Coordinated optimization method for distributed new energy consumption capacity of load concentration area power grid

Abstract

The application discloses a load concentration area power grid distributed new energy consumption capacity collaborative optimization method, and belongs to the field of power grid operation control and optimal scheduling, which comprises the following steps: acquiring and credibility processing real-time data; dynamically evaluating the peak regulation safety margin gap and the equipment thermal safety margin constraint value; constructing and solving a multi-objective collaborative optimization model with the safety margin gap, new energy consumption capacity and system cost as the target, generating a regulation and control strategy containing distributed new energy, energy storage and controllable flexible load; monitoring the gap and triggering dynamic regulation and control; credit rating of controllable resources, and the results are fed back to the optimization model for dynamic adjustment of the weight. The application adopts a multi-dimensional safety margin sensing, high credibility data fusion and resource credit feedback mechanism, performs closed-loop self-adaptive adjustment on the collaborative optimization model, can accurately grasp the safety boundary of the power grid, fully excavate the potential of the flexible load, and improves the consumption capacity and operation reliability of the regional power grid to the distributed new energy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power grid operation control and optimal scheduling, in particular to a method for collaborative optimization of distributed new energy consumption capacity in a load concentration area power grid. BACKGROUND

[0002] A load concentration area power grid refers to a distribution network or local area network with high power load density and a large number of users within a specific geographical range. With the rapid development of distributed new energy, especially distributed photovoltaic and wind power, a large number of such power sources are connected to the load concentration area power grid, bringing new challenges to the planning, operation and control of the power grid. In order to manage these distributed resources and ensure the stability of the power grid, advanced power grid scheduling and optimization control systems are usually required, which belong to the category of power supply and distribution system control technology.

[0003] In existing technical solutions, in order to cope with the volatility of distributed new energy, scheduling methods based on power flow calculation and static security constraints are usually used. These methods will develop power generation and power consumption plans according to the next day's new energy output and load forecast. When deviations occur in actual operation, passive adjustments such as reducing new energy output or starting standby units are made. Some advanced methods also attempt to use energy storage or demand side response resources, but usually they are called as independent backup resources, lacking coordination among multiple types of resources.

[0004] There are inherent defects in the existing technical solutions. First, the operation data relied on are often not subjected to credibility screening, and communication interference or equipment failure in the data collection process may lead to decision errors. Second, the safety constraints used are usually static thresholds based on design constants, which fail to consider the impact of real-time working conditions such as environmental temperature on the current-carrying capacity of equipment, resulting in inaccurate safety margin evaluation. In addition, the optimization objective is relatively single, often focusing on economy while ignoring the coordinated improvement of safety margin and new energy consumption potential, and lacking a mechanism for effectively evaluating and utilizing the response performance of the resources involved in the regulation. SUMMARY

[0005] To solve the above problems, the present application provides a method for collaborative optimization of distributed new energy consumption capacity in a load concentration area power grid, which adopts an adaptive regulation system constructed from data credibility processing, multi-dimensional safety margin evaluation, multi-objective collaborative optimization to credit loop feedback, which can realize the dynamic balance and continuous optimization of power grid safety, economy and new energy consumption.

[0006] The above objectives can be achieved by the following solutions:

[0007] A collaborative optimization method for the distributed renewable energy absorption capacity of a power grid in a load-concentrated area includes: acquiring real-time operating data of the load-concentrated area and performing credibility processing on the real-time operating data to generate credibility-weighted data, wherein the credibility-weighted data consists of load data, renewable energy output prediction data, and ambient temperature and equipment load data; dynamically evaluating the multi-dimensional safety margin of the power grid based on the credibility-weighted data to generate a safety margin assessment result, wherein the safety margin assessment result includes peak-shaving safety margin gap and equipment thermal safety margin constraint values; and constructing and solving a method based on the safety margin assessment result, using the peak-shaving safety margin gap and renewable energy output prediction data as the basis for optimization. A multi-objective collaborative optimization model is used, with energy consumption and total system operating cost as optimization objectives and equipment thermal safety margin constraints as constraints. This model generates an optimized control strategy that includes distributed renewable energy output commands, energy storage control commands, and controllable flexible load control commands. The model monitors the peak-shaving safety margin gap and triggers a collaborative dynamic control mode when the gap exceeds a preset threshold, performing control actions according to the optimized control strategy. It also assesses the creditworthiness of controllable resources participating in the control and generates creditworthiness assessment results. These results are then fed back to the multi-objective collaborative optimization model, dynamically adjusting its weight parameters.

[0008] Optionally, the step of acquiring real-time operational data of the load-concentrated area and performing credibility processing on the real-time operational data to generate credibility-weighted data includes: acquiring communication signal strength parameters in the real-time operational data and calculating data deviation compensation values; using distributed ledger technology to perform multi-party verification on the real-time operational data and generating data verification results; and based on the data deviation compensation values ​​and data verification results, performing weighted fusion on the real-time operational data to generate credibility-weighted data.

[0009] Optionally, the dynamic assessment of the multidimensional safety margin of the power grid and the generation of safety margin assessment results include: calculating the required negative reserve capacity threshold and actual negative reserve capacity of the power grid based on the load data and new energy output prediction data in the credibility-weighted data to obtain the peak-shaving safety margin gap; predicting the maximum allowable temperature of key equipment based on the ambient temperature and equipment load data in the credibility-weighted data through an equipment temperature model to obtain the equipment thermal safety margin constraint value; and combining the peak-shaving safety margin gap and the equipment thermal safety margin constraint value to generate the safety margin assessment result.

[0010] Optionally, the construction and solution of a multi-objective collaborative optimization model with peak-shaving safety margin gap, renewable energy consumption, and total system operating cost as optimization objectives and equipment thermal safety margin constraints as constraints, generating an optimized control strategy that includes distributed renewable energy output commands, energy storage control commands, and controllable flexible load control commands, includes: constructing a multi-objective function with renewable energy consumption, peak-shaving safety margin gap, and total system operating cost as optimization objectives; forming a multi-objective collaborative optimization model with power flow balance constraints, voltage safety constraints, equipment thermal stability threshold constraints, and energy storage and controllable flexible load control capability constraints as constraints; and solving the multi-objective collaborative optimization model using a multi-objective optimization algorithm to obtain the optimized control strategy.

[0011] Optionally, the step of using a multi-objective optimization algorithm to solve the multi-objective collaborative optimization model includes: initializing a particle swarm, where each particle represents a control scheme; calculating the fitness value of each particle based on the multi-objective function and constraints; finding a Pareto optimal solution set by iteratively updating the particle position and velocity; and selecting an optimization scheme from the Pareto optimal solution set as the optimization control strategy.

[0012] Optionally, monitoring the peak-shaving safety margin gap and triggering a collaborative dynamic control mode when the peak-shaving safety margin gap exceeds a preset threshold, and performing control actions according to the optimized control strategy, includes: real-time monitoring of the peak-shaving safety margin gap and generating a trigger signal when the peak-shaving safety margin gap exceeds a preset threshold; extracting energy storage control instructions and controllable flexible load control instructions from the optimized control strategy based on the trigger signal; and controlling the energy storage system and controllable flexible load to perform control actions according to the energy storage control instructions and controllable flexible load control instructions.

[0013] Optionally, the control of the energy storage system and the controllable flexible load to perform regulation actions includes: controlling the energy storage system to perform charging and discharging operations within a preset charging and discharging power limit and state of charge range based on the energy storage regulation command; acquiring the operating status data and user preference data of the controllable flexible load; calculating the regulation power and time of each flexible load unit according to the controllable flexible load regulation command, combined with the operating status data and user preference data; and sending control signals to the controllable flexible load through a communication network to perform load-side regulation operations.

[0014] Optionally, the step of assessing the creditworthiness of controllable resources involved in regulation and generating creditworthiness assessment results includes: obtaining actual and planned output data of resources during the regulation process, and calculating regulation accuracy indicators; assessing the contribution of resource regulation to economic efficiency and security, and generating economic and security indicators; and generating creditworthiness assessment results for each resource by combining the regulation accuracy indicators, economic indicators, and security indicators through a creditworthiness calculation model.

[0015] Optionally, the credit rating assessment results are fed back to the multi-objective collaborative optimization model, and the weight parameters of the multi-objective collaborative optimization model are dynamically adjusted, including: adjusting the scheduling priority weight of resources in the multi-objective collaborative optimization model according to the credit rating assessment results; adaptively adjusting the weight coefficients of each optimization objective in the multi-objective function based on historical regulation effect data, and updating the multi-objective collaborative optimization model using the adjusted weight parameters.

[0016] Based on the same inventive concept, this invention also provides a collaborative optimization system for the distributed renewable energy absorption capacity of a power grid in a load-concentrated area. The system includes: a data acquisition and reliability processing module, used to acquire real-time operating data of the load-concentrated area and perform reliability processing on the real-time operating data to generate reliability-weighted data, wherein the reliability-weighted data consists of load data, renewable energy output prediction data, and ambient temperature and equipment load data; a safety margin assessment module, used to dynamically assess the multi-dimensional safety margin of the power grid based on the reliability-weighted data and generate a safety margin assessment result, wherein the safety margin assessment result includes peak-shaving safety margin gap and equipment thermal safety margin constraint values; and a multi-objective optimization module, used to construct... A multi-objective collaborative optimization model is constructed and solved, with the peak-shaving safety margin gap, renewable energy consumption, and total system operating cost as optimization objectives and the equipment thermal safety margin constraint value as a constraint condition. This model generates an optimized control strategy that includes distributed renewable energy output commands, energy storage control commands, and controllable flexible load control commands. A dynamic control triggering module monitors the peak-shaving safety margin gap and triggers a collaborative dynamic control mode when the gap exceeds a preset threshold, performing control actions according to the optimized control strategy. A credit rating assessment module evaluates the credit rating of controllable resources participating in the control and generates a credit rating assessment result. A feedback adjustment module feeds back the credit rating assessment result to the multi-objective collaborative optimization model, dynamically adjusting the weight parameters of the model.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention constructs a more accurate and realistic power grid operation boundary by performing credibility processing on real-time data and dynamically evaluating multi-dimensional safety margins such as power grid peak shaving and equipment thermal safety. This ensures that all optimized control strategies are based on reliable data and dynamic safety constraints, thereby improving the safety and stability of power grid operation.

[0019] 2. This invention constructs a multi-objective collaborative optimization model with safety margin, renewable energy consumption and total operating cost as objectives, and coordinates and regulates diverse heterogeneous resources such as distributed power sources, energy storage and controllable flexible loads, to achieve a comprehensive balance between power grid security, economy and environmental protection. Compared with traditional single-objective optimization methods, it can more effectively tap into system flexibility, maximize renewable energy consumption and reduce total operating costs while ensuring safety.

[0020] 3. This invention introduces a closed-loop mechanism that evaluates the creditworthiness of resources involved in regulation and feeds it back to the optimization model. This enables the entire optimization and regulation system to have the ability to learn and adapt itself. It can dynamically select high-reliability resources and calibrate the focus of the optimization strategy based on historical regulation effects, thereby achieving long-term performance iteration improvement and dynamic adaptation to changes in the power grid environment.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the method for collaborative optimization of distributed renewable energy absorption capacity in power grids in areas with concentrated loads, according to an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the Pareto optimal solution set for multi-objective optimization according to an embodiment of the present invention.

[0025] Figure 3 This is a comparison chart of the dynamic changes and control effects of the peak-shaving safety margin gap in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the structure of the distributed renewable energy absorption capacity collaborative optimization system of the power grid in the load-concentrated area according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0028] Reference Figure 1 One embodiment of the present invention proposes a collaborative optimization method for the distributed renewable energy absorption capacity of power grids in areas with concentrated loads. The method employs multi-dimensional safety margin perception, high-reliability data fusion, and resource credit feedback mechanism to perform closed-loop adaptive adjustment of the collaborative optimization model. This method can accurately grasp the boundary of power grid safe operation, fully explore the potential of controllable flexible loads, and improve the absorption capacity and operational reliability of power grids in areas with concentrated loads for distributed renewable energy.

[0029] The method described in this embodiment specifically includes:

[0030] Real-time operating data of the load concentration area is acquired, and the real-time operating data is processed for credibility to generate credibility-weighted data. The credibility-weighted data consists of load data, new energy output prediction data, and ambient temperature and equipment load data.

[0031] Based on the aforementioned credibility-weighted data, the multidimensional security margin of the power grid is dynamically evaluated, and a security margin evaluation result is generated. The security margin evaluation result includes the peak-shaving security margin gap and the equipment thermal security margin constraint value.

[0032] Based on the safety margin assessment results, a multi-objective collaborative optimization model is constructed and solved with the peak-shaving safety margin gap, renewable energy consumption and total operating cost as optimization objectives and the equipment thermal safety margin constraint value as constraint condition. This model generates an optimized control strategy that includes distributed renewable energy output commands, energy storage control commands and controllable flexible load control commands.

[0033] The system monitors the peak shaving safety margin gap and triggers a collaborative dynamic control mode when the peak shaving safety margin gap exceeds a preset threshold, and performs control actions according to the optimized control strategy.

[0034] Creditworthiness assessment is conducted on controllable resources involved in regulation, and creditworthiness assessment results are generated;

[0035] The credit rating assessment results are fed back to the multi-objective collaborative optimization model, and the weight parameters of the multi-objective collaborative optimization model are dynamically adjusted.

[0036] Specifically, a credibility processing mechanism, integrating communication quality and distributed verification technologies, ensures the accuracy and reliability of input data. A multi-dimensional dynamic evaluation model for peak-shaving security and equipment thermal safety is established to quantify grid operation risks and provide precise boundary constraints and target guidance for optimization decisions. Based on this, a multi-objective collaborative optimization model is constructed, centered on safety margin gap, renewable energy absorption capacity, and total operating cost. This model coordinates diverse controllable resources such as distributed power sources, energy storage, and flexible loads to generate a collaborative control strategy that balances safety, economy, and environmental protection. The method further designs a dynamic triggering mechanism based on the safety margin gap to achieve precise intervention of control actions. By establishing a resource credit assessment and feedback adjustment mechanism, the actual effect of control is dynamically linked to the model's optimization parameters, forming a closed-loop control capable of self-learning and continuous optimization. This enables in-depth exploration and intelligent management of grid absorption capacity.

[0037] Optionally, the step of acquiring real-time operational data of the load-concentrated area and performing credibility processing on the real-time operational data to generate credibility-weighted data includes:

[0038] Obtain the communication signal strength parameters from the real-time operating data and calculate the data deviation compensation value;

[0039] Specifically, real-time operational data is collected from measurement units in the power grid of concentrated load areas, such as smart meters, sensors, and phasor measurement units. This data covers key information such as load power, renewable energy output, and power flow. Simultaneously, the corresponding communication signal strength parameters, such as received signal strength indication, are acquired for each data point. These parameters reflect the physical layer quality of the data transmission link. Based on a pre-defined mapping relationship, the communication signal strength parameters are converted into quantified data deviation compensation values, which essentially reflect the weight of communication reliability. For example, the stronger the communication signal, the lower the probability of distortion or packet loss during data transmission, and the higher the corresponding data deviation compensation value, indicating a higher reliability of the data at the physical transmission layer.

[0040] The real-time operational data is verified from multiple parties using distributed ledger technology to generate data verification results.

[0041] Specifically, the acquired real-time operational data is submitted to a verification network based on distributed ledger technology. This network is jointly maintained by multiple trusted nodes within the power grid, such as the master station system, regional control centers, and aggregators. Upon receiving the data, each node independently verifies its validity using its own historical data, physical models, or correlation data from neighboring nodes. The verification opinions of all nodes are used to form a unified data verification result through a consensus algorithm. This result is a quantified confidence score; a higher score indicates successful verification by a majority of nodes, while a lower score indicates abnormal data or data inconsistent with the state. This process leverages the decentralized and tamper-proof characteristics of distributed ledgers to effectively resist single points of failure and malicious data attacks.

[0042] Based on the data deviation compensation value and data verification results, the real-time running data is weighted and fused to generate credibility-weighted data.

[0043] Specifically, it integrates the credibility assessment of both the communication physical layer and the data content logical layer. The final credibility weight for each data point is calculated using a formula:

[0044] ;

[0045] in, It is the first The final credibility weight of each real-time running data point; It is a data deviation compensation value calculated based on the communication signal strength parameters and then normalized. It is a data verification result generated by multi-party verification using distributed ledger technology, and is also a normalized confidence score. This is a preset weighting coefficient used to adjust the relative importance of communication quality and multi-party verification in the final credibility assessment. The final generated credibility-weighted data is the original real-time running data sequence, and each data point is accompanied by the final credibility weight calculated comprehensively. This weighted data will serve as the input for subsequent calculations.

[0046] Optionally, the dynamic assessment of the multidimensional security margin of the power grid, generating security margin assessment results, includes:

[0047] Based on the load data and new energy output prediction data in the credibility-weighted data, the required negative reserve capacity threshold and actual negative reserve capacity of the power grid are calculated to obtain the peak-shaving safety margin gap.

[0048] Specifically, based on load forecast data and renewable energy output forecast data in the confidence-weighted data, the required negative reserve capacity threshold for the power grid is calculated. Negative reserve capacity refers to the downward adjustment capacity reserved to cope with situations where renewable energy output exceeds expectations or load falls below expectations, including increasing load or reducing generation. The negative reserve capacity threshold typically considers forecast uncertainty to ensure safe operation at a certain confidence level. The actual negative reserve capacity that all currently controllable resources can provide is statistically analyzed. This includes the maximum rechargeable power of energy storage systems, the potential for capacity expansion of controllable flexible loads, and the deep peak-shaving capacity of traditional units. The peak-shaving safety margin gap is calculated using the following formula:

[0049]

[0050] in, This represents the peak-shaving safety margin gap, measured in megawatts (MW). This is the threshold of negative reserve capacity required by the power grid. The value is determined based on statistical analysis of new energy sources and load forecasting errors, combined with safety criteria; This is the actual negative reserve capacity that can be called up in real time, obtained by summarizing the current operating status and adjustment capabilities of various controllable resources. When A value greater than zero indicates insufficient negative reserve capacity and a risk of renewable energy consumption.

[0051] Based on the ambient temperature and equipment load data in the confidence-weighted data, the maximum allowable temperature of key equipment is predicted through the equipment temperature model, and the equipment thermal safety margin constraint value is obtained.

[0052] Specifically, in equipment thermal safety margin assessment, the goal is to determine the dynamic current-carrying capacity of critical power equipment, such as transformers and transmission cables, in the current and future period. This involves using an equipment temperature model, which describes the dynamic physical relationship between the internal hotspot temperature of the equipment and the equipment load and ambient temperature. Real-time ambient temperature and equipment load data from confidence-weighted data are used as input to the equipment temperature model. By solving the equipment temperature model, the trend of hotspot temperature changes in critical equipment under expected load and renewable energy output scenarios can be predicted. Based on the equipment's design specifications and operating procedures, each piece of equipment has a maximum permissible temperature that cannot be exceeded. Through reverse engineering, the maximum power or current that the equipment can carry without exceeding the maximum permissible temperature is calculated. This dynamically calculated maximum transmission limit is the equipment thermal safety margin constraint value. This constraint value is dynamically changing; it increases as the ambient temperature decreases and decreases as it increases, reflecting the equipment's true, real-time transmission capacity.

[0053] The peak-shaving safety margin gap and the equipment thermal safety margin constraint value are combined to generate a safety margin assessment result.

[0054] Specifically, the calculated peak-shaving safety margin gap and the thermal safety margin constraints of each key device are combined to form a multi-dimensional safety margin assessment result. This result comprehensively reveals the operational bottlenecks that the power grid may face in the future, including both overall power balance risks and equipment-level physical overheating risks.

[0055] Optionally, the construction and solution of a multi-objective collaborative optimization model with peak-shaving safety margin gap, renewable energy consumption, and total system operating cost as optimization objectives and equipment thermal safety margin constraints as constraints, generating an optimized control strategy that includes distributed renewable energy output commands, energy storage control commands, and controllable flexible load control commands, includes:

[0056] A multi-objective function is constructed with the optimization objectives of renewable energy consumption, peak-shaving safety margin gap, and total system operating cost.

[0057] Specifically, based on the safety margin assessment results, a multi-objective function is constructed to simultaneously optimize three interrelated but conflicting objectives. The specific objective function... Represented as:

[0058] ;

[0059] in, It is a set of decision variables, representing the controllable quantities of all controllable resources, including the output adjustment of distributed renewable energy sources, the charging and discharging power of energy storage systems, and the adjustment power of each controllable flexible load. (Function) Aimed at minimizing the peak-shaving safety margin gap, i.e. ,in Is implementing regulatory strategies Then, the recalculated peak-shaving safety margin gap is used; the smaller the value, the more abundant the peak-shaving capacity and the higher the safety. (Function) The aim is to maximize the absorption of renewable energy, which is equivalent to minimizing the amount of renewable energy wasted. ,in It is to implement control strategies The total amount of renewable energy curtailed. (Function) The aim is to minimize the total operating cost of the system, i.e. This includes compensation costs for utilizing resources such as energy storage and flexible loads, opportunity costs for curtailed renewable energy, and network loss costs.

[0060] A multi-objective collaborative optimization model is formed by using power flow balance constraints, voltage safety constraints, equipment thermal stability threshold constraints, and constraints on energy storage and controllable flexible load regulation capabilities as constraints.

[0061] Specifically, while optimizing the aforementioned objective function, a series of stringent constraints must be met to ensure the physical laws and operational safety of the power grid. These constraints constitute the boundary of the multi-objective collaborative optimization model. These mainly include power flow balance constraints, requiring that power injection at any node in the power grid equal power outflow to maintain power balance; voltage safety constraints, requiring that the voltage amplitude of all nodes must be maintained within a specified safe range; equipment thermal stability threshold constraints, requiring that the load current or transmission power of all critical lines and transformers must not exceed the dynamically calculated equipment thermal safety margin constraint value; and the self-operational constraints of various controllable resources, such as the upper and lower limits of the state of charge and charging / discharging power limits of energy storage systems, and the adjustable power range and response time limits of controllable flexible loads.

[0062] The multi-objective collaborative optimization model is solved using a multi-objective optimization algorithm to obtain the optimized control strategy.

[0063] Specifically, multi-objective optimization algorithms, such as multi-objective particle swarm optimization, are used to solve the constructed multi-objective collaborative optimization model. The algorithm's characteristic is that it does not seek a unique so-called optimal solution, but rather generates a set of Pareto optimal solutions. Each solution in this set represents a control scheme that achieves different balances between different objectives. For example, some schemes may sacrifice a small amount of economic efficiency to achieve a higher renewable energy absorption rate, while another scheme may focus more on minimizing costs. From the Pareto optimal solution set, the scheme that best suits the current operating strategy can be selected as the final optimized control strategy based on preset preferences or higher-level decision logic. The optimized control strategy is specifically manifested as a set of explicit instructions, namely, distributed renewable energy output instructions, energy storage control instructions, and controllable flexible load control instructions.

[0064] Optionally, solving the multi-objective collaborative optimization model using a multi-objective optimization algorithm includes:

[0065] Initialize the particle swarm, where each particle represents a control scheme;

[0066] Specifically, a particle swarm consists of several particles, each representing a complete, potential optimal control strategy. More specifically, a particle's position vector... It consists of the controllable quantities of all controllable resources, for example... This includes information on each distributed new energy source. Energy storage unit and controllable flexible load The system provides precise output or power adjustment commands. During initialization, within the allowable adjustment range of each control resource, a set of position vectors is randomly generated to form an initial particle swarm. Simultaneously, each particle is randomly assigned an initial velocity vector. .

[0067] Based on the multi-objective function and constraints, the fitness value of each particle is calculated;

[0068] Specifically, in each iteration, the fitness value of each particle is calculated based on the multi-objective function and constraints. For each particle... Representative regulatory plan A power flow calculation or state assessment will be performed to obtain the corresponding three objective function values, namely the peak shaving safety margin gap. New energy consumption Total system operating cost Simultaneously, the scheme is checked to ensure it meets all constraints, such as voltage safety constraints and equipment thermal safety margin constraints. Particles that violate constraints will be penalized, and their fitness will be significantly reduced. Fitness evaluation employs the principle of non-dominated ranking, where a particle's quality depends on how many other particles it dominates, and how many other particles dominate it, thus determining its position in the Pareto order.

[0069] By iteratively updating the particle position and velocity, a Pareto optimal solution set is found, and an optimal scheme is selected from the Pareto optimal solution set as an optimization control strategy.

[0070] Specifically, the Pareto optimal solution set is searched in the solution space by iteratively updating the particle's position and velocity. Each particle's trajectory is guided by both its own historical best position and the group's historical best position. The formulas for calculating the particle's velocity and position updates are:

[0071] ;

[0072] ;

[0073] in, This represents the current iteration number; and They are particles velocity and position vectors; It is the inertia weight, which adjusts the global and local search capabilities of the algorithm; and These are learning factors, representing the proportions of a particle learning towards its individual optimum and the global optimum, respectively. and It is a random number between 0 and 1, increasing the randomness of the search; It is a particle The best position I have ever been in; The entire particle swarm is at the th The globally optimal position discovered in the next iteration. In multi-objective optimization, there is no unique optimal position. ,therefore Typically, solutions are selected from a dynamically maintained external archive, which stores all non-dominated solutions discovered so far, i.e., Pareto optimal solutions. The iterative process continues until a preset termination condition is met, such as reaching the maximum number of iterations or the Pareto optimal solution set no longer changing significantly. After the algorithm ends, the final Pareto optimal solution set is stored in the external archive. A decision-making mechanism, such as a fuzzy membership function or the TOPSIS method based on entropy weights, is employed to select the optimal solution from the solution set that best balances safety, assimilation, and economy, as the final output optimization control strategy. Figure 2 As shown, this illustrates the non-dominant trade-off between renewable energy curtailment and total operating costs, where solid dots represent the final optimization strategy selected based on preset preferences.

[0074] Optionally, the step of monitoring the peak-shaving safety margin gap and triggering a collaborative dynamic control mode when the peak-shaving safety margin gap exceeds a preset threshold, and performing control actions according to the optimized control strategy, includes:

[0075] The peak shaving safety margin gap is monitored in real time, and a trigger signal is generated when the peak shaving safety margin gap is greater than a preset threshold.

[0076] Specifically, a real-time monitoring mechanism is established to continuously and frequently calculate and track the peak-shaving safety margin gap of the power grid. The monitoring process is closely linked to the safety margin assessment, utilizing the latest reliability-weighted data on load forecasts and renewable energy output forecasts to dynamically update the required negative reserve capacity threshold and the actual available negative reserve capacity. By continuously calculating the peak-shaving safety margin gap, the changes in the power grid's safety margin in responding to renewable energy fluctuations can be monitored in real time. Simultaneously, thresholds required for triggering control measures are pre-set internally. The threshold setting comprehensively considers factors such as the power grid's operational safety standards, the statistical distribution of prediction errors, and the response delay of control resources. It is an engineering parameter designed to provide early warning and allow sufficient response time. When a peak-shaving safety margin gap is detected in real time... When it exceeds this preset threshold, that is This indicates that the power grid is about to or has already been in a state of severe insufficient negative reserve capacity, posing a high risk of new energy consumption. At this time, a trigger signal will be automatically generated.

[0077] Based on the trigger signal, energy storage control instructions and controllable flexible load control instructions are extracted from the optimized control strategy.

[0078] Specifically, based on trigger signals, the collaborative dynamic control mode is activated. From the multi-objective optimization-generated and selected control strategies, control instructions directly related to increasing negative reserve capacity are precisely extracted. Since the peak-shaving safety margin gap is mainly caused by low load, it is necessary to increase the load-side absorption capacity or equivalent load. Accordingly, control instructions for energy storage systems and controllable flexible loads are selected, namely energy storage control instructions and controllable flexible load control instructions. The energy storage control instructions will clearly indicate the charging power required for each energy storage unit, while the controllable flexible load control instructions will specify the increased power consumption required for various flexible load clusters. At this point, for the output instructions of distributed renewable energy, a decision is made based on the optimization results regarding whether small-scale power curtailment control is needed as a final safeguard. Figure 3 As shown, the curves of the peak shaving safety margin gap changing over time under typical operating scenarios are displayed, and the changes of the peak shaving gap under the collaborative optimization method and the traditional control are compared. The dashed line represents the preset control trigger threshold.

[0079] Based on the energy storage control command and the controllable flexible load control command, control the energy storage system and the controllable flexible load to perform control actions.

[0080] Specifically, the extracted energy storage control commands and controllable flexible load control commands are transmitted to the corresponding control terminals via the communication network, driving physical equipment to execute control actions. Upon receiving the commands, the battery management system and energy management system of the energy storage system control the inverter to adjust its operating state and begin absorbing electrical energy. Similarly, upon receiving the commands, flexible load aggregators or user-side smart terminals adjust the operating power of equipment such as air conditioners, water heaters, and electric vehicle charging stations according to the command requirements, increasing the instantaneous load on the grid. This series of actions works together to rapidly increase the actual negative reserve capacity of the grid, thereby effectively reducing or even eliminating the peak-shaving safety margin gap, ensuring the stable operation of the grid and maximizing the absorption of new energy sources.

[0081] Optionally, the control of the energy storage system and the controllable flexible load to perform regulation actions includes:

[0082] Based on the energy storage control command, the energy storage system is controlled to perform charging and discharging operations within the preset charging and discharging power limits and state of charge range;

[0083] Specifically, once a power control command containing the target charge and discharge power of each energy storage unit for one or more future time periods is issued, the local energy management system (EMS) of the energy storage system will verify the compliance of the command and check whether the charge and discharge power required by the command is within the preset charge and discharge power limit of the energy storage converter (PCS). and Within this range. Simultaneously, the EMS will predict the future SOC change trajectory after executing the command based on the current state of charge (SOC) and the commanded power, ensuring that it always remains within the preset safe operating range. This verification process ensures that the control commands will not cause the energy storage equipment to operate beyond its limits, thereby guaranteeing equipment safety and extending its service life. After the verification is passed, the EMS decomposes the power command into specific control signals and sends them to the PCS. The PCS then precisely controls the energy exchange between the DC-side battery stack and the AC grid to achieve the charging and discharging operations required by the command.

[0084] Acquire operational status data and user preference data for controllable flexible loads;

[0085] Specifically, the control of controllable flexible loads needs to consider the diversity and personalized needs of users. Once an aggregated control command is issued for a specific type of controllable flexible load, the load aggregator or regional energy management system will obtain real-time operating status data for each flexible load unit within the cluster, such as the current set temperature of the air conditioner, indoor and outdoor temperatures, and operating mode. Simultaneously, it will retrieve pre-stored user preference data, which may include the user's set comfort temperature range, allowed control time windows, and sensitivity to electricity price changes.

[0086] Based on the controllable flexible load regulation command, combined with the operating status data and user preference data, the regulation power and time of each flexible load unit are calculated;

[0087] Specifically, by combining aggregated control commands, real-time operating status data of each unit, and user preference data, an internal load allocation algorithm calculates the specific adjustment power and adjustment time required for each independent flexible load unit. The goal is to minimize the impact on user comfort while meeting overall control needs. For example, priority is given to controlling air conditioners whose current indoor temperature deviates significantly from the user's set comfort zone, or adjustments are made within the user-permitted control period.

[0088] Control signals are sent to controllable flexible loads through the communication network to perform load-side regulation operations.

[0089] Specifically, control signals are sent to each selected controllable flexible load unit via home energy management, smart sockets, or dedicated communication networks. These signals may directly adjust the device's set parameters, such as temporarily raising the air conditioner's set temperature by one degree or briefly interrupting the water heater's heating process, thereby achieving precise load-side regulation. The entire process is completed almost imperceptibly by the user, achieving a balance between grid friendliness and user comfort.

[0090] Optionally, the process of assessing the creditworthiness of controllable resources involved in regulation and generating creditworthiness assessment results includes:

[0091] Obtain actual and planned resource output data during the regulation process, and calculate regulation accuracy indicators;

[0092] Specifically, after each controllable event, a controllability index is calculated for each controllable resource involved in the control, such as energy storage units and flexible load aggregates. To do this, the actual output data sequence of each resource during the control process is obtained from measurements, and the planned output data sequence issued to it is retrieved. The controllability index aims to quantify the deviation between the actual response and the planned command, and can be obtained by calculating the root mean square error or the mean absolute percentage error. It can be represented as:

[0093] ;

[0094] in, It is a resource The accuracy of the regulation is an indicator; the closer its value is to 1, the more accurate the response. It is a resource exist Actual output data at any given moment; It is to distribute resources exist Planned output data at any given time. Summation symbol. This indicates that all sampling points within the entire control period are accumulated.

[0095] Assess the contribution of resource regulation to economic efficiency and security, and generate economic and security indicators.

[0096] Specifically, the contribution of resource regulation to the economy and security of the power grid is assessed, generating economic indicators and security indicators respectively. Economic Indicators It is measuring resources The cost-effectiveness of invocation. It can be achieved through computational resources. The cost savings relative to baseline resources when providing the same adjustment amount are quantified; the lower the cost, the higher the economic efficiency index. Safety index. Used to assess resources The actual contribution of regulatory actions to mitigating security risks. This indicator can be calculated by analyzing the changes in key safety margin indicators before and after regulation; the greater the contribution, the higher the safety index.

[0097] By using a credit rating calculation model, and combining indicators of regulatory accuracy, economic efficiency, and security, a credit rating assessment result is generated for each resource.

[0098] Specifically, a comprehensive credit score calculation model integrates the three dimensions of indicators into a final credit score assessment result. The credit score calculation model uses a weighted summation method:

[0099] ;

[0100] in, It is a resource The final credit rating is a comprehensive score. , , These are preset weighting coefficients, reflecting the different levels of emphasis that power grid operators place on the accuracy, economy, and security of regulation. These weights can be dynamically adjusted based on the power grid's operating status and management objectives. A credit score is calculated for each controllable resource participating in regulation and recorded in the resource archive for subsequent dynamic updates and applications.

[0101] Optionally, the credit rating assessment results are fed back to a multi-objective collaborative optimization model, and the weight parameters of the multi-objective collaborative optimization model are dynamically adjusted, including:

[0102] Based on the credit rating assessment results, adjust the scheduling priority weights of resources in the multi-objective collaborative optimization model;

[0103] Specifically, based on the latest credit rating assessment results of each controllable resource, the scheduling priority weights of these resources in the multi-objective collaborative optimization model are dynamically adjusted. During the optimization model solution process, when it is necessary to select from multiple feasible control schemes, or when there is competition in resource allocation, the credit rating assessment results... This will be a key decision factor. For example, in constructing the objective function for total operating cost. In this scenario, the compensation cost for calling different resources can be linked to creditworthiness. Resources with high creditworthiness can have a lower virtual cost per unit of adjustment power, and vice versa. In this way, the optimization algorithm, in the process of seeking cost minimization, will naturally tend to prioritize calling resources with good historical performance and high reliability. This essentially quantifies and embeds the soft indicator of "credit" into the economic objective of the optimization model, realizing resource optimization based on market incentives.

[0104] Based on historical data on the effects of regulation, the weight coefficients of each optimization objective in the multi-objective function are adaptively adjusted, and the adjusted weight parameters are used to update the multi-objective collaborative optimization model.

[0105] Specifically, based on long-term historical data on regulatory effects, the multi-objective function is adaptively adjusted. The weighting coefficients of each optimization objective are determined. The actual operating status of the power grid after each adjustment is continuously tracked to evaluate the actual achievement of the optimization objectives. For example, analysis is conducted on whether peak-shaving safety margin gaps frequently occur, whether renewable energy curtailment rates are effectively controlled, and whether total operating costs meet expectations over a period of time. If historical data shows that despite each optimization attempting to minimize the peak-shaving safety margin gap, the gap still frequently exceeds the limit in actual operation, this may indicate that the current optimization model does not adequately prioritize safety. In this case, an adaptive adjustment mechanism is triggered, employing methods such as reinforcement learning or expert systems to automatically increase the safety objective in the multi-objective function based on historical performance evaluation results. The weighting coefficients are adjusted accordingly, and the economic objectives are adjusted accordingly. and absorption targets The weights are adjusted to ensure the total weight remains constant. The adjusted weight parameters are immediately used to update the multi-objective collaborative optimization model, so that subsequent optimization calculations place greater emphasis on ensuring peak shaving safety.

[0106] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a collaborative optimization system for the distributed renewable energy absorption capacity of power grids in areas with concentrated loads, the system comprising:

[0107] The data acquisition and credibility processing module is used to acquire real-time operating data of the load-concentrated area and perform credibility processing on the real-time operating data to generate credibility-weighted data. The credibility-weighted data consists of load data, new energy output prediction data, and ambient temperature and equipment load data.

[0108] The safety margin assessment module is used to dynamically assess the multidimensional safety margin of the power grid based on the credibility-weighted data and generate a safety margin assessment result, wherein the safety margin assessment result includes peak-shaving safety margin gap and equipment thermal safety margin constraint value;

[0109] The multi-objective optimization module is used to construct and solve a multi-objective collaborative optimization model based on the safety margin assessment results. The model takes the peak-shaving safety margin gap, the amount of new energy consumption, and the total system operating cost as optimization objectives and the equipment thermal safety margin constraint value as the constraint condition. The model generates an optimized control strategy that includes distributed new energy output commands, energy storage control commands, and controllable flexible load control commands.

[0110] The dynamic control triggering module is used to monitor the peak shaving safety margin gap, and when the peak shaving safety margin gap is greater than a preset threshold, it triggers the collaborative dynamic control mode and performs control actions according to the optimized control strategy.

[0111] The credit rating assessment module is used to assess the credit rating of controllable resources involved in regulation and generate credit rating assessment results.

[0112] The feedback adjustment module is used to feed the credit rating assessment results back to the multi-objective collaborative optimization model and dynamically adjust the weight parameters of the multi-objective collaborative optimization model.

[0113] To verify the feasibility of this invention in practice, it was applied to a power grid in a certain industrial park. This park's power grid has a concentrated load and a high penetration rate of distributed photovoltaic (PV) power. During the midday hours on weekdays, PV output reaches its peak while some industrial loads are at their lowest, easily leading to problems such as difficulties in absorbing renewable energy, reverse power flow in transmission lines, and voltage exceeding limits. The park's power grid dispatch center adopted the method of this invention to improve the local absorption capacity of distributed PV power while ensuring the safe and stable operation of the power grid.

[0114] In this embodiment, the collaborative optimization method proposed in this invention is deployed in the park's power grid dispatch center. Through data acquisition and credibility processing, real-time operational data and ambient temperature data from various distributed photovoltaic power stations, energy storage systems, controllable air conditioning loads, electric vehicle charging stations, etc., within the park are obtained. The multi-dimensional safety margin of the power grid is dynamically assessed, a multi-objective collaborative optimization model is constructed and solved, and when the peak-shaving safety margin gap exceeds a threshold, collaborative dynamic control is triggered. The credibility of the resources participating in the control is assessed, and the assessment results are ultimately fed back to the optimization model, forming a closed-loop adaptive control.

[0115] To verify the beneficial effects of the present invention, the operating data of a typical summer day of the park's power grid were selected for analysis, and the effects were compared with those of a control day using a traditional fixed threshold control strategy.

[0116] In the data acquisition and credibility processing stage, at 12:30 PM that day, the output data of a rooftop photovoltaic power station was collected as 500kW, and the RSSI value of the communication signal strength was recorded as -72dBm. The calculated data deviation compensation value was 0.92. After this data was verified by a multi-party verification network based on distributed ledger technology, the data verification result was 0.96. Finally, the real-time data was weighted and fused to generate credibility-weighted data with a credibility score of 0.94, providing high-quality input for subsequent accurate evaluation.

[0117] In the safety margin assessment and dynamic control triggering phase, at 13:00 on the same day, based on credibility-weighted data, the total photovoltaic output of the park was predicted to reach 50MW, while the load forecast was only 40MW. The calculated negative reserve capacity threshold to cope with the forecast uncertainty was 12MW, but at that time, all available energy storage and flexible loads in the park could only provide an actual negative reserve capacity of 8MW. Therefore, the peak-shaving safety margin gap was calculated to be 12MW - 8MW = 4.0MW. Since the gap exceeded the preset control trigger threshold of 2.0MW, a trigger signal was immediately generated, initiating the collaborative dynamic control mode.

[0118] In the multi-objective optimization and strategy execution phase, upon triggering, a multi-objective particle swarm optimization algorithm was immediately employed to solve the collaborative optimization model, selecting the most balanced control scheme from the Pareto optimal solution set. Based on this scheme, specific optimization and control strategies were generated and issued: instructing the 5MWh energy storage system within the park to charge at a power of 2.0MW; instructing the central air conditioning clusters in office buildings within the park to increase their capacity by 1.5MW; and issuing instructions to reduce the output of photovoltaic power stations in some non-critical areas by 0.5MW. After the control actions were executed, the actual negative reserve capacity of the power grid rapidly increased, the peak-shaving safety margin gap was effectively eliminated, and large-scale power curtailment events were avoided.

[0119] In the credit rating assessment and feedback adjustment phase, the resources involved in the regulation were evaluated after the event concluded. Data showed that the actual response power of the energy storage system was 1.98MW, with a regulation accuracy index of 0.99; the actual response power of the office building air conditioning load cluster was 1.4MW, with an accuracy index of 0.93. Considering their contribution to mitigating voltage exceedances during this regulation, the energy storage system and the air conditioning load cluster were ultimately given credit ratings of 95 and 88 respectively. Based on this assessment, the scheduling priority weight of the energy storage system was increased from 0.8 to 0.9 in subsequent optimized scheduling. Simultaneously, given the recent frequent peak-shaving safety margin gaps during midday, the feedback adjustment adaptively increased the weight coefficient of the objective "minimizing the peak-shaving safety margin gap" in the multi-objective function from 0.4 to 0.5, enhancing the proactive response to safety risks.

[0120] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0121] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for coordinated optimization of distributed renewable energy absorption capacity in power grids in areas with concentrated loads, characterized in that, The method includes: Real-time operating data of the load concentration area is acquired, and the real-time operating data is processed for credibility to generate credibility-weighted data. The credibility-weighted data consists of load data, new energy output prediction data, and ambient temperature and equipment load data. Based on the aforementioned credibility-weighted data, the multidimensional security margin of the power grid is dynamically evaluated, and a security margin evaluation result is generated. The security margin evaluation result includes the peak-shaving security margin gap and the equipment thermal security margin constraint value. Based on the safety margin assessment results, a multi-objective collaborative optimization model is constructed and solved with the peak-shaving safety margin gap, renewable energy consumption and total system operating cost as optimization objectives and the equipment thermal safety margin constraint value as constraint condition. This model generates an optimized control strategy that includes distributed renewable energy output commands, energy storage control commands and controllable flexible load control commands. The peak shaving safety margin gap is monitored, and when the peak shaving safety margin gap is greater than a preset threshold, a collaborative dynamic control mode is triggered, and control actions are performed according to the optimized control strategy. Creditworthiness assessment is conducted on controllable resources involved in regulation, and creditworthiness assessment results are generated; The credit rating assessment results are fed back to the multi-objective collaborative optimization model, and the weight parameters of the multi-objective collaborative optimization model are dynamically adjusted.

2. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas according to claim 1, characterized in that, The process of acquiring real-time operational data of the concentrated load area and performing credibility processing on the real-time operational data to generate credibility-weighted data includes: Obtain the communication signal strength parameters from real-time operational data and calculate the data deviation compensation value; The real-time operational data is verified from multiple parties using distributed ledger technology to generate data verification results. Based on the data deviation compensation value and data verification results, the real-time running data is weighted and fused to generate credibility-weighted data.

3. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in concentrated load areas according to claim 1, characterized in that, The dynamic assessment of the multidimensional security margin of the power grid generates security margin assessment results including: Based on the load data and new energy output prediction data in the credibility-weighted data, the required negative reserve capacity threshold and actual negative reserve capacity of the power grid are calculated to obtain the peak-shaving safety margin gap. Based on the ambient temperature and equipment load data in the confidence-weighted data, the maximum allowable temperature of key equipment is predicted through the equipment temperature model, and the equipment thermal safety margin constraint value is obtained. The peak-shaving safety margin gap and the equipment thermal safety margin constraint value are combined to generate a safety margin assessment result.

4. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas according to claim 1, characterized in that, The construction and solution of a multi-objective collaborative optimization model with peak-shaving safety margin gap, renewable energy consumption, and total system operating cost as optimization objectives and equipment thermal safety margin constraints as constraints, generates an optimized control strategy that includes distributed renewable energy output commands, energy storage control commands, and controllable flexible load control commands. A multi-objective function is constructed with the optimization objectives of renewable energy consumption, peak-shaving safety margin gap, and total system operating cost. A multi-objective collaborative optimization model is formed by using power flow balance constraints, voltage safety constraints, equipment thermal stability threshold constraints, and constraints on energy storage and controllable flexible load regulation capabilities as constraints. The multi-objective collaborative optimization model is solved using a multi-objective optimization algorithm to obtain the optimized control strategy.

5. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas according to claim 4, characterized in that, The step of solving the multi-objective collaborative optimization model using a multi-objective optimization algorithm includes: Initialize the particle swarm, where each particle represents a control scheme; Based on the multi-objective function and constraints, the fitness value of each particle is calculated; By iteratively updating the particle position and velocity, a Pareto optimal solution set is found, and an optimal scheme is selected from the Pareto optimal solution set as an optimization control strategy.

6. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas according to claim 1, characterized in that, The monitoring of the peak-shaving safety margin gap, and the triggering of a collaborative dynamic control mode when the peak-shaving safety margin gap exceeds a preset threshold, and the control actions performed according to the optimized control strategy, include: The peak shaving safety margin gap is monitored in real time, and a trigger signal is generated when the peak shaving safety margin gap is greater than a preset threshold. Based on the trigger signal, energy storage control instructions and controllable flexible load control instructions are extracted from the optimized control strategy. Based on the energy storage control command and the controllable flexible load control command, control the energy storage system and the controllable flexible load to perform control actions.

7. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas according to claim 6, characterized in that, The control and regulation actions performed by the energy storage system and the controllable flexible load include: Based on the energy storage control command, the energy storage system is controlled to perform charging and discharging operations within the preset charging and discharging power limits and state of charge range; Acquire operational status data and user preference data for controllable flexible loads; Based on the controllable flexible load regulation command, combined with the operating status data and user preference data, the regulation power and time of each flexible load unit are calculated; Control signals are sent to controllable flexible loads through the communication network to perform load-side regulation operations.

8. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas according to claim 1, characterized in that, The process of assessing the creditworthiness of controllable resources involved in regulation and generating creditworthiness assessment results includes: Obtain actual and planned resource output data during the regulation process, and calculate regulation accuracy indicators; Assess the contribution of resource regulation to economic efficiency and security, and generate economic and security indicators. By using a credit rating calculation model, and combining indicators of regulatory accuracy, economic efficiency, and security, a credit rating assessment result is generated for each resource.

9. The method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas according to claim 1, characterized in that, The credit rating assessment results are fed back to the multi-objective collaborative optimization model, and the weight parameters of the multi-objective collaborative optimization model are dynamically adjusted, including: Based on the credit rating assessment results, adjust the scheduling priority weights of resources in the multi-objective collaborative optimization model; Based on historical data on the effects of regulation, the weight coefficients of each optimization objective in the multi-objective function are adaptively adjusted, and the adjusted weight parameters are used to update the multi-objective collaborative optimization model.

10. A system for coordinated optimization of distributed renewable energy absorption capacity in power grids in areas with concentrated loads, characterized in that: The system is used in the method for coordinated optimization of distributed renewable energy absorption capacity in power grids in load-concentrated areas as described in any one of claims 1-9, and the system includes: The data acquisition and credibility processing module is used to acquire real-time operating data of the load-concentrated area and perform credibility processing on the real-time operating data to generate credibility-weighted data. The credibility-weighted data consists of load data, new energy output prediction data, and ambient temperature and equipment load data. The safety margin assessment module is used to dynamically assess the multidimensional safety margin of the power grid based on the credibility-weighted data and generate a safety margin assessment result, wherein the safety margin assessment result includes peak-shaving safety margin gap and equipment thermal safety margin constraint value; The multi-objective optimization module is used to construct and solve a multi-objective collaborative optimization model based on the safety margin assessment results. The model takes the peak-shaving safety margin gap, the amount of new energy consumption, and the total system operating cost as optimization objectives and the equipment thermal safety margin constraint value as the constraint condition. The model generates an optimized control strategy that includes distributed new energy output commands, energy storage control commands, and controllable flexible load control commands. The dynamic control triggering module is used to monitor the peak shaving safety margin gap, and when the peak shaving safety margin gap is greater than a preset threshold, it triggers the collaborative dynamic control mode and performs control actions according to the optimized control strategy. The credit rating assessment module is used to assess the credit rating of controllable resources involved in regulation and generate credit rating assessment results. The feedback adjustment module is used to feed the credit rating assessment results back to the multi-objective collaborative optimization model and dynamically adjust the weight parameters of the multi-objective collaborative optimization model.

Images