Power balance optimization method for power systems based on the coordinated load regulation capabilities of aggregator clusters

Abstract

This invention discloses a power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters. It relates to the field of aggregator cluster coordination optimization technology, specifically including: predicting the data interaction demand between the aggregated total load model and other equipment models in the aggregator platform system at the next moment based on historical data, and adjusting the data interaction amount at the next moment according to the prediction results. This power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters improves the data analysis and calculation speed of the aggregator platform system during the power balance regulation process, and helps avoid the problem of decreased calculation accuracy caused by the difficulty of analyzing massive amounts of data during the power balance regulation process.

Description

Technical Field

[0001] This invention relates to the field of aggregator cluster coordination and optimization technology, specifically a power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters. Background Technology

[0002] With the increasing problems arising from the integration of distributed energy resources into power systems, these systems face growing volatility and uncertainty. Therefore, coordinating and managing the loads of multiple distributed energy resources to achieve energy supply and demand balance and optimization is essential. In achieving this balance and optimization, power system power balancing based on aggregator load coordination is a widely adopted optimization method.

[0003] The construction of a load aggregator platform mainly includes functions such as load forecasting, scheduling, and control. Through technical means, it aggregates scattered small-amount electricity loads to form a larger total load, thereby enabling more efficient operation and management, realizing large-scale electricity trading, and achieving power balance in the power system.

[0004] However, in existing research, studies on power balance in new power systems with large-scale distributed energy grid connection are still in their early stages. The unified aggregation and management of distributed energy output and scattered small-scale electricity loads are currently limited to setting fixed values. This inevitably leads to a disproportion between the production and consumption of new energy. In terms of energy utilization and system operating costs, there are currently no results that have gained widespread recognition in the industry.

[0005] Therefore, in order to address the power balance optimization problem in power systems, this patent has conducted research and proposed a power balance optimization method for power systems that coordinates the load regulation capabilities of aggregators and adjusts the output of a new type of power system by predicting the power balance demand index of the power system. Summary of the Invention

[0006] To address the shortcomings of the existing technologies, this invention provides a power system power balance optimization method that coordinates the load regulation capabilities of aggregators, which can accurately determine the power system power balance requirements.

[0007] This invention provides the following technical solution: a power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters. This method predicts the data interaction requirements between the aggregated total load model and other equipment models in the aggregator platform system at the next moment based on historical data, and adjusts the data interaction amount at the next moment according to the prediction results.

[0008] The method includes the following steps:

[0009] Step 1: Define the power system power balance demand index for the coordinated load regulation capabilities of aggregator clusters;

[0010] Step 2: Measure the data interaction between the aggregator cluster load model and other equipment models, as well as the relevant parameters of the power system power balance demand index of the aggregator cluster load regulation capability coordination, and establish a time series of relevant parameters of the power system power balance demand index of the aggregator cluster load regulation capability coordination based on the obtained measurements.

[0011] Step 3: Normalize the measurement data of relevant parameters of the power system power balance demand index for the coordinated load regulation capabilities of aggregator clusters.

[0012] Step 4: Calculate the influence factors of historical data on the power system power balance demand index of the aggregator cluster load regulation capability coordination on the power system power balance demand index of the aggregator cluster load regulation capability coordination at the next moment.

[0013] Step 5: Calculate the predicted value of the power system power balance demand index for the next time step based on the coordinated load regulation capabilities of the aggregator cluster.

[0014] Step 6: Adjust the amount of data interaction between the aggregator cluster load model and other equipment models based on the predicted value of the power system power demand index of the aggregator cluster load regulation capability coordination at the next moment.

[0015] Preferably, the power system power balance demand index is related to the output power of wind power in the power system, the maximum and minimum output power of wind power in the power system, the output power of photovoltaic power, the maximum and minimum output power of photovoltaic power, the charging and discharging power of energy storage batteries, the maximum and minimum charging and discharging power of energy storage batteries, the output power of traditional thermal power units, the maximum and minimum output power of traditional thermal power units, the total load of distributed electricity loads aggregated by aggregators, and the maximum and minimum total load of distributed electricity loads aggregated by aggregators.

[0016] Preferably, the sampling frequency of the input data for the power system power balance demand index is fixed, the sampling time interval is a natural number, and the sampling is performed sequentially.

[0017] Preferably, in step two, the amount of data interaction between the aggregator cluster load model and other equipment models at each sampling time is measured, as well as the relevant parameters of the power system power balance demand index of the aggregator cluster load regulation capability coordination.

[0018] Preferably, the measurement data of the power system power balance demand index related parameters of the load regulation capability coordination of the aggregator cluster include the output power of wind power in the aggregator platform system, the output power of photovoltaic power in the aggregator platform system, the charging and discharging power of energy storage batteries in the aggregator platform system, the output power of traditional thermal power units in the aggregator platform system, and the total load of the distributed electricity loads aggregated in the aggregator platform system.

[0019] Preferably, if the power system power balance demand coordinated by the load regulation capabilities of the aggregator cluster decreases in the next moment, the amount of data interaction between the aggregated total load model and other equipment models in the next moment will decrease; if the power system power balance demand coordinated by the load regulation capabilities of the aggregator cluster remains unchanged in the next moment, the amount of data interaction between the aggregated total load model and other equipment models will remain unchanged; if the power system power balance demand coordinated by the load regulation capabilities of the aggregator cluster increases, the amount of data interaction between the aggregated total load model and other equipment models in the next moment will increase.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. This power system power balance optimization method, which coordinates the load regulation capabilities of aggregator clusters, can predict the data interaction demand between the aggregated total load model and other equipment models in the aggregator platform system at the next moment based on historical data, and adjust the data interaction volume at the next moment according to the prediction results. This improves the data analysis and calculation speed of the aggregator platform system during the power system power balance regulation process, helping to avoid the problem of decreased calculation accuracy caused by the difficulty of analyzing massive amounts of data during the power system power balance regulation process. Attached Figure Description

[0022] Figure 1 The flowchart illustrates the power system power balance demand index prediction method for coordinating the load regulation capabilities of aggregators provided by this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] This invention provides a power system power balance optimization method based on the coordinated load regulation capabilities of aggregators, comprising the following steps:

[0025] Step 1: Define the power system power balance demand index for the coordinated load regulation capabilities of aggregators. The power system power balance demand index is related to the output power of wind power, photovoltaic power, energy storage battery charging and discharging power, traditional thermal power units, and the total load of distributed electricity loads aggregated by aggregators in the power system. The input data sampling frequency of the power system power balance demand index is fixed, the sampling time interval is a natural number, and sampling is performed sequentially.

[0026] Power system power balance demand index (IE) PB As shown in formula (1):

[0027]

[0028] Where t1, t2, Λ, t z ,Λ,t k Let k be the times of fixed time intervals, where k is a natural number, k∈1,2,Λ, and z is the z-th time, z is a natural number, and z∈{1,2,Λ,k}; For t z The output power of wind power in the power system at any given time; For t z The output power of photovoltaic power sources in the power system at any given time; For t z The charging and discharging power of energy storage batteries in the power system at any given time; For t z The output power of traditional thermal power units in the power system at any given time; For t z The total load of distributed electrical loads aggregated by the aggregator is utilized at all times; P w,min P w,max They are t1, t2, Λ, t respectively. z ,Λ,t k The minimum and maximum output power of wind power sources in the power system at time P; ph,min P ph,max They are t1, t2, Λ, t respectively. z ,Λ,t k The minimum and maximum output power of the photovoltaic power source in the power system at any given time; Q es,min Q es,max They are t1, t2, Λ, t respectively. z ,Λ,t k The minimum and maximum charging and discharging power of the power system's energy storage batteries at any given time; E c,min E c,max They are t1, t2, Λ, t respectively. z ,Λ,t kThe minimum and maximum output power of traditional thermal power units in the power system at any given time; L pa,min L pa,max They are t1, t2, Λ, t respectively. z ,Λ,t k The minimum and maximum total load of distributed electrical loads aggregated by the aggregator at any given time;

[0029] Step 2: Measure t k Data interaction volume (IA) between the time-aggregator cluster load model and the power generation equipment model tz The parameters related to the power system power balance demand index, which are determined by the coordinated load regulation capabilities of aggregator clusters, are used to establish a time series of these parameters based on the measured values.

[0030]

[0031] If the measurement interval is set to 1s and k = 200, the time series of relevant parameters of the power system power balance demand index for the coordinated load regulation capacity of the aggregator cluster is as shown in formula (2):

[0032]

[0033] Step 3: Normalize the measurement data of the power system power balance demand index related parameters of the load regulation capability coordination of the aggregator cluster. The measurement data of the power system power balance demand index related parameters of the load regulation capability coordination of the aggregator cluster include the output power of wind power in the aggregator platform system, the output power of photovoltaic power in the aggregator platform system, the charging and discharging power of energy storage batteries in the aggregator platform system, the output power of traditional thermal power units in the aggregator platform system, and the total load of the distributed electricity loads aggregated in the aggregator platform system.

[0034] The formula obtained after normalization is shown in equation (3):

[0035]

[0036] In the formula, For t z Normalized output power of wind power in the real-time aggregation platform system;

[0037] For t z Normalized output power of photovoltaic power source in the real-time aggregation platform system; For t z Normalized value of energy storage battery charging and discharging power in the real-time aggregation platform system; For t zNormalized output power of traditional thermal power units in the real-time aggregation platform system; For t z The normalized value of the total load of distributed electricity loads aggregated in the real-time aggregator platform system;

[0038] Step 4: Calculate the influence factors of historical data on the power system power balance demand index of the aggregator cluster load regulation capability coordination on the power system power balance demand index of the aggregator cluster load regulation capability coordination at the next moment, as shown in Equation (4):

[0039]

[0040] In the formula, The impact factor of wind power output measurement data in the aggregator platform system on the power system power balance demand index of the aggregator cluster load regulation capability coordination at the next moment; The impact factor of photovoltaic power output power measurement data in the aggregator platform system on the power system power balance demand index of the aggregator cluster load regulation capability coordination at the next moment; The impact factor of the output power measurement data of traditional thermal power units in the aggregator platform system on the power system power balance demand index of the aggregator cluster load regulation capability coordination at the next moment; The total load measurement data of the distributed electricity loads aggregated in the aggregator platform system is an influence factor on the power system power balance demand index of the aggregator cluster load regulation capability coordination at the next moment.

[0041] Step 5: Calculate the predicted value of the power system power balance demand index for the next time step based on the coordinated load regulation capabilities of the aggregator cluster, as shown in equation (5):

[0042]

[0043] Step 6: Adjust the data interaction between the aggregator cluster load model and other equipment models based on the predicted value of the power system power demand index of the aggregator cluster load regulation capability coordination at the next time step. If the power system power balance demand of the aggregator cluster load regulation capability coordination decreases at the next time step, the data interaction between the aggregator total load model and other equipment models at the next time step decreases; if the power system power balance demand of the aggregator cluster load regulation capability coordination remains unchanged at the next time step, the data interaction between the aggregator total load model and other equipment models remains unchanged; if the power system power balance demand of the aggregator cluster load regulation capability coordination increases, the data interaction between the aggregator total load model and other equipment models at the next time step increases.

[0044] As shown in equation (6):

[0045]

[0046] In the formula, IA t201 This represents the amount of data interaction between the energy storage battery model and other device models at the next moment; it can be seen from equation (6) that if the obtained IE pre If the result is less than 0.326, it is considered that the power balance demand of the power system in the next moment is reduced due to the coordinated load regulation capabilities of the aggregator cluster. Therefore, the data interaction volume between the aggregated total load model and other equipment models in the next moment is also considered to be less. It should be reduced to 0.5 times its original value; if the calculated IE pre If the result is between 0.326 and 0.653, it is assumed that the power system power balance demand in the next moment, based on the coordinated load regulation capabilities of the aggregator cluster, will remain unchanged. Therefore, the data interaction between the aggregated total load model and other equipment models will remain constant. If the IE is obtained... pre If the value is greater than 0.653, it is considered that the power system power balance demand of the aggregator cluster load regulation capability coordination has increased, and the amount of data interaction between the aggregated total load model and other equipment models at the next moment will increase to 1.5 times the original amount.

[0047] In summary, this power system power balance optimization method, which integrates the load regulation capabilities of aggregator clusters, can predict the data interaction requirements between the aggregated total load model and other equipment models in the aggregator platform system at the next moment, based on historical data, and adjust the data interaction volume according to the prediction results. This method improves the data analysis and calculation speed of the aggregator platform system during the power system power balance regulation process, and helps to avoid the problem of decreased calculation accuracy caused by the difficulty of analyzing massive amounts of data during the power system power balance regulation process.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters, characterized by: Based on historical data, this method predicts the data interaction requirements between the aggregated total load model and other device models in the aggregator platform system at the next moment, and adjusts the data interaction volume at the next moment according to the prediction results. The method includes the following steps: Step 1: Define the power system power balance demand index for the coordinated load regulation capabilities of aggregator clusters; Step 2: Measure the data interaction between the aggregator cluster load model and other equipment models, as well as the relevant parameters of the power system power balance demand index of the aggregator cluster load regulation capability coordination, and establish a time series of relevant parameters of the power system power balance demand index of the aggregator cluster load regulation capability coordination based on the obtained measurements. Step 3: Normalize the measurement data of relevant parameters of the power system power balance demand index for the coordinated load regulation capabilities of aggregator clusters. Step 4: Calculate the influence factors of historical data on the power system power balance demand index of the aggregator cluster load regulation capability coordination on the power system power balance demand index of the aggregator cluster load regulation capability coordination at the next moment. Step 5: Calculate the predicted value of the power system power balance demand index for the next time step based on the coordinated load regulation capabilities of the aggregator cluster. Step 6: Adjust the amount of data interaction between the aggregator cluster load model and other equipment models based on the predicted value of the power system power demand index of the aggregator cluster load regulation capability coordination at the next time step. If the predicted power system power balance demand based on the load regulation capabilities of the aggregator cluster is less than 0.326 in the next time step, the data interaction between the aggregated total load model and other equipment models will decrease to 0.5 times the current value. If the power system power balance demand based on the load regulation capabilities of the aggregator cluster remains unchanged in the next time step, and the predicted power system power balance demand index based on the load regulation capabilities of the aggregator cluster is between 0.326 and 0.653, the data interaction between the aggregated total load model and other equipment models will remain unchanged. If the predicted power system power balance demand based on the load regulation capabilities of the aggregator cluster is greater than 0.653, the data interaction between the aggregated total load model and other equipment models will increase to 1.5 times the current value in the next time step.

2. The power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters according to claim 1, characterized in that: The power system power balance demand index is related to the output power of wind power in the power system, the maximum and minimum output power of wind power in the power system, the output power of photovoltaic power, the maximum and minimum output power of photovoltaic power, the charging and discharging power of energy storage batteries, the maximum and minimum charging and discharging power of energy storage batteries, the output power of traditional thermal power units, the maximum and minimum output power of traditional thermal power units, the total load of distributed electricity loads aggregated by aggregators, and the maximum and minimum total load of distributed electricity loads aggregated by aggregators.

3. The power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters according to claim 2, characterized in that: The input data sampling frequency for the power system power balance demand index is fixed, the sampling time interval is a natural number, and sampling is performed sequentially.

4. The power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters according to claim 1, characterized in that: In step two, the amount of data interaction between the aggregator cluster load model and other equipment models at each sampling time is measured, as well as the relevant parameters of the power system power balance demand index of the aggregator cluster load regulation capability coordination.

5. The power system power balance optimization method based on the coordinated load regulation capabilities of aggregator clusters according to claim 1, characterized in that: The measurement data of relevant parameters of the power system power balance demand index for the coordinated load regulation capabilities of aggregators include the output power of wind power in the aggregator platform system, the output power of photovoltaic power in the aggregator platform system, the charging and discharging power of energy storage batteries in the aggregator platform system, the output power of traditional thermal power units in the aggregator platform system, and the total load of distributed electricity loads aggregated in the aggregator platform system.

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