Micro-grid adaptive optimization regulation and control method based on frequency deviation

Abstract

The invention discloses a frequency deviation-based micro-grid adaptive optimization regulation and control method, which comprises the following steps of: firstly, dividing frequency deviation into a plurality of intervals by comparing the deviation between the frequency of a micro-grid and a rated value in real time; secondly, considering the system frequency deviation and the system operation cost to establish a micro-grid multi-target optimization regulation and control model; a weighted summation method is adopted to convert the micro-grid multi-target optimization regulation and control model into a single-target optimization regulation and control model, and weight coefficients of different optimization targets are set; and finally, based on the divided frequency deviation intervals, providing an optimization regulation and control method for adaptively adjusting different optimization target weight coefficients according to the frequency deviation intervals. Compared with the prior art, the frequency deviation-based micro-grid adaptive optimization regulation and control method provided by the invention can improve the frequency stability and the operation efficiency of the micro-grid, and considers both the frequency deviation and the system frequency modulation cost.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimization and control, and in particular to a microgrid adaptive optimization and control method based on frequency deviation. Background Art

[0002] Microgrids, as small-scale power systems, can operate independently or in parallel with the main grid, offering high flexibility and reliability. However, with the integration of large-scale renewable energy sources such as wind power and photovoltaics into microgrids, the randomness and volatility of their output, combined with the volatility of their load, pose numerous challenges to the frequency stability of microgrids. Frequency is a key indicator of microgrid system stability, and frequency deviations can lead to instability or even collapse of the microgrid. Therefore, research on the frequency stability of microgrids is urgently needed.

[0003] Although many methods have been proposed to improve the frequency stability of microgrids, the following problems still need to be solved.

[0004] 1) Most existing studies focus on the frequency deviation of microgrid systems. However, they ignore the economic efficiency of the system when considering frequency deviation regulation, making it difficult to balance the system frequency regulation cost and frequency deviation.

[0005] 2) Existing microgrid frequency deviation control methods usually use controllers with fixed parameters, which are difficult to adapt to the dynamic changes of the system operating status and lack the ability to adaptively control frequency deviations. Summary of the Invention

[0006] To address the technical problem of frequency deviation regulation and system frequency regulation costs in existing technologies, the present invention partitions the frequency deviation of a microgrid and proposes a microgrid frequency optimization and control method that adaptively adjusts the weight coefficients of different optimization targets according to the frequency deviation interval. This method balances system frequency regulation costs and frequency deviation, improving the frequency stability and operating efficiency of the microgrid.

[0007] The technical solution adopted by the present invention is:

[0008] The microgrid adaptive optimization control method based on frequency deviation includes the following steps:

[0009] Step 1: Monitor the frequency of the microgrid in real time, calculate the difference between the frequency and the rated value, and partition the frequency deviation of the microgrid system based on the difference;

[0010] Step 2: Considering the frequency deviation and system operation cost of the microgrid system, a multi-objective optimization control model of the microgrid is established;

[0011] Step 3: Use the weighted summation method to convert the microgrid multi-objective optimization control model into a single-objective optimization control model, and set the weight coefficients of different optimization objectives;

[0012] Step 4: Adaptively adjust the weight coefficients of different optimization objectives according to the frequency deviation interval to optimize the control of the microgrid.

[0013] In step 1, the method for partitioning the frequency deviation of the microgrid system is as follows:

[0014] Compare the microgrid frequency with the rated value of 50Hz, and record the difference as △f. The microgrid frequency deviation is divided into zones according to the value range of △f, as follows:

[0015] When |△f|≤0.1Hz, it is recorded as a slight frequency deviation interval;

[0016] When 0.1Hz<|△f|≤0.4Hz, it is recorded as the moderate frequency deviation interval;

[0017] When |△f|>0.4Hz, it is recorded as a severe frequency deviation interval.

[0018] In step 2, a multi-objective optimization control model of a microgrid is established, specifically as follows:

[0019] The microgrid system frequency deviation optimization objective function C1 is defined as follows;

[0020] C1=△f=|ff ref | (1);

[0021] In formula (1): f and f ref They represent the actual value and rated value of the microgrid system frequency respectively.

[0022] The microgrid system operation cost optimization objective function C2 is defined as follows:

[0023] C2=C s +C p -C load (2);

[0024] In formula (2): C s represents the equipment operating cost function in the microgrid, including the operating costs of gas turbines, diesel generators, and fuel cells; C p represents the frequency regulation cost of controllable distributed generation participating in microgrid frequency regulation, including power storage units and fuel cells; C load It represents the benefit generated by participating in microgrid frequency regulation by shedding loads.

[0025] The overall multi-objective optimization objective function C of the microgrid is established as follows:

[0026] minC=|ff ref |+C s +Cp -C load (3);

[0027] In step 3, considering that the dimensions of the sub-optimization objective functions are inconsistent, in order to make the dimensions and magnitudes of the optimization objective functions the same, the following formula is used to normalize the sub-optimization objective functions;

[0028]

[0029] In formula (4): F pu represents the normalized per-unit value of the sub-optimization objective function, F represents the sub-optimization objective function, and F max and F min Represent the maximum and minimum values of the sub-optimization objective function respectively.

[0030] After normalization, let λ1 and λ2 represent the weight coefficients of the sub-optimization objective functions C1 and C2 respectively, and the single-objective optimization control model of the microgrid can be obtained as shown below.

[0031] minC=λ1C 1,pu +λ2C 2,pu (5);

[0032] In formula (5): C 1,pu Indicates the per-unit value of the frequency deviation optimization target, C 2,pu Represents the per-unit value of the system operation cost optimization objective function.

[0033] The step 4 includes the following steps:

[0034] S4.1: Determine the weights of different sub-optimization objectives in different frequency deviation intervals, as follows;

[0035] When the frequency of the microgrid is in the range of slight frequency deviation, the optimization goal of the microgrid should consider the economic efficiency of its own operation more, and minimize the operating cost of the microgrid by coordinating the operation of each energy unit.

[0036] When the microgrid frequency is in the moderate frequency deviation range, the optimization goal of the microgrid mainly considers the microgrid frequency deviation, while taking into account the economic efficiency of the microgrid operation.

[0037] When the microgrid frequency is in a severe frequency deviation range, the optimization goal of the microgrid is to quickly restore the microgrid frequency deviation to near the rated value by cutting off the load and frequency regulation unit output. At this time, the economic efficiency of the microgrid does not need to be considered. S4.2: Determine the adaptive control rules for the weight coefficients of different sub-optimization objectives, as follows:

[0038] Based on the above analysis of the weights of different sub-optimization objectives in different frequency deviation intervals, three variables s1, s2, and s3 are defined to indicate whether the microgrid frequency is in the slight frequency deviation interval, the moderate frequency deviation interval, and the severe frequency deviation interval, respectively. When the variables s1, s2, and s3 are 1, it means that the microgrid frequency is in the corresponding interval, otherwise it is not in the interval; the adaptive control rules for the weight coefficients of different sub-optimization objectives can be obtained, as follows:

[0039]

[0040] The present invention provides a microgrid adaptive optimization control method based on frequency deviation, and the technical effects are as follows:

[0041] 1) The present invention partitions the frequency deviation of the microgrid and can selectively adjust the optimization target according to the partition, thereby improving the flexibility of frequency regulation.

[0042] 2) The present invention proposes an optimization control method that adaptively adjusts the weight coefficients of different optimization objectives according to the frequency deviation interval, taking into account both the system frequency regulation cost and the frequency deviation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below with reference to the accompanying drawings and examples:

[0044] Figure 1 Flow chart of the method of the present invention.

[0045] Figure 2 A comparison chart of three different control strategies. DETAILED DESCRIPTION

[0046] A frequency deviation-based adaptive optimization and control method for microgrids is used in microgrid systems with a high proportion of renewable energy, solving the technical problem in existing technologies where frequency deviation regulation and system frequency regulation costs cannot be balanced. This method first divides the frequency deviation into multiple intervals by comparing the deviation between the frequency of the microgrid and the rated value in real time. Secondly, a multi-objective optimization and control model for the microgrid is established by considering the system frequency deviation and the system operating cost. Then, a weighted summation method is used to convert the multi-objective optimization and control model of the microgrid into a single-objective optimization and control model, and weight coefficients for different optimization objectives are set. Finally, based on the divided frequency deviation intervals, an optimization and control method is proposed that adaptively adjusts the weight coefficients of different optimization objectives according to the frequency deviation intervals. Compared with existing technologies, the strategy proposed in this invention can improve the frequency stability and operating efficiency of the microgrid, while taking into account both frequency deviation and system frequency regulation costs.

[0047] like Figure 1 As shown, the present invention discloses a microgrid adaptive optimization control method based on frequency deviation, and its specific steps are:

[0048] Step 1: Partition the frequency deviation of the microgrid system;

[0049] Compare the microgrid frequency with the rated value of 50Hz, and record the difference as △f. The microgrid frequency deviation is divided into different zones according to the value range of △f, as shown below:

[0050] When |△f|≤0.1Hz, it is recorded as a slight frequency deviation interval;

[0051] When 0.1Hz<|△f|≤0.4Hz, it is recorded as the moderate frequency deviation interval;

[0052] When |△f|>0.4Hz, it is recorded as a severe frequency deviation interval;

[0053] Step 2: Considering the system frequency deviation and system operation cost, a multi-objective optimization control model of the microgrid is established. The steps are as follows;

[0054] 1): Define the system frequency deviation optimization objective function C1 as shown below;

[0055] C1=△f=|ff ref | (1);

[0056] Where: f and f ref They represent the actual value and rated value of the microgrid system frequency respectively.

[0057] 2): Define the system operation cost optimization objective function C2 as shown below;

[0058] C2=C s +C p -C load (2);

[0059] Where: C s represents the operating cost function of the equipment in the microgrid, including the operating costs of the gas turbine (GT), diesel generator (DLS), and fuel cell (FC). The expression is as follows:

[0060]

[0061] Where: P i Indicates the output of the device, α i , β i and γ i They represent the operating cost coefficient of the equipment respectively.

[0062] C p represents the frequency regulation cost of controllable distributed generation participating in microgrid frequency regulation, including the battery storage unit (BS) and fuel cell. The expression is as follows:

[0063]

[0064] Where: and They represent the cost coefficients of upward and downward frequency regulation of controllable distributed generation, P i + and P i - They represent the upward and downward frequency modulation powers of the controllable distributed power source respectively.

[0065] C load It represents the benefit of participating in microgrid frequency regulation by shedding loads. The expression is as follows:

[0066]

[0067] Where: σ i and η i They respectively represent the benefit function coefficients generated by the load participating in the frequency regulation of the microgrid.

[0068] 3): Establish the overall multi-objective optimization objective function C of the microgrid as shown below.

[0069] minC=|ff ref |+C s +C p -C load (6);

[0070] Step 3: Use the weighted summation method to convert the microgrid multi-objective optimization control model into a single-objective optimization control model. The specific process is as follows:

[0071] 1): Normalization of sub-optimization objective function;

[0072] Taking into account the inconsistency of the dimensions of the sub-optimization objective functions, in order to make the dimensions and orders of magnitude of the optimization objective functions the same, the following formula is used to normalize the sub-optimization objective functions.

[0073]

[0074] Where: F pu represents the normalized per-unit value of the sub-optimization objective function, F represents the sub-optimization objective function, and F max and F min Represent the maximum and minimum values of the sub-optimization objective function respectively.

[0075] 2): The multi-objective optimization and control model is converted into a single-objective optimization and control model;

[0076] After normalization, let λ1 and λ2 represent the weight coefficients of the sub-optimization objective functions C1 and C2 respectively, and the single-objective optimization control model of the microgrid can be obtained as shown below.

[0077] minC=λ1C 1,pu +λ2C 2,pu (8);

[0078] Where: C 1,pu Indicates the per-unit value of the frequency deviation optimization target, C 2,pu Represents the per-unit value of the system operation cost optimization objective function.

[0079] Step 4: Propose an optimization control method that adaptively adjusts the weight coefficients of different optimization objectives according to the frequency deviation interval. The specific steps are as follows:

[0080] 1) Determine the weights of different sub-optimization objectives in different frequency deviation intervals, as shown below;

[0081] When the frequency of the microgrid is in the range of slight frequency deviation, the optimization goal of the microgrid should consider the economic efficiency of its own operation more, and minimize the operating cost of the microgrid by coordinating the operation of each energy unit.

[0082] When the microgrid frequency is in the moderate frequency deviation range, the optimization goal of the microgrid mainly considers the microgrid frequency deviation, while taking into account the economic efficiency of the microgrid operation.

[0083] When the microgrid frequency is in the severe frequency deviation range, the optimization goal of the microgrid is to quickly restore the microgrid frequency deviation to near the rated value by cutting off the load and frequency regulation unit output. At this time, there is no need to consider the economic efficiency of the microgrid.

[0084] 2): Determine the adaptive control rules for the weight coefficients of different sub-optimization objectives, as shown below;

[0085] Based on the above analysis of the weights of different sub-optimization objectives in different frequency deviation ranges, three variables s1, s2, and s3 are defined to indicate whether the microgrid frequency is in the slight frequency deviation range, the moderate frequency deviation range, and the severe frequency deviation range, respectively. When the values of variables s1, s2, and s3 are 1, the microgrid frequency is in the corresponding range; otherwise, it is not in the range.

[0086] Then the adaptive control rules of the weight coefficients of different sub-optimization objectives can be obtained as follows:

[0087]

[0088] In order to prove the effectiveness of the method proposed in this invention, three strategies are set for verification:

[0089] Strategy 1: λ1 = 1, λ2 = 0, which only considers the frequency deviation and ignores the system operation cost;

[0090] Strategy 2: λ1 = 0, λ2 = 1, that is, only the system operation cost is considered, and the frequency deviation is not considered;

[0091] Strategy 3: Set weight coefficients and make adaptive adjustments.

[0092] The results obtained are as follows Figure 2 As shown, from Figure 2 As can be seen, Strategy 1 only considers frequency deviation and ignores system operating costs. Therefore, the frequency deviation is minimized throughout the simulation, but its operating costs are the highest. Strategy 2 only considers system operating costs and ignores frequency deviation. Therefore, its operating costs are minimized throughout the simulation, but its frequency deviation is the highest. Strategy 3, the adaptive adjustment strategy proposed in this paper, shows good economic efficiency. Although the frequency deviation is large, it remains within the range allowed by the system, and its operating costs are minimized, thus balancing frequency deviation and system frequency regulation costs.

Claims

1. Microgrid adaptive optimization control method based on frequency deviation, characterized by The following steps are involved: Step 1: Monitor the frequency of the microgrid in real time, calculate the difference between the frequency and the rated value, and partition the frequency deviation of the microgrid system based on the difference; Step 2: Considering the frequency deviation and system operation cost of the microgrid system, a multi-objective optimization control model of the microgrid is established; Step 3: Use the weighted summation method to convert the microgrid multi-objective optimization control model into a single-objective optimization control model, and set the weight coefficients of different optimization objectives; Step 4: Adaptively adjust the weight coefficients of different optimization objectives according to the frequency deviation interval to optimize the control of the microgrid.

2. The microgrid adaptive optimization control method based on frequency deviation according to claim 1 is characterized in that: In step 1, the method for partitioning the frequency deviation of the microgrid system is as follows: Compare the microgrid frequency with the rated value of 50Hz, and record the difference as △f. The microgrid frequency deviation is divided into zones according to the value range of △f, as follows: When |△f|≤0.1Hz, it is recorded as a slight frequency deviation interval; When 0.1Hz<|△f|≤0.4Hz, it is recorded as the moderate frequency deviation interval; When |△f|>0.4Hz, it is recorded as a severe frequency deviation interval.

3. The microgrid adaptive optimization control method based on frequency deviation according to claim 1 is characterized in that: In step 2, a multi-objective optimization control model of a microgrid is established, specifically as follows: The microgrid system frequency deviation optimization objective function C1 is defined as follows; C1=△f=|f-f ref | (1); In formula (1): f and f ref Represent the actual value and rated value of the microgrid system frequency respectively; The microgrid system operation cost optimization objective function C2 is defined as follows: C2=C s +C p -C load (2); In formula (2): C s represents the equipment operating cost function in the microgrid, including the operating costs of gas turbines, diesel generators, and fuel cells; C p represents the frequency regulation cost of controllable distributed generation participating in microgrid frequency regulation, including power storage units and fuel cells; C load It represents the benefit generated by participating in microgrid frequency regulation by shedding loads.

4. The microgrid adaptive optimization control method based on frequency deviation according to claim 3 is characterized by: The overall multi-objective optimization objective function C of the microgrid is established as follows: minC=|f-f ref |+C s +C p -C load (3)。 5. The microgrid adaptive optimization control method based on frequency deviation according to claim 4 is characterized in that: Considering the inconsistency of the dimensions of each sub-optimization objective function, in order to make the dimensions and magnitudes of the optimization objective functions the same, the following formula is used to normalize each sub-optimization objective function; In formula (4): F pu represents the normalized per-unit value of the sub-optimization objective function, F represents the sub-optimization objective function, and F max and F min Represent the maximum and minimum values of the sub-optimization objective function respectively.

6. The microgrid adaptive optimization control method based on frequency deviation according to claim 5 is characterized in that: After normalization, let λ1 and λ2 represent the weight coefficients of the sub-optimization objective functions C1 and C2 respectively, and the single-objective optimization control model of the microgrid can be obtained as shown below; minC=λ1C 1,pu +λ2C 2,pu (5); In formula (5): C 1,pu Indicates the per-unit value of the frequency deviation optimization target, C 2,pu Represents the per-unit value of the system operation cost optimization objective function.

7. The microgrid adaptive optimization control method based on frequency deviation according to claim 2 is characterized in that: The step 4 includes the following steps: S4.1: Determine the weight of different sub-optimization objectives in different frequency deviation intervals; S4.2: Determine the adaptive control rules for the weight coefficients of different sub-optimization objectives.

8. The microgrid adaptive optimization control method based on frequency deviation according to claim 7 is characterized in that: In S4.1, the weights of different sub-optimization objectives in different frequency deviation intervals are determined as follows: When the microgrid frequency is in the range of slight frequency deviation, the optimization goal of the microgrid should consider the economic efficiency of its own operation more, and minimize the operating cost of the microgrid by coordinating the operation of each energy unit; When the microgrid frequency is in the moderate frequency deviation range, the optimization goal of the microgrid mainly considers the microgrid frequency deviation while taking into account the economic efficiency of the microgrid operation; When the microgrid frequency is in the severe frequency deviation range, the optimization goal of the microgrid is to quickly restore the microgrid frequency deviation to near the rated value by cutting off the load and frequency regulation unit output. At this time, there is no need to consider the economic efficiency of the microgrid.

9. The microgrid adaptive optimization control method based on frequency deviation according to claim 7 is characterized in that: In S4.2, the adaptive control rules for the weight coefficients of different sub-optimization objectives are determined as follows: Based on the above analysis of the weights of different sub-optimization objectives in different frequency deviation intervals, three variables s1, s2, and s3 are defined to indicate whether the microgrid frequency is in the slight frequency deviation interval, the moderate frequency deviation interval, and the severe frequency deviation interval, respectively. When the variables s1, s2, and s3 take the value of 1, it means that the microgrid frequency is in the corresponding interval, otherwise it is not in the interval.

10. The microgrid adaptive optimization control method based on frequency deviation according to claim 9 is characterized in that: The adaptive control rules of the weight coefficients of different sub-optimization objectives are as follows:

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