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CN118659412B - Virtual power plant frequency adjusting method based on balance information and margin mechanism - Google Patents

Virtual power plant frequency adjusting method based on balance information and margin mechanism Download PDF

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CN118659412B
CN118659412B CN202411154463.3A CN202411154463A CN118659412B CN 118659412 B CN118659412 B CN 118659412B CN 202411154463 A CN202411154463 A CN 202411154463A CN 118659412 B CN118659412 B CN 118659412B
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戴光
赵宏斌
蔡昕原
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Shaanxi Siji Technology Co ltd
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Abstract

The invention discloses a virtual power plant frequency adjusting method based on a balance information and margin mechanism, which relates to the field of virtual power plant frequency adjustment and comprises the following steps: acquiring a micro-grid set, micro-grid position information, weather conditions and historical power data and preprocessing; predicting the future power condition of each micro-grid by using DeepAR model to obtain the power data of each micro-grid for 1 hour in future; setting a balance allowance mechanism of the micro-grid, and dividing all the micro-grids into balanced stable micro-grids and unbalanced stable micro-grids; aiming at the unbalanced stable micro-grid, a target optimization function is established aiming at realizing the lowest transfer of the loss cost of the excessive electric energy to the load overload micro-grid; solving a target optimization function by using an ant colony algorithm to obtain an adjustment strategy of each unbalanced stable micro-grid; and adding the power grid operation constraint to obtain the frequency adjustment strategy of the virtual power plant. The method and the device can improve the stability and reliability of frequency adjustment of the virtual power plant.

Description

Virtual power plant frequency adjusting method based on balance information and margin mechanism
Technical Field
The invention relates to the field of virtual power plant frequency adjustment, in particular to a virtual power plant frequency adjustment method based on a balance margin mechanism.
Background
With the advancement of global energy conversion and the rapid development of renewable energy sources, conventional power systems face challenges such as instability of the power supply and load pressure of the grid. Virtual power plants (Virtual Power Plant, VPP) are increasingly receiving widespread attention and application as an innovative solution. The VPP realizes the optimal management and scheduling of energy sources by integrating distributed energy sources, and improves the flexibility and reliability of the power system. However, frequency regulators of virtual power plants face some unique challenges. Virtual power plants consist of multiple distributed energy resources, each with independent response capability and regulation capability. The virtual power plant needs to coordinate and co-operate these resources to achieve overall performance optimization and frequency adjustment goals; and frequency adjustment of virtual power plants requires efficient scheduling strategies and algorithms. Meanwhile, the virtual power plant needs to be capable of adjusting the output power of the energy resources according to the power grid demand and the market signal, and the response degree and the adjustment speed of the distributed energy resources need to be flexibly controlled.
CN108429280a, named as patent document of passive power grid wide area virtual frequency control method and system, the method comprises: calculating virtual frequency according to actual active power, power frequency and preset planned power of the converter station; calculating the power demand of the new energy power station according to the virtual frequency; and determining the power adjustment quantity of the new energy power station according to the power required adjustment quantity and the power adjustable quantity of the new energy power station, and controlling each new energy power station to generate power according to the adjustment quantity. According to the invention, the virtual frequency is provided by the substation to reflect the fluctuation degree of the new energy power station in the transmitting end passive power grid deviating from the plan, so that the fluctuation is reduced as much as possible, the problems of intermittence, fluctuation and the like of wind power and photovoltaic power generation are improved, the pressure of the pumping power storage station for regulating the fluctuation of the new energy power is reduced, and the regulating frequency of the pumping power storage station is reduced. However, uncertainty of distributed energy resources and loads is not considered, resulting in poor robustness of grid frequency regulation.
CN114938006a, named as a patent document of a virtual inertia-based power grid frequency control method and system, the method includes: acquiring a change value of the angular frequency of the power grid; when the angular frequency of the power grid is reduced, reducing the direct-current voltage of the multi-active bridge based on the direct-current capacitance of the multi-active bridge of the energy router, injecting active power into a medium-voltage alternating-current port of the electric energy router, releasing the active power to the power grid, and completing the direct-current voltage control of the multi-active bridge; when the angular frequency of the power grid rises, the direct-current voltage of the multi-active bridge is raised based on the direct-current capacitor of the multi-active bridge, the medium-voltage alternating-current port of the electric energy router absorbs active power from the power grid, the active frequency of the power grid is released, the direct-current voltage of the multi-active bridge is controlled through virtual inertia, the rising rate of the angular frequency of the power grid is reduced, and the frequency of the power grid is regulated. According to the invention, the frequency characteristic of the power grid is improved by simulating the control strategy of virtual inertia; however, the abnormal condition of the grid frequency is not considered, so that the stability of the system is poor.
Disclosure of Invention
The invention mainly solves the technical problems that the relation between distributed energy resources and loads at future time cannot be predicted, and the virtual power plant cannot make resource scheduling and frequency adjustment in advance.
In order to solve the technical problems, the invention adopts a technical scheme that: the utility model provides a virtual power plant frequency adjustment method based on a balance margin mechanism, which comprises the following steps:
S100: acquiring a micro-grid set, micro-grid position information, weather conditions and historical power data, and preprocessing the historical power data;
s200: predicting the future power condition of each micro-grid by using DeepAR model according to the historical power data to obtain the power data of each micro-grid for 1 hour in the future;
S300: setting a micro-grid balance allowance mechanism, dividing all micro-grids into balanced stable micro-grids and unbalanced stable micro-grids according to power data of each micro-grid for 1 hour in the future, and further dividing the unbalanced stable micro-grids into excessive micro-grids and overload micro-grids;
S400: aiming at the unbalanced stable micro-grid, a target optimization function is established aiming at realizing the lowest transfer of the loss cost of the excessive electric energy to the load overload micro-grid;
S500: solving the target optimization function by using an ant colony algorithm to obtain an adjustment strategy of each unbalanced stable micro-grid;
S600: adding power grid operation constraint according to the regulation strategy of each unbalanced stable micro-power grid to obtain a frequency regulation strategy of the virtual power plant;
The microgrid location information is expressed as: Wherein, the method comprises the steps of, wherein, Is the firstThe longitude of the individual micro-grid,Is the firstLatitude of the individual micro-grid;
the set of micro-grids is expressed as: Wherein, the method comprises the steps of, wherein, Is the firstThe number of micro-grids is one,Is the micro-grid quantity;
the historical power data includes: power supply power, power of an electrical load, state of an energy storage system, charging power and discharging power of the energy storage system;
The electric power supply power refers to the generated power of all power generation equipment in the micro-grid, and comprises the following components: the power generation device comprises wind power generation equipment power generation, photovoltaic power generation equipment power generation and micro gas turbine generator set power generation;
the electric load power comprises load power of various electric equipment in life and charging power of an energy storage system;
the energy storage system refers to a system for storing electric energy in a micro-grid formed by a plurality of energy storage devices, and is used for realizing the balance of power supply and demand in the micro-grid and maintaining the stability of voltage and frequency, wherein the energy storage devices comprise: the device comprises a storage battery, a fuel cell, an electrolytic cell, a super capacitor and a flywheel;
The state of the energy storage system includes: a discharge state and a charge state;
The preprocessing refers to normalization processing, abnormal value removal and missing value filling operation on the historical power data.
Further, the S200 includes:
S210: dividing the historical power data of each micro-grid into a 70% training set and a 30% testing set;
S220: constructing DeepAR models by utilizing an autoregressive recursion network, and designating hidden layer dimensions, RNN types, RNN layers, dropout rates, learning rates and batch sizes;
S230: using a negative log likelihood loss function as a loss function of the model, training the model by using the training set, and optimizing model parameters by using an Adam optimizer;
S240: using a DeepAR model after training for each micro-grid, acquiring the probability distribution of the electric power data of each micro-grid for 1 hour in the future by using the test set, and using the binary digits of the predicted value as a predicted result to acquire the electric power data of each micro-grid for 1 hour in the future;
the quantile refers to the median in the probability distribution;
each micro-grid is provided with 1 hour of power data in future, and the power data comprises: the power supply power, the power of the electrical load, the state of the energy storage system, the charging power and the discharging power of the energy storage system.
Further, the step of setting the balance margin mechanism of the micro-grid is to set a power balance discriminant of the micro-grid, and judge all the micro-grids according to the power data of each micro-grid for 1 hour in the future, if the power balance discriminant is met, the micro-grid is balanced and stable, otherwise, the micro-grid is unbalanced and stable;
The power balance discriminant of the micro-grid is as follows:
；
；
；
Wherein, In order to balance the lower limit of the amount,In order to balance the upper limit of the number,In order to balance the lower-limit coefficient,，In order to balance the upper-limit coefficient,，Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,For the number of energy storage devices in the energy storage system,Is the firstPower generation equipmentThe power generated at the moment of time,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,For the number of load devices,In order to achieve a number of power generation devices,For the current time of the energy storage system to be the electric energy,Rated power for the energy storage system;
The power generation apparatus includes: wind power generation equipment, photovoltaic power generation equipment and miniature gas units;
the miniature gas unit is a power generation group consisting of a plurality of miniature gas engines in a virtual power plant;
the excess supply micro-grid means that the power supply power of the unbalanced stable micro-grid is larger than the power load power;
The load overload micro-grid means that the power supply power of the unbalanced stable micro-grid is smaller than the power load power.
Further, the S400 includes:
S410: establishing an electric power overload function, and calculating the overload power of the micro-grid;
S420: establishing an electric power surplus function, and calculating the surplus power of the micro-grid;
S430: establishing a power conversion loss function, and calculating the scheduling loss power caused by conversion between voltages during micro-grid power scheduling;
s440: establishing a weather condition index, and calculating a stability index of new energy generation power of the micro-grid;
s450: establishing an electric power transmission loss function, and calculating transmission loss power transmitted between micro-grids along with the distance;
s460: establishing a target optimization function according to the overload power, the surplus power, the scheduling loss power, the stability index and the transmission loss power of the micro-grid;
the electric power overload function has a calculation formula as follows:
；
Wherein, As a function of the power overload,Is the firstPower generation equipmentThe power generated at the moment of time,Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,For the number of load devices,For the number of energy storage devices in the energy storage system,The number of the power generation equipment;
The electric power surplus function has a calculation formula as follows:
；
Wherein, As a function of the excess of power,Is the firstPower generation equipmentThe power generated at the moment of time,Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,For the number of load devices,For the number of energy storage devices in the energy storage system,The number of the power generation equipment;
the power conversion loss function has a calculation formula:
；
Wherein, As a function of the power conversion loss,For the voltage before the conversion of the micro-grid,For the converted output voltage of the micro-grid,For the converted current of the micro-grid,Is conversion efficiency;
The weather condition index is calculated according to weather, the weather condition index is 1.0 when the weather is sunny, the weather condition index is 0.6 when the weather is other weather, and the calculation formula is as follows:
；
Wherein, Is a weather condition index;
The power transmission loss function has a calculation formula as follows:
；
Wherein, As a function of the power transmission loss,For the converted current of the micro-grid,Is the resistance of the power line,For the length of the transmission line between the micro-grids,Is the current speed;
The target optimization function has a calculation formula as follows:
；
Wherein, For the objective optimization function, constraint conditions of the objective optimization function include:， A kind of electronic device ，Is a weight parameter, and，For the length of the transmission line between the micro-grids,Is a constant value of the distance to be measured,As a function of the power overload,As a function of the excess of power,As a function of the power conversion loss,As an index of the weather condition,As a function of the power transmission loss,Is the firstThe maximum electrical load power of the individual micro-grids,Is the firstThe individual micro-grids are at maximum power supply.
Further, the S500 includes:
s510: initializing ant colony scale, pheromone factors, heuristic function factors, pheromones, volatilization factors, pheromone constants, maximum iteration times and position information of each micro-grid;
s520: randomly placing ants in different micro-grids, and calculating the next micro-grid to be accessed for each ant until all the micro-grids accessed by the ants are in the updated pheromone table;
S530: calculating the path length of each ant passing by, recording the optimal solution in the current iteration times, and updating the pheromone concentration on each micro-grid connection path;
S540: judging whether the maximum iteration times are reached, if not, returning to the step S520, and if so, terminating the program to obtain the adjustment strategy of each unbalanced stable micro-grid;
The regulation strategy refers to finding the lowest cost transfer of electrical energy to supply the excess microgrid to the load-overloaded microgrid.
Further, the operation constraint refers to that in order to make the virtual power plant safely operate, the operation constraint is added to each device in the virtual power plant, and the operation constraint comprises: power balance constraints, micro gas turbine set operation constraints, energy storage system constraints, and interactive power constraints;
The power balance constraint is to keep balance of power supply power and power load power in a virtual power plant, power generation equipment and load equipment in the virtual power plant need to be constrained, the power generation equipment comprises wind power generation equipment, photovoltaic power generation equipment and a micro gas turbine unit, and a power balance constraint formula is as follows:
；
Wherein, Is the firstTyphoon power generation equipmentThe power generated at the moment of time,For the number of wind power plants,Is the firstBench photovoltaic power generation equipmentThe power generated at the moment of time,For the number of photovoltaic power generation devices,Is the firstGroup miniature gas engine setThe power generated at the moment of time,Is the number of the micro gas units,Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,For the number of energy storage devices in the energy storage system,Is a virtual power plantThe power of the interaction at the moment in time,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,The number of load devices;
the micro gas turbine unit operation constraint comprises: generating constraint of the micro gas unit and generating climbing constraint of the micro gas unit;
the generation constraint formula of the miniature gas turbine unit is as follows:
；
Wherein, Is the firstGroup miniature gas engine setThe power generated at the moment of time,The lower limit and the upper limit of the power generation power of the micro gas unit are respectively set;
the generation climbing constraint formula of the miniature gas turbine unit is as follows:
；
Wherein, Is the firstThe power generation climbing rate of the miniature gas turbine units,Is thatGroup miniature gas engine setThe power generated at the moment of time,Is thatGroup miniature gas engine setGenerating power at moment;
The constraint formula of the energy storage system is as follows:
；
；
Wherein, Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,Is the first in the energy storage systemThe maximum discharge power of the energy storage device,Is the first in the energy storage systemEnergy storage device for a tableThe charging power at the moment of time is,Is the first in the energy storage systemMaximum charging power of the energy storage device;
The interactive power constraint formula is as follows:
；
Wherein, Is a virtual power plantThe power of the interaction at the moment in time,The lower limit and the upper limit of the interaction power of the virtual power plant are respectively.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. According to the virtual power plant frequency adjustment method based on the balance information and margin mechanism, provided by the invention, the power data of each micro power grid for 1 hour in the future is predicted through the DeepAR model, so that the virtual power plant is effectively ensured to be capable of making corresponding power scheduling and control decisions for each micro power grid in advance, and the running risk is reduced.
2. According to the virtual power plant frequency adjusting method based on the balance margin mechanism, balance stability of each micro-grid in the virtual power plant is judged through the margin mechanism, and stability and reliability of frequency adjustment of a virtual power plant system are ensured.
3. According to the virtual power plant frequency adjusting method based on the balance information and margin mechanism, provided by the invention, a target optimization function is established by considering various factors influencing power resource scheduling, and the optimal scheme is solved by using the ant colony algorithm, so that the waste of power resources is effectively reduced, and the utilization rate of the power resources and the scheduling response speed are improved.
Drawings
Fig. 1 is a flowchart of a virtual power plant frequency adjustment method based on a balance margin mechanism.
Fig. 2 is a flow chart of a virtual power plant frequency adjustment method based on a balance margin mechanism according to the present invention using DeepAR model prediction.
FIG. 3 is a flow chart of an objective optimization function established by the virtual power plant frequency adjustment method based on the balance margin mechanism.
Fig. 4 is an ant colony algorithm solving flow chart of the virtual power plant frequency adjusting method based on the balance margin mechanism.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the 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 otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the invention.
Referring to fig. 1, 2, 3 and 4, an embodiment of the present invention includes:
As shown in fig. 1, a flowchart of a virtual power plant frequency adjustment method based on a balance margin mechanism includes:
S100: and acquiring a micro-grid set, micro-grid position information, weather conditions and historical power data, and preprocessing the historical power data.
Further, the microgrid location information is expressed as: Wherein, the method comprises the steps of, wherein, Is the firstThe longitude of the individual micro-grid,Is the firstLatitude of the individual micro-grid;
the set of micro-grids is expressed as: Wherein, the method comprises the steps of, wherein, Is the firstThe number of micro-grids is one,Is the micro-grid quantity;
the historical power data includes: power supply power, power of an electrical load, state of an energy storage system, charging power and discharging power of the energy storage system;
The electric power supply power refers to the generated power of all power generation equipment in the micro-grid, and comprises the following components: the power generation device comprises wind power generation equipment power generation, photovoltaic power generation equipment power generation and micro gas turbine generator set power generation;
the electric load power comprises load power of various electric equipment in life and charging power of an energy storage system;
the energy storage system refers to a system for storing electric energy in a micro-grid formed by a plurality of energy storage devices, and is used for realizing the balance of power supply and demand in the micro-grid and maintaining the stability of voltage and frequency, wherein the energy storage devices comprise: the device comprises a storage battery, a fuel cell, an electrolytic cell, a super capacitor and a flywheel;
The state of the energy storage system includes: a discharge state and a charge state;
The preprocessing refers to normalization processing, abnormal value removal and missing value filling operation on the historical power data.
S200: and predicting the future power condition of each micro-grid by using DeepAR model according to the historical power data to obtain the power data of each micro-grid for 1 hour in future.
Further, as shown in fig. 2, the S200 includes:
S210: dividing the historical power data of each micro-grid into a 70% training set and a 30% testing set;
S220: constructing DeepAR models by utilizing an autoregressive recursion network, and designating hidden layer dimensions, RNN types, RNN layers, dropout rates, learning rates and batch sizes;
S230: using a negative log likelihood loss function as a loss function of the model, training the model by using the training set, and optimizing model parameters by using an Adam optimizer;
S240: using a DeepAR model after training for each micro-grid, acquiring the probability distribution of the electric power data of each micro-grid for 1 hour in the future by using the test set, and using the binary digits of the predicted value as a predicted result to acquire the electric power data of each micro-grid for 1 hour in the future;
the quantile refers to the median in the probability distribution;
each micro-grid is provided with 1 hour of power data in future, and the power data comprises: the power supply power, the power of the electrical load, the state of the energy storage system, the charging power and the discharging power of the energy storage system.
S300: and setting a micro-grid balance allowance mechanism, dividing all micro-grids into balanced stable micro-grids and unbalanced stable micro-grids according to the power data of each micro-grid for 1 hour in the future, and further dividing the unbalanced stable micro-grids into excessive micro-grids and overload micro-grids.
Further, the step of setting the balance margin mechanism of the micro-grid is to set a power balance discriminant of the micro-grid, and judge all the micro-grids according to the power data of each micro-grid for 1 hour in the future, if the power balance discriminant is met, the micro-grid is balanced and stable, otherwise, the micro-grid is unbalanced and stable;
The power balance discriminant of the micro-grid is as follows:
；
；
；
Wherein, In order to balance the lower limit of the amount,In order to balance the upper limit of the number,In order to balance the lower-limit coefficient,，In order to balance the upper-limit coefficient,，Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,For the number of energy storage devices in the energy storage system,Is the firstPower generation equipmentThe power generated at the moment of time,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,For the number of load devices,In order to achieve a number of power generation devices,For the current time of the energy storage system to be the electric energy,Rated power for the energy storage system;
The power generation apparatus includes: wind power generation equipment, photovoltaic power generation equipment and miniature gas units;
the miniature gas unit is a power generation group consisting of a plurality of miniature gas engines in a virtual power plant;
the excess supply micro-grid means that the power supply power of the unbalanced stable micro-grid is larger than the power load power;
The load overload micro-grid means that the power supply power of the unbalanced stable micro-grid is smaller than the power load power.
S400: for unbalanced stable micro-grids, a target optimization function is established aiming at realizing the lowest transfer of the loss cost of the surplus electric energy to the load overload micro-grid.
Further, as shown in fig. 3, the S400 includes:
S410: establishing an electric power overload function, and calculating the overload power of the micro-grid;
S420: establishing an electric power surplus function, and calculating the surplus power of the micro-grid;
S430: establishing a power conversion loss function, and calculating the scheduling loss power caused by conversion between voltages during micro-grid power scheduling;
s440: establishing a weather condition index, and calculating a stability index of new energy generation power of the micro-grid;
s450: establishing an electric power transmission loss function, and calculating transmission loss power transmitted between micro-grids along with the distance;
s460: establishing a target optimization function according to the overload power, the surplus power, the scheduling loss power, the stability index and the transmission loss power of the micro-grid;
the electric power overload function has a calculation formula as follows:
；
Wherein, As a function of the power overload,Is the firstPower generation equipmentThe power generated at the moment of time,Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,For the number of load devices,For the number of energy storage devices in the energy storage system,The number of the power generation equipment;
The electric power surplus function has a calculation formula as follows:
；
Wherein, As a function of the excess of power,Is the firstPower generation equipmentThe power generated at the moment of time,Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,For the number of load devices,For the number of energy storage devices in the energy storage system,The number of the power generation equipment;
the power conversion loss function has a calculation formula:
；
Wherein, As a function of the power conversion loss,For the voltage before the conversion of the micro-grid,For the converted output voltage of the micro-grid,For the converted current of the micro-grid,Is conversion efficiency;
The weather condition index is calculated according to weather, the weather condition index is 1.0 when the weather is sunny, the weather condition index is 0.6 when the weather is other weather, and the calculation formula is as follows:
；
Wherein, Is a weather condition index;
The power transmission loss function has a calculation formula as follows:
；
Wherein, As a function of the power transmission loss,For the converted current of the micro-grid,Is the resistance of the power line,For the length of the transmission line between the micro-grids,Is the current speed;
The target optimization function has a calculation formula as follows:
；
Wherein, For the objective optimization function, constraint conditions of the objective optimization function include:， A kind of electronic device ，Is a weight parameter, and，For the length of the transmission line between the micro-grids,Is a constant value of the distance to be measured,As a function of the power overload,As a function of the excess of power,As a function of the power conversion loss,As an index of the weather condition,As a function of the power transmission loss,Is the firstThe maximum electrical load power of the individual micro-grids,Is the firstThe individual micro-grids are at maximum power supply.
S500: and solving the target optimization function by using an ant colony algorithm to obtain the adjustment strategy of each unbalanced stable micro-grid.
Further, as shown in fig. 4, the S500 includes:
s510: initializing ant colony scale, pheromone factors, heuristic function factors, pheromones, volatilization factors, pheromone constants, maximum iteration times and position information of each micro-grid;
s520: randomly placing ants in different micro-grids, and calculating the next micro-grid to be accessed for each ant until all the micro-grids accessed by the ants are in the updated pheromone table;
S530: calculating the path length of each ant passing by, recording the optimal solution in the current iteration times, and updating the pheromone concentration on each micro-grid connection path;
S540: judging whether the maximum iteration times are reached, if not, returning to the step S520, and if so, terminating the program to obtain the adjustment strategy of each unbalanced stable micro-grid;
The regulation strategy refers to finding the lowest cost transfer of electrical energy to supply the excess microgrid to the load-overloaded microgrid.
S600: and adding power grid operation constraint according to the regulation strategy of each unbalanced stable micro-power grid to obtain the frequency regulation strategy of the virtual power plant.
Further, the operation constraint refers to that in order to make the virtual power plant safely operate, the operation constraint is added to each device in the virtual power plant, and the operation constraint comprises: power balance constraints, micro gas turbine set operation constraints, energy storage system constraints, and interactive power constraints;
The power balance constraint is to keep balance of power supply power and power load power in a virtual power plant, power generation equipment and load equipment in the virtual power plant need to be constrained, the power generation equipment comprises wind power generation equipment, photovoltaic power generation equipment and a micro gas turbine unit, and a power balance constraint formula is as follows:
；
Wherein, Is the firstTyphoon power generation equipmentThe power generated at the moment of time,For the number of wind power plants,Is the firstBench photovoltaic power generation equipmentThe power generated at the moment of time,For the number of photovoltaic power generation devices,Is the firstGroup miniature gas engine setThe power generated at the moment of time,Is the number of the micro gas units,Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,For the number of energy storage devices in the energy storage system,Is a virtual power plantThe power of the interaction at the moment in time,Is the firstTable load deviceThe moment load power comprises the load power of various electric equipment in life and the charging power of an energy storage system,The number of load devices;
the micro gas turbine unit operation constraint comprises: generating constraint of the micro gas unit and generating climbing constraint of the micro gas unit;
the generation constraint formula of the miniature gas turbine unit is as follows:
；
Wherein, Is the firstGroup miniature gas engine setThe power generated at the moment of time,The lower limit and the upper limit of the power generation power of the micro gas unit are respectively set;
the generation climbing constraint formula of the miniature gas turbine unit is as follows:
；
Wherein, Is the firstThe power generation climbing rate of the miniature gas turbine units,Is thatGroup miniature gas engine setThe power generated at the moment of time,Is thatGroup miniature gas engine setGenerating power at moment;
The constraint formula of the energy storage system is as follows:
；
；
Wherein, Is the first in the energy storage systemEnergy storage device for a tableThe discharge power at the moment of time is,Is the first in the energy storage systemThe maximum discharge power of the energy storage device,Is the first in the energy storage systemEnergy storage device for a tableThe charging power at the moment of time is,Is the first in the energy storage systemMaximum charging power of the energy storage device;
The interactive power constraint formula is as follows:
；
Wherein, Is a virtual power plantThe power of the interaction at the moment in time,The lower limit and the upper limit of the interaction power of the virtual power plant are respectively.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.

Claims (5)

1.一种基于平衡信裕机制的虚拟电厂频率调节方法，其特征在于，包括：1. A frequency regulation method for a virtual power plant based on a balance margin mechanism, characterized by comprising: S100：获取微电网集合、微电网位置信息、天气状况和历史电力数据，并对历史电力数据进行预处理；S100: Acquire a microgrid set, microgrid location information, weather conditions, and historical power data, and pre-process the historical power data; S200：根据所述历史电力数据，使用DeepAR模型预测各个微电网的未来电力情况，得到各个微电网未来1小时电力数据；S200: Based on the historical power data, use the DeepAR model to predict the future power situation of each microgrid to obtain the power data of each microgrid in the next hour; S300：设定微电网平衡信裕机制，根据所述各个微电网未来1小时电力数据，将所有微电网划分为平衡稳定微电网和非平衡稳定微电网，并将非平衡稳定微电网进一步划分为供应过剩微电网和负载过载微电网；S300: Setting a microgrid balance margin mechanism, dividing all microgrids into balanced stable microgrids and unbalanced stable microgrids according to the power data of each microgrid in the next hour, and further dividing the unbalanced stable microgrid into oversupplied microgrids and overloaded microgrids; S400：针对非平衡稳定微电网，将实现供应过剩的电能以损失成本最低转移至负载过载微电网为目标建立目标优化函数；S400: For the unbalanced stable microgrid, a target optimization function is established with the goal of transferring the excess power to the overloaded microgrid with the lowest loss cost; S500：使用蚁群算法对所述目标优化函数进行求解，得到各个非平衡稳定微电网的调节策略；S500: using an ant colony algorithm to solve the target optimization function to obtain a regulation strategy for each unbalanced stable microgrid; S600：根据所述各个非平衡稳定微电网的调节策略，添加电网运行约束，得到虚拟电厂的频率调节策略；S600: According to the regulation strategies of the respective unbalanced stable microgrids, grid operation constraints are added to obtain a frequency regulation strategy of the virtual power plant; 所述微电网位置信息，表示为：，其中，为第个微电网的经度，为第个微电网的纬度；The microgrid location information is expressed as: ,in, For the The longitude of the microgrid, For the The latitude of a microgrid; 所述微电网集合，表示为：，其中，为第个微电网，为微电网数量；The microgrid set is expressed as: ,in, For the Microgrids, is the number of microgrids; 所述历史电力数据包括：电力供应功率、电力负载功率、储能系统的状态、储能系统的充电功率和放电功率；The historical power data includes: power supply power, power load power, state of energy storage system, charging power and discharging power of energy storage system; 所述电力供应功率，是指微电网中所有的发电设备的发电功率，包括：风力发电设备发电功率、光伏发电设备发电功率以及微型燃气机组发电功率；The power supply power refers to the power generation power of all power generation equipment in the microgrid, including: the power generation power of wind power generation equipment, the power generation power of photovoltaic power generation equipment and the power generation power of micro gas units; 所述电力负载功率包括生活中各种用电设备的负载功率以及储能系统的充电功率；The electric load power includes the load power of various electrical equipment in life and the charging power of the energy storage system; 所述储能系统是指微电网内由若干台储能设备组成存储电能的系统，实现微电网内部的电力供需平衡，维持电压和频率的稳定，储能设备包括：蓄电池、燃料电池、电解池、超级电容以及飞轮；The energy storage system refers to a system that stores electric energy in a microgrid and is composed of several energy storage devices to achieve a balance between power supply and demand within the microgrid and maintain voltage and frequency stability. The energy storage devices include: batteries, fuel cells, electrolytic cells, supercapacitors, and flywheels. 所述储能系统的状态包括：放电状态及充电状态；The states of the energy storage system include: a discharge state and a charge state; 所述预处理，是指对所述历史电力数据进行归一化处理、去除异常值以及填充缺失值操作;The preprocessing refers to normalizing the historical power data, removing outliers, and filling missing values; 所述设定微电网平衡信裕机制，是指设定微电网的功率平衡判别式，根据所述各个微电网未来1小时电力数据，判别所有微电网，如果满足功率平衡判别式，则为平衡稳定微电网，否则为非平衡稳定微电网；The setting of the microgrid balance margin mechanism refers to setting the power balance discriminant of the microgrid, and judging all microgrids according to the power data of each microgrid in the next one hour. If the power balance discriminant is satisfied, it is a balanced and stable microgrid, otherwise it is an unbalanced and stable microgrid; 所述微电网的功率平衡判别式为：The power balance discriminant of the microgrid is: ； ; ； ; ； ; 其中，为平衡下限，为平衡上限，为平衡下限系数，，为平衡上限系数，，为储能系统中第台储能设备时刻的放电功率，为储能系统中的储能设备数量，为第台发电设备时刻的发电功率，为第台负载设备时刻的负载功率，包括生活中各种用电设备的负载功率以及储能系统的充电功率，为负载设备数量，为发电设备数量，为储能系统当前时刻电能，为储能系统额定电能；in, is the lower limit of equilibrium, For the balance limit, is the lower limit coefficient of equilibrium, , is the upper balance coefficient, , For energy storage system Energy storage equipment The discharge power at the moment, is the number of energy storage devices in the energy storage system, For the Power generation equipment The power generation at the moment, For the Load equipment The load power at each moment, including the load power of various electrical equipment in life and the charging power of the energy storage system, is the number of load devices, is the number of power generation equipment, is the current electrical energy of the energy storage system, is the rated power of the energy storage system; 所述发电设备，包括：风力发电设备、光伏发电设备以及微型燃气机组；The power generation equipment includes: wind power generation equipment, photovoltaic power generation equipment and micro gas generator set; 所述微型燃气机组，是指由虚拟电厂中若干台微型燃气机组成的发电组；The micro gas generator set refers to a power generation group composed of several micro gas generators in a virtual power plant; 所述供应过剩微电网是指非平衡稳定微电网的电力供应功率大于电力负载功率；The oversupplied microgrid refers to an unbalanced stable microgrid whose power supply power is greater than the power load power; 所述负载过载微电网是指非平衡稳定微电网的电力供应功率小于电力负载功率。The overloaded microgrid refers to an unbalanced stable microgrid whose power supply power is less than the power load power. 2.如权利要求1所述的一种基于平衡信裕机制的虚拟电厂频率调节方法，其特征在于，所述S200，包括：2. A virtual power plant frequency regulation method based on a balance margin mechanism according to claim 1, characterized in that said S200 comprises: S210：将各个微电网的所述历史电力数据划分为70%的训练集和30%的测试集；S210: Divide the historical power data of each microgrid into a 70% training set and a 30% test set; S220：利用自回归递归网络构建DeepAR模型，指定隐含层维度、RNN类型、RNN层数、Dropout率、学习率和批量大小；S220: Build a DeepAR model using an autoregressive recurrent network, specifying the hidden layer dimension, RNN type, number of RNN layers, dropout rate, learning rate, and batch size; S230：采用负对数似然损失函数作为模型的损失函数，使用所述训练集来训练模型，并利用Adam优化器优化模型参数；S230: using a negative log-likelihood loss function as a loss function of the model, using the training set to train the model, and using an Adam optimizer to optimize model parameters; S240：对各个微电网使用训练后的DeepAR模型，利用所述测试集获取各个微电网未来1小时电力数据概率分布，使用预测值的二分位数作为预测结果，得到各个微电网未来1小时电力数据；S240: Using the trained DeepAR model for each microgrid, using the test set to obtain the probability distribution of the power data of each microgrid in the next hour, using the quantile of the predicted value as the prediction result, and obtaining the power data of each microgrid in the next hour; 所述二分位数是指概率分布中的中位数；The quantile is the median in the probability distribution; 所述各个微电网未来1小时电力数据，包括：电力供应功率、电力负载功率、储能系统的状态、储能系统的充电功率和放电功率。The power data of each microgrid for the next hour includes: power supply power, power load power, status of the energy storage system, and charging power and discharging power of the energy storage system. 3.如权利要求1所述的一种基于平衡信裕机制的虚拟电厂频率调节方法，其特征在于，所述S400，包括：3. A virtual power plant frequency regulation method based on a balance margin mechanism according to claim 1, characterized in that said S400 comprises: S410：建立电力过载函数，计算微电网过载功率；S410: Establishing a power overload function to calculate the overload power of the microgrid; S420：建立电力过剩函数，计算微电网过剩功率；S420: Establishing a power surplus function to calculate the excess power of the microgrid; S430：建立电力转换损耗函数，计算微电网电力调度时电压之间转换时导致的调度损失功率；S430: Establishing a power conversion loss function to calculate the dispatching loss power caused by the conversion between voltages during microgrid power dispatching; S440：建立天气状况指数，计算微电网的新能源发电功率的稳定指标；S440: Establish a weather condition index and calculate the stability index of the renewable energy power generation power of the microgrid; S450：建立电力输送损失函数，计算微电网之间随着距离传输的传输损失功率；S450: Establishing a power transmission loss function to calculate the transmission loss power between microgrids as the distance is transmitted; S460：根据所述微电网过载功率、过剩功率、调度损失功率、稳定指标和传输损失功率，建立目标优化函数；S460: Establishing a target optimization function according to the microgrid overload power, excess power, dispatch loss power, stability index and transmission loss power; 所述电力过载函数，计算公式为：The power overload function is calculated as follows: ； ; 其中，为电力过载函数，为第台发电设备时刻的发电功率，为储能系统中第台储能设备时刻的放电功率，为第台负载设备时刻的负载功率，包括生活中各种用电设备的负载功率以及储能系统的充电功率，为负载设备数量，为储能系统中的储能设备数量，为发电设备数量；in, is the power overload function, For the Power generation equipment The power generation at the moment, For energy storage system Energy storage equipment The discharge power at the moment, For the Load equipment The load power at each moment, including the load power of various electrical equipment in life and the charging power of the energy storage system, is the number of load devices, is the number of energy storage devices in the energy storage system, is the number of power generation equipment; 所述电力过剩函数，计算公式为：The power surplus function is calculated as follows: ； ; 其中，为电力过剩函数，为第台发电设备时刻的发电功率，为储能系统中第台储能设备时刻的放电功率，为第台负载设备时刻的负载功率，包括生活中各种用电设备的负载功率以及储能系统的充电功率，为负载设备数量，为储能系统中的储能设备数量，为发电设备数量；in, is the power surplus function, For the Power generation equipment The power generation at the moment, For energy storage system Energy storage equipment The discharge power at the moment, For the Load equipment The load power at each moment, including the load power of various electrical equipment in life and the charging power of the energy storage system, is the number of load devices, is the number of energy storage devices in the energy storage system, is the number of power generation equipment; 所述电力转换损耗函数，计算公式为：The power conversion loss function is calculated as follows: ； ; 其中，为电力转换损耗函数，为微电网转换前的电压，为微电网转换后的输出电压，为微电网转换后的电流，为转换效率；in, is the power conversion loss function, is the voltage of the microgrid before conversion, is the output voltage of the microgrid after conversion, is the current converted by the microgrid, is the conversion efficiency; 所述天气状况指数，是根据天气进行计算当天气为晴天时天气状况指数为1.0，其他天气时天气状况指数为0.6，计算公式为：The weather condition index is calculated based on the weather. When the weather is sunny, the weather condition index is 1.0, and when the weather is other than sunny, the weather condition index is 0.6. The calculation formula is: ； ; 其中，为天气状况指数；in, is the weather condition index; 所述电力输送损失函数，计算公式为：The power transmission loss function is calculated as follows: ； ; 其中，为电力输送损失函数，为微电网转换后的电流，为输电线的电阻，为微电网间输电线的长度，为电流速度；in, is the power transmission loss function, is the current converted by the microgrid, is the resistance of the transmission line, is the length of the transmission line between microgrids, is the current speed; 所述目标优化函数，计算公式为：The target optimization function is calculated as follows: ； ; 其中，为目标优化函数，目标优化函数的约束条件包括：，，以及，为权重参数，且，为微电网间输电线的长度，为一个距离常量，为电力过载函数，为电力过剩函数，为电力转换损耗函数，为天气状况指数，为电力输送损失函数，为第个微电网最大电力负载功率，为第个微电网最大电力供应功率。in, is the target optimization function, and the constraints of the target optimization function include: , ,as well as , is the weight parameter, and , is the length of the transmission line between microgrids, is a distance constant, is the power overload function, is the power surplus function, is the power conversion loss function, is the weather condition index, is the power transmission loss function, For the The maximum power load power of a microgrid, For the The maximum power supply power of a microgrid. 4.如权利要求1所述的一种基于平衡信裕机制的虚拟电厂频率调节方法，其特征在于，所述S500，包括：4. The frequency regulation method of a virtual power plant based on a balance margin mechanism according to claim 1, characterized in that said S500 comprises: S510：初始化蚁群规模、信息素因子、启发函数因子、信息素、挥发因子、信息素常数、最大迭代次数，以及各个微电网位置信息；S510: Initialize the ant colony size, pheromone factor, heuristic function factor, pheromone, volatility factor, pheromone constant, maximum number of iterations, and location information of each microgrid; S520：随机将蚂蚁放于不同的微电网，对每个蚂蚁计算下一个待访问的微电网，直至所更新的信息素表有蚂蚁访问完的所有微电网；S520: Randomly place the ants in different microgrids, and calculate the next microgrid to be visited for each ant, until the updated pheromone table contains all the microgrids visited by the ants; S530：计算各个蚂蚁经过的路径长度，记录当前迭代次数中的最优解，同时对各个微电网连接路径上的信息素浓度进行更新；S530: Calculate the length of the path that each ant has passed, record the optimal solution in the current iteration number, and update the pheromone concentration on each microgrid connection path; S540：判断是否达到最大迭代次数，若否，则返回步骤S520，是则终止程序，得到各个非平衡稳定微电网的调节策略；S540: Determine whether the maximum number of iterations has been reached. If not, return to step S520. If yes, terminate the program to obtain the regulation strategy of each unbalanced stable microgrid. 所述调节策略是指寻找到供应过剩微电网的电能以损失成本最低转移至负载过载微电网。The regulation strategy refers to finding a way to transfer the electric energy of the oversupplied microgrid to the overloaded microgrid with the lowest loss cost. 5.如权利要求1所述的一种基于平衡信裕机制的虚拟电厂频率调节方法，其特征在于，所述运行约束，是指为使得虚拟电厂安全运行，对虚拟电厂中的各设备添加运行约束，包括：功率平衡约束、微型燃气机组运行约束、储能系统约束以及交互功率约束；5. A virtual power plant frequency regulation method based on a balance margin mechanism as claimed in claim 1, characterized in that the operation constraint refers to adding operation constraints to each device in the virtual power plant to ensure the safe operation of the virtual power plant, including: power balance constraints, micro gas unit operation constraints, energy storage system constraints and interactive power constraints; 所述功率平衡约束，是指为保持虚拟电厂中电力供电功率和电力负载功率的平衡，需要对虚拟电厂中的发电设备和负载设备进行约束，发电设备包括风力发电设备、光伏发电设备以及微型燃气机组，功率平衡约束公式为：The power balance constraint means that in order to maintain the balance between the power supply power and the power load power in the virtual power plant, it is necessary to constrain the power generation equipment and load equipment in the virtual power plant. The power generation equipment includes wind power generation equipment, photovoltaic power generation equipment and micro gas units. The power balance constraint formula is: ； ; 其中，为第台风力发电设备时刻的发电功率，为风力发电设备数量，为第台光伏发电设备时刻的发电功率，为光伏发电设备数量，为第组微型燃气机组时刻的发电功率，为微型燃气机组数量，为储能系统中第台储能设备时刻的放电功率，为储能系统中的储能设备数量，为虚拟电厂时刻的交互功率，为第台负载设备时刻的负载功率，包括生活中各种用电设备的负载功率以及储能系统的充电功率，为负载设备数量；in, For the Wind power generation equipment The power generation at the moment, is the number of wind power generation equipment, For the Photovoltaic power generation equipment The power generation at the moment, is the number of photovoltaic power generation equipment, For the Micro gas unit The power generation at the moment, is the number of micro gas units, For energy storage system Energy storage equipment The discharge power at the moment, is the number of energy storage devices in the energy storage system, Virtual Power Plant The interaction power at each moment, For the Load equipment The load power at each moment, including the load power of various electrical equipment in life and the charging power of the energy storage system, is the number of load devices; 所述微型燃气机组运行约束，包括：微型燃气机组发电约束及微型燃气机组发电爬坡约束；The micro gas unit operation constraints include: micro gas unit power generation constraints and micro gas unit power generation ramp constraints; 所述微型燃气机组发电约束公式为：The power generation constraint formula of the micro gas turbine unit is: ； ; 其中，为第组微型燃气机组时刻的发电功率，分别为微燃气机组的发电功率下限和上限；in, For the Micro gas unit The power generation at the moment, They are the lower and upper limits of power generation of the micro gas generator set respectively; 所述微型燃气机组发电爬坡约束公式为：The power generation ramp constraint formula of the micro gas turbine is: ； ; 其中，为第组微型燃气机组的发电爬坡率，为组微型燃气机组时刻的发电功率，为组微型燃气机组时刻的发电功率；in, For the The power generation ramp rate of the micro gas turbine unit, for Micro gas unit The power generation at the moment, for Micro gas unit The power generation at the time; 所述储能系统约束公式为：The energy storage system constraint formula is: ； ; ； ; 其中，为储能系统中第台储能设备时刻的放电功率，为储能系统中第台储能设备最大放电功率，为储能系统中第台储能设备时刻的充电功率，为储能系统中第台储能设备最大充电功率；in, For energy storage system Energy storage equipment The discharge power at the moment, For energy storage system Maximum discharge power of energy storage equipment, For energy storage system Energy storage equipment Charging power at the moment, For energy storage system Maximum charging power of energy storage equipment; 所述交互功率约束公式为：The interactive power constraint formula is: ； ; 其中，为虚拟电厂时刻的交互功率，分别为虚拟电厂交互功率的下限和上限。in, Virtual Power Plant The interaction power at each moment, are the lower and upper limits of the interactive power of the virtual power plant respectively.
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