CN113285490A - Power system scheduling method and device, computer equipment and storage medium - Google Patents

Power system scheduling method and device, computer equipment and storage medium Download PDF

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CN113285490A
CN113285490A CN202110633541.8A CN202110633541A CN113285490A CN 113285490 A CN113285490 A CN 113285490A CN 202110633541 A CN202110633541 A CN 202110633541A CN 113285490 A CN113285490 A CN 113285490A
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power
wind
energy storage
output power
battery energy
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CN113285490B (en
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聂涌泉
胡亚平
周华锋
赵化时
何宇斌
顾慧杰
彭超逸
高红亮
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China Southern Power Grid Co Ltd
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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
    • H02J3/46—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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • 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
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/10—Flexible AC transmission systems [FACTS]
    • 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
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The application relates to a power system scheduling method, a power system scheduling device, computer equipment and a storage medium. The method comprises the following steps: according to the obtained wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, aiming at the minimum of the total operation cost of the power system, solving a preset system operation cost objective function to obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit, the optimal output power of the battery energy storage system and the minimum of the total operation cost of the power system, and scheduling the power system considering wind power integration and the battery energy storage system according to the minimum of the total operation cost of the power system; the objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function. By adopting the method, the power system can be reasonably planned and scheduled, the minimization of the total operation cost of the system is realized, and the method is more environment-friendly.

Description

Power system scheduling method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of power system technologies, and in particular, to a power system scheduling method, apparatus, computer device, and storage medium.
Background
Due to the concerns about air pollution and global warming, the establishment of a low-carbon society has attracted extensive attention, and the use of clean energy is increasingly common. The consumption of renewable energy is a key factor for reducing air pollution and reducing clean energy environment.
Wind power has important roles in reducing global greenhouse gas emission and relieving global energy shortage due to the characteristics of cleanness and reproducibility. Wind power generation has been increasingly used in recent years because it does not produce harmful emissions as an effective measure for reducing carbon emissions.
According to the traditional power system scheduling scheme considering wind power grid connection, because uncertain factors are brought by the intermittency and volatility of wind power, difficulty is brought to power grid scheduling and planning, the power system scheduling is unreasonable, and the power system operation cost is high.
Disclosure of Invention
In view of the above, it is necessary to provide a power system scheduling method, apparatus, computer device and storage medium capable of reducing the operation cost of a power system.
A power system scheduling method is suitable for a power system considering wind power integration and a battery energy storage system, and comprises the following steps:
acquiring a wind power predicted value, a wind turbine generator parameter, a thermal power generator unit parameter, power grid unit load demand data and a battery energy storage system parameter;
based on the wind power predicted value, the wind power unit parameters, the thermal power unit parameters, the power grid unit load demand data, the battery energy storage system parameters and preset constraint conditions, with the minimum total operating cost of the power system as a target, solving a preset system operating cost objective function to obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system;
obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
scheduling the power system considering the wind power integration and the battery energy storage system according to the minimum total operation cost of the power system;
the preset system operation total cost target function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
In one embodiment, based on the wind power predicted value, the wind turbine generator parameter, the thermal power generator parameter, the grid generator load demand data, the battery energy storage system parameter and the preset constraint condition, with the minimum total operating cost of the power system as a target, solving a preset system operating cost objective function to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system comprises:
based on the wind power predicted value, the wind power generation unit parameters, the thermal power generation unit parameters, the power grid unit load demand data, the battery energy storage system parameters and preset constraint conditions, the method aims at minimizing the total operation cost of the power system, adopts a quantum particle swarm optimization algorithm to solve a preset system operation cost objective function so as to optimize the output power of the wind power generation unit, the output power of the thermal power generation unit and the output power of the battery energy storage system, and obtains the optimal output power of the wind power generation unit, the optimal output power of the thermal power generation unit and the optimal output power of the battery energy storage system.
In one embodiment, based on a wind power predicted value, a wind turbine generator parameter, a thermal power generator parameter, a grid generator load demand data, a battery energy storage system parameter and a preset constraint condition, with a goal of minimum total operating cost of a power system, a quantum particle swarm optimization algorithm is adopted to solve a preset system operating cost objective function so as to optimize the wind turbine generator output power, the thermal power generator output power and the battery energy storage system output power, and the obtaining of the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system comprises:
initializing quantum bits and angles of quanta based on the output power of the wind turbine generator, the output power of the thermal power generator and the output power of the battery energy storage system, and constructing an initial random population;
counting the iteration times and the population scale of the algorithm, and judging whether the initial random population meets the running suspension condition of the preset algorithm;
when the initial random population does not meet the operation stopping condition of the preset algorithm, continuing iteration to obtain and update a local optimal solution and a global optimal solution of the quantum, wherein the local optimal solution and the global optimal solution comprise the initial wind turbine generator optimal output power, the initial thermal power generator optimal output power and the initial battery energy storage system optimal output power;
if the current global optimal solution is consistent with the last global optimal solution, calculating quantum affinity and concentration;
selecting quanta and carrying out sequence mutation by using a self-adaptive probability selection algorithm and chaotic sequence variation;
updating quantum bits and angles of the quantum through a quantum rotation gate algorithm;
and adding new quantum individuals into the initial random population, returning to the step of counting the preset iteration times and the preset population scale, and judging whether the initial random population meets the preset algorithm operation stopping condition or not until the initial random population meets the preset algorithm operation stopping condition, so as to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system.
In one embodiment, obtaining the wind power prediction value comprises:
acquiring historical wind speed data;
fitting historical wind speed data by using a Weibull probability distribution function, and obtaining a probability density function of wind power by combining the relation between wind speed and output power;
and acquiring a wind power predicted value according to the probability density function of the wind power.
In one embodiment, fitting historical wind speed data by using a weibull probability distribution function, and obtaining a probability density function of wind power by combining a relation between wind speed and output power includes:
fitting historical wind speed data by utilizing a Weibull probability distribution function to construct a cumulative distribution function of wind speed;
obtaining a probability density function of the wind speed based on the cumulative distribution function of the wind speed;
according to the cumulative distribution function of the wind speed and the probability density function of the wind speed, combining the relation between the wind speed and the output power to obtain the cumulative distribution function of the wind power;
and obtaining a probability density function of the wind power based on the cumulative distribution function of the wind power.
In one embodiment, the preset constraints include: the system active load constraint condition, the thermal power unit output power constraint condition, the fan output power constraint condition, the battery energy storage system charge and discharge power constraint condition and the storage capacity constraint condition of the battery energy storage system.
In one embodiment, the carbon emission cost function is derived based on:
acquiring carbon emission tax prices and an emission function of greenhouse gases of a thermal power generating unit;
and constructing a carbon emission cost function according to the carbon emission tax price and the emission function of the greenhouse gas of the thermal power generating unit.
An electric power system dispatching device is suitable for considering the electric power system of wind power grid-connected and battery energy storage system, the device includes:
the data acquisition module is used for acquiring a wind power predicted value, wind turbine generator set parameters, thermal power generator set parameters, power grid set load demand data and battery energy storage system parameters;
the cost optimization module is used for solving a preset system operation cost objective function to obtain the optimal output power of the wind generation set, the optimal output power of the thermal power generation set and the optimal output power of the battery energy storage system based on a wind power predicted value, wind generation set parameters, thermal power generation set parameters, power grid set load demand data, battery energy storage system parameters and preset constraint conditions, and taking the minimum total operation cost of the power system as a target;
the cost calculation module is used for obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
the system scheduling module is used for scheduling the power system considering wind power integration and a battery energy storage system according to the minimum total operation cost of the power system;
the preset system operation total cost target function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a wind power predicted value, a wind turbine generator parameter, a thermal power generator unit parameter, power grid unit load demand data and a battery energy storage system parameter;
based on the wind power predicted value, the wind power unit parameters, the thermal power unit parameters, the power grid unit load demand data, the battery energy storage system parameters and preset constraint conditions, with the minimum total operating cost of the power system as a target, solving a preset system operating cost objective function to obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system;
obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
scheduling the power system considering the wind power integration and the battery energy storage system according to the minimum total operation cost of the power system;
the preset system operation total cost target function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a wind power predicted value, a wind turbine generator parameter, a thermal power generator unit parameter, power grid unit load demand data and a battery energy storage system parameter;
based on the wind power predicted value, the wind power unit parameters, the thermal power unit parameters, the power grid unit load demand data, the battery energy storage system parameters and preset constraint conditions, with the minimum total operating cost of the power system as a target, solving a preset system operating cost objective function to obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system;
obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
scheduling the power system considering the wind power integration and the battery energy storage system according to the minimum total operation cost of the power system;
the preset system operation total cost target function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
The power system scheduling method, the device, the computer equipment and the storage medium take the power system of the wind power integration and the battery energy storage system into consideration as a research object, utilize the battery energy storage system to stabilize the randomness of the wind power and relieve the fluctuation influence caused by the wind power integration, design the objective function comprising the carbon emission cost function, the wind power government subsidy function and the battery energy storage system operation cost function, solve the objective function by aiming at the minimization of the total operation cost of the power system, obtain the optimal output power, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system, further obtain the minimum total operation cost of the power system, reasonably plan and schedule the power system according to the minimum total operation cost of the power system, realize the minimization of the total operation cost of the system, enhance the consumption of new energy output and effectively reduce the carbon emission, is more environment-friendly.
Drawings
FIG. 1-1 is a diagram of an exemplary implementation of a method for scheduling power systems;
FIGS. 1-2 are system block diagrams of an electrical power system in one embodiment;
FIG. 2 is a flow diagram illustrating a method for scheduling power systems in one embodiment;
FIG. 3-1 is a schematic diagram of a conventional wind turbine and associated parameters of a wind turbine in one embodiment;
3-2 are schematic diagrams of three cases of load prediction and wind power prediction in one embodiment;
3-3 are schematic diagrams of battery energy storage system related parameters in one embodiment;
FIG. 4 is a schematic diagram of an output power curve of a fan in another embodiment;
FIG. 5 is a diagram illustrating results of an embodiment of a power system that considers carbon emission taxes and includes wind farm taxes;
FIG. 6 is a schematic diagram illustrating the results of an embodiment of the power system under wind-powered and carbon emission tax considerations for battery energy storage systems of different capacities;
FIG. 7 is a block diagram showing the structure of a scheduling apparatus of an electric power system according to an embodiment;
FIG. 8 is a block diagram of an electric power system dispatching device in another embodiment;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The power system scheduling method provided by the application can be applied to the application environment shown in fig. 1-1. Wherein the terminal 102 communicates with the server 104 via a network. Specifically, a manager logs in the service management system at the terminal 102, operates the service management system at a system operation interface, sends a system cost analysis message to the server 104, and the server 102 receives the system cost analysis message to obtain an existing wind power predicted value, wind turbine generator parameters, thermal power generator parameters, grid generator load demand data and battery energy storage system parameters; then, on the basis of a wind power predicted value, wind power unit parameters, thermal power unit parameters, power grid unit load demand data, battery energy storage system parameters and preset constraint conditions, with the minimum total operating cost of the power system as a target, solving a preset system operating cost objective function to obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system, and obtaining the minimum total operating cost of the power system according to the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system; scheduling the power system considering the wind power integration and the battery energy storage system according to the minimum total operation cost of the power system; the preset system operation total cost target function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
As shown in fig. 1-2, the present application takes an electric power system including a conventional unit, a wind turbine and a battery energy storage system as a research object, and considers a carbon emission constraint in the system, and establishes an environment-economic combined dispatching unit combination model aiming at the minimum system operation cost and the highest renewable energy consumption rate, so as to reduce the operation cost and the carbon emission. The conventional wind generating set and the wind generating set supply energy, the battery energy storage device is connected with a power grid through a power converter, and when the predicted value of wind power is smaller than the actual power output (underestimated), redundant electric quantity can be stored in an energy storage system. And if the actual wind power value is smaller than the predicted value (overestimation), the stored electric quantity can meet the load requirement of the system. The minimum electric quantity and the maximum electric quantity stored by the storage battery pack are specified, the lower limit of the SOC is 20% of the full capacity of the battery, and the upper limit of the SOC is 80% of the full capacity of the battery. By limiting SOC to between 20% and 80%, deep charge and discharge cycles have been minimized to extend battery life. During the charging and discharging process, the charging and discharging can be carried out in only one state, namely, the charging and the discharging can not be carried out simultaneously.
In an embodiment, as shown in fig. 2, a power system scheduling method is provided, and this embodiment is illustrated by applying the method to a server, and it is to be understood that the method may also be applied to a server, and may also be applied to a system including a terminal and a server, and is implemented by interaction between the terminal and the server. In this embodiment, the method includes the steps of:
step 202, acquiring a wind power predicted value, wind turbine generator parameters, thermal power generator unit parameters, grid unit load demand data and battery energy storage system parameters.
The wind turbine parameters are turbine characteristic parameters reported by each wind power plant, and specifically include a weibull distribution ratio parameter, a shape parameter, a rated wind speed, a cut-in wind speed, a cut-out wind speed, a rated output power, a fan production cost coefficient, a wind power government subsidy coefficient, a cost coefficient of purchasing backup power from other operators due to wind power overestimation, a cost coefficient of not completely using wind power due to wind power underestimation, and the like, and refer to fig. 3-1. The parameters of the thermal power generating unit are unit characteristic parameters reported by each thermal power plant, and specifically include a fuel cost coefficient, a fuel consumption coefficient, unit capacity upper and lower limit constraints, a carbon dioxide fuel emission coefficient, a carbon emission tax price, and the like, as shown in fig. 3-1. The power grid unit load demand data is load demand data of a system in the future 24 hours obtained by a power grid unit dispatching center, specifically comprises predicted system load demand, wind power installed capacity and the like, and can be seen in fig. 3-2. The battery energy storage system parameters are related characteristic data of the battery energy storage system capable of accessing the network, and specifically include upper and lower limits of battery energy storage capacity, initial energy storage capacity, upper and lower limits of battery state of charge, maximum charge and discharge power, cost coefficient consumed by the energy storage system, and charge and discharge efficiency of the battery, as shown in fig. 3-3. The battery energy storage system can be used for relieving fluctuation of wind power randomness to a power grid, the battery energy storage system can store electric energy when wind power is too large, and the battery energy storage system discharges to the power grid through the electric energy converter when the wind power is too small.
In one embodiment, obtaining the wind power prediction value comprises:
step 220, acquiring historical wind speed data;
step 222, fitting historical wind speed data by using a Weibull probability distribution function, and obtaining a probability density function of wind power by combining the relation between wind speed and output power;
and 224, acquiring a wind power predicted value according to the probability density function of the wind power.
In this embodiment, the wind power prediction value is obtained according to a probability density function of the wind power. Wind power is also uncertain, as wind speed is uncertain. The probability density function of wind power can be obtained by assuming that the wind speed obeys the Weibull probability density function. In this embodiment, the wind speed uncertainty is modeled by using the weibull probability distribution, and the cumulative distribution function of the wind speed is obtained as follows:
Figure BDA0003104540030000081
where c is a scale parameter, k is a shape parameter, c >0, k > 0. The probability density function of the wind speed can be obtained by the cumulative distribution function of the wind speed as follows:
Figure BDA0003104540030000082
the mathematical modeling of wind speed and output power is as follows, see fig. 4:
Figure BDA0003104540030000083
wherein the content of the first and second substances,
Figure BDA0003104540030000084
vinis the cut-in wind speed, vrIs rated wind speed, voutIs the cut-out wind speed, wrIs the rated output power. As can be seen from equations (1) and (2), the cumulative distribution function of the wind power is as follows:
if W is 0:
Figure BDA0003104540030000091
if 0 < W < Wr：
Figure BDA0003104540030000092
If W is equal to Wr：
Figure BDA0003104540030000093
The probability density function of the wind power obtained from equations (1) to (6) is as follows:
Figure BDA0003104540030000094
after the probability density function of the wind power is obtained, the wind power predicted value can be analyzed and obtained. In the embodiment, the uncertainty of the wind speed and the wind power can be accurately described by using the Weibull distribution function, so that the accuracy of the wind power predicted value is improved.
And 204, solving a preset system operation cost objective function based on the wind power predicted value, the wind power unit parameter, the thermal power unit parameter, the power grid unit load demand data, the battery energy storage system parameter and a preset constraint condition, wherein the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function, and the preset system operation total cost objective function is used for aiming at the minimum total operation cost of the power system to obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system.
In this embodiment, the system operation cost objective function is:
Figure BDA0003104540030000095
m, N are the number of thermal power generating units and wind power generating units respectively, and as can be seen from the above function expressions, the system operation cost objective function is an objective function containing 7 sub-functions, specifically, the system operation cost objective function respectively comprises a power generation cost function of the wind power generating units
Figure BDA0003104540030000101
Power generation cost function of thermal power generating unit
Figure BDA0003104540030000102
Cost function of carbon emissions
Figure BDA0003104540030000103
Wind power government subsidy function
Figure BDA0003104540030000104
Wind power overestimation penalty cost function
Figure BDA0003104540030000105
Wind power underestimation penalty cost function
Figure BDA0003104540030000106
And battery energy storage system operating cost function
Figure BDA0003104540030000107
Wherein the cost coefficient of the thermal power generating unit
Figure BDA0003104540030000108
Expressed as:
Figure BDA0003104540030000109
where p (i, t) is the output power of the thermal power generating unit i at time t, ai,t、bi,tAnd ci,tAnd (4) the fuel cost coefficient of the thermal power generating unit i at the time t.
Cost coefficient of wind turbine generator system
Figure BDA00031045400300001010
Comprises the following steps:
Figure BDA00031045400300001011
wherein alpha isw,jAnd the coefficient of the production cost of the fan j is shown, and Q (j, t) shows the starting and stopping states of the fan j at the moment t. Wav(j, t) is the actual output power of wind power (i.e. the actual output of wind power), and if the wind farm is owned by the grid, W isav(j, t) takes the value 0.
The carbon emission cost function is:
Figure BDA00031045400300001012
Figure BDA00031045400300001013
wherein EMi(pi) Expressed is the carbon dioxide emission, ef, of the thermal power generating unit iiAnd represents the fuel emission coefficient of carbon dioxide of the thermal power generating unit i. f. ofi、giAnd hiExpressed is the fuel consumption coefficient, where CTaxRepresenting a carbon emission tax price.
Figure BDA00031045400300001014
What shows is the government subsidy of fan j at time t, specifically:
Figure BDA00031045400300001015
wherein alpha iss,jThe government subsidy factor of wind power is shown.
According to the probability density function of the wind power, constructing a wind power overestimation penalty cost function as follows:
Figure BDA0003104540030000111
wherein wjIs the predicted wind power of fan j, fW(w) is wind power Co,jRepresenting the cost factor for purchasing reserve power from other operators due to wind overestimation.
Likewise, the wind underestimation penalty cost is as follows:
Figure BDA0003104540030000112
wherein wr,jRepresenting the rated power of fan j. Cu,jRepresenting a cost factor for not fully using wind power due to wind power underestimation.
The last term is the operating cost of the battery energy storage system, which can be expressed as:
Figure BDA0003104540030000113
whereinπBESSRepresenting the cost factor of the battery energy storage system drain. PBESSIs the charge and discharge power of the battery.
In specific implementation, the objective function may be optimized with the minimum total operating cost (i.e., the power generation cost of the power system) of the power system as a target, so as to obtain the optimal output power of the wind turbine, the optimal output power of the thermal power unit, and the optimal output power of the battery energy storage system, which minimize the total operating cost of the power system.
And step 206, obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system.
And after the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system are obtained, substituting the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system into a formula (8) to obtain the minimum total operation cost of the power system.
And 208, scheduling the power system considering the wind power integration and the battery energy storage system according to the minimum power system total operation cost.
After the minimum cost of the power system is obtained, the power system with the wind power grid-connected and battery energy storage systems taken into consideration can be scheduled according to the cost by combining the optimal output power of the wind power generation unit, the optimal output power of the thermal power generation unit and the optimal output power of the battery energy storage system, and relevant parameters of the power system, such as the output power of the wind power generation unit, the output power of the thermal power generation unit and the output power of the battery energy storage system, are adjusted, so that the operation cost of the power system is minimum.
According to the power system scheduling method, a power system considering wind power grid connection and a battery energy storage system is taken as a research object, the battery energy storage system is utilized to stabilize the randomness of wind power and relieve the fluctuation influence caused by the wind power grid connection, an objective function comprising a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function is designed, the objective function is solved by taking the total operation cost minimization of the power system as a target, the optimal output power of a thermal power generating unit and the optimal output power of the battery energy storage system are obtained, the total operation cost of the minimum power system is further obtained, the power system can be reasonably scheduled according to the total operation cost of the minimum power system, the total power generation cost minimization of the system is realized, the consumption of new energy output is enhanced, the carbon emission is effectively reduced, and the method is more environment-friendly.
In one embodiment, the preset constraints include: the system active load constraint condition, the thermal power unit output power constraint condition, the fan output power constraint condition, the battery energy storage system charge and discharge power constraint condition and the storage capacity constraint condition of the battery energy storage system.
And summarizing the established system operation cost objective function and the corresponding model to obtain corresponding constraint conditions. Specifically, the constraints are as follows:
the system active load constraint conditions are as follows:
Figure BDA0003104540030000121
wherein P isdIs the total system demand. The thermal power generating unit upper and lower limits are constrained as follows:
pi,min≤p(i,t)≤pi,max (18)
the constraint conditions of the output power of the fan are as follows:
0≤wj≤wr,j (19)
and (3) restricting the charging and discharging power of the battery energy storage system:
Figure BDA0003104540030000122
Figure BDA0003104540030000123
wherein
Figure BDA0003104540030000131
And
Figure BDA0003104540030000132
respectively, represent the maximum charge and discharge rates. This constraint indicates that the cells cannot be charged and discharged at the same time.
Figure RE-GDA0003176457930000133
The storage capacity constraint conditions of the battery energy storage system are as follows:
SOCL≤SOC(t)≤SOCU (22)
Figure BDA0003104540030000134
where SOC is the state of charge of the battery energy storage system at time t. SOCLAnd SOCURespectively, the upper and lower limits of the battery state of charge. Stored electric quantity C of battery energy storage systemBESSIs represented as follows:
Figure RE-GDA0003176457930000135
wherein C isiniIs an initial value, eta, of the stored electric quantity of the battery energy storage systemchAnd ηdisRespectively, the charge-discharge efficiency of the battery.
In practical applications, the constraint conditions may be set according to practical situations.
In one embodiment, step 204 includes: and 224, solving a preset system operation cost objective function by adopting a quantum particle swarm optimization algorithm based on the wind power predicted value, the wind power unit parameter, the thermal power unit parameter, the power grid unit load demand data, the battery energy storage system parameter and a preset constraint condition so as to optimize the output power of the wind power unit, the output power of the thermal power unit and the output power of the battery energy storage system, and obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system.
A Quantum Particle Swarm Optimization (QPSO) is a probability search algorithm, in which Quantum behaviors are added to the Particle Swarm Optimization. In this algorithm, the state of the particle is described by qubits and angles, rather than by position and velocity as in the classical particle swarm algorithm. In the embodiment, the target function is simulated by adopting a quantum particle swarm optimization algorithm, so that the optimal output power of the wind generation set, the optimal output power of the thermal power generation set and the optimal output power of the battery energy storage system can be quickly and accurately obtained, and the total operation cost of the power system is minimized.
In one embodiment, step 224 comprises:
step 240, initializing quantum bits and angles of quanta based on the output power of the wind turbine generator, the output power of the thermal power generator and the output power of the battery energy storage system, and constructing an initial random population;
241, counting the iteration times and the population scale of the algorithm, and judging whether the initial random population meets the running termination condition of the preset algorithm;
step 242, when the initial random population does not meet the operation suspension condition of the preset algorithm, continuing iteration to obtain and update a local optimal solution and a global optimal solution of the quantum, wherein the local optimal solution and the global optimal solution comprise the initial wind turbine generator optimal output power, the initial thermal power generator optimal output power and the initial battery energy storage system optimal output power;
step 243, if the current global optimal solution is consistent with the last global optimal solution, calculating quantum affinity and concentration;
step 244, selecting quanta and carrying out sequence mutation by using a self-adaptive probability selection algorithm and chaotic sequence variation;
step 245, updating quantum bits and angles of the quantum through a quantum rotating gate algorithm;
and 246, adding new quantum individuals into the initial random population, returning to the step of counting the preset iteration times and the preset population scale, and judging whether the initial random population meets the preset algorithm operation stopping condition or not until the initial random population meets the preset algorithm operation stopping condition, so that the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system are obtained.
In specific implementation, the specific flow of the quantum particle swarm optimization algorithm is as follows:
1) and (5) quantum initialization. Based on the output power of the wind turbine generator, the output power of the thermal power generator and the output power of the battery energy storage system, n parameter vectors with the dimension of m are defined and serve as a population of each iteration, and each individual in the population can be represented as a quantum bit. Qubits (the smallest unit in quantum heuristic particle swarm optimization algorithms) are defined as a pair of m-dimensional parameter vectors:
Figure BDA0003104540030000141
modulus | αji(t)|2And | βji(t)|2The probability that the qubit is in the states "0" and "1", respectively, is given and satisfies:
|αji(t)|2+|βji(t)|2＝1 (26)
a string of qubits is composed of n quantum bodies, which can be represented as:
Figure BDA0003104540030000151
the quantum angle can be expressed as:
Figure BDA0003104540030000152
in this embodiment, the value of m is 3, that is, a three-dimensional parameter vector is defined, and the initialization parameter setting is performed on the qubits and the angles of each quantum according to equations (25) to (28), so as to obtain an initial random population. The final objective of the algorithm is to search a global optimal quantum, namely the optimal power output of the system in combined operation (including the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system), and calculate the minimum power generation cost according to the formula (8). The iteration number Run and the population size Gen are counted, and the initial values of the two are both 0. The number of stop iterations is set to N.
2) It is determined whether to terminate the iteration. If the initial random population meets the operation termination condition, namely the random population updates 100 times of global optimal solutions and keeps unchanged, counting Run +1 by the iteration times and continuing the iteration until the times reach N, and ending the program; if not, go to step 3).
3) And obtaining a local optimal solution and a global optimal solution of the quantum. And (3) evaluating the fitness of the quanta in the population, optimizing by taking the minimum total operating cost of the electric power system as a target, solving the formula (8), updating the local optimal solution of the quanta and the global optimal solution of the population, namely obtaining the optimal output power of the wind generation set, the optimal output power of the thermal power generation set and the optimal output power of the battery energy storage system, and calculating the total operating cost of the electric power system by substituting the formula (8).
4) And judging whether the global optimal solution is kept unchanged. If the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator, the optimal output power of the battery energy storage system and the total operation cost of the power system obtained by the current iterative computation are consistent with the last iterative result, the step 5) is carried out; otherwise, go to step 8).
5) Quantum affinity and concentration were calculated. Individual affinity and individual concentration can be expressed as:
Figure BDA0003104540030000153
Figure BDA0003104540030000161
Figure BDA0003104540030000162
where r is a random number in the range of (0, 1).
6) And (4) carrying out roulette selection and chaotic sequence variation on the quanta. In the embodiment, the problem of premature convergence is solved by adopting self-adaptive probability selection and chaotic sequence variation, and the defects that the original particle swarm algorithm is premature convergence and is easy to fall into local optimum are effectively overcome. The selection and sequence mutation were performed in order according to equation (32):
g(t+1)＝μg(t)[1-g(t)],μ∈[0,4] (32)
small differences in initial values can lead to significant differences in long-term behavior. μ ═ 4 was chosen here, and the mutation operation was defined as:
Figure BDA0003104540030000163
Figure BDA0003104540030000164
7) the qubits and angles are updated. The basic update mechanism of quantum-behaved particle swarm algorithm is the evolution of qubits and angles, so the update of qubits still satisfies the normalization condition. The update equation of the quantum revolving gate is as follows:
Figure BDA0003104540030000165
Figure BDA0003104540030000166
wherein the content of the first and second substances,
Figure BDA0003104540030000167
is a change in the angle of the beam,
Figure BDA0003104540030000168
is the current angle of the light beam that is being projected,
Figure BDA0003104540030000169
is a local optimum angle of the mirror,
Figure BDA00031045400300001610
is a global optimum angle.
8) And (5) evolving quantum new individuals. And (3) after updating the quantum bits and the angles, adding new quantum individuals into the initial random population, counting the population size by Gen +1, and returning to the step 2) for iteration. Wherein, new quantum individuals are obtained by quantum bit and revolving gate evolution.
The quantum particle swarm optimization algorithm has the advantages of fast convergence and strong optimal solution searching capability. In the embodiment, the optimal output power of thermal power, the optimal output power of wind power and the optimal output power of battery energy storage are obtained by utilizing a quantum particle group optimization algorithm, and the minimum power generation cost of the system is further calculated, so that the minimum total power generation cost of the combined dispatching system is realized, the consumption of new energy output is enhanced, and the carbon emission is effectively reduced.
In one embodiment, assume a carbon emissions tax of 23 dollars/ton, πBESS$ 0.1/Kwh. The carbon emission cost is added into the total running cost of the system, so that the scheduling conforms to the environmental protection-economy. By setting a series of constraints and solving the constraints by applying a quantum particle swarm optimization algorithm, referring to fig. 5, it can be seen that part of the power provided by the highly polluted coal-fired unit (G1-G6) in the objective function is replaced by the less polluted gas generator (G7-G10) and the oil generator (G11, G12). Although the cost of electricity generation for carbon tax containing models is much higher than the cost of electricity generation for carbon tax free; but the carbon-containing tax model can lead the carbon emission to be less and the dispatching to be more environment-friendly.
In one embodiment, a unit combination problem can be solved by adopting a quantum particle swarm optimization algorithm, and simulation is carried out on several scenes of whether a wind power plant is contained, whether carbon emission cost is contained and different battery capacities are contained. It can be seen from fig. 5 that when the model includes a wind turbine, the output of a part of conventional turbines is reduced, and the wind turbine is responsible for reducing the total scheduling cost even though the wind turbine output cost is expensive. The wind power output reduces the output of a conventional unit, thereby reducing carbon emission. In CaseA, G13 produces less than the predicted wind. In CaseC, the wind power produced is greater than predicted. The scheduling cost is increased due to the overestimated cost and the underestimated cost of the wind power, so that the prediction deviation of the wind power needs to be compensated, and a battery energy storage system is added. Different battery energy storage system capacities also have different rates of consumption of wind power, and in view of the fact that the battery energy storage systems can improve the capacity of consumption of the power grid for wind power, in this embodiment, three battery energy storage systems with different capacity levels are considered to be added, which are respectively 15%, 20% and 25%, and the influence of different battery energy storage system capacities on the scheduling result can be seen in fig. 6. It can be seen from fig. 6 that the battery energy storage system with 20% capacity participates in wind-fire coordinated scheduling, so that the scheduling total cost is the lowest, and the wind power consumption rate is the highest, thereby reducing the output of the conventional unit. Compared with battery energy storage systems with other capacity levels, the battery energy storage system with 20% capacity has less carbon emission and is more environment-friendly to dispatch.
It should be understood that, although the steps in the flowcharts related to the above embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily executed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in each flowchart related to the above embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
In one embodiment, as shown in fig. 7, there is provided a power system dispatching device, including: a data acquisition module 510, a cost optimization module 520, a cost calculation module 530, and a system scheduling module 540, wherein:
the data obtaining module 510 is configured to obtain a wind power predicted value, a wind turbine generator parameter, a thermal power generator parameter, a grid generator load demand data, and a battery energy storage system parameter.
And the cost optimization module 520 is used for solving a preset system operation cost objective function based on the wind power predicted value, the wind power unit parameter, the thermal power unit parameter, the power grid unit load demand data, the battery energy storage system parameter and a preset constraint condition, and taking the minimum total operation cost of the power system as a target to obtain the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system, wherein the preset system operation total cost objective function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
The cost calculation module 530 is used for obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
and the system scheduling module 540 is configured to schedule the power system considering the wind power integration and the battery energy storage system according to the minimum total operation cost of the power system.
In one embodiment, the cost optimization module 520 is further configured to solve a preset system operation cost objective function by using a quantum particle swarm optimization algorithm based on a wind power predicted value, a wind power generation unit parameter, a thermal power generation unit parameter, grid unit load demand data, a battery energy storage system parameter and a preset constraint condition, with a goal of minimizing a total operation cost of the power system, so as to optimize the wind power generation unit output power, the thermal power generation unit output power and the battery energy storage system output power, and obtain an optimal output power of the wind power generation unit, an optimal output power of the thermal power generation unit and an optimal output power of the battery energy storage system.
In one embodiment, the cost optimization module 520 is further configured to:
initializing quantum bits and angles of quanta based on the output power of the wind turbine generator, the output power of the thermal power generator and the output power of the battery energy storage system, and constructing an initial random population;
counting the iteration times and the population scale of the algorithm, and judging whether the initial random population meets the running suspension condition of the preset algorithm;
when the initial random population does not meet the operation stopping condition of the preset algorithm, continuing iteration to obtain and update a local optimal solution and a global optimal solution of the quantum, wherein the local optimal solution and the global optimal solution comprise the initial wind turbine generator optimal output power, the initial thermal power generator optimal output power and the initial battery energy storage system optimal output power;
if the current global optimal solution is consistent with the last global optimal solution, calculating quantum affinity and concentration;
selecting quanta and carrying out sequence mutation by using a self-adaptive probability selection algorithm and chaotic sequence variation;
updating quantum bits and angles of the quantum through a quantum rotation gate algorithm;
and adding new quantum individuals into the initial random population, returning to the step of counting the preset iteration times and the preset population scale, and judging whether the initial random population meets the preset algorithm operation stopping condition or not until the initial random population meets the preset algorithm operation stopping condition, so as to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generating unit and the optimal output power of the battery energy storage system.
In an embodiment, the data obtaining module 510 is further configured to obtain historical wind speed data, fit the historical wind speed data by using a weibull probability distribution function, obtain a probability density function of the wind power according to a relation between the wind speed and the output power, and obtain a predicted value of the wind power according to the probability density function of the wind power.
In an embodiment, the data obtaining module 510 is further configured to fit the historical wind speed data by using a weibull probability distribution function, construct a cumulative distribution function of the wind speed, obtain a probability density function of the wind speed based on the cumulative distribution function of the wind speed, and obtain the cumulative distribution function of the wind power and the cumulative distribution function of the wind power by combining a relationship between the wind speed and the output power according to the cumulative distribution function of the wind speed and the probability density function of the wind speed, so as to obtain the probability density function of the wind power.
As shown in fig. 8, in an embodiment, the system further includes a carbon emission cost function construction module 550, configured to obtain the carbon emission tax price and the emission function of the greenhouse gas of the thermal power generating unit, and construct the carbon emission cost function according to the carbon emission tax price and the emission function of the greenhouse gas of the thermal power generating unit.
For specific embodiments of the power system scheduling apparatus, reference may be made to the above embodiments of the power system scheduling method, which is not described herein again. The modules in the power system dispatching device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing wind power predicted value, wind turbine generator set parameters, thermal power generator set parameters, power grid set load demand data, battery energy storage system parameters and other data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a power system scheduling method.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the power system scheduling method when executing the computer program.
In one embodiment, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor, performs the steps in the above power system scheduling method. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A power system scheduling method is applicable to a power system considering wind power integration and a battery energy storage system, and comprises the following steps:
acquiring a wind power predicted value, a wind turbine generator parameter, a thermal power generator unit parameter, power grid unit load demand data and a battery energy storage system parameter;
based on the wind power predicted value, the wind power unit parameters, the thermal power unit parameters, the power grid unit load demand data, the battery energy storage system parameters and preset constraint conditions, a preset system operation cost objective function is solved by taking the minimum total operation cost of the power system as a target, and the optimal output power of the wind power unit, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system are obtained;
obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
scheduling the power system considering the wind power integration and the battery energy storage system according to the minimum power system total operation cost;
the preset system operation total cost target function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
2. The power system scheduling method according to claim 1, wherein based on the predicted wind power value, the wind turbine generator parameter, the thermal power generator parameter, the grid generator load demand data, the battery energy storage system parameter and the preset constraint condition, with the minimum total operating cost of the power system as a target, solving a preset system operating cost objective function to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system comprises:
based on the wind power predicted value, the wind power generation unit parameters, the thermal power generation unit parameters, the power grid unit load demand data, the battery energy storage system parameters and preset constraint conditions, a quantum particle swarm optimization algorithm is adopted to solve a preset system operation cost objective function by taking the minimum total operation cost of the power system as a target so as to optimize the output power of the wind power generation unit, the output power of the thermal power generation unit and the output power of the battery energy storage system, and the optimal output power of the wind power generation unit, the optimal output power of the thermal power generation unit and the optimal output power of the battery energy storage system are obtained.
3. The power system scheduling method of claim 2, wherein the obtaining of the optimal output power of the wind turbine generator, the optimal output power of the thermal power unit and the optimal output power of the battery energy storage system by using a quantum particle swarm optimization algorithm to solve a preset system operation cost objective function and optimizing the output power of the wind turbine generator, the output power of the thermal power unit and the output power of the battery energy storage system based on the wind turbine power predicted value, the wind turbine generator parameters, the thermal power unit parameters, the power grid unit load demand data, the battery energy storage system parameters and preset constraint conditions with the minimum total operation cost of the power system as a target comprises:
initializing quantum bits and angles of quanta based on the output power of the wind turbine generator, the output power of the thermal power generator and the output power of the battery energy storage system, and constructing an initial random population;
counting the iteration times and the population scale of the algorithm, and judging whether the initial random population meets the running suspension condition of the preset algorithm;
when the initial random population does not meet the operation stopping condition of the preset algorithm, continuing iteration to obtain and update a local optimal solution and a global optimal solution of the quantum, wherein the local optimal solution and the global optimal solution comprise the initial wind turbine generator optimal output power, the initial thermal power generator optimal output power and the initial battery energy storage system optimal output power;
if the current global optimal solution is consistent with the last global optimal solution, calculating quantum affinity and concentration;
selecting quanta and carrying out sequence mutation by using a self-adaptive probability selection algorithm and chaotic sequence variation;
updating quantum bits and angles of the quantum through a quantum rotation gate algorithm;
and adding new quantum individuals into the initial random population, returning to the step of counting the preset iteration times and the preset population scale, and judging whether the initial random population meets the preset algorithm operation stopping condition or not until the initial random population meets the preset algorithm operation stopping condition, so as to obtain the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system.
4. The power system scheduling method of claim 1, wherein obtaining a wind power prediction value comprises:
acquiring historical wind speed data;
fitting the historical wind speed data by utilizing Weibull probability distribution, and obtaining a probability density function of the wind power by combining the relation between the wind speed and the output power;
and acquiring a wind power predicted value according to the probability density function of the wind power.
5. The power system scheduling method of claim 4, wherein the fitting the historical wind speed data by using Weibull probability distribution and obtaining the probability density function of the wind power by combining the relation between the wind speed and the output power comprises:
fitting the historical wind speed data by utilizing a Weibull probability distribution function, analyzing wind speed and wind power uncertainty, and constructing a cumulative distribution function of the wind speed;
obtaining a probability density function of the wind speed based on the cumulative distribution function of the wind speed;
according to the cumulative distribution function of the wind speed and the probability density function of the wind speed, combining the relation between the wind speed and the output power to obtain the cumulative distribution function of the wind power;
and obtaining a probability density function of the wind power based on the cumulative distribution function of the wind power.
6. The power system scheduling method according to claim 1, wherein the preset constraint condition comprises: the system active load constraint condition, the thermal power unit output power constraint condition, the fan output power constraint condition, the battery energy storage system charge and discharge power constraint condition and the storage capacity constraint condition of the battery energy storage system.
7. The power system scheduling method of claim 1, wherein the carbon emission cost function is derived based on:
acquiring carbon emission tax prices and an emission function of greenhouse gases of a thermal power generating unit;
and constructing a carbon emission cost function according to the carbon emission tax price and the emission function of the greenhouse gas of the thermal power generating unit.
8. An electric power system dispatching device, adapted to an electric power system considering wind power integration and a battery energy storage system, the device comprising:
the data acquisition module is used for acquiring a wind power predicted value, wind turbine generator set parameters, thermal power generator set parameters, power grid set load demand data and battery energy storage system parameters;
the cost optimization module is used for solving a preset system operation cost objective function to obtain the optimal output power of the wind generation set, the optimal output power of the thermal power generation set and the optimal output power of the battery energy storage system based on the wind power predicted value, the wind generation set parameters, the thermal power generation set parameters, the power grid set load demand data, the battery energy storage system parameters and preset constraint conditions, and taking the minimum total operation cost of the power system as a target;
the cost calculation module is used for obtaining the total operation cost of the minimum power system according to the optimal output power of the wind turbine generator, the optimal output power of the thermal power generator and the optimal output power of the battery energy storage system;
the system scheduling module is used for scheduling the power system considering the wind power integration and the battery energy storage system according to the minimum power system total operation cost;
the preset system operation total cost target function comprises a carbon emission cost function, a wind power government subsidy function and a battery energy storage system operation cost function.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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