wherein R is the wind-solar power generation utilization rate; e (t) istThe electric quantity provided by the wind and the light to the load at the moment;
Figure BDA0002306628330000032
load after scheduling for time t; rrThe rated wind-solar power generation utilization rate is obtained.
In one embodiment, step S2 specifically includes:
step S2.1: optimally constructing a second objective function F on the power grid side according to the generation income2：
Figure BDA0002306628330000033
Wherein p ism(t) represents the amount of power purchased to the grid at time t, cm(t) represents the price of electricity purchased at time t, pb(t) is the grid-connected electric quantity at the moment t, cb(t) represents grid-connected electricity price at time t;
step S2.2: determining constraints on the power grid side, the constraints comprising: the load variance is less than or equal to the rated load variance:
μ≤μa
Figure BDA0002306628330000034
Figure BDA0002306628330000035
wherein μ is the load variance, μaFor the rated load variance, PaIs the average load after scheduling.
In one embodiment, step S3 specifically includes:
step S3.1: optimally constructing a third objective function F of the user side according to the user income3：
Figure BDA0002306628330000036
Wherein, PL(t) load before scheduling at time t, cb(t) represents grid-connected electricity prices at time t,
Figure BDA0002306628330000037
represents the ith user load participating in scheduling at the time t, i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents interruptible loadElectric vehicle load; p (t) represents the user load not participating in scheduling;
Figure BDA0002306628330000038
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t;
step S3.2: determining the constraint conditions of the user side, wherein the constraint conditions comprise:
upper and lower transferable load limits:
Figure BDA0002306628330000041
upper and lower limits of transferable load transferable time:
Figure BDA0002306628330000042
the total amount of transferable load remains constant over a period of time:
Figure BDA0002306628330000043
upper and lower limits of interruptible load:
Figure BDA0002306628330000044
upper and lower limits of interruptible load interruptible time:
Figure BDA0002306628330000045
upper and lower limits of the electric vehicle charge state:
Figure BDA0002306628330000046
and the satisfaction degree of the expenditure of the electricity fee of the user:
s≤sr
Figure BDA0002306628330000047
wherein the content of the first and second substances,
Figure BDA0002306628330000048
representing the ith type of user load participating in scheduling at the time t, wherein i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents electric vehicle load; p (t) represents the user load not participating in scheduling;
Figure BDA0002306628330000049
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t, PL(t) load before scheduling at time t; ptrb(t)、Ptrl(t) total transferable loads before and after transfer at time t, respectively;
Figure BDA00023066283300000410
Figure BDA00023066283300000411
respectively, the total upper and lower transferable load limits;
Figure BDA00023066283300000412
respectively the upper and lower limits of the transferable time range of the transferable load; pin(t) is the total interruptible load at time t;
Figure BDA00023066283300000413
respectively the upper and lower limits of the total interruptible load,
Figure BDA00023066283300000414
respectively the upper limit and the lower limit of the interruptible load interruptible time range; SOCEVmin、SOCEVmaxRespectively the upper and lower limits of the electric vehicle state of charge, SOCEV(t) is the state of charge of the electric vehicle at time t; s is the satisfaction degree of the user electricity expense; srTo be rated satisfactory.
In one embodiment, step S6 specifically includes:
judging whether the wind and light power generation capacity is larger than a load or not;
when the wind and light power generation amount is larger than the load, further judging whether the power grid price is higher than a threshold value, if so, adopting a grid-connected mode, and if not, reporting the price through the electric vehicle load, wherein the information of the price report comprises the load type of report, the time node of report, the reported price and the state of charge;
when the wind and light power generation amount is not larger than the load, further judging whether the power grid power price is higher than a threshold value, if so, reporting the power price through the adjustable load, wherein the adjustable load comprises an interruption load and a transferable load, and the information of the power price report comprises the type of the reported load, the reported time node and the reported power price; and if the voltage is not higher than the preset voltage, purchasing power from the power grid.
In one embodiment, step S7 specifically includes: solving the power generation and utilization integrated scheduling model by adopting a multi-target particle swarm algorithm solving method according to the electricity price declaration information, wherein the optimal income of the wind-solar-storage combined power generation system model, the optimal income of the power grid side system and the optimal income of the user side are used as objective functions;
and obtaining the optimal benefits of the wind-light-storage combined power generation system, the power grid side and the user side under three load types by three types of user side loads, namely, interruptible loads, transferable loads and electric vehicle loads.
In one embodiment, the method for solving the power generation and power utilization integrated scheduling model by adopting a multi-target particle swarm algorithm comprises the following steps:
step S7.1: using latitude nlatitudeLongitude n, longitudelongitudeAverage light intensity nlight_intAverage wind speed nwind_speedObtaining a wind power output curve and a photovoltaic output curve; using the original load P at each momentload(t) obtaining a load curve; obtaining parameters solved by the model, including the capacity P of wind power field in the wind-light-storage combined power generation systemwindCapacity P of photovoltaic power plantsolarCapacity of the energy storage system PenergyInterruptible load and rotatableElectric quantity P applied by each user for load shiftinguserCharging and discharging power P of electric automobileelectricX of time-of-use electricity price in charge declaration of electricity price for transferable load and electric vehiclei～xjIn the time-of-use electricity price y when the load can be interrupted and the electric automobile discharges to declare the electricity pricei～yjGrid-connected electricity price c at time tb(t) electricity purchase price at time tm(t)；
Step S7.2: initializing a particle swarm, specifically comprising: initializing the declared price p of each user at the moment tdeclareAnd the number N of three types of users participating in schedulingusersSetting the population size as N, the iteration number as K, the particle dimension as D, and the acceleration factor as c1、c2Random number r1、r2Inertial weight w0；
Step S7.3: taking the first target function, the second target function and the third target function as fitness functions of the multi-target particle swarm, and taking constraint conditions corresponding to the three target functions as constraint conditions of the multi-target particle swarm;
step S7.4: solving a non-inferior solution set which meets the requirement by utilizing a multi-target particle swarm algorithm;
step S7.5: selecting a group of relatively optimal solutions from the obtained series of non-inferior solutions as a final optimal solution, and taking the output final optimal solution as the optimal benefits of the optimized wind-solar-energy-storage combined power generation system, the power grid side and the user side;
step S7.6: for the transferable load, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal profit of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the transferable load as a scheduling object;
step S7.7: aiming at interruptible loads, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal profit of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the interruptible loads as scheduling objects;
step S7.8: and (4) aiming at the electric automobile load, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal income of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the electric automobile as a dispatching object.
In one embodiment, after step S7, the method further comprises: and judging whether the scheduling result meets the requirement, if so, feeding the scheduling result back to the user, and otherwise, returning to the step S5 to perform scheduling again.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
the invention discloses a wind-solar-storage combined power generation system-based power generation and power utilization integrated scheduling method, which comprises the steps of firstly establishing a wind-solar-storage combined power generation system model according to power generation income, establishing a power grid side model according to a difference value between income for selling power to a system and grid-connected cost, and establishing a user side model according to the change condition of user power utilization cost before and after scheduling, wherein the user side model comprises a third objective function and a corresponding constraint condition; then, according to the wind-solar-storage combined power generation system model, the power grid side model and the user side model, taking the wind-solar-storage combined power generation system income, the power grid side income and the user side income as optimization targets, constructing a power generation and power utilization integrated scheduling model; secondly, when scheduling is prepared, collecting current load information, photovoltaic power generation conditions and wind power generation conditions, and calculating wind and light power generation amount according to the photovoltaic power generation conditions and the wind power generation conditions; determining the load type for reporting the electricity price according to the relation between the wind-solar power generation amount and the load and whether the electricity price of the power grid is higher than a threshold value, and reporting the electricity price by the load of the corresponding type; and finally, according to the electricity price declaration information, taking the first objective function, the second objective function and the third objective function as optimization objectives, and scheduling by using the power generation and utilization integrated scheduling model to obtain a scheduling result, wherein the scheduling result comprises the user type and the user number.
According to the method, an integrated scheduling model of a wind-solar-energy-storage combined power generation system model, a power grid side and a user side is built, three types of user side loads such as interruptible loads, transferable loads and electric vehicle loads are added to participate in scheduling, a user side information declaration mechanism is provided, namely, different types of accords are used for declaring the price of electricity, then a first objective function, a second objective function and a third objective function are used as optimization targets according to the price declaration information, the power generation and utilization integrated scheduling model is used for scheduling to obtain a scheduling result, the interaction level between the user side and the power generation side is improved, the adjustable load and the electric vehicle load are comprehensively considered, and therefore a better scheduling effect can be achieved.
Further, three types of user side loads, namely interruptible loads, transferable loads and electric vehicles, are considered respectively, and the optimal benefits of the wind-solar-energy-storage combined power generation system, the power grid side and the user side under the three conditions are obtained. The invention optimizes the benefits of the three components, and has important practical significance for improving the consumption capability of renewable energy sources and optimizing power generation and utilization resources.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is an interaction relationship diagram of a wind-solar-energy-storage combined power generation system, a power grid side and a user side according to the invention;
fig. 2 is a flowchart of a power generation and utilization integrated scheduling method based on a wind-solar-storage combined power generation system according to an embodiment of the present invention;
FIG. 3 is a basic flow diagram of the integrated scheduling of power generation and utilization in an embodiment;
FIG. 4 is a structural diagram of a wind-solar-energy-storage combined power generation system in an embodiment of the invention;
FIG. 5 is a flow chart of a calculation of an integrated scheduling model for power generation and utilization according to an embodiment of the present invention;
FIG. 6 is a chart of annual wind speed prediction data for an embodiment of the present invention;
FIG. 7 is a graph of annual illumination intensity prediction data for an embodiment of the present invention;
FIG. 8 is a load graph after optimized scheduling according to an embodiment of the present invention;
FIG. 9 is a graph of various load power curves participating in scheduling at each moment in accordance with an embodiment of the present invention;
FIG. 10 is a graph of grid-connected power, stored energy, and purchased power according to an embodiment of the present invention;
fig. 11 is a load graph after different types of loads participate in the optimization scheduling according to an embodiment of the present invention.
Detailed Description
The inventor of the application finds out through a great deal of research and practice that:
in the prior art, the research on power generation and power utilization integrated scheduling at home and abroad mainly aims at reducing the operation cost of a power grid, the research on the simultaneous consideration of the income of a power generation side and a user side is less, and from the power generation side, most methods only consider a wind power system, a photovoltaic power generation system or a traditional thermal power generation system independently, but use a wind-light-storage combined power generation system as a research object is less; from the perspective of a user side, many researches are conducted on the scheduling problem of the adjustable load and the electric vehicle load, but the adjustable load and the electric vehicle are mostly considered independently, and the researches on comprehensively considering the adjustable load and the electric vehicle load are few.
The interaction level between the user side and the power generation side is improved, the adjustable load is guided and adjusted to transfer to the time period when the wind power generation amount is high, and the method is one of effective ways for improving the renewable energy consumption capacity and optimizing power generation and utilization resources.
In view of the above, the invention innovatively provides a dispatching strategy for power generation and power utilization integration, and the dispatching strategy has important practical significance for improving the interaction level between a user side and a power generation side, improving the renewable energy consumption capacity and optimizing power generation and power utilization resources.
In order to achieve the above object, the main concept of the present invention is as follows:
the wind-solar-storage combined power generation system-based power generation and utilization integrated scheduling strategy is provided, three types of user side loads such as interruptible loads, transferable loads and electric vehicle loads are added to participate in scheduling, and a power generation and utilization integrated scheduling model taking profits of the wind-solar-storage combined power generation system, the power grid side and the user side as optimization targets and taking the wind-solar power generation utilization rate, the user electricity expense satisfaction degree and the like as constraint conditions is established. Solving the model by using a multi-target particle swarm algorithm to obtain the optimal profit of the wind-solar-energy-storage combined power generation system, the power grid side and the user side; and respectively considering three types of user side loads including interruptible loads, transferable loads and electric vehicles to obtain the optimal benefits of the wind-light-storage combined power generation system, the power grid side and the user side under three conditions. The invention optimizes the benefits of the three components, and has important practical significance for improving the consumption capability of renewable energy sources and optimizing power generation and utilization resources.
Overall, the invention is improved in three main aspects:
firstly, an optimized scheduling strategy is provided, three types of user side loads such as interruptible loads, transferable loads and electric vehicle loads are added to participate in scheduling, a user side information declaration mechanism is introduced, and a basic flow of power utilization integrated scheduling is provided;
secondly, a power generation and utilization integrated scheduling model taking the benefits of the wind-light-storage combined power generation system, the power grid side and the user side as optimization targets is established;
thirdly, solving the model by using a multi-target particle swarm algorithm to obtain the optimal benefits of the wind-solar-energy-storage combined power generation system, the power grid side and the user side; and respectively considering three types of user side loads such as interruptible loads, transferable loads and electric vehicle loads to obtain the optimal benefits of the wind-light-storage combined power generation system, the power grid side and the user side under three conditions.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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 embodiment provides a power generation and utilization integrated scheduling method based on a wind-solar-storage combined power generation system, please refer to fig. 2, and the method includes:
step S1: and establishing a wind-solar-energy-storage combined power generation system model according to the power generation income, wherein the wind-solar-energy-storage combined power generation system model comprises a first objective function and corresponding constraint conditions.
Specifically, the power generation benefit of the wind-solar-energy-storage combined power generation system is related to the benefit of user load, the expenditure of electric quantity purchased to a power grid, the unit time operation cost of the wind-solar-energy-storage combined power generation system and the like, and according to the factors, a wind-solar-energy-storage combined power generation system model with the optimal power generation benefit as a first objective function can be constructed.
In one embodiment, step S1 specifically includes:
step S1.1: first objective function F of wind-light-storage combined power generation system is optimally constructed according to power generation income1：
Figure BDA0002306628330000091
Wherein the content of the first and second substances,
Figure BDA0002306628330000092
representing the ith type of user load participating in scheduling at the time t, wherein i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents electric vehicle load; p (t) represents the user load not participating in scheduling; p is a radical ofb(t) is grid-connected electric quantity at the moment t; p is a radical ofm(t) represents the amount of power purchased to the grid at time t;
Figure BDA0002306628330000093
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t; c. Cm(t) represents the electricity purchase price at the time t; c. CbRepresenting the unit time operation cost of the wind-solar-energy storage combined power generation system; Δ t represents the operating time period;
step S1.2: determining constraint conditions of the wind-solar-energy-storage combined power generation system, wherein the constraint conditions comprise:
the residual capacity is within a set range:
SOCmin≤SOC(t)≤SOCmax
wherein, SOC (t) is the state of charge of the energy storage system at the time t; SOCmin、SOCmaxRespectively the upper limit and the lower limit of the charge state of the energy storage system;
the wind-solar power generation utilization rate is less than or equal to the rated wind-solar power generation utilization rate:
Figure BDA0002306628330000101
wherein R is the wind-solar power generation utilization rate; e (t) the electric quantity provided by the wind and light to the load at the time t;
Figure BDA0002306628330000102
load after scheduling for time t; rrThe rated wind-solar power generation utilization rate is obtained.
In a specific implementation, SOCmin、SOCmaxThe method can be set according to actual conditions, and can be calculated by the following formula: SOCmin＝0.5×300＝150MW、SOCmax＝0.9×300＝270MW。
Step S2: and establishing a power grid side model according to the difference between the income for selling the electricity to the system and the grid-connected cost, wherein the power grid side model comprises a second objective function and corresponding constraint conditions.
Specifically, the optimal power grid side profit is related to the difference between the profit for selling the electricity to the system and the grid-connected cost, so that a power grid side model with the optimal power grid side profit as a second objective function is constructed.
In one embodiment, step S2 specifically includes:
step S2.1: optimally constructing a second objective function F on the power grid side according to the generation income2：
Figure BDA0002306628330000103
Wherein p ism(t) represents the amount of power purchased to the grid at time t, cm(t) represents the price of electricity purchased at time t, pb(t) ist moment of grid connection electric quantity, cb(t) represents grid-connected electricity price at time t;
step S2.2: determining constraints on the power grid side, the constraints comprising: the load variance is less than or equal to the rated load variance:
μ≤μa
Figure BDA0002306628330000104
Figure BDA0002306628330000105
wherein μ is the load variance, μaFor the rated load variance, PaIs the average load after scheduling.
Specifically, in order to avoid excessive peak shifting during scheduling, which results in "peak-to-peak" of the load, the load variance is constrained, so that the above constraint condition on the power grid side can be obtained.
Step S3: and establishing a user side model according to the change condition of the user electricity consumption cost before and after scheduling, wherein the user side model comprises a third objective function and a corresponding constraint condition.
Specifically, the user side profit is optimal and is related to the change of the user electricity consumption cost before and after scheduling, so that a user side model with the user side profit optimal as a third objective function is constructed.
In one embodiment, step S3 specifically includes:
step S3.1: optimally constructing a third objective function F of the user side according to the user income3：
Figure BDA0002306628330000111
Wherein, PL(t) load before scheduling at time t, cb(t) represents grid-connected electricity prices at time t,
Figure BDA0002306628330000112
representing the i-th user load participating in scheduling at the time t, i-1 tableIndicating transferable load, wherein i is 2 to indicate interruptible load, and i is 3 to indicate electric vehicle load; p (t) represents the user load not participating in scheduling; c. Ci *(t) reporting the electricity price of the ith class user participating in scheduling at the time t; c. Cb(t) represents grid-connected electricity price at time t;
step S3.2: determining the constraint conditions of the user side, wherein the constraint conditions comprise:
upper and lower transferable load limits:
Figure BDA0002306628330000113
upper and lower limits of transferable load transferable time:
Figure BDA0002306628330000114
the total amount of transferable load remains constant over a period of time:
Figure BDA0002306628330000115
upper and lower limits of interruptible load:
Figure BDA0002306628330000116
upper and lower limits of interruptible load interruptible time:
Figure BDA0002306628330000117
upper and lower limits of the electric vehicle charge state:
SOCEVmin≤SOCEV(t)≤SOCEVmax
and the satisfaction degree of the expenditure of the electricity fee of the user:
s≤sr
Figure BDA0002306628330000118
wherein the content of the first and second substances,
Figure BDA0002306628330000121
representing the ith type of user load participating in scheduling at the time t, wherein i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents electric vehicle load; p (t) represents the user load not participating in scheduling;
Figure BDA0002306628330000122
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t, PL(t) load before scheduling at time t; ptrb(t)、Ptrl(t) total transferable loads before and after transfer at time t, respectively;
Figure BDA0002306628330000123
Figure BDA0002306628330000124
respectively, the total upper and lower transferable load limits;
Figure BDA0002306628330000125
respectively the upper and lower limits of the transferable time range of the transferable load; pin(t) is the total interruptible load at time t;
Figure BDA0002306628330000126
respectively the upper and lower limits of the total interruptible load,
Figure BDA0002306628330000127
respectively the upper limit and the lower limit of the interruptible load interruptible time range; SOCEVmin、SOCEVmaxRespectively the upper and lower limits of the electric vehicle state of charge, SOCEV(t) is the state of charge of the electric vehicle at time t; s is the satisfaction degree of the user electricity expense; srTo be rated satisfactory.
In a specific implementation process, values of the parameters may be:
Figure BDA0002306628330000128
respectively, the total upper and lower transferable load limits;
Figure BDA0002306628330000129
respectively the upper and lower limits of the transferable time range of the transferable load; pin(t) is the total interruptible load at time t;
Figure BDA00023066283300001210
respectively, the total interruptible load upper and lower limits;
Figure BDA00023066283300001211
respectively the upper limit and the lower limit of the interruptible load interruptible time range; SOCEVmin、SOCEVmaxRespectively representing the upper limit and the lower limit of the electric vehicle charge state; SOCEV(t) the state of charge of the electric vehicle at time t, wherein SOCEVminIs 20% of the rated capacity of the battery, SOCEVmaxIs 90% of the rated capacity of the battery; s is the satisfaction degree of the user electricity expense; srRated satisfaction was given as 1.5. The above parameters can be set according to actual conditions, and the above values are only used as examples and do not limit the protection range.
Step S4: and constructing a power generation and power utilization integrated scheduling model by taking the benefits of the wind-solar-storage combined power generation system, the benefits of the power grid side and the benefits of the user side as optimization targets according to the wind-solar-storage combined power generation system model, the power grid side model and the user side model.
Specifically, referring to fig. 1, the power generation and utilization integrated scheduling model includes a wind-solar-energy-storage combined power generation system model, a power grid-side model and a user-side model, that is, the foregoing first objective function and its corresponding constraint condition, the second objective function and its corresponding constraint condition, and the third objective function and its corresponding constraint condition are included.
Step S5: and when the dispatching is prepared, collecting the current load information, the photovoltaic power generation condition and the wind power generation condition, and calculating the wind and light power generation amount according to the photovoltaic power generation condition and the wind power generation condition.
Step S6: and determining the load type for reporting the electricity price according to the relation between the wind-solar power generation amount and the load and whether the electricity price of the power grid is higher than a threshold value, and reporting the electricity price by the load of the corresponding type.
Specifically, the wind-solar-energy-storage combined power generation system collects original load information, judges the magnitude of wind-solar power generation and load by combining the conditions of photovoltaic power generation and wind power generation, and selects a corresponding scheduling strategy. According to the load type, the time point and the electricity price submitted by the user, the system selects the type and the number of the users according to the residual or lacked electric quantity and the proper electricity price, and measures the constraint conditions such as the utilization rate of wind-solar power generation and the satisfaction degree of user electricity expense expenditure, so that the benefits of the wind-solar-energy-storage combined power generation system, the power grid side and the user side are optimal. If the scheduling requirement is met, the system feeds the scheduling information back to the user, and the user responds according to the fed-back information. The proposed scheduling strategy can take the user's will into consideration to the maximum extent, improve the satisfaction of the user's electricity charge expenditure, and simultaneously improve the safety and economy of the wind-solar-energy-storage combined power generation system.
In one embodiment, step S6 specifically includes:
judging whether the wind and light power generation capacity is larger than a load or not;
when the wind and light power generation amount is larger than the load, further judging whether the power grid price is higher than a threshold value, if so, adopting a grid-connected mode, and if not, reporting the price through the electric vehicle load, wherein the information of the price report comprises the load type of report, the time node of report, the reported price and the state of charge;
when the wind and light power generation amount is not larger than the load, further judging whether the power grid power price is higher than a threshold value, if so, reporting the power price through the adjustable load, wherein the adjustable load comprises an interruption load and a transferable load, and the information of the power price report comprises the type of the reported load, the reported time node and the reported power price; and if the voltage is not higher than the preset voltage, purchasing power from the power grid.
Specifically, referring to fig. 3, only two situations are possible in the operation of the wind-solar-energy-storage combined power generation system, wherein the wind-solar power generation amount is larger than the load and the wind-solar power generation amount is smaller than the load. Therefore, the scheduling is performed according to the following optimal scheduling strategy in consideration of the actual output of the wind-solar power generation amount and the actual load situation.
(1) When the load is smaller than the wind-light generating capacity, the system has residual electric quantity besides meeting the traditional load requirement, and three modes of grid connection, energy storage system electricity storage and arrangement of electric automobile charging are adopted for scheduling of the residual electric quantity. Based on the time-of-use electricity price, if the electricity price of the power grid is high, grid connection is carried out; and if the power grid is low in price, the electric automobile load starts to report the price. The mode determines the scheduling mode of the residual energy by the system.
(2) When the load is larger than the wind and light power generation amount, for the insufficient load part, the electric energy is dispatched in various modes such as power grid purchase, energy storage system discharge, arrangement of electric automobile discharge and load interruption and transfer. Based on the time-of-use electricity price, if the electricity price of the power grid is low, purchasing the power grid; if the electricity price of the power grid is high, selecting an energy storage system to discharge; if not, the electric vehicle load, interruptible load and transferable load begin reporting electricity prices, and the system determines the scheduling mode of the underloaded part.
The user-side information declaration mechanism (for reporting the electricity price of the load on the user side) in the above process is specifically described as follows:
although the user habits of the user can be changed to a certain extent by the time-of-use electricity price, and the user is guided to use electricity at a low electricity price, direct information sharing is lacked between the electricity generation side and the electricity utilization side in the mode, the load of the user side is difficult to effectively schedule, the wind and light consumption effect is limited, and the profits of the electricity generation side and the user side are not balanced. Therefore, in order to balance the income, the load at the user side is stimulated to better respond to the optimized scheduling strategy, and the user self-declares the electricity price. The declaration process needs to satisfy the following conditions: (1) the real-time electricity price mechanism in the power market is not considered, and in order to avoid the situation that a user optionally declares the electricity price, the user declares the electricity price within a certain range on the basis of the time-of-use electricity price; (2) for the adjustable load, the electric quantity declared by each user is kept constant; (3) the method comprises the following steps of selecting a lithium battery electric vehicle as a scheduling object, wherein the influence on the lithium battery electric vehicle caused by short-time charging or discharging can be ignored, and the lithium battery electric vehicle can be scheduled at will after a user declares the lithium battery electric vehicle; (4) the time length of the user reporting the participation in the scheduling is 1h each time, and if the user wants to continue the participation, other time intervals need to be reported again.
The controllable load comprises an interruptible load, a transferable load and the like, and the electric Vehicle load is used as a special transferable load and has a V2G (Vehicle-to-grid) function, and the transferable load has no discharging function. Therefore, interruptible load, transferable load and electric vehicle load are selected to participate in scheduling, and the adjustable load needs to reflect the following information in the aspects during reporting: the declared load type, the declared time node and the declared price of electricity, and the electric vehicle load user needs to upload the charge state of the electric vehicle in addition to reflecting the information when declaring. The declaration process depends on an energy internet platform, and the user uploads declaration types and proper time points to the system according to the characteristics and time arrangement of the user and declares the price of electricity within a certain price range.
Step S7: and according to the electricity price declaration information, taking the first objective function, the second objective function and the third objective function as optimization objectives, and scheduling by using the power generation and utilization integrated scheduling model to obtain a scheduling result, wherein the scheduling result comprises the user type and the user number.
Wherein, step S7 specifically includes: solving the power generation and utilization integrated scheduling model by adopting a multi-target particle swarm algorithm solving method according to the electricity price declaration information, wherein the optimal income of the wind-solar-storage combined power generation system model, the optimal income of the power grid side system and the optimal income of the user side are used as objective functions;
and obtaining the optimal benefits of the wind-light-storage combined power generation system, the power grid side and the user side under three load types by three types of user side loads, namely, interruptible loads, transferable loads and electric vehicle loads.
In one embodiment, the method for solving the power generation and power utilization integrated scheduling model by adopting a multi-target particle swarm algorithm comprises the following steps:
step S7.1: using latitude nlatitudeLongitude n, longitudelongitudeAverage light intensity nlight_intAverage wind speed nwind_speedObtaining a wind power output curve and a photovoltaic outputA curve; using the original load P at each momentload(t) obtaining a load curve; obtaining parameters solved by the model, including the capacity P of wind power field in the wind-light-storage combined power generation systemwindCapacity P of photovoltaic power plantsolarCapacity of the energy storage system PenergyThe amount of power P applied by each user for interruptible load and transferable loaduserCharging and discharging power P of electric automobileelectricX of time-of-use electricity price in charge declaration of electricity price for transferable load and electric vehiclei～xjIn the time-of-use electricity price y when the load can be interrupted and the electric automobile discharges to declare the electricity pricei～yjGrid-connected electricity price c at time tb(t) electricity purchase price at time tm(t)；
Step S7.2: initializing a particle swarm, specifically comprising: initializing the declared price p of each user at the moment tdeclareAnd the number N of three types of users participating in schedulingusersSetting the population size as N, the iteration number as K, the particle dimension as D, and the acceleration factor as c1、c2Random number r1、r2Inertial weight w0；
Step S7.3: taking the first target function, the second target function and the third target function as fitness functions of the multi-target particle swarm, and taking constraint conditions corresponding to the three target functions as constraint conditions of the multi-target particle swarm;
step S7.4: solving a non-inferior solution set which meets the requirement by utilizing a multi-target particle swarm algorithm;
step S7.5: selecting a group of relatively optimal solutions from the obtained series of non-inferior solutions as a final optimal solution, and taking the output final optimal solution as the optimal benefits of the optimized wind-solar-energy-storage combined power generation system, the power grid side and the user side;
step S7.6: for the transferable load, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal profit of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the transferable load as a scheduling object;
step S7.7: aiming at interruptible loads, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal profit of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the interruptible loads as scheduling objects;
step S7.8: and (4) aiming at the electric automobile load, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal income of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the electric automobile as a dispatching object.
Specifically, the model is solved by adopting a multi-target particle swarm algorithm solving method, so that the calculation efficiency can be improved, and a better scheduling result can be obtained.
Referring to fig. 4 to 11, a specific embodiment of the method of the present invention is described, fig. 4 is a structural diagram of a wind-solar-energy-storage combined power generation system, fig. 5 is a calculation flow chart of solving a power generation and utilization integrated scheduling model by using a multi-target particle swarm optimization solving method, and fig. 6 and 7 are respectively a year-round wind speed prediction data graph and a year-round illumination intensity prediction data graph in the embodiment of the present invention.
In a specific implementation process, referring to the power generation and utilization integrated scheduling model in the foregoing, actual parameters are needed in the solving process, wherein the actual parameters are utilized, and the latitude is 35 degrees 22 degrees, the longitude is 105 degrees 1 degrees, and the average illumination intensity is 4.32kW/m2Obtaining a wind power and photovoltaic output curve at the average wind speed of 4.87 m/s; using the original load P at each momentload(t) obtaining a load curve; the data used in the model comprises the capacity 1200MW of a wind power field in the wind-solar-energy-storage combined power generation system, the capacity 1600MW of a photovoltaic power station, the capacity 300MW of an energy storage system, the electric quantity 2MW applied by each user of an interruptible load and a transferable load, the charging and discharging power of an electric automobile 30kW, the grid-connected electricity price of the transferable load and the electric automobile within 0.05-0.15 of the time-of-use electricity price when the transferable load and the electric automobile charge the electricity price, the grid-connected electricity price of the interruptible load and the electric automobile discharge the electricity price within 1.05-1.15 of the time-of-use electricity price, the peak time period (11: 00-15: 00, 19: 00-22: 00) is 650 yuan/(MW · h), the flat time period (07: 00-10: 00, 16: 00-18: 00, 22: 00-23: 00) is 380 yuan/(MW · h), the valley time period (00: 00-06: 00) is 130 yuan/(MW · h, the peak time period (11: 00-15: 00, 19: 00-22: 00) has a purchase price of 830 Yuan-(MW & h), the electricity purchasing price in the flat period (07: 00-10: 00, 16: 00-18: 00, 22: 00-23: 00) is 490 yuan/(MW & h), the electricity purchasing price in the valley period (00: 00-06: 00) is 170 yuan/(MW & h), and the like;
then, the particle group is initialized, namely, the declaration price p of each user is initialized to 24hdeclareAnd the number N of three types of users participating in schedulingusersSetting the population size to be 500, the iteration number to be 150, the particle dimension to be 72, and the acceleration factor to be c1＝0.8、c20.9, random number r1＝0.5、r20.5, inertial weight w0＝0.5；
Then, steps S3.4 to S3.8 are performed according to the aforementioned parameters to obtain the final scheduling result.
In one embodiment, after step S7, the method further comprises: and judging whether the scheduling result meets the requirement, if so, feeding the scheduling result back to the user, and otherwise, returning to the step S5 to perform scheduling again.
Specifically, after the scheduling result is fed back to the user, a load response may be performed, and the scheduling is completed. Fig. 8 to 11 are related result graphs obtained by the method of the present invention, including a load graph after optimized scheduling, a load graph after different types of loads participate in optimized scheduling, and the like.
Compared with the prior art, the method has the advantages that the multi-objective particle swarm algorithm can be utilized to solve the power generation and utilization integrated scheduling model, the power generation and utilization integrated scheduling strategy enables the benefits of the three to be optimal, the user will can be considered to the maximum extent, the renewable energy consumption capacity is improved, the power generation and utilization resources are optimized, and the effect of peak clipping and valley filling on the original load is achieved to a certain extent.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.

Claims (8)

1. A power generation and utilization integrated scheduling method based on a wind-solar-storage combined power generation system is characterized by comprising the following steps:
step S1: establishing a wind-solar-energy-storage combined power generation system model according to the power generation income, wherein the wind-solar-energy-storage combined power generation system model comprises a first objective function and corresponding constraint conditions;
step S2: establishing a power grid side model according to the difference value between the income for selling power to the system and the grid-connected cost, wherein the power grid side model comprises a second objective function and a corresponding constraint condition;
step S3: establishing a user side model according to the change condition of the user electricity consumption cost before and after scheduling, wherein the user side model comprises a third objective function and a corresponding constraint condition;
step S4: according to the wind-solar-storage combined power generation system model, the power grid side model and the user side model, taking the wind-solar-storage combined power generation system income, the power grid side income and the user side income as optimization targets, and constructing a power generation and power utilization integrated scheduling model;
step S5: when scheduling is prepared, collecting current load information, photovoltaic power generation conditions and wind power generation conditions, and calculating wind and light power generation amount according to the photovoltaic power generation conditions and the wind power generation conditions;
step S6: determining the load type for reporting the electricity price according to the relation between the wind-solar power generation amount and the load and whether the electricity price of the power grid is higher than a threshold value, and reporting the electricity price by the load of the corresponding type;
step S7: and according to the electricity price declaration information, taking the first objective function, the second objective function and the third objective function as optimization objectives, and scheduling by using the power generation and utilization integrated scheduling model to obtain a scheduling result, wherein the scheduling result comprises the user type and the user number.
2. The method according to claim 1, wherein step S1 specifically comprises:
step S1.1: first objective function F of wind-light-storage combined power generation system is optimally constructed according to power generation income1：
Figure FDA0002306628320000011
Wherein the content of the first and second substances,
Figure FDA0002306628320000012
representing the ith type of user load participating in scheduling at the time t, wherein i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents electric vehicle load; p (t) represents the user load not participating in scheduling; p is a radical ofb(t) is grid-connected electric quantity at the moment t; p is a radical ofm(t) represents the amount of power purchased to the grid at time t;
Figure FDA0002306628320000013
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t; c. Cm(t) represents the electricity purchase price at the time t; c. CbRepresenting the unit time operation cost of the wind-solar-energy storage combined power generation system; Δ t represents the operating time period;
step S1.2: determining constraint conditions of the wind-solar-energy-storage combined power generation system, wherein the constraint conditions comprise:
the residual capacity is within a set range:
SOCmin≤SOC(t)≤SOCmax
wherein, SOC (t) is the state of charge of the energy storage system at the time t; SOCmin、SOCmaxRespectively the upper limit and the lower limit of the charge state of the energy storage system;
the wind-solar power generation utilization rate is less than or equal to the rated wind-solar power generation utilization rate:
Figure FDA0002306628320000021
wherein R is the wind-solar power generation utilization rate; e (t) istThe electric quantity provided by the wind and the light to the load at the moment; pL *(t) is the load scheduled at time t; rrThe rated wind-solar power generation utilization rate is obtained.
3. The method according to claim 1, wherein step S2 specifically comprises:
step S2.1: optimally constructing a second objective function F on the power grid side according to the generation income2：
Figure FDA0002306628320000022
Wherein p ism(t) represents the amount of power purchased to the grid at time t, cm(t) represents the price of electricity purchased at time t, pb(t) is the grid-connected electric quantity at the moment t, cb(t) represents grid-connected electricity price at time t;
step S2.2: determining constraints on the power grid side, the constraints comprising: the load variance is less than or equal to the rated load variance:
μ≤μa
Figure FDA0002306628320000023
Figure FDA0002306628320000024
wherein μ is the load variance, μaFor the rated load variance, PaIs the average load after scheduling.
4. The method according to claim 1, wherein step S3 specifically comprises:
step S3.1: optimally constructing a third objective function F of the user side according to the user income3：
Figure FDA0002306628320000031
Wherein, PL(t) load before scheduling at time t, cb(t) represents grid-connected electricity prices at time t,
Figure FDA0002306628320000032
representing the ith type of user load participating in scheduling at the time t, wherein i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents electric vehicle load; p (t) represents the user load not participating in scheduling;
Figure FDA0002306628320000033
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t;
step S3.2: determining the constraint conditions of the user side, wherein the constraint conditions comprise:
upper and lower transferable load limits:
Figure FDA0002306628320000034
upper and lower limits of transferable load transferable time:
Figure FDA0002306628320000035
the total amount of transferable load remains constant over a period of time:
Figure FDA0002306628320000036
upper and lower limits of interruptible load:
Figure FDA0002306628320000037
upper and lower limits of interruptible load interruptible time:
Figure FDA0002306628320000038
upper and lower limits of the electric vehicle charge state:
SOCEVmin≤SOCEV(t)≤SOCEVmax
and the satisfaction degree of the expenditure of the electricity fee of the user:
s≤sr
Figure FDA0002306628320000039
wherein the content of the first and second substances,
Figure FDA00023066283200000310
representing the ith type of user load participating in scheduling at the time t, wherein i-1 represents transferable load, i-2 represents interruptible load, and i-3 represents electric vehicle load; p (t) represents the user load not participating in scheduling;
Figure FDA00023066283200000311
the declaration price of the ith class of users participating in scheduling at the time t is represented; c. Cb(t) represents grid-connected electricity price at time t, PL(t) load before scheduling at time t; ptrb(t)、Ptrl(t) total transferable loads before and after transfer at time t, respectively;
Figure FDA00023066283200000312
Figure FDA0002306628320000041
respectively, the total upper and lower transferable load limits;
Figure FDA0002306628320000042
respectively the upper and lower limits of the transferable time range of the transferable load; pin(t) is the total interruptible load at time t;
Figure FDA0002306628320000043
are respectively a totalThe upper and lower limits of the interruptible load of (c),
Figure FDA0002306628320000044
respectively the upper limit and the lower limit of the interruptible load interruptible time range; SOCEVmin、SOCEVmaxRespectively the upper and lower limits of the electric vehicle state of charge, SOCEV(t) is the state of charge of the electric vehicle at time t; s is the satisfaction degree of the user electricity expense; srTo be rated satisfactory.
5. The method according to claim 1, wherein step S6 specifically comprises:
judging whether the wind and light power generation capacity is larger than a load or not;
when the wind and light power generation amount is larger than the load, further judging whether the power grid price is higher than a threshold value, if so, adopting a grid-connected mode, and if not, reporting the price through the electric vehicle load, wherein the information of the price report comprises the load type of report, the time node of report, the reported price and the state of charge;
when the wind and light power generation amount is not larger than the load, further judging whether the power grid power price is higher than a threshold value, if so, reporting the power price through the adjustable load, wherein the adjustable load comprises an interruption load and a transferable load, and the information of the power price report comprises the type of the reported load, the reported time node and the reported power price; and if the voltage is not higher than the preset voltage, purchasing power from the power grid.
6. The method according to claim 1, wherein step S7 specifically comprises: solving the power generation and utilization integrated scheduling model by adopting a multi-target particle swarm algorithm solving method according to the electricity price declaration information, wherein the optimal income of the wind-solar-storage combined power generation system model, the optimal income of the power grid side system and the optimal income of the user side are used as objective functions;
and obtaining the optimal benefits of the wind-light-storage combined power generation system, the power grid side and the user side under three load types by three types of user side loads, namely, interruptible loads, transferable loads and electric vehicle loads.
7. The method of claim 6, wherein the power generation and utilization integrated scheduling model is solved by a multi-objective particle swarm optimization solving method, and the method comprises the following steps:
step S7.1: using latitude nlatitudeLongitude n, longitudelongitudeAverage light intensity nlight_intAverage wind speed nwind_speedObtaining a wind power output curve and a photovoltaic output curve; using the original load P at each momentload(t) obtaining a load curve; obtaining parameters solved by the model, including the capacity P of wind power field in the wind-light-storage combined power generation systemwindCapacity P of photovoltaic power plantsolarCapacity of the energy storage system PenergyThe amount of power P applied by each user for interruptible load and transferable loaduserCharging and discharging power P of electric automobileelectricX of time-of-use electricity price in charge declaration of electricity price for transferable load and electric vehiclei～xjIn the time-of-use electricity price y when the load can be interrupted and the electric automobile discharges to declare the electricity pricei～yjGrid-connected electricity price c at time tb(t) electricity purchase price at time tm(t)；
Step S7.2: initializing a particle swarm, specifically comprising: initializing the declared price p of each user at the moment tdeclareAnd the number N of three types of users participating in schedulingusersSetting the population size as N, the iteration number as K, the particle dimension as D, and the acceleration factor as c1、c2Random number r1、r2Inertial weight w0；
Step S7.3: taking the first target function, the second target function and the third target function as fitness functions of the multi-target particle swarm, and taking constraint conditions corresponding to the three target functions as constraint conditions of the multi-target particle swarm;
step S7.4: solving a non-inferior solution set which meets the requirement by utilizing a multi-target particle swarm algorithm;
step S7.5: selecting a group of relatively optimal solutions from the obtained series of non-inferior solutions as a final optimal solution, and taking the output final optimal solution as the optimal benefits of the optimized wind-solar-energy-storage combined power generation system, the power grid side and the user side;
step S7.6: for the transferable load, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal profit of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the transferable load as a scheduling object;
step S7.7: aiming at interruptible loads, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal profit of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the interruptible loads as scheduling objects;
step S7.8: and (4) aiming at the electric automobile load, repeatedly executing the step S7.1-7.5, and outputting a final optimization result as the optimal income of the wind-solar-energy-storage combined power generation system, the power grid side and the user side which are optimized by taking the electric automobile as a dispatching object.
8. The method of claim 1, wherein after step S7, the method further comprises: and judging whether the scheduling result meets the requirement, if so, feeding the scheduling result back to the user, and otherwise, returning to the step S5 to perform scheduling again.
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