Power balancing system and method based on micro-grid
By designing a power balance system in the microgrid, predicting photovoltaic and PCS power, and using PID control to achieve power balance of load data, the problem of lag in the energy storage system adjustment in the prior art is solved, and better power balance and maximum returns are achieved.
Patent Information
- Application Number
- CN202411634257.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-15
- Publication Date
- 2025-03-07
AI Technical Summary
When optimizing the operation of multi-micro grids, the prior art fails to effectively predict the power changes of photovoltaic and photothermal systems, resulting in lag in the charging and discharging power adjustment of the energy storage system and taking power from the power grid.
A power balance system based on microgrid is designed, including a data acquisition module, a photovoltaic power prediction module, a PCS power prediction module and a power balance logic module. Power balance of load data is achieved by predicting the photovoltaic output power and PCS charging and discharging power at the next moment, and using PID control.
It realizes timely prediction and response to power changes in photovoltaic systems, avoids the situation of withdrawing power from the power grid, reduces user electricity bills, and maximizes the benefits of enterprises in the microgrid.
Smart Images
Figure CN119582337A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of photovoltaic storage technology, and in particular to a power balancing system and method based on a microgrid. Background Art
[0002] Microgrids are modular power systems with their own independent power sources and loads. Microgrids allow the application of distributed energy and multiple renewable energy sources. When multiple microgrids are interconnected, they can share power generation and storage resources.
[0003] The problem of optimal operation of interconnected multi-microgrids has been solved in many applications. These can be mainly divided into solutions for optimal scheduling of power generation, energy storage energy management, and multi-agent consensus power management of energy resources in microgrid networks. Microgrid models usually include some external factors, such as energy prices, load energy demand, demand-side management, and weather condition forecasts. Many of these solutions focus on how to minimize the design and operation costs of energy in microgrids. For example, energy storage optimization considering demand-side management and interconnected microgrid distributed agent models that minimize the cost of scheduling distributed generation. However, they are often based only on power optimization and benefits in microgrids, but there is no advance prediction of the power calculation of photovoltaic and solar thermal systems. Therefore, although the charging and discharging power of the energy storage system is dynamically adjusted, it still lags behind the changes in the photovoltaic and solar thermal systems, so there will still be a situation where power is taken from the grid. Summary of the invention
[0004] In view of the defects in the prior art, an object of the present invention is to provide a power balancing system and method based on a microgrid.
[0005] According to one aspect of the present invention, there is provided a power balancing system based on a microgrid, comprising:
[0006] Data acquisition module: obtain weather data, historical power generation data of equipment, current photovoltaic output power, current PCS charging and discharging power, temperature data, irradiation data and load data;
[0007] Photovoltaic power prediction module: based on the weather data, temperature data, irradiation data, historical power generation data of the equipment, and the photovoltaic output power at the current moment, predict the photovoltaic output power at the next moment;
[0008] PCS power prediction module: predicts the PCS charging and discharging power at the next moment based on the PCS charging and discharging power at the current moment and the predicted photovoltaic output power at the next moment;
[0009] Power balancing logic module: Based on the PCS charging and discharging power at the next moment and the PCS charging and discharging power at the current moment, PID control is performed to achieve power balancing based on the load data.
[0010] Preferably, the photovoltaic power prediction module, the power prediction module and the power balancing logic module are all arranged in the hub of the microgrid, namely, the energy management module.
[0011] Preferably, the data acquisition module is arranged outside the energy management module; the data acquisition module comprises:
[0012] The cloud obtains weather data and transmits it to the energy management module;
[0013] A photovoltaic controller transmits the photovoltaic output power at the current moment to the energy management module;
[0014] The PCS controller transmits the current PCS charging and discharging power to the energy management module;
[0015] A temperature sensor collects temperature data and transmits it to the energy management module;
[0016] An irradiation sensor, which collects irradiation data and transmits it to the energy management module;
[0017] The load meter transmits the load data of the microgrid to the energy management module.
[0018] According to a second aspect of the present invention, there is provided a power balancing method based on a microgrid, comprising:
[0019] Obtain weather data, historical power generation data of equipment, current photovoltaic output power, current PCS charging and discharging power, temperature data, irradiation data and load data;
[0020] Based on the weather data, temperature data, irradiation data, historical power generation data of the equipment, and the photovoltaic output power at the current moment, predict the photovoltaic output power at the next moment;
[0021] Based on the PCS charging and discharging power at the current moment and the predicted photovoltaic output power at the next moment, predict the PCS charging and discharging power at the next moment;
[0022] Based on the PCS charging and discharging power at the next moment and the PCS charging and discharging power at the current moment, PID control is performed to achieve power balance based on the load data.
[0023] Preferably, the predicting of the photovoltaic output power at the next moment based on the weather data, temperature data, irradiation data, historical power generation data of the equipment, and the photovoltaic output power at the current moment includes:
[0024] Get the state vector P t is the photovoltaic output power at time t, I t is the solar radiation intensity within the photovoltaic panel at time t, T t is the temperature within the photovoltaic panel at time t;
[0025] The state transfer equation is established to describe the state change relationship of the power balance system based on the microgrid from time t to time t+1, specifically: A is the state transfer matrix, B is the control input matrix, u t is the control input vector, w t is the process noise vector;
[0026] Obtain the covariance matrix Q of the process noise;
[0027] Get the observation vector P t is the photovoltaic output power at time t, I t is the solar radiation intensity within the photovoltaic panel at time t;
[0028] An observation equation is established to describe the relationship between the observation vector and the state vector, specifically: H is the measurement matrix, v t is the observation noise vector;
[0029] Obtain the covariance matrix R of the observation noise;
[0030] Kalman filter is used for prediction and update, specifically:
[0031] The predictions are: U t+|t =AU t A T +Q;
[0032] The update is: Calculate the Kalman gain K t+1 =U t+1|t H T (HU t+1|t H T +R) -1 , update the state vector Update the covariance matrix U t+1 =(IK t+1 H)U t+1|t ;
[0033] represents the state vector at time t, A is the state transfer matrix, B is the control input matrix, u t is the control input vector; represents the estimated value of the state vector at time t+1 for the state vector information at time t; Q represents the covariance matrix of the process noise; U t represents the estimation error covariance at time t; U t+1|t represents the estimated error covariance at time t+1 calculated based on the information at time t; H represents the observation matrix, and R represents the covariance of the observation noise;
[0034] extract The first component of is used as the predicted photovoltaic output power at time t+1.
[0035] Preferably, the state transfer matrix is where a ij Indicates the influence of the jth state variable in the current state vector on the ith state variable in the state vector at the next moment;
[0036] The control input matrix B represents the influence of the control input on the state of the power balance system. Specifically, b 11 is the impact of weather forecast data on output power, b 12 is the impact of weather forecast data on solar radiation intensity, b 13 It represents the impact of weather data on ambient temperature;
[0037] The control input vector u t =[W t ],W t is the weather forecast data at time t;
[0038] The process noise vector w t represents the random noise of the power balancing system, where w P，t represents the effect of process noise on output power, w I，t represents the effect of process noise on solar radiation intensity, w T，t Represents the effect of process noise on temperature;
[0039] The process noise covariance matrix Q is specifically: q 11 is the process noise variance of the output power, q 22 is the process noise variance of solar radiation intensity, q 33 is the process noise variance of the ambient temperature, q 12 =q 21 is the process noise variance between output power and radiation intensity, q13=q31 is the process noise variance between output power and ambient temperature, and q23=q32 is the process noise variance between radiation intensity and ambient temperature.
[0040] Preferably, the observation matrix H represents the relationship between the observation vector and the state vector.
[0041] The observation noise vector v t Represents the random noise in the observation process where v P，t and v I，t They represent the influence of observation noise on output power and radiation intensity respectively;
[0042] The observation noise covariance matrix R represents the covariance of the observation noise,
[0043] Preferably, the predicting the PCS charging and discharging power at the next moment based on the PCS charging and discharging power at the current moment and the predicted photovoltaic output power at the next moment includes:
[0044] When the photovoltaic and energy storage systems can meet the load data, the state vector changes are obtained. P b，t is the PCS charging and discharging power at the current time t, P pv，t is the photovoltaic output power at the current time t, ΔP pv，t is the change in photovoltaic power between the current moment t and the next moment t+1;
[0045] Use a linear model to define the state transition model. A' is the state transfer matrix, w t ′ is the process noise;
[0046] Define the observation vector Define the observation model as z′ t =H′y t +v′ t , H′ is the observation matrix, v t ′ is the observation noise;
[0047] Use Kalman filter for prediction and update:
[0048] The prediction is:
[0049] Predicted state vector and covariance matrix, V t|t+1 =A′V t-1|t-1 A′ T +Q′, Q′ is the process covariance matrix;
[0050] The update is:
[0051] Calculate the Kalman gain:
[0052] Kt =V t|t-1 H′ t (H′V t|t-1 H′ T +R′) -1
[0053] Update the state vector and covariance matrix:
[0054]
[0055] V t|t =(IK t H)′V t|t-1
[0056] in, represents the state vector at time t-1, A' is the state transfer matrix, represents the estimated value of the state vector at time t+1 for the state vector information at time t; Q' represents the covariance matrix of the process noise; V t-1|t-1 represents the estimated error covariance at time t-1; V t|t+1 represents the estimated error covariance at time t+1 calculated based on the information at time t; H' represents the observation matrix, and R' represents the covariance of the observation noise;
[0057] Extract the PCS charging and discharging power value of the predicted energy storage system: P b，t+1 Right now The first component of .
[0058] Preferably, the PID control is performed based on the PCS charging and discharging power at the next moment and the PCS charging and discharging power at the current moment to achieve power balance based on the load data, including:
[0059] Calculate the PID control output: u t is the control output, e t =P b，t+1 -P b，t , represents the difference between the current charging and discharging power and the predicted charging and discharging power at the next moment, K p K i K d , are the proportional, integral, and differential gains respectively;
[0060] Based on the PID control output, the PCS charging and discharging power at the current moment is adjusted to satisfy the load data equal to the sum of the PCS charging and discharging power and the photovoltaic output power, thereby achieving power balance.
[0061] According to a third aspect of the present invention, there is provided a control terminal, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the microgrid-based power balancing method.
[0062] Compared with the prior art, the embodiments of the present invention have at least one of the following beneficial effects:
[0063] The microgrid-based power balancing system in the embodiment of the present invention deploys the photovoltaic power prediction module and the PCS power prediction module into the EMS, which can timely adjust the charging and discharging power of the PCS to achieve better power balance and prevent power from being taken from the grid when both photovoltaic and energy storage have surplus power, thereby reducing user benefits.
[0064] The microgrid-based power balancing system and method in the embodiment of the present invention adjusts the PCS charging and discharging power in advance by predicting the power value of the photovoltaic power at the next moment on the EMS side, so that the power of the load is equal to the power of the photovoltaic power plus the charging and discharging power of the PCS, thereby achieving better power balance, that is, preventing the situation where power is drawn from the grid during peak hours due to the PCS power change always lagging behind the photovoltaic power change, and at the same time, it can well avoid the situation of reverse flow to the grid, thereby reducing the amount of electricity drawn by enterprises from the grid, reducing unnecessary electricity bills, and realizing the construction of microgrids for small and medium-sized enterprises to obtain maximum benefits from the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS
[0065] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:
[0066] Figure 1 The figure is a structural diagram of a microgrid-based power balancing system in one embodiment of the present invention. DETAILED DESCRIPTION
[0067] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements may be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.
[0068] In order to achieve the best power balance of the photovoltaic energy storage system, in one embodiment of the present invention, a power balance system based on a microgrid is provided, such as Figure 1As shown, it includes: a data acquisition module, a photovoltaic power prediction module, a PCS power prediction module (power conversion system PCS, Power Conversion System), and an EMS (Energy Management System) power balance logic module.
[0069] Data acquisition module: acquires weather data, historical power generation data of the equipment, photovoltaic output power at the current moment, PCS charge and discharge power at the current moment, temperature data, irradiation data and load data; Photovoltaic power prediction module: predicts the photovoltaic output power at the next moment based on the weather data, temperature data, irradiation data, historical power generation data of the equipment and photovoltaic output power at the current moment; Power prediction module: predicts the PCS charge and discharge power at the next moment based on the PCS charge and discharge power at the current moment and the predicted photovoltaic output power at the next moment; Power balance logic module: performs PID control based on the PCS charge and discharge power at the next moment and the PCS charge and discharge power at the current moment to achieve power balance based on the load data.
[0070] Generally, the photovoltaic power prediction module and the PCS power prediction module are set up in the cloud, and the data is uploaded to the cloud. The data predicted by the model in the cloud is then sent to the EMS end. In actual operation, the intermediate network conditions are often delayed. In cloudy conditions, the photovoltaic power changes quickly and frequently. Therefore, the EMS end cannot receive the prediction data from the cloud in time, and there will be a reverse flow to the power grid and power drawn from the power grid. In a preferred embodiment, the photovoltaic power prediction module and the PCS power prediction module are deployed in the energy management module EMS, which can timely adjust the charging and discharging power of the PCS to achieve better power balance and prevent power from being drawn from the power grid when there is surplus in photovoltaic and energy storage, thereby reducing user benefits.
[0071] In order to collect data more quickly and comprehensively, in a preferred embodiment of the present invention, the data collection module includes a cloud, a photovoltaic controller, a PCS controller, a temperature sensor, an irradiation sensor and a load meter.
[0072] The cloud obtains weather data and transmits it to the energy management module. The cloud can also obtain historical power generation data; the photovoltaic controller transmits the current photovoltaic output power to the energy management module; the PCS controller transmits the current PCS charging and discharging power to the energy management module; the temperature sensor collects temperature data and transmits it to the energy management module EMS; the irradiation sensor collects irradiation data and transmits it to the energy management module EMS; the load meter transmits the load data of the microgrid to the energy management module.
[0073] Furthermore, in a preferred embodiment of the present invention, power balance is achieved through the EMS power balance logic module, which mainly calculates the difference between the predicted PCS power and the current PCS power and inputs it into the PID controller. This embodiment can make the load power equal to the photovoltaic power plus the PCS charging and discharging power to achieve better power balance.
[0074] In the aforementioned embodiments, the specific implementation of each module / unit may refer to the step implementation technology of the microgrid-based power balancing method described below, which will not be described here to avoid redundancy.
[0075] Based on the same inventive concept, in some other preferred embodiments of the present invention, a power balancing method based on a microgrid is provided, and its main steps are as follows:
[0076] Step 1, obtain weather data, historical power generation data of the equipment (mainly refers to the historical power generation power of photovoltaic), the current photovoltaic output power, the current PCS charging and discharging power, temperature data, irradiation data and load data;
[0077] Step 2: Based on the weather data, temperature data, irradiation data, historical power generation data of the equipment, and the photovoltaic output power at the current moment collected in step 1, predict the photovoltaic output power at the next moment;
[0078] Step 3, based on the PCS charge and discharge power at the current moment collected in step 1, the photovoltaic output power at the current moment, and the photovoltaic output power at the next moment predicted in step 2, predict the PCS charge and discharge power at the next moment;
[0079] Step 4: Based on the PCS charge and discharge power at the next moment predicted in step 3 and the PCS charge and discharge power at the current moment collected in step 1, PID control is performed to achieve power balance based on the load data.
[0080] In a preferred embodiment of the present invention, the weather conditions and historical power generation data of the specific location of the equipment in the microgrid system are collected from the cloud through the EMS. The photovoltaic output power at the current moment is collected through the photovoltaic controller, and the PCS charging and discharging power at the current moment is collected through the PCS controller. The ambient temperature data is collected through the temperature sensor, and the irradiation intensity data is collected through the irradiation sensor. Therefore, the power of the load is relatively stable, so only the data of the load-side electric meter needs to be collected.
[0081] In a microgrid system, based on the collected load data, it can be confirmed that the load data is relatively stable over a period of time. After obtaining the current environment, temperature, and power data, the photovoltaic power at the next moment can be predicted. In a preferred embodiment, step S2, the step of predicting the photovoltaic output power at the next moment can be as follows:
[0082] S2.1, set the state vector x t .
[0083] The state vector x t : state vector x t Contains all state variables required for PV system output power prediction Among them, P t is the photovoltaic output power at time t, I t is the solar radiation intensity within the photovoltaic panel at time t, T t is the temperature within the photovoltaic panel at time t;
[0084] S2.2, set the state transfer equation.
[0085] The state transition equation describes the state change relationship of the system from time t to time t+1, specifically:
[0086] A is the state transfer matrix, B is the control input matrix, u t is the control input vector, w t is the process noise vector.
[0087] Among them, the state transfer matrix: where a ij It represents the influence of the jth state variable in the current state vector on the ith state variable in the state vector at the next moment. The state transfer matrix is obtained by summarizing the historical data and the physical characteristics of the system.
[0088] Among them, the control input matrix B represents the influence of the control input on the system state, which is obtained by analyzing the influence of weather data on the system state, such as radiation, temperature, and output power; specifically, b11 represents the impact of weather forecast data on output power, b12 represents the impact of weather forecast data on solar radiation intensity, and b13 represents the impact of weather data on ambient temperature.
[0089] Among them, the control input vector u t =[W t ],W t is the weather forecast data at time t;
[0090] Among them, the process noise vector w t Represents the random noise in the system model, which obeys Gaussian distribution, specifically: Among them, w P，t represents the effect of process noise on output power, w I，t represents the effect of process noise on solar radiation intensity, w T，tRepresents the effect of process noise on temperature.
[0091] S2.3, obtain the covariance matrix of the process noise.
[0092] By analyzing the historical data of weather, temperature, radiation and photovoltaic output power, the statistical characteristics of system state changes are obtained and calculated, thereby obtaining the covariance matrix representing the process noise:
[0093] Among them, q 11 is the process noise variance of the output power, q 22 is the process noise variance of solar radiation intensity, q 33 is the process noise variance of the ambient temperature, q 12 =q 21 is the process noise variance between the output power and the radiation intensity, q 13 =q 31 is the process noise variance between output power and ambient temperature, q 23 =q 32 is the process noise variance between the radiation intensity and the ambient temperature
[0094] S2.4, set the observation vector.
[0095] Observation vector z t contains vectors that can be measured directly, namely P t is the photovoltaic output power at time t, I t Radiation intensity at time t.
[0096] S2.5, set the observation equation.
[0097] The observation equation describes the relationship between the observation vector and the state vector: H is the measurement matrix, v t is the observation noise vector.
[0098] Among them, the observation matrix H represents the relationship between the observation vector and the state vector. Let
[0099] Among them, the observation noise vector v t Represents the random noise in the observation process, which obeys the Gaussian distribution, specifically: where v P，t and v I，t They respectively represent the influence of observation noise on output power and radiation intensity.
[0100] S2.6, obtain the observation noise covariance matrix.
[0101] The observation noise covariance matrix R represents the covariance of the observation noise,
[0102] Generally, the data collected from various sensors is obtained through actual measurement.
[0103] S2.7, prediction and update steps of Kalman filter:
[0104] Prediction Steps: U t+|t =AU t A T +Q
[0105] Update step: Calculate the Kalman gain K t+1 =U t+1|t H T (HU t+1|t H T +R) -1 , update the state vector Update the covariance matrix U t+1 =(IK t+1 H)U t+1|t .
[0106] in, represents the state vector at time t, A is the state transfer matrix, B is the control input matrix, u t is the control input vector; represents the estimated value of the state vector at time t+1 for the state vector information at time t; Q represents the covariance matrix of the process noise; U t represents the estimation error covariance at time t; U t+1|t represents the estimated error covariance at time t+1 calculated based on the information at time t; H represents the observation matrix, and R represents the covariance of the observation noise.
[0107] S2.8 Prediction The first vector is the photovoltaic power at the next moment.
[0108] In the prior art, the photovoltaic power obtained by the EMS always lags behind the photovoltaic change. The above embodiment actively predicts the photovoltaic output power at the next moment, thus avoiding the lag.
[0109] In this embodiment, a Kalman filter is used to predict the photovoltaic output power. The filter effectively addresses the measurement noise and system uncertainty issues, and exhibits high flexibility and real-time update capabilities. Therefore, the prediction calculation for photovoltaic output power has low complexity and is suitable for embedded systems and real-time applications.
[0110] Further, in another embodiment of the present invention, in step S3, when judging that the current photovoltaic and energy storage system can fully meet the load demand based on the obtained load power, the charge and discharge power in the PCS at the next moment is predicted, and the following steps can be adopted:
[0111] S3.1, determine the state vector change.
[0112] P b，t is the PCS charging and discharging power of the energy storage cabinet at the current time t, P pv，t is the photovoltaic output power at the current time t, ΔP pv，t is the change in photovoltaic power between the current moment t and the next moment t+1;
[0113] S3.2, define the state transition model.
[0114] The charging and discharging rules of the energy storage cabinet are mainly affected by the current photovoltaic power and the change in photovoltaic power. A linear model is used to approximate A' is the state transfer matrix, w t ′ is the process noise, The state transfer matrix is obtained by summarizing historical data and the physical characteristics of the system.
[0115] S3.3, set the observation model.
[0116] Define the observation vector Define the observation model as z′ t =H′y t +v′ t , H′ is the observation matrix, v t ′ is the observation noise.
[0117] S3.4, Calculate predictions.
[0118] Prediction Steps:
[0119] Predicted state vector and covariance matrix:
[0120] V t|t+1 =A′V t-1|t-1 A′ T +Q′, Q′ is the process covariance matrix;
[0121] Update steps:
[0122] Calculate the Kalman gain:
[0123] K t =V t|t-1 H′ t (H′V t|t-1 H′ T +R′)-1
[0124] Update the state vector and covariance matrix:
[0125]
[0126] V t|t =(IK t H)′V t|t-1
[0127] S3.5, extract the predicted PCS charging and discharging power of the energy storage system: P b，t+1 Right now The first component of .
[0128] in, represents the state vector at time t-1, A' is the state transfer matrix, represents the estimated value of the state vector at time t+1 for the state vector information at time t; Q' represents the covariance matrix of the process noise; V t-1|t-1 represents the estimated error covariance at time t-1; V t|t+1 represents the estimated error covariance at time t+1 calculated based on the information at time t; H' represents the observation matrix, and R' represents the covariance of the observation noise.
[0129] Through the above embodiment, the charging and discharging power of the power conversion system at the next moment is accurately predicted.
[0130] Furthermore, another preferred embodiment of the present invention performs power regulation based on the predicted PCS charging and discharging power at the next moment. Specifically, the predicted energy storage cabinet charging and discharging power value is input into the PID to ensure that the actual power value of the energy storage cabinet can quickly and stably reach the predicted power value. The control process is as follows:
[0131] Calculate the PID control output: u t is the control output, e t =P b，t+1 -P b，t , represents the difference between the current charging and discharging power and the predicted charging and discharging power at the next moment. K p K i K d , are the proportional, integral, and differential gains respectively.
[0132] Regulation control: Based on the PID control output, adjust the current PCS charging and discharging power to ensure that the load data is equal to the sum of the PCS charging and discharging power and the photovoltaic output power, thus achieving power balance.
[0133] In the above embodiment, when the photovoltaic and energy storage systems can fully meet the load demand, the output power of the photovoltaic system at the next time point is predicted in advance by using Kalman filtering technology, and the PCS charging and discharging power is adjusted in advance accordingly, so as to ensure that the load power is exactly equal to the sum of the photovoltaic output power and the PCS charging and discharging power, thereby achieving a more accurate power balance. Pre-adjusting the PCS charging and discharging power effectively avoids the phenomenon in the prior art that additional power needs to be drawn from the main grid during peak hours due to the lag in PCS power regulation relative to photovoltaic power changes. At the same time, this solution also significantly reduces the backflow to the main grid, thereby reducing the company's dependence on the main grid power and reducing unnecessary electricity expenses. For small and medium-sized enterprises that build microgrids, this strategy helps to maximize the benefits they obtain from microgrids.
[0134] Based on the same inventive concept, in other embodiments, a control terminal is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the microgrid-based power balancing method.
[0135] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various modifications or variations within the scope of the claims, which does not affect the essence of the present invention. The above preferred features can be used in any combination without conflicting with each other.
Claims
1. A power balancing system based on a microgrid, characterized in that: include: Data acquisition module: obtain weather data, historical power generation data of equipment, current photovoltaic output power, current PCS charging and discharging power, temperature data, irradiation data and load data; Photovoltaic power prediction module: based on the weather data, temperature data, irradiation data, historical power generation data of the equipment, and the photovoltaic output power at the current moment, predict the photovoltaic output power at the next moment; PCS power prediction module: predicts the PCS charging and discharging power at the next moment based on the PCS charging and discharging power at the current moment and the predicted photovoltaic output power at the next moment; Power balancing logic module: Based on the PCS charging and discharging power at the next moment and the PCS charging and discharging power at the current moment, PID control is performed to achieve power balancing based on the load data.
2. A power balancing system based on a microgrid according to claim 1, characterized in that: The photovoltaic power prediction module, the power prediction module and the power balancing logic module are all arranged in the hub of the microgrid, namely, the energy management module.
3. A microgrid-based power balancing system according to claim 2, characterized in that: The data acquisition module is arranged outside the energy management module; the data acquisition module comprises: The cloud obtains weather data and transmits it to the energy management module; A photovoltaic controller transmits the photovoltaic output power at the current moment to the energy management module; The PCS controller transmits the current PCS charging and discharging power to the energy management module; A temperature sensor collects temperature data and transmits it to the energy management module; An irradiation sensor, which collects irradiation data and transmits it to the energy management module; The load meter transmits the load data of the microgrid to the energy management module.
4. A power balancing method based on a microgrid, characterized in that: include: Obtain weather data, historical power generation data of equipment, current photovoltaic output power, current PCS charging and discharging power, temperature data, irradiation data and load data; Based on the weather data, temperature data, irradiation data, historical power generation data of the equipment, and the photovoltaic output power at the current moment, predict the photovoltaic output power at the next moment; Based on the PCS charging and discharging power at the current moment and the predicted photovoltaic output power at the next moment, predict the PCS charging and discharging power at the next moment; Based on the PCS charging and discharging power at the next moment and the PCS charging and discharging power at the current moment, PID control is performed, and combined with the load data, power balance based on the load data is achieved.
5. A power balancing method based on a microgrid according to claim 4, characterized in that: The method of predicting the photovoltaic output power at the next moment based on the weather data, temperature data, irradiation data, historical power generation data of the equipment, and the photovoltaic output power at the current moment includes: Get the state vector P t is the photovoltaic output power at time t, I t is the solar radiation intensity within the photovoltaic panel at time t, T t is the temperature within the photovoltaic panel at time t; The state transfer equation is established to describe the state change relationship of the power balance system based on the microgrid from time t to time t+1, specifically: A is the state transfer matrix, B is the control input matrix, u t is the control input vector, w t is the process noise vector; Obtain the covariance matrix Q of the process noise; Get the observation vector P t is the photovoltaic output power at time t, I t is the solar radiation intensity within the photovoltaic panel at time t; An observation equation is established to describe the relationship between the observation vector and the state vector, specifically: H is the observation matrix, v t is the observation noise vector; Obtain the covariance matrix R of the observation noise; Kalman filter is used for prediction and update, specifically: The predictions are: U t+|t =AU t A T +Q; The update is: Calculate the Kalman gain K t+1 =U t+1|t H T (HU t+1|t H T +R) -1 , update the state vector Update the covariance matrix U t+1 =(IK t+1 H)U t+1|t ; represents the state vector at time t, A is the state transfer matrix, B is the control input matrix, u t is the control input vector; represents the estimated value of the state vector at time t+1 for the state vector information at time t; Q represents the covariance matrix of the process noise; U t represents the estimation error covariance at time t; U t+1|t represents the estimated error covariance at time t+1 calculated based on the information at time t; H represents the observation matrix, and R represents the covariance of the observation noise; extract The first component of is used as the predicted photovoltaic output power at time t+1.
6. A power balancing method based on a microgrid according to claim 5, characterized in that: The state transfer matrix is where a ij Indicates the influence of the jth state variable in the current state vector on the ith state variable in the state vector at the next moment; The control input matrix B represents the influence of the control input on the state of the power balance system. Specifically, b 11 is the impact of weather forecast data on output power, b 12 is the impact of weather forecast data on solar radiation intensity, b 13 It represents the impact of weather data on ambient temperature; The control input vector u t =[W t ],W t is the weather forecast data at time t; The process noise vector w t represents the random noise of the power balancing system, where w P，t represents the effect of process noise on output power, w I，t represents the effect of process noise on solar radiation intensity, w T，t Represents the effect of process noise on temperature; The process noise covariance matrix Q is specifically: q 11 is the process noise variance of the output power, q 22 is the process noise variance of solar radiation intensity, q 33 is the process noise variance of the ambient temperature, q 12 =q 21 is the process noise variance between the output power and the radiation intensity, q 13 =q 31 is the process noise variance between output power and ambient temperature, q 23 =q 32 is the process noise variance between the radiation intensity and the ambient temperature.
7. A power balancing method based on a microgrid according to claim 5, characterized in that: The observation matrix H represents the relationship between the observation vector and the state vector, The observation noise vector v t Represents the random noise in the observation process where v P，t and v I，t They represent the influence of observation noise on output power and radiation intensity respectively; The observation noise covariance matrix R represents the covariance of the observation noise, 8. A power balancing method based on a microgrid according to claim 4, characterized in that: The predicting of the PCS charging and discharging power at the next moment based on the PCS charging and discharging power at the current moment and the predicted photovoltaic output power at the next moment includes: When the photovoltaic and energy storage systems can meet the load data, the state vector changes are obtained. P b，t is the PCS charging and discharging power at the current time t, P pv，t is the photovoltaic output power at the current time t, ΔP pv，t is the change in photovoltaic power between the current moment t and the next moment t+1; Use a linear model to define the state transition model. A' is the state transfer matrix, w t ′ is the process noise; Define the observation vector Define the observation model as z′ t =H′y t +v′ t , H′ is the observation matrix, v t ′ is the observation noise; Use Kalman filter for prediction and update: The prediction is: Predicted state vector and covariance matrix, V t|t-1 =A′V t-1|t-1 A′ T +Q′, Q′ is the process covariance matrix; The update is: Calculate the Kalman gain: K t =V t|t-1 H′ t (H′V t|t-1 H′ T +R′) -1 Update the state vector and covariance matrix: V t|t =(I-K t H)′V t|t-1 in, represents the state vector at time t-1, A' is the state transfer matrix, represents the estimated value of the state vector at time t+1 for the state vector information at time t; Q' represents the covariance matrix of the process noise; V t-1|t-1 represents the estimated error covariance at time t-1; V t|t+1 represents the estimated error covariance at time t+1 calculated based on the information at time t; H' represents the observation matrix, and R' represents the covariance of the observation noise; Extract the PCS charging and discharging power value of the predicted energy storage system: P b，t+1 Right now The first component of .
9. A power balancing method based on a microgrid according to claim 8, characterized in that: The PID control is performed based on the PCS charging and discharging power at the next moment and the PCS charging and discharging power at the current moment to achieve power balance based on the load data, including: Calculate the PID control output: u t is the control output, e t =P b，t+1 -P b，t , represents the difference between the current charging and discharging power and the predicted charging and discharging power at the next moment, K p K i K d , are the proportional, integral, and differential gains respectively; Based on the PID control output, the PCS charging and discharging power at the current moment is adjusted to satisfy that the load data is equal to the sum of the PCS charging and discharging power and the photovoltaic output power, thereby achieving power balance.
10. A control terminal, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the microgrid-based power balancing method described in any one of claims 4-9.