Power distribution network load optimization regulation and control method based on micro-grid self-balancing

Abstract

The invention discloses a power distribution network load optimization regulation and control method based on micro-grid self-balancing, and relates to the technical field of power system optimization control, and the method comprises the following steps: obtaining the historical time sequence operation data of a micro-grid, and obtaining the power distribution network load optimization regulation and control data of the micro-grid based on the proportion limitation of the exchange electric quantity of the micro-grid and the power distribution network; determining a quantitative constraint relationship among the generating capacity, the power consumption and the exchanged power; based on the quantitative constraint relation, performing time sequence matching analysis on the annual power generation curve and the load curve, and determining a micro-grid source load configuration range; in the source load configuration range, performing optimization by taking the minimum energy storage capacity as a target, and determining the optimal source load ratio, the corresponding minimum energy storage capacity and the allowable maximum exchange electric quantity; and performing load peak clipping analysis based on the minimum energy storage capacity to obtain a maximum optimizable load proportion, and generating a micro-grid and power distribution network cooperative operation regulation and control strategy according to the proportion. According to the invention, the load optimization regulation and control of the power distribution network are accurately supported while the efficient operation of the micro-grid is ensured.

Description

Technical Field

[0001] This invention relates to the field of power system optimization control technology, and more specifically, to a method for optimizing and controlling the load of a distribution network based on microgrid self-balancing. Background Technology

[0002] With the large-scale integration of distributed energy sources, represented by photovoltaics, into the distribution network, microgrids based in industrial parks and communities have become important carriers for improving local energy consumption and enhancing power supply reliability. In regions such as Zhejiang, distributed power sources are mainly photovoltaics, whose output is characterized by significant intermittency and randomness, resulting in microgrids in these areas generally exhibiting weak self-balancing capabilities and strong operational fluctuations. When faced with scenarios such as continuous rainy weather or sudden load surges, microgrids have to rely heavily on the upstream distribution network for power support, which not only increases the regulation pressure on the distribution network but also restricts the efficient utilization of clean energy.

[0003] To promote the coordination between distribution networks and microgrids, existing technologies have initially introduced load analysis into microgrid planning and operation. However, the methods are relatively crude and have the following drawbacks: First, existing methods typically treat "self-balancing" as a vague operational objective, failing to translate it into a mathematical constraint that can be precisely applied during the planning phase. This leads to microgrid source-storage configuration schemes often being based on simple power balance calculations for typical days or single scenarios, failing to fully consider the time-series matching characteristics throughout the year and all time periods (8760 hours). As a result, in actual operation, the planned schemes frequently encounter situations where the power exchange with the distribution network exceeds the limits, rendering the self-balancing objective ineffective and necessitating additional reliance on the distribution network to provide substantial reserve capacity.

[0004] Secondly, due to the lack of precise quantification of the theoretical maximum control capability of microgrids under specific configurations, distribution network dispatch centers find it difficult to regard them as a knowable and controllable high-quality regulation resource. Existing methods cannot answer a key question: what is the maximum load reduction potential that a microgrid that meets self-balancing requirements can provide to the distribution network? This leads to overly conservative or unfounded coordinated control strategies, failing to fully exploit the flexible value of microgrids and resulting in low overall energy utilization efficiency.

[0005] To address the above problems, this invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a distribution network load optimization and control method based on microgrid self-balancing, so as to solve the problems of insufficient microgrid self-balancing capability, high dependence on distribution network and poor coordinated control effect caused by the lack of systematic analysis in the existing methods.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing and controlling the load of a distribution network based on microgrid self-balancing includes the following steps: acquiring historical time-series operation data of the microgrid; determining the quantitative constraint relationship between power generation, power consumption, and exchanged power based on the proportional limit of the power exchange between the microgrid and the distribution network; performing time-series matching analysis on the annual power generation curve and load curve based on the quantitative constraint relationship to determine the source-load configuration range of the microgrid; within the source-load configuration range, optimizing with the minimum energy storage capacity as the objective to determine the optimal source-load ratio and the corresponding minimum energy storage capacity and the maximum allowable exchanged power; performing load peak shaving analysis based on the minimum energy storage capacity to obtain the maximum optimizable load ratio, and generating a coordinated operation and control strategy for the microgrid and the distribution network based on this ratio.

[0008] In a preferred embodiment, the acquisition of historical time-series operation data of the microgrid includes the microgrid's electricity consumption, distributed power generation, energy storage charging and discharging, and the amount of electricity exchanged with the distribution network.

[0009] In a preferred embodiment, determining the quantitative constraint relationship between power generation, power consumption, and power exchange based on the proportional limit of power exchange between the microgrid and the distribution network specifically involves: converting the data of power generation, power consumption, and power exchange into percentage parameters relative to the total power consumption of the microgrid; establishing a balance relationship between the percentage parameters based on the energy conservation principle; applying the proportional limit to the balance relationship, i.e., the proportion of power exchange to total power consumption does not exceed a preset threshold; and, based on the balance relationship after applying the proportional limit, deriving the range of power generation percentage and power exchange percentage that satisfy the proportional limit conditions under different operating conditions, thus forming the quantitative constraint relationship system.

[0010] In a preferred embodiment, the quantitative constraint relationship includes: the power generation ratio is not lower than the lower limit threshold corresponding to the self-balancing preset threshold; the exchange power ratio does not exceed the upper limit threshold jointly determined by the power generation ratio and the ratio limit.

[0011] In a preferred embodiment, determining the microgrid source-load configuration range specifically includes: calculating the minimum source-load ratio that meets the minimum requirement of the ratio limit based on the constraint of the power generation ratio; selecting different candidate source-load ratio values ​​within the range greater than or equal to the minimum source-load ratio; fitting and superimposing the annual load curve and the distributed power generation output curve at each time step to obtain the net load curve under each candidate source-load ratio value; screening out all candidate source-load ratio values ​​that can make the actual exchange power ratio meet the constraint relationship; and determining the set of all screened candidate source-load ratio values ​​as the source-load configuration range.

[0012] In a preferred embodiment, the calculation of the minimum source-load ratio that satisfies the minimum requirement of the proportional limit specifically involves: statistically analyzing the cumulative power generation per unit capacity of distributed power sources and the cumulative electricity consumption per unit load of microgrids; establishing a proportional relationship equation between the installed capacity of distributed power sources and the load access capacity based on the principle of energy conservation, using the lower limit of power generation ratio as a constraint; solving the proportional relationship equation by combining the cumulative power generation and cumulative electricity consumption to obtain the source-load configuration ratio corresponding to the total power generation reaching the lower limit of the proportional limit within the statistical period, and determining the source-load configuration ratio as the minimum source-load ratio.

[0013] In a preferred embodiment, the step of optimizing within the source-load configuration range with the goal of minimizing energy storage capacity to determine the optimal source-load ratio and the corresponding minimum energy storage capacity specifically involves: within the allowable range of source-load configuration, minimizing energy storage capacity as the optimization objective, and satisfying the quantified constraint relationship as the boundary condition, calculating the energy storage capacity for each candidate source-load ratio; selecting the result with the minimum required energy storage capacity from the calculation results, determining the candidate source-load ratio corresponding to this result as the optimal source-load ratio, and determining the energy storage capacity under this result as the minimum energy storage capacity.

[0014] In a preferred embodiment, the maximum allowable exchange capacity is determined as follows: based on the optimal source-load ratio, the corresponding net load curve is obtained; based on the net load curve and in conjunction with the upper limit condition for exchange capacity in the quantification constraint relationship, the maximum allowable exchange capacity is calculated.

[0015] In a preferred embodiment, the step of performing load peak shaving analysis based on minimum energy storage capacity to obtain the maximum optimizable load ratio specifically involves: analyzing the peak shaving capability of energy storage participation in regulation on the microgrid load curve based on the minimum energy storage configuration to obtain the equivalent load curve after energy storage regulation; comparing the equivalent load curve with the original net load curve before regulation to identify the target period with the largest load peak decrease, and calculating the maximum load reduction during that period; and calculating the maximum optimizable load ratio based on the ratio of the maximum load reduction to the original load peak before regulation.

[0016] In a preferred embodiment, the generation of the microgrid and distribution network coordinated operation and control strategy specifically involves: quantifying the maximum load regulation capacity that the microgrid can safely participate in the coordinated regulation of the distribution network based on the maximum optimizable load ratio; determining the power interaction constraints between the microgrid and the distribution network using the maximum load regulation capacity as the coordinated operation boundary; and generating a load optimization and control strategy that includes regulation time, power limits, and energy storage operation constraints based on the power interaction constraints.

[0017] The technical effects and advantages of the distribution network load optimization and control method based on microgrid self-balancing proposed in this invention are as follows: 1. This invention determines the quantitative constraint relationship between power generation, power consumption, and exchanged power by limiting the proportion of power exchanged between the microgrid and the distribution network. Based on the quantitative constraint relationship, it performs time-series matching analysis on the annual power generation curve and load curve to determine the source-load configuration range of the microgrid. This enables the microgrid to meet the self-balancing requirements during the planning and operation phases, reduces the microgrid's dependence on the backup support of the distribution network, and improves the local consumption capacity and operational stability of distributed energy.

[0018] 2. This invention optimizes the source-load ratio and the corresponding minimum energy storage capacity within the source-load configuration range, and determines the maximum allowable exchange power by targeting the minimum energy storage capacity. Based on the minimum energy storage capacity, load peak shaving analysis is performed to obtain the maximum optimizable load ratio. This transforms the microgrid's controllability from empirical judgment to calculable parameters, providing a clear, safe, and executable control boundary for the coordinated operation of the distribution network and microgrid, thereby improving the overall control efficiency and security of the distribution network. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a distribution network load optimization and control method based on microgrid self-balancing according to the present invention. Detailed Implementation

[0020] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1, Figure 1 This invention presents a method for optimizing and controlling the load of a distribution network based on microgrid self-balancing, comprising the following steps: S1, acquire historical time-series operation data of the microgrid, and determine the quantitative constraint relationship between power generation, power consumption and exchanged power based on the ratio limit of power exchange between the microgrid and the distribution network; It should be noted that, in this invention, the exchanged electricity between the microgrid and the distribution network refers to the net electricity exchange between the microgrid and the distribution network within the statistical period. This includes both the electricity sent from the microgrid to the distribution network and the electricity received from the distribution network to the microgrid. To maintain a consistent calculation method, the exchanged electricity described herein is preferably calculated using the absolute value of the net exchanged electricity, and this value is used as an evaluation indicator for self-balancing constraints and capacity configuration.

[0022] S101, in this embodiment, obtaining historical time-series operation data of the microgrid specifically involves: obtaining historical time-series operation data from the past year through an energy management system (EMS) or smart meters, with a time resolution of 1 hour, totaling 8760 time points. The operation data specifically includes: microgrid electricity consumption. Distributed power generation Energy storage charge and discharge capacity (charge and discharge power through the energy storage system) (kW) sequence representation. During charging. (Negative during operation, positive during discharge) and the exchanged electricity between the microgrid and the distribution network, wherein the exchanged electricity includes the electricity supplied and the electricity fed into the grid, and the electricity obtained by the microgrid from the distribution network is the electricity supplied. The electricity fed back from the microgrid to the distribution network is the electricity fed into the grid. .

[0023] S102, in this embodiment, the quantitative constraint relationship between power generation, power consumption, and exchanged power is determined based on the proportional limit of power exchange between the microgrid and the distribution network. Specifically: It should be noted that, in this embodiment, the ratio limit is set according to the regional grid connection technology regulations, that is, the ratio of the total energy exchanged between the microgrid and the distribution network (i.e., the sum of the power supplied and the power fed into the grid) to its total power consumption shall not exceed a preset threshold. In this embodiment, .

[0024] Calculate the total annual electricity consumption of the microgrid From this, we can obtain the proportion of power generation f, the proportion of power supply g, and the proportion of power grid connection s.

[0025] According to the law of conservation of energy, the electricity consumption of a microgrid is met by its own power generation and by drawing electricity from the grid, with any surplus electricity being fed back into the grid. The total amount of electricity generated can be distributed as follows:

[0026] Substituting the above percentage definitions, we obtain the core balance equation: Formula 1 The ratio limit This can be expressed as: Formula 2 Equation 1 ( Substituting into equation 2, we get:

[0027] Considering that in actual operation, power supply and grid connection usually do not occur simultaneously, combined with Given the non-negativity constraint, we need to discuss the above inequality in two cases: when the microgrid has net electricity consumption and no grid connection or zero grid connection (… From equation 2, we get Substituting into equation 1, we get When the microgrid has net grid connection, there is no power supply or the power supply is 0 ( From equation 2, we get Substituting into equation 1, we get Therefore, both conditions must be met simultaneously. and .

[0028] Therefore, the necessary condition (i.e., the quantitative constraint relationship) for satisfying the proportional limit can be derived as follows: The power generation ratio f is not lower than the lower limit threshold corresponding to the self-balancing preset threshold, that is... ; The proportion of exchanged electricity does not exceed the upper limit threshold jointly determined by the proportion of electricity generated and the ratio limit, i.e. And because This constraint is in It only has practical significance at that time.

[0029] This invention transforms the operational management regulation of limiting the proportion of exchanged electricity into a precise inequality constraint that can be directly used in mathematical programming, through the principle of energy conservation and derivation under different operating conditions. In existing technologies, "self-balancing" is only used as a qualitative goal or empirical concept, lacking quantitative tools. This invention creatively introduces three proportion parameters (f, g, s), establishes the relationship g + f = 1 + s, and, given |g + s| ≤ α, derives two constraints with clear physical meaning through mathematical derivation (distinguishing between power supply g > 0 and grid connection s > 0 operating conditions): and Provides for subsequent calculations. S2, based on quantitative constraint relationships, performs time-series matching analysis on the annual power generation curve and load curve to determine the source-load configuration range of the microgrid; In this embodiment, determining the microgrid source-load configuration range specifically includes: S21, the calculation of the minimum source-to-load ratio that satisfies the minimum requirement of the proportional limitation is specifically as follows: From S102, the constraint condition for the power generation ratio is: This involves calculating the cumulative power generation per unit capacity of distributed power sources and the cumulative electricity consumption per unit load of a microgrid. Let the cumulative power generation per unit capacity of distributed photovoltaic power in a microgrid (e.g., 1 kW) within the statistical period (one year) be... The cumulative electricity consumption per unit load (e.g., 1kW) of a microgrid during the same period is The source-to-load ratio k represents the installed capacity of distributed power sources. With microgrid rated load The ratio. Based on the principle of energy conservation, establish the installed capacity of distributed power sources. With the rated load capacity of the microgrid The proportional relationship equation between them:

[0030] Let the annual electricity consumption per unit capacity be... ,but To meet Required:

[0031] This allows us to calculate the minimum source-load ratio required to meet the lower limit of the power generation ratio. .

[0032] S22, Select different candidate source-load ratio values ​​within the range that are greater than or equal to the lowest source-load ratio. For each candidate source-charge ratio Obtain the original load curve of the microgrid throughout the year. Compared with the original output curve of distributed power generation (photovoltaics) The net load power value at each time point t is obtained by fitting and superimposing the data at each time point t using the following formula (t=1 to 8760). :

[0033] From all moments The curve formed represents the candidate source-to-charge ratio. Net load curve below .

[0034] S23, calculate the actual exchanged electricity percentage. (This refers to the net load curve.) The absolute value of all negative values ​​(i.e., power surplus) is integrated over the statistical period to obtain the theoretical total surplus power. Without energy storage, this electricity will entirely become the electricity exchanged with the distribution network (the portion connected to the grid). Therefore, the actual percentage of electricity connected to the grid is... It can be estimated as follows:

[0035] Obtain the candidate source-to-charge ratio. Corresponding percentage of power generation ,Will and Substitute the aforementioned quantitative constraint relationship into the judgment, that is, check whether it is satisfied simultaneously. and If both of the above conditions are met simultaneously, then the candidate source-to-charge ratio is... Those that ensure the actual exchanged power ratio meets the constraints are retained. The set of all selected candidate source-load ratios is determined as the source-load configuration range.

[0036] Existing technologies typically employ calculations based on a single typical day or simple capacity matching, neglecting the year-round temporal fluctuations. This invention first derives, based on the inequality f≥α from the first part, the minimum source-load ratio required to meet the annual total power generation requirement. This established the first screening threshold. Secondly, it creatively addressed... Each of the above candidate configurations A time-series simulation of 8760 hours was conducted throughout the year. The photovoltaic and load curves were superimposed hourly to obtain the net load curve, and the proportion of naturally generated grid-connected electricity was calculated. Ultimately, it passed verification. Does it meet the requirements? This method filters out all configurations that can guarantee no exceedances during actual operation, forming the source-load configuration range. This approach verifies both the long-term power adequacy and short-term power compliance of the configuration schemes during the planning phase, resulting in schemes that possess both static rationality and dynamic adaptability.

[0037] S3, within the range of source-load configuration, optimizes the minimum energy storage capacity to determine the optimal source-load ratio and the corresponding minimum energy storage capacity and the maximum allowable exchange power. In this embodiment, the step of optimizing within the source-load configuration range with the goal of minimizing energy storage capacity to determine the optimal source-load ratio and the corresponding minimum energy storage capacity specifically involves: Within the allowable range of the source-load configuration, the rated power of the energy storage system is... Rated capacity is If a fixed discharge duration (e.g., 2 hours) is set as the decision variable, then... .

[0038] The optimization objective is to minimize energy storage capacity, typically targeting rated power or rated energy. .

[0039] The constraints include physical constraints and quantitative constraints for energy storage operation. The physical constraints for energy storage operation include power constraints and energy (SOC) continuity and boundary constraints.

[0040]

[0041]

[0042] in, The energy output at time t is the energy stored (positive for discharging, negative for charging). (While charging) or (During discharge) and For charging and discharging efficiency, The state of charge (SCC) range is the lower and upper limits set for battery SCC operation to ensure battery life and safety. .

[0043] After energy storage participates in regulation, the proportion of electricity exchanged between the system and the distribution network must meet the upper limit constraint determined in the aforementioned quantitative constraint relationship. This requires increasing the output of energy storage. Superimposed on the net load curve Above, the adjusted exchange power curve is formed:

[0044] Calculate the actual on-grid power ratio corresponding to the adjusted switching power curve, and ensure that it meets the following requirements: .

[0045] The above model is solved using optimization algorithms (such as linear programming, mixed integer programming, or intelligent optimization algorithms) to obtain the candidate source-load ratio values. Minimum rated energy storage power required to meet all constraints And the corresponding energy storage configuration scheme.

[0046] For all candidate source-charge ratio values After calculating the minimum rated power of energy storage, a set of corresponding minimum energy storage capacity results are obtained. All results are iterated through, and the minimum rated power of energy storage is selected. Let the value be the smallest, such that The candidate source charge ratio that yields the global minimum is ,Will The optimal source-to-load ratio is determined, and its corresponding minimum energy storage capacity is: .

[0047] It should be noted that the minimum energy storage capacity is usually expressed as a power value, and its corresponding energy capacity can be determined based on the rated discharge duration.

[0048] In this embodiment, the maximum allowable exchange capacity is determined as follows: Based on the optimal source-load ratio Obtain the corresponding net load curve Extracting from quantitative constraints Upper limit of the proportion of exchanged power : ,in .

[0049] Maximum battery power allowed for internet access Determined by the upper limit of the percentage and the total electricity consumption:

[0050] At the operational level, the maximum allowable exchange capacity is typically expressed as a power limit. Therefore, it is necessary to... This translates into a reasonable power constraint. One approach is to analyze the net load curve. The statistical characteristics of the negative portion (surplus power) ensure that, within any reasonable time period, the cumulative on-grid electricity consumption does not exceed [a certain value]. A simpler and safer approach is to convert it into a continuous power cap. ,For example:

[0051] in, This is an agreed-upon permitted internet access time (such as the total number of hours per year multiplied by a coefficient, or the total duration during peak periods). Ultimately, this will be... (or rules containing its calculation logic) are used to determine the maximum allowable exchange capacity.

[0052] In existing technologies, energy storage configuration often relies on experience or independent economic calculations, which are disconnected from the system's self-balancing requirements, easily leading to over-configuration or under-configuration. This invention establishes a mathematical model within the source-load configuration range, with minimizing energy storage capacity as the optimization objective and quantitative constraints as insurmountable boundaries. Through system optimization, it automatically solves for a unique optimal solution (optimal source-load ratio, minimum energy storage capacity) that simultaneously satisfies self-balancing requirements and minimizes investment, and simultaneously derives its corresponding operating boundary (maximum allowable exchange capacity). This achieves global economic automatic optimization of the system under the strict premise of meeting the annual self-balancing hard constraint. The optimization process automatically generates the key operating parameter, the maximum allowable exchange capacity. This means that the safe operating boundary has been automatically and accurately calculated, directly providing setpoints for the operation control system, completely breaking down the data barriers between planning and operation.

[0053] S4 performs load peak shaving analysis based on the minimum energy storage capacity to obtain the maximum optimizable load ratio, and generates a coordinated operation and control strategy for microgrid and distribution network based on this ratio.

[0054] In this embodiment, the process of performing load peak shaving analysis based on the minimum energy storage capacity to obtain the maximum optimizable load ratio specifically involves: Minimum energy storage capacity and its corresponding rated energy (Usually calculated based on a 2-hour discharge duration) and the original net load curve As input, the charging and discharging process of the energy storage system is simulated with the goal of reducing peak net load. In the simulation, the charging and discharging power of the energy storage system must not exceed [a certain limit]. Furthermore, its state of charge (SOC) needs to be maintained within a safe range, resulting in an optimized energy storage charge and discharge power sequence. Then, the equivalent load curve after energy storage regulation is calculated. This curve reflects the actual power demand of the microgrid on the distribution network after the intervention of energy storage. The calculation formula is as follows:

[0055] The equivalent load curve Compared with the original net load curve By comparing data from different time periods, the period with the largest load decrease is identified and denoted as... This period is when the peak-shaving effect of energy storage is most significant. During this target period... Calculate the maximum load reduction. Its value is the original net load peak value. With equivalent load The difference (in kW) represents the maximum power reduction capability that the microgrid can provide to the distribution network in a single instance.

[0056] Calculate the maximum load reduction Peak value of the original net load curve (Original net load curve before adjustment) The maximum optimizable load ratio is obtained by taking the proportion of the maximum value over the entire statistical period.

[0057] in, A dimensionless ratio between 0 and 1 represents the maximum relative reduction capacity that the energy storage system can provide relative to the microgrid's original maximum net load. For example, This indicates that the microgrid can reduce its peak net load by up to 15%.

[0058] In this embodiment, the coordinated operation and control strategy for generating microgrids and distribution networks specifically includes: The calculated maximum optimizable load ratio This is converted into a specific power capacity value, namely the maximum load regulation capacity that the microgrid can safely participate in the coordinated regulation of the distribution network. :

[0059] Adjusting capacity based on the maximum load To establish a collaborative operation boundary, power interaction constraints between the microgrid and the distribution network are defined. This constraint specifies the microgrid's response boundary when the distribution network issues a support request: when the distribution network requires load shedding (peak shaving) support from the microgrid, the microgrid promises to provide a maximum shedding power not exceeding [a certain threshold]. Similarly, the maximum load increase power constraint can be defined based on the remaining charging capacity of energy storage and the load increase space. The determination method is similar to peak shaving, but may be based on different scenario analyses.

[0060] The process involves generating a load optimization and control strategy based on the power interaction constraints, including adjustment time, power limits, and energy storage operation constraints. Specifically, based on historical data analysis, it specifies which date types (e.g., weekdays, summer) and which hour periods (e.g., 10:00-16:00) the microgrid energy storage system possesses effective peak-shaving capabilities, and stipulates the maximum allowable delay (e.g., ≤15 minutes) from receiving the distribution network dispatch command to starting to output regulating power. This is explicitly written into the code. The specific values ​​are specified (e.g., maximum peak shaving support power is 200kW), and to ensure equipment safety, a power ramp-up rate limit (e.g., ≤100kW / min) is stipulated for energy storage during the response process. It is also stipulated that the state of charge of the energy storage system must remain constant before, during, and after the execution of the coordinated regulation task. Within the range, based on energy storage capacity And SOC window, calculate and agree on the total energy limit for a single continuous adjustment or daily cumulative adjustment (e.g., the maximum duration of a single peak reduction is 2 hours or the daily cumulative peak reduction power is ≤400kWh).

[0061] This invention, based on a predetermined minimum energy storage capacity as the optimal configuration, simulates its operation in peak-shaving mode, accurately calculating the maximum load reduction and maximum optimizable load ratio that the microgrid can provide to the distribution network. Furthermore, these capability indicators are transformed into a coordinated operation and control strategy incorporating specific power, time, and energy limits, thereby improving the overall operating efficiency of the regional power grid and the level of renewable energy absorption.

[0062] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0063] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0064] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0065] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0066] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0067] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing and controlling the load of a distribution network based on microgrid self-balancing, characterized in that, Includes the following steps: Historical time-series operation data of microgrids are obtained, and quantitative constraint relationships between power generation, power consumption and exchanged power are determined based on the proportional limit of power exchange between microgrids and distribution networks. Based on quantitative constraints, a time-series matching analysis of the annual power generation curve and load curve is performed to determine the source-load configuration range of the microgrid. Within the range of source-load configuration, optimization is carried out with the minimum energy storage capacity as the target to determine the optimal source-load ratio and the corresponding minimum energy storage capacity and the maximum allowable exchange power. Load shaving analysis is performed based on the minimum energy storage capacity to obtain the maximum optimizable load ratio, and a coordinated operation and control strategy for microgrids and distribution networks is generated based on this ratio.

2. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 1, characterized in that, The acquisition of historical time-series operation data of the microgrid includes the microgrid's electricity consumption, distributed power generation, energy storage charging and discharging, and the amount of electricity exchanged with the distribution network.

3. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 2, characterized in that, The quantitative constraint relationship between power generation, power consumption, and exchanged power is determined based on the proportional limit of power exchange between the microgrid and the distribution network, specifically as follows: The data on power generation, power consumption, and power exchange are converted into a percentage parameter relative to the total power consumption of the microgrid. Based on the law of conservation of energy, a balance relationship is established between the aforementioned percentage parameters; The proportional limit is applied to the balance relationship, that is, the proportion of exchanged electricity to total electricity consumption does not exceed a preset threshold. Based on the balance relationship after applying proportional constraints, and combined with different operating conditions, the range of power generation ratio and power exchange ratio that meet the proportional constraints is derived, thus forming the quantitative constraint relationship.

4. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 3, characterized in that, The quantization constraint relationships include: The proportion of power generation is not lower than the lower limit threshold corresponding to the preset self-balancing threshold; The percentage of exchanged electricity does not exceed the upper limit threshold determined jointly by the percentage of electricity generated and the ratio limit.

5. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 4, characterized in that, Determining the microgrid source-load configuration range specifically includes: Based on the constraint of the power generation ratio, calculate the minimum source-load ratio that meets the minimum requirement of the ratio limit; Different candidate source load ratio values ​​are selected within the range of the minimum source load ratio. The load curve and the distributed power output curve for the whole year are fitted and superimposed on time-by-time to obtain the net load curve under each candidate source load ratio value. All candidate source-load ratios that can satisfy the constraint relationship for the actual exchange power ratio are selected, and the set of all selected candidate source-load ratios is determined as the source-load configuration range.

6. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 5, characterized in that, The calculation of the minimum source-to-load ratio that satisfies the minimum requirement of the proportional limit is specifically as follows: The cumulative power generation per unit capacity of distributed power sources and the cumulative electricity consumption per unit load of microgrids are statistically analyzed. Using the lower limit of the proportion of power generation as a constraint, and based on the principle of energy conservation, a proportional relationship equation between the installed capacity of distributed power generation and the load access capacity is established. By combining the cumulative power generation and cumulative power consumption, the proportional correlation equation is solved to obtain the source-load configuration ratio corresponding to the lower limit of the proportion when the total power generation reaches the lower limit within the statistical period. The source-load configuration ratio is then determined as the minimum source-load ratio.

7. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 6, characterized in that, Within the source-load configuration range, optimization is performed with the minimum energy storage capacity as the objective to determine the optimal source-load ratio and the corresponding minimum energy storage capacity. Specifically: Within the allowable range of source-load configuration, with minimizing energy storage capacity as the optimization objective and satisfying the quantified constraint relationship as the boundary condition, the energy storage capacity of each candidate source-load ratio is calculated. The result with the smallest required energy storage capacity is selected from the calculation results, and the candidate source-load ratio corresponding to the result is determined as the optimal source-load ratio, and the energy storage capacity under the result is determined as the minimum energy storage capacity.

8. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 7, characterized in that, The method for determining the maximum allowable exchange capacity is as follows: Based on the optimal source-load ratio, the corresponding net load curve is obtained; Based on the net load curve and the upper limit condition for the exchanged power in the quantitative constraint relationship, the maximum allowable exchanged power is calculated.

9. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 8, characterized in that, The load peak shaving analysis based on the minimum energy storage capacity yields the maximum optimizable load ratio, specifically as follows: Based on the minimum energy storage configuration, the peak shaving capability of energy storage in regulating the microgrid load curve is analyzed, and the equivalent load curve after energy storage regulation is obtained. The equivalent load curve is compared with the original net load curve before adjustment to identify the target period with the largest drop in load peak and to calculate the maximum load reduction during that period. The maximum optimizable load ratio is calculated based on the ratio of the maximum load reduction to the original peak load before adjustment.

10. The method for optimizing and controlling distribution network load based on microgrid self-balancing according to claim 9, characterized in that, The specific strategy for coordinated operation and control of the generated microgrid and distribution network is as follows: Based on the maximum optimizable load ratio, the maximum load regulation capacity of the microgrid that can safely participate in the coordinated regulation of the distribution network is quantified. Using the maximum load regulation capacity as the collaborative operation boundary, the power interaction constraints between the microgrid and the distribution network are determined; Based on the power interaction constraints, a load optimization and control strategy is generated that includes adjustment time, power limits, and energy storage operation constraints.

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