CN115964843A - Evaluation method and system for improving new energy bearing capacity of regional power grid by considering energy storage - Google Patents
Evaluation method and system for improving new energy bearing capacity of regional power grid by considering energy storage Download PDFInfo
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Abstract
The evaluation method and the system for improving the new energy bearing capacity of the regional power grid in consideration of energy storage provide a method for evaluating the new energy bearing capacity of the regional power grid based on a stochastic programming theory and dynamic risk management under the condition of considering energy storage, and comprise the following steps: acquiring original data of a regional power grid; analyzing the new energy output characteristic, and constructing a new energy output characteristic index system; establishing a new energy bearing capacity decision model based on a stochastic programming theory, evaluating a current probability distribution result of the new energy bearing capacity of the power grid, and providing decision guidance for power grid scheduling; and (3) considering constraint conditions such as load increase, minimum thermal power output characteristics and outgoing channels, establishing a new energy bearing capacity collaborative optimization prediction model for energy storage configuration based on load prediction, new energy planning and output characteristics, and providing technical support for power grid development planning and scheduling decisions.
Description
Technical Field
The invention belongs to the technical field of power systems, relates to new energy bearing capacity assessment, and particularly relates to a new energy bearing capacity assessment method and system.
Background
In recent years, the installed capacity and the generated energy of new energy generation in China are higher and higher, and new energy is certainly developed more and more in the future. Due to the fact that new energy is subjected to large-scale grid connection, the new energy bearing pressure of a power grid is increased, the new energy bearing capacity needs to be evaluated, and safe and stable operation of the current and future power grids is guaranteed.
In the prior art, research on evaluation of new energy bearing capacity of a power system includes:
a linear optimal power flow model of the new energy bearing capacity of the power system is established based on analysis of the new energy bearing capacity of the power system based on linear optimal power flow (Chinese power 2022.55 (3), dong Yi and the like), and the problem that a global optimal solution is difficult to obtain by a traditional high-order non-convex model of the optimal power flow is solved on the premise of accuracy permission. And comprehensively considering constraint conditions such as node voltage, line tide, power return, unit climbing and the like, and evaluating the new energy bearing capacity of the power system. And (3) evaluating the bearing capacity of the power distribution network to a multi-access body under the double-carbon background (power grid technology, 2021.11, li hong Zhong and the like), and analyzing the association relation between the new energy characteristics and the bearing capacity of the power distribution network by using a similarity measurement method. In order to realize that the new energy can participate in the energy interaction process to the maximum extent, a two-stage optimization evaluation model with the maximum similarity and the maximum new energy consumption as targets is established. Cai BaoRui et al, yunan Power technology (10 months in 2021), published research on operation boundary of New energy resources of the Yunan Power grid considering frequency safety constraints, established a comprehensive model of frequency response of the Yunan Power grid considering a traditional unit, direct-current frequency limit control and new energy, and under the condition of considering two safety constraints of maximum frequency deviation and frequency change rate of a system, evaluating the bearing capacity of the New energy resources of the Yunan Power grid to guide the actual operation control of the Yunan Power grid.
In the aspect of new energy bearing capacity evaluation, in chinese patent No. cn201811532482.X, "a method for determining new energy bearing capacity of a power grid through system frequency stability constraint", under a system power disturbance condition, a load proportion assumed by a new energy generator set in a model and an equivalent inertia time constant are continuously changed to obtain a system frequency response curve when new energy permeability is different, and the new energy bearing capacity of the power grid is determined according to the lowest frequency of the system under the current new energy permeability, the lowest frequency lower limit value in the system frequency stability constraint, and the lowest frequency of the system after the current new energy permeability is increased by a predetermined percentage value. Chinese patent CN202110638082.2 "evaluation method and system for new energy carrying capacity of power grid considering primary frequency modulation of new energy unit" proposes concepts such as power shortage of multistage power transmission section, proposes evaluation indexes considering power grid frequency response factor, and constructs an evaluation method for new energy carrying capacity of interconnected provincial power grid based on multistage power transmission section stability constraint by considering that new energy unit is connected and has frequency modulation capacity and allocating primary frequency modulation spare capacity borne by conventional unit and new energy unit.
Chinese patent CN201910337063.9, "a new energy bearing capacity early warning method based on online inertia estimation of power system", relates to a new energy bearing capacity early warning method based on online inertia estimation of power system, and determines key parameters determining frequency change of system according to a power grid frequency response characteristic curve; obtaining the limit moment of inertia of the system under the maximum disturbance according to the constraint condition of the frequency change of the system; and evaluating the bearing capacity of the new energy according to the rotational inertia and the limit rotational inertia. Chinese patent CN202110647848.3 discloses a new energy bearing capacity assessment method and system for an electric power system, which analyzes relevant influence factors, equipment data and operation data of each node of a new energy unit, determines a new energy bearing capacity assessment model, obtains the sensitivity of the influence factors of the new energy bearing capacity through load flow calculation, and assesses the new energy bearing capacity according to the sensitivity. Chinese patent CN202111303623.2, "a method for measuring and calculating new energy bearing capacity of regional power grid considering regional power exchange", provides an influence factor for measuring and calculating new energy bearing capacity of a target region in a planning year based on a new energy installation basic scheme of each partition in the planning year and a regional power flow exchange condition, and obtains the influence of the regional power flow exchange considered on the new energy bearing capacity of the regional power grid through load flow calculation results in different ways, thereby providing more practical and reliable guidance for making new energy scheduling and planning decisions in power grid operation, and providing scientific basis for planning and construction of grid structure and power supply structure. Chinese patent CN202110683011.4, a method for displaying the bearing capacity of new energy in a region in real time, calculates the maximum bearing capacity of new energy in a certain period of a regional power grid in real time, displays the maximum new energy capacity which can be installed in the certain period of the regional power grid in real time, can replace bearing capacity evaluation work, saves a large amount of manual time, can early warn the change trend of the bearing capacity of the new energy, and provides guidance for the operation of regional power grid equipment.
Chinese patent CN201911152362.1, "a method for calculating new energy bearing capacity of regional power grid considering energy storage", calculates and calculates new energy bearing capacity index of the regional power grid by comprehensively considering load level, power generation capacity of conventional energy, configuration capacity of energy storage and transmission limit of partition section and taking stable operation margin of the regional power grid, stable operation margin of conventional support energy and electric power and electric quantity balance margin of the regional power grid as the indexes of the new energy bearing capacity of the regional power grid. Chinese patent CN201910851227.X 'evaluation method of power grid source bearing capacity based on data drive and scenario analysis method' carries out scientific clustering division on new energy and power grid operation scenarios based on historical operation data, further realizes refined evaluation of new energy bearing capacity of a power grid under various operation scenarios, and is beneficial to promoting development and synchronization of large-scale new energy and ensuring safe and stable operation of the power grid.
In summary, the research on the bearing capacity of new energy in the prior art mainly focuses on two aspects. In the first aspect, a new energy bearing capacity evaluation model is established according to the power grid constraint condition, and a new energy bearing capacity evaluation result within a certain time period under the power grid constraint condition is obtained. However, representing the bearing capacity of the new energy by using a determined numerical value cannot reflect the uncertainty of the new energy output, and cannot provide more comprehensive and reasonable guidance for the dispatching and operation of the power grid. In the second aspect, energy storage is studied to improve the carrying capacity of new energy. However, the method is lack of new energy bearing capacity evaluation research under the time sequence condition based on the prediction data, and cannot be applied to the characteristic of new energy time sequence uncertainty.
Therefore, the research of the new energy bearing capacity evaluation technology is mainly characterized in that a new energy bearing capacity evaluation model is established, the bearing capacity result of uncertainty of new energy output can be reflected under the current state of the power grid, meanwhile, sensitive influence factors of power grid load, a conveying channel, a traditional unit and the like on the new energy bearing capacity are comprehensively considered, the new energy bearing capacity of the regional power grid for energy storage is predicted and considered, and guidance is provided for power grid planning and scheduling operation.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an evaluation method and system for improving the new energy bearing capacity of a regional power grid by considering energy storage.A new energy bearing capacity evaluation model of the regional power grid is established by taking the current running condition of the power grid as a constraint condition, and a new energy bearing capacity set of the regional power grid with different confidence levels is evaluated; and then, considering the condition changes of power grid load, traditional units, outgoing channels and the like, establishing a regional power grid new energy bearing capacity optimization evaluation model, obtaining new energy bearing capacities of different confidence intervals of the regional power grid, and providing technical support for power grid dispatching operation.
The invention adopts the following technical scheme.
The evaluation method for improving the new energy bearing capacity of the regional power grid in consideration of energy storage comprises the following steps:
step 1, analyzing the output characteristics of the new energy, collecting the output data of the new energy under multiple time scales and multiple operation working conditions, calculating and extracting uncertainty indexes of the output of the new energy, and constructing an index system of the output characteristics of the new energy;
step 2, analyzing a multi-working-condition operation scene of the power grid, analyzing the sensitivity of the new energy bearing capacity to each influence factor according to the operation condition of the power grid, and constructing a typical scene considering the actual operation of the power grid;
step 3, establishing a multi-scene new energy bearing capacity probability evaluation model to obtain a new energy bearing capacity evaluation result set under different confidence levels under the existing constraint condition of the power grid;
and 4, establishing a new energy bearing capacity probability analysis model based on dynamic risk management, developing dynamic evaluation of new energy bearing capacity, and obtaining new energy bearing capacity and energy storage configuration schemes under different confidence levels.
Preferably, in step 1, the output characteristic index includes output rate and interval distribution thereof, a continuous output curve, volatility, a sunrise output curve, and field station output correlation analysis.
Preferably, in step 3, the objective function of the mathematical model of the current stage bearing capacity of the power grid is under all scenes in the scheduling period, so that the bearing capacity of the new energy is maximum. The objective function is:
wherein t is the interval length of the scheduling time interval; s is a scene set; s is a scene label; p is a radical of s The probability value of s corresponding to the scene is obtained; p is ren,t New energy output for a scene s and a time period t;
the constraint conditions comprise power and electric quantity balance, the power generation output and climbing rate of a conventional unit, the output of a new energy unit, a power transmission section and the like:
electric power and electric quantity balance equation:
in the formula, P g,t 、P ren,j,t Respectively represents the power of the conventional unit and the new energy unit at the moment t, P k,t 、P D,t Respectively representing the outgoing channel power and the system load power at the time t;
and (3) power generation output constraint of a conventional unit:
γ g P gmin ≤P g,t ≤γ g P gmax
in the formula, P gmin 、P gmax Respectively representing the lower limit and the upper limit of the output of the conventional unit; gamma ray g Is a variable of 0 to 1, gamma g When =0, it means the unit is stopped, gamma g If the number is not less than 1, the unit is started;
and (3) conventional unit climbing rate constraint:
ΔP gD ≤P g,t -P g,t-1 ≤ΔP gU
in the formula,. DELTA.P gD 、ΔP gU Respectively representing the lower limit and the upper limit of the climbing rate of the conventional unit;
and (3) output constraint of the new energy unit:
P ren,j,min ≤P ren,j,t ≤P ren,j,max
in the formula, P ren,j,min 、P ren,j,max Respectively representing the lower limit and the upper limit of the output of the new energy unitLimiting;
and (3) power transmission section constraint:
P D,min ≤P D,t ≤P D,max
in the formula, P D,min 、P D,max Respectively representing the lower limit and the upper limit of the output power of the outgoing channel;
preferably, in step 4, the random scene risks are defined as:
where Risk(s) represents a scene Risk value, z 0 Indicating the expected value of the bearer capability and Pna(s) indicating the actual bearer capability.
Defining the expected corresponding risk:
in the formula, EDR (z) 0 ) Representing the scene expectation risk, pi(s) representing the probability of scene s occurring, and N representing the scene set value.
The new energy bearing capacity evaluation objective function considering energy storage is as follows:
the new energy bearing capacity evaluation constraint condition considering the energy storage comprises the following steps:
equation of load balance
Wherein L(s) is,
P es Respectively representing system load, power of an outgoing channel, energy storage charging power and other variablesAs before.
Upper and lower energy storage capacity constraints
0≤Pes≤Pg min
In the formula Pg min Representing the upper limit of the energy storage power. Other variables are the same as before.
New energy bearing capacity upper and lower limit constraint
In the formula, L min,t 、
Respectively representing minimum load, minimum value of outgoing power of outgoing channel, L max,t 、/>
Respectively representing the maximum load and the maximum value of the outgoing power of the outgoing channel. Other variables are the same as before.
Discharge channel capacity constraint
In the formula (I), the compound is shown in the specification,
respectively representing the lower limit and the upper limit of the transmission power of the outgoing channel.
Preferably, in step 4, dynamic risk management is adopted to evaluate new energy bearing capacity evaluation results under different expectations and the effect of the energy storage capacity on improving the new energy bearing capacity.
By imposing a large penalty on the expected risk in the objective function, a correspondingly minimized risk value is obtained:
the risk level that the regional grid is willing to withstand is represented by adding the expected lower risk constraint:
EDR(z 0 )≤EDR 0
the dynamic risk management is to solve the coordination optimization scheduling problem of the new energy bearing capacity and the energy storage capacity configuration of the regional power grid under the risk neutral condition so as to bear a target value z 0 Calculating a risk value for the benchmark under the corresponding expectation; and if the risk level can meet the requirement of the power grid on the risk, stopping calculation, and obtaining the current solution as the optimal solution.
And (4) adding a great punishment to the expected risk in the objective function, and solving a corresponding power grid coordination optimization scheduling problem to obtain the minimum expected risk.
And based on the risk under the minimum expectation, selecting an acceptable risk level, and solving a corresponding regional power grid coordination optimization scheduling problem by dynamically adjusting corresponding risk constraints to obtain a satisfactory optimal scheduling decision. The scheduling decision obtained under the corresponding risk level is a balance solution between the bearing capacity and the risk.
The system for evaluating the bearing capacity of the new energy of the regional power grid in consideration of energy storage comprises an output characteristic analysis module, an operation scene analysis module, a bearing capacity probability evaluation module and a dynamic bearing capacity probability analysis module.
And the output characteristic analysis module analyzes the output characteristics of the new energy on the time scale and the operation working condition and constructs a full-process output characteristic index system. And the operation scene analysis module analyzes the influence degree of different operation scenes on the new energy bearing capacity to obtain different influence weights. And the bearing capacity probability evaluation module analyzes uncertainty by using a stochastic programming method to obtain a new energy bearing capacity evaluation result set under different confidence levels. And the dynamic bearing capacity probability analysis module establishes a new energy bearing capacity probability analysis model based on dynamic risk management to obtain new energy bearing capacity and energy storage configuration schemes under different confidence levels.
The invention has the advantages that compared with the prior art,
1. aiming at the uncertainty of the new energy output, a new energy bearing capacity evaluation model considering the uncertainty is established, new energy bearing capacity evaluation results reflecting different confidence levels of the uncertainty of the new energy output are obtained, and scientific and reasonable technical support is provided for scheduling operation;
2. the future change trend of the power grid is comprehensively considered, the new energy bearing capacity of the power grid is evaluated by applying the stored energy, the change trend of the future new energy bearing capacity of the regional power grid is obtained, the effect of the stored energy on improving the new energy bearing capacity is achieved, and important reference values are provided for planning and scheduling operation of the power grid.
Drawings
Fig. 1 is a flowchart of a method for evaluating the new energy bearing capacity of a regional power grid in consideration of energy storage according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are only some embodiments of the invention, and not all embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step on the basis of the spirit of the present invention are within the scope of protection of the present invention.
The embodiment 1 of the invention provides an evaluation method for improving the new energy bearing capacity of a regional power grid in consideration of energy storage, as shown in fig. 1, the evaluation method comprises the following steps:
step 1, analyzing the output characteristics of the new energy, collecting the output data of the new energy under multiple time scales and multiple operation working conditions, calculating and extracting uncertainty indexes of the output of the new energy, and constructing an index system of the output characteristics of the new energy.
In the step 1, the uncertainty indexes comprise new energy output rate, output curve correlation of different dates, fluctuation amplitude and the like; the time scale comprises years, seasons, months and days, the space scale comprises stations, station groups and the like, and the operation conditions comprise typical operation conditions of the regional power grid in different seasons.
And 2, analyzing a multi-working-condition operation scene of the power grid, analyzing the sensitivity of the new energy bearing capacity to each influence factor according to the power grid operation conditions such as a delivery channel, a traditional unit starting mode, a load curve and the like, and constructing a typical scene considering the actual operation of the power grid.
The scene analysis comprises a large/small load operation mode, an extreme operation mode and the like, and the influence degree of different operation scenes on the new energy bearing capacity is analyzed, namely the change condition of the new energy bearing capacity when a single scene is changed is analyzed, and the influence weight of different influence factors on the bearing capacity is obtained;
step 3, establishing a multi-scene probability evaluation model of the new energy bearing capacity to obtain a new energy bearing capacity evaluation result set under different confidence levels under the existing constraint condition of the power grid;
the new energy bearing capacity evaluation result is a set consisting of bearing capacities with different confidence levels, namely different bearing capacities correspond to different confidence levels.
The new energy bearing capacity probability evaluation model comprises an objective function and constraint conditions under different scenes, wherein the objective function is the maximum new energy bearing capacity under all scenes in a scheduling period; the constraint conditions comprise power and electric quantity balance, the power generation output and the climbing rate of the conventional unit, the output of the new energy unit and the power transmission section.
Based on the above, it is clear for those skilled in the art how to establish the objective function with the maximum new energy carrying capacity, and how to establish the corresponding constraint condition. The objective function and each constraint condition disclosed by the embodiment of the invention are only a better technical scheme, and are not specific limitations on the new energy bearing capacity probability evaluation model.
In the preferred embodiment of the invention, the objective function of the mathematical model of the bearing capacity of the power grid at the present stage is under all scenes in the scheduling period, so that the bearing capacity of the new energy is maximum. The objective function is:
in the formula: t isThe interval of the scheduling time interval is long; s is a scene set; s is a scene label; p is a radical of s The probability value of s corresponding to the scene is obtained; p ren,t And (4) outputting new energy in a scene s and a time period t.
The constraint conditions comprise power and electric quantity balance, the output and climbing rate of the conventional unit, the output of the new energy unit, the transmission section and the like:
and (4) a power electricity quantity balance equation.
In the formula, P g,t 、P ren,j,t Respectively represents the power of the conventional unit and the new energy unit at the moment t, P k,t 、P D,t Respectively representing the outgoing channel power and the system load power at the time t.
Power generation output restriction of conventional unit
γ g P gmin ≤P g,t ≤γ g P gmax (3)
In the formula, P gmin 、P gmax Respectively representing the lower limit and the upper limit of the output of the conventional unit; gamma ray g Is a variable of 0 to 1, gamma g When =0, it means the unit is stopped, gamma g And when the number is not less than 1, the unit is started.
Conventional unit ramp rate constraint
ΔP gD ≤P g,t -P g,t-1 ≤ΔP gU (4)
In the formula,. DELTA.P gD 、ΔP gU Respectively representing the lower limit and the upper limit of the climbing rate of the conventional unit.
Output constraint of new energy unit
P ren,j,min ≤P ren,j,t ≤P ren,j,max (5)
In the formula, P ren,j,min 、P ren,j,max Respectively representing the lower limit and the upper limit of the output of the new energy source unit.
Transmission cross section constraint
P D,min ≤P D,t ≤P D,max (6)
In the formula, P D,min 、P D,max Respectively representing the lower and upper limits of the outgoing channel output power.
And solving the model by adopting a stochastic programming theory suitable for solving the uncertainty problem, and realizing the maximum expectation of the new energy bearing capacity.
And 4, considering factors such as the future load increase condition of the power grid, channel planning, traditional unit planning and the like, establishing a new energy bearing capacity probability analysis model based on dynamic risk management, and developing dynamic evaluation on the new energy bearing capacity to obtain new energy bearing capacity and energy storage configuration schemes under different confidence levels.
The risk of each scene defining randomness is:
where Risk(s) represents a scene Risk value, z 0 Indicating the expected value of the bearer capability and Pna(s) indicating the actual bearer capability.
Defining the expected corresponding risk:
in the formula, EDR (z) 0 ) Representing the scene expectation risk, pi(s) representing the probability of scene s occurring, and N representing the scene set value.
The new energy bearing capacity evaluation objective function considering energy storage is as follows:
the new energy bearing capacity evaluation constraint condition considering the energy storage comprises the following steps:
equation of load balance
Wherein L(s) is,
P es Respectively representing system load, outgoing channel power and stored energy charging power, and other variables are the same as the above.
Upper and lower energy storage capacity constraints
0≤Pes≤Pg min (11)
In the formula, pg min Representing the upper limit of the energy storage power. Other variables are the same as before.
New energy bearing capacity upper and lower limit constraint
In the formula, L min,t 、
Respectively representing minimum load, minimum value of outgoing power of outgoing channel, L max,t 、/>
Respectively representing the maximum load and the maximum value of the outgoing power of the outgoing channel. Other variables are the same as before.
Discharge channel capacity constraint
In the formula (I), the compound is shown in the specification,
respectively representing the lower limit and the upper limit of the transmission power of the outgoing channel.
And evaluating new energy bearing capacity evaluation results under different expectations and the effect of improving the new energy bearing capacity by the energy storage capacity by adopting a dynamic risk management theory.
By imposing a large penalty on the expected risk in the objective function, a correspondingly minimized risk value is obtained:
the risk level that the regional grid is willing to withstand is represented by adding the expected lower risk constraint:
EDR(z 0 )≤EDR 0 (14)
solving the coordination optimization scheduling problem of the new energy bearing capacity and the energy storage capacity configuration of the regional power grid under the risk neutral condition to bear the target value z 0 The risk value under the corresponding expectation is calculated for the benchmark. And if the risk level can meet the requirement of the power grid on the risk, stopping calculation, and obtaining the current solution as the optimal solution.
And (4) adding a great punishment to the expected risk in the objective function, and solving a corresponding power grid coordination optimization scheduling problem to obtain the minimum expected risk.
And based on the risk under the minimum expectation, selecting an acceptable risk level, and solving a corresponding regional power grid coordination optimization scheduling problem by dynamically adjusting corresponding risk constraints to obtain a satisfactory optimal scheduling decision. The obtained scheduling decision under the corresponding risk level is a balanced solution between the bearing capacity and the risk.
The embodiment 2 of the present invention provides an evaluation system for improving a new energy bearing capacity of a regional power grid in consideration of energy storage, and the evaluation method for improving the new energy bearing capacity of the regional power grid in consideration of energy storage according to the embodiment 1 is operated, where the evaluation method includes: the device comprises an output characteristic analysis module, an operation scene analysis module, a bearing capacity probability evaluation module and a dynamic bearing capacity probability analysis module.
And the output characteristic analysis module analyzes the output characteristics of the new energy on the time scale and the operation working condition and constructs a full-process output characteristic index system. And the operation scene analysis module analyzes the influence degree of different operation scenes on the new energy bearing capacity to obtain different influence weights. And the bearing capacity probability evaluation module analyzes uncertainty by using a random planning method to obtain a new energy bearing capacity evaluation result set under different confidence levels. And the dynamic bearing capacity probability analysis module establishes a new energy bearing capacity probability analysis model based on dynamic risk management to obtain new energy bearing capacity and an energy storage configuration scheme under different confidence levels.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer-readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Finally, it should be noted that the above embodiments are only used 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 (10)
1. The evaluation method for improving the new energy bearing capacity of the regional power grid in consideration of energy storage is characterized by comprising the following steps of:
step 1, analyzing new energy output characteristics, collecting new energy output data under multiple time scales and multiple operation conditions, calculating and extracting uncertainty indexes of new energy output, and constructing a new energy output characteristic index system;
step 2, analyzing a multi-working-condition operation scene of the power grid, analyzing the sensitivity of the new energy bearing capacity to each influence factor according to the operation condition of the power grid, and constructing a typical scene considering the actual operation of the power grid;
step 3, establishing a multi-scene new energy bearing capacity probability evaluation model to obtain a new energy bearing capacity evaluation result set under different confidence levels under the existing constraint condition of the power grid;
and 4, establishing a new energy bearing capacity probability analysis model based on dynamic risk management, developing dynamic evaluation of new energy bearing capacity, and obtaining new energy bearing capacity and energy storage configuration schemes under different confidence levels.
2. The method for evaluating the new energy bearing capacity of the regional power grid in consideration of energy storage according to claim 1, wherein the method comprises the following steps:
in the step 1, the output characteristic indexes comprise output rate and interval distribution thereof, a continuous output curve, volatility, a sunrise output curve and station output correlation analysis.
3. The evaluation method for improving the new energy bearing capacity of the regional power grid in consideration of energy storage according to claim 1, wherein the evaluation method comprises the following steps:
in step 3, the objective function of the mathematical model of the bearing capacity of the power grid at the current stage is that under all scenes in a scheduling period, the bearing capacity of the new energy is maximized, and the objective function is as follows:
wherein t is the interval length of the scheduling time interval; s is a scene set; s is a scene label; p is a radical of formula s The probability value of s corresponding to the scene is obtained; p is ren，t New energy output for a scene s and a time period t;
the constraint conditions comprise power and electric quantity balance, the power generation output and climbing rate of a conventional unit, the output of a new energy unit, a power transmission section and the like:
electric power and electric quantity balance equation:
in the formula, P g，t 、P ren，j，t Respectively represents the power of the conventional unit and the new energy unit at the moment t, P k，t 、P D，t Respectively representing the outgoing channel power and the system load power at the time t;
the conventional unit generates power and output constraint:
γ g P gmin ≤P g，t ≤γ g P gmax
in the formula, P gmin 、P gmax Respectively representing the lower limit and the upper limit of the output of the conventional unit; gamma ray g Is a variable of 0 to 1, gamma g When =0, it means the unit is stopped, gamma g If =1, the unit is started;
and (3) conventional unit climbing rate constraint:
ΔP gD ≤P g，t -P g，t-1 ≤ΔP gU
in the formula,. DELTA.P gD 、ΔP gU Respectively representing the lower limit and the upper limit of the climbing rate of the conventional unit;
and (3) output constraint of the new energy unit:
P ren，j，min ≤P ren，j，t ≤P ren，j，max
in the formula, P ren，j，min 、P ren，j，max Respectively representing the lower limit and the upper limit of the output of the new energy unit;
and (3) power transmission section constraint:
P D，min ≤P D，t ≤P D，max
in the formula, P D，min 、P D，max Respectively representing the lower and upper limits of the output power of the outgoing channel.
4. The method for evaluating the new energy bearing capacity of the regional power grid in consideration of energy storage according to claim 1, wherein the method comprises the following steps:
in step 4, the random risk of each scene is defined as:
where Risk(s) represents a scene Risk value, z 0 An expected value representing the bearer capability, pna(s) representing the actual bearer capability;
defining the expected corresponding risk:
in the formula, EDR (z) 0 ) Representing the scene expectation risk, pi(s) representing the probability of scene s occurring, and N representing the scene set value.
5. The method for evaluating the new energy bearing capacity of the regional power grid in consideration of energy storage according to claim 1, wherein the method comprises the following steps:
in step 4, the new energy bearing capacity evaluation objective function considering energy storage is as follows:
the new energy bearing capacity evaluation constraint condition considering the energy storage comprises the following steps:
equation of load balance
Wherein L(s) is,
P es Respectively representing system load, outgoing channel power and energy storage charging power;
upper and lower energy storage capacity constraints
0≤Pes≤Pg min
In the formula, pg min Representing an upper limit of energy storage power;
new energy bearing capacity upper and lower limit constraint
In the formula, L min，t 、
Respectively represents the minimum load, the minimum value of the outgoing power of the outgoing channel, L max，t 、/>
Respectively representing the maximum load and the maximum value of the outgoing power of an outgoing channel;
discharge channel capacity constraint
In the formula (I), the compound is shown in the specification,
respectively representing the lower limit and the upper limit of the transmission power of the outgoing channel.
6. The evaluation method for improving the new energy bearing capacity of the regional power grid in consideration of energy storage according to claim 1, wherein the evaluation method comprises the following steps:
and 4, evaluating new energy bearing capacity evaluation results under different expectations and improving the new energy bearing capacity by the energy storage capacity by adopting dynamic risk management.
By imposing a large penalty on the expected risk in the objective function, a correspondingly minimized risk value is obtained:
the risk level that the regional grid is willing to withstand is represented by adding the expected lower risk constraint:
EDR(z 0 )≤EDR 0
7. the method for evaluating the new energy bearing capacity of the regional power grid in consideration of energy storage according to claim 5, wherein the method comprises the following steps:
the dynamic risk management is to solve the problem of coordinated optimization scheduling of the new energy bearing capacity and the energy storage capacity configuration of the regional power grid under the risk neutral condition so as to bear a target value z 0 Calculating a risk value for the benchmark under the corresponding expectation; if the risk level can meet the requirement of the power grid on the risk, stopping calculation, and obtaining the current solution as the optimal solution;
adding great punishment to the expected risk in the objective function, and solving the corresponding power grid coordination optimization scheduling problem to obtain the minimum expected risk;
and based on the risk under the minimum expectation, selecting an acceptable risk level, and solving a corresponding regional power grid coordination optimization scheduling problem by dynamically adjusting corresponding risk constraints to obtain a satisfactory optimal scheduling decision. The obtained scheduling decision under the corresponding risk level is a balanced solution between the bearing capacity and the risk.
8. The system for evaluating the new energy bearing capacity of the regional power grid in consideration of energy storage is operated according to the method for evaluating the new energy bearing capacity of the regional power grid in consideration of energy storage in claims 1 to 7, and comprises an output characteristic analysis module, an operation scene analysis module, a bearing capacity probability evaluation module and a dynamic bearing capacity probability analysis module, and is characterized in that:
the output characteristic analysis module analyzes the output characteristics of the new energy on the time scale and the operation working condition, and constructs a full-flow output characteristic index system; the operation scene analysis module analyzes the influence degree of different operation scenes on the new energy bearing capacity to obtain different influence weights; the bearing capacity probability evaluation module analyzes uncertainty by using a random planning method to obtain a new energy bearing capacity evaluation result set under different confidence levels; and the dynamic bearing capacity probability analysis module establishes a new energy bearing capacity probability analysis model based on dynamic risk management to obtain new energy bearing capacity and energy storage configuration schemes under different confidence levels.
9. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is to store instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 7.
10. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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