CN117578602A - Power grid peak shaving method and device based on virtual power plant and peak shaving equipment - Google Patents
Power grid peak shaving method and device based on virtual power plant and peak shaving equipment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract
The invention provides a power grid peak shaving method and device based on a virtual power plant and peak shaving equipment. According to the method, the dispatching electric quantity between the virtual power plant and the large power grid in each time period in the day is obtained through load prediction and power generation prediction, a two-stage optimization model with maximum peak regulation capacity and minimum running cost as targets is constructed, and an optimal distribution scheme of each controllable load in the virtual power plant is determined. The invention introduces controllable load to realize peak regulation, and improves the peak regulation capacity of the power grid by scheduling load side resources to cut peaks and fill valleys. Furthermore, the invention determines the optimal allocation scheme of each controllable load through a two-stage optimization model with the maximum peak regulation capacity and the lowest running cost as targets, and comprehensively considers the peak regulation capacity and the running cost factors. The controllable load operation is controlled by the determined optimal allocation scheme, so that the problem that the peak regulation capacity of the power grid is limited at present can be solved, and the peak regulation capacity of the power grid can be improved at lower cost.
Description
Technical Field
The invention relates to the technical field of new energy power generation, in particular to a power grid peak shaving method and device based on a virtual power plant and peak shaving equipment.
Background
With the improvement of economic development and the living standard of people, the installed capacity of the power grid is continuously improved, and correspondingly, the peak shaving pressure of the power grid is also increased year by year. The power grid load is based on industrial and commercial electricity, the electricity consumption time period of agriculture and resident life and seasonal characteristics are obvious, and peak Gu Chalv is higher. The new energy power generation duty ratio in the power grid is continuously increased, so that peak regulation pressure of the power grid is increased.
At present, the peak shaving of the power grid is mainly performed through a power supply side generator set. However, as the peak shaving demand of the power grid increases, the potential excavation of the power supply side adjusting resource enters a bottleneck period, the peak shaving demand and the valley shaving demand of the power grid are difficult to meet, and if the peak shaving resource is further excavated, the transformation cost of the power supply side generating set is greatly increased. Therefore, the problem of limited peak shaving capacity of the power grid exists at present.
Disclosure of Invention
The invention provides a power grid peak shaving method, device and equipment based on a virtual power plant, which can solve the problem that the current power grid peak shaving capacity is limited, and can improve the power grid peak shaving capacity with lower cost.
In a first aspect, the present invention provides a virtual power plant-based power grid peak shaving method, including: acquiring historical operation data, installation data and operation constraint data of each device in the virtual power plant; based on historical operation data, load prediction and power generation prediction are carried out, and the dispatching electric quantity between the virtual power plant and the large power grid in each period of the day is obtained; based on installed data and operation constraint data, establishing a two-stage optimization model with maximum peak shaving capacity and minimum operation cost as targets; performing resource allocation based on the scheduling electric quantity of each time period in the day and a two-stage optimization model to obtain an optimal allocation scheme of each controllable load in the virtual power plant, wherein the optimal allocation scheme comprises the predicted peak shaving quantity of each controllable load in each time period; and controlling the controllable load to operate based on the optimized distribution scheme of each controllable load.
In a second aspect, an embodiment of the present invention provides a power grid peak shaving device based on a virtual power plant, where the device includes: a communication module and a processing module; the communication module is used for acquiring historical operation data, installation data and operation constraint data of each device in the virtual power plant; the processing module is used for carrying out load prediction and power generation prediction based on the historical operation data to obtain the dispatching electric quantity between the virtual power plant and the large power grid in each period of the day; based on installed data and operation constraint data, establishing a two-stage optimization model with maximum peak shaving capacity and minimum operation cost as targets; performing resource allocation based on the scheduling electric quantity of each time period in the day and a two-stage optimization model to obtain an optimal allocation scheme of each controllable load in the virtual power plant, wherein the optimal allocation scheme comprises the predicted peak shaving quantity of each controllable load in each time period; and controlling the controllable load to operate based on the optimized distribution scheme of each controllable load.
In a third aspect, an embodiment of the present invention provides a virtual power plant based power grid peak shaving device, comprising a memory and a processor, the memory storing a computer program, the processor being configured to invoke and run the computer program stored in the memory to perform the steps of the method according to the first aspect and any possible implementation manner of the first aspect.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described in the first aspect and any possible implementation manner of the first aspect.
The invention provides a power grid peak shaving method, a device and peak shaving equipment based on a virtual power plant, which are used for obtaining the dispatching electric quantity between the virtual power plant and a large power grid in each period of the day through load prediction and power generation prediction, constructing a two-stage optimization model with maximum peak shaving capacity and minimum running cost as targets, and determining an optimal allocation scheme of each controllable load in the virtual power plant. On one hand, the invention introduces controllable load to realize peak shaving, and ensures the safe, high-quality and high-efficiency operation of the power grid by dispatching the load side resources to cut peaks and fill valleys, thereby improving the peak shaving capacity of the power grid. On the other hand, the invention determines the optimal allocation scheme of each controllable load through a two-stage optimization model with the maximum peak regulation capacity and the lowest running cost as targets, and comprehensively considers the peak regulation capacity and the running cost factors. The controllable load operation is controlled by the determined optimal allocation scheme, so that the problem that the peak regulation capacity of the power grid is limited at present can be solved, and the peak regulation capacity of the power grid can be improved at lower cost.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an application scenario of a power grid peak shaving method based on a virtual power plant according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a power grid peak shaving method based on a virtual power plant according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a power grid peak shaving device based on a virtual power plant according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a power grid peak shaving device based on a virtual power plant according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the description of the present invention, "/" means "or" unless otherwise indicated, for example, A/B may mean A or B. "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. Further, "at least one", "a plurality" means two or more. The terms "first," "second," and the like do not limit the number and order of execution, and the terms "first," "second," and the like do not necessarily differ.
In the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as examples, illustrations, or descriptions. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion that may be readily understood.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or modules is not limited to only those steps or modules but may, alternatively, include other steps or modules not listed or inherent to such process, method, article, or apparatus.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made with reference to the accompanying drawings of the present invention by way of specific embodiments.
Fig. 1 is a schematic diagram of an application scenario of a power grid peak shaving method based on a virtual power plant according to an embodiment of the present invention.
Under the background of the energy revolution in China, the energy Internet rapidly develops, and the smart grid and the electric power Internet of things are strongly fused deeply, so that the smart grid and the electric power Internet of things become an important form of a new generation energy system. The virtual power plant is a power coordination management system which is used as a special power plant to participate in the operation of an electric power market and a power grid by realizing the aggregation and coordination optimization of distributed power sources such as a distributed power source, an energy storage system, a controllable load, an electric vehicle and the like through an advanced information communication technology and a software system.
The virtual power grid can focus on important characteristics of synchronous power generation of an energy Internet production side and a consumption side, relies on a new generation intelligent control technology and an interactive business mode of clean development of aggregation optimization of 'source network charge storage', aggregates multiple types, multiple energy flows and multiple main resources with electricity as a center, realizes multi-energy complementation of the power supply side and flexible interaction of the load side, and provides a prospective solution for solving the difficult problem of clean energy consumption and low-carbon energy transformation.
Illustratively, as shown in FIG. 1, a virtual power plant includes a distributed power source, a controllable load, an energy storage system, and a conventional load. The distributed power sources comprise wind power plants, photovoltaic power plants and conventional power plants. The controllable load comprises an electric automobile charging pile, electric heating, commercial complex double storage, intelligent building, central heating and other adjustable loads.
It should be noted that the virtual power plant realizes energy interaction with the large power grid through a connecting wire between the virtual power plant and the large power grid. When the virtual power plant generates more power than electricity, the virtual power plant supplies power to the large power grid. When the power generation of the virtual power plant is smaller than the power consumption, the large power grid supplies power to the virtual power plant.
The power grid peak shaving method based on the virtual power plant provided by the embodiment of the invention aims at assisting the power grid peak shaving income and the running cost, researches the virtual power plant running mechanism, builds a two-stage optimization model, proposes an overall optimization distribution scheme of the virtual power plant, optimizes a cost distribution algorithm, improves the satisfaction degree and the enthusiasm of each controllable load user in the electric auxiliary service market, expands the flexible load and the energy storage resource at the user side, and comprehensively improves the power grid peak shaving capacity.
As shown in fig. 2, an embodiment of the present invention provides a power grid peak shaving method based on a virtual power plant. The execution subject is a virtual power plant-based power grid peak shaving device. The power grid peak shaving method comprises steps S101-S105.
S101, acquiring historical operation data, installation data and operation constraint data of each device in the virtual power plant.
In some embodiments, the virtual power plant includes a distributed power source, a controllable load, an energy storage system, and a conventional load.
Exemplary distributed power sources include wind farms, photovoltaic farms, and conventional farms.
Illustratively, the controllable loads include transferable loads and adjustable loads. Wherein, adjustable load is electric heating, intelligent building, centralized heating and commercial complex double storage. The transferable load may be an electric vehicle charging stake.
In some embodiments, the historical operating data includes historical power generation data for the wind farm, historical power generation data for the photovoltaic farm, historical load power for the controllable load, historical load power for the conventional load, and historical charge-discharge power for the energy storage system.
In some embodiments, the installed data includes installed capacity of each distributed power source, installed capacity of each type of load, and installed capacity of the energy storage system.
In some embodiments, the operational constraint data includes output constraints of each distributed power source, power and regulation constraints of each controllable load, power and capacity constraints of the energy storage system, and power constraints of each type of load.
And S102, carrying out load prediction and power generation prediction based on the historical operation data to obtain the scheduling electric quantity between the virtual power plant and the large power grid in each period of the day.
As a possible implementation manner, the power grid peak shaving device may obtain the scheduled power between the virtual power plant and the large power grid in each period of the day based on steps S1021-S1023.
S1021, carrying out power generation prediction based on historical operation data of each generator set in the virtual power plant to obtain predicted power generation data of each period in the day.
As one possible implementation manner, the prediction device may construct an equivalent model of each generator set based on the historical power generation data, determine a predicted power generation amount of each generator set in each period of the day based on the equivalent model of each generator set, and determine predicted power generation data of each period of the day based on a sum of the predicted power generation amounts of each generator set in each period of the day.
As another possible implementation manner, for the wind farm and the photovoltaic farm, the grid peak shaver device can predict the power generation data in the day by analyzing the historical operation data and the historical weather data of the wind farm and the photovoltaic farm.
The power grid peak shaver predicts the predicted power generation data of each period in the day through steps 11-15.
And 11, performing time division on historical operation data and historical weather data of the wind power plant and the photovoltaic electric field, and determining the generated energy and the weather data of each period.
In some embodiments, the weather data includes illumination intensity, temperature, and wind speed.
And 12, determining a training sample by taking weather data of each period as input and the generated energy of each period as output.
And step 13, training the neural network based on the training sample to obtain a power generation prediction model.
And 14, obtaining predicted weather data of each period in the day.
And 15, inputting the predicted weather data of each time period in the day into a power generation prediction model, and predicting to obtain the predicted power generation data of each time period in the day.
And S1022, carrying out load prediction based on the historical operation data of each load in the virtual power plant, and obtaining predicted load data of each period in the day.
As a possible implementation manner, the power grid peak shaving device can analyze historical operation data of each load and historical weather data of the virtual power plant in the range of each load, so as to predict daily loads. For example, the grid peaking device may predict predicted load data for each period of the day based on steps 21-27.
And step 21, performing data fitting by taking historical weather data as independent variables and historical operation data of each load as dependent variables to obtain a fitting regression module.
Wherein the historical weather data includes historical temperature data, historical humidity data, and historical precipitation data.
And 22, inputting the historical weather data into a fitting regression module to obtain fitting load data.
Step 23, taking the historical weather data and the fitting load data as input and the historical operation data of each load as output to generate a training sample.
And step 24, training the neural network based on the training sample to obtain a neural network module.
And step 25, determining a load prediction model based on the fitting regression module and the neural network module.
Step 26, obtaining predicted weather data of each period in the day.
And step 27, inputting the predicted weather data of each time period in the day into a load prediction model, and predicting to obtain the predicted load data of each time period in the day.
S1023, determining the dispatching electric quantity between the virtual power plant and the large power grid in each period of the day based on the predicted power generation data and the predicted load data of each period of the day.
In some embodiments, the predicted power generation data includes a predicted power generation amount, and the predicted load data includes a predicted load amount.
For example, for any period, the grid peaking device may determine a scheduled power level between the virtual power plant and the large grid for the period based on a difference between the predicted power generation and the predicted load level for the period.
And S103, based on the installed data and the operation constraint data, establishing a two-stage optimization model with maximum peak shaving capacity and minimum operation cost as targets.
As a possible implementation manner, the grid peak shaving device may build a two-stage optimization model based on steps S1031-S1035.
S1031, determining an equivalent model of each device of the virtual power plant based on the installed data.
As a possible implementation manner, the grid peak shaving device may determine a wind power generation equivalent model in the wind farm based on the following formula.
Wherein A is eq The cross-over area (m 2) of the equivalent wind driven generator; c (C) p-eq Is a power coefficient; v (V) v-eq Is equal to the wind speed (m/s); p (P) e-eq Is the mechanical power (W), J 'of the equivalent wind driven generator' eq The inertia coefficient (kg/m 2) of the equivalent wind driven generator; w (W) r The rotor speed (rad/s) of the equivalent wind driven generator; h is the system inertia time constant (S) of the equivalent wind driven generator; p (P) N Is the rated power (kW) of the equivalent wind driven generator.
The invention uses the generator rotor equivalent method to determine the wind power generation equivalent model. The core idea of the generator rotor equivalence method is to consider all wind driven generators to be equivalent to a simple model under the condition of not considering the model difference and the input wind speed change of the wind driven generators, and research the model. And carrying out applicability correction on the existing generator rotor equivalent method so that the generator rotor equivalent method is suitable for a wind power generation system.
For equivalent wind power generator sweep area A eq The sum of the radiuses of all the wind power generators is the radius of the equivalent wind power generator; to minimize errors and simplify computation, the power coefficient C p-eq Equal to the optimal power coefficient of all wind turbines.
As a possible implementation manner, the grid peak shaving device may determine the photovoltaic power generation equivalent model based on the following formula.
Wherein G is T Is the illumination intensity; k (k) c Is the temperature coefficient; t (T) c Is the actual working temperature; GSTC, TSTC, PSTC are rated illumination intensity, operating temperature and output power, respectively. The detailed relationship between the available output power Ppv and G, T is that the greater the illumination intensity, the greater the output power, within a certain range at constant temperature. Under constant illumination intensity, the temperature is increased and the maximum output power is reduced within a certain range.
As a possible implementation manner, the power grid peak shaving device may determine the equivalent model of the energy storage system based on the following formula. The equivalent model of the energy storage system comprises a charging model and a discharging model.
The charging power of the energy storage system BESS at the t hour is represented by Pc BESS (t), the maximum charging power of the energy storage system BESS is represented by PBCmax, the discharging power of the energy storage system BESS at the t hour is represented by Pd BESS (t), the maximum discharging power of the energy storage system BESS at the t hour is represented by PBDmax, the SOC of the energy storage system BESS at the t hour is represented by Soc (t), the SOC of the energy storage system BESS at the t+1hour is represented by Soc (t+1), the maximum value of the SOC of the energy storage system during operation is Socmax, the minimum value of the SOC of the energy storage system during operation is Socmin, and Snom represents the rated capacity of the energy storage system during operation. In different scenarios, the BESS charge and discharge power is also related to the specific charge and discharge strategy in the scenario.
As a possible implementation manner, the power grid peak shaving device may determine an equivalent model of the fuel generator set in the conventional power generation field based on the following formula.
Wherein PDGS (t) -the diesel generator output power (kW) at hour t; pdmin—minimum output power (kW) of diesel generator; pdmax—maximum output power (kW) of a diesel generator; the active power output of the Rup- (t-1, t) period diesel engine can be increased or decreased by a maximum value (kW).
S1032, constructing a first-stage objective function by taking the maximum peak shaving capacity as a target based on the equivalent model of each device of the virtual power plant.
In some embodiments, the peak shaver capability maximum represents that an interaction total energy between the virtual power plant and a large power grid is minimum.
For example, the grid peak shaver may construct the first stage objective function based on the following formula;
wherein M is i And (3) the interactive energy between the virtual power plant and the large power grid in the ith period, wherein n is the period number.
It should be noted that, for any period, the power grid peak shaving device may determine the difference between the generated energy and the load of the period as the interactive energy between the virtual power plant and the large power grid in the period. The power grid peak regulating device can determine the sum of the total power generation amount of wind power generation, photovoltaic power generation and conventional power generation and the discharge power of the energy storage system as the power generation amount of the period; the sum of the load power of the controllable load and the conventional load, and the charging power of the energy storage system is determined as the load amount of the period.
The load power of the controllable load is the load power of the controllable load after being regulated. The electricity consumption valley period is the load power of the controllable load after the electric quantity is increased. The electricity consumption peak period is the load power after the controllable load reduces the electric quantity.
It is understood that the distributed energy storage system is disposed at a plurality of nodes in the virtual power plant. The control device of each energy storage system can control the charge and discharge states and the charge and discharge power of the energy storage system based on the real-time electric quantity of the energy storage system. As such, during the same period, different energy storage systems may be in different charge and discharge states. For example, the energy storage system 1 is in a charged state. The energy storage system 2 is in a discharged state.
S1033, constructing a second-stage objective function by taking the lowest running cost as a target based on the equivalent model of each device of the virtual power plant.
For example, the grid peaking device may construct a second stage objective function based on the following formula.
Wherein,represents the unit cost of electricity generation of the jth electricity generation device in the ith period, < >>Represents the generated power of the jth power generation device of the ith period,/th power generation device of the ith period>Representing the unit cost of the kth energy storage device of the ith period,/>Representing the charge-discharge power of the kth energy storage device of the ith period, +.>Represents the cost per unit of adjustment of the z-th controllable load of the i-th period,/th period>Representing the regulated power of the ith period of time, z-th energy storage device,>unit cost representing scheduled power between virtual power plant and large grid in the ith period, +. >And the power consumption of the power plant is calculated according to the power consumption of the power plant, the power generation equipment and the energy storage equipment, and the power consumption of the power plant is calculated according to the power consumption of the power plant.
The power generation equipment includes wind power generation equipment, photovoltaic power generation equipment and conventional power generation equipment. The charge and discharge power of the energy storage device includes a charge power or a discharge power.
S1034, determining constraint conditions of the two-stage optimization model based on the operation constraint conditions.
In some embodiments, the operating constraints include power balance constraints, node voltage constraints, upper and lower limits of output and ramp rate constraints of a conventional generator set, upper and lower limits of regulation and rate constraints of a controllable load, regulation power constraints of a controllable load, charge and discharge power constraints of an energy storage system, maximum output constraints of wind power generation and maximum output constraints of photovoltaic power generation.
The power balance constraint is, for example, that the difference in the amount of generated load of the virtual power plant is equal to the scheduled power between the virtual power plant and the large power grid.
Illustratively, the node voltage constraints are such that the node voltages of the nodes within the virtual power plant meet a voltage criterion. For example, the node voltage of each node should be varied in a range of 50.+ -. 0.2Hz.
Illustratively, the upper and lower limits of the output force of a conventional generator set should meet design criteria. If the output power of the conventional generator set is larger than the minimum limit value and smaller than the maximum limit value. The ramp rate of a conventional genset should be less than a set point.
Illustratively, the upper and lower limits of the regulation of the controllable load are constrained such that the regulated power of the controllable load should be greater than a minimum limit and less than a maximum limit. The minimum limit value and the maximum limit value of the regulated power of the controllable load are comprehensively determined according to the operation condition and the installed capacity of the controllable load.
The adjustment electric quantity constraint of the controllable loads is that the sum of the adjustment electric quantity of each controllable load in the electricity consumption valley period and the electric quantity of the controllable load participating in the valley adjustment in the electricity consumption valley period are smaller than the first error; and the error between the sum of the regulated electric quantity of each controllable load in the electricity consumption peak period and the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period is smaller than the second error.
Illustratively, the charge and discharge power constraints of the energy storage system are such that the charge power and the discharge power of the energy storage system should satisfy greater than a minimum limit and less than a maximum limit.
Illustratively, the maximum output force constraint of the wind power generation is that the output power of the wind power generation should be less than a maximum limit value.
Illustratively, the maximum output of photovoltaic power generation is constrained. The output power for photovoltaic power generation should be less than the maximum limit.
S1035, constructing the two-stage optimization model based on the first-stage objective function, the second-stage objective function and the constraint condition.
And S104, performing resource allocation based on the scheduling electric quantity of each time period in the day and the two-stage optimization model to obtain an optimal allocation scheme of each controllable load in the virtual power plant.
In some embodiments, the optimal allocation scheme includes a predicted peak shaver amount for each period of time for each controllable load.
In some embodiments, the controllable load includes an adjustable load and a transferable load.
In some embodiments, the optimal allocation scheme includes a first optimal allocation scheme and a second optimal allocation scheme, where the first optimal allocation scheme is used to determine an increased amount of power for each controllable load during each period of the electricity consumption valley period; the second optimal allocation scheme is used for determining the reduction electric quantity of each controllable load in each period of the electricity consumption peak period.
Accordingly, as a possible implementation manner, the step S104 may be specifically implemented as steps S1041 to S1043.
S1041, respectively calculating the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period and the electric quantity of the controllable load participating in valley regulation in the electricity consumption valley period based on the dispatching electric quantity of each period in the day.
The power grid peak regulation device can sum the dispatching electric quantity of each period in the electricity consumption peak period to obtain the total dispatching electric quantity in the electricity consumption peak period, and then determine the set proportion of the total dispatching electric quantity in the electricity consumption peak period as the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period.
Still further exemplary, the grid peak shaver may first sum the scheduled power for each period of the peak power usage period to obtain the total scheduled power for the peak power usage period, and comparing the total dispatching electric quantity in the electricity consumption peak period with the peak regulating electric quantity of the controllable load before the day, and determining the electric quantity of the controllable load participating in peak regulating in the electricity consumption peak period based on a comparison result.
For example, if the error between the total power consumption peak period scheduling electric quantity and the peak shaving electric quantity of the load controllable before the day is smaller than the third error, the total power consumption peak period scheduling electric quantity is determined to be the electric quantity of the load controllable in the peak shaving in the power consumption peak period. If the error between the total dispatching electric quantity in the electricity consumption peak period and the peak regulating electric quantity of the daily controllable load is larger than the third error, the peak regulating electric quantity of the daily controllable load is determined to be the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period.
S1042, determining a first optimal allocation scheme of the electricity consumption valley period based on the electric quantity of the controllable load participating in valley adjustment in the electricity consumption valley period and the two-stage optimization model.
S1043, determining a second optimal allocation scheme of the electricity consumption peak period based on the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period and the two-stage optimization model.
The power grid peak shaving device may calculate the first optimal allocation scheme or the second optimal allocation scheme by using a particle swarm algorithm.
Step 31, initializing algorithm parameters. And randomly initializing a population, wherein the population is the adjusted electric quantity of each controllable load.
Step 32, initializing the iteration number k=1.
Step 33, calculating a first stage objective function, and if the interaction energy corresponding to the current population is the minimum value of the interaction energy before the current iteration times, determining the current population as an optimal solution; if the difference between the interaction energy corresponding to the current population and the minimum value of the interaction energy before the current iteration number is smaller than the set value, determining the current population as a candidate solution.
And step 34, updating the population.
And 35, adding 1 to the iteration number, and judging whether the current iteration number is larger than the first iteration number. If so, step 36 is performed. If not, repeating steps 33-35.
Step 36, initializing the iteration number k=1. And randomly selecting any population from the optimal solution and the candidate solution to be the current population.
And 37, calculating a second stage objective function, and if the running cost corresponding to the current population is the minimum value of the running cost before the current iteration times, determining the current population as an optimal solution.
Step 38, updating the population.
And 39, adding 1 to the iteration number, and judging whether the current iteration number is greater than the second iteration number. If yes, outputting the optimal solution, and exiting the iterative process. If not, repeating steps 37-39.
It should be noted that, the optimal solution is the first optimal allocation scheme or the second optimal allocation scheme. When the time period is the electricity consumption valley period, the optimal solution is the first optimal allocation scheme. And when the time period is the electricity consumption peak period, the optimal solution is the second optimal allocation scheme.
Illustratively, the first number of iterations may be 200. The second number of iterations may be 100. The present application is not limited thereto.
It should be noted that, when the particle swarm optimization algorithm is applied, in the iterative process of each step, the population can be updated according to the following formula, so as to control the speed and the position of the particles (i.e. the adjusted electric quantity of each controllable load).
Wherein v is k Indicating the speed of the particles, v, at an iteration number k k+1 Indicating the speed of the control particles when the iteration number is k+1; x is x k Representing the spatial position of the control particles at an iteration number k, x k+1 Represents the spatial position of the control particle, pbest, at an iteration number of k+1 k Represents the self-optimal solution, gbest, of the control particle at the kth iteration k Representing a globally optimal solution for the control particles at the kth iteration c 1 Representing the first learning factor, c 2 Representing a second learning factor, r 1 And r 2 Are random numbers uniformly distributed between (0, 1), wherein omega represents an inertia factor and omega max Representing the most significant of the inertial factorsLarge limit, ω min Represents the minimum limit of the inertia factor, k represents the number of iterations, k max Representing the maximum number of iterations.
When the iteration number k is greater than the maximum iteration number k max The iteration process is exited to obtain the adjustment electric quantity (gbest) of each controllable load k The value is the optimal solution of the two-stage optimization model).
S105, controlling the controllable loads to run based on the optimized distribution scheme of each controllable load.
As a possible implementation manner, the grid peak shaving device may control the controllable load operation based on steps S1051-S1052.
S1051, controlling the starting of the transferable load and controlling the adjustable load to increase the power consumption based on the increased electric quantity of each controllable load in each period of the electricity consumption valley period in the first optimized distribution scheme.
S1052, controlling the closing of the transferable load and controlling the reducing of the power consumption of the adjustable load based on the electric quantity of each controllable load in each period of the power consumption peak period in the second optimal allocation scheme.
The power grid peak shaving device can also monitor the running state of each controllable load in real time, adjust the peak shaving amount of each controllable load in real time and ensure that the virtual power plant runs with the maximum peak shaving capacity.
Step S105 may be embodied as steps A1-A4, for example.
A1, monitoring the actual peak shaving amount of each controllable load.
A2, if the error between the actual peak regulation quantity and the predicted peak regulation quantity is larger than the set error, re-determining the constraint condition of the two-stage optimization model based on the actual running condition of each controllable load.
For example, the power grid peak shaving device may redetermine the upper and lower limit adjustment constraints of the controllable loads and the adjustment electric quantity constraint of the controllable loads based on the actual operation conditions of the controllable loads.
And A3, re-distributing resources based on the constraint condition of the re-determined two-stage optimization model, and updating the optimal distribution scheme of each controllable load in the virtual power plant.
And A4, controlling the controllable load to operate based on the updated optimal allocation scheme.
The invention provides a power grid peak shaving method, a device and peak shaving equipment based on a virtual power plant, which are used for obtaining the dispatching electric quantity between the virtual power plant and a large power grid in each period of the day through load prediction and power generation prediction, constructing a two-stage optimization model with maximum peak shaving capacity and minimum running cost as targets, and determining an optimal allocation scheme of each controllable load in the virtual power plant. On one hand, the invention introduces controllable load to realize peak shaving, and ensures the safe, high-quality and high-efficiency operation of the power grid by dispatching the load side resources to cut peaks and fill valleys, thereby improving the peak shaving capacity of the power grid. On the other hand, the invention determines the optimal allocation scheme of each controllable load through a two-stage optimization model with the maximum peak regulation capacity and the lowest running cost as targets, and comprehensively considers the peak regulation capacity and the running cost factors. The controllable load operation is controlled by the determined optimal allocation scheme, so that the problem that the peak regulation capacity of the power grid is limited at present can be solved, and the peak regulation capacity of the power grid can be improved at lower cost.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 3 shows a schematic structural diagram of a power grid peak shaving device based on a virtual power plant according to an embodiment of the present invention. The grid peak shaver 200 comprises a communication module 201 and a processing module 202.
And the communication module 201 is used for acquiring historical operation data, installation data and operation constraint data of each device in the virtual power plant.
The processing module 202 is configured to perform load prediction and power generation prediction based on the historical operation data, so as to obtain a scheduled electric quantity between the virtual power plant and the large power grid in each period of the day; based on the installed data and the operation constraint data, establishing a two-stage optimization model with maximum peak shaving capacity and minimum operation cost as targets; performing resource allocation based on the scheduling electric quantity of each time period in the day and the two-stage optimization model to obtain an optimal allocation scheme of each controllable load in the virtual power plant, wherein the optimal allocation scheme comprises the predicted peak shaving quantity of each controllable load in each time period; and controlling the controllable load to run based on the optimized distribution scheme of each controllable load.
In one possible implementation manner, the processing module 202 is specifically configured to perform power generation prediction based on historical operation data of each generator set in the virtual power plant, so as to obtain predicted power generation data of each period in a day; carrying out load prediction based on historical operation data of each load in the virtual power plant to obtain predicted load data of each time period in the day; and determining the scheduling electric quantity between the virtual power plant and the large power grid in each period of the day based on the predicted power generation data and the predicted load data of each period of the day.
In a possible implementation manner, the processing module 202 is specifically configured to determine an equivalent model of each device of the virtual power plant based on the installed data; constructing a first-stage objective function by taking peak shaving capacity as a target based on an equivalent model of each device of the virtual power plant; the maximum peak shaving capacity represents the minimum total energy of interaction between the virtual power plant and a large power grid; constructing a second-stage objective function by taking the lowest running cost as a target based on an equivalent model of each device of the virtual power plant; determining constraints of the two-stage optimization model based on the operating constraints; the two-stage optimization model is constructed based on the first-stage objective function, the second-stage objective function, and the constraint condition.
In one possible implementation manner, the optimal allocation scheme includes a first optimal allocation scheme and a second optimal allocation scheme, where the first optimal allocation scheme is used to determine an increased electric quantity of each controllable load in each period of the electricity consumption valley period; the second optimal allocation scheme is used for determining the reduction electric quantity of each controllable load in each period of the electricity consumption peak period; correspondingly, the processing module 202 is specifically configured to calculate, respectively, an electric quantity of the controllable load participating in peak regulation during a peak period of electricity consumption and an electric quantity of the controllable load participating in valley regulation during a valley period of electricity consumption based on the scheduled electric quantity of each period in the day; determining a first optimal allocation scheme of the electricity consumption valley period based on the electric quantity of the controllable load participating in valley adjustment in the electricity consumption valley period and the two-stage optimization model; and determining a second optimal allocation scheme of the electricity consumption peak period based on the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period and the two-stage optimization model.
In one possible implementation, the processing module 202 is specifically configured to perform the following steps: step 31, initializing algorithm parameters. And randomly initializing a population, wherein the population is the adjusted electric quantity of each controllable load. Step 32, initializing the iteration number k=1. Step 33, calculating a first stage objective function, and if the interaction energy corresponding to the current population is the minimum value of the interaction energy before the current iteration times, determining the current population as an optimal solution; if the difference between the interaction energy corresponding to the current population and the minimum value of the interaction energy before the current iteration number is smaller than the set value, determining the current population as a candidate solution. And step 34, updating the population. And 35, adding 1 to the iteration number, and judging whether the current iteration number is larger than the first iteration number. If so, step 36 is performed. If not, repeating steps 33-35. Step 36, initializing the iteration number k=1. Randomly selecting any population from the optimal solution and the candidate solution to be the current population; step 37, calculating a second stage objective function, and if the running cost corresponding to the current population is the minimum value of the running cost before the current iteration times, determining the current population as an optimal solution; step 38, updating the population. And 39, adding 1 to the iteration number, and judging whether the current iteration number is greater than the second iteration number. If yes, outputting the optimal solution, and exiting the iterative process. If not, repeating the steps 37-39; the optimal solution is a first optimal allocation scheme in the electricity consumption valley period.
In a possible implementation manner, the processing module 202 is specifically configured to control the start of the transferable load and control the adjustable load to increase the power consumption based on the increased power of each controllable load in the first optimal allocation scheme in each period of the electricity consumption valley period; and controlling the closing of the transferable load and controlling the adjustable load to reduce the power consumption based on the subtracted electric quantity of each controllable load in each period of the power consumption peak period in the second optimal distribution scheme.
In one possible implementation, the processing module 202 is specifically configured to monitor an actual peak shaver amount of each controllable load; if the error between the actual peak shaving amount and the predicted peak shaving amount is larger than the set error, re-determining the constraint condition of the two-stage optimization model based on the actual running condition of each controllable load; based on the constraint conditions of the two-stage optimization model which are determined again, resource allocation is carried out again, and the optimization allocation scheme of each controllable load in the virtual power plant is updated; and controlling the controllable load to run based on the updated optimal allocation scheme.
Fig. 4 is a schematic structural diagram of a power grid peak shaving device based on a virtual power plant according to an embodiment of the present invention. As shown in fig. 4, the power grid peak shaver 300 of this embodiment includes: a processor 301, a memory 302 and a computer program 303 stored in said memory 302 and executable on said processor 301. The steps of the method embodiments described above, such as steps 101 to 105 shown in fig. 2, are implemented when the processor 301 executes the computer program 303. Alternatively, the processor 301 may implement the functions of the modules/units in the above-described embodiments of the apparatus when executing the computer program 303, for example, the functions of the communication module 201 and the processing module 202 shown in fig. 3.
Illustratively, the computer program 303 may be partitioned into one or more modules/units that are stored in the memory 302 and executed by the processor 301 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions describing the execution of the computer program 303 in the grid peaking device 300. For example, the computer program 303 may be divided into the communication module 201 and the processing module 202 shown in fig. 3.
The processor 301 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 302 may be an internal storage unit of the grid peaking device 300, such as a hard disk or a memory of the grid peaking device 300. The memory 302 may also be an external storage device of the grid peak shaver 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the grid peak shaver 300. Further, the memory 302 may also include both internal and external memory units of the grid peaking device 300. The memory 302 is used for storing the computer program as well as other programs and data required by the terminal. The memory 302 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. A virtual power plant-based power grid peak shaving method, comprising:
acquiring historical operation data, installation data and operation constraint data of each device in the virtual power plant;
based on the historical operation data, load prediction and power generation prediction are carried out, and the scheduling electric quantity between the virtual power plant and the large power grid in each period of the day is obtained;
based on the installed data and the operation constraint data, establishing a two-stage optimization model with maximum peak shaving capacity and minimum operation cost as targets;
performing resource allocation based on the scheduling electric quantity of each time period in the day and the two-stage optimization model to obtain an optimal allocation scheme of each controllable load in the virtual power plant, wherein the optimal allocation scheme comprises the predicted peak shaving quantity of each controllable load in each time period;
And controlling the controllable load to run based on the optimized distribution scheme of each controllable load.
2. The virtual power plant-based power grid peak shaving method according to claim 1, wherein the load prediction and the power generation prediction are performed based on the historical operation data to obtain a scheduled power amount between the virtual power plant and a large power grid in each period of the day, and the method comprises the following steps:
generating electricity prediction is carried out based on historical operation data of each generator set in the virtual power plant, and predicted generating electricity data of each period in the day are obtained;
carrying out load prediction based on historical operation data of each load in the virtual power plant to obtain predicted load data of each time period in the day;
and determining the scheduling electric quantity between the virtual power plant and the large power grid in each period of the day based on the predicted power generation data and the predicted load data of each period of the day.
3. The virtual power plant-based grid peaking method of claim 1, wherein the building a two-stage optimization model targeting maximum peaking capability and minimum operating cost based on the installed data and operating constraint data comprises:
based on the installed data, determining an equivalent model of each device of the virtual power plant;
Constructing a first-stage objective function by taking peak shaving capacity as a target based on an equivalent model of each device of the virtual power plant; the maximum peak shaving capacity represents the minimum total energy of interaction between the virtual power plant and a large power grid;
constructing a second-stage objective function by taking the lowest running cost as a target based on an equivalent model of each device of the virtual power plant;
determining constraints of the two-stage optimization model based on the operating constraints;
the two-stage optimization model is constructed based on the first-stage objective function, the second-stage objective function, and the constraint condition.
4. The virtual power plant-based grid peak shaving method according to claim 1, wherein the optimal allocation scheme comprises a first optimal allocation scheme and a second optimal allocation scheme, and the first optimal allocation scheme is used for determining the increased electric quantity of each controllable load in each period of the electricity consumption valley period; the second optimal allocation scheme is used for determining the reduction electric quantity of each controllable load in each period of the electricity consumption peak period;
correspondingly, the resource allocation is performed based on the scheduling electric quantity of each time period in the day and the two-stage optimization model, so as to obtain an optimal allocation scheme of each controllable load in the virtual power plant, which comprises the following steps:
Based on the dispatching electric quantity of each time period in the day, respectively calculating the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period and the electric quantity of the controllable load participating in valley regulation in the electricity consumption valley period;
determining a first optimal allocation scheme of the electricity consumption valley period based on the electric quantity of the controllable load participating in valley adjustment in the electricity consumption valley period and the two-stage optimization model;
and determining a second optimal allocation scheme of the electricity consumption peak period based on the electric quantity of the controllable load participating in peak regulation in the electricity consumption peak period and the two-stage optimization model.
5. The virtual power plant-based grid peak shaving method according to claim 4, wherein the determining a first optimal allocation scheme for the electricity consumption valley period based on the amount of electricity that the controllable load participates in peak shaving during the electricity consumption valley period and the two-stage optimization model includes:
step 31, initializing algorithm parameters; randomly initializing a population, wherein the population is the adjusted electric quantity of each controllable load;
step 32, initializing iteration times k=1;
step 33, calculating a first stage objective function, and if the interaction energy corresponding to the current population is the minimum value of the interaction energy before the current iteration times, determining the current population as an optimal solution; if the difference between the interaction energy corresponding to the current population and the minimum value of the interaction energy before the current iteration times is smaller than a set value, determining the current population as a candidate solution;
Step 34, updating the population;
step 35, adding 1 to the iteration number, and judging whether the current iteration number is greater than the first iteration number; if yes, go to step 36; if not, repeating the steps 33-35;
step 36, initializing iteration times k=1; randomly selecting any population from the optimal solution and the candidate solution to be the current population;
step 37, calculating a second stage objective function, and if the running cost corresponding to the current population is the minimum value of the running cost before the current iteration times, determining the current population as an optimal solution;
step 38, updating the population;
step 39, adding 1 to the iteration number, and judging whether the current iteration number is greater than the second iteration number; if yes, outputting an optimal solution, and exiting the iterative process; if not, repeating the steps 37-39;
the optimal solution is a first optimal allocation scheme in the electricity consumption valley period.
6. The virtual power plant-based grid peaking method of claim 4, wherein the controlling the controllable load operation based on the optimized distribution scheme of each controllable load comprises:
controlling the starting of the transferable loads and controlling the adjustable loads to increase the power consumption based on the increased electric quantity of each controllable load in each period of the electricity consumption valley period in the first optimal allocation scheme;
And controlling the closing of the transferable load and controlling the adjustable load to reduce the power consumption based on the subtracted electric quantity of each controllable load in each period of the power consumption peak period in the second optimal distribution scheme.
7. The virtual power plant-based grid peaking method of claim 1, wherein the controlling the controllable loads to operate based on the peaking contribution of the controllable loads at each time period further comprises:
monitoring the actual peak shaving amount of each controllable load;
if the error between the actual peak shaving amount and the predicted peak shaving amount is larger than the set error, re-determining the constraint condition of the two-stage optimization model based on the actual running condition of each controllable load;
based on the constraint conditions of the two-stage optimization model which are determined again, resource allocation is carried out again, and the optimization allocation scheme of each controllable load in the virtual power plant is updated;
and controlling the controllable load to run based on the updated optimal allocation scheme.
8. A virtual power plant-based power grid peak shaving apparatus, comprising:
the communication module is used for acquiring historical operation data, installation data and operation constraint data of each device in the virtual power plant;
The processing module is used for carrying out load prediction and power generation prediction based on the historical operation data to obtain the scheduling electric quantity between the virtual power plant and the large power grid in each time period in the day; based on the installed data and the operation constraint data, establishing a two-stage optimization model with maximum peak shaving capacity and minimum operation cost as targets; performing resource allocation based on the scheduling electric quantity of each time period in the day and the two-stage optimization model to obtain an optimal allocation scheme of each controllable load in the virtual power plant, wherein the optimal allocation scheme comprises the predicted peak shaving quantity of each controllable load in each time period; and controlling the controllable load to run based on the optimized distribution scheme of each controllable load.
9. A virtual power plant based grid peaking device, characterized in that the peaking device comprises a memory storing a computer program and a processor for invoking and running the computer program stored in the memory to perform the method according to any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.
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| CN117791627A (en) * | 2024-02-26 | 2024-03-29 | 国网山东省电力公司东营供电公司 | Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant |
| CN117933667A (en) * | 2024-03-21 | 2024-04-26 | 广州疆海科技有限公司 | Resource scheduling method and device for virtual power plant, computer equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117791627A (en) * | 2024-02-26 | 2024-03-29 | 国网山东省电力公司东营供电公司 | Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant |
| CN117791627B (en) * | 2024-02-26 | 2024-05-14 | 国网山东省电力公司东营供电公司 | Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant |
| CN117933667A (en) * | 2024-03-21 | 2024-04-26 | 广州疆海科技有限公司 | Resource scheduling method and device for virtual power plant, computer equipment and storage medium |
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