CN115693652A - Power distribution network frame optimization method and device based on power balance and performance cost - Google Patents
Power distribution network frame optimization method and device based on power balance and performance cost Download PDFInfo
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Abstract
The invention discloses a power distribution network frame optimization method and system based on power balance and performance cost, wherein the method comprises the following steps: acquiring power loads of a power distribution network, predicting the power loads by combining historical power loads and determining power adequacy information; based on the electric power adequacy information, carrying out load transfer optimization on the power distribution network by a space-time transfer load optimization algorithm; setting a performance cost constraint condition and an optimization function with the minimum total cost as a target, and determining the maximum economic benefit by utilizing a particle swarm algorithm; and performing coordination control according to the power grid optimized load curve to realize real-time power balance, and performing power distribution network frame optimization by combining the maximized economic benefit. The invention fully considers the volatility and randomness of various flexible resources, and effectively eliminates the influence of prediction error on operation optimization by correcting the power load curve; the real-time power balance and the optimization of the power distribution network frame are realized by setting the performance constraint conditions, the target optimization function and the maximum economic benefit.
Description
Technical Field
The invention relates to the technical field of power distribution network optimization, in particular to a power distribution network frame optimization method and device based on power balance and performance cost.
Background
The power and power balance is an important criterion in the joint optimization configuration of the flexible resources and the new Energy, and usually, a system reliability index, such as a load of load probability (Loss of power) index or an Energy over demand (Expected value of power) index, is calculated by using an equivalent continuous load curve method, and the flexible resources and the new Energy joint optimization configuration scheme can pass through only if the judgment index meets the requirement.
However, the current equivalent continuous load curve method or equivalent electric quantity function method does not consider the effects of distributed power supply and flexible resource control, which results in larger actually calculated LOLP and EENS results, and the traditional power balance method can not adapt to the access of a large amount of flexible resources and new energy.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and title of the application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides a power distribution network frame optimization method and device based on power balance and performance cost, and solves the problem of how to realize power balance when a large amount of flexible resources and new energy are accessed.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a power distribution network frame optimization method based on power balance and performance cost, including:
acquiring power loads of a power distribution network, predicting the power loads by combining historical power loads and determining power adequacy information;
based on the electric power adequacy information, carrying out load transfer optimization on the power distribution network by a time-space transfer load optimization algorithm;
setting a performance cost constraint condition and an optimization function with the minimum total cost as a target, and determining the maximum economic benefit by utilizing a particle swarm algorithm;
and performing coordination control according to the power grid optimized load curve to realize real-time power balance, and performing power distribution network frame optimization by combining the maximized economic benefit.
As a preferred scheme of the power distribution network frame optimization method based on power balance and performance cost, the method comprises the following steps: further comprising:
performing power balance calculation based on the power adequacy information, and determining the external main capacity of the newly-added system, the newly-added installed capacity and the capacity of the new energy unit;
performing electric quantity balance calculation based on the electric power adequacy information, and determining the off-grid electric quantity of the main transformer outside the system and the electric quantity to be generated by the generator set;
predicting according to historical data to obtain the probability corresponding to the peak regulation margin, and acquiring the probability information of the peak regulation margin according to the peak regulation margin and the probability corresponding to the peak regulation margin to form a peak regulation margin probability table;
calculating the wind curtailment and the light curtailment electric quantity based on the peak regulation margin probability table;
and correcting the power load curve based on the abandoned wind and abandoned light electric quantity.
As a preferred scheme of the power distribution network frame optimization method based on power balance and performance cost, the method comprises the following steps: the load transfer optimization through the space-time transfer load optimization algorithm comprises the following steps: planning optimization and operation mode optimization are carried out on high-capacity loads of the power distribution network, and the method specifically comprises the following steps:
by taking a single group of load transfer routes as a unit, under the condition that a transformer substation and a network structure are determined, the number and the positions of switches on the lines in the group are adjusted, the existing line where the load is located is changed, and planning and optimization are performed on the large-capacity load of the power distribution network in a mode of determining N-1 safety and load rate balance;
according to the real-time load condition in the power distribution network, the power supply path of the load is changed by adjusting different combinations of the contact switch and the section switch so as to optimize the operation mode of the high-capacity load of the power distribution network;
the model for planning and optimizing the large-capacity load of the power distribution network can be expressed as follows:
in the formula, N 1 Indicating the number of lines in the group, T 1R Representing the load factor, T, of the line after load transfer R1 And (4) line load rate average value after the table load is transferred.
As a preferred scheme of the power distribution network frame optimization method based on power balance and performance cost, the method comprises the following steps: the total cost minimization is the objective optimization function, expressed as:
where minF represents the objective function, N represents the total number of lines, X k Denotes the kth line code element, C nl Represents the construction investment cost, C om Represents the cost of operation and maintenance, C D Representing the depreciation cost, C the total cost, X the set of line coding elements, W ll An offset value, C, representing the recovery cost 0 Represents the recovery cost, W ens An offset value, C, representing the performance cost i Representing the cost of performance.
As a preferred scheme of the power distribution network frame optimization method based on power balance and performance cost, the method comprises the following steps: the method for determining the maximized economic benefit by utilizing the particle swarm algorithm comprises the following steps:
establishing a population and generating initial population individuals based on the power distribution network parameters, setting the iteration number to be 1, and initializing an optimal target value and corresponding target power distribution network parameters;
judging whether the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index;
when the numerical value of the population individual does not meet the power distribution network autonomous operation index constraint condition, adjusting a power distribution network consumption scheme based on an autonomous operation index limit value, so that the adjusted numerical value of the population individual meets the power distribution network autonomous operation index constraint condition;
when the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index, judging whether the numerical value of the population individual meets the constraint condition of the power distribution network operation and the constraint condition of the power distribution network controllable resource calling;
when the numerical value of the population individual does not meet the operation constraint condition of the power distribution network or the calling constraint condition of the controllable resources of the power distribution network, crossing and varying the population individual based on a genetic algorithm, and updating the numerical value of the population individual so that the updated numerical value of the population individual meets the operation constraint condition of the power distribution network and the calling constraint condition of the controllable resources of the power distribution network;
when the numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network, calculating the value of the new energy consumption capacity maximum target function and judging whether the value of the new energy consumption capacity maximum target function is larger than the optimal target value or not;
and when the value of the new energy absorption capacity maximum objective function is larger than the optimal target value, updating the optimal target value to the value of the new energy absorption capacity maximum objective function, and updating the target power distribution network related parameters to the values of the corresponding power distribution network related parameters when the value of the new energy absorption capacity maximum objective function is obtained, wherein the value of the new energy absorption capacity maximum objective function is equal to the sum of the numerical values of various groups of individuals.
As a preferred scheme of the power distribution network frame optimization method based on power balance and performance cost, the method comprises the following steps: the implementation of real-time power balance is represented as:
wherein minF represents a power balance value,
representing the parameter values corresponding to the distribution network in the load curve,
representing parameter values corresponding to all flexible resources in the load curve, and delta t represents time difference;
f(A)＝[a j +b j +c j ]+SU j +Z j D j
wherein, a j 、b j 、c j Representing a plurality of load values corresponding to the jth value in the load curve, S representing the power deviation, U j Representing the voltage load, Z j Representing the equivalent impedance load, D j Representing a reactive power output value.
As a preferred scheme of the power distribution network frame optimization method based on power balance and performance cost, the method comprises the following steps: the optimization of the power distribution network frame comprises the following steps:
the power distribution network control center generates a set flexible resource model after summing based on the flexible resource model parameters reported by each resource, and determines the set flexible resource model parameters;
the power transmission network layer dispatching center calculates the power consumption curve guided by each power distribution layer control center and sends the curve to the corresponding power distribution network control center;
the power distribution network layer adopts a rolling optimization method to schedule the price of each flexible resource according to the real-time power demand information reported by each flexible resource and simultaneously follows a power utilization guiding power curve issued by a control center of a power transmission network layer in the day ahead;
and each flexible resource optimizes the self output according to the optimized price signal of the power distribution network by taking the maximum economic benefit as an optimization target, and returns the optimization result to the power distribution network control center.
In a second aspect, an embodiment of the present invention provides an apparatus for optimizing a grid structure of a power distribution network based on power balance and performance cost, including,
the acquisition module is used for acquiring the power load of the power distribution network;
the processing module is used for predicting the power load of the power distribution network;
the optimization module is used for carrying out high-capacity load transfer optimization on the power distribution network;
the driving module is used for setting performance cost constraint conditions and optimizing an objective function and determining the maximized economic benefit;
and the management module is used for carrying out real-time power balance and power distribution network frame optimization.
In a third aspect, an embodiment of the present invention provides a computing device, including:
a memory and a processor;
the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the power balance and performance cost-based power distribution network rack optimization method according to any embodiment of the invention.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the method for optimizing a grid of a power distribution network based on power balance and performance cost is implemented.
Compared with the prior art, the invention has the beneficial effects that: the invention fully considers the volatility and the randomness of various flexible resources, and effectively eliminates the influence of prediction errors on operation optimization by correcting the power load curve according to the peak regulation margin probability information; the real-time power balance and the optimization of the power distribution network frame are realized by setting the performance constraint conditions, the target optimization function and the maximum economic benefit.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
fig. 1 is a schematic overall flow chart of a power distribution network frame optimization method based on power balance and performance cost according to an embodiment of the present invention;
fig. 2 is a schematic architecture diagram of a power distribution network frame optimization method based on power balance and performance cost according to an embodiment of the present invention;
fig. 3 is a schematic device diagram of a scenario management system based on event-driven configuration according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 to 3, in an embodiment of the present invention, a method for optimizing a power distribution network frame based on power balance and performance cost is provided, including:
s100: and acquiring the power load of the power distribution network, predicting the power load by combining the historical power load and determining the power adequacy information.
Specifically, the historical power load may include a power load acquired in a past period of time, and the power load information may include at least one of power distribution network load data, temperature, humidity, and weather data.
The method for predicting the power load according to the power load of the power distribution network and the historical power load can comprise the following steps:
fitting the historical power load data; and training the power load prediction neural network model by taking the historical power load data as training data, wherein the power load prediction neural network model can comprise a radial basis function neural network model, and power load prediction is carried out through the trained power load prediction neural network model.
It should be noted that, due to the discreteness of the data of the historical power load, the discrete data can be processed into relatively more continuous data by the data fitting process.
It should be noted that, for the power load prediction, an existing prediction method may be referred to, and this is not described in the embodiment of the present invention.
Further, a adequacy probability table is calculated based on the predicted power load value.
It should be noted that, for the new energy source unit, the adequacy probability table is calculated based on the historical output data thereof.
It is to be appreciated that the new energy cluster includes existing new energy clusters and planning new energy clusters.
It should be further noted that the output data of the existing new energy unit is measured, and the output data of the planned new energy unit is measured as the typical value of the existing similar units in the local or nearby area.
It should be noted that the power adequacy information includes the ability of the system to maintain a continuous supply of the total power and power demand to the customer in view of planned outages and reasonably expected unplanned outages of system components, wherein the power adequacy information may include power supply capability, power transmission capability, and the like.
Further, taking the unit power supply capacity adequacy as an example, the power adequacy information may be determined by the number of load nodes in the load unit, the power supply adequacy of the load nodes, and the unit power supply adequacy value, wherein the specific method may be shown by the following formula:
wherein K represents the number of load nodes in the selected load unit, ω i 、L i Respectively representing the power supply adequacy and the unit power supply adequacy of the ith load node.
Further, a adequacy probability table is calculated by the plurality of adequacy information.
Furthermore, power balance calculation is carried out based on the adequacy probability table and is used for determining the capacity of the newly-added system external main machine, the newly-added installed capacity and the new energy machine set capacity.
It should be noted that the power balance calculation is a primary basis for evaluating the supply and demand situation of the power system and reasonably arranging the production scale and time sequence of the power supply, and is an important component in the aspects of power engineering early planning and design work/power scheduling operation, reasonable arrangement of new energy consumption and the like.
Specifically, the power balance calculation method in the embodiment of the present disclosure may include obtaining a rated parameter of the energy storage device, obtaining a load curve of the power system in a charge-discharge cycle of the energy storage device, determining a target output power value of the energy storage device according to the rated parameter and the load curve, and performing power balance calculation according to the target output power value and the composite curve.
Furthermore, electric quantity balance calculation is carried out based on the adequacy probability table and is used for determining the electric quantity of the external main transformer grid-off of the system and the electric quantity to be generated by the generator set.
Specifically, the calculation method for the electric quantity balance in the embodiment of the disclosure may include acquiring historical load preset hour data, annual regional maximum load, annual output preset hour data of various power supply installation machines, annual installed scale of various power supply installation machines and annual generated energy; dividing a power balance characteristic period and a load period; calculating a load probability coefficient, probability output coefficients of various power supplies and utilization hours; acquiring a load prediction result, an electric quantity prediction result and planning an installation scale; and carrying out electric quantity balance calculation.
Furthermore, the probability corresponding to the peak shaver margin is obtained through prediction according to the historical data, and the probability information of the peak shaver margin is obtained according to the peak shaver margin and the probability corresponding to the peak shaver margin, so that a peak shaver margin probability table is formed.
Furthermore, the wind curtailment and the light curtailment electric quantity are calculated based on the peak regulation margin probability table.
Specifically, the method for calculating the wind curtailment and light curtailment amount in the embodiment of the disclosure includes:
1) Determining adequacy and peak regulation margin of a grid-connected point, a generator set and a power load of the micro-grid system;
2) Predicting the probability corresponding to the adequacy, and forming an adequacy probability table according to the adequacy and the probability corresponding to the adequacy;
3) According to the adequacy probability table, carrying out power balance calculation to obtain the required new installed capacity and the new energy unit capacity value;
4) According to the adequacy probability table, carrying out electric quantity balance calculation to obtain the electric quantity to be generated by the generator set;
5) And predicting according to historical data to obtain the probability corresponding to the peak regulation margin, forming a peak regulation margin probability table according to the peak regulation margin and the probability corresponding to the peak regulation margin, and calculating the wind curtailment and the light curtailment electric quantity according to the peak regulation margin probability table.
Furthermore, the power load curve is corrected based on the wind curtailment and the light curtailment electric quantity.
It should be noted that the power load curve is corrected to make the power load curve satisfy a preset condition, where the preset condition is that the wind curtailment and the light curtailment amount are less than a predetermined value.
S200: and based on the electric power adequacy information, carrying out load transfer optimization on the power distribution network by a space-time transfer load optimization algorithm.
Further, a load fluctuation curve of the power distribution network and a complementary load characteristic curve of the power distribution network and an adjacent power distribution network are determined according to the power adequacy information.
It should be noted that, in the embodiments of the present disclosure, a load fluctuation curve and a complementary load characteristic curve may be constructed by acquiring load fluctuation and complementary load of a power distribution network; the high-capacity load transfer characteristics of the power distribution network can be obtained by analyzing the load fluctuation curve and the complementary load characteristic curve, wherein the high-capacity load transfer characteristics can include whether the load of the power distribution network fluctuates greatly or not, for example, when the daily load rate is below 70%, or the daily peak-valley difference rate is above 50%, the fluctuation can be considered to be large; and the load rate of the system is improved by more than 10% or the peak-to-valley difference rate is reduced by more than 10% after the load curves are superposed at different time points.
It should also be noted that the analysis of the load fluctuation curve and the complementary load characteristic curve can also be used for the optimization of the two curves in a subsequent space-time transfer optimization method.
Furthermore, the method for optimizing the spatial transfer of the large-capacity load on the power distribution network comprises the following steps: planning optimization and operation mode optimization.
Furthermore, planning and optimizing the large-capacity load of the power distribution network; the method specifically comprises the following steps: and taking a single group of load transfer route as a unit, under the condition of determining the structure of the transformer substation and the network, changing the route of the existing load by adjusting the number and the positions of switches on the route in the group, and determining the safety of N-1 and the load rate balance.
The model for planning and optimizing the large-capacity load of the power distribution network can be expressed as follows:
wherein N is 1 Indicating the number of lines in the group, T 1R Representing the load factor, T, of the line after load transfer R1 And (4) line load rate average value after the table load is transferred.
Furthermore, the operation mode of the large-capacity load of the power distribution network is optimized, and specifically, the power supply path of the load is changed by adjusting different combinations of the interconnection switch and the section switch according to the real-time load condition in the power distribution network.
S300: and setting an optimization function with a performance cost constraint condition and a minimum total cost as targets, and determining the maximum economic benefit by using a particle swarm algorithm.
Specifically, the performance cost constraints include power balance constraints, network topology constraints, normal operating state constraints, and flexible resource constraints.
Further, the optimization function with the minimum total cost as the target is expressed as:
where minF represents the objective function, N represents the total number of lines, X k Denotes the kth line code element, C nl Represents the construction investment cost, C om Represents the cost of operation and maintenance, C D Representing the depreciation cost, C the total cost, X the set of line coding elements, W ll An offset value, C, representing the recovery cost 0 Represents the recovery cost, W ens An offset value, C, representing the performance cost i Express properties toThis is true.
Furthermore, based on the relevant parameters of the power distribution network, a population is established and initial population individuals are generated, the iteration times are set to be 1, and the optimal target value and the corresponding relevant parameters of the target power distribution network are initialized.
It should be noted that the optimal target value is a value of the target function with the maximum new energy absorption capacity, and the target power distribution network related parameter is a value of the power distribution network related parameter when the optimal target value is obtained.
Further, whether the numerical value of the population individual meets each index constraint condition of the power distribution network is judged;
specifically, when the numerical value of the population individual does not satisfy the power distribution network autonomous operation index constraint condition, the power distribution network consumption scheme is adjusted based on the autonomous operation index limit value, so that the adjusted numerical value of the population individual satisfies the power distribution network autonomous operation index constraint condition; when the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index, judging whether the numerical value of the population individual meets the constraint condition of the power distribution network operation and the constraint condition of the power distribution network controllable resource calling; when the numerical value of the population individual does not meet the operation constraint condition of the power distribution network or the calling constraint condition of the controllable resources of the power distribution network, crossing and varying the population individual based on a genetic algorithm, and updating the numerical value of the population individual so that the updated numerical value of the population individual meets the operation constraint condition of the power distribution network and the calling constraint condition of the controllable resources of the power distribution network; when the numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network, calculating the value of the new energy absorption capacity maximum target function and judging whether the value of the new energy absorption capacity maximum target function is larger than the optimal target value, updating the optimal target value to the value of the new energy absorption capacity maximum target function when the value of the new energy absorption capacity maximum target function is larger than the optimal target value, and updating the relevant parameters of the target power distribution network to the values of the corresponding power distribution network relevant parameters when the value of the new energy absorption capacity maximum target function is obtained, wherein the value of the new energy absorption capacity maximum target function is equal to the sum of the numerical values of various population individuals.
S400: and performing coordination control according to the optimized load curve of the power distribution network to realize real-time power balance, and performing power distribution network frame optimization by combining the maximized economic benefit.
Furthermore, the power distribution network control center generates a set flexible resource model after summing up based on the flexible resource model parameters reported by each resource, and determines the relevant parameters of the flexible resource model.
It should be noted that the flexible resources of the embodiments of the present disclosure may include energy storage devices and demand response, wherein the energy storage devices include input and output of energy and substance, energy conversion and storage devices, and the common evaluation indexes are energy storage density, energy storage power, energy storage efficiency, and energy storage price, influence on environment, and the like.
The demand response, that is, the short-term power demand response, refers to that when the power wholesale market price increases or the system reliability is threatened, after receiving a direct compensation notification of an inductive reduction load or a power price increase signal sent by a power supplier, a power consumer changes its inherent conventional power consumption mode to reduce or push a certain period of power consumption load to respond to power supply, so as to ensure the stability of a power grid and suppress a short-term behavior of power price increase.
The flexible resource model may include a model that calculates energy storage devices and demand response related parameters; the model may be specifically constructed based on a neural network model, which is not limited in the embodiment of the present disclosure.
The relevant parameters of the flexible resource model comprise parameters such as the layer number, the weight parameter, the number of nerve cells and the like of each layer of the model.
Specifically, in a planning stage before the day, flexible resources on a demand side generate flexible resource model parameters according to a prediction result of the next-day power demand, meanwhile, distributed energy sources also generate an output plan, and renewable energy sources perform output prediction and report the output plan to a control center of a power distribution network layer; and the control center of the power distribution network layer generates a set flexible resource model after summing the flexible resource model parameters reported by the resources, and reports the corresponding parameters to the scheduling center of the power transmission network layer.
Furthermore, the power transmission network layer dispatching center calculates the power consumption curve guided by each power distribution layer control center and sends the curve to the corresponding power distribution network control center.
It should be noted that, the method for calculating the power consumption curve according to the embodiment of the present disclosure may include:
acquiring power consumption parameters of control centers of all power distribution layers and generating a power consumption parameter matrix; classifying the electricity utilization parameter matrix to obtain a distribution layer control center with a preset classification number; solving a daily load standard curve of each type of distribution layer control center group; and obtaining the power consumption guiding curve of each power distribution layer control center by using the daily electric quantity of each power distribution layer control center and the corresponding daily load standard curve.
Specifically, after the transmission network layer dispatching center receives the next day set flexible resource model of the distribution network layer, the start-stop and output plans of the next day unit are optimized, and the power consumption curve guided by each distribution layer control center on the next day is calculated and sent to the corresponding distribution network layer control center.
Furthermore, the power distribution network control center and each flexible resource carry out coordination control according to the optimized load curve of the top-layer transmission network, so that the power distribution network and the flexible resources can meet real-time power balance while the economic benefits are optimal.
Specifically, the power distribution network control center and each flexible resource perform coordinated control according to the optimized load curve of the top-level transmission network, so that the optimal economic benefits of the power distribution network and the flexible resources are realized, and meanwhile, the method for meeting real-time power balance is shown in the following formula:
wherein minF represents a power balance value,
representing the parameter values corresponding to the distribution network in the load curve,
representing load curveThe parameter values corresponding to all flexible resources in the line, delta t represents the time difference;
f(A)＝[a j +b j +c j ]+SU j +Z j D j
wherein, a j 、b j 、c j Representing a plurality of load values corresponding to the jth value in the load curve, S representing the power deviation, U j Representing the voltage load, Z j Representing the equivalent impedance load, D j Representing a reactive power output value.
Furthermore, the power distribution network layer adopts a rolling optimization method to schedule the price of each flexible resource according to the real-time power demand information reported by each flexible resource and simultaneously follows a power utilization guiding power curve issued by a control center of a power transmission network layer in the day ahead.
Furthermore, each flexible resource optimizes the self output according to the optimized price signal of the power distribution network with the maximum economic benefit as the optimization target, and returns the optimization result to the power distribution network control center.
The above is an illustrative scheme of the power distribution network frame optimization method based on power balance and performance cost in this embodiment. It should be noted that the technical solution of the power distribution network frame optimization device based on power balance and performance cost and the technical solution of the power distribution network frame optimization method based on power balance and performance cost belong to the same concept, and details of the technical solution of the power distribution network frame optimization device based on power balance and performance cost in this embodiment, which are not described in detail, can be referred to in the description of the technical solution of the power distribution network frame optimization method based on power balance and performance cost.
Fig. 3 is a schematic structural diagram of a power distribution network frame optimization device based on power balance and performance cost according to the present invention, which is applicable to the case of a power distribution network frame optimization method based on power balance and performance cost in this embodiment.
Referring to fig. 3, the power distribution network frame optimization device based on power balance and performance cost in this embodiment includes:
the acquisition module 201 is used for acquiring the power load of the power distribution network;
the processing module 202 is configured to perform power load prediction of the power distribution network;
the optimization module 203 is used for performing high-capacity load transfer optimization on the power distribution network;
the driving module 204 is used for setting a performance cost constraint condition and an optimization objective function and determining the maximized economic benefit;
and the management module 205 is used for performing real-time power balance and power distribution network frame optimization.
In one embodiment, the model building module 201 is further configured to plan an optimal configuration path, guide visual configuration wiring, check automatic configuration wiring, and build an automatic experiment scene;
in one embodiment, the management module 203 is further configured to control the sequence starting and running processes of all events according to the time point information after receiving the time point information; specifically, the status of the configuration equipment can be read in real time during the control of the operation process.
The embodiment further provides a computing device, which is suitable for a power distribution network frame optimization method based on power balance and performance cost, and the computing device comprises:
a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the power distribution network frame optimization method based on power balance and performance cost as set forth in the embodiment.
The computer device may be a terminal comprising a processor, a memory, a communication interface, a display screen and an input means connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
The present embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, the method for implementing power balance and performance cost-based grid optimization of a power distribution network as proposed in the above embodiments is implemented.
The storage medium proposed in this embodiment belongs to the same inventive concept as the data storage method proposed in the above embodiment, and the technical details that are not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
Example 2
Referring to table 1, a comparative explanation with the conventional scheme is provided for verifying the advantageous effects thereof based on the above method for one embodiment of the present invention.
TABLE 1 COMPARATIVE TABLE
As can be seen from the above table, in the conventional optimization method, calculating the reliability index of the system by using an equivalent continuous load curve method or an equivalent electric quantity function method leads to a relatively large actual calculation result, and further leads to a failure in meeting the requirement of the reliability index of westernen, so that the formed flexible resource and new energy combined optimization configuration scheme cannot pass through, and the power grid system cannot adapt to access of a large amount of flexible resources and new energy; in contrast, the power distribution network frame optimization method based on power balance and performance cost can accurately obtain the calculation result of the system reliability index, and further can meet the reliability requirement of the system to form a flexible resource and new energy combined optimization configuration scheme, so that the flexible resources and new energy which are accessed to the power grid in large quantity are optimally configured.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods of the embodiments of the present invention.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. A power distribution network frame optimization method based on power balance and performance cost is characterized by comprising the following steps:
acquiring power loads of a power distribution network, predicting the power loads by combining historical power loads and determining power adequacy information;
based on the electric power adequacy information, carrying out load transfer optimization on the power distribution network by a space-time transfer load optimization algorithm;
setting a performance cost constraint condition and an optimization function with the minimum total cost as a target, and determining the maximum economic benefit by using a particle swarm algorithm;
and performing coordination control according to the power grid optimized load curve to realize real-time power balance, and performing power distribution network frame optimization by combining the maximized economic benefit.
2. The power balance and performance cost based power distribution network rack optimization method of claim 1, further comprising:
performing power balance calculation based on the power adequacy information, and determining the external main capacity of the newly-added system, the newly-added installed capacity and the capacity of the new energy unit;
performing electric quantity balance calculation based on the electric power adequacy information, and determining the off-grid electric quantity of the main transformer outside the system and the electric quantity to be generated by the generator set;
predicting according to historical data to obtain the probability corresponding to the peak regulation margin, and acquiring the probability information of the peak regulation margin according to the peak regulation margin and the probability corresponding to the peak regulation margin to form a peak regulation margin probability table;
calculating the curtailed wind and curtailed light electric quantity based on the peak regulation margin probability table;
and correcting the power load curve based on the abandoned wind and abandoned light electric quantity.
3. The power balance and performance cost based power distribution network frame optimization method according to claim 1 or 2, wherein the load transfer optimization through a space-time transfer load optimization algorithm comprises the following steps: planning optimization and operation mode optimization are carried out on high-capacity loads of the power distribution network, and the method specifically comprises the following steps:
by taking a single group of load transfer routes as a unit, under the condition that a transformer substation and a network structure are determined, the number and the positions of switches on the lines in the group are adjusted, the existing line where the load is located is changed, and planning and optimization are performed on the large-capacity load of the power distribution network in a mode of determining N-1 safety and load rate balance;
according to the real-time load condition in the power distribution network, the power supply path of the load is changed by adjusting different combinations of the contact switch and the section switch so as to optimize the operation mode of the high-capacity load of the power distribution network;
the model for planning and optimizing the large-capacity load of the power distribution network can be expressed as follows:
in the formula, N 1 Indicating the number of lines in the group, T 1R Representing the load factor, T, of the line after load transfer R1 And (4) line load rate average value after the table load is transferred.
4. The power balance and performance cost based power distribution grid architecture optimization method of claim 3, wherein the total cost minimization target optimization function is expressed as:
where minF represents the objective function, N represents the total number of lines, X k Denotes the kth line code element, C nl Represents the construction investment cost, C om Represents the cost of operation and maintenance, C D Representing the depreciation cost, C the total cost, X the set of line coding elements, W ll An offset value, C, representing the recovery cost 0 Represents the recovery cost, W ens An offset value, C, representing the performance cost i Representing the cost of performance.
5. The power balance and performance cost based power distribution network frame optimization method according to claim 4, wherein the determining the maximized economic benefit by using the particle swarm algorithm comprises the following steps:
establishing a population and generating an initial population individual based on the power distribution network parameters, setting the iteration number to be 1, and initializing an optimal target value and corresponding target power distribution network parameters;
judging whether the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index;
when the numerical value of the population individual does not meet the power distribution network autonomous operation index constraint condition, adjusting a power distribution network consumption scheme based on an autonomous operation index limit value, so that the adjusted numerical value of the population individual meets the power distribution network autonomous operation index constraint condition;
when the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index, judging whether the numerical value of the population individual meets the constraint condition of the power distribution network operation and the constraint condition of the power distribution network controllable resource calling;
when the numerical value of the population individual does not meet the operation constraint condition of the power distribution network or the calling constraint condition of the controllable resources of the power distribution network, crossing and varying the population individual based on a genetic algorithm, and updating the numerical value of the population individual so that the updated numerical value of the population individual meets the operation constraint condition of the power distribution network and the calling constraint condition of the controllable resources of the power distribution network;
when the numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network, calculating the value of the new energy consumption capacity maximum target function and judging whether the value of the new energy consumption capacity maximum target function is larger than the optimal target value or not;
and when the value of the new energy absorption capacity maximum objective function is larger than the optimal target value, updating the optimal target value to the value of the new energy absorption capacity maximum objective function, and updating the target power distribution network related parameters to the values of the corresponding power distribution network related parameters when the value of the new energy absorption capacity maximum objective function is obtained, wherein the value of the new energy absorption capacity maximum objective function is equal to the sum of the numerical values of various groups of individuals.
6. The power balance and performance cost based power distribution network frame optimization method according to claim 4 or 5, wherein the real-time power balance is realized by:
wherein minF represents a power balance value,
representing the parameter values corresponding to the distribution network in the load curve,
representing parameter values corresponding to all flexible resources in the load curve, and delta t represents time difference;
f(A)＝[a j +b j +c j ]+SU j +Z j D j
wherein, a j 、b j 、c j Representing a plurality of load values corresponding to the jth value in the load curve, S representing the power deviation, U j Representing the voltage load, Z j Representing the equivalent impedance load, D j Representing a reactive power output value.
7. The power balance and performance cost based power distribution network frame optimization method according to claim 6, wherein the power distribution network frame optimization comprises the following steps:
the power distribution network control center generates a set flexible resource model after summing up based on the flexible resource model parameters reported by each resource, and determines the set flexible resource model parameters;
the power transmission network layer dispatching center calculates the power consumption curve guided by each power distribution layer control center and sends the curve to the corresponding power distribution network control center;
the power distribution network layer adopts a rolling optimization method to schedule the price of each flexible resource according to the real-time power demand information reported by each flexible resource and simultaneously follows a power utilization guiding power curve issued by a control center of a power transmission network layer in the day ahead;
and each flexible resource optimizes the self output according to the optimized price signal of the power distribution network by taking the maximum economic benefit as an optimization target, and returns the optimization result to the power distribution network control center.
8. An apparatus for configurable scene management based on event driving, comprising,
the acquisition module is used for acquiring the power load of the power distribution network;
the processing module is used for predicting the power load of the power distribution network;
the optimization module is used for carrying out high-capacity load transfer optimization on the power distribution network;
the driving module is used for setting a performance cost constraint condition and an optimization objective function and determining the maximized economic benefit;
and the management module is used for carrying out real-time power balance and power distribution network frame optimization.
9. A computing device, comprising:
a memory and a processor;
the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, and the computer executable instructions are executed by the processor to realize the steps of the power distribution network rack optimization method based on power balance and performance cost in any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the power balance and performance cost based power distribution grid optimization method of any of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116914751A (en) * | 2023-09-12 | 2023-10-20 | 济南舜信达电力科技有限公司 | Intelligent power distribution control system |
| CN117254505A (en) * | 2023-09-22 | 2023-12-19 | 南方电网调峰调频(广东)储能科技有限公司 | Energy storage power station optimal operation mode decision method and system based on data processing |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116914751A (en) * | 2023-09-12 | 2023-10-20 | 济南舜信达电力科技有限公司 | Intelligent power distribution control system |
| CN116914751B (en) * | 2023-09-12 | 2023-12-05 | 济南舜信达电力科技有限公司 | Intelligent power distribution control system |
| CN117254505A (en) * | 2023-09-22 | 2023-12-19 | 南方电网调峰调频(广东)储能科技有限公司 | Energy storage power station optimal operation mode decision method and system based on data processing |
| CN117254505B (en) * | 2023-09-22 | 2024-03-26 | 南方电网调峰调频(广东)储能科技有限公司 | Energy storage power station optimal operation mode decision method and system based on data processing |
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