CN116488264B - Optimal scheduling method, device and equipment for power distribution network and storage medium - Google Patents

Optimal scheduling method, device and equipment for power distribution network and storage medium Download PDF

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CN116488264B
CN116488264B CN202310736097.1A CN202310736097A CN116488264B CN 116488264 B CN116488264 B CN 116488264B CN 202310736097 A CN202310736097 A CN 202310736097A CN 116488264 B CN116488264 B CN 116488264B
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distribution network
scheduling
power
power distribution
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CN116488264A (en
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王蕾
刘曌煜
戴攀
高强
孙志鹏
叶承晋
潘弘
王曦冉
周灵刚
钟磊
王新禹
侯文浩
梁邱
姜巍
柴炜
朱超
张曼颖
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Zhejiang University ZJU
State Grid Zhejiang Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Zhejiang University ZJU
State Grid Zhejiang Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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    • 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
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    • 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
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    • G06Q50/06—Energy 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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

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Abstract

The invention discloses an optimal scheduling method, device and equipment for a power distribution network and a storage medium, wherein the method comprises the following steps: in the day-ahead scheduling stage, constructing and solving a day-ahead planning model to obtain a day-ahead scheduling scheme by taking the minimum running cost of the power distribution network and the maximum new energy duty ratio as objective functions; in the intra-day scheduling stage, based on a pre-day scheduling scheme, constructing an intra-day rolling model by taking the minimum network loss of the power distribution network and the maximum active power output of the distributed energy source as objective functions, and solving the intra-day rolling model at preset time intervals to obtain the intra-day scheduling scheme; and in the real-time scheduling stage, in each preset time interval, correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling steps. The invention can solve the problem that the precision of the optimized dispatching of the power distribution network is reduced along with the time.

Description

Optimal scheduling method, device and equipment for power distribution network and storage medium
Technical Field
The present invention relates to the field of power distribution network control technologies, and in particular, to an optimized scheduling method and apparatus for a power distribution network, a terminal device, and a computer readable storage medium.
Background
Along with the fact that a large amount of renewable energy sources and related equipment are connected into a power distribution network, the traditional power distribution network is gradually evolved into an active power distribution network with a plurality of controllable resources, a distributed power source, particularly renewable energy sources such as new energy sources, has the advantages that the power generation output has randomness, intermittence and volatility, the prediction accuracy is low, the prediction error is increased along with the increase of time, and great challenges are brought to the regulation and control of the power distribution network. Although the capability of the power distribution network in coping with the uncertainty of renewable energy sources is effectively improved through model predictive control in the existing research, the power distribution network is mainly subjected to optimized scheduling in the future, and uncertain factors influencing new energy sources and load prediction are increased along with the time, so that the optimized scheduling precision of the power distribution network is also reduced.
Disclosure of Invention
The invention provides an optimal scheduling method, device, terminal equipment and computer readable storage medium for a power distribution network, which can solve the problem that the optimal scheduling precision of the power distribution network is reduced along with the time by adopting a scheme of performing optimal scheduling on the power distribution network in the prior art.
The embodiment of the invention provides an optimal scheduling method of a power distribution network, which comprises the following steps:
In a day-ahead scheduling stage, constructing a day-ahead planning model according to two objective functions of minimum running cost of the power distribution network and maximum new energy duty ratio, and solving the day-ahead planning model to obtain a day-ahead scheduling scheme;
in the intra-day scheduling stage, based on the pre-day scheduling scheme, constructing an intra-day rolling model by taking the minimum network loss of the power distribution network and the maximum active power output of the distributed energy source as objective functions, and solving the intra-day rolling model at preset time intervals to obtain an intra-day scheduling scheme;
and in the real-time scheduling stage, correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling steps in each preset time interval.
As an improvement of the scheme, the objective function with the minimum running cost of the power distribution network is specifically as follows:
wherein,for the running cost of the distribution network, < > for>For the electricity purchasing cost of the distribution network in the day of the day-ahead stage,distributed energy operation costs for average day before,/-day before>Cost is controlled for the daily average daily flexible load,/-day>Energy storage system operating costs for average day before, < +.>For average daily pollution control costs before day, < - >For the all-day gear adjusting cost of the power on-load voltage regulator in the day-ahead stage,/for the power on-load voltage regulator>And adjusting the cost for the all-day gear of the capacitance compensator in the day-ahead stage.
As an improvement of the above scheme, the objective function with the largest new energy duty ratio is specifically:
wherein,the new energy ratio of the distribution network is +.>For the new energy quantity in the distributed energy, < +.>For the controllable energy quantity in the distributed energy source, +.>Is->Purchase power of time node, +.>Is->New energy is at the->Power supply of time node, ">Is->The controllable energy source is at the +.>The power supply of the time node.
As an improvement of the scheme, the objective function of the minimum network loss of the power distribution network and the maximum active power output of the distributed energy sources is specifically as follows:
wherein,for the loss of the distribution network, < >>For the start time of each of said preset time intervals of the intra-day scheduling phase,for the number of said preset time intervals of the intra-day scheduling phase +.>For the duration of said preset time interval, < > j->For the collection of distribution network branches, < > for>Branch->Resistance of->Is->Time branch->Is>Is a node set of distributed energy sources, +.>Is->Time->Active power of the individual distributed energy sources.
As an improvement of the above scheme, the objective function with the smallest adjustable resource output adjustment amount is specifically:
Wherein,for the adjustable resource output adjustment, +.>For the control variable of the adjustable resources in the power distribution network in real-time scheduling phase +.>For the preset sampling step size,/->Is->Time-adjustable actual output feedback value of resource, < + >>Is->Output adjustment of time,/->Is->The output of each adjustable resource of the time-of-day scheduling scheme.
As an improvement of the above solution, the constraint conditions of the objective functions of the day-ahead scheduling stage, the day-in scheduling stage, and the real-time scheduling stage include: flow constraint, safe operation constraint of a power distribution network, active output constraint of an adjustable resource and reactive output constraint of the adjustable resource.
As an improvement of the above solution, the method further includes:
and carrying out convex relaxation on the load flow constraint of the objective functions of the day-ahead dispatching stage, the day-in dispatching stage and the real-time dispatching stage by a second order cone planning method.
Correspondingly, another embodiment of the present invention provides an optimized scheduling device for a power distribution network, including:
the day-ahead scheduling module is used for constructing a day-ahead planning model according to two objective functions of minimum running cost of the power distribution network and maximum new energy duty ratio in a day-ahead scheduling stage, and solving the day-ahead planning model to obtain a day-ahead scheduling scheme;
The intra-day rolling module is used for constructing an intra-day rolling model by taking the minimum network loss of the power distribution network and the maximum active power output of the distributed energy source as objective functions based on the pre-day scheduling scheme, and solving the intra-day rolling model at preset time intervals to obtain an intra-day scheduling scheme;
and the real-time correction module is used for correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling step length in each preset time interval in the real-time scheduling stage.
As an improvement of the scheme, the objective function with the minimum running cost of the power distribution network is specifically as follows:
wherein,for the running cost of the distribution network, < > for>For the electricity purchasing cost of the distribution network in the day of the day-ahead stage,distributed energy operation costs for average day before,/-day before>Cost is controlled for the daily average daily flexible load,/-day>Average daily for the day beforeEnergy storage system operating costs of->For average daily pollution control costs before day, < ->For the all-day gear adjusting cost of the power on-load voltage regulator in the day-ahead stage,/for the power on-load voltage regulator>And adjusting the cost for the all-day gear of the capacitance compensator in the day-ahead stage.
As an improvement of the above scheme, the objective function with the largest new energy duty ratio is specifically:
Wherein,the new energy ratio of the distribution network is +.>For the new energy quantity in the distributed energy, < +.>For the controllable energy quantity in the distributed energy source, +.>Is->Purchase power of time node, +.>Is->New energy is at the->TimePower supply of node, ">Is->The controllable energy source is at the +.>The power supply of the time node.
As an improvement of the scheme, the objective function of the minimum network loss of the power distribution network and the maximum active power output of the distributed energy sources is specifically as follows:
wherein,for the loss of the distribution network, < >>For the start time of each of said preset time intervals of the intra-day scheduling phase,for the number of said preset time intervals of the intra-day scheduling phase +.>For the duration of said preset time interval, < > j->For the collection of distribution network branches, < > for>Branch->Resistance of->Is->Time branch->Is>Is a node set of distributed energy sources, +.>Is->Time->Active power of the individual distributed energy sources.
As an improvement of the above scheme, the objective function with the smallest adjustable resource output adjustment amount is specifically:
wherein,for the adjustable resource output adjustment, +.>For the control variable of the adjustable resources in the power distribution network in real-time scheduling phase +.>For the preset sampling step size,/- >Is->Time-adjustable actual output feedback value of resource, < + >>Is->Output adjustment of time,/->Is->The output of each adjustable resource of the time-of-day scheduling scheme.
As an improvement of the above solution, the constraint conditions of the objective functions of the day-ahead scheduling stage, the day-in scheduling stage, and the real-time scheduling stage include: flow constraint, safe operation constraint of a power distribution network, active output constraint of an adjustable resource and reactive output constraint of the adjustable resource.
As an improvement of the above scheme, the day-ahead scheduling module is further configured to perform convex relaxation on a power flow constraint of an objective function in the day-ahead scheduling stage by using a second order cone planning method;
the intra-day rolling module is further used for performing convex relaxation on the power flow constraint of the objective function in the intra-day scheduling stage through the second order cone planning method;
the real-time correction module is further used for performing convex relaxation on the power flow constraint of the objective function in the real-time scheduling stage through the second-order cone planning method.
Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor executes the computer program to implement the optimized scheduling method of the power distribution network according to any one of the above.
Another embodiment of the present invention provides a computer readable storage medium, where the computer readable storage medium includes a stored computer program, where when the computer program runs, the device where the computer readable storage medium is controlled to execute the optimized scheduling method of the power distribution network according to any one of the foregoing.
Compared with the prior art, the optimal scheduling method, the optimal scheduling device, the terminal equipment and the computer readable storage medium of the power distribution network disclosed by the embodiment of the invention solve the problem by using two objective functions of minimum running cost and maximum new energy duty ratio of the power distribution network to obtain a daily day-ahead scheduling scheme, thereby fully utilizing new energy and reducing cost in a large direction. Secondly, in order to avoid the increase of uncertain factors of new energy and load prediction caused by time scale, the optimal scheduling precision of the power distribution network is reduced, so that the power distribution network is solved by taking the minimum network loss of the power distribution network and the maximum active output of the distributed energy as objective functions based on the daily scheduling scheme, and fluctuation of the new energy and the load in the day is further regulated, so that the daily scheduling scheme is obtained; and correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling step length in each preset time interval, so that the optimal scheduling precision of the power distribution network can be further improved.
Drawings
Fig. 1 is a flow chart of an optimized scheduling method of a power distribution network according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an optimized dispatching device for a power distribution network according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, fig. 1 is a flow chart of an optimized scheduling method for a power distribution network according to an embodiment of the present invention.
The optimal scheduling method of the power distribution network provided by the embodiment of the invention comprises the following steps:
s11, in a day-ahead scheduling stage, constructing a day-ahead planning model according to two objective functions of minimum running cost of the power distribution network and maximum new energy duty ratio, and solving the day-ahead planning model to obtain a day-ahead scheduling scheme;
S12, in a day scheduling stage, based on the day scheduling scheme, constructing a day rolling model by taking the minimum network loss of a power distribution network and the maximum active power output of a distributed energy source as objective functions, and solving the day rolling model at preset time intervals to obtain a day scheduling scheme;
and S13, in the real-time scheduling stage, correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling steps in each preset time interval.
The invention is worth to be noted, the day-ahead scheduling scheme is to perform active power and reactive power coordinated optimization scheduling of the active power distribution network according to the load and the new energy prediction information in the distributed energy, and the purpose is to determine the action state and the action quantity of the slow dynamic equipment in the power distribution network in a future day; wherein, slow dynamic device mainly includes: the slow dynamic devices are not suitable for short-term adjustment due to limited adjustment speed, and a scheduling plan needs to be determined in a day-ahead scheduling stage. As the prediction time scale becomes longer, uncertain factors affecting new energy and load prediction increase, prediction accuracy gradually decreases, and a day-ahead scheduling scheme often cannot meet power balance requirements, so that fluctuation of new energy and load in the day needs to be further adjusted according to the day-ahead scheduling scheme. Although the influence of new energy fluctuation on power distribution network scheduling is reduced in the daily scheduling stage, the method still belongs to an open-loop optimization scheduling method, and closed-loop optimization control is formed by utilizing the information fed back at the current moment according to the latest ultra-short-term prediction error in the real-time correction stage, so that the scheduling result deviation generated by the prediction error is corrected in time, and the optimization scheduling precision of the active power distribution network is improved.
It can be understood that the method firstly solves the objective function with the minimum running cost and the maximum new energy duty ratio of the power distribution network to obtain a day-ahead scheduling scheme of a certain day in the future so as to schedule the slow dynamic equipment and the equipment except the slow dynamic equipment of the power distribution network day-ahead. That is, the day-ahead scheduling scheme includes scheduling of slow-motion devices and other devices than the slow-motion devices, that is, full utilization of new energy in a large direction and cost reduction. However, the longer the time scale is, the more inaccurate the prediction and the load prediction of new energy power generation are, so that the day-ahead scheduling scheme of the slow dynamic equipment is kept unchanged in the future, the minimum network loss and the maximum active output of the power distribution network are used as regulation targets, the day-ahead rolling model is solved at preset time intervals to obtain the day-ahead scheduling scheme, and the day-ahead scheduling scheme is used for carrying out the day-ahead scheduling on other equipment except the slow dynamic equipment in the power distribution network.
It should be noted that, the duration of the preset sampling step length is smaller than the duration of the preset time interval. And setting preset time intervals, starting periods and preset sampling step sizes in each scheduling stage, and reasonably setting by considering the space-time difference of each adjustable resource in the power distribution network and combining the adjusting speed and other related operation characteristics of the adjustable resource, so as to realize reasonable matching of each adjustable resource and each optimizing stage, thereby ensuring reasonable and efficient optimization and control of the active power distribution network.
Specifically, the objective function with the minimum running cost of the power distribution network is specifically:
wherein,for the running cost of the distribution network, < > for>For the electricity purchasing cost of the distribution network in the day of the day-ahead stage,distributed energy operation costs for average day before,/-day before>Cost is controlled for the daily average daily flexible load,/-day>Energy storage system operating costs for average day before, < +.>For average daily pollution control costs before day, < ->For the all-day gear adjusting cost of the power on-load voltage regulator in the day-ahead stage,/for the power on-load voltage regulator>And adjusting the cost for the all-day gear of the capacitance compensator in the day-ahead stage.
It will be appreciated that the minimum running cost of the distribution network is used as an objective function in the daily planning model in the present embodiment, and the purpose is that: on one hand, the operation cost of the day-ahead scheduling stage is enabled to be the lowest so as to generate larger economic benefit, and under the condition that the cost in the day-ahead large direction is the lowest, the day-ahead and real-time correction scheduling does not cause larger deviation of the cost, so that the requirement of the operation benefit is met; on the other hand, the operation cost of each access device in the power distribution network is included in the calculation of the operation cost of the power distribution network, including distributed energy sources (such as new energy sources and controllable energy sources), flexible loads, energy storage systems, OLTC (organic light emitting diode) and compensation capacitors, and the like, and meanwhile, the pollutant treatment cost generated in the power generation process of the controllable energy sources is considered, so that the economical efficiency and the environmental protection performance are balanced.
Further, the electricity purchasing cost of the distribution network in the day of the day-ahead stageCalculated by the following formula:
wherein,is->Purchase power of time node, +.>Is->Electricity prices of the time nodes.
The power in the calculation of the running cost of the power distribution network is a function of active power and reactive power, and the power purchasing of the power distribution network is positive and negative.
Further, the average daily distributed energy operation costCalculated by the following formula:
wherein,for the new energy quantity in the distributed energy, < +.>For the controllable energy quantity in the distributed energy source, +.>For the number of time nodes in a day, +.>Is->New energy is at the->Power supply of time node, ">Is the firstThe controllable energy source is at the +.>Power supply of time node, ">Is->Operating cost per unit power supply of new energy source of time node, < >>Is->The controllable energy source of the time node generates power per unit power supply and the running cost.
It should be noted that, the distributed energy operation cost includes: the operation cost of new energy power generation, the operation cost of controllable energy power generation and the power generation cost of fuel combustion; wherein, the new energy includes at least: wind power generation and photovoltaic power generation, and controllable energy sources are fuel units such as micro gas turbines and the like.
Further, the average daily pollution treatment cost before the dayCalculated by the following formula:
wherein,to govern the cost factor of nitride, +.>To treat the cost coefficient of sulfides, +.>Is->Amount of nitride per unit generated power emission of the controllable energy source, +.>Is->The amount of sulfide emitted by each controllable energy source per unit of generated power.
It should be noted that, in the embodiment of the present invention, the distributed energy source includes: new energy sources such as wind power generation, photovoltaic power generation and the like, and controllable energy sources powered by fuel sets and the like. It should be noted that, during the transmission process of the distribution network, no waste gas is generated basically, so the pollution control cost mainly considers the treatment of the waste gas generated at the controllable energy source. Among them, the nitrides and sulfides are used as the main components in all the exhaust gases, so that the daily average daily pollution control cost in this embodiment mainly calculates the treatment costs of the nitrides and sulfides.
Further, the day-ahead average daily flexible load control costCalculated by the following formula:
wherein,node set for flexible load, +.>Is->The controlled state of the flexible load of the time node, Is->Controlled active power of flexible load of time node, < ->Is->The compensation cost of the flexible load unit power of the time node.
It should be noted that the number of the substrates,indicating controlled->Indicating uncontrolled.
In particular, the flexible load in the present invention mainly refers to an interruptible class load.
Further, the all-day gear adjusting cost of the on-load power voltage regulator in the day-ahead stageCalculated by the following formula:
wherein,unit regulation cost for power on-load voltage regulator, < >>The frequency of the gear of the power on-load voltage regulator is adjusted all the day.
Further, the all-day gear adjustment cost of the day-ahead stage capacitance compensatorCalculated by the following formula:
wherein,for the unit adjustment cost of the compensation capacitor, +.>The number of times of adjustment of the switching gear of the compensating capacitor is adjusted all the day.
It should be noted that, the power on-load voltage regulator gear and the compensation capacitor switching gear can be adjusted by only 1 gear at a time.
It should be noted that, the power on-load voltage regulator and the capacitor component connector are limited by the manufacturing technology and the service life of the equipment, the switching speed and the daily switching times are limited to a certain extent, when the switching times are exceeded, the corresponding cost is generated by the replacement, and the interruptible load needs to be informed to a user in advance in the implementation process to avoid the excessive power failure loss, and the early informing time is generally 2 hours to 1 day. Thus, for flexible loads, OLTC and compensation capacitors, the schedule plan can only be determined during the day-ahead schedule phase, while no changes occur during the following day-ahead schedule phase and the real-time schedule phase.
As one specific embodiment, the objective function with the largest new energy duty ratio is specifically:
wherein,the new energy ratio of the distribution network is +.>For the new energy quantity in the distributed energy, < +.>For the controllable energy quantity in the distributed energy source, +.>Is->Purchase power of time node, +.>Is->New energy is at the->Power supply of time node, ">Is->The controllable energy source is at the +.>The power supply of the time node.
It should be noted that, in this embodiment, the new energy duty ratio is the maximum as an objective function in the daily planning model, and the purpose is: the electric energy generated by the new energy is fully utilized in the day-ahead scheduling stage, so that the day-ahead scheduling scheme can ensure the consumption of the power supply of the new energy by the randomness and the intermittence of the power output, and can reduce the power supply of the main power grid and the controllable energy to the power distribution network, thereby indirectly reducing pollutants generated by the fuel combustion power generation.
Specifically, the objective function of the minimum network loss and the maximum active power output of the distributed energy sources of the power distribution network is specifically as follows:
wherein,for the loss of the distribution network, < >>For the start time of each of said preset time intervals of the intra-day scheduling phase,for the number of said preset time intervals of the intra-day scheduling phase +. >For the duration of said preset time interval, < > j->For the collection of distribution network branches, < > for>Branch->Resistance of->Is->Time branch->Is>Is a node set of distributed energy sources, +.>Is->Time->Active power of the individual distributed energy sources.
It should be noted that, in this embodiment, the solution is performed at preset time intervals for the futureAnd predicting new energy output and load information in time, so that an output plan of other regulation and control resources except slow dynamic equipment is determined by taking the minimum network loss of the power distribution network and the maximum active output of the distributed energy in the time period as objective functions according to a prediction result, and the aim is to maximally utilize the active output of the distributed energy and reduce the network loss of the power distribution network.
The current amplitude of the actual branch is determined by the distributed energy source, the energy storage system and the power purchased by the distribution network connected in the branch. However, because the devices accessed by different branches are different,time branch->Is>Is comprised of: distributed energy, energy storage systems, and distribution networks purchase active and reactive power as a function of these variables.
In a specific embodiment, the objective function with the smallest adjustable resource output adjustment amount is specifically:
Wherein,output for adjustable resourceWhole amount, ->For the control variable of the adjustable resources in the power distribution network in real-time scheduling phase +.>For the preset sampling step size,/->Is->Time-adjustable actual output feedback value of resource, < + >>Is->Output adjustment of time,/->Is->The output of each adjustable resource of the time-of-day scheduling scheme.
It will be appreciated that the number of components,is->、/>、/>The general terms of the same are with +.>、/>、/>The relevant values are all by +.>The control variable represented is determined.
It should be noted that, in this embodiment, at each sampling time of the real-time scheduling stage, ultra-short-term prediction is performed on new energy output and load information at the current time, and the actual active reactive output (i.e., the actual output feedback value of the current time adjustable resource) of each adjustable resource at the current time and the output of each adjustable resource of the intra-day scheduling scheme are taken as known quantities, and optimal power flow calculation is performed with the minimum output adjustment quantity of the adjustable resource as an objective function, so as to perform feedback correction on the output of each adjustable resource. The real-time scheduling stage is mainly used for adjusting the output of each adjustable resource in the daily scheduling stage, so that the start-stop state of each adjustable resource in the system is consistent with the daily scheduling stage.
As one of the optional embodiments, the constraint conditions of the objective functions of the day-ahead scheduling stage, the day-in scheduling stage and the real-time scheduling stage include: flow constraint, safe operation constraint of a power distribution network, active output constraint of an adjustable resource and reactive output constraint of the adjustable resource.
The power flow constraint is to perform constraint calculation on node voltage amplitude, current amplitude, injected active power and reactive power, and active power and reactive power of a load of each branch circuit of the power distribution network.
It should be noted that, for the setting of the corresponding constraint conditions of the objective function, the purpose is to ensure safe and stable operation of the power distribution network. The active and reactive output constraints of the adjustable resources can be mainly divided into three types: 1. constraints that only participate in active scheduling, such as: a flexible load; 2. only constraints that participate in reactive power scheduling, such as: compensation capacitors, OLTC, etc.; 3. constraints that provide both active and reactive power, such as: energy storage systems and distributed energy sources.
In the day-ahead scheduling stage, the scheduling program is executedAnd a day scheduling stage, wherein variables of the controllable resources at each moment are required to meet tide constraint, safe operation constraint of the power distribution network, active output constraint of the adjustable resources and reactive output constraint of the adjustable resources, and in the real-time scheduling stage, corrected regulated resource output values are required, namely: The method meets the requirements of power flow constraint, safe operation constraint of the power distribution network, active output constraint of the adjustable resource and reactive output constraint of the adjustable resource.
Further, the method further comprises:
and carrying out convex relaxation on the load flow constraint of the objective functions of the day-ahead dispatching stage, the day-in dispatching stage and the real-time dispatching stage by a second order cone planning method.
It will be appreciated that the power flow constraint is a non-convex, non-linear constraint that would make model solution an NP-hard problem. Therefore, it is necessary to linearize OLTC constraints containing integer variables by using a piecewise linearization method, then perform convex relaxation on the power flow constraint conditions, so that the power flow constraint conditions are converted into a second order cone programming problem, and then solve the problem. After the above processing, the model in the embodiment is converted into a second order cone optimization problem containing mixed integer variables, and the solution can be directly solved by using mature commercial software, so that the calculation efficiency and the optimality of the solution are ensured.
Preferably, the preset time interval is 1h.
Specifically, the optimization control variables of the day-ahead scheduling scheme include: the method comprises the steps of new energy reactive power output, controllable energy active power reactive power output, flexible load adjustment quantity, energy storage system active power reactive charge and discharge power, switching gear of a compensation capacitor and an OLTC transformation ratio. It should be noted that, for the distributed power source and energy storage at the user side, the limitation is imposed by all the conditions such as control and control degree, and the like, and the control resource is not considered here.
Specifically, the trend constraints of the objective functions of the day-ahead scheduling stage, the day-in scheduling stage and the real-time scheduling stage are specifically:
wherein,is->Time branch->Active power of head end of +.>For slave node->Flow to downstream node->Active power, < >>Is->Time branch->Reactive power of head end>For slave node->Flow to downstream node->Is used for the reactive power of the (c),for node->Head-end active power net injection of (2)Value of->For node->Net injection value of reactive power of>For branch->Reactance of->For node->Load active power, +.>For node->Active charging power of energy storage system>For node->Active power amplifier of energy storage system>For node->Active power of new energy source +.>For node->Active power of the controllable energy source, < >>For node->Active power of flexible load>For node->Is used for the load reactive power of the (a),for node->Reactive power of energy storage system->For node->SVC reactive power>For node->Reactive power of new energy source>For node->Reactive power of the controllable energy source, +.>For node->Reactive power of the compensation capacitor->For node->Voltage amplitude->Is->Time branch->Tunable ratio at OLTC, +. >For node->Voltage amplitude->For branch->Is set in the above-described range).
Specifically, the safe operation constraint of the power distribution network specifically includes:
wherein,for node->Voltage amplitude>For node->Upper limit of voltage amplitude, ">For node->Lower limit of voltage amplitude, ">For branch->Upper limit of current amplitude, ">For the upper limit of active power between the transmission network and the distribution network, < > for>For the lower limit of active power between the transmission network and the distribution network, < > for>Is the active power between the transmission network and the distribution network.
It should be noted that the purpose of the safe operation constraint of the power distribution network is to avoid the adverse effect of the power variation of the active power distribution network on the upper-level power transmission network and the line.
As can be appreciated, the energy storage system is generally connected to the power distribution network through an inverter, and the inverter is regulated to enable the energy storage to have a certain reactive adjustable capacity while sending/absorbing active power, but the active and reactive output of the energy storage is limited by the capacity of the inverter:
wherein,for maximum apparent power of the energy storage inverter, < >>The active power for the discharge of the energy storage inverter,reactive power for energy storage system, +.>Charging active power for energy storage inverter, < >>Reactive power for the energy storage system.
It should be noted that the energy storage system cannot be in both the charging and discharging states at the same time, and the stored power must be within 20% -90% of its capacity limit. Thus, the energy storage system charge-discharge and stored power obeys the following constraints:
Wherein,for node->At the upper limit of the charging power of the energy storage system, +.>For node->Upper limit of discharge power of energy storage system is set +.>Is->Time energy storage system state of charge->Is->Time energy storage system discharge state->Is->The time energy storage system stores electric quantity, < >>Is->The time energy storage system stores electric quantity, < >>Charging efficiency for energy storage system->For the energy storage system discharge efficiency, < >>Is the energy storage capacity limit.
It is to be noted that,、/>、/>and->Is a 0-1 variable.
It should be noted that, for the constraint condition of new energy, taking photovoltaic power generation as an example, when the photovoltaic cell is connected to the power distribution network through the inverter, reactive power can be generated by the multiplexing technology of the inverter, and the active reactive power output constraint is also limited by the maximum apparent power of the inverter:
wherein,for node->Active power of photovoltaic power generation +.>For node->Active power of photovoltaic power generation +.>Is the maximum apparent power of the photovoltaic inverter.
It should be noted that, in engineering applications, the power factor of the common connection point at the photovoltaic access point is required to be greater than 0.9, specifically shown in the following formula:
wherein,for node->Load active power, +.>For node->Is used for the reactive power of the load.
It should be noted that, for the constraint condition of the controllable energy, taking the controllable energy of the micro gas turbine as an example for illustration, the grid-connected mode of the controllable energy of the micro gas turbine is similar to energy storage, photovoltaic and the like, that is, the power transmission is realized through the grid connection of the inverter, and the PQ decoupling control is adopted. Therefore, the active and reactive power output of the distributed power supply is mainly limited by the capacity of the inverter:
wherein,is->Time node->Active power of miniature gas turbine>Is->Time node->Reactive power of miniature gas turbine>Is the installed capacity of the micro gas turbine.
Wherein, for active force, the micro gas turbine has a hill climbing rate constraint of:
wherein,for node->Up hill speed limit of micro gas turbine,/->For node->Downhill ramp rate for miniature gas turbineRestriction (S)>Is->Time node->The active power of the micro gas turbine.
Referring to fig. 2, a schematic structural diagram of an optimized scheduling device for a power distribution network according to an embodiment of the present invention is shown.
The optimal scheduling device for the power distribution network provided by the embodiment of the invention comprises the following components:
the day-ahead scheduling module 21 is configured to construct a day-ahead planning model according to two objective functions, i.e., the minimum running cost of the power distribution network and the maximum new energy duty ratio, in a day-ahead scheduling stage, and solve the day-ahead planning model to obtain a day-ahead scheduling scheme;
The intra-day rolling module 22 is configured to construct an intra-day rolling model based on the pre-day scheduling scheme by taking the minimum power loss of the power distribution network and the maximum active power output of the distributed energy as objective functions, and solve the intra-day rolling model at preset time intervals to obtain an intra-day scheduling scheme;
the real-time correction module 23 is configured to correct the intra-day scheduling scheme in a real-time scheduling stage by taking the minimum adjustable resource output adjustment amount as an objective function every preset sampling step length in each preset time interval.
Specifically, the objective function with the minimum running cost of the power distribution network is specifically:
wherein,for the running cost of the distribution network, < > for>For the electricity purchasing cost of the distribution network in the day of the day-ahead stage,distributed energy operation costs for average day before,/-day before>Cost is controlled for the daily average daily flexible load,/-day>Energy storage system operating costs for average day before, < +.>For average daily pollution control costs before day, < ->For the all-day gear adjusting cost of the power on-load voltage regulator in the day-ahead stage,/for the power on-load voltage regulator>And adjusting the cost for the all-day gear of the capacitance compensator in the day-ahead stage.
As one specific embodiment, the objective function with the largest new energy duty ratio is specifically:
/>
Wherein,the new energy ratio of the distribution network is +.>For the new energy quantity in the distributed energy, < +.>For the controllable energy quantity in the distributed energy source, +.>Is->Purchase power of time node, +.>Is->New energy is at the->Power supply of time node, ">Is->The controllable energy source is at the +.>The power supply of the time node.
Specifically, the objective function of the minimum network loss and the maximum active power output of the distributed energy sources of the power distribution network is specifically as follows:
wherein,for the loss of the distribution network, < >>For the start time of each of said preset time intervals of the intra-day scheduling phase,for the number of said preset time intervals of the intra-day scheduling phase +.>For the duration of said preset time interval, < > j->For the collection of distribution network branches, < > for>Branch->Resistance of->Is->Time branch->Is>Is a node set of distributed energy sources, +.>Is->Time->Active power of the individual distributed energy sources.
Specifically, the objective function with the smallest adjustable resource output adjustment amount is specifically:
wherein,for the adjustable resource output adjustment, +.>For real-time scheduling stageControl variable of an adjustable resource in a power distribution network, +.>For the preset sampling step size,/->Is->Time-adjustable actual output feedback value of resource, < + > >Is->Output adjustment of time,/->Is->The output of each adjustable resource of the time-of-day scheduling scheme.
As one of the optional embodiments, the constraint conditions of the objective functions of the day-ahead scheduling stage, the day-in scheduling stage and the real-time scheduling stage include: flow constraint, safe operation constraint of a power distribution network, active output constraint of an adjustable resource and reactive output constraint of the adjustable resource.
Further, the day-ahead scheduling module 21 is further configured to perform convex relaxation on a power flow constraint of an objective function in the day-ahead scheduling stage by using a second-order cone planning method, so as to convert a solution problem of the day-ahead planning model into a second-order cone planning solution problem;
the intra-day rolling module 22 is further configured to perform convex relaxation on a power flow constraint of an objective function in the intra-day scheduling stage by using a second-order cone planning method, so as to convert a solution problem of the intra-day rolling model into a second-order cone planning solution problem;
the real-time correction module 23 is further configured to perform convex relaxation on the power flow constraint of the objective function in the real-time scheduling stage by using a second-order cone planning method, so as to convert the problem of solving the intra-day scheduling scheme by using the minimum adjustable resource output adjustment as the objective function into a second-order cone planning problem of solving.
It should be noted that, the relevant specific description and the beneficial effects of each embodiment of the optimal scheduling device for a power distribution network in this embodiment may refer to the relevant specific description and the beneficial effects of each embodiment of the optimal scheduling method for a power distribution network described above, which are not described herein again.
Referring to fig. 3, a schematic structural diagram of a terminal device according to an embodiment of the present invention is shown.
The terminal device provided by the embodiment of the invention comprises a processor 10, a memory 20 and a computer program stored in the memory 20 and configured to be executed by the processor 10, wherein the optimal scheduling method of the power distribution network according to any one of the embodiments is realized when the processor 10 executes the computer program.
The steps in the above-described embodiments of the method for optimizing the scheduling of a power distribution network, such as all the steps of the method for optimizing the scheduling of a power distribution network shown in fig. 1, are implemented when the processor 10 executes the computer program. Alternatively, the processor 10 may implement the functions of each module/unit in the above embodiment of the optimal scheduling apparatus for a power distribution network when executing the computer program, for example, the functions of each module of the optimal scheduling apparatus for a power distribution network shown in fig. 2.
Illustratively, the computer program may be partitioned into one or more modules that are stored in the memory 20 and executed by the processor 10 to perform the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the terminal device.
The terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The terminal device may include, but is not limited to, a processor 10, a memory 20. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a terminal device and does not constitute a limitation of the terminal device, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor 10 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 10 is the control center of the terminal device, and connects the various parts of the entire terminal device using various interfaces and lines.
The memory 20 may be used to store the computer program and/or module, and the processor 10 implements various functions of the terminal device by running or executing the computer program and/or module stored in the memory 20 and invoking data stored in the memory 20. The memory 20 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the terminal device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the terminal device integrated modules/units may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as stand alone products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Another embodiment of the present invention provides a computer readable storage medium, where the computer readable storage medium includes a stored computer program, where when the computer program runs, the device where the computer readable storage medium is controlled to execute the optimized scheduling method of the power distribution network according to any one of the foregoing method embodiments.
In summary, the optimized scheduling method, the optimized scheduling device, the terminal equipment and the computer readable storage medium for the power distribution network provided by the embodiment of the invention solve the problem by using two objective functions of minimum running cost and maximum new energy duty ratio of the power distribution network to obtain a daily day-ahead scheduling scheme, thereby fully utilizing new energy and reducing cost in a large direction. Secondly, in order to avoid the increase of uncertain factors of new energy and load prediction caused by time scale, the optimal scheduling precision of the power distribution network is reduced, so that the power distribution network is solved by taking the minimum network loss of the power distribution network and the maximum active output of the distributed energy as objective functions based on the daily scheduling scheme, and fluctuation of the new energy and the load in the day is further regulated, so that the daily scheduling scheme is obtained; and correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling step length in each preset time interval, so that the optimal scheduling precision of the power distribution network can be further improved.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the scope of the invention.

Claims (14)

1. An optimized scheduling method for a power distribution network is characterized by comprising the following steps:
in a day-ahead scheduling stage, constructing a day-ahead planning model according to two objective functions of minimum running cost of the power distribution network and maximum new energy duty ratio, and solving the day-ahead planning model to obtain a day-ahead scheduling scheme;
in the intra-day scheduling stage, based on the pre-day scheduling scheme, constructing an intra-day rolling model by taking the minimum network loss of the power distribution network and the maximum active power output of the distributed energy source as objective functions, and solving the intra-day rolling model at preset time intervals to obtain an intra-day scheduling scheme;
in a real-time scheduling stage, correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling steps in each preset time interval;
the objective function with the largest new energy duty ratio is specifically as follows:
Wherein,the new energy ratio of the distribution network is +.>For the new energy quantity in the distributed energy, < +.>For the controllable energy quantity in the distributed energy source, +.>Is->Purchase power of time node, +.>Is->New energy is at the->Power supply of time node, ">Is->The controllable energy source is at the +.>The power supply of the time node.
2. The optimal scheduling method for a power distribution network according to claim 1, wherein the objective function with the minimum running cost of the power distribution network is specifically:
wherein,for the running cost of the distribution network, < > for>The cost of purchasing electricity of the distribution network in the day of the day-ahead stage is +.>Distributed energy operation costs for average day before,/-day before>Cost is controlled for the daily average daily flexible load,/-day>Energy storage system operating costs for average day before, < +.>Is the pollution treatment cost of average daily before the day,for the all-day gear adjusting cost of the power on-load voltage regulator in the day-ahead stage,/for the power on-load voltage regulator>And adjusting the cost for the all-day gear of the capacitance compensator in the day-ahead stage.
3. The optimal scheduling method for a power distribution network according to claim 1, wherein the objective function of minimum power loss and maximum active power output of the distributed energy source of the power distribution network is specifically:
Wherein,for the loss of the distribution network, < >>For the start time of each of said preset time intervals of the intra-day scheduling phase +.>For the number of said preset time intervals of the intra-day scheduling phase +.>For the duration of said preset time interval, < > j->For the collection of distribution network branches, < > for>Branch->Resistance of->Is->Time branch->Is>Is a node set of distributed energy sources, +.>Is->Time->Active power of the individual distributed energy sources.
4. The optimal scheduling method for a power distribution network according to claim 1, wherein the objective function with the smallest adjustable resource output adjustment amount is specifically:
wherein,for the adjustable resource output adjustment, +.>For the control variable of the adjustable resources in the power distribution network in real-time scheduling phase +.>For the preset sampling step size,/->Is->Time-adjustable actual output feedback value of resource, < + >>Is->Output adjustment of time,/->Is->The output of each adjustable resource of the time-of-day scheduling scheme.
5. The optimal scheduling method of a power distribution network according to claim 1, wherein constraints of objective functions of the day-ahead scheduling stage, the day-in scheduling stage, and the real-time scheduling stage include: flow constraint, safe operation constraint of a power distribution network, active output constraint of an adjustable resource and reactive output constraint of the adjustable resource.
6. The optimal scheduling method of a power distribution network of claim 5, further comprising:
and carrying out convex relaxation on the load flow constraint of the objective functions of the day-ahead dispatching stage, the day-in dispatching stage and the real-time dispatching stage by a second order cone planning method.
7. An optimized scheduling device for a power distribution network, comprising:
the day-ahead scheduling module is used for constructing a day-ahead planning model according to two objective functions of minimum running cost of the power distribution network and maximum new energy duty ratio in a day-ahead scheduling stage, and solving the day-ahead planning model to obtain a day-ahead scheduling scheme;
the intra-day rolling module is used for constructing an intra-day rolling model by taking the minimum network loss of the power distribution network and the maximum active power output of the distributed energy source as objective functions based on the pre-day scheduling scheme, and solving the intra-day rolling model at preset time intervals to obtain an intra-day scheduling scheme;
the real-time correction module is used for correcting the intra-day scheduling scheme by taking the minimum adjustable resource output adjustment amount as an objective function at intervals of preset sampling step length in each preset time interval in a real-time scheduling stage;
The objective function with the largest new energy duty ratio is specifically as follows:
wherein,the new energy ratio of the distribution network is +.>For the new energy quantity in the distributed energy, < +.>For the controllable energy quantity in the distributed energy source, +.>Is->Purchase power of time node, +.>Is->New energy is at the->Power supply of time node, ">Is->The controllable energy source is at the +.>The power supply of the time node.
8. The optimal scheduling device for a power distribution network according to claim 7, wherein the objective function with the minimum running cost of the power distribution network is specifically:
wherein,for the running cost of the distribution network, < > for>The cost of purchasing electricity of the distribution network in the day of the day-ahead stage is +.>Distributed energy operation costs for average day before,/-day before>Cost is controlled for the daily average daily flexible load,/-day>Energy storage system operating costs for average day before, < +.>Is the pollution treatment cost of average daily before the day,for the all-day gear adjusting cost of the power on-load voltage regulator in the day-ahead stage,/for the power on-load voltage regulator>Capacitance compensator for day-ahead stageIs a whole day gear adjustment cost.
9. The optimal scheduling device for a power distribution network according to claim 7, wherein the objective function of minimum power loss and maximum active power output of the distributed energy source of the power distribution network is specifically:
Wherein,for the loss of the distribution network, < >>For the start time of each of said preset time intervals of the intra-day scheduling phase +.>For the number of said preset time intervals of the intra-day scheduling phase +.>For the duration of said preset time interval, < > j->For the collection of distribution network branches, < > for>Branch->Resistance of->Is->Time branch->Is>Is a node set of distributed energy sources, +.>Is->Time->Active power of the individual distributed energy sources.
10. The optimal scheduling apparatus for a power distribution network according to claim 7, wherein the objective function with the smallest adjustable resource output adjustment amount is specifically:
wherein,for the adjustable resource output adjustment, +.>For the control variable of the adjustable resources in the power distribution network in real-time scheduling phase +.>For the preset sampling step size,/->Is->Time-adjustable actual output feedback value of resource, < + >>Is->Output adjustment of time,/->Is->The output of each adjustable resource of the time-of-day scheduling scheme.
11. The optimal scheduling apparatus for a power distribution network according to claim 7, wherein constraints of objective functions of the day-ahead scheduling stage, the day-in scheduling stage, and the real-time scheduling stage include: flow constraint, safe operation constraint of a power distribution network, active output constraint of an adjustable resource and reactive output constraint of the adjustable resource.
12. The optimal scheduling device for a power distribution network according to claim 11, wherein the day-ahead scheduling module is further configured to perform convex relaxation on a power flow constraint of an objective function in the day-ahead scheduling stage by using a second order cone planning method;
the intra-day rolling module is further used for performing convex relaxation on the power flow constraint of the objective function in the intra-day scheduling stage through the second order cone planning method;
the real-time correction module is further used for performing convex relaxation on the power flow constraint of the objective function in the real-time scheduling stage through the second-order cone planning method.
13. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform the optimized scheduling method of the power distribution network according to any one of claims 1 to 6.
14. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method of optimizing scheduling of a power distribution network according to any one of claims 1 to 6 when the computer program is executed.
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