CN114418232A - Energy storage system operation optimization method and system, server and storage medium - Google Patents

Energy storage system operation optimization method and system, server and storage medium Download PDF

Info

Publication number
CN114418232A
CN114418232A CN202210096555.5A CN202210096555A CN114418232A CN 114418232 A CN114418232 A CN 114418232A CN 202210096555 A CN202210096555 A CN 202210096555A CN 114418232 A CN114418232 A CN 114418232A
Authority
CN
China
Prior art keywords
energy storage
storage system
load
power
output
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202210096555.5A
Other languages
Chinese (zh)
Inventor
李煜阳
李相俊
贾学翠
赵波
倪筹帷
章雷其
靳文涛
修晓青
马会萌
全慧
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, China Electric Power Research Institute Co Ltd CEPRI, Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202210096555.5A priority Critical patent/CN114418232A/en
Publication of CN114418232A publication Critical patent/CN114418232A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
    • G06Q10/06313—Resource planning in a project environment
    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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]
    • 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
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Power Engineering (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Educational Administration (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

A method, system, server and storage medium for optimizing the operation of an energy storage system comprises the steps of obtaining the probability distribution of the total load prediction deviation as the uncertain factor input of a load side; obtaining maximum charge state and minimum charge state allowed by a multipoint distribution energy storage system at the side of a distribution network and charge-discharge power constraint as constraint conditions of power output; setting output of the energy storage system as decision output without considering load uncertainty factors and decision output for bearing load prediction deviation, and setting load prediction deviation bearing coefficients of each energy storage system aiming at the energy storage systems distributed at multiple points, wherein the sum of all the bearing coefficients is 1; setting probability constraint of the output of the energy storage system; setting an objective function of the energy storage system operation optimization to minimize the peak value of the main network injection power; and obtaining an operation optimization control model of the energy storage system under probability constraint, performing equivalent transformation, and solving to obtain an energy storage system output plan. The invention can reduce the impact of uncertain factors on the load side on the main network.

Description

Energy storage system operation optimization method and system, server and storage medium
Technical Field
The invention relates to the technical field of power system operation planning, in particular to an energy storage system operation optimization method, an energy storage system operation optimization system, a server and a storage medium.
Background
Electric energy cannot be stored in a large scale under the traditional visual angle, the supply and demand balance is kept at the running time of the power system, namely the deviation of the load side at each time needs to be adjusted by the power supply side to ensure the power balance, and the load shedding phenomenon is avoided. However, in recent years, the rapid development of energy storage technology is gradually reversing this feature, because of its bidirectional charge and discharge capability to perform space-time transfer of energy.
In the past, research aiming at energy storage mainly focuses on stabilizing new energy fluctuation and eliminating wind and light abandonment on the source side, but less focuses on the balance of uncertain factors on the load side. In recent years, with the large-power loads such as electric vehicles and the like being connected to a distribution network in a large scale, the fluctuation and uncertainty of the load side are gradually intensified, the impact on a main network is more obvious, how to adopt an energy storage system as a flexible resource adjusting means at a user side or a distribution network side with lower voltage level and realize local balance on the basis of considering load uncertainty factors is realized, and the technical problem to be solved in the technical field is to avoid introducing more uncertainty factors into the main network.
The traditional control optimization theory based on the deterministic idea cannot account for the influence of uncertainty factors in the decision process, but the operation of the power system is a multi-time scale process. For example, a scheduling department may compile a day-ahead output plan of a thermal power generating unit according to a load prediction situation of the next day and a new energy power generation situation, but the actual situations of the load and the new energy usually have a certain deviation from the prediction, and at this time, a control optimization theory based on a deterministic idea cannot account for the influence of uncertain factors, which may cause insufficient operation margin of a power grid or suboptimal economy. In order to solve the above problems, other technical solutions apply a stochastic programming-based idea to perform operation control, that is, first, scene generation is performed to obtain load conditions under different scenes and expectations corresponding to the scenes, and then, optimization control strategies considering all the scenes are researched and weighted. However, the operation scene cannot be accurately generated, and when the scene is too many, the solution time is too long, so that the requirements of many projects on the calculation time cannot be met.
Disclosure of Invention
The invention aims to provide an energy storage system operation optimization method, an energy storage system operation optimization system, a server and a storage medium, which can take the influence of the prediction deviation of a load on power supply and demand balance into consideration, take the energy storage as a flexible adjusting resource to balance the load in place, increase the autonomy of regional power grid operation, reduce the impact of uncertain factors on a main network by a load side and increase the operation margin of the system.
In order to achieve the purpose, the invention has the following technical scheme:
in a first aspect, a method for optimizing the operation of an energy storage system is provided, which includes the following steps:
acquiring probability distribution of total load forecasting deviation according to historical load forecasting and actual data, and inputting the probability distribution as uncertain factors of a load side of the energy storage system;
obtaining the maximum charge state allowed by a multipoint distribution energy storage system at the side of a distribution network
Figure BDA0003491013660000021
And minimum state of charge
Figure BDA0003491013660000022
And charge-discharge power constraints
Figure BDA0003491013660000023
As a constraint on the power output of the energy storage system;
setting the output of the energy storage system as decision output without considering load uncertainty factors and decision output bearing load prediction deviation, and setting the load prediction deviation bearing coefficients of all the energy storage systems aiming at the energy storage systems distributed at multiple points, wherein the sum of all the load prediction deviation bearing coefficients is 1;
setting probability constraint of the output of the energy storage system according to uncertain factor input at the load side of the energy storage system, constraint conditions of the power output of the energy storage system, the output of the energy storage system and all load prediction deviation bearing coefficients of the energy storage system in multipoint distribution;
setting an objective function of the energy storage system operation optimization to minimize the peak value of the main network injection power;
integrating the probability constraint of the output of the energy storage system and an objective function of the operation optimization of the energy storage system to obtain an operation optimization control model of the energy storage system under the probability constraint;
and performing equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint, and solving to obtain an energy storage system output plan.
Preferably, in the step of acquiring the probability distribution of the total load forecast deviation according to the historical load forecast and the actual data and inputting the probability distribution as the uncertain factor on the load side of the energy storage system, the expected mu of the load forecast deviation probability distribution of each node i in each time period t is obtainedi,tAnd variance
Figure BDA0003491013660000031
Obtaining probabilistic expectations of load forecast deviations based on data-driven approach
Figure BDA0003491013660000032
Probability variance Var [ omega ] of deviation from load predictioni,t]And is and
Figure BDA0003491013660000033
total load forecast deviation
Figure BDA0003491013660000034
The expectation of the total load forecast deviation is:
Figure BDA0003491013660000035
the variance of the total load prediction bias is:
Figure BDA0003491013660000036
and obtaining the probability distribution omega of the total load prediction deviation as the uncertain factor input of the load side of the energy storage system.
Furthermore, the maximum charge state allowed by the multipoint distribution energy storage system at the side of the acquisition distribution network
Figure BDA0003491013660000037
And minimum state of charge
Figure BDA0003491013660000038
And charge-discharge power constraints
Figure BDA0003491013660000039
In the step of serving as the constraint condition of the power output of the energy storage system, if the node i is not connected with any energy storage system, the maximum state of charge is set
Figure BDA00034910136600000310
Is 0, charge and discharge power constraint
Figure BDA00034910136600000311
Is 0.
Preferably, in the step of setting the energy storage system capacity as a decision capacity without considering load uncertainty factors and a decision capacity for bearing load prediction deviation, setting load prediction deviation bearing coefficients of each energy storage system for the energy storage systems distributed at multiple points, and setting the sum of all the load prediction deviation bearing coefficients as 1, the actual capacity of each active source is set as:
Figure BDA00034910136600000312
in the formula, p0,tFor the injected power of the superior grid in each time period t,
Figure BDA00034910136600000313
for higher-level grid injection power, alpha, without taking into account load prediction deviations for each period0,tΩtPredicting deviation power for the balance load borne by the superior power grid in each period;
Figure BDA00034910136600000314
in order to take into account the energy storage system charging power at different periods of time at each node under the load prediction deviation,
Figure BDA00034910136600000315
energy storage system for not considering load prediction deviationThe power of the system is charged by the system,
Figure BDA00034910136600000316
predicting deviation power for the load borne by the charging power of the energy storage system at each time interval;
Figure BDA00034910136600000317
in order to consider the energy storage system discharge power of each node at different time periods under the load prediction deviation,
Figure BDA0003491013660000041
to account for the energy storage system discharge power in the event of load forecast deviations,
Figure BDA0003491013660000042
predicting deviation power for the load borne by the discharge power of the energy storage system at each time interval;
the expression that the sum of all the load prediction deviation bearing coefficients is 1 is as follows:
Figure BDA0003491013660000043
further, the probability constraint of the energy storage system output is as follows:
Figure BDA0003491013660000044
Figure BDA0003491013660000045
Figure BDA0003491013660000046
Figure BDA0003491013660000047
in the formula, 1-epsilonch、1-εdisConfidence coefficients of energy storage system charge-discharge power step-out-of-limit are respectively obtained;
SOCi,tfor the state of charge, SOC, of each node in each period of the energy storage systemi,t-1For a period of time of state of charge, η, on each node energy storage systemiFor energy storage system charge-discharge conversion efficiency, pestmaxIs the maximum charge-discharge power of the energy storage system,
Figure BDA0003491013660000048
in order to satisfy the probability distribution of the constraint,
Figure BDA0003491013660000049
to satisfy the probability distribution expectations of the constraints.
Further, the objective function of the energy storage system operation optimization is as follows:
Figure BDA00034910136600000410
in the formula, p0,tAnd (3) the injection power of each time period T of the superior power grid, wherein T belongs to T and is the set of the time periods, and T is the total time period number in the day.
Furthermore, the objective function of the energy storage system operation optimization is equivalently transformed as follows:
Figure BDA00034910136600000411
the expression after the equivalent transformation of the objective function for the operation optimization of the energy storage system is as follows:
Figure BDA00034910136600000412
further, the expression is expressed in the probability constraint of the energy storage system output
Figure BDA00034910136600000413
And
Figure BDA00034910136600000414
performing equivalent transformation by using an inverse function of the load prediction deviation probability distribution, and obtaining an expression after the equivalent transformation as follows:
Figure BDA0003491013660000051
Figure BDA0003491013660000052
Figure BDA0003491013660000053
Figure BDA0003491013660000054
in the formula phi-1(1-εch)、Φ-1(1-εdis) Respectively is an inverse function of the probability distribution of the charging and discharging power of the energy storage system in the corresponding confidence interval,
Figure BDA0003491013660000055
predicting a standard deviation of the deviation for the load;
for expression
Figure BDA0003491013660000056
After performing the equivalent transformation:
Figure BDA0003491013660000057
in a second aspect, an energy storage system operation optimization system is provided, which includes:
the uncertainty factor input module is used for acquiring probability distribution of total load prediction deviation according to historical load prediction and actual data and inputting the probability distribution as uncertainty factors of the load side of the energy storage system;
the power output constraint condition acquisition module is used for acquiring the maximum charge state allowed by the multipoint distribution energy storage system at the distribution network side
Figure BDA0003491013660000058
And minimum state of charge
Figure BDA0003491013660000059
And charge-discharge power constraints
Figure BDA00034910136600000510
As a constraint on the power output of the energy storage system;
the system output setting module is used for setting the output of the energy storage system into decision output without considering load uncertainty factors and decision output bearing load prediction deviation, setting the load prediction deviation bearing coefficients of all the energy storage systems aiming at the energy storage systems distributed at multiple points, and setting the sum of all the load prediction deviation bearing coefficients to be 1;
the system output probability constraint setting module is used for setting the probability constraint of the output of the energy storage system according to the uncertain factor input at the load side of the energy storage system, the constraint condition of the power output of the energy storage system, the output of the energy storage system and the prediction deviation undertaking coefficient of all the loads of the energy storage system in multipoint distribution;
the target function setting module is used for setting the target function of the energy storage system operation optimization to minimize the peak value of the main network injection power;
the operation optimization control model establishing module is used for integrating the probability constraint of the output of the energy storage system and an objective function of the operation optimization of the energy storage system to obtain an operation optimization control model of the energy storage system under the probability constraint;
and the model solving module is used for performing equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint and solving to obtain an energy storage system output plan.
Preferably, the uncertainty factor input module obtains the load prediction deviation probability distribution of each node i in each time period tExpectation of mui,tAnd variance
Figure BDA0003491013660000061
Obtaining probabilistic expectations of load forecast deviations based on data-driven approach
Figure BDA0003491013660000062
Probability variance Var [ omega ] of deviation from load predictioni,t]And is and
Figure BDA0003491013660000063
total load forecast deviation
Figure BDA0003491013660000064
The expectation of the total load forecast deviation is:
Figure BDA0003491013660000065
the variance of the total load prediction bias is:
Figure BDA0003491013660000066
and obtaining the probability distribution omega of the total load prediction deviation as the uncertain factor input of the load side of the energy storage system.
Furthermore, the power output constraint condition obtaining module sets the maximum state of charge if the node i is not connected with any energy storage system
Figure BDA0003491013660000067
Is 0, charge and discharge power constraint
Figure BDA0003491013660000068
Is 0.
Preferably, the system output setting module sets the actual output of each active source to be:
Figure BDA0003491013660000069
in the formula, p0,tFor the injected power of the superior grid in each time period t,
Figure BDA00034910136600000610
for higher-level grid injection power, alpha, without taking into account load prediction deviations for each period0,tΩtPredicting deviation power for the balance load borne by the superior power grid in each period;
Figure BDA00034910136600000611
in order to take into account the energy storage system charging power at different periods of time at each node under the load prediction deviation,
Figure BDA00034910136600000612
to account for the energy storage system charging power for load forecast deviations,
Figure BDA0003491013660000071
predicting deviation power for the load borne by the charging power of the energy storage system at each time interval;
Figure BDA0003491013660000072
in order to consider the energy storage system discharge power of each node at different time periods under the load prediction deviation,
Figure BDA0003491013660000073
to account for the energy storage system discharge power in the event of load forecast deviations,
Figure BDA0003491013660000074
predicting deviation power for the load borne by the discharge power of the energy storage system at each time interval;
the expression that the sum of all the load prediction deviation bearing coefficients is 1 is as follows:
Figure BDA0003491013660000075
furthermore, the system output probability constraint setting module sets the probability constraint of the energy storage system output as follows:
Figure BDA0003491013660000076
Figure BDA0003491013660000077
Figure BDA0003491013660000078
Figure BDA0003491013660000079
in the formula, 1-epsilonch、1-εdisConfidence coefficients of energy storage system charge-discharge power step-out-of-limit are respectively obtained;
SOCi,tfor the state of charge, SOC, of each node in each period of the energy storage systemi,t-1For a period of time of state of charge, η, on each node energy storage systemiFor energy storage system charge-discharge conversion efficiency, pestmaxIs the maximum charge-discharge power of the energy storage system,
Figure BDA00034910136600000710
in order to satisfy the probability distribution of the constraint,
Figure BDA00034910136600000711
to satisfy the probability distribution expectations of the constraints.
Further, the objective function setting module sets an objective function for operation optimization of the energy storage system as follows:
Figure BDA00034910136600000712
in the formula, p0,tAnd injecting power into the superior power grid in each time period, wherein T belongs to T as a set of time periods, and T is the whole time period in one day.
Furthermore, the model solving module performs equivalent transformation on the objective function of the operation optimization of the energy storage system according to the following mode:
Figure BDA00034910136600000713
the expression after the equivalent transformation of the objective function for the operation optimization of the energy storage system is as follows:
Figure BDA00034910136600000714
furthermore, the model solving module is used for solving an expression in probability constraint of the output of the energy storage system
Figure BDA0003491013660000081
And
Figure BDA0003491013660000082
performing equivalent transformation by using an inverse function of the load prediction deviation probability distribution, and obtaining an expression after the equivalent transformation as follows:
Figure BDA0003491013660000083
Figure BDA0003491013660000084
Figure BDA0003491013660000085
Figure BDA0003491013660000086
in the formula phi-1(1-εch)、Φ-1(1-εdis) Respectively is an inverse function of the probability distribution of the charging and discharging power of the energy storage system in the corresponding confidence interval,
Figure BDA0003491013660000087
predicting a standard deviation of the deviation for the load;
for expression
Figure BDA0003491013660000088
After performing the equivalent transformation:
Figure BDA0003491013660000089
in a third aspect, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements a method for optimizing the operation of an energy storage system according to the first aspect.
In a fourth aspect, a server is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the energy storage system operation optimization method according to the first aspect when executing the computer program.
Compared with the prior art, the first aspect of the invention has at least the following beneficial effects:
the invention obtains the probability distribution of load prediction deviation by a data driving and curve fitting mode based on historical load prediction and actual data, takes the maximum and minimum charge states allowed by a multipoint distribution energy storage system at the side of a distribution network into consideration and inputs the maximum and minimum charge states as the constraint conditions of energy storage system power output, simultaneously sets the output of the energy storage system as two parts of decision output without considering load uncertainty factors and decision output for bearing load prediction deviation, sets the load prediction deviation bearing coefficients of each energy storage system aiming at the multipoint distribution energy storage system, sums the load prediction deviation bearing coefficients of all the energy storage systems to be 1, sets the probability constraint of the output of the energy storage system according to the sum, obtains the operation optimization control model of the energy storage system under the probability constraint, can take the influence of the load prediction deviation on power supply and demand balance, and performs equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint based on probability theory, the invention realizes an energy storage system operation optimization strategy considering the uncertain factors of the load, can balance the load in situ by taking the energy storage as flexible regulation resources, increases the autonomy of regional power grid operation, reduces the impact of the uncertain factors of the load side on a main network, and further increases the operation margin of the system.
It is understood that the beneficial effects of the second aspect to the fourth aspect of the present invention can be seen from the description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of an energy storage system operation optimization method according to an embodiment of the present invention;
fig. 2 is a block diagram of an energy storage system operation optimization system according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 shows a flow of an energy storage system operation optimization method according to an embodiment of the present invention, which mainly includes:
step 1, acquiring probability distribution of total load forecasting deviation according to historical load forecasting and actual data, and inputting the probability distribution as uncertain factors of a load side of an energy storage system;
step 2, obtaining the maximum charge state allowed by the multipoint distribution energy storage system at the distribution network side
Figure BDA0003491013660000101
And minimum state of charge
Figure BDA0003491013660000102
And charge-discharge power constraints
Figure BDA0003491013660000103
As a constraint on the power output of the energy storage system;
step 3, setting the output of the energy storage system as a decision output without considering the load uncertainty factor and a decision output for bearing the load prediction deviation, and setting the load prediction deviation bearing coefficient of each energy storage system aiming at the energy storage systems distributed at multiple points, wherein the sum of all the load prediction deviation bearing coefficients is 1;
step 4, setting probability constraint of the output of the energy storage system according to uncertain factor input at the load side of the energy storage system, constraint conditions of the power output of the energy storage system, the output of the energy storage system and all load prediction deviation bearing coefficients of the energy storage system in multipoint distribution;
step 5, setting an objective function for optimizing the operation of the energy storage system to minimize the peak value of the main network injection power;
step 6, integrating the probability constraint of the output of the energy storage system and an objective function of the operation optimization of the energy storage system to obtain an operation optimization control model of the energy storage system under the probability constraint;
and 7, performing equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint, and solving to obtain an energy storage system output plan.
In a possible embodiment, in step 1, the probability distribution of the load prediction deviation is obtained by data-driven and curve-fitting, and the expected μ of the probability distribution of the load prediction deviation of each node i in each time period t is obtainedi,tAnd variance
Figure BDA0003491013660000104
Obtaining probabilistic expectations of load forecast deviations based on data-driven approach
Figure BDA0003491013660000105
Variance Var [ omega ] of deviation from load predictioni,t]And is and
Figure BDA0003491013660000106
total load forecast deviation
Figure BDA0003491013660000107
The load prediction deviations are independent of each other, so that the load prediction deviations are additive.
The expectation of the total load forecast deviation is:
Figure BDA0003491013660000108
the variance of the total load prediction bias is:
Figure BDA0003491013660000111
and obtaining the probability distribution omega of the total load prediction deviation as the uncertain factor input of the load side of the energy storage system.
In a possible embodiment, in step 2, ifSetting the maximum state of charge if the node i is not connected with any energy storage system
Figure BDA0003491013660000112
Is 0, charge and discharge power constraint
Figure BDA0003491013660000113
Is 0. The step is mainly used for avoiding excessive charging and discharging of the energy storage system.
In a possible implementation manner, in the step 3, the actual output of each active source is set as follows:
Figure BDA0003491013660000114
in the formula, p0,tFor the injected power of the superior grid in each time period t,
Figure BDA0003491013660000115
for higher-level grid injection power, alpha, without taking into account load prediction deviations for each period0,tΩtPredicting deviation power for the balance load borne by the superior power grid in each period;
Figure BDA0003491013660000116
in order to take into account the energy storage system charging power at different periods of time at each node under the load prediction deviation,
Figure BDA0003491013660000117
to account for the energy storage system charging power for load forecast deviations,
Figure BDA0003491013660000118
predicting deviation power for the load borne by the charging power of the energy storage system at each time interval;
Figure BDA0003491013660000119
in order to consider the energy storage system discharge power of each node at different time periods under the load prediction deviation,
Figure BDA00034910136600001110
to account for the energy storage system discharge power in the event of load forecast deviations,
Figure BDA00034910136600001111
predicting deviation power for the load borne by the discharge power of the energy storage system at each time interval;
in order to ensure that the extra power reserved for the uncertain factors on the load side can just balance the load prediction deviation, the sum of the bearing coefficients of all active sources needs to be 1. According to equation (3), the expression that the sum of all load prediction deviation share coefficients is 1 is:
Figure BDA00034910136600001112
in a possible embodiment, the step 4 sets the probability constraint of the energy storage system output as follows:
Figure BDA00034910136600001113
Figure BDA00034910136600001114
Figure BDA00034910136600001115
Figure BDA00034910136600001116
in the formula, the charge-discharge power of the energy storage system constrained by the formulas (5) and (6) is not out of limit within a certain confidence interval, and is 1-epsilonch、1-εdisConfidence coefficients of energy storage system charge-discharge power step-out-of-limit are respectively, and the values of the confidence coefficients can be selected according to the autonomous degree of the regional power grid;
SOCi,tfor the state of charge, SOC, of each node in each period of the energy storage systemi,t-1For a period of time of state of charge, η, on each node energy storage systemiFor energy storage system charge-discharge conversion efficiency, pestmaxIs the maximum charge-discharge power of the energy storage system,
Figure BDA0003491013660000121
in order to satisfy the probability distribution of the constraint,
Figure BDA0003491013660000122
to satisfy the probability distribution expectations of the constraints.
In a possible embodiment, the objective function for optimizing the operation of the energy storage system set in step 5 is:
Figure BDA0003491013660000123
in the formula, p0,tAnd (3) the injection power of each time period T of the superior power grid, wherein T belongs to T and is the set of the time periods, and T is the total time period number in the day.
Furthermore, in the step 6, the probability constraint of the output of the energy storage system and the objective function of the operation optimization of the energy storage system are integrated to obtain an operation optimization control model of the energy storage system under the probability constraint, which is expressed by the formulas (4) to (9).
In a possible embodiment, step 7 performs an equivalent transformation of formulae (4) to (9), specifically as follows:
the target function of the energy storage system operation optimization is equivalently transformed according to the following mode:
Figure BDA0003491013660000124
and (3) the expression after the equivalent transformation of the objective function of the energy storage system operation optimization in the formula (9) is as follows:
Figure BDA0003491013660000125
performing equivalent transformation on the equations (5) and (6) in the probability constraint of the output of the energy storage system by using an inverse function of the probability distribution of the load prediction deviation to obtain an expression after the equivalent transformation as follows:
Figure BDA0003491013660000126
Figure BDA0003491013660000127
Figure BDA0003491013660000128
Figure BDA0003491013660000129
in the formula phi-1(1-εch)、Φ-1(1-εdis) Respectively is an inverse function of the probability distribution of the charging and discharging power of the energy storage system in the corresponding confidence interval,
Figure BDA0003491013660000131
predicting a standard deviation of the deviation for the load;
analogously, the equivalent transformation of formula (7) gives:
Figure BDA0003491013660000132
further, in the step 7, the operation optimization control model of the energy storage system under the probability constraint is subjected to equivalent transformation based on the probability theory, and is solved to obtain the output plan of the energy storage system. And obtaining an operation optimization control model with an objective function of formula (11) and constraint conditions of (4), (8) and (12) to (16), wherein the model is a linear model and can be directly solved through a solver.
The invention provides an energy storage system operation optimization strategy considering load uncertainty factors, which can take the influence of the prediction deviation of the load on power supply and demand balance into consideration, and take the energy storage as a flexible adjusting resource to balance the load in place, thereby increasing the autonomy of regional power grid operation, reducing the impact of the load side uncertainty factors on a main grid and increasing the system operation margin.
Example 2
Referring to fig. 2, the present embodiment provides an energy storage system operation optimization system, which includes an uncertain factor input module 1, a power output constraint condition obtaining module 2, a system output setting module 3, a system output probability constraint setting module 4, an objective function setting module 5, an operation optimization control model establishing module 6, and a model solving module 7, where main functions and uses of each module are embodied in the following aspects:
the uncertainty factor input module 1 is used for acquiring probability distribution of total load prediction deviation according to historical load prediction and actual data and inputting the probability distribution as uncertainty factors of a load side of the energy storage system;
a power output constraint condition obtaining module 2, configured to obtain a maximum state of charge allowed by a multipoint distribution energy storage system on the distribution network side
Figure BDA0003491013660000133
And minimum state of charge
Figure BDA0003491013660000134
And charge-discharge power constraints
Figure BDA0003491013660000135
As a constraint on the power output of the energy storage system;
the system output setting module 3 is used for setting the output of the energy storage system as decision output without considering load uncertainty factors and decision output for bearing load prediction deviation, setting load prediction deviation bearing coefficients of all the energy storage systems aiming at the energy storage systems distributed at multiple points, and setting the sum of all the load prediction deviation bearing coefficients as 1;
the system output probability constraint setting module 4 is used for setting the probability constraint of the output of the energy storage system according to the uncertain factor input at the load side of the energy storage system, the constraint condition of the power output of the energy storage system, the output of the energy storage system and the prediction deviation undertaking coefficient of all the loads of the energy storage system in multipoint distribution;
the objective function setting module 5 is used for setting an objective function for optimizing the operation of the energy storage system to minimize the peak value of the main network injection power;
the operation optimization control model establishing module 6 is used for integrating the probability constraint of the output of the energy storage system and an objective function of the operation optimization of the energy storage system to obtain an operation optimization control model of the energy storage system under the probability constraint;
and the model solving module 7 is used for performing equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint and solving to obtain an energy storage system output plan.
Further, the uncertainty factor input module 1 obtains the expected μ of the load prediction deviation probability distribution of each node i in each time period ti,tAnd variance
Figure BDA0003491013660000141
Obtaining probabilistic expectations of load forecast deviations based on data-driven approach
Figure BDA0003491013660000142
Probability variance Var [ omega ] of deviation from load predictioni,t]And is and
Figure BDA0003491013660000143
total load forecast deviation
Figure BDA0003491013660000144
The expectation of the total load forecast deviation is:
Figure BDA0003491013660000145
the variance of the total load prediction bias is:
Figure BDA0003491013660000146
and obtaining the probability distribution omega of the total load prediction deviation as the uncertain factor input of the load side of the energy storage system.
Furthermore, the power output constraint condition obtaining module 2 sets the maximum state of charge if the node i is not connected to any energy storage system
Figure BDA0003491013660000147
Is 0, charge and discharge power constraint
Figure BDA0003491013660000148
Is 0.
Further, the system output setting module 3 sets the actual output of each active source to be:
Figure BDA0003491013660000151
in the formula, p0,tFor the injected power of the superior grid in each time period t,
Figure BDA0003491013660000152
for higher-level grid injection power, alpha, without taking into account load prediction deviations for each period0,tΩtPredicting deviation power for the balance load borne by the superior power grid in each period;
Figure BDA0003491013660000153
in order to take into account the energy storage system charging power at different periods of time at each node under the load prediction deviation,
Figure BDA0003491013660000154
to account for the energy storage system charging power for load forecast deviations,
Figure BDA0003491013660000155
load prediction deviation work born by charging power of energy storage system in each time periodRate;
Figure BDA0003491013660000156
in order to consider the energy storage system discharge power of each node at different time periods under the load prediction deviation,
Figure BDA0003491013660000157
to account for the energy storage system discharge power in the event of load forecast deviations,
Figure BDA0003491013660000158
predicting deviation power for the load borne by the discharge power of the energy storage system at each time interval;
the expression that the sum of all load prediction deviation share coefficients is 1 is as follows:
Figure BDA0003491013660000159
further, the system output probability constraint setting module 4 sets the probability constraint of the energy storage system output as follows:
Figure BDA00034910136600001510
Figure BDA00034910136600001511
Figure BDA00034910136600001512
Figure BDA00034910136600001513
in the formula, 1-epsilonch、1-εdisConfidence coefficients of energy storage system charge-discharge power step-out-of-limit are respectively obtained;
SOCi,tcharging state for each time period of energy storage system of each nodeState, SOCi,t-1For a period of time of state of charge, η, on each node energy storage systemiFor energy storage system charge-discharge conversion efficiency, pestmaxIs the maximum charge-discharge power of the energy storage system,
Figure BDA00034910136600001514
in order to satisfy the probability distribution of the constraint,
Figure BDA00034910136600001515
to satisfy the probability distribution expectations of the constraints.
Further, the objective function setting module 5 sets the objective function for optimizing the operation of the energy storage system as follows:
Figure BDA00034910136600001516
in the formula, p0,tAnd injecting power into the superior power grid in each time period, wherein T belongs to T as a set of time periods, and T is the whole time period in one day.
Furthermore, the model solving module 7 performs equivalent transformation on the objective function of the operation optimization of the energy storage system as follows:
Figure BDA0003491013660000161
the expression after the equivalent transformation of the objective function for the operation optimization of the energy storage system is as follows:
Figure BDA0003491013660000162
on the other hand, the model solving module 7 is an expression in the probability constraint of the output of the energy storage system
Figure BDA0003491013660000163
And
Figure BDA0003491013660000164
using load predictionPerforming equivalent transformation on the inverse function of the deviation probability distribution to obtain an expression after the equivalent transformation as follows:
Figure BDA0003491013660000165
Figure BDA0003491013660000166
Figure BDA0003491013660000167
Figure BDA0003491013660000168
in the formula phi-1(1-εch)、Φ-1(1-εdis) Respectively is an inverse function of the probability distribution of the charging and discharging power of the energy storage system in the corresponding confidence interval,
Figure BDA0003491013660000169
predicting a standard deviation of the deviation for the load;
in the same way, for expression
Figure BDA00034910136600001610
After performing the equivalent transformation:
Figure BDA00034910136600001611
the invention provides an energy storage system optimization operation model construction and equivalent model conversion method considering load prediction deviation, avoids introducing uncertainty factors into a main network, and can be applied to energy storage system output planning considering load prediction deviation.
Example 3
Another embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the energy storage system operation optimization method according to embodiment 1 of the present invention. The computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice. For convenience of explanation, the above description only shows the relevant parts of the embodiments of the present invention, and the detailed technical details are not disclosed, please refer to the method parts of the embodiments of the present invention. The computer-readable storage medium is non-transitory, and may be stored in a storage device formed by various electronic devices, and is capable of implementing the execution process described in the method of the embodiment of the present invention.
Example 4
Another embodiment of the present invention further provides a server, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the method for optimizing the operation of the energy storage system according to embodiment 1 of the present invention is implemented. Similarly, for convenience of explanation, the above description only shows the relevant parts of the embodiments of the present invention, and the detailed technical details are not disclosed, please refer to the method parts of the embodiments of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (18)

1. An energy storage system operation optimization method is characterized by comprising the following steps:
acquiring probability distribution of total load forecasting deviation according to historical load forecasting and actual data, and inputting the probability distribution as uncertain factors of a load side of the energy storage system;
obtaining the maximum charge state allowed by a multipoint distribution energy storage system at the side of a distribution network
Figure FDA0003491013650000011
And minimum state of charge
Figure FDA0003491013650000012
And charge-discharge power constraints
Figure FDA0003491013650000013
As a constraint on the power output of the energy storage system;
setting the output of the energy storage system as decision output without considering load uncertainty factors and decision output bearing load prediction deviation, and setting the load prediction deviation bearing coefficients of all the energy storage systems aiming at the energy storage systems distributed at multiple points, wherein the sum of all the load prediction deviation bearing coefficients is 1;
setting probability constraint of the output of the energy storage system according to uncertain factor input at the load side of the energy storage system, constraint conditions of the power output of the energy storage system, the output of the energy storage system and all load prediction deviation bearing coefficients of the energy storage system in multipoint distribution;
setting an objective function of the energy storage system operation optimization to minimize the peak value of the main network injection power;
integrating the probability constraint of the output of the energy storage system and an objective function of the operation optimization of the energy storage system to obtain an operation optimization control model of the energy storage system under the probability constraint;
and performing equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint, and solving to obtain an energy storage system output plan.
2. The energy storage system operation optimization method according to claim 1, wherein in the step of obtaining the probability distribution of the total load forecast deviation from the historical load forecast and the actual data as the uncertainty factor input at the load side of the energy storage system, the expected μ of the load forecast deviation probability distribution of each node i at each time t is obtainedi,tAnd variance
Figure FDA0003491013650000014
Obtaining probabilistic expectations of load forecast deviations based on data-driven approach
Figure FDA0003491013650000015
Variance Var [ omega ] of deviation from load predictioni,t]And is and
Figure FDA0003491013650000016
total load forecast deviation
Figure FDA0003491013650000017
The expectation of the total load forecast deviation is:
Figure FDA0003491013650000021
the variance of the total load prediction bias is:
Figure FDA0003491013650000022
and obtaining the probability distribution omega of the total load prediction deviation as the uncertain factor input of the load side of the energy storage system.
3. The energy storage system operation optimization method according to claim 2, wherein the maximum state of charge allowed by the multipoint distribution energy storage system at the acquisition distribution network side is obtained
Figure FDA0003491013650000023
And minimum state of charge
Figure FDA0003491013650000024
And charge-discharge power constraints
Figure FDA0003491013650000025
In the step of serving as the constraint condition of the power output of the energy storage system, if the node i is not connected with any energy storage system, the maximum state of charge is set
Figure FDA0003491013650000026
Is 0, charge and discharge power constraint
Figure FDA0003491013650000027
Is 0.
4. The energy storage system operation optimization method according to claim 3, wherein in the step of setting the energy storage system output as the decision output without considering the load uncertainty and the decision output for bearing the load prediction deviation, setting the load prediction deviation bearing coefficient of each energy storage system for the energy storage systems distributed at multiple points, and setting the sum of all the load prediction deviation bearing coefficients as 1, the actual output of each active source is set as follows:
Figure FDA0003491013650000028
in the formula, p0,tFor the injected power of the superior grid in each time period t,
Figure FDA0003491013650000029
for higher-level grid injection power, alpha, without taking into account load prediction deviations for each period0,tΩtPredicting deviation power for the balance load borne by the superior power grid in each period;
Figure FDA00034910136500000210
in order to take into account the energy storage system charging power at different periods of time at each node under the load prediction deviation,
Figure FDA00034910136500000211
to account for the energy storage system charging power for load forecast deviations,
Figure FDA00034910136500000212
predicting deviation power for the load borne by the charging power of the energy storage system at each time interval;
Figure FDA00034910136500000213
in order to consider the energy storage system discharge power of each node at different time periods under the load prediction deviation,
Figure FDA00034910136500000214
to account for the energy storage system discharge power in the event of load forecast deviations,
Figure FDA00034910136500000215
predicting deviation power for the load borne by the discharge power of the energy storage system at each time interval;
the expression that the sum of all the load prediction deviation bearing coefficients is 1 is as follows:
Figure FDA00034910136500000216
5. the method of claim 4, wherein the probability constraint on energy storage system output is:
Figure FDA0003491013650000031
Figure FDA0003491013650000032
Figure FDA0003491013650000033
Figure FDA0003491013650000034
in the formula, 1-epsilonch、1-εdisConfidence coefficients of energy storage system charge-discharge power step-out-of-limit are respectively obtained;
SOCi,tfor the state of charge, SOC, of each node in each period of the energy storage systemi,t-1For a period of time of state of charge, η, on each node energy storage systemiFor energy storage system charge-discharge conversion efficiency, pestmaxIs the maximum charge-discharge power of the energy storage system,
Figure FDA0003491013650000035
in order to satisfy the probability distribution of the constraint,
Figure FDA0003491013650000036
to satisfy the probability distribution expectations of the constraints.
6. The energy storage system operation optimization method according to claim 5, wherein the objective function of the energy storage system operation optimization is as follows:
Figure FDA0003491013650000037
in the formula, p0,tAnd (3) the injection power of each time period T of the superior power grid, wherein T belongs to T and is the set of the time periods, and T is the total time period number in the day.
7. The energy storage system operation optimization method according to claim 6, wherein the objective function of the energy storage system operation optimization is equivalently transformed as follows:
Figure FDA0003491013650000038
the expression after the equivalent transformation of the objective function for the operation optimization of the energy storage system is as follows:
Figure FDA0003491013650000039
8. the method of claim 5, wherein the probabilistic constraint on energy storage system output is expressed as an expression
Figure FDA00034910136500000310
And
Figure FDA00034910136500000311
performing equivalent transformation by using an inverse function of the load prediction deviation probability distribution, and obtaining an expression after the equivalent transformation as follows:
Figure FDA0003491013650000041
Figure FDA0003491013650000042
Figure FDA0003491013650000043
Figure FDA0003491013650000044
in the formula phi-1(1-εch)、Φ-1(1-εdis) Respectively is an inverse function of the probability distribution of the charging and discharging power of the energy storage system in the corresponding confidence interval,
Figure FDA0003491013650000045
predicting a standard deviation of the deviation for the load;
for expression
Figure FDA0003491013650000046
After performing the equivalent transformation:
Figure FDA0003491013650000047
9. an energy storage system operation optimization system, comprising:
the uncertainty factor input module is used for acquiring probability distribution of total load prediction deviation according to historical load prediction and actual data and inputting the probability distribution as uncertainty factors of the load side of the energy storage system;
the power output constraint condition acquisition module is used for acquiring the maximum charge state allowed by the multipoint distribution energy storage system at the distribution network side
Figure FDA0003491013650000048
And minimum state of charge
Figure FDA0003491013650000049
And charge-discharge power constraints
Figure FDA00034910136500000410
As a constraint on the power output of the energy storage system;
the system output setting module is used for setting the output of the energy storage system into decision output without considering load uncertainty factors and decision output bearing load prediction deviation, setting the load prediction deviation bearing coefficients of all the energy storage systems aiming at the energy storage systems distributed at multiple points, and setting the sum of all the load prediction deviation bearing coefficients to be 1;
the system output probability constraint setting module is used for setting the probability constraint of the output of the energy storage system according to the uncertain factor input at the load side of the energy storage system, the constraint condition of the power output of the energy storage system, the output of the energy storage system and the prediction deviation undertaking coefficient of all the loads of the energy storage system in multipoint distribution;
the target function setting module is used for setting the target function of the energy storage system operation optimization to minimize the peak value of the main network injection power;
the operation optimization control model establishing module is used for integrating the probability constraint of the output of the energy storage system and an objective function of the operation optimization of the energy storage system to obtain an operation optimization control model of the energy storage system under the probability constraint;
and the model solving module is used for performing equivalent transformation on the operation optimization control model of the energy storage system under the probability constraint and solving to obtain an energy storage system output plan.
10. The energy storage system operation optimization system of claim 9, wherein: the uncertain factor input module obtains expected mu of load prediction deviation probability distribution of each node i in each time period ti,tAnd variance
Figure FDA0003491013650000051
Obtaining probabilistic expectations of load forecast deviations based on data-driven approach
Figure FDA0003491013650000052
Probability variance Var [ omega ] of deviation from load predictioni,t]And is and
Figure FDA0003491013650000053
total load forecast deviation
Figure FDA0003491013650000054
The expectation of the total load forecast deviation is:
Figure FDA0003491013650000055
the variance of the total load prediction bias is:
Figure FDA0003491013650000056
and obtaining the probability distribution omega of the total load prediction deviation as the uncertain factor input of the load side of the energy storage system.
11. The energy storage system operation optimization system of claim 10, wherein: the power output constraint condition acquisition module sets the maximum charge state if the node i is not connected with any energy storage system
Figure FDA0003491013650000057
Is 0, charge and discharge power constraint
Figure FDA0003491013650000058
Is 0.
12. The energy storage system operation optimization system of claim 11, wherein: the system output setting module sets the actual output of each active source as follows:
Figure FDA0003491013650000059
in the formula, p0,tFor the injected power of the superior grid in each time period t,
Figure FDA00034910136500000510
for higher-level grid injection power, alpha, without taking into account load prediction deviations for each period0,tΩtPredicting deviation power for the balance load borne by the superior power grid in each period;
Figure FDA00034910136500000511
in order to take into account the energy storage system charging power at different periods of time at each node under the load prediction deviation,
Figure FDA0003491013650000061
to account for the energy storage system charging power for load forecast deviations,
Figure FDA0003491013650000062
predicting deviation power for the load borne by the charging power of the energy storage system at each time interval;
Figure FDA0003491013650000063
in order to consider the energy storage system discharge power of each node at different time periods under the load prediction deviation,
Figure FDA0003491013650000064
to account for the energy storage system discharge power in the event of load forecast deviations,
Figure FDA0003491013650000065
predicting deviation power for the load borne by the discharge power of the energy storage system at each time interval;
the expression that the sum of all the load prediction deviation bearing coefficients is 1 is as follows:
Figure FDA0003491013650000066
13. the energy storage system operation optimization system according to claim 12, wherein the system output probability constraint setting module sets the probability constraint on energy storage system output as:
Figure FDA0003491013650000067
Figure FDA0003491013650000068
Figure FDA0003491013650000069
Figure FDA00034910136500000610
in the formula, 1-epsilonch、1-εdisConfidence coefficients of energy storage system charge-discharge power step-out-of-limit are respectively obtained;
SOCi,tfor the state of charge, SOC, of each node in each period of the energy storage systemi,t-1For a period of time of state of charge, η, on each node energy storage systemiFor energy storage system charge-discharge conversion efficiency, pestmaxIs the maximum charge-discharge power of the energy storage system,
Figure FDA00034910136500000611
in order to satisfy the probability distribution of the constraint,
Figure FDA00034910136500000612
to satisfy the probability distribution expectations of the constraints.
14. The energy storage system operation optimization system according to claim 13, wherein the objective function setting module sets the objective function for energy storage system operation optimization to:
Figure FDA00034910136500000613
in the formula, p0,tAnd injecting power into the superior power grid in each time period, wherein T belongs to T as a set of time periods, and T is the whole time period in one day.
15. The energy storage system operation optimization system of claim 14, wherein the model solving module performs equivalent transformation on the objective function of the energy storage system operation optimization according to the following way:
Figure FDA0003491013650000071
the expression after the equivalent transformation of the objective function for the operation optimization of the energy storage system is as follows:
Figure FDA0003491013650000072
16. the energy storage system operation optimization system of claim 13, wherein the model solution module expresses the probability constraint on the energy storage system output
Figure FDA0003491013650000073
And
Figure FDA0003491013650000074
performing equivalent transformation by using an inverse function of the load prediction deviation probability distribution, and obtaining an expression after the equivalent transformation as follows:
Figure FDA0003491013650000075
Figure FDA0003491013650000076
Figure FDA0003491013650000077
Figure FDA0003491013650000078
in the formula phi-1(1-εch)、Φ-1(1-εdis) Respectively is an inverse function of the probability distribution of the charging and discharging power of the energy storage system in the corresponding confidence interval,
Figure FDA0003491013650000079
predicting a standard deviation of the deviation for the load;
for expression
Figure FDA00034910136500000710
After performing the equivalent transformation:
Figure FDA00034910136500000711
17. a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out a method for optimizing the operation of an energy storage system according to any one of claims 1 to 8.
18. A server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a method for optimizing the operation of an energy storage system according to any one of claims 1 to 8 when executing the computer program.
CN202210096555.5A 2022-01-26 2022-01-26 Energy storage system operation optimization method and system, server and storage medium Pending CN114418232A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210096555.5A CN114418232A (en) 2022-01-26 2022-01-26 Energy storage system operation optimization method and system, server and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210096555.5A CN114418232A (en) 2022-01-26 2022-01-26 Energy storage system operation optimization method and system, server and storage medium

Publications (1)

Publication Number Publication Date
CN114418232A true CN114418232A (en) 2022-04-29

Family

ID=81276346

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210096555.5A Pending CN114418232A (en) 2022-01-26 2022-01-26 Energy storage system operation optimization method and system, server and storage medium

Country Status (1)

Country Link
CN (1) CN114418232A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115207950A (en) * 2022-07-27 2022-10-18 中国华能集团清洁能源技术研究院有限公司 Energy storage system control method and device based on random disturbance

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115207950A (en) * 2022-07-27 2022-10-18 中国华能集团清洁能源技术研究院有限公司 Energy storage system control method and device based on random disturbance
CN115207950B (en) * 2022-07-27 2024-02-06 中国华能集团清洁能源技术研究院有限公司 Random disturbance-based energy storage system control method and device

Similar Documents

Publication Publication Date Title
Khodabakhsh et al. Optimal control of energy storage in a microgrid by minimizing conditional value-at-risk
Wu et al. A multi-agent-based energy-coordination control system for grid-connected large-scale wind–photovoltaic energy storage power-generation units
Ranamuka et al. Flexible AC power flow control in distribution systems by coordinated control of distributed solar-PV and battery energy storage units
CN110929964A (en) Energy-storage-containing power distribution network optimal scheduling method based on approximate dynamic programming algorithm
Alfaverh et al. Optimal vehicle-to-grid control for supplementary frequency regulation using deep reinforcement learning
KR20210100699A (en) hybrid power plant
Chen et al. Power output smoothing for renewable energy system: Planning, algorithms, and analysis
CN111682529B (en) Flexible scheduling method for mobile energy storage vehicle based on node electricity price information
Zhang et al. Efficient design of energy microgrid management system: a promoted Remora optimization algorithm-based approach
CN115345380A (en) New energy consumption electric power scheduling method based on artificial intelligence
Zhu et al. Energy optimal dispatch of the data center microgrid based on stochastic model predictive control
CN114418232A (en) Energy storage system operation optimization method and system, server and storage medium
CN114498638A (en) Source network load storage coordination planning method and system considering source load bilateral uncertainty
CN108288132B (en) Modeling method based on source-load interactive power system scheduling
Zhang et al. Grouping consistency control strategy based on DMPC and energy storage unit constraints
CN115360768A (en) Power scheduling method and device based on muzero and deep reinforcement learning and storage medium
Yu et al. A fuzzy Q-learning algorithm for storage optimization in islanding microgrid
US20200177025A1 (en) Predictive operational planning in a microgrid taking into account high-load time windows of a main electricity grid
Chen et al. Leaky bucket-inspired power output smoothing with load-adaptive algorithm
Liu et al. Optimal Aggregation Method of a Virtual Power Plant Based on a Nash-Stackelberg Game
Mion et al. Model predictive control of electrical energy storage systems for microgrids-integrated smart buildings
CN113162124B (en) New energy concentrated grid-connected node output optimization method, device, equipment and medium
Liu et al. Stochastic energy management for microgrids with constraints under uncertainty
Zhu et al. A new pricing scheme for controlling energy storage devices in future smart grid
Namba et al. Distributed Microgrid Management Using Passivity-based Generalized Primal-Dual Dynamics

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination