A charging power control method and EMS system

Abstract

The present application discloses a charging power control method and EMS system, including: the SCADA system receives the historical load data of the user in the historical time period sent by the data acquisition module, and predicts the power load of the user in the predicted time period based on the historical load data; the SCADA system receives the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and builds a prediction model based on the first state parameter and the second state parameter; the SCADA system obtains the charging power corresponding to each energy storage power station in the predicted time period based on the power load and the prediction model, and controls each energy storage power station to charge based on the charging power. The present application can flexibly control the power of the energy storage power station during charging according to the user load, thereby reducing the time that the energy storage battery is stationary at too low or too high SOC.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage systems, and in particular to a charging power control method and an EMS system. Background Art

[0002] At present, battery industry experts generally recommend that when the battery is not in use, it is the best choice to maintain its state of charge (SOC) in the range of 25% to 75%. The reason is that when the battery SOC is at a high level, the internal temperature of the battery will increase. This change not only accelerates the aging of the battery, but also causes a decrease in the number of active substances and a decrease in battery capacity, thereby accelerating the decline of battery performance; and too low SOC state may cause polarization of electrode materials, loss of active substances in electrode materials, damage to electrode structures, etc., accelerating battery capacity decay. In addition, excessive control of the SOC state may also cause the battery to be unable to be fully charged in the corresponding period, directly affecting the benefits of peak shifting and valley filling. Therefore, how to ensure that the energy storage power station reaches a fully charged state and effectively alleviate the problem of accelerated battery degradation caused by full-charge static has become an urgent problem to be solved.

[0003] At present, the charging and discharging plans of energy storage power stations mostly follow the traditional mode of fixed time periods and power. Although this mode is simple and easy to implement, it is difficult to flexibly respond to the dynamic changes in users' daily loads and it is difficult to achieve dynamic optimization of charging and discharging plans. Especially during the low-load period at night, due to the relatively ample charging time, energy storage power stations tend to adopt high-power fast charging technology. Excessive charging power will cause the batteries of energy storage power stations to be fully charged in advance, causing the battery cells to remain stationary at a high SOC state for a long time. This operating mode not only accelerates the aging process of the battery and shortens the battery life, but also reduces the conversion efficiency of the energy storage power station, which directly affects the economic benefits of the energy storage power station.

[0004] Application Contents

[0005] The present application provides a charging power control method and an EMS system to flexibly control the power of an energy storage power station during charging according to user load, thereby reducing the time that the energy storage battery is stationary at too low or too high SOC.

[0006] In a first aspect, the present application provides a charging power control method applicable to an EMS system, wherein the EMS system comprises: a data acquisition module and a SCADA system;

[0007] The charging power control method comprises:

[0008] The SCADA system receives the historical load data of the user in the historical time period sent by the data acquisition module, and predicts the power load of the user in the predicted time period based on the historical load data;

[0009] The SCADA system receives the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, respectively, and constructs a prediction model based on the first state parameter and the second state parameter;

[0010] The SCADA system obtains the charging power corresponding to each energy storage power station in the predicted time period based on the power load and the prediction model, and controls each energy storage power station to charge based on the charging power.

[0011] The embodiment of the present application can accurately predict the user's power load in the predicted time period based on the historical load data by receiving the historical load data of the user in the historical time period sent by the data acquisition module, so as to facilitate the subsequent prediction of the charging power of the energy storage power station; by receiving the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and constructing a prediction model, it is convenient to predict the charging power of the energy storage power station through the prediction model; based on the power load and the prediction model, the charging power corresponding to each energy storage power station can be accurately predicted, so as to flexibly control the power of the energy storage power station during charging according to the user load, thereby reducing the time that the energy storage battery is stationary at too low or too high SOC.

[0012] Furthermore, the power load of the user in the prediction time period is predicted based on the historical load data, specifically:

[0013] The SCADA system receives the first load data of the user in the historical time period sent by the data acquisition module, and divides and sets weight coefficients for the first load data respectively to obtain second load data;

[0014] The SCADA system constructs a baseline load curve based on the second load data;

[0015] The SCADA system predicts the power load of the user within a prediction time period based on the historical load data and the baseline load curve.

[0016] In this way, by receiving the historical load data of the user in the historical time period sent by the data acquisition module and constructing a baseline load curve, the user's power load in the predicted time period can be accurately predicted based on the historical load data, which facilitates the subsequent prediction of the charging power of the energy storage power station.

[0017] Furthermore, the calculation formula for predicting the power load of the user in the prediction time period is specifically:

[0018]

[0019] Where P 预测,tis the power load at the predicted time t in the baseline load curve, N max C is the maximum number of dates of the partition type. n is the weight coefficient set for each type of date in the second load data, P n,t It is the historical load of the most recent nth date of the same type at the forecast time t.

[0020] In this way, the historical load data can be used to accurately predict the user's electricity load during the predicted time period, which is convenient for subsequent prediction of the charging power of the energy storage power station.

[0021] Furthermore, the prediction model includes: an objective function and constraints; the constraints include: a charging power constraint, an SOC constraint, a high SOC holding state constraint, a low SOC holding state constraint and a demand constraint;

[0022] The prediction model with the best charging benefit is established according to the charging power constraint, the SOC constraint, the high SOC maintaining state constraint, the low SOC maintaining state constraint and the demand constraint.

[0023] In this way, by setting multiple constraints to constrain the charging power, it is convenient to establish the prediction model with the best charging benefit, and then facilitate the subsequent prediction of the charging power that meets the optimal charging benefit conditions.

[0024] Furthermore, the constraint condition is expressed as:

[0025]

[0026] E min ≤E t ≤E max ;

[0027] E t -α t E 高soc ≤E 高soc ;

[0028] E t +β t E 低soc ≥E 低soc ;

[0029]

[0030] P 需量防守值 ≥P 需量 ;

[0031]

[0032] In the formula, E t and E t-1are the SOC states of each energy storage unit at time t and time t-1 respectively; η ch The charging efficiency of each energy storage power station; is the charging power of each energy storage station at time t; Δt is the time period; E is the rated electrical energy capacity of each energy storage unit; E min and E min They are the upper and lower limits of the allowed state of charge of each energy storage power station, and the values ​​can be customized; E 高soc and E 低soc are the high SOC reference value and the low SOC reference value respectively; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; P max The maximum power for charging each energy storage unit is generally less than or equal to the rated power; Rated power for charging each energy storage unit; P 需量防守值 For a custom set demand target, P 需量 is the maximum demand value during the forecast period; 预测,t is the user's electricity load at the predicted time t.

[0033] In this way, by setting multiple constraints to constrain the charging power, it is convenient to establish the prediction model with the best charging benefit, and then facilitate the subsequent prediction of the charging power that meets the optimal charging benefit conditions.

[0034] In a second aspect, the present application provides an EMS system, including: a data acquisition module and a SCADA system;

[0035] The SCADA system comprises: a first receiving module, a second receiving module and a control module;

[0036] The first receiving module is used to receive the historical load data of the user in the historical time period sent by the data acquisition module, and predict the power load of the user in the predicted time period based on the historical load data;

[0037] The second receiving module is used to respectively receive the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and to build a prediction model based on the first state parameter and the second state parameter;

[0038] The control module is used to obtain the charging power corresponding to each energy storage power station in the predicted time period based on the power load and the prediction model, and control each energy storage power station to charge based on the charging power.

[0039] The embodiment of the present application can accurately predict the user's power load in the predicted time period based on the historical load data by receiving the historical load data of the user in the historical time period sent by the data acquisition module, so as to facilitate the subsequent prediction of the charging power of the energy storage power station; by receiving the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and constructing a prediction model, it is convenient to predict the charging power of the energy storage power station through the prediction model; based on the power load and the prediction model, the charging power corresponding to each energy storage power station can be accurately predicted, so as to flexibly control the power of the energy storage power station during charging according to the user load, thereby reducing the time that the energy storage battery is stationary at too low or too high SOC.

[0040] Furthermore, the first receiving module includes: a receiving unit, a building unit and a prediction unit;

[0041] The receiving unit is used to receive the first load data of the user in the historical time period sent by the data acquisition module, and respectively divide the first load data and set weight coefficients to obtain second load data;

[0042] The construction unit is used to construct a baseline load curve based on the second load data;

[0043] The prediction unit is used to predict the power load of the user within a prediction time period based on the historical load data and the baseline load curve.

[0044] Furthermore, the calculation formula for predicting the power load of the user in the prediction time period is specifically:

[0045]

[0046] Where P 预测,t is the power load at the predicted time t in the baseline load curve, N max C is the maximum number of dates of the partition type. n is the weight coefficient set for each type of date in the second load data, P n,t It is the historical load of the most recent nth date of the same type at the forecast time t.

[0047] Furthermore, the prediction model includes: an objective function and constraints; the constraints include: a charging power constraint, an SOC constraint, a high SOC holding state constraint, a low SOC holding state constraint and a demand constraint;

[0048] The prediction model with the best charging benefit is established according to the charging power constraint, the SOC constraint, the high SOC maintaining state constraint, the low SOC maintaining state constraint and the demand constraint.

[0049] Furthermore, the constraint condition is expressed as:

[0050]

[0051] E min ≤E t ≤E max ;

[0052] E t -α t E 高soc ≤E 高soc ;

[0053] E t +β t E 低soc ≥E 低soc ;

[0054]

[0055] P 需量防守值 ≥P 需量 ;

[0056]

[0057] In the formula, E t and E t-1 are the SOC states of each energy storage unit at time t and time t-1 respectively; η ch The charging efficiency of each energy storage power station; is the charging power of each energy storage station at time t; Δt is the time period; E is the rated electrical energy capacity of each energy storage unit; E min and E min They are the upper and lower limits of the allowed state of charge of each energy storage power station, and the values ​​can be customized; E 高soc and E 低soc are the high SOC reference value and the low SOC reference value respectively; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; P max The maximum power for charging each energy storage unit is generally less than or equal to the rated power; Rated power for charging each energy storage unit; P 需量防守值 For a custom set demand target, P 需量 is the maximum demand value during the forecast period; 预测,t is the user's electricity load at the predicted time t. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1It is a flow chart of an embodiment of a charging power control method provided by the present application;

[0059] Figure 2 It is a structural schematic diagram of an embodiment of an EMS system provided by the present application;

[0060] Figure 3 It is a structural diagram of an embodiment of the SCADA system provided by the present application. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] It should be understood that the step numbers used in this article are only for the convenience of description and are not intended to limit the order in which the steps are executed.

[0063] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0064] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0065] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0066] In the battery industry, experts recommend that when the battery is not in use, its state of charge (SOC) should be maintained at an optimal level of 25%-75%. The reason is that high SOC accelerates battery aging and reduces capacity, while low SOC affects the benefits of peak shifting and valley filling. The charging and discharging plans of energy storage power stations are traditionally fixed and difficult to dynamically optimize. Therefore, at night when the load is low, high-power fast charging can easily cause the battery to be fully charged in advance, causing the battery to remain stationary at a high SOC state for a long time, thereby accelerating battery aging, shortening the battery life, and further affecting the conversion efficiency and economic benefits of the energy storage power station.

[0067] Next, the nouns involved in this application are analyzed:

[0068] SOC (State of Charge) refers to the state of charge of the battery, which indicates the ratio of the current remaining power of the battery to the total capacity of the battery, usually expressed as a percentage. This parameter is an important parameter in the battery management system (BMS) and is used to evaluate the battery's state of charge, thereby helping to optimize the battery's use and charging strategy.

[0069] Energy Management System (EMS) is an intelligent system integrating software and hardware, which is used to monitor, control and optimize the energy flow and energy consumption in the energy system. It includes: data acquisition module and SCADA system. The data acquisition module can monitor the operating status, energy consumption and environmental conditions of energy equipment in real time, and the SCADA system can analyze the collected data and provide decision support technology, so as to achieve efficient management and optimization of energy.

[0070] Based on this, the embodiments of the present application provide a charging power control method and an EMS system, which can flexibly control the power of the energy storage power station during charging according to the user load, thereby reducing the time that the energy storage battery is stationary at too low or too high SOC.

[0071] A charging power control method and an EMS system provided in an embodiment of the present application are specifically illustrated through the following embodiments. First, the charging power control method in an embodiment of the present application is described.

[0072] Embodiment 1

[0073] Please refer to Figure 1 , Figure 1 It is a flow chart of an embodiment of a charging power control method provided by the present application, which is applicable to an EMS system, wherein the EMS system includes: a data acquisition module and a SCADA system;

[0074] The charging power control method includes steps S101 to S103, each of which is specifically as follows:

[0075] Step S101, the SCADA system receives the historical load data of the user in the historical time period sent by the data acquisition module, and predicts the power load of the user in the predicted time period based on the historical load data;

[0076] It is understandable that it is necessary to determine which subject's historical load data needs to be collected, and then consider the differences in the collection frequencies in different time periods (such as peak, flat peak, and valley) to set the corresponding data collection frequencies. Among them, the collection frequency can be once every 15 minutes, once every 30 minutes, once every hour, etc. When collecting the load data, it is also necessary to record the date types corresponding to the load data, including but not limited to date types such as day of the week, whether it is a legal holiday, and whether it is a legal rest day. After the data collection module collects the historical load data of the user in the historical time period, it will send the historical load data to the SCADA system for user load prediction.

[0077] It should be noted that the user terminals collected include but not limited to subjects such as power grids, transportation, communities, and industrial users. That is to say, the present application can flexibly control the charging power of the energy storage power station when supplying power to other user terminals.

[0078] When the SCADA system receives the historical load data, it is necessary to predict the electricity load of the user in the prediction time period based on the historical load data. It includes: receiving the first load data of the user in the historical time period sent by the data collection module, and respectively dividing and setting weight coefficients for the first load data to obtain the second load data; the SCADA system constructs a baseline load curve based on the second load data; the SCADA system predicts the electricity load of the user in the prediction time period based on the historical load data and the baseline load curve.

[0079] Specifically: First, receive the first load data of the user in the historical time period, and divide the first load data according to the date type, which can but is not limited to dividing according to legal holidays, according to rest holidays, according to Monday to Sunday and other date types. Secondly, after dividing the first load data, corresponding weights can be set for the same type of dates and the sum of the weights is guaranteed to be 100%. Among them, the weights can be attenuated according to the principle of "larger for the near and smaller for the far". Exemplarily rather than restrictively, when setting the weight of the most recent same type of day as C1, (C1 < 1), and customizing the weight attenuation coefficient coef, (0 < coef < 1), so for the nth same type of day (N max ≥ n > 1), where N max is the maximum number of this type of day, the weight of the load corresponding to the nth same type of day is C n =(1 - C1)×coef n-1 , therefore, the weight of the last load is After dividing the first load data and setting the weight coefficient, the second load data can be obtained. Again, the data in the second load data is processed, that is, the loads of the same type of date are weighted averaged using the weight coefficient, and the baseline load curve at each moment of the forecast day can be obtained. Finally, the historical load data is substituted into the baseline load curve to predict the user's power load in the forecast time period. The calculation formula for predicting the user's power load in the forecast time period is specifically:

[0080]

[0081] Where P 预测,t is the power load at the predicted time t in the baseline load curve, N max C is the maximum number of dates of the partition type. n is the weight coefficient set for each type of date in the second load data, P n,t It is the historical load of the most recent nth date of the same type at the forecast time t.

[0082] In this way, by receiving the historical load data of the user in the historical time period sent by the data acquisition module and constructing a baseline load curve, the user's power load in the predicted time period can be accurately predicted based on the historical load data, which facilitates the subsequent prediction of the charging power of the energy storage power station.

[0083] Step S102, the SCADA system receives the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and constructs a prediction model based on the first state parameter and the second state parameter;

[0084] It can be understood that the data acquisition module needs to collect the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit within a certain time length Δt, wherein the energy storage unit can be the user-side transformer demand value, wherein the first state parameter can be but is not limited to the energy storage charging incentive electricity price p ch The value can be set to the valley electricity price, the charging efficiency η of the energy storage power station ch That is, the ratio of the actual amount of electricity stored in the battery to the input amount of the energy storage gateway meter and the demand unit price p 需量 etc.; the second state parameter may be, but is not limited to, the charge and discharge state, voltage, current, and rated electric energy capacity E of the energy storage unit, where E comes from the energy storage installed configuration or later actual capacity measurement, and the first state parameter and the second state are sent to the SCADA system via the data acquisition module. Upon receiving the first state parameter and the second state parameter, the SCADA system will construct a prediction model based on the first state parameter and the second state parameter.

[0085] It should be noted that demand usually refers to the average power value measured within a specified time period (such as 15 minutes), reflecting the average demand level for electricity by users within a specific time period.

[0086] The prediction model includes: an objective function and constraints; the constraints include: a charging power constraint, an SOC constraint, a high SOC holding state constraint, a low SOC holding state constraint and a demand constraint; wherein the constraints are expressed as:

[0087] The relationship between charging power and SOC:

[0088]

[0089] SOC is within the maximum and minimum allowable SOC range:

[0090] E min ≤E t ≤E max ;

[0091] High SOC hold state constraints:

[0092] E t -α t E 高soc ≤E 高soc ;

[0093] Low SOC hold state constraints:

[0094] E t +β t E 低soc ≥E 低soc ;

[0095] Charging power within the maximum power range:

[0096]

[0097] The demand during the current period cannot exceed the demand defense value:

[0098] P 需量防守值 ≥P 需量 ;

[0099] Charging does not raise the demand control target:

[0100]

[0101] In the formula, E t and E t-1 are the SOC states of each energy storage unit at time t and time t-1 respectively; η ch The charging efficiency of each energy storage power station; is the charging power of each energy storage station at time t; Δt is the time period; E is the rated electrical energy capacity of each energy storage unit; E min and E min They are the upper and lower limits of the allowed state of charge of each energy storage power station, and the values ​​can be customized; E 高soc and E 低soc are the high SOC reference value and the low SOC reference value respectively; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; P max The maximum power for charging each energy storage unit is generally less than or equal to the rated power; Rated power for charging each energy storage unit; P 需量防守值 For a custom set demand target, P 需量 is the maximum demand value during the forecast period; 预测,t is the user's electricity load at the predicted time t.

[0102] It should be noted that E 高soc It can be set to 75% but is not limited to it. When it is greater than this value, it is considered to enter a high SOC state. 低soc It can be but is not limited to being set to 25%. When it is less than this value, it is considered to enter a low SOC state.

[0103] In this way, by setting multiple constraints to constrain the charging power, it is convenient to establish the prediction model with the best charging benefit, and then facilitate the subsequent prediction of the charging power that meets the optimal charging benefit conditions.

[0104] Subsequently, according to the charging power constraint, the SOC constraint, the high SOC maintaining state constraint, the low SOC maintaining state constraint and the demand constraint, the prediction model with the best charging benefit is established, wherein the objective function of the prediction model is:

[0105]

[0106] Where, F is the charging efficiency; is the charging power of each energy storage power station at time t; p ch P is the charging incentive electricity price of the energy storage power station, which can be set to the valley electricity price; 需量 is the maximum demand value during the forecast period; p 需量 P is the demand price. This value needs to be averaged every day. For example, in Guangdong, the demand price is 36.1 yuan / kW. There are 30 days in a month, so the value is 36.1 / 30. max is the maximum actual charging power of the energy storage power station; p 高功率This is the benefit penalty for the actual maximum power of the energy storage power station. This value should be set lower than the charging incentive electricity price p ch , the value can be set to the demand unit price p 需量 Same; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; p 高soc and p 低soc They are the benefit penalty for maintaining a high SOC state and the benefit penalty for maintaining a low SOC state. Both values ​​must be set lower than the charging incentive electricity price p. ch , the value can be set to the charging incentive electricity price p ch half.

[0107] The forecast model takes into account the enterprise demand after superimposing energy storage The maximum actual charging power P of the energy storage station max , high SOC and low SOC holding time are used as function penalties, and the corresponding penalty benefits can be regarded as penalty weights, that is, the prediction model will try its best to meet the company's higher demand, the actual high operating power of energy storage, and high SOC and low SOC will reduce the benefits of the energy storage power station.

[0108] Step S103: The SCADA system obtains the charging power corresponding to each energy storage power station in the predicted time period based on the power load and the prediction model, and controls each energy storage power station to charge based on the charging power.

[0109] It is understandable that the power load is input into the prediction model, and a mathematical solver such as gurobi or cplex may be called to solve the constraints of the prediction model to obtain the optimization variables corresponding to different charging benefits (which may be but not limited to the charging power of each energy storage station at time t). The maximum demand value P during the forecast period 需量 、Is SOC higher than the reference value E 高soc Is the sum lower than the set value E? 低soc Alpha t and β t ), and by comparing the charging benefits and the values ​​of the optimization variables, the optimal value of the charging benefits and the values ​​of the optimization variables corresponding to the optimal value of the charging benefits are determined, that is, the most appropriate charging power corresponding to each energy storage power station in the predicted time period is determined. That is, the charging plan curve corresponding to each moment is obtained, and the instructions are sent to the EMS system through the data interface between the cloud and the EMS system. The EMS system uses the group control allocation strategy to send the allocated power to the converter PCS of each energy storage power station to control the charging and discharging of the energy storage power station.

[0110] It should be noted that, in order to prevent the prediction deviation of the prediction model, the rated electric energy capacity E of the energy storage unit may be amplified, for example, by 20%, and then input into the prediction model.

[0111] After obtaining the corresponding charging power of each energy storage power station in the predicted time period, since the excessive charging power of the energy storage power station may increase the power load on the user side, it is necessary to monitor the power load on the user side at each moment in real time through EMS to reasonably allocate the charging power to the energy storage unit, thereby realizing the charging of the energy storage power station.

[0112] It should be noted that in order to prevent communication interruptions caused by accidents, the EMS system should also be able to set different types of days and universal charging power curves in advance, and realize dynamic updates every week or day to dynamically control the charging power of the energy storage power station.

[0113] The embodiment of the present application can accurately predict the user's power load in the predicted time period based on the historical load data by receiving the historical load data of the user in the historical time period sent by the data acquisition module, so as to facilitate the subsequent prediction of the charging power of the energy storage power station; by receiving the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and constructing a prediction model, it is convenient to predict the charging power of the energy storage power station through the prediction model; based on the power load and the prediction model, the charging power corresponding to each energy storage power station can be accurately predicted, so as to flexibly control the power of the energy storage power station during charging according to the user load, thereby reducing the time that the energy storage battery is stationary at too low or too high SOC.

[0114] Embodiment 2

[0115] Please refer to Figure 2 , Figure 2 2 is a schematic diagram of the structure of an embodiment of an EMS system provided by the present application, including: a data acquisition module 201 and a SCADA system 202; wherein the schematic diagram of the structure of an embodiment of the SCADA system 202 is as follows Figure 3 As shown:

[0116] The SCADA system 202 includes: a first receiving module 301, a second receiving module 302 and a control module 303;

[0117] The first receiving module 301 is used to receive the historical load data of the user in the historical time period sent by the data acquisition module, and predict the power load of the user in the predicted time period based on the historical load data;

[0118] It can be understood that it is necessary to determine which subject's historical load data needs to be collected, and then consider the differences in the collection frequencies in different time periods (such as peak, flat peak, and valley) to set the corresponding data collection frequencies. Among them, the collection frequency can be once every 15 minutes, once every 30 minutes, once every hour, etc. When collecting the load data, it is also necessary to record the date types corresponding to the load data, including but not limited to date types such as day of the week, whether it is a legal holiday, and whether it is a legal adjusted holiday. After the data collection module collects the historical load data of the user in the historical time period, it will send the historical load data to the SCADA system for user load prediction.

[0119] It should be noted that the user terminals collected include but not limited to subjects such as power grids, transportation, communities, and industrial users. That is to say, the present application can flexibly control the charging power of the energy storage power station when it supplies power to other user terminals.

[0120] The first receiving module 301 includes: a receiving unit, a constructing unit, and a predicting unit; the receiving unit is used to receive the first load data of the user in the historical time period sent by the data collection module, and divide and set weight coefficients for the first load data respectively to obtain second load data; the constructing unit is used to construct a baseline load curve based on the second load data; the predicting unit is used to predict the electricity load of the user in the prediction time period based on the historical load data and the baseline load curve.

[0121] Specifically: First, receive the first load data of the user in the historical time period, and divide the first load data according to the date type, which can but is not limited to dividing according to legal holidays, adjusted holiday, from Monday to Sunday and other date types. Secondly, after dividing the first load data, corresponding weights can be set for the same type of dates and the sum of the weights is ensured to be 100%. Among them, the weights can be attenuated according to the principle of "larger for the near and smaller for the far". Exemplarily rather than restrictively, when setting the weight of the most recent same type of day as C1, (C1 < 1), and customizing the weight attenuation coefficient coef, (0 < coef < 1), so for the nth same type of day (N max ≥ n > 1), where N max is the maximum number of this type of day, the weight of the load corresponding to the nth same type of day is C n =(1 - C1)×coef n-1 , therefore, the weight of the last load is After dividing the first load data and setting the weight coefficient, the second load data can be obtained. Again, the data in the second load data is processed, that is, the loads of the same type of date are weighted averaged using the weight coefficient, and the baseline load curve at each moment of the forecast day can be obtained. Finally, the historical load data is substituted into the baseline load curve to predict the user's power load in the forecast time period. The calculation formula for predicting the user's power load in the forecast time period is specifically:

[0122]

[0123] Where P 预测,t is the power load at the predicted time t in the baseline load curve, N max C is the maximum number of dates of the partition type. n is the weight coefficient set for each type of date in the second load data, P n,t It is the historical load of the most recent nth date of the same type at the forecast time t.

[0124] In this way, by receiving the historical load data of the user in the historical time period sent by the data acquisition module and constructing a baseline load curve, the user's power load in the predicted time period can be accurately predicted based on the historical load data, which facilitates the subsequent prediction of the charging power of the energy storage power station.

[0125] The second receiving module 302 is used to respectively receive the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and to build a prediction model based on the first state parameter and the second state parameter;

[0126] It can be understood that the data acquisition module needs to collect the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit within a certain time length Δt, wherein the energy storage unit can be the user-side transformer demand value, wherein the first state parameter can be but is not limited to the energy storage charging incentive electricity price p ch The value can be set to the valley electricity price, the charging efficiency η of the energy storage power station ch That is, the ratio of the actual amount of electricity stored in the battery to the input amount of the energy storage gateway meter and the demand unit price p 需量 etc.; the second state parameter may be, but is not limited to, the charge and discharge state, voltage, current, and rated electric energy capacity E of the energy storage unit, where E comes from the energy storage installed configuration or later actual capacity measurement, and the first state parameter and the second state are sent to the SCADA system via the data acquisition module. Upon receiving the first state parameter and the second state parameter, the SCADA system will construct a prediction model based on the first state parameter and the second state parameter.

[0127] It should be noted that demand usually refers to the average power value measured within a specified time period (such as 15 minutes), reflecting the average demand level for electricity by users within a specific time period.

[0128] The prediction model includes: an objective function and constraints; the constraints include: a charging power constraint, an SOC constraint, a high SOC holding state constraint, a low SOC holding state constraint and a demand constraint; wherein the constraints are expressed as:

[0129] The relationship between charging power and SOC:

[0130]

[0131] SOC is within the maximum and minimum allowable SOC range:

[0132] E min ≤E t ≤E max ;

[0133] High SOC hold state constraints:

[0134] E t -α t E 高soc ≤E 高soc ;

[0135] Low SOC hold state constraints:

[0136] E t +β t E 低soc ≥E 低soc ;

[0137] Charging power within the maximum power range:

[0138]

[0139] The demand in the current period cannot exceed the demand defense value:

[0140] P 需量防守值 ≥P 需量 ;

[0141] Charging does not raise the demand control target:

[0142]

[0143] In the formula, E t and E t-1 are the SOC states of each energy storage unit at time t and time t-1 respectively; η ch The charging efficiency of each energy storage power station; is the charging power of each energy storage station at time t; Δt is the time period; E is the rated electrical energy capacity of each energy storage unit; E min and E min They are the upper and lower limits of the allowed state of charge of each energy storage power station, and the values ​​can be customized; E 高soc and E 低soc are the high SOC reference value and the low SOC reference value respectively; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; P max The maximum power for charging each energy storage unit is generally less than or equal to the rated power; Rated power for charging each energy storage unit; P 需量防守值 For a custom set demand target, P 需量 is the maximum demand value during the forecast period; 预测,t is the user's electricity load at the predicted time t.

[0144] It should be noted that E 高soc It can be set to 75% but is not limited to it. When it is greater than this value, it is considered to enter a high SOC state. 低soc It can be but is not limited to being set to 25%. When it is less than this value, it is considered to enter a low SOC state.

[0145] In this way, by setting multiple constraints to constrain the charging power, it is convenient to establish the prediction model with the best charging benefit, and then facilitate the subsequent prediction of the charging power that meets the optimal charging benefit conditions.

[0146] Subsequently, according to the charging power constraint, the SOC constraint, the high SOC maintaining state constraint, the low SOC maintaining state constraint and the demand constraint, the prediction model with the best charging benefit is established, wherein the objective function of the prediction model is:

[0147]

[0148] Where, F is the charging efficiency; is the charging power of each energy storage power station at time t; p ch P is the charging incentive electricity price of the energy storage power station, which can be set to the valley electricity price; 需量 is the maximum demand value during the forecast period; p 需量 P is the demand price. This value needs to be averaged every day. For example, in Guangdong, the demand price is 36.1 yuan / kW. There are 30 days in a month, so the value is 36.1 / 30. max is the maximum actual charging power of the energy storage power station; p 高功率This is the benefit penalty for the actual maximum power of the energy storage power station. This value should be set lower than the charging incentive electricity price p ch , the value can be set to the demand unit price p 需量 Same; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; p 高soc and p 低soc They are the benefit penalty for maintaining a high SOC state and the benefit penalty for maintaining a low SOC state. Both values ​​must be set lower than the charging incentive electricity price p. ch , the value can be set to the charging incentive electricity price p ch half.

[0149] The forecast model takes into account the enterprise demand after superimposing energy storage The maximum actual charging power P of the energy storage station max , high SOC and low SOC holding time are used as function penalties, and the corresponding penalty benefits can be regarded as penalty weights, that is, the prediction model will try its best to meet the company's higher demand, the actual high operating power of energy storage, and high SOC and low SOC will reduce the benefits of the energy storage power station.

[0150] The control module 303 is used to obtain the charging power corresponding to each energy storage power station in the predicted time period based on the power load and the prediction model, and control each energy storage power station to charge based on the charging power.

[0151] It is understandable that the power load is input into the prediction model, and a mathematical solver such as gurobi or cplex may be called to solve the constraints of the prediction model to obtain the optimization variables corresponding to different charging benefits (which may be but not limited to the charging power of each energy storage station at time t). The maximum demand value P during the forecast period 需量 、Is SOC higher than the reference value E 高soc Is the sum lower than the set value E? 低soc Alpha t and β t ), and by comparing the charging benefits and the values ​​of the optimization variables, the optimal value of the charging benefits and the values ​​of the optimization variables corresponding to the optimal value of the charging benefits are determined, that is, the most appropriate charging power corresponding to each energy storage power station in the predicted time period is determined. That is, the charging plan curve corresponding to each moment is obtained, and the instructions are sent to the EMS system through the data interface between the cloud and the EMS system. The EMS system uses the group control allocation strategy to send the allocated power to the converter PCS of each energy storage power station to control the charging and discharging of the energy storage power station.

[0152] It should be noted that, in order to prevent the prediction deviation of the prediction model, the rated electric energy capacity E of the energy storage unit may be amplified, for example, by 20%, and then input into the prediction model.

[0153] After obtaining the corresponding charging power of each energy storage power station in the predicted time period, since the excessive charging power of the energy storage power station may increase the power load on the user side, it is necessary to monitor the power load on the user side at each moment in real time through EMS to reasonably allocate the charging power to the energy storage unit, thereby realizing the charging of the energy storage power station.

[0154] It should be noted that in order to prevent communication interruptions caused by accidents, the EMS system should also be able to set different types of days and universal charging power curves in advance, and realize dynamic updates every week or day to dynamically control the charging power of the energy storage power station.

[0155] The embodiment of the present application can accurately predict the user's power load in the predicted time period based on the historical load data by receiving the historical load data of the user in the historical time period sent by the data acquisition module, so as to facilitate the subsequent prediction of the charging power of the energy storage power station; by receiving the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and constructing a prediction model, it is convenient to predict the charging power of the energy storage power station through the prediction model; based on the power load and the prediction model, the charging power corresponding to each energy storage power station can be accurately predicted, so as to flexibly control the power of the energy storage power station during charging according to the user load, thereby reducing the time that the energy storage battery is stationary at too low or too high SOC.

[0156] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only a specific embodiment of the present application and is not intended to limit the scope of protection of the present application. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A charging power control method, characterized in that: Applicable to EMS system, the EMS system comprises: data acquisition module and SCADA system; The charging power control method comprises: The SCADA system receives the historical load data of the user in the historical time period sent by the data acquisition module, and predicts the power load of the user in the predicted time period based on the historical load data; The SCADA system receives the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, respectively, and constructs a prediction model based on the first state parameter and the second state parameter; The SCADA system obtains the charging power corresponding to each energy storage power station in the predicted time period based on the power load and the prediction model, and controls each energy storage power station to charge based on the charging power.

2. The charging power control method according to claim 1, characterized in that: The predicting of the user's power load in the prediction time period based on the historical load data is specifically: The SCADA system receives the first load data of the user in the historical time period sent by the data acquisition module, and divides and sets weight coefficients for the first load data respectively to obtain second load data; The SCADA system constructs a baseline load curve based on the second load data; The SCADA system predicts the power load of the user within a prediction time period based on the historical load data and the baseline load curve.

3. The charging power control method according to any one of claims 1 to 2, characterized in that: The calculation formula for predicting the power load of the user within the prediction time period is specifically: Where P 预测,t is the power load at the predicted time t in the baseline load curve, N max C is the maximum number of dates of the partition type. n is the weight coefficient set for each type of date in the second load data, P n,t It is the historical load of the most recent nth date of the same type at the forecast time t.

4. The charging power control method according to claim 1, characterized in that: The prediction model includes: an objective function and constraints; the constraints include: a charging power constraint, an SOC constraint, a high SOC holding state constraint, a low SOC holding state constraint and a demand constraint; The prediction model with the best charging benefit is established according to the charging power constraint, the SOC constraint, the high SOC maintaining state constraint, the low SOC maintaining state constraint and the demand constraint.

5. The charging power control method according to claim 4, characterized in that: The constraints are expressed as: AND min ≤E t ≤E max ; AND t -α t AND 高soc ≤E 高soc ; AND t +β t AND 低soc ≥E 低soc ; P 需量防守值 ≥P 需量 ; In the formula, E t and E t-1 are the SOC states of each energy storage unit at time t and time t-1 respectively; η ch The charging efficiency of each energy storage power station; is the charging power of each energy storage station at time t; Δt is the time period; E is the rated electrical energy capacity of each energy storage unit; E min and E min They are the upper and lower limits of the allowed state of charge of each energy storage power station, and the values ​​can be customized; E 高soc and E 低soc are the high SOC reference value and the low SOC reference value respectively; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; P max The maximum power for charging each energy storage unit is generally less than or equal to the rated power; Rated power for charging each energy storage unit; P 需量防守值 For a custom set demand target, P 需量 is the maximum demand value during the forecast period; 预测,t is the user's electricity load at the predicted time t.

6. An EMS system, characterized in that: include: Data acquisition modules and SCADA systems; The SCADA system comprises: a first receiving module, a second receiving module and a control module; The first receiving module is used to receive the historical load data of the user in the historical time period sent by the data acquisition module, and predict the power load of the user in the predicted time period based on the historical load data; The second receiving module is used to respectively receive the first state parameter and the second state parameter corresponding to the energy storage power station and each energy storage unit sent by the data acquisition module, and to build a prediction model based on the first state parameter and the second state parameter; The control module is used to obtain the charging power corresponding to each energy storage power station in the predicted time period based on the power load and the prediction model, and control each energy storage power station to charge based on the charging power.

7. The EMS system according to claim 6, characterized in that: The first receiving module includes: a receiving unit, a building unit and a prediction unit; The receiving unit is used to receive the first load data of the user in the historical time period sent by the data acquisition module, and respectively divide the first load data and set weight coefficients to obtain second load data; The construction unit is used to construct a baseline load curve based on the second load data; The prediction unit is used to predict the power load of the user within a prediction time period based on the historical load data and the baseline load curve.

8. The EMS system according to any one of claims 6 to 7, characterized in that: The calculation formula for predicting the power load of the user within the prediction time period is specifically: Where P 预测,t is the power load at the predicted time t in the baseline load curve, N max C is the maximum number of dates of the partition type. n is the weight coefficient set for each type of date in the second load data, P n,t It is the historical load of the most recent nth date of the same type at the forecast time t.

9. The EMS system according to claim 6, characterized in that: The prediction model includes: an objective function and constraints; the constraints include: a charging power constraint, an SOC constraint, a high SOC holding state constraint, a low SOC holding state constraint and a demand constraint; The prediction model with the best charging benefit is established according to the charging power constraint, the SOC constraint, the high SOC maintaining state constraint, the low SOC maintaining state constraint and the demand constraint.

10. The EMS system according to claim 9, characterized in that: The constraints are expressed as: AND min ≤E t ≤E max ; AND t -α t AND 高soc ≤E 高soc ; AND t +β t AND 低soc ≥E 低soc ; P 需量防守值 ≥P 需量 ; In the formula, E t and E t-1 are the SOC states of each energy storage unit at time t and time t-1 respectively; η ch The charging efficiency of each energy storage power station; is the charging power of each energy storage station at time t; Δt is the time period; E is the rated electrical energy capacity of each energy storage unit; E min and E min They are the upper and lower limits of the allowed state of charge of each energy storage power station, and the values ​​can be customized; E 高soc and E 低soc are the high SOC reference value and the low SOC reference value respectively; α t and β t Indicates whether the SOC is higher than the reference value E 高soc Is the sum lower than the set value E? 低soc , the value is 0 or 1; P max The maximum power for charging each energy storage unit is generally less than or equal to the rated power; Rated power for charging each energy storage unit; P 需量防守值 For a custom set demand target, P 需量 is the maximum demand value during the forecast period; 预测,t is the user's electricity load at the predicted time t.

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